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Using induced pluripotent stem cells for tissue regeneration in cardiovascular diseases

Rishya Gutti — Fri, 04 Feb 2022 10:06:54 +0000

Author: Rishya Gutti
Mentor: Dr. Fabiola Munarin
Neuqua Valley High School

Abstract

Heart attacks, or Myocardial Infarctions (MI), lead to death of tissue due to lack of blood supply to the portion of the organ. Resulting scar tissue does not contract or function as well as healthy muscle tissue. On the other hand, stem cells have shown propensity to be guided into becoming specific cells that can be used to regenerate and repair diseased or damaged tissues in people. This paper will explore the latest research that supports using induced pluripotent stem cells for tissue reparation in cardiovascular disease.

Introduction

Over the past few decades, stem cell therapies have evolved considerably and one of their many potential applications could be to repair the scarring caused by myocardial infarctions. Myocardial Infarction (MI), which is a reduction or blockage of blood flow in the coronary arteries, commonly referred to as heart attack, is one of the leading causes of death in the United States with 805,000 people experiencing one every year (CDC). Unfortunately, out of those 805,000, 12% will die (CDC). Following MI, the inadequate blood flow to the infarcted tissue causes a severe reduction of oxygen and nutrients, leading to cardiomyocyte necrosis (reduced contractility), and therefore compromised heart function. MI does not traditionally have any treatment since once the tissue has necrotized, it can not regain its function. MI’s can only be managed with preventative measures taken to inhibit another incident. Medicines like aspirin and other anti-clotting drugs are used to keep clots from forming and causing another MI (CDC). ACE inhibitors reduce the strain on the heart by lowering blood pressure and this helps to not weaken the damaged tissues any further (NIH). Similarly, Betablockers also reduce the strain on the organ by blocking the release of stress hormones like noradrenaline and adrenaline to keep heart rate constant (NIH). All of these, however, only reduce the risk of a recurrence and do not regenerate the dead tissue, whereas a different form of therapy for the damaged tissue could bring about a brighter prospect. Stem cells can regenerate tissues suitable to one’s own body without having to use a transplant. This makes it less risky when it comes to a patient’s body rejecting the cells. Some varieties are also easily accessible, usable, and effective in their respective needs.

Out of the many varieties of stem cells, induced pluripotent stem cells, are some of the most promising to study. Induced pluripotent stem cells (iPS cells) are derived from somatic cells that are reprogrammed into iPS cells. These cells can then be made to differentiate into whatever tissue cell is needed (Shi et al, 2016). They are also important to observe because of their accessibility and high turnover rate (Krzysztof et al, 2018). In this review, we will focus on two types of stem cells: induced pluripotent stem cells, and their abilities in tissue regeneration in regards to therapies to treat the infarcted myocardium (Yoshida et al, 2017).

Induced Pluripotent Stem cells

Induced Pluripotent Stem Cells (iPSCs) are adult somatic cells that are reprogrammed into a pluripotent state. These cells are adults and unipotent, meaning they are capable of regenerating only their own specific tissue type (Tweedell, 2017). For example, an adult somatic cell in the skin could only generate skin cells. When these cells are reprogrammed into iPSCs, they become pluripotent, and are able to differentiate into any type of tissue with appropriate differentiation factors (Tweedell, 2017).

The use of iPSCs for regenerative medicine bears significant advantages. In fact, the somatic cells generally used for reprogramming are highly accessible and they are already part of the body of the person who needs them. Therefore, there is no risk of rejection when they are implanted for regenerating damaged tissues or organs (Arjmand et al, 2017). One further advantage is that they are not controversial like embryonic stem cells that are isolated from embryos while having similar properties. Generating the iPSCs is completed by taking any healthy adult somatic cells from the body and reverse engineering them into a pluripotent state where they can then differentiate into whatever cell type is needed. How this occurs is that first, the cells organize spatially and then divide into three areas. The middle section, differentiates into the middle portion of the three’s lineage and this activates certain genes.

Cardiovascular Regeneration

As mentioned above, one of the capabilities of iPSCs is tissue regeneration, which is paramount for cardiovascular tissue regeneration. The basic process for cardiovascular tissue engineering consists in isolating somatic cells of the patient or from healthy donors, which are then reprogrammed to iPSCs. Next, the obtained iPSCs are differentiated into the specific cell type that is needed (such as cardiomyocytes, cardiac fibroblasts, or endothelial cells). The differentiated cells must be cultured in the lab to grow, and during this process they can be stimulated with chemical or physical cues to mimic the mechanical properties of the beating heart. The last step is to inject or implant the cells into the patient.

Cardiovascular tissue engineering has shown promising results in vitro and in preclinical in vivo studies. Several groups have used small animal models, including mice and rats model of myocardial infarction to assess the ability of repairing the damaged heart tissue with iPSCs-derived cardiomyocytes. An example of implantation of cardiac engineered tissues in a small animal model is provided by (Tompkins et. al. 2018), that used 3D bioprinted iPSC-derived cardiomyocytes, fibroblasts and endothelial cells to produce 3D patches that were implanted in n=6 infarcted rats.

Additionally, vivo models further demonstrate that this path of study is incredibly promising. The work of Tompkins et. al. describes small animal models where iPSCs are implanted. This article demonstrates how this work is viable in live models as they tested various species of small animals to prove efficacy. Moreover, the same study considered large animal studies and deduced that they too have promising results. More specifically, in swine models, which are known to have extremely similar cardiac structure to that of humans, these studies further the thought that using iPSCs to repair tissues is a viable solution. Kawamura et al. placed a sheet of dermal fibroblast-derived hiPSC-CMs over the infarcted area in an ischemic swine model, which produced improved cardiac performance, angiogenesis (increased number of blood vessels in the infarct), and an attenuated LV remodeling 8-weeks post implantation.

While in the lab, stimuli of stretching and current are used to help the cells mature faster and grow more resilient. This is one place of research that is continuing to challenge researchers, since they do not have years to culture mature cells and there is risk with implanting immature cells regarding their ability to adapt to the heart’s environment. However, it can and has been done, as explained above, which has drawn tremendous attention to this field of pursuit. Moreover, cardiovascular regeneration is one of the newest technologies in repairing damaged tissues in the heart. This breakthrough has made it possible to just regrow healthy and functional tissue instead of needing a transplant since it is already known that once tissue is dead from a myocardial infarction, there is no way to salvage it. As the MI damages the tissue, it makes it impossible for the original tissue to be functional, so inputting fresh, cultivated tissues open up new possibilities in life for the patient after their MI episode.

Drug Screening

During the process of drug screening, various drugs are tested on the cardiac engineered tissues to gauge safety and efficacy of the tested molecules and drugs. One of the commonly tested side effects of newly developed drugs are for drug induced arrhythmias. By testing in-vitro with iPSCs outside of a patient’s body, it is not only more convenient to do so but also safer so as to not involve a live subject (Smith et. al. 2017). Various types of cell models are used, ranging from flat, 2D monolayers to more complex 3D tissues, organ-on-a-chip models show a wide range of functionality. Each of these model types show a range from the least to most complex levels of organization in order to understand how drugs can affect the cardiomyocytes on a basic to fully vascularized level (Smith et. al. 2017 Fig. 1). This is one place of development in the field of iPSCs that would be of great benefit to the scientific community and to the general population as well. If drugs can be screened and tested within a lab without having to use in vivo models until much later in the process, it can be much more ethical and more varieties of medicines that may or may not be viewed as viable could potentially be trialed in this way due to the reduced ethics concerns.

One example of a clinical trial is in the research of Blinova et. al. which shows a personalized drug screen model that highlights how iPSCs derived from 22 healthy subjects can be grown and tested within a dish. Safety and efficacy of two drugs, dofetilide and moxifloxacin (hERG‐blocking and QT prolonging), were tested on iPSCs isolated from the peripheral blood mononuclear cells and differentiated in cardiomyocytes. There were no drug induced arrhythmia-like events observed at the studied drug concentration rate. In vivo model of testing that highlights how tissue can be grown and tested within a dish. In this trial, the researchers tested and analyzed for arrhythmias in the iPSCs. This demonstrates how various environments of the heart can be simulated in the lab and that it is necessary to do so (Blinova et. al. 2019)

Various different types of trials can be used to screen for arrhythmias in a drug screening. One method researchers historically and commonly use is the analysis of hERG channel response which is the standard procedure for in vitro preclinical trials of drug screening. While this is a method commonly used, it is not as accurate as could be desired since false positive results are frequent occurrences (Smith et. al. 2017). This is why iPSC-CMs are making headway in the field of drug screening since they offer a more accurate option. There are various tests researchers can run with in vitro models of iPSCs to represent the function of the heart more fully and effectively. Out of the multitude of options researchers now have with iPSC-CMs as an option, an example presented in the above research is that researchers measure cell contraction to observe the cardiomyocytes’ contractile function (Smith et. al. 2017).

Conclusion

After an episode of myocardial infarction, heart tissue is damaged irreversibly and the prognosis only entails either drug therapeutics or organ transplant. Cardiovascular regeneration is one of the newest technologies in regards to repairing damaged tissues in the heart. With reprogrammed iPSCs , the patient is able to have their healthy cells cultured in a lab and remediate the scarred tissue resulting from an MI episode. Furthermore, progress has also been made in labs to accommodate the new research and to screen drugs to ensure their safety with the cultured tissues when implanted in a person. All together, these breakthroughs have made it possible to regrow healthy and functioning tissue and using iPSCs could make this possibility a reality.

References

Arjmand B, Goodarzi P, Mohamadi-Jahani F, Falahzadeh K, Larijani B. Personalized Regenerative Medicine. Acta Med Iran. 2017 Mar;55(3):144-149.

Blinova K, Schocken D, Patel D, Daluwatte C, Vicente J, Wu JC, Strauss DG. Clinical Trial in a Dish: Personalized Stem Cell-Derived Cardiomyocyte Assay Compared With Clinical Trial Results for Two QT-Prolonging Drugs. Clin Transl Sci. 2019 Nov;12(6):687-697.

CDC, Heart Disease in the United States, 2020 Sept.

Csöbönyeiová M, Polák Š, Danišovič L. Perspectives of induced pluripotent stem cells for cardiovascular system regeneration. Exp Biol Med (Maywood). 2015 May;240(5):549-56.

Hoang P, Wang J, Conklin BR, Healy KE, Ma Z. Generation of spatial-patterned early-developing cardiac organoids using human pluripotent stem cells. Nat Protoc. 2018 Apr;13(4):723-737.

Justin Liu, Kathleen Miller, Xuanyi Ma, Sukriti Dewan, Natalie Lawrence, Grace Whang, Peter Chung, Andrew D. McCulloch, Shaochen Chen,Direct 3D bioprinting of cardiac micro-tissues mimicking native Biomaterials, Volume 256, 2020, 120204, ISSN 0142-9612myocardium,

Kawamura M, Miyagawa S, Miki K, Saito A, Fukushima S, Higuchi T, Kawamura T, Kuratani T, Daimon T, Shimizu T, Okano T, Sawa Y. Feasibility, safety, and therapeutic efficacy of human induced pluripotent stem cell-derived cardiomyocyte sheets in a porcine ischemic cardiomyopathy model. Circulation. 2012 Sep 11;126(11 Suppl 1):S29-37.

Madigan M, Atoui R. Therapeutic Use of Stem Cells for Myocardial Infarction. Bioengineering (Basel). 2018 Apr 6;5(2):28

Müller P, Lemcke H, David R. Stem Cell Therapy in Heart Diseases – Cell Types, Mechanisms andImprovement Strategies. Cell Physiol Biochem. 2018;48(6):2607-2655.

NIH, What is a Heart Attack?, 2018 June.

Polonchuk L, Chabria M, Badi L, Hoflack JC, Figtree G, Davies MJ, Gentile C. Cardiac spheroids as promising in vitro models to study the human heart microenvironment. Sci Rep. 2017 Aug 1;7(1):7005.

Rikhtegar R, Pezeshkian M, Dolati S, Safaie N, Afrasiabi Rad A, Mahdipour M, Nouri M, Jodati AR, Yousefi M. Stem cells as therapy for heart disease: iPSCs, ESCs, CSCs, and skeletal myoblasts. Biomed Pharmacother. 2019 Jan;109:304-313.

Shi Y, Inoue H, Wu JC, Yamanaka S. Induced pluripotent stem cell technology: a decade of progress. Nat Rev Drug Discov. 2017 Feb;16(2):115-130.

Smith AS, Macadangdang J, Leung W, Laflamme MA, Kim DH. Human iPSC-derived cardiomyocytes and tissue engineering strategies for disease modeling and drug screening. Biotechnol Adv. 2017 Jan-Feb;35(1):77-94.

Tompkins BA, Balkan W, Winkler J, Gyöngyösi M, Goliasch G, Fernández-Avilés F, Hare JM. Preclinical Studies of Stem Cell Therapy for Heart Disease. Circ Res. 2018 Mar 30;122(7):1006-1020.

Tweedell KS. The Adaptability of Somatic Stem Cells: A Review. J Stem Cells Regen Med. 2017 May 30;13(1):3-13

Ye L, Swingen C, Zhang J. Induced pluripotent stem cells and their potential for basic and clinical sciences. Curr Cardiol Rev. 2013 Feb 1;9(1):63-72.

Yoshida Y, Yamanaka S. Induced Pluripotent Stem Cells 10 Years Later: For Cardiac Applications. Circ Res. 2017 Jun 9;120(12):1958-1968.

Zhao Y, Rafatian N, Wang EY, Wu Q, Lai BFL, Lu RX, Savoji H, Radisic M. Towards chamber specific heart-on-a-chip for drug testing applications. Adv Drug Deliv Rev. 2020;165-166:60-76.

About the author

Rishya Gutti

Rishya is a junior at Neuqua Valley High School. She is interested in biological sciences and is an aspiring medical student. Research programs like RISE (Research, Inquiry Skills & Experimentation) have equipped Rishya with necessary skills to conduct independent research. She is a third degree black belt in Taekwondo and has won several national titles in her age group. Rishya enjoys volunteering her time to teach mathematics to younger students and to promote mental health awareness through a non-profit organization. In her free time, you will find her reading, working out, or watching her favorite tv shows.

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Technologies Reshaping Agriculture in Tamil Nadu, India

Harnishya Palanichamy — Tue, 23 Nov 2021 13:37:36 +0000

Author: Harnishya Palanichamy
Hebron School, Lushington Campus

Introduction

“If agriculture is to continue to feed the world, it needs to become more like manufacturing”, says Geoffrey Carrand fortunately, that is already beginning to happen.¹Advances in technology are key to the future of agriculture as farmers strive to feed the world with limited natural resources.² In Tamil Nadu state, located in South India, Agriculture is the greatest overriding sector of the state’s economy and nearly 70% of states population is based on agriculture. The agriculture in Tamil Nadu has executed a good performance over the years with the help of efficient farmers who are both receptive and responsive to the technological development announced in the agricultural sector of the state.³ Innovations for small agricultural operations can significantly increase profit margins by minimizing the need for manual labour with automation, expediting machinery commands with remote and real-time monitoring, and allowing farmers to utilize resources more efficiently with preventative maintenance and environmental prediction. Mass embracement of these technology advancements in agriculture will allow small land holding farmers to achieve higher potential for profit, and higher yields on the upfront investments.⁴I live in Tamil Nadu and our family own an agricultural field and that interests me to investigate this study, for which I researched and collected primary and secondary data regarding the topic. The technologies that are currently reshaping the agriculture of Tamil Nadu state are detailed below.

PRECISION FARMING

Precision Farming helps farmers to generate data with the help of sensors and analyse that information to take intelligent and quick decisions.⁵ It is the application of modern information technologies to provide, process and analyse multisource data of high spatial and temporal resolution for decision making and operations in the management of crop production⁶and helps in changing the socio-economic status of farmers.⁷

CASE STUDY

Mr.Rajamani, a farmer in Coimbatore district, Tamil Nadu, follows the precision farming in his field, where he broadcasted the coriander seeds in between the turmeric crop, small onion and chilly as intercrops, red gram as border crop and irrigated the field through drip system. He harvested the coriander in 30 – 35 days, onion in 70 days, chillies on 90th day, red gram on 250th day and turmeric fingers on 275 days after sowing. He acquired yield of 7 tonnes of turmeric fingers and 13 tonnes of onion, 2 tonnes of green and dry chilies and 50 kg red gram in one hectare of land. He sold turmeric fingers at the rate of Rs. 135 / kg, onion at Rs. 20/ kg, chillies at Rs. 12/ kg, red gram at Rs. 100/ kg, tender coriander leaves Rs.4/kg and coriander seed at Rs. 15/ kg. He invested Rs. 3,35,400 for cultivation practices and attained a high profit of Rs. 9,66,000 per hectare and this was possible since he shifted from conventional farming to precision farming.⁸

ORGANIC FARMING

This process involves the use of biological materials, avoiding synthetic substances to maintain soil fertility and ecological balance thereby minimizing pollution and wastage. It relies on ecological processes, biodiversity and cycles adapted to local conditions, rather than the use of inputs with adverse effects and care for the larger environment and conservation of natural habitats and wildlife.⁹

CASE STUDY

Organic farming is the in-thing now in Thanjavur district, the rice bowl of Tamil Nadu, where one hundred farmers are successful in practising organic farming, for crops like- banana, maize and paddy. Mr.Kulandaisamy, a progressive farmer has raised Rasthali and Robust variety of bananas adopting organic farming methods. With respect to Rasthali, a bunch has five to six hands (hands means “seeppu” in Tamil language), instead of three to four hands, which are normally seen in ordinary cultivation and a single bunch weighs 20 kgs and fetches Rs.200. And the Robust variety of banana has 12 to 15 hands in a bunch and the bunch weighs 30 to 35 kgs, which are popularly sold in Tiruchirappalli city (my native), market. Fertiliser used by the farmer was composed using organic matters, neem, and pancha kavya prepared using cow’s urine and cow dung. The farmer has cultivated maize, paddy and Vanila (a profit-oriented venture), using organic farming.¹⁰

DRONES

Drones help farmers optimize the use of inputs such as seeds, fertilizers, water, and pesticides more efficiently. This allows timely protection of crops from pests, saves time for crop scouting, reduces overall cost in farm production, and secures high yield, increasing the farm profitability.¹¹Initially used for chemical spraying, today drones are a great tool used to assess different aspects of plant health, weeds, and assets.

CASE STUDY

Tamil Nadu Agricultural University has developed drones, that could carry 15kg agricultural input, that helps to spray pesticides, insecticides and herbicides to protect crops, with a capability of covering one hectare in five minutes and a maximum of three hectares in 15 minutes in a single flight. It could cover large areas in a short time, so that pests could be destroyed across massive tracts of land before they could spread. However, drifting due to varying wind speed is a risk. During windy days it may not be a good choice, says the officials.¹² And in future, usage of a drone would eliminate dependence on the already scarce farm labour.

IOT BASED SMART FARMING

In IoT-based smart farming, a system is built for monitoring the crop field with the help of sensors and automating the irrigation system. The farmers can monitor the field conditions from anywhere.In terms of environmental issues, IoT-based smart farming can provide great benefits including more efficient water usage, or optimization of inputs and treatments.¹³

CASE STUDY

A team of college students from Salem city, Tamil Nadu, have come into the spotlight recently for their Internet-of-Things (IoT) based software solution that seeks to give a push to smart farming, and this crop guidance software is a set of three applications that enable remote monitoring of pest control, automated watering and growth. The objective is to bring in smart farming solutions that allow farmers to produce maximum yields with minimum resources such as water, fertiliser and seeds, and reduce wastage or losses. One of the applications of their IoT-based comprehensive solution is the Plant Growth Monitoring System, which uses colour sensors to check the growth of the plants and sprays fertiliser as and when needed. Another application with an additional WiFi module identifies pest attacks and sprays pesticide precisely on the affected parts of the agricultural land. Another application, the Automatic Plant Watering System, uses a moisture sensor and utilises water resource judiciously as it triggers watering based on the moisture level of the soil. The systems work automatically- for example- in the watering system when the sufficient water level is reached, it will switch off on its own. This makes it easier for farmers and makes the process seamless. As the applications are IoT-based, they also have a feature that provides weather conditions for proper crop growth to the farmers. ¹⁴

MOBILE APPS

There are mobile applications that provide latest agricultural information about trends, equipment, technologies and methods being used, help identify pests and diseases, provide real-time data about weather, early warnings about storms, local markets offering best prices, seeds, fertilizers etc. In addition, farmers can also interact and get guidance from agriculture experts across the country via the apps. These apps help in providing market information, facilitating market links, providing access to extension services, farm related information etc.¹⁵

CASE STUDY

About 4,91,811 users of Uzhavan App, launched by Tamil Nadu Agricultural Ministry, educate farmers about soil quality, seeds and fertilizers. FarmMOJO app provides real time solutions for aquaculture, including shrimp and fish farming and this app records data like pH balance, ammonia and nitrate levels and water quality. Tumaini app allows farmers, cultivating banana, to scan plants and detects symptoms on any part of the crop with an accuracy rate of 98%.¹⁶

HYDROPONICS

Hydroponics is a way to skip the soil and grow crops directly in nutrient-rich water, that gives higher yield with fewer resources.¹⁷

CASE STUDY

According to Mr.Srinivasan, a resident of Chennai city, perform commercial hydroponics by growing crops like spinach, kale, and lettuce. With nearly 250 plants across 50 square feet, Rahul Dhoka runs Acqua Farms in Chennai city. The rate of crop growth is around 30 to 40 per cent faster and have a 30 to 40 per cent higher yield, than soil-based agriculture, with a less cost of production ie-lettuce costs Rs 15 to 17 per kg, and basil costs Rs 20 to 25 per kg.¹⁸

SOIL MOISTURE INDICATOR

Soil moisture indicator has sensors to detect the soil moisture content for proper development of plants and notify the user when the soil gets too dry or too wet.¹⁹ This device works based on the principle that electrical conductivity of the soil is directly proportional to soil moisture or soil electrical resistance is indirectly proportional to soil moisture content.^20.It is suitable for different types of soils and can be used in nurseries, farms, potted plants, etc.²¹

CASE STUDY

The Soil Moisture Indicator, a handy and user-friendly electronic moisture–indicating device, was first launched at Sugarcane Breeding Institute at Coimbatore city, Tamil Nadu, which is used for the efficient irrigation management practices such as irrigation-scheduling, particularly in sugarcane fields, that helps the farmers in deciding when to irrigate their fields and as a result there would be considerable saving of irrigation water. The sensor rods of the device need to be inserted into the soil to a required depth to assess the soil moisture, which is indicated by glowing LEDs and the device is suitable for use in agricultural farms as well as in potted plants.²²

RFID TECHNOLOGY

Radio Frequency Identification Technology offers monitoring systems, which protects the crops from pests and wireless sensors can be used to monitor cattle. It uses dedicated software and hardware to monitor livestock management.²³It supports Animal population trackingand Animal data base monitoring.²⁴

CASE STUDY

“Mr. Vijaykumar, Perambalur district, Tamil Nadu, relies on computerisation to monitor the dairy along with the 60 staff who stay on the farm. Each animal has a blue-collar tag with a unique microchip number. “Of the 368 cows here, 160 are used for milking and the microchip helps us not just to track each cow’s health and daily milk output, but also to decide if it can be selected for breeding in future.²⁵

CONCLUSION:

The agriculture technologies have changed almost all the domains of farming from sowing to harvesting. These technologies are continued to evolve and invent new innovations that act as catalyst to ameliorate farmers’ life by increasing incoming and providing the access to research stations and agro-scientists. The use of technology can make farmers feel more empowered and enable them to adopt required measures in needful time. It has potential capabilities to transform agriculture into a better prospect to get aware of climatic change and appropriate use of limited natural resources in agricultural land.²⁶ However technologies like hydroponics; drone farming; use of mobile applications and IOT based devices had get tremendous success in Tamil Nadu. Many government organizations and private bodies are working on to give a clear view about new technologies to the farmers of Tamil Nadu, like robotic farming, chromosomal technique and application of remote sensing etc… which are practiced globally.

REFERENCES

About the author

Harnishya Palanichamy

Harnishya is currently in Grade 10 at the Hebron School, Ootacamund, Tamil Nadu, India. She’s passionate about the field of computer science, particularly the coding languages, Java and JavaScript. She enjoys coding and researching about AI. She started her coding journey by coding games in JavaScript, and she also has experience with robotics; being in the school robotics club. In the future, she wants to develop her coding knowledge by creating more complex apps.

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How do lightning rods work?

Winnie Shi — Wed, 20 Oct 2021 15:02:48 +0000

Author: Winnie Shi
Mentor: Dr. De La Torre, Caltech
Shanghai Starriver Bilingual School
October 1, 2021

1.Introduction

Lightning rods, mostly made of copper, is a structure that protects buildings from being damaged by attracting flashes through electric-magnetic force and guide the current to the ground. After learning a bit about electricity and experiencing a night of thunder and lightning, I intend to explore how lightning rods work. Therefore, in this presentation, I will first introduce the historical research on lightning rods, and then explain how lightning rods work in general using electrostatic principles and some easy-to-understand analogies. I will then write a program to calculate the effective range of the lightning rod based on the Monte Carlo technique and finally propose a lightning protection solution in conjunction with the 3D street view.

2. The History of Lightning Rod

2.1 Franklin Kite Experiment

In 1746, Franklin turned his home into an electrical laboratory after occasionally discovering the electrical experiments of other scientists, and in a letter he described receiving an electric shock as “a numbing sensation from the beginning to the end”.

In 1747, thanks to Franklin’s discoveries, people stopped using glassy and resinous to describe electricity. They began to use positive and negative electricity.

In 1749, Franklin began to make analogies between lightning and batteries, and from then on lightning became palpable. He explained by analogy the bifurcation in lightning, the color of the lightning, and the deafening sound, and was determined to prove that lightning and electricity were directly related. in 1750, he began to focus his research on the protective devices for lightning. This was man’s first step toward the lightning rod

Fifteen years later, Franklin’s close friend recorded in his diary Franklin’s famous kite experiment. He took the risk of using a kite to try to get up close and personal with lightning. He even tied a key to the kite in order to attract an electrical charge. Even though the string of the kite was already made of insulating silk, this was still a very risky act considering the strength of the lightning bolt could even make the insulator relatively conductive. The conclusion of this experiment was that the key was seen to receive the electric charge brought by the lightning, and Franklin thus proved that lightning is electricity.

2.2 Tip Lightning Rod or Round-end Ones?

Almost simultaneously with the kite experiment, Franklin realized the fact that iron needles can conduct electricity, and tried to integrate this into the “lightning rod” invention. In his diary, he envisioned, “Could there be a way to protect people from sudden lightning strikes by inserting thin needles directly into clouds and pulling the electricity out of them before the lightning strikes the ground?”

Franklin focused on elevating the tip of the lightning rod, while Benjamin Wilson, a member of the Royal Court circle of George III, believed that the pointed lightning rod would attract lightning (and this property remained unchanged and became the main principle of the modern lightning rod) and was not as safe as the round-headed lightning rod. Most scholars at the time also supported Benjamin Wilson’s view, so much so that this eventually turned into a political showdown, with proponents of Franklin’s lightning rod being falsely accused of “trying to establish their own political group in England. The war between science and politics officially ended when an East India Company was struck by lightning, and Franklin’s spiked design is still used today.

2.3 Three primary Modern lightning rods

2.3.1 Early Streamline Emmision (ESE)

ESE systems are more similar to conventional lightning rods. They are designed to trigger early initiation of upward flow, which increases the effective protection range. This discharge trigger increases the probability of triggering a “streamline” discharge at or near the tip of the rod as the ionized “leader” approaches.

Fig 1 Early Streamline Emmision(plotted by AXIS house)

2.3.2 Charge Transfer System (CTS)

The CTS is characterized by its designated protection zone. It is the only system that deters lightning strikes, rather than encouraging them. CTS technology is based on existing physical and mathematical principles. The CTS collects the induced charge from the thunderstorm clouds in the area and transfers it to the surrounding air via an ionizer, thereby reducing the electric field strength in the protected area. The resulting reduction in the potential difference between the site and the clouds inhibits the formation of upward currents and thus reduces electric shocks.

Fig 2 Charge Transfer System

2.3.3 Dissipation Array System (DAS)

DAS is a special type of CTS. Based on the “protected area” of CTS, DAS can completely isolate a facility from direct lightning strikes during a thunderstorm by releasing the induced charge within the protected area to 55% of its pre-installation level in relation to its surroundings. When the electric field is reduced, the upward current does not get enough energy, and it is the connection of the upward and downward currents that is required for lightning to occur. Without energy, the connection cannot be made, so lightning cannot be generated.

Fig 3 Dissipation Array System (DAS) (Plotted by India Mart)

3. The working principle of the lightning rod

3.1 Electron distribution

3.1.1 Electrons in the earth’s crust

Before asking how lightning rods come into use, let’s first examine the function of electrons that makes lightning occurs. Before we go into how lightning rods work, let’s take a look at how electrons work to cause lightning. To begin with, the ground’s surface is made up of positive charges because the dipole cloud produces an electric field that forces electrons to flow to the earth’s core. The earth’s crust is plainly devoid of negatively charged electrons, resulting in a positively charged ground. Colors have been employed to depict the phase cancellation process, with yellow indicating negative charges and blue representing positive charges. The green hue created by combining blue and yellow is neutral, but the absence of either color gives it a bluish/yellowish appearance.

The positive charge upon the ground produces an electric field between the earth and the clouds, resulting in a negative charge covering the bottom of the clouds. And this electric field can reach 400,000 volts, creating a powerful electric field that lingers in the atmosphere. The procedure of positive and negative charge exchange in the clouds is essentially like an ion engine that repels the negative charge of the entire planet to the opposite side, according to the principle that different charges attract and the same charges repel.

We all know that when the electric field’s dipole reaches a particular level, clouds unleash lightning, which neutralizes the charge at the cloud’s bottom compared to the ground, and then the clouds repeat the process to rebuild the potential difference in the form of an exponential equation. Here’s a visual representation of this.

Fig 4. Resetting time. At t=5 the cloud releases the lightning

The time it takes to recharge is known as the resetting time, and we use it to determine the power of the ion pump described before, for which we have data of around 5 seconds. I=Q/T. When the experiment is replaced, the result is a cloud with a charge of -20C and a resetting time of 5 seconds.

Despite the fact that 4 amps may appear to be a little quantity, comparable to double the current of a mobile phone charger (2A) or the current used in street lights (4A), it will inflict a great deal of damage due to the fact that it is released from a very small hole.

3.1.2 Electrons in clouds

Clouds that carries lightning consists soft hail particles and ice particles. Soft hails has more weight than ice particles, therefore they fall to the bottom during a thunderstorm while the small crystals were uplifted to the top. This falling process allowed negatively charged hails stay at the bottom(6-8km) and positively charged ice floats to the upper part of the cloud to 10km.

3.1.3 Generation of lightning

There are three major hypothesis about how lightning comes into place.

The electric field inside a stormy cloud is far higher that what has been calibrated.

Lightning is created via hydrometeors, which means water particles in the cloud
Energetic runaway electrons initiate the lightning.

Because the overactive electrons (hypothesis 3) in the above figure are hydrometeors (hypothesis 2), ice crystals and water droplets traveling through the cloud, and the situation presented by hypothesis 2 usually boosts the electric field strength by a large margin, due to the equation of Coulomb’s law, these three points are actually interconnected.

It can be found that the electric field strength is inversely proportional to the square of the distance. Thus overactive electrons are in between many electrons of different charges, causing a huge electric field and thus the birth of lightning.

Most lightning is intra-cloud lightning, while lightning that occurs outside of clouds is divided into four main types, two from the ground to the thunderclouds, which are beyond the scope of this report, and two from the thunderclouds to the ground. One of them is downward lightning negatively-charged leader caused by a negative charge at the bottom of the cloud and a positive charge for activation, and the other is downward lightning positively-charged leader caused by a negative charge leading from the positive charge at the top of the cloud connected to the ground charged leader.

Fig 6 cloud-to-ground lightning flashes

3.2 Lightning propagation methods

The negative step leader, as its name suggests, will extend the length of the leader channel by step propagation. In the study of lightning pathways, early studies based on photography were skewed because some of the steps were too tiny to be seen with the human eye. The multiple-station dE/dt technique was utilized by J. Howard, M.A. Uman, C. Biagi, D. Hill, V.A. Rakov, and D.M. Jordan in 2011 to localize each step. When Step brings the lightning to the ground, the length charge is around 10^-3C/m, and the earth sends a return stroke to contact with it, resulting in lightning. TThis generally happens in the conductor nearest to the elevation, since lightning, like an item in an automated pathfinding system, will want to walk on the side with the least “resistance,” that is, the side with the quickest potential reduction movement. Because the charges at the ground and at the bottom of the cloud are generally different, the ground attracts the leader in this scenario.

Fig 7 This graph shows four main lightning strokes that have been witnessed in Florida, which records the waveform of the electric field they generate

Fig 9. The figure shows the path of lightning in 10 video frames plotted by Biagi et al. The lightning originates from a 150 m high cloud layer and the return stroke is shown in frame 10.[Source: Adapted from Biagi et al.]

Fig 10 The figure shows a zoomed-in schematic of the first 9 frames of the leader, and it is easy to see the trend with increased contrast and brightness.[Source: Adapted from Biagi et al.]

3.3 How do conductors work?

A conductor is a substance that electrons are relatively free to move compared to the insulators, which is the property needed to create the lightning rod–any net charge resides on the surface because ρ=0 inside a conductor according to Gauss’s law. So that negative charges are attaching on the surface of the lightning rod, making it easy to be strike. The reason is, usually a ground is conductive and there are negative charges throughout the ground. When you put a conductor such as a metal on the ground, the electrons of the ground moved to the metal, and the protons in the metal moved to the ground until the metal and the ground are equipotential and the metal and the ground can be regard as a system because of the formula E = -⊽ V , Where E represents the electric field and it equals the negative product of divergence of electric potential. When the system is a closed loop the divergence is 0 so that there is no electric field and electrons are static again. The metal can then be seen as a whole with the ground. This reasoning is also valid for conductive buildings and lightning rods, which become more vulnerable to lightning strikes as if they were a mountain range raised on the natural landscape.

What will the lightning rods do to the lightning that it had intercepted? When lightning occurs, the lightning rod can attract the discharge channel of lightning, so that the lightning current flows from the lightning rod into the earth’s land, avoiding huge currents to cause damage to buildings, equipment, trees or injury to people or animals that happen to walk above the ground.

4. The effective area of lightning rods.

4.1 Monte Carlo Technique

Abhay Srivastava calculated the protection of the lightning rods by applying a mathematic model conducting rod using Monte Carlo technique. It is a computer simulated model that randomized the distribution of lightning strokes. It assumes a concave lateral surface of the cone, using the concept of striking distance in Golde’s formula, where d_s means the striking distance, A=10 and σ = .65 are constants.

Fig 11 The coordinate system of the model

As the graph suggests, it initialized the sky with height k, the starting point of the lightning as h0, which will randomly stepping down towards the ground until it sees a postive charged object in its detecting sphere, which is assigned H_n. H_n-1 and H_n-2 are the last two steps from the striking point, and each point in this graph is given a three dimension coordinate.

The author assumes that the cube is 100*100*1000, and that a Cumulonimbus Cloud capable of storing lightning has a height of 500-16,000 meters.

The lightning will begin at a random location at the maximum height,1000m.

int[][] origin=new int[Math.random()*101][Math.random()*101];

In 5% to 80% range of the striking distance it generate some random variables to select the step length of leader.。Then it designate two angels of the spherical polar coordinates: the inclination angle α lying between π/2 and 3π/2 and the azimuthal angle β lying between 0 and 2π.

The mathematic formula is as follow. Iteration in Java should be used to infer where the lightning will strike. The loop terminates when the program determines that the lightning leader has reached the monitoring range, which is simulated as a sphere of radius 20m.

/*

*Precondition: it checks every step of the lightening

*Postcondition: true means that the lightning has successfully been
*intercepted, and therefore will not be accounted as lightenings that have

*caused damage.

*/

Public boolean inRegion(Object leader,int r){

if(Math.sqrt(Math.pow(leader.getx()-rod.getx(),2)+Math.pow(leader.gety()-rod.gety(),2)+Math.pow(leader.getz()-rod.getz(),2)<=r)}

//Euclidean distance

return true;

return false;

}

Then comes the main program for generating the path, written according to the following mathematical equation.

public int[][] stepProcess(int[][] before,Object leader,int r){

int[][] after=int[][] before;

I=leader.getx();

j=leader.getz();

K=leader.gety();

while(k!=0&&!inRegion(leader,r)){

i=i+r*Math.sin( )*Math.cos(β);

j=j+r*Math.sin( )*Math.sin(β);

k=k+r*Math.sin( );

}

if(inRegion(leader,r)){

return before;

}

else{

after[i][j]=after[i][j]+1;

return after;

}

//the higher the number in the array is, the more dangerous is the area

}

After running the program, I tested 1000 times, assuming 10 strikes per year in this area, which would be all the lightning this virtual area has suffered in 100 years, and drew a graph of the conclusions drawn, where the yellow area represents relative safety(have been struck once), green represents absolute safety, and red represents danger(more than once).

Fig 12/13 The output of the program

4.2 Real Life Application

In the 3D street view of Gaudet Map, I intercepted a dense map of high-rise buildings of about 1000*1000 and used the model for the simulation of lightning rod placement. It is assumed that all the buildings need protection, but we can ignore the open space. Here are the before-after graph of the map. When lightning rods were applied in that area, it meant to make sure every building to stay in the green or yellow circle of fig 12/13, which take the height of the lightning rods, r, as a variable and execute the program.

‘
Fig 14 An actual overview of Lujiazui

Fig 15 Protection after applying the program.

5. Reference List

1.“Modern Lightning Protection Lightning Rods with Lightning Eliminators.” Edited by LEC By admin, LEC, 19 Sept. 2018, www.lightningprotection.com/lightning-rods-are-old-new-lightning-protection-part-3/.

2. J. Howard, M.A. Uman, C. Biagi, D. Hill, V.A. Rakov, D.M. Jordan, Measured close lightning leader-step electric-field-derivative waveforms, J. Geophys.

Res. 116 (2011) http://dx.doi.org/10.1029//2010JD015249.

3. E.P. Krider, C.D. Weidman, R.C. Noggle, The electric field produced by lightning stepped leaders, J. Geophys. Res. 82 (1977) 951–960.

4. Srivastava, Abhay, and Mrinal Mishra. “Lightning Modeling And Protection Zone Of Conducting Rod Using Monte Carlo Technique – ScienceDirect.” Lightning Modeling And Protection Zone Of Conducting Rod Using Monte Carlo Technique – ScienceDirect, Www.sciencedirect.com, 13 June. 2013, https://www.sciencedirect.com/science/article/pii/S0307904X13003478?via%3Dihub.

“Franklin’s Lightning Rod | The Franklin Institute.” The Franklin Institute, Www.fi.edu, 8 March. 2014, https://www.fi.edu/history-resources/franklins-lightning-rod.

5. Godwin, Ian . “Franklin Letter To King Fans Flames Of Lightning Debate › News In Science (ABC Science).” Franklin Letter To King Fans Flames Of Lightning Debate › News In Science (ABC Science), Www.abc.net.au, 26 March. 2003, https://www.abc.net.au/science/articles/2003/03/26/816484.htm.

6. M. Vargas, H. Torres. On the development of a lightning leader model for tortuous or branched channels – Part II: model description

7. J. Electrostat., 66 (2008), pp. 489-495

8. M.A. Uman, The Lightning Discharge, Academic Press, London, 1987, 376 pages, revised paperback edition, Dover, New York, 2001.

9. K. Berger, Blitzstrom-Parameter von Aufwärtsblitzen, Bull. Schweiz. Elektrotech. Ver. 69 (1978) 353–360.

About the author

Douyun (Winnie) Shi

Winnie is a Physics learner at the Starriver Bilingual School in Shanghai, China.

The post How do lightning rods work? appeared first on Exploratio Journal.

Why Mask Compliance Differed in the United States and Taiwan During the COVID-19 Pandemic: How Individualist vs. Collectivist Cultures Respond in Uncertain Times

Alena Powell — Mon, 18 Oct 2021 14:19:24 +0000

Author: Alena Powell
Avenues: The World School
October 01, 2021

Abstract

This paper investigates why the mask compliance rates were significantly higher in Taiwan than in the United States during the COVID-19 pandemic. This distinction can primarily be represented by an individualist vs. collectivist mindset, associated with Western and Eastern countries, respectively. Mask wearing was influenced by collectivism; Taiwan’s proximity to the 2003 Severe Acute Respiratory Syndrome (SARS) epidemic and the subsequent policies implemented; cultural norms; psychological factors including higher risk attitude, sensitivity to social norms, and compliance with personal surveillance; and demographics including race, political ideology, and social class. Mask wearing is negatively associated with infection rates but is not fact based or logical: multiple psychological and cultural factors contribute to this compliance variability. Therefore, those that don’t comply are not purely defiant; individualists and collectivists just have a different belief system in what they value and how they behave. As a paper that explores reasons for noncompliance, from a public policy perspective, the message in compliance requests must be tailored to a specific belief system that serves an individual and group’s best interest while respecting personal values.

Keywords: COVID-19, mask-wearing, culture, individualist vs. collectivist, psychological factors

Why Mask Compliance Differed in the United States and Taiwan During the COVID-19 Pandemic: How Individualist vs. Collectivist Cultures Respond in Uncertain Times

COVID-19, a disease caused from SARS-CoV-2 virus, first detected in Wuhan, China, in December 2019, has been a test of responding to health regulations. Common symptoms include cough, fever, chills, loss of taste and smell, just to name a few. Most cases are mild, with symptoms persisting a few days, but some cases are very severe, requiring hospitalization. The virus has ravaged through borders and taken the lives of millions worldwide. Even though the severity of the pandemic varied by country and demographics, the COVID-19 pandemic was an experience that everyone dealt with. However, the responses, attitudes, and behaviors of the citizens of different countries shed light on how people deal during times of uncertainty. Two contrasting examples include the United States and Taiwan. These two countries have significant differences in mask wearing compliance, defined as wearing a mask when in close contact (within 6 feet) of non household members (Key, 2021).

In a literature search of studies on the mask compliance rates between Eastern and Western cultures, there were multiple studies on the compliance rates and reasoning behind this behavior in Western countries, but limited studies in Eastern countries. This would suggest that because the compliance rates are so high in Eastern countries, researchers aren’t conducting studies on why people complied or how to get people to comply, instead they’re more interested in why people DON’T comply.

According to a study conducted by the University of Southern California’s Dornsife Center for Economic and Social Research, approximately 83% of Americans agree that masks are an effective way to protect themselves from contracting Americans report actually wearing masks when in public places or in close contact with members not of the same household (Key, 2021). Another study found that 64% of Americans that report not wearing a mask responded, “It is my right as an American to not wear a mask” or “It is uncomfortable.” (Vargas & Sanchez, 2020).

The Taiwanese government, on the other hand, instituted a mask mandate with a fine between $100-500 USD for noncompliance (Ministry of Health and Welfare, n.d.; Ministry of Health and Welfare, n.d.). However, there were some reports of non-compliance in some cities in Taiwan. For instance, 604 fines were given in Kaohsiung within 1.5 days (Zheng, 2021) and 848 fines given in Taichung within 2 months (Hong, T. & Lǚ, Z., 2021). Both cities have a population of around 2.7 million, so based on this statistic it can be speculated that the non-compliance rate in Kaohsiung and Taichung is about 0.02% which is still significantly lower than the approximately 50% noncompliance rate in the United States. This finding raises questions on why there is such a big disparity.

The United States has over 330 million people with diverse backgrounds, socioeconomic levels, and beliefs. When the pandemic hit, those outside of the United States saw how a high-income country like the United States dealt with unprecedented circumstances. As of October 2021, the US has over 43 million confirmed cases and 688,000 deaths (World Health Organization, 2021).

Conversely, Taiwan is a densely populated island off the coast of Mainland China with over 23 million people. Due to its proximity to China, where the virus originated, and constant air travel to and from, Taiwan was expected to have the 2nd highest number of cases. However, this was proved to be incorrect. Taiwan along with other countries like Singapore and New Zealand were able to implement policies and community-based preventative measures to slow the rate of transmission and infection rates. By April 2020, the local transmission was at zero (The Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, 2021).It stayed that way for about a year. When comparing infection and mortality rates, as of October 2, 2021, the confirmed cases per million people in Taiwan and the United States is 680 and 131,020, respectively. The confirmed number of deaths per million people in Taiwan and the United States in 35 and 2,103, respectively (Ritchie et al., 2020). These statistics illustrate the significant contrast in the severity of the pandemic in these two countries with the US infection rate about 200 times that of Taiwan and the US mortality rate about 60 times that of Taiwan. Why is there such a major difference? How did this happen? What lessons can other countries learn and what do the actions by Taiwan tell us about their attitudes and cultural norms?

Specific factors that can explain why the Taiwanese and Americans responded differently to the pandemic lie mainly in cultural differences. These distinctions include Taiwan’s past experience with SARS, established social norms, different healthcare systems and access to resources, an individualist vs. collectivist mindset that serves as the foundation for psychological factors, and diversity in the population.

Past Experience with SARS: Proximal vs. Distal Threat

Taiwan had a greater proximal distance than the United States did to the SARS epidemic in 2003. General psychological principles suggest that first-hand experience has a greater impact on someone than watching from far away. From Taiwan’s experience with SARS, the government put policies in place for controlling another global health crisis, such as universal mask-wearing, quarantine requirements instituted in February 2020, closing down borders to foreigners in March 2020, and contact tracing systems after the first identified case in China (Taiwan Centers for Disease Control, 2020). However, Americans had no prior experience with a pandemic to this level. Given Taiwan’s past experience in dealing with a health care crisis, the Taiwanese were more familiar than Americans were with healthcare recommendations when these preventative measures were put in place to curb the spread of COVID-19. Additionally, in the beginning of the pandemic, Americans were not directly involved or affected by the pandemic because of its origin in China. This feeling was bolstered by Trump’s rhetoric calling COVID-19 the China virus, resulting in some Americans believing that they could not get the virus because they had limited a relationship with China. For instance, they weren’t Chinese or planning on visiting China soon.

Differences in the Governmental Leadership

Health Care Services

Another reason is the difference in access to health care services. In the United States, there is no universal health care. Universal health care ensures that all citizens have access to health care services when they need it without financial burden. About 8% of the US population is uninsured (Keisler-Starkey & Bunch, 2020). Given the dozens of insurance companies, including in the public and private sectors, Americans pay different fees, resulting in the fragmented health care system that provides them varying degrees of access to certain medical services. The average annual health insurance in the United States is $5,940. This number fluctuates given location and different insurance tiers. Some plans can reach an upward annual cost of $8000 (Price, 2021).

Taiwan, on the other hand, has the National Health Insurance (NHI) System which provides universal health care to 99% of the population. The NHI provides citizens with “SMART” cards, which store a patient’s medical history and records.

After the first confirmed COVID case was identified in China, Taiwan took strict actions to prevent the transmission to its island, given the frequent flights between Mainland China and Taiwan. Taiwan already had a public health agency, the Central Epidemic Command Center (CECC), instituted after Taiwan’s experience with SARS in 2003. The CECC responded to the COVID-19 outbreak and followed pre-established protocols to control a pandemic and had access to other data from various government agencies.

On January 20, 2020, when the CECC was activated, patients’ medical history from the “SMART” cards was integrated with their travel history and data. From there, a system categorized each citizen into high risk or low risk for contracting the virus. High-risk individuals were those who had traveled to high-risk areas, such as Wuhan, and low-risk individuals included those who had not traveled abroad and had no preexisting health condition. After this integrated information was stored on a citizen’s “SMART” card, low-risk individuals were ordered to buy a week’s worth of masks and could live normal lives. High-risk individuals, on the other hand, were sent into a two-week quarantine after which they could join everyone else (Wang et al., 2020; Vox, 2021). Quarantines as such were effective because it controlled the spread and didn’t rely on quarantining only symptomatic individuals, as asymptomatic individuals have a high chance of transmitting the virus before developing symptoms, if they develop symptoms (Summers et al, 2020).

Taiwan also banned foreigners from entering and in March 2020, the CECC categorized everyone flying into Taiwan to be considered high risk so they all had to undergo isolation quarantine. To make sure no citizens left their quarantine facility, the CECC tracked people’s location using cell phone data. There were also daily phone call check-ins to monitor any possible symptoms as well as occasional in-person check-ins (Vox, 2021).Taiwan also instituted a fine between NT $200,000 and NT $1,000,000 (approximately $7000 USD and $36,0000 USD) for breaking quarantine rules (Ministry of Health and Welfare, 2020).

However, studies have shown that only relying on case-based preventative measures such as quarantine and contact tracing wouldn’t have been sufficient for controlling the pandemic. Instead, population-based measures, such as wearing masks and social distancing, were useful in the initial containment of the virus (Ng et al., 2020). Taiwanese attitudes towards wearing masks and having a collectivist mindset, discussed later in the paper, also helped enforce these measures. Additionally, the then Vice President of Taiwan, epidemiologist Chen Chein-Jen, had broadcast announcements to assist citizens in population based measures such as mask wearing, frequent hand washing, and preventing mask hoarding. Similarly, the CECC set a fixed price for masks and used funds and the military to increase mask production. By January 20, 2020, when the CECC was activated, the government had 44 million surgical masks and 1.9 million N95 masks (Wang et al., 2020).With an integrated health insurance system, quarantine requirements, and resource allocation for mask production, Taiwan was organized and prepared to contain the virus.

U.S. Response to COVID-19

Compared to Taiwan’s approach, the United States’ response to the pandemic was completely different. To start off, the federal government put the responsibility of controlling the pandemic onto the state and local governments. This led to a divided nation, with different states instituting different policies, resulting largely from political ideology (Lewis, 2021).

Additionally, during the beginning of the pandemic, there was limited testing and even so, testing criteria was too high, mainly for symptomatic individuals admitted to hospitals, likely to have COVID-19 (Lewis, 2021). The Centers for Disease Control and Prevention (CDC) also released a flawed test, reporting that it could fail 33% of the time (Temple-Raston, 2020). Furthermore, the CDC reported that the spread of COVID-19 likely started in January/February 2020. However, the surveillance systems for detecting the virus and reports of flu-like symptoms were insufficient allowing the virus to spread undetected for more than a month (Jorden et al., 2020).

There was also mixed information from then-President Trump, government agencies including the CDC and the World Health Organization (WHO), and the behaviors from local officials. Examples include Trump’s denial of the seriousness of the virus as well as government agencies changing their message for mask guidance in part due to medical supply shortages for hospitals and health care workers (Molteni & Rogers, 2020; World Health Organization, 2020). The mask guidance during the beginning of the pandemic sent confusing messages for further encouragement of mask-wearing. Until April 2020 for the CDC and June 2020 for the WHO, these agencies only recommended masks for those experiencing symptoms, but it has now been established that the virus can also spread from asymptomatic individuals. Consequently, it creates confusing mask guidance as well as making it hard to know who and which government agency to trust.

Lastly, the US had insufficient contact tracing and quarantine policies put in place, which seen from other countries, such as Taiwan and New Zealand, had a role in attenuating the transmission (Lewis, 2021).

Individualist vs. Collectivist

One way to further understand the striking difference between these two countries is by looking at contrasting social and cultural norms. These perspectives can differ broadly and are learned distinctions in behavior imposed by cultures, through family, friends, classmates, and more. Psychologists that study cultural differences have found a distinction between Eastern and Western culture which provides insight into the difference in pandemic responses. This distinction can be represented by an individualist vs. collectivist mindset, ideas put forth by Markus and Kitayama. An individualist mindset, associated with many Western countries, puts the individual or self above the group. These individuals value and have personal independence. Collectivists, on the other hand, associated with many Eastern countries, have strong social ties and a sense of belonging to their group. Collectivists are more likely to agree that they are willing to sacrifice their own self-interests for the well-being of the group and that their happiness depends largely on the happiness of those around them. Individualists are more likely to agree that they often do their own thing and that whatever happens to them is their own doing, emphasizing the responsibility for personal well-being (Lu et al., 2021).

To illustrate the prevalence of individualist vs. collectivist cultures, in collectivist cultures, it’s more normal to see families of multiple generations living together. In the United States, a record-breaking 64 million Americans live in multi-generational households, including sizable immigrant collectivist populations. Asian and Hispanic populations, many of which are considered collectivist countries, are rapidly increasing in the US. Asians and Hispanics are more likely than whites to live in a multi-generational household, with approximately 29% of Asians and 27% of Hispanics doing so (Cohn & Passel, 2018). This sense of belonging and community from collectivist beliefs, carried over into the United States, include taking care of elderly and 1 putting others’ interests before theirs, such as potentially sacrificing personal health, commitments, or time to help out. Research suggests that collectivists are more likely to care for elderly family members as a means to strengthen family ties whereas individualists are more likely to limit caregiving and use formal social services as a means of support (Pyke & Bengtson, 1996).

This individualist and collectivist mindset can be used to understand how individual and group rights and responsibilities influenced behavior during the pandemic. For example, individual rights include the personal freedom of choosing whether or not to wear a mask and take the vaccine. To further illustrate, an individualist is more likely to say that they don’t want to wear a mask because it’s uncomfortable whereas a collectivist is more likely to agree that discomfort is not a valid excuse for going against group norms. Individual responsibility entails taking care of one’s health, through social distancing and wearing a mask. For instance, an individual wearing a mask for their personal health and not contracting COVID.

Group rights mean that being part of a collective gives access to specific privileges: a right to health care and access to masks and vaccines. Being a member of a group also implies specific behavior expectations. This can include taking the vaccine and following policies such as travel restrictions, quarantine, social distancing, mask mandates, to prevent others from possibly contracting the virus. These important distinctions highlight the different reasons individuals give in mask behavior, with individualists more likely to put themselves before the group and collectivists prioritizing group needs.

Cultural Norms

Even before policies for stopping the spread of the virus were implemented, Taiwan and many other Eastern countries had a norm for wearing surgical masks when experiencing the common cold or similar viruses to protect others and for taking care of the elderly or groups that were at higher risk (Jennings, 2021). So during a pandemic, it seemed normal if not obvious to be wearing masks in public places, on public transportation, and walking around. This mindset and behavior echoes a collectivist mindset present in many Eastern cultures.

For Americans, on the other hand, the preventative measures seemed unusual and unprecedented, since they’ve never experienced a global health crisis to this scale before. Consequently, the pandemic was an anxiety-provoking experience with changes in daily routine, with economic, financial, and health threats, as well as immense uncertainty: lots of unknowns from long-term COVID-19 effects, how to deal with variants, and confusing guidance on preventative measures from government officials and agencies. As a result, the link between behavior and curbing COVID-19 transmission might not have been as straightforward for Americans as it was for the Taiwanese based on different experiences and how the pandemic was handled. Along with the diverse backgrounds of its citizens, the United States found itself divided. As policies such as mask mandates and isolation requirements slowly rolled in, some Americans refused to follow these rules.

Collectivism Predicts Mask-Wearing

It has been well established that masks are an effective way to slow the transmission of COVID-19. Studies have also shown that there is a negative correlation between mask wearing and infection rates. As stated earlier, a USC study reported an approximate 50% mask noncompliance rate in the United States and reports of noncompliance in Taiwan predict an approximate 0.02% noncompliance rate (Key, 2021; Zheng, 2021; Hong, T. & Lǚ, Z., 2021).

Furthermore, studies have also shown that collectivism is positively correlated with mask-wearing. This holds true not only to illustrate the Taiwan vs. United States distinction, but also amongst many individualist and collectivist countries. Countries that scored higher on a reserve-coded scale of Hofstede’s individualism index (represented as a collectivism scale) such as the Philippines, Indonesia, and Thailand, had higher mask compliance rates than individualist countries that scored lower on the scale such as Sweden, The Netherlands, and Finland. These results are after controlling for other factors (e.g., political affiliation and government stringency) (Lu et al., 2021).

This is true not only when comparing the United States to other countries but stays consistent in the United States, with people in more collectivist regions (states and counties) more likely to wear a mask. For instance, states such as New Jersey, California, and Maryland scored higher on the state-level collectivism scale sourced from Vandello and Cohen (1999) and in mask compliance compared to states such as Arizona, Ohio, and Wisconsin, which scored lower on both the state-level collectivism scale and in mask compliance (Lu et al., 2021).

Masks can create physical inconvenience and be uncomfortable. As said earlier, one study found that 64% of Americans that report not wearing a mask responded, “It is my right as an American to not wear a mask” or “It is uncomfortable.” (Vargas & Sanchez, 2020). These actions follow an individualist mindset of protecting personal choice and freedom, but disregard that their actions can affect others (Stewart, 2020). Conversely, collectivists are more willing to put aside their personal inconvenience for the collective welfare and well-being (Biddlestone et al., 2020).

As mentioned previously, there is a $100-500 USD fine for not complying with mask mandates in Taiwan, along with limited reports of noncompliance (Ministry of Health and Welfare, n.d.; Ministry of Health and Welfare, n.d.). In collectivist cultures, the rules are more strict, with hefty consequences for non compliance, because the norm is an expectation to follow the policies implemented. In individualist cultures, on the other hand, the mandates are less strict and more complex and ambiguous because individualists are less likely to comply with rules that sacrifice personal freedom for the well-being and welfare of others. Cultural and personal beliefs can influence how rules are put into place and how people respond.

Additionally, in the US, there is a large divide between democrats and republicans based on their political ideology. Republicans can be seen as more individualist because they value personal freedom and limited government interference in daily personal matters whereas democrats can be seen as more collectivist because they value greater government intervention in economics affairs and a balance between orderly society and liberty. When looking at the difference between mask compliance in democrats and republicans, a striking difference is revealed. Democratically leaning Americans, aligned with collectivist values, have a higher mask-compliance rate than republican leaning counterparts, aligned with more individualist values, have a lower rate of mask compliance (Xu & Cheng, 2020).

To conclude, it is crucial to note that lower mask compliance rates in the United States is not because of Americans being defiant against preventative behaviors, but because of contrasting belief systems and pandemic unpredictability. These findings do not suggest that Americans are associated with various personality traits but instead shed light on the distinct cultural norms affecting behavior.

Psychological Factors

Personal Freedom and Surveillance

Psychological factors, supported by an individualist and collectivist mindset, can also influence mask wearing behavior. The first factor is the idea of personal surveillance. Collectivists are more likely to agree that groups can intrude on an individual’s privacy, especially if it’s for the greater good, since collectivists are more likely to sacrifice their personal freedom for the collective (Bellman et al., 2004). Individualist cultures are more likely to put themselves before the collective to protect their personal freedom, a value that the nation was founded on. This can be seen through the reactions that Americans had towards tracking devices. Before the pandemic, tech companies shared consumer location data with the government to make it easier to track the location of Americans. According to results from a survey in December 2020 conducted on American adults, 42% of the men who responded and 52% of women who responded were very uncomfortable with this (Johnson, 2020). During the pandemic, other companies, such as Google and Apple, used consumer data to track potential exposure to COVID-19. Over 60% of US adults found this COVID-19 exposure tracking tool to be very or somewhat concerning for their privacy (Johnson, 2020).

In South Korea, a collectivist country, government surveillance and tracking has been implemented even before the pandemic. For example, the government has access to credit and bank transaction records to prevent fraud. This system was then repurposed during the pandemic to track where people went, from restaurants to subways. Additionally, because 95% of adults own a smartphone, data location, which was originally used in criminal investigations, is now used for contact tracing. Surveillance footage utilized for investigative purposes and can now provide real time, to the minute, tracking of someone’s location. Koreans can also get sent text messages for outbreak updates. The use of South Korea’s established government surveillance network made it easier to ensure public health safety. Even though there was some talk about privacy concerns, there are limited reports on noncompliance, emphasizing the collectivist tendency to allow personal surveillance for public health purposes (Fendos, 2020).

Risk Attitude

Risk attitude is another psychological factor that affects mask wearing and can be explained through the individualist vs. collectivist mindset. Recent studies show that risk aversion, defined as less likely to engage in risky behaviors, was correlated with compliance to engage in protective behaviors during the pandemic. This was not only true in a pandemic setting but in general, with individuals that have higher levels of risk aversion less likely to smoke or engage in heavy drinking. (Xu & Cheng, 2021).

During a study conducted on Italians, results revealed that emerging adults were more concerned with their relatives and other individuals/community members contracting COVID-19, potentially through them being an asymptomatic carrier, than testing positive for COVID-19 themselves. This collectivist mindset was correlated with a higher perceived risk of infection (Germani et al., 2020). This perceived risk was positively associated with engaging in protective behaviors such as mask wearing and social distancing, a US study found (Duong et al., 2021).

Mask-wearing behavior has similarly been observed and studied in many Asian countries, including Taiwan’s long-standing cultural norm of wearing surgical masks when experiencing symptoms, such as a sore throat and runny nose, as a means to protect others, mentioned earlier (Jennings, 2021).The collectivist mindset and risk perception associated with mask-wearing in different regions can help to support the reasoning behind the Taiwanese mask compliance.

Additionally, as said earlier, amongst the Americans that report not wearing masks, 64% of those Americans said that they didn’t wear a mask because it was uncomfortable or that it’s their right as an American to choose not to wear a mask (Vargas & Sanchez, 2020). An individualist mindset provides reason for these attitudes and behaviors present in some individuals.

Sensitivity Towards Social Norms

The Taiwanese have strong responsiveness to social norms. There is a sense of pressure for wearing masks in subways and public areas. The community will also shame those for non-compliance. For instance, this mentality towards social norms is epitomized in what one Taiwanese said in a CNBC article, “We have this phrase in Taiwan that roughly translates to, ‘This is your country, and it’s up to you to save it’” (Farr, 2020).The government policies also add to this, with hefty fines, up to $500, for non-compliance (Ministry of Health and Welfare, n.d.; Ministry of Health and Welfare, n.d.).

These distinctions can again be supported by an individualist vs. collectivist mindset, in terms of emotional reactions. For example, one study conducted by Matsumoto, Kudoh, Scherer, and Wallbott (1988) found that Americans and Japanese experienced similar emotional reactions but Americans experienced emotions longer, with greater intensity and more bodily symptoms such as verbal reactions, lumps in the throat, breath changes. To conclude the study, more Japanese agreed that acting on these events when coping with these emotional situations was unnecessary, showing a weaker association between emotion and behavior (Scherer, Matsumoto, Wallbot, & Kudoh, 1988). The findings can be expanded out and offer an explanation to how individualists vs. collectivists in the US and Taiwan behaved in mask compliance. The Taiwanese held each other accountable and were less likely to act on their emotions if they didn’t fully agree/want to wear a mask. Americans were more likely to act and go against these mandates, as can be seen through countless protests across many states, even if they had felt similar levels of emotion towards masks as some Taiwanese did.

One of the possible explanations for this is that many of the emotions experienced are ego-focused emotions, meaning they mainly concern the individual’s internal attributes or characteristics. Some examples include anger, frustration, and pride. Therefore, it is logical that individualists are more likely to attend to and act on these emotions than collectivists are, say if they feel their personal freedom is being violated, because these ego-focused emotions are at the heart of an independent self (Markus & Kitayama, 1991). Through the exploration of how psychological factors influenced mask compliance, the prevalence of an individualist vs. collectivist mindset underscores the application to attitudes and behaviors.

Differences in Diversity among Populations

The United States has great diversity with Americans having their own distinct identity, from various demographics, gender, race, ethnicity, and social groups. The United States is rapidly becoming more complex, with data estimates from the US Census Bureau showing that nearly 4 of 10 Americans identify with a race or ethnic group other than white (Frey, 2020; US Census Bureau, 2021). Some Americans then form subgroups with those of similar demographic identities, and base social behavior off of their beliefs and backgrounds.

One way of measuring ethnic diversity is based on an analysis of ethnic fractionalization, the probability that two random individuals from the same country are not from the same group (race, ethnicity, or other criteria). This can be done through Fearon’s analysis in which ethnic fractionalization is on a scale from 0 to 1, with 1 being the most ethnically diverse. When comparing the numbers on Fearon’s analysis, the United States is 0.49 and Taiwan is 0.274 (Alesina et al., 2002; Fisher, 2019).

Diversity is a descriptive factor in the individualist vs. collectivist mindset, with individualism associated with more heterogeneous cultures and collectivism associated with more homogeneous cultures. This diversity in mindset can explain why some states have higher mask compliance rates, as mentioned in the “Collectivism Predicts Mask Wearing” section (Lu et al., 2021).

]From a racial perspective, in a study conducted by USC, the group that was least likely to consistently wear a mask when in close contact with non-household members were whites, with a compliance rate of 46%. Compared to whites, other races including latinos, blacks, and others had higher compliance rates with 63%, 67%, and 65%, respectively (Key, 2021). Diversity in all demographics, from race, locale, and ethnicity, had significant contributions the way individualists and collectivists engaged in mask wearing. This emphasizes the dynamic intricacies of various societies in which no single factor can predict mask wearing.

Conclusion

Culture is an important factor in behavior that has intrigued me as someone who is mixed and spends time with those of various ethnicities, races, and social groups. When the pandemic hit, I spoke to many family and friends that had completely different views on how the virus affected them and what appropriate measures they believed should be taken. At times it was overwhelming and I sought to understand if there was an underlying cultural factor at the root of different attitudes and behaviors. I found that my relatives in Taiwan had one of the most striking contrasts compared to my relatives in the United States in the way they viewed how the government and our societies should be responding.

Since the onset of the pandemic, the infection and mortality rates have been significantly higher in the United States: the US infection rate is about 200 times that of Taiwan and the US mortality rate is about 60 times that of Taiwan (Ritchie et al., 2020). I chose mask wearing as my control factor because it is a universal way to lower the rate of transmission. From talking with my family and friends, I observed that mask-wearing was one of the most heavily debated topics.

The mask compliance rates are significantly higher in Taiwan than in the United States. Through my literature search, I found multiple demographic, cultural, and psychological factors, influenced by an individualist vs. collectivist mindset, that predicted mask wearing. Taiwan’s proximal distance to SARS in 2003 resulted in public health regulations that gave public health agencies access to patient medical and travel records for contact tracing and testing. Along with this, Eastern countries have norms for wearing masks to protect others. Race, locale, and political ideology was associated with mask wearing. Psychological factors involving higher risk attitude, sensitivity to social norms, and personal surveillance compliance were affected by a collectivist mindset. As a caveat, individualism tends to be correlated with Western countries but there is still a large percentage of Americans that do not associate with an individualist mindset. This results in greater diversity within the United States and Americans having differing views of cultural beliefs. Further, Taiwan’s cultural norms and policy preparedness proved to be significant in Taiwanese compliance with preventative measures.

At the heart of a collectivist is having compassion and taking in another perspective by wearing a mask to protect others. On the other hand, a reason individualists are not complying with mask mandates is not because of pure defiance but because they have a different belief system. For instance, for some individualists, it may be harder to conceptualize that they’re part of a collective and that their individual behavior is affecting the group.

These findings are important because it provides insights into how people react to governmental health regulations during times of uncertainty. Neither individualists nor collectivists are “better” than the other. There are specific attributes of each that may better serve during specific circumstances, such as a global health crisis, but I am not stereotyping individualists or collectivists with specific personality traits. I am not here to convince anyone to change their belief system but in global health crises it may be useful to adopt more collectivist actions while also taking steps to protect themselves. This can be achieved without taking away key components of identity and protecting personal values. One big question is how can we get people to comply without making them change their belief systems?

This paper explores the reasons behind noncompliance, so we can get insight into how to frame compliance requests for individualists and collectivists in different manners with the goal of showing that mask-wearing benefits the health of the public. For collectivists, explaining how mask-wearing benefits the group. Ironically, individualists that are not complying with mask mandates are presenting potential health risks to themselves and the group; these individuals are more likely valuing personal freedom over health. When framing compliance requests for individualists, it may help to emphasize that wearing masks acts in their own interests as well as establish the link between individual behavior and group health. These changes in reframing requests appeal to the individualist and collectivist belief systems while respecting personal values.

It is also important to note that extreme collectivism and extreme individualism can also harm self-interest. To further illustrate, extreme collectivism is primarily not taking into account individual needs and extreme individualism is solely focused on personal desires. Neither of these extremes act in one’s best interest because it fails to take into account other perspectives and people.

To conclude, in everyday experiences, it’s good to find some common ground. That way different perspectives can be acknowledged to create a more informed and dynamic view of the world. Sometimes it’s better to be an individual, sometimes it’s better to be a collectivist. In general, it’s hard to change belief systems to adopt other views but being able to empathize and understand why people are the way they are is beneficial not only in a pandemic, but in daily life.

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About the author

Alena Powell

Alena is a senior at Avenues: The World School in NYC. She is passionate about the social sciences, public policy, and global/cultural studies. Her academic interests are interdisciplinary and experiential as she hopes to continue immersion in different cultures, learning about various economic and government systems, and explore pathways to apply her knowledge through social impact.

The post Why Mask Compliance Differed in the United States and Taiwan During the COVID-19 Pandemic: How Individualist vs. Collectivist Cultures Respond in Uncertain Times appeared first on Exploratio Journal.

Music Genres Effect on Heart Rate

Rishubh Madaboosi — Sun, 17 Oct 2021 13:43:06 +0000

Author: Rishubh Madaboosi
Naperville Central High School
October 1, 2021

Abstract

Purpose: The purpose of this investigation was to determine if different genres of music had an effect on heart rate.

Procedure: Firstly, please go to a quiet room with all required materials, then record initial heart rate on google form using the heart rate monitor. After this, please connect headphones to the device and set volume to medium setting. Then please start to listen to the song given in the google form’s description, and at the halfway mark of the song, please record your heart rate on google form using the heart rate monitor. After finishing the song, please record the final heart rate in google form using the heart rate monitor.

Conclusion: After analyzing the data, the result was consistent with what the hypothesis assumed. When looking at the initial, ongoing, and final heart rates for classical music, 100% of the volunteers recorded a lower ongoing and final heart rate when compared to the initial heart rate. When looking at rock music, 60% of the volunteers recorded a lower ongoing and final heart rate when compared to the initial heart rate, while the other 40% recorded higher ongoing and final heart rates when compared to the initial heart rate.

Safety Sheet and Endorsements

All the volunteers in this experiment were asked to listen to music while recording their initial, ongoing, and final heart rates. Therefore, There are no safety concerns in this experiment.

Acknowledgements

I would like to acknowledge Mr. Golab for helping through the entire process of my research and for helping me understand how to do proper research at a higher standard. I would like to thank Dr. Rohit Loomba for helping to advise me and guide me through my research project. I would also like to thank all the people who helped during the testing stage of my experiment for volunteering and taking the time out of your day to help me in my research. I would like to thank my family for helping motivate me to push through with my work. I would also like to thank them for recommending me to take up this program.

Purpose

The purpose of this investigation was to determine if different genres of music had an effect on the heart rate of a human.

Hypothesis

If classical music was chosen to lower heart rate, it would be more successful in doing so when compared to rock music

Rationale: Classical music triggers certain emotions through different dopamine levels to order the brain to send messages through the nervous system to the sinus node telling it to either lower or increase the heart rate.

Review of Literature

Music is a big part of people’s lives and sometimes affects us in ways that we don’t always notice. With the growing popularity of listening to music while doing various activities like reading, exercising, etc, the question that comes to mind which music is best? The answer depends on the activity that is being accomplished. This is because different genres of music can have different effects on your body, like heart rate, blood pressure, and others. The purpose of
this investigation, then, is to determine if different genres of music have an effect on heart rate. This will be done in order to inform the people on how certain genres can be beneficial in certain circumstances.

According to an article from the National Library of Medicine, “How Does Music Affect the Human Body,” music is known to have certain effects on different parts and physiological variables of the body. To figure out how it could have an affect on a human’s heart rate, we need to examine how the body regulates the heart rate in different circumstances. According to royalsocietypublishing.org, the brain controls the heart rate directly through the sympathetic and parasympathetic nervous systems. These two systems are a part of a bigger nervous system
called the autonomic nervous system. According to an article from byjus.com titled Difference Between Sympathetic And Parasympathetic, the sympathetic nervous system is used to respond to perceived dangers, and the parasympathetic nervous system is used as a calming mechanism. These systems release different hormones that either accelerate or decelerate the heart rate. The sinus node is the pacemaker on the heart’s right atrium that releases electric impulses which start each beat of the heart. In an article from medicinenet.com, they say that the autonomic nervous system is said to directly control the sinus node which starts the cardiac cycle. The article goes on to say that the sinus node initiates the cycle by “generating electrical impulses and conducting them throughout the muscle of the heart, stimulating the heart to contract and pump blood.”.

It makes sense then, how the brain can send a message through the nervous system towards the sinus node to either speed up or slow down the heart rate. Music affects the heart rate of a human being because certain types of music trigger certain emotions in our body. According to dailygood.org, music can be used to peak emotions by increasing the amount of dopamine in your body. Dopamine is known as a feel good hormone, meaning that an increase in dopamine in a person’s body would make them feel very happy. Music acts as an independent variable by changing the amount of dopamine that flows through the body depending on the genre of music. Dopamine therefore can be used to change a person’s emotions depending on how much of it is used. A change in emotion can cause the brain to order different organs to act in a manner that best fits the setting that the human is in. To summarize this, music triggers a certain emotion through different dopamine levels and the brain sends messages through different nervous systems ordering the heart to change its pace.

The independent variable in this experiment are the genres of music because the genres are the variables being changed in order to get a different result from the heart rate. The dependent variable is the heart rate because the level of heart rate depends on what genre of music is being played.

In previous experimentation, an article titled, “Effects of music on systolic blood pressure, diastolic blood pressure, and heart rate: a meta-analysis”, Dr. Rohit S. Loomba and Rohit Arora came to the conclusion that music has a beneficial effect in settings like emergency care units and other high anxiety environments, because it reduced heart rate, systolic, and diastolic blood pressure. This helps us understand that music does have an effect on our emotions, which in consequently has an effect on heart rate.

Other concepts that relate to the investigation could be, “What effect do different tones, tempos, and rhythms have on the entire body and the brain?” This question is similar to the different genres of music because different genres of music usually have different rhythms and beats that go along with them.

In conclusion, different genres of music cause different emotions to form based on the amount of dopamine that is produced as a result of listening to a certain genre. This then tells the brain to order certain actions from different organs to adjust to the new environment. One of these messages travel as neurons through the nervous system and go to the heart, where they order the sinus node to change the pace of the heartbeat. Based on this research, I can hypothesize that different genres of music will have an effect on heart rate and it will differ based on which genre of music is used.

Materials

1 heart rate monitor(or any device that can record heart rate)
headphones
device to play music
A quiet room to do experiment

Procedure

go to a quiet room with all required materials
record initial heart rate on google form using the heart rate monitor
connect headphones to device and set volume to medium setting
listen to the song given in the google form’s description
at halfway mark of the song, record heart rate on google form using the heart rate monitor
after finishing the song, record final heart rate in google form using the heart rate monitor

Variables

Independent variable: Genre of music (Rock or Classical)
Controlled variables: gender, age, setting of location, volume, length of each song (within 30
seconds of each other)
Dependent variable: heart rates recorded
Control: A comparison among the two genres of music

Results

Data Analysis: The data shows the heart rates for each song from different genres. Song 1 is rock and song 2 is classical music. Along with the heart rates, the table also shows the average heart rate for each song, the standard deviation, standard error, and 95% confidence interval. The graph shows the average for each time when listening to the song (initial, ongoing, and final). The error bars represent the 95% confidence intervals.

Statistical analysis: In the graphs, the error bars are not overlapping which means that the data is significant, which shows that different genres of music have an effect on heart rate. In this experiment, there was evidence showing that classical music causes a decrease in heart rate 100% of the time, and rock music causes an increase in heart rate 60% of the time.

Error analysis: one of the main causes of error in the experiment could have been the preference of music. Because different people prefer different types of music to others, this can cause an increase in dopamine which can affect the heart rate of a person. For example, someone who enjoys rock music would most likely have a more evident heart rate change, whereas someone who does not enjoy it or has no opinion on it would have a less evident heart rate change or a heart rate change that is opposite to the trend.

Conclusion

To conclude my research, the data suggests that different genres of music do have an effect on heart rate. The data shows this idea because classical music is shown to lower the heart rate 100% of the time, while rock music is shown to increase the heart rate 60% of the time. The data is significant because the error bars between the two graphs do not overlap. Some things that could have been done to make the data more accurate is to find test subjects that do not have a preference for a certain genre of music, and do not have a disliking for a certain genre of music.

Reference List

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Alessandro, S., Alessandro Silvani Alessandro Silvani http://orcid.org/0000-0003-3992-3892 PRISM Lab, Silvani, A., Alessandro Silvani http://orcid.org/0000-0003-3992-3892 PRISM Lab, Calandra-Buonaura, G., Giovanna Calandra-Buonaura Autonomic Unit, . . . Al., E. (2016, May 13). Brain–heart interactions: Physiology and clinical implications. Retrieved January 11, 2021, from https://royalsocietypublishing.org/doi/10.1098/rsta.2015.0181#:~:text=10.1098%2Frsta.2015.0181-,Abstract ,central%20preganglionic%20and%20premotor%20neurons.

Bhandari, S. (2019, June 19). Dopamine: What It Is & What It Does. Retrieved January 11, 2021, from https://www.webmd.com/mental-health/what-is-dopamine#1

Change in Heartbeat. (n.d.). Retrieved January 11, 2021, from
https://www.uofmhealth.org/health-library/aa53422#:~:text=Your%20heart%20rate%20or%20rh
ythm,changes%20in%20your%20heart%20rhythm.

Emotions and Heart Health. (n.d.). Retrieved January 11, 2021, from https://www.urmc.rochester.edu/encyclopedia/content.aspx?ContentTypeID=134&ContentID=165

Heart arrhythmia. (2020, August 09). Retrieved January 11, 2021, from https://www.mayoclinic.org/diseases-conditions/heart-arrhythmia/symptoms-causes/syc-20350668

Loomba, R., Arora, R., Shah, P., Chandrasekar, S., & Molnar, J. (2012, May). Effects of music on systolic blood pressure, diastolic blood pressure, and heart rate: A meta-analysis. Retrieved January 11, 2021, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3860955/ M;, M. (n.d.). [How does music affect the human body?]. Retrieved January 31, 2021, from https://pubmed.ncbi.nlm.nih.gov/10863350/#:~:text=Research%20has%20shown%20that%20mu
sic,influences%20immune%20and%20endocrine%20function.

Music & the Brain: The Fascinating Ways Music Affects Your Mood and Mind. (n.d.). Retrieved January 11, 2021, from http://www.dailygood.org/story/1613/music-and-the-brain-the-fascinating-ways-music-affects-yo
ur-mood-and-mind/#:~:text=Listening%20to%20music%20can%20create,brain’s%20reward%20
and%20pleasure%20centers.&text=The%20study%20incorporated%20specific%20songs%
20to%20portray%20different%20emotions.

Seladi-Schulman, J. (2018, July 23). What Part of the Brain Controls Emotions? Retrieved January 6, 2021, from
https://www.healthline.com/health/what-part-of-the-brain-controls emotions#:~:text=The%20limbic%20system%20is%20a,for%20behavioral%20and%20emotional%20responses

UC Davis Health, S. (n.d.). Heart Rate. Retrieved January 11, 2021, from https://health.ucdavis.edu/sportsmedicine/resources/heart_rate_description.html#:~:text=The%20 sympathetic%20nervous%20system%20(SNS)%20releases%20the%20hormones%20(catechola mines,to%20slow%20the%20heart%20rate.

William C. Shiel Jr., M. (2018, December 27). Definition of Sinus node. Retrieved January 11, 2021, from https://www.medicinenet.com/sinus_node/definition.htm

About the author

Rishubh Madaboosi

Rishubh is a Senior at Naperville Central High school in Naperville, Illinois. He has a passion for behavioral science and different fields involving biology. Besides his academic interests in biology and history, he enjoys playing tennis for the school team and playing clarinet in his free time.

The post Music Genres Effect on Heart Rate appeared first on Exploratio Journal.

Neural Data Analysis Using Spectral Techniques

Gitika Tirumishi Jada — Sun, 10 Oct 2021 13:07:13 +0000

Author: Gitika Tirumishi Jada
CMR Institute Of Technology, Bangalore
September 1, 2021

1. Introduction

Data science is the study of data. It is a concept that unifies statistics, data analysis,
informatics and their related methods to understand the actual phenomena with data. Data
science in an interdisciplinary field focused on extracting large data sets (for example big
data) and applying the knowledge gained from that data to solve problems in a wide
range of application domains.

The methods used in processing the data seen in this paper are similar to that of signal
processing. Digital signal processing is used to process discrete time signals. Some of the
algorithms or techniques used in this are, Discrete time Fourier Transform (DFT), Fast
Fourier Transform (FFT), Finite Impulse Response (FIR), etc,. Along with these, we
make use of spectrograms to study the properties of these signals in different domains.

In this report, we will make use of LFP (local field potential) data, which is a form of
neural data. The data is read from the brain by a certain probe inserted in it. These
micro-needles are inserted in various parts of the brain, thus giving rise to many signals
recorded at different spatial locations. We want to discuss how to perform neural analysis
of these brain signals in both the frequency and time domain, therefore we introduce the
DFT and FFT techniques as well as the short time Fourier Transform STFT and the
spectrogram. Correlations of these LFP signals are introduced towards the end of the
report to investigate the relation among the signals and give an idea on how the brain
functions when subjected to certain tasks and which parts of it are functionally connected.

2. Time series

Time series is simply the collection of data over a period of time or at different points in
time. In most cases, a time series is a sequence taken at successive, equally spaced points.
Therefore it is called a sequence of discrete time data or a regular time series. In other
cases, if the time series is not taken over equally spaced points in time, it is called an
irregular time series. There can also be a change in the number of variables, resulting in a
multivariate time series.

This time series provides a source of additional information that can be analysed and used
in the prediction process. Time series analysis refers to the relationships between
different points in time within a single series.

Often while dealing with time series and data in the time domain, we use sampling as a
method to analyse the signal. In signal processing, when we are comparing and sampling
multiple signals, we come across an effect called aliasing. This is an effect that causes
different signals to be indistinguishable when sampled if they are sampled at different
rates. It can also refer to the distortion or artifact that results when a signal reconstructed
from its samples has not the same sample rate as the original signal.

One such example of a time series is neural data signals. There are many types, the
Electro-encephalogram (EEG), the Local field potential (LPF), etc,. These are the data
taken from the brain signals. They are used in understanding how the brain works,
essentially which part of the brain has more activity when subjected to certain tasks. We
will be discussing more on the LFP in the later sections.

2.1 Time domain and frequency domain

The time domain is where signals are plotted with respect to time. Time domain analysis
is the analysis of this time series with reference to time. In the time domain, the signal’s
value is understood as a real number at various instances. A graph in the time domain
shows how the signals change with respect to time.

Fig 1: A cosine wave represented in the time domain, with a time period of 1000s. This wave was generated by mixing (adding) two different cosine waves with different periods.

The frequency domain is where the signals are plotted with respect to frequency rather
than time. Now we can say, the frequency domain analysis is the analysis of a function or
a series in the frequency domain. A frequency domain displays how much of the signal
exists within a given frequency band concerning a range of frequencies.

Fig 2: This is the same mixture of cosine waves shown in Fig. 1 here displayed in the frequency domain. As we can see, there are two frequency components, one at 10000 Hz with an amplitude of 0.9 and another at 10 Hz with an amplitude of 2.8

2.2 Fourier Transform

A given function or signal can be converted between the time domain and the frequency domain by using certain mathematical operators called transforms. The most commonly used is the Fourier transform. What this does is, it converts the time function into an integral of simple waves like sines and cosines. The spectrum of the frequency components is the frequency domain representation of the signal. The Fourier transform of a signal x(t) can be represented as

The original signal can be reconstructed by applying an inverse Fourier transform. This can be written as-

2.3 Discrete Time Fourier Transform

The Fourier transform deals with infinite number of samples, whereas the discrete time Fourier transform otherwise known as discrete Fourier transform (DFT) is a type of Fourier transform which converts finite number of equally spaced samples of a function in the time domain into a complex valued function of the same length in the frequency domain. Since experimentally we never have infinite time series acquisition, we always have to deal with finite time series and, therefore, we use the Discrete Fourier transform instead. The latter is expressed in formulas as

This creates a spectrum of all the frequency components present in the signal, similarly to the Fourier Transform. One of the many applications of the discrete Fourier transform is spectral analysis. When a sequence is represented as x{t} with samples uniformly spaced, the DFT can tell us about the frequency components of the signal or, in other words, the spectral content of the signal.

2.4 Power Spectral Density

We can also derive a power spectrum from a time series. The power spectrum S_xx(ω) of a time series denoted by x(t) is the absolute value of the Frequency spectrum obtained by taking the DFT of the said time series.

Where Y(ω) is the DFT of the signal x(t).

The spectral density obeys an important theorem, called the Parseval’s theorem which states that the integral of the spectral density equals the squared sum of the absolute value of the time signal, expressed in the following as-

In the above equation, we can notice one of the important features of this theorem is that the integral of the components in the frequency domain is equal to the sum of all components in the time domain.

2.5 Short Time Fourier Transform

Another type of the Fourier transform is the short time Fourier transform (STFT). This transform is used to measure the sinusoidal frequency and phase content of a particular window in the signal. This involves dividing the signals into shorter time segments of equal length and then computing the DFT on each segment separately. This shows us the Fourier of each segment individually. Then we plot this to see the changes in the spectra. Taking an example for a signal x(t). The short time Fourier transform of this signal is essentially the product of this function and a window function which is non zero for a particular period of time.

Fig 3: Several segments of the same signals are taken one after another and the DFT is computed for each of them

3. Spectrogram

The spectrogram is a 2-dimensional representation of the STFT where the time and frequency are expressed in the same plot on each of the two axes respectively. As we can see in Fig. 3, a shorter time segment of the original signal is considered. In the spectrogram, the squared absolute value of the power spectrum (i.e. the spectral energy density) of a segment is represented on the y axis and colored accordingly to its intensity. Consecutive DFTs are represented one after the other on the time axis vs frequency.

We consider the time signal shown in fig.4. There is an active signal between the time period 0 and 3 seconds, after which there is zero frequency for one second length. Then the active signal continues from the 4th second and continues till the end of the signal.

Fig 4: Time signal consisting of various frequencies.

Fig.5 represents the spectrogram of the time signal shown above. As depicted, there are various frequencies from the zeroth second till the third second. There is a gap in the frequencies corresponding to the gap in the signal above. This spectrogram shows which frequency has what value at precisely which instant of time. The four distinct yellow lines represent the highest of frequencies occurring in the time signal. The colour bar helps the reader understand the magnitude of the various frequencies present in it.

Fig 5: Spectrogram of the given time signal shown in Fig. 4.

3.1 Time-Frequency Uncertainty Principle

Coming to look at this spectrogram, we can wonder how one gets a precise value of the time or frequency component. There are several parameters we can adjust to achieve this precision. One of them being the size of the window we consider while taking the Fourier transform. If we have a narrow window, the temporal (time) precision will be high but there will be very few frequencies between 0 and Nyquist. As this window gets longer, there will be more frequencies so the frequency resolution will increase, but at the same time the temporal precision will decrease as the integration occurs over large periods of time. Therefore, there has to be a trade-off between the frequency resolution and the temporal resolution for us to attain a decent spectrogram.

Fig 6: Time-Frequency Trade-off

Depending on the length of the window we consider, we can have two types of spectrograms – Narrowband spectrogram and a Wideband spectrogram.

3.2 Narrowband Spectrum

Narrowband spectrogram is where the window length is long. This means, there will be more points for computation of DFT. Therefore, more frequency resolution. The drawback here is that there is less time resolution as there are many points. As shown in the figure below, the frequency lines are very sharp, indicating exactly where these frequencies lie, but the time scale is not very clear.

Fig 7: Narrowband spectrogram where the frequency resolution is very precise, but the time resolution is less accurate.

3.3 Wideband Spectrum

Wideband spectrum is where window length is short. This means, there are numerous time segments which account for precise location of transitions i.e., high time resolution. However, as the window is short, there are fewer DFT points which results in a poor frequency resolution. In Fig. 7, we can see that the timelines are very accurate but the frequency lines are vague and it is harder to identify the precise frequencies of the signal.

Fig 8: Wideband spectrogram where we can see exactly where the time events happen, but the frequency resolution is less accurate and frequency lines are blurry

3.4 Neural Data Analysis of Brain Signal

From this point forward, all the graphs and pictures have been derived from actual brain data. This data is the LFP signal from the brain of an animal when subjected to certain testing conditions.

3.5 Local Field Potential

The Local Field Potential is the electric potential recorded in the extracellular space around the neurons, typically using microneedles. They differ from electroencephalogram (EEG), which is recorded at the surface of the scalp with macro-electrodes.

When messages are transmitted from one neuron to another, there is a spike in potential known as action potential. This is what the LFP picks up. These are very refined signals as they are taken from such close proximity to the neurons, whereas in the case of the EEG, the signal must propagate through various media like the cranium, the cerebrospinal fluid, dura mater, muscle and skin.

Fig.9: Neural data taken from the same electrode: two different trials of the same experiment

The LFP data shown in Fig.9 is just two trials conducted for a particular experiment, recorded by the same electrode. Each signal corresponds to the data collected by a single electrode. These probes were located at different locations, but the data was taken during the same period of time. The cumulative of all the signals is shown in fig. 10.

Fig.10: LFP data of five trails.

The mean of all these trials is then used for computation of the spectrogram. Fig.11 represents the mean of all the signals in the five trials.

Fig 11: Graph showing the mean LPF of different trials of an experiment

Figure 11 shows the LFP that varies with time. As we can see, the y-axis has both negative and positive voltages. From the zeroth second, the signal shoots up from negative value of 4 to a positive value of 2, and then keeps on varying subsequently.

Fig 12: This figure shows the DFT of the LFP signal shown in Fig. 10

The graph in Fig.12 shows the DFT of the signal depicted in Fig.11. The graph of the positive frequencies looks like a mirror image of the negative frequencies. This is because the DFT has both positive and negative components which are similar in magnitude.

Fig 13: The expanded view of the DFT of Fig. 12 after eliminating the negative portion

The negative frequencies are eliminated and the positive ones are enhanced. As we can see, the graph is more readable now, the frequencies have distinguished values.

Fig.14: Spectrogram of the LFP data

The spectrogram of the given LFP data is depicted in Fig.14. The purple color indicates low frequencies whereas the blue, green and yellow colors indicate high frequencies.

3.6 Correlation Function

A useful tool for comparing two signals which are a function of time is the correlation function. It measures how similar two signals are. It is a function which is dependent on a certain amount of time shift. There are two types – autocorrelation and cross-correlation.

3.7 Cross-Correlation

Cross-correlation is defined as the correlation between a signal and a time shifted version of another signal. This is also known as the sliding dot product or sliding inner product. We can consider two signals x(t) and y(t) which are functions of time. The cross-correlation function can be written as-

Where s is the shift in time.

3.8 Autocorrelation

Autocorrelation, also known as serial correlation, is the correlation of a signal with a delayed version of itself. In other words, it is the observations of the time lag in the signal. In signal analysis we can use this to analyze functions or series of values. Considering the same example as above, we can write the autocorrelation functions as-

3.9 Covariance

Covariance is defined as the measure of correlation. In other words, covariance gives an exact number to the similarities between two variables. It is represented by eqn (x).

Where, x_i and y_i are data values of x and y respectively, x and y are mean values and N is the number of data values.

3.10 Pearson Correlation

Another method to calculate correlation is to find the Pearson coefficient. It is defined as the measure of linear correlation between two sets of data. As with covariance itself, the measure can only reflect a linear correlation of variables, and ignores many other types of relationship or correlation.

Eqn.(xi) represents the formula to calculate the Pearson coefficient. From this we can obtain the correlation matrix as follows-

3.11 Correlation Of Neural Data

We consider two signals of the LFP data from two different electrodes, to calculate the correlation. The two signals are represented by two distinct colors. We can see how each of these signals changes with time, and how similar they are to each other.

Fig 15: LFP data taken into consideration for calculation of correlation

We now calculate the correlation among the LFP signals acquired in 5 different electrodes at different brain locations and obtain the correlation matrix represented in Fig. 16. For simplicity, we restricted ourselves to only 5 electrodes for the computation of the correlation matrix.

Fig.16: Correlation matrix for LFP data

Note that the diagonal elements of the matrix are all ‘1’, since they are the correlation of a column with itself. Another point of observation is that this matrix is also symmetric as shown in Pearson correlation.

Fig.17: Correlation plot for LFP data corresponding to 5 data points

Fig.17 shows the corresponding plot for the correlation matrix in Fig.16. The diagonal elements of the plot are shaded white: indicating high correlation, which is true because all the diagonal elements are one. On the contrary, the elements shaded as black have zero correlation. Taking the heat map as reference, we can locate which part of the signal has high correlation and which part does not.

Here below, we plot two LFP signals vs time that have small correlation in Fig. 17, these are the electrodes 1 and 2 which show low correlation in Fig. 17. This way we can conclude to what extent these signals are similar.

Fig.18: LFP data from electrode 1 and electrode 2

We can observe here in fig.18, these signals are not very similar: the correlation indeed is very small as one can observe from the correlation matrix for these electrodes.

Fig.19: LFP data from electrode 1 and 3

Fig.19 represents data which is much more similar than the data from Fig.18, these are electrodes 1 and 3. The peaks and dips of the signal from trail 1 are consistent with that of trial 2. Taking these observations into consideration, we can propose that the parts of the brain from where this data was taken, are connected or work in coordination when subjected to certain tasks. These two electrodes have, indeed, higher correlation as one can observe from the correlation matrix in Fig. 17.

4. Coding

All the graphs and plots in this paper have been coded using python. The codes for these respective figures can be found in the link given below-

https://github.com/giti21/Neural-Data-Analysis

5. References

Mentor: Dr. Gino Del Ferraro, NYU

Time Series – Stoica, P and Moses, R. (2004). Spectral Analysis of signals. Prentice Hall. Wikipedia
Frequency vs. time Domain – Wikipedia
Fourier Transform – Wikipedia STFT
Power Spectral density – Stoica, P and Moses, R. (2004). Spectral Analysis of signals. Prentice Hall.
Spectrogram – Wikipedia Spectrogram code
Correlation – Wikipedia, Tutorial Covariance Pearson Correlation
Local Field Potential – LFP

About the author

Gitika Tirumishi Jada

Gitika is a Senior in college where she studies Electronics. She recently picked up interest in the field of Data science and its applications in the medical field.

The post Neural Data Analysis Using Spectral Techniques appeared first on Exploratio Journal.

SARS Data Analysis

Seoyeon Cho — Mon, 30 Aug 2021 03:57:20 +0000

Author: Seoyeon Cho
February 9, 2021

1. Introduction

Since the beginning of 2020, the economies and social activities of global markets ground to a halt due to the rapid spread of the novel contagious disease caused by Coronavirus from Wuhan, China. Scientists named this virus as ”SARS CoV 2” and the disease it causes as ”Coronavirus Disease 2019” (COVID-19). SARS, also known as Severe Acute Respiratory Syndrome, is a disease that can be easily spread through surfaces and saliva. It was first identified in February 2003, and has reemerged since then. The World Health Organization (WHO) first declared the new outbreak as a public health emergency of international concern in January, 2020 but soon declared the crisis a pandemic as the virus spread exponentially in March, 2020. COVID 19 has now spread to more than 200 countries in the world and has caused 1.9 million deaths.

2: R script

This is an R chunk with no plots:

>sarsdataset <- read_csv("sars_2003_complete_dataset_clean.csv", + col_types = cols(Date = col_date(format = "%Y-%m-%d")))

>#View(sarsdataset)

>names(sarsdataset

[1]	"Date"	"Country"
[3]	"Cumulative number of case(s)" "Number of deaths"	
library(readr)
[5] "Number recovered"

names(sarsdataset)[3]<-"CumNumber"

names(sarsdataset)[4]<-"Deaths"

names(sarsdataset)[5]<-"Recovered"

#install.packages("tidyverse")

library(tidyverse)

#install.packages("ggplot2")

library(ggplot2)

str(sarsdataset)
tibble [2,538		5] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
$ Date	:	Date[1:2538], format: "2003-03-17" "2003-03-17" ...
$ Country	:	chr [1:2538] "Germany" "Canada" "Singapore" "Hong Kong SAR, China" ...
$ CumNumber:		num [1:2538] 1 8 20 95 2 1 40 2 8 0 ...
$ Deaths	:	num [1:2538] 0 2 0 1 0 0 1 0 2 0 ...
$ Recovered:		num [1:2538] 0 0 0 0 0 0 0 0 0 0 ...
- attr(*, "spec")=

.. cols(

..   Date = col_date(format = "%Y-%m-%d"),

..   Country = col_character(),

..Cumulative number of case(s)  = col_double(),

..Number of deaths  = col_double(),

..Number recovered  = col_double()

.. )

>sarsdataset$Country <- as.factor(sarsdataset$Country)

# Explore data for individual country

>

>list_Country<-levels(sarsdataset$Country)

>sarsdataset$Country <- as.factor(sarsdataset$Country)

>rev(sort(table(sarsdataset$Country)))[1:5]
Thailand	Singapore Hong Kong SAR, China	
96	96	96
Germany	China	
96	96	
>Country0<-list_Country[11]
>countryindexes<-c(6,11,27)
>countryindexes<-c(1:length(list_Country))
>list_Country[countryindexes]
[1]	"Australia"	"Belgium"	"Brazil"
[4]	"Bulgaria"	"Canada"	"China"
[7]	"Colombia"	"Finland"	"France"
[10]	"Germany"	"Hong Kong SAR, China" "India"	
[13]	"Indonesia"	"Italy"	"Japan"
[16]	"Kuwait"	"Macao SAR, China"	"Malaysia"
[19]	"Mongolia"	"New Zealand"	"Philippines"
[22]	"Poland"	"Republic of Ireland"	"Republic of Korea"
[25]	"Romania"	"Russian Federation"	"Singapore"
[28]	"Slovenia"	"South Africa"	"Spain"
[31]	"Sweden"	"Switzerland"	"Taiwan, China"
[34]	"Thailand"	"United Kingdom"	"United States"
[37]	"Viet Nam"

With Sweave files, there is only one figure per chunk

countryindexes<- c(6)

for (j in countryindexes){

+   Country0<- list_Country[j]

+ sarsdataset0<- sarsdataset[ sarsdataset$Country== Country0,] + #print(sarsdataset0)

+ #ggplot(data=sarsdataset0, aes(x=Date, y=CumNumber)+ geom_line()

+

+ par(mfcol=c(2,2))

+ plot(sarsdataset0$Date, sarsdataset0$CumNumber, type="l",

main=Country0,

xlab="Date",

ylab="Cumulative Number of Cases")


plot(sarsdataset0$Date, sarsdataset0$Deaths, type="l",

main=Country0,

xlab="Date",

ylab="Deaths")

+

plot(sarsdataset0$Date, sarsdataset0$Recovered, type="l",

main=Country0,

xlab="Date",

ylab="Recovered")

+

plot(sarsdataset0$Date, sarsdataset0$Deaths/sarsdataset0$CumNumber,type="l",

main=Country0,

xlab="Date",
ylab="Death Rate")

}

3. From Google Docs

Background information

The purpose of this research paper is to critically understand the past situation of the SARS outbreak in 2003 with the objective of projecting the near future conditions of the COVID 19 crisis. This effort com-pares and contrasts quantitative information available from the two crises. It now has been almost a year since the outbreak started with significant impacts on human beings all over the world but to date, there have been no significant improvements or comprehensive solutions in countries of the world. The critical, analytical understanding of the SARS outbreak presented in this paper, improves the potential to better predict possible outcomes of the COVID 19.

4. Research Topic

4.1 Issues

Contrasting each country and analyze the SARS impact * examining the progression of disease in each country Interesting points that may arise: * Death rates may be higher in lower income countries * World Health Organization classifies countries by geography and by income level; perhaps there are patterns in expo-sure levels and death rates * When comparing progression of disease by country, accounting for different population sizes of the countries (focus on percentage increases/changes) and possibly residential density of countries. * Degree of government control on quarantines and isolation of active cases.

4.2 Connection between the covid and SARS

Countries that experienced SARS were possibly better prepared to deal with COVID-19 (e.g., testing protocols and other .)

SARS – data, analysis (organized discussion with logical flow) (list the actual number on the data)

5. Plots from GGPlot

6. Analysis

The similarities between the countries that had most death rates.

This graph indicates the death rates of different countries along the time due to the impact of SARS outbreak back in 2003. One can easily notice the highest deaths has been recorded by China, second by Hong Kong, third by Taiwan, and fourth by Canada.

One can also notice that these four countries can be grouped into two parts , where one has recorded relatively significant number of deaths and another with less significant death rate. China and Hong Kong can be grouped as ”most death rate” group whereas Taiwan and Canada can be grouped as ”non-significant death rate” group. This separation could have been done easily because of the distinct difference in the number of deaths.

The very distinct similarities that ”most death rate” group shared was the exponential growth of number of death from April to late May. The green and blue graphs showed almost an identical exponential growth during that time span and slowly stopped increasing and remained almost constant from June to July with constant death rate of 380 and 300 for China and Hong Kong respectively.

Other than the information from given graph, one can also assume that the ”most death rate” group were similar in a sense that they were both sharing / touching the border of China. This may be very obvious and does not seem like an important fact but can’t be ignored as both SARS and COVID-19 were emerged in China and spread to other countries rapidly. (Add another graph with percentage deaths (death rate) as a percent of cases to compare countries)

The similarities between the countries that did not have many cases From the figure 1, one can also notice the similarities between the countries that did not have such high death cases; Taiwan and Canada. It is that compared to ”most death rate” group, the increase of the death case of these two countries was not very exponential but quite steady. Canada showed very linear increase of death cases along time and Taiwan with small exponential growth during May but not as significant as that of China or Hong Kong.

The another distinct similarity that one can know is that these two countries, Canada and Taiwan, are not touching China’s national border directly. There may be a lot of Chinese immigrants in Taiwan and Canada but they both are not directly touching China in terms of country’s borer line.

7. Worst Experienced Country

In order to pin down which country experienced the worst outbreak, one has to define ”worst experience” first. This is important because one could think the country with most death cases had the worst experience whereas another can argue that the worst experience should be based on the percentage of number of death per national population. In this report, the ”worst experience” will be based on the total number of SARS case. This is because the absolute amount of people who suffered from this disease matters more than the numbers of death rate or the number of deaths. According to figure 2, it is clear that China had the highest cumulated number of SARS case among 4 countries with overwhelming 4800 cases, almost the double of Hong Kong’s SARS case

8. Worst Impacted Country Among Top Rated Countries

Just like how ”worst experienced” was defined in the past paragraph, ”impact” will be defined as how much of a shock SARS brought to a country. In other words, ”impact” will be measured based on the percentage of death per the nation’s population.

Taiwan: 24 mil Canada: 38 mil Hong Kong: 7.5 mil China: 1.4 bil

Even without the detail calculation, one can see that the death / nation pop-ulation is the highest for Hong Kong as it has the smallest number of population but almost the same number of death cases according to figure 1. Therefore, one can argue that Hong Kong had the worst impact among the four given countries.

9. Seriousness of SARS Crisis

Unlike other diseases like cancer or heart disease, the problem with SARS was the fact that this disease was so contagious. This is important because person with cancer or heart disease can live their normal life even after recognizing they have those diseases. However, in case of SARS, the other people who do not carry the disease cannot function properly because of the fear that this unseeable virus might be traveling as an aerosol in mid air and infect them. This fear cannot be sorely explained or quantified with graphs and numbers on figures. Which also explains why the numbers and the figures can not properly explain the seriousness of SARS. SARS did not only kill people but killed the economics of not only these four countries but the many other countries who were closely related to China. Obviously, the death of many people is a deep pain that can not be forgotten but the economic damage that SARS caused was very significant.

From figure 3, one can distinguish the differences and similarities between four countries.

First of all, Canada showed a very distinctive characteristic that other 3 countries never shown. During the late February, the deaths/cumnumber of Canada spiked all the way up to 0.3 but it soon died to 0.1 and slowly increased and remained constant at about 0.15. This kind of instant spike, decrease and remaining of the rate has not been shown in any other countries. The other two countries, Hong Kong, and Taiwan, never shown an instant spike like Canada did but rather slower exponential increases. However China has shown a very different rate graph as well. China was the only country that had linear increase among 4 countries in figure 3.

For the death rates why does death rates stop dramatical increases during June?

From the figure 1, one can see that the exponential growth of death rate for China and Hong Kong has been remained until early June but the growth has significantly died from June. A lot of reasons could be the answer to this dra-matic ease of SARS such as the change of weather, countless efforts of scientists, researchers, and doctors to stop the disease. As all four countries’ death rate has decreased dramatically as time went by, one could assume that the obvious change of weather (from winter to summer) along time has helped the human population to stop the contagious disease, SARS. Another reason for a sudden ease of exponential growth of SARS would be the fact that all four countries’ governments were well aware of the situation with the help of WHO and well managed to stop the spread of virus.

There could be multiple reasons for a stop of dramatical increase during June. One is the change of weather from winter to summer and the increased temperature. Another is the cumulated effort of human beings to stop the virus with research for cures and governments’ desperate effort to stop people from going out.

The country that had most severe crisis and what happened to their country Among these four countries, Hong Kong had the most severe crisis with the highest percentage of both death and cumulated case number per nation’s pop-ulation. The death rate of SARS case in Hong Kong were usually around 20% but it went up close to 55% among elders.

Because other normal respiratory diseases’ death rate were about 1% back then, the SARS did not seem to be a significant issue at the beginning of the outbreak. However, the death rate was significantly high specifically in Hong Kong since they were most vulnerable to the disease as they had the highest portion of elders among the four given countries in the data. This was a very significant fact that changed the SARS’ outcome back in 2003. Furthermore, Hong Kong was not very well prepared for these kind of outbreaks as they have one of the highest population density in the world which meant Hong Kong was the best possible option for SARS outbreak to exponentially damage the country and the economics of it.

Due to SARS outbreak in 2003, Hong Kong’s tourism industry had to face the worst period so far. As you could imagine, with this outbreak ongoing in Hong Kong, no one in other countries would have voluntarily wanted to visit and explore Hong Kong because of the SARS. According to Paul Chan Mo Po, finance minister of Hong Kong in 2003, the number of foreign visitors decreased to 40% compared to that of 2002. This kind of economic impact did not only hurt the tourism industry but the local and international economics of Hong Kong as well. This damage was more severe for Hong Kong since they had such a dense country compared to others like Canada or China in terms of both landscape and population density. It completely frozen the economics and the country and took years of effort for Hong Kong and their people to recover from SARS.

10. Prediction

Compare the conclusion of sars and the current cove pandemic SARS could have been completely controlled within only 6 months of time with international effort back in 2003 but the current ongoing COVID 19 pandemic already has surpassed 12 months of time and does not seem to end in the near future. This could be explained with the fact that back in 2003, the world was not as connective as it is now. For instance, traveling was treated as a huge privilege back in 2003 and was not a very common. However, as the world started to globalize exponentially, traveling was not a big privilege for mange people and became common for everyone in 2021. One can believe that the significant reason why SARS could have ended much faster, only in 6 months, compared to COVID was because it was not as globalized back in 2003 as it is now in 2021.

SARS ended only in 6 months and only caused 774 deaths but the current COVID already has killed more than 1.9 million people internationally all over the world. It is very distinct that the SARS was only spread to few countries whereas COVID has been spread to more than 200 countries. This may be the reason why COVID is not concluding like how SARS has concluded, the exponent globalization of the world.

11. Comparison Between SARS and COVID 19

SARS and COVID 19 share the very same virus called coronavirus which is very contagious and causes respiratory diseases. These two also are very similar in a sense that these diseases are much more dangerous for elders or one with existing diseases like obesity, cancer or respiratory disease than others.

It is also similar how the outbreak originated from China and spread to other close countries. As Hong Kong is literally next to China and easy to travel, the virus was able to spread in Hong Kong rapidly just like how COVID19 had spread in Hon Kong as one of the earliest countries in the first several months of the outbreak.

12. Conclusion

In conclusion, the SARS and COVID 19 share the very similar characteristic in a sense that both of them are caused by coronavirus, are very contagious, and are dramatically more dangerous to elders and people with existing diseases. Back in 2003, people were able to successfully stop the SARS as the world was not as globalized as it is now with efforts of scientists, researchers, doctors, and the governments as the virus only spread to few countries. However, the COVID19 now has been spread to more than 200 countries and caused almost 2 million deaths. From the close observation of SARS case, one can conclude that it has surpassed singularity point but the time, effort from researchers and governments will stop this COVID19 in foreseeable future.

13. References

Seladi-Schulman, J. S. S. (2020, April 2). COVID-19 vs. SARS: How Do They Differ? Healthline. https://www.healthline.com/health/coronavirus-vs-sars

Severe Acute Respiratory Syndrome (SARS). (2019, November 1). World Health Organisation. https://www.who.int/health-topics/severe-acute-respiratory-syndrome

E.P.M.K.U.G.D.H.H.N.P.F.C. (2020, July 3). Comparing SARS-CoV-2 with SARS-CoV and influenza pandemics. The Lancet. https://www.thelancet.com/journals/laninf/article/PIIS147 3099(21)00054-2/fulltext

About the author

Seoyeon Cho

The post SARS Data Analysis appeared first on Exploratio Journal.

Can Perovskite Solar Cells Power Aircraft?

Advait Harish Shirbhayye — Mon, 21 Dec 2020 14:52:25 +0000

Author: Advait Harish Shirbhayye
Hiranandani Foundation School
December, 2020

Introduction

It is no secret that aircraft are gas guzzlers. They account for nearly 3% of the total C02 emissions in the United States, although not many can complain about this, as air travel is indispensable. However, what we can do is use our advances in science and technology paired with our innate curiosity, to solve this pressing issue.

Historically, there has been a lot of research regarding the use of silicon and gallium arsenide solar cells to power aircraft, however Perovskite Solar Cells (PSCs) have been a rather recent innovation, so minimal research has been conducted in that regard. In this article, I shall discuss everything from the basics of PSCs to their potential applications in aircraft.

Basics of PSCs

Fig 1. Promotion of electron to conduction band upon absorption of light. A hole is left behind in the valence band. (https://outreach.phy.cam.ac.uk/

When light (photons) strikes the perovskite layer, the energy of the photons energizes the electrons, thus imparting kinetic energy. This kinetic energy enables the conduction of electrons in the electron transport layer (ETL) (See fig.2). However, something interesting happens. When an electron ‘moves’ up to the ETL and conduction band, it leaves a gap in its original position. This gap is filled by a particle called a ‘hole’. Holes essentially have the opposite charge to an electron. The concept of holes stems from the Law of Conservation of Charge. The holes then migrate to the Hole Transport Layer (HTL) (See Fig.2). The electron-hole pairs generated are conductive, so they can move about in their respective layers, and moving charges produce electricity. This is the basic principle of a solar cell.

Fig. 2:
https://www.ossila.com/pages/perovskites-and-perovskite-solar-cells-an-introduction

The mesoporous Tio2 layer is engineered such that the perovskite material can be drawn into it, which increases the surface area for photon absorption, thus increasing the efficiency. The optimal thickness for PSCs is in the ballpark range of 500nm (which is almost 50X thinner than a human hair!).

Desirable properties of PSCs which make them ideal to power aircraft

PSCs are flexible and mechanically durable. This is the single most important property of a PSC, as it can withstand the stresses of flight. Research conducted into using PSCs as a wearable source of energy quantified the mechanical durability of PSCs. The researchers conducted numerous bending cycles and analyzed FEM simulations. These are the strategies they used to make PSC’s flexible which are published in the Royal Society of Chemistry Journal (Citation: Energy Environ. Sci., 2019, 12, 3182):

They introduced a parylene protective layer on top of the PSC, which protected the perovskite layer from scratching, contamination, etc. and shifted the neutral plane (NP) to the photon absorbing and mechanically unstable perovskite layer (See Fig.3). A neutral plane is essentially the plane where the strain is zero, as the compression and tension of bending is balanced out. Thus, shifting the NP to the mechanically sensitive layer is desirable, as no strain would be experienced.
Bending can reduce the conductive power of a PSC, so to find the optimal tradeoff, a gold metal mesh was introduced.

Fig. 3 (Citation: Energy Environ. Sci., 2019, 12, 3182)

In separate research, a scaffold like structure was designed to be placed within the mechanically fragile perovskite layer, to internally reinforce the layer (See Fig.4). This ‘honeycomb’ structure divides the layer into different grids, thus isolating defects to one grid area only, and the mechanical durability imparted makes the cell resistant to the mechanically harsh environment of flight.

Fig. 4 (Citation: Energy Environ. Sci., 2017, 10, 2500)

Fig. 5 shows a magnified section of a microcell within the honeycomb structure. As you can see, there is a continuous point of contact between the perovskite material and the scaffold wall. Since there is contact, adhesion must prevail, i.e. the two materials must be ‘glued’ together. This can be achieved by mechanical interlocking, that is the static friction between the two layers. Mechanical interlocking can be enhanced by increasing the roughness between the two surfaces.

Fig. 5 (Citation: Energy Environ. Sci., 2017, 10, 2500)

However, a new way to increase adhesion we researched is the use of organosilicate. In a nutshell, organosilicate is a good adhesive. It also possesses extra durability as organosilicate has Si (silicon) bonded to the classic organic compound C-C bonds. Organosilicate is also a good diffusion barrier. Perovskite is highly sensitive to moisture, so organosilicate could also act as a good protective barrier/layer. These qualities make organosilicate promising for use in PSC powered aircraft.

You may have realized that a C60 layer has been added below the perovskite layer in Fig.4. But why? This has to do with the inherent salt-like weak nature of perovskite.

Fig. 6: science.sciencemag.org/content/367/6482/1097

As you can see in the figure above, the structure of perovskite has many gaps/defects. These make the material fragile and the solar cell loses efficiency. Thus, to correct this, C60 (fullerene) acts as a defect filler by filling in the gaps.

On a side note, graphene is an exciting innovation that could be used to make the grid structure discussed above. Although graphene is difficult to control and harness at this stage, new research emerges every day that makes the mechanical use of graphene inch closer to us. In fact, researchers from the University of Texas at Austin discovered that by limiting the oxygen during graphene crystal growth, much larger crystals can be obtained, which could have potential uses in flexible electronics.

Can PSCs power aircraft?

Now that we have discussed the properties and mechanics of PSCs, we can move forward to the actual integration of PSCs into aircraft.

Temperature decreases as altitude increases. At such cold temperatures, the efficiency of PSCs increases, because the hysteresis energy loss is reduced considerably. Hysteresis is the resistance offered to the flow of electrons by iodine ions. At low temperatures, the movement of iodine ions is lowered, which is beneficial. Resistance due to phonons (heat particles) is also reduced.

What aircraft surfaces can harbor the solar cells?

The wings and upper fuselage surfaces. The PSCs cannot be placed on the control surfaces of aircraft such as the ailerons and flaps, but the relatively flat mid-section of the wing is suitable.

The upper part of the fuselage is displayed below:

Figure 7. Illustration by author

However, because of the inherent bi-axial curvature of the fuselage, the surface is not flat, which affects the angle of incidence of sunlight. To correct this, micro lenses could be used. The material for the micro lenses needs to withstand the stresses of flight, thus organosilicate or reinforced carbon glass may be the best candidates.

On the ground, the solar power we get on a daily basis comes from operating solar farms containing solar panels. All of these solar panels are divided into individual modules connected by conducting bus-bars. Generally, each module is connected to a micro-converter which converts DC to AC current. However, this requires a large and heavy transformer. This is not feasible for aircraft due to weight constraints, so a Super Capacitor may be used instead. Super Capacitors have high power densities as well, which is great for instantaneous thrust (bursts of power). This is achieved by placing an ion-permeable membrane between two charged plates.

Batteries will also need to be modified and enhanced for cold temperature operations, and current research is quite promising.

Current technology makes a PSC powered aircraft tricky to design. However, the field moves at a rapid pace and detailed research can be conducted into this topic. According to calculations we ran using a simple model, we can roughly suggest that PSCs, when used on the surfaces of an aircraft mentioned above, can produce approx. 10-12% of the required power of a current aircraft.

Our model accounted for the bi-axial curvature of the fuselage by including an angle x:

The aircraft we used for our calculations was a Cessna 172 Skyhawk, one of the most popular general aviation aircraft ever produced. However, do keep in mind that the Cessna was developed many decades ago. Technology has obviously advanced a lot since then, and we can design an ultra-light aircraft using lightweight composites, which could potentially fly for 30-60 minutes while being powered solely by PSCs. Such short-haul flights are very useful for logistics. For instance, Amazon is developing special drones to deliver packages. These drones could potentially be replaced by ultra-light aircraft powered by PSCs, with larger cargo capacities and higher weight limits This could be an ideal testbed to further expand this idea.

Moreover, aircraft have APUs (Auxiliary Power Units) that are used to provide initial electrical power to the aircraft to start its engines. Aircraft also spend a large chunk of their time taxiing to the runway, which requires propulsion by the engines. So, if the APU could be enhanced to provide power for taxiing, the valuable PSC power could be conserved and used only for flying.

Conclusion

As you can probably tell, this is an exciting prospect, and PSCs do have many desirable qualities that can enable them to power aircraft. Of course, many innovations will need to be developed, however it does seem quite likely that this rather bold thought experiment can come to life within the next 20 years. Electrically powered aircraft can reduce CO2 emissions anywhere from 40 to 50X the current emission rates. This is something to look forward to.

Works Cited

Royal Society of Chemistry Journal (Citation: Energy Environ. Sci., 2019, 12, 3182):
https://www.graphene-info.com/ut-austin-researchers-grow-large-graphene-crystals-exceptional-electrical-properties
outreach.phy.cam.ac.uk
www.ossila.com/pages/perovskites-and-perovskite-solar-cells-an-introduction
Citation: Energy Environ. Sci., 2019, 12, 3182
Citation: Energy Environ. Sci., 2017, 10, 2500
science.sciencemag.org/content/367/6482/1097

About the author

Advait Harish Shirbhayye

Advait is a senior at the Hiranandani Foundation School.

The post Can Perovskite Solar Cells Power Aircraft? appeared first on Exploratio Journal.

The Relationship Between the Dark Triad Personality Traits and Decision Making

Sijia (Scarlett) Dong — Mon, 10 Aug 2020 08:49:05 +0000

Author: Sijia (Scarlett) Dong
Foxcroft School
May, 2020

Introduction

Decision making has always been an important role in human life, especially in economics. In the market of oligopoly competition, a few companies set the price interdependent from each other, competing using the game theory. Each decision they make on the price determines their gains and losses. In economics, people serve as rational agents who make decisions that maximize their utility; however, this is not normally the case. Individuals’ decisions are affected by various factors (e.g. personality, educational background and also psychological state characteristics such as mood), and people do not always choose the profit-maximizing decision. The present study examines the relationship between personality and economic decision making.

Personality is closely associated with behaviors, cognition, and emotions. Assessing personality can be used to predict behavior tendencies. One way to measure personality is by using the Big Five personality test. The Big Five personality traits includes conscientiousness (disorganized / impulse vs. careful / disciplined), agreeableness (ruthless / suspicious vs. soft-hearting / trusting), neuroticism (calm / secure / self-satisfied vs. anxious / insecure / self-pitying), openness (practical / conforming vs. imaginative / independent), and extraversion (retiring / reserved vs. sociable / affectionate) (McCrae & Costa, 2008). The Big Five personality factors are relatively stable in adulthood and appear to be present in every culture. It also clearly depicts the whole picture of individual’s traits and as a result, studies mostly apply the Big Five personality test for predicting behaviors.

The present study employed the Dark Triad personality measure as a form of personality assessment. The Dark Triad is a measure of malevolent personality developed by Paulhus and Williams (2002). People who score high on the Dark triad are more likely to commit crime and cause social distress. The three dimensions of the dark triad are Machiavellianism, Narcissism, and Psychopathy. Narcissism stands for strong ego, greediness, and a sense of superiority and dominance. Machiavellianism stands for the manipulative, calculating, and amoral personality and people with high levels of Machiavellianism typically focus on self-interest and self-gain. Psychopathy, on the other hand, is marked by cruelness, impulsivity, and anti-social behaviors. The Dark triad, in general, is related to antisocial behaviors, such as aggression and violence, and emotional deficit, such as a lack of empathy and theory of mind (Paulhus & Williams, 2002). It is also related to poor well-being such as loneliness and anxiety. People who score high on the Dark triad are more likely to score low on agreeableness in the Big 5 personality.

Personality has been linked with cognitive functioning, such as attention, reaction time, but also decision making. One study found that personality measured using the Big 5 questionnaire influences decision-making during the Ultimatum Game, which measures the willingness to share (Fiori et al., 2013). In the Ultimatum Game, the first participant was asked to divide 10 dollars with the second participant. If the second participant rejects, then neither can get the money. According to the theory of rational agent, the proposer will always offer the smallest amount, and the receiver will always accept. However, the experiment findings do not agree with the prediction. It was found that participants who scored higher on conscientiousness and lower on extroversion were more likely to accept the offer in the game (Fiori et al., 2013). Individuals with high neuroticism seem to have decreased willingness to take risk. In another study, participants were asked to choose between a lottery and a certain amount, six times. The lottery is always the same but the fixed payment varies in each choice. The task was repeated four times. For the first two times, the lottery is positive, and the lottery can incur a loss of $1 for the third time and $5 for the last time. The researchers would record the number of times participants choose the lottery over a fixed payment and associated these with the person’s personality (Anderson et al., 2011). In the experiment of sequential prisoner’s Dilemma, two players each had $5, and the first player decided whether to send $5 or $0 to the second player. The amount would double after the transfer. Then the second player decides to transfer back a number between $0 and $5. The researchers recorded the money transfers and found that agreeableness increases the fraction of first moves transferring $5 and increases also the average amount transferring back. Cognitive skills and IQ also influenced the result (Anderson et al., 2011). Another study found that individuals with Dark Triad personality traits were more likely to engage in opportunistic decision-making, which is the actions assessed toward an immediate circumstance in favor of the individual or company (D’Souza & Lima, 2015). In business, people with Narcissism are visionary; they seek superior position to influence over others and make decisions that are beneficial to their reputation. On the other hand, narcissistic people are also innovative with prominent leadership with the ability to lead their company to glory. Evidence has shown that most managers and people in executive positions in a corporation score low in Psychopathy; they are not self-centered, cruel, or cynical, but can contribute to the longevity and gains of the companies. People with high levels of Machiavellianism seek management and leadership positions, which allow them to manage, control and manipulate people inferior to them. They focus on their personal power and can easily influence others (D’Souza & Lima, 2015).

Most previous research has been done on the Big 5 personality measure in relation to decision-making, including willingness to share and take risks and the game theory, while the relationship between the Dark Triad and various decision-making games has not been assessed in detail. Moreover, previous research mostly focuses on adults rather than younger population groups. The present study aims to explore the relationship between the Dark Triad personality and economic decision-making, specifically, the wiliness to take risk, in a female sample of high school students. It was hypothesised that the Dark Triad personality traits would be associated with high school students’ decision in risks-taking contexts.

Method

Participants and sampling

Participants in the present study were female students from an all-girl high school in Virginia (US), with the age range from 14 to 20. Convenience sampling method was used: the questionnaire was sent to the school email, so every student in the high school had access to the questionnaire and could fill it out voluntarily. It was attempted to obtain a homogenous sample, with all participants being females with similar age range and similar education level.

Design

This study was based on a non-experimental cross-sectional design. The dependent variable was decision-making/ risks-taking and the independent variable was personality based on the Dark Triad.

Measures

Personality was measured by the short Dark Triad questionnaire (Jones & Paulhus, 2014). It contains 27 questions: 9 questions measured Machiavellianism (e.g. “I like to use clever manipulation to get my way”), 9 questions measured narcissism (e.g. “People see me as a natural leader”), and 9 questions measured psychopathy (e.g. “I like to get revenge on authorities”). Each item was rated on a 5-point Likert-type scale (strongly disagree-strongly agree; Jones & Paulhus, 2014). Sum scores for each of the sub-scales and the overall global scales were computed and higher scores indicated higher attributes of the respective trait.

Decision-making was measured by Iowa Gambling Task on the online simulation on Psytoolkit (Stoet, 2010; Stoet, 2017). Participants would be given $2,000 at the start and asked to choose from one of the four card decks (A, B, C, and D) for 50 trials. Deck A and B yield $100 each time and deck C and D yield $50. Each time a participant chooses a deck of card, there is a 50% chance to get a penalty. The penalty is $250 for choosing deck A and/or B and $50 for choosing deck C and/or D. Participants need to gain as much money as possible. Without knowing how much each deck of card yields and the amount of penalty it incurs, participants would have to make a decision each time. The online experiment recorded participants’ reaction times (used to determine cognitive skills), their choice, whether there was a penalty and money they gained and lost. This study focused on how many times participants chose deck A/B versus deck C/D to determine the willingness to take risk: the more deck A/B was chosen, the more willing the individual to take risk. An index was calculated to determine decision-making based on A/B choices divided by C/D choices and values >1 indicate risk taking and <1 indicate risk aversion.

Further, math ability was assessed with rating from 1 to 5 (very low – very high); math liking was assessed as 3 ratings (Yes/No/Neutral). Students reported whether they have studied Psychology or Economics (Yes/No) and also gave their mood ratings on a 3-scale (Low or negative mood/Neutral mood/High or positive mood).

Statistical analyses

All data analyses were performed on SPSS (IBM, version 25). Normality tests were performed on the outcome measure and revealed that decision-making was highly positively skewed (Skewness: 4.00 and Kurtosis: 17.70) and the Shapiro-Wilk test (p<.001) suggested violation of normality. Log-transformation (ln method) was applied and normalised the distribution (Skewness: 0.03 and Kurtosis: 2.70; Shapiro-Wilk test, p=.080) and hence the log-transformed data was used with parametric testing. Robustness checks were conducted prior to analyses. To explore whether individuals who dropped out prior to the DM task differed in personality from individuals who completed the survey three independent-sample t-tests were conducted. Findings showed that the three sub-scales Machiavellianism, Narcissism and Psychopathy were not statistically significantly different between the non-completers and the completers (all p’s >.410), suggesting that the final sample seems to be representative of the whole sample that took part.

Results

Descriptive

Table 1 depicts the characteristics of the sample. The sample was aged on average 16.12 years (SD=1.30). The average maths ability was 3.31 (SD=0.84). Most students had not studied Economics (80.8%) or Psychology (80.8%).

From the personality traits, the average score on Machiavellianism was 3.27 (SD=0.59); the average score on Narcissism was 2.77 (SD=0.60) and the average score on psychopathy was 2.15 (SD=0.70). The global score of the three traits was 8.19 (SD=0.87).

The mean score of decision-making after applying log-transformation was 0.09 (SD=0.87), suggesting that participants on average were more risk-taking.

The effect of personality on decision making

Bivariate Pearson’s correlation analyses were conducted and showed that Machiavellianism was not correlated with DM, r=.217, p=.286. Narcissism was also not related to DM, r=-.214, p=.293. Psychopathy was also not related to DM, r=.082, p=.690. Further, the global score was also not related to DM, r=.035, p=.865. This suggests that the three personality traits were not related to DM. The other study variables, i.e. maths levels, maths liking and mood did not associate with DM either (all p’s >.392).

A multiple linear regression model was run to explore whether there is an effect of personality on DM, when controlling for the other potential confounding variables (i.e. maths levels, maths liking and mood) and the findings revealed that none of the variables predicted DM, F(6,19)=.507, p=.796.

Table 1. Study variables for the full sample.

Variable	Full sample (N=26) M(SD)/N(%)
Age	16.12 (1.30)
Maths ability (1-5)	3.31 (0.84)
Maths liking Yes No Neutral	12 (46.2) 8 (30.8) 6 (23.1)
Economics study No Yes	21 (80.8) 5 (19.2)
Psychology study No Yes	21 (80.8) 5 (19.2)
Mood Low/negative mood Neutral High/positive mood	4 (15.4) 20 (76.9) 2 (7.7)
Personality Machiavellianism Narcissism Psychopathy Global score	3.27 (0.59) 2.77 (0.60) 2.15 (0.70) 8.19 (1.42)
Decision making (ln)	0.09 (0.87)

Discussion

The present study aimed to explore the relationship between personality and decision-making in the context of risk taking. The study employed young females as a sample. It was hypothesised that Dark Triad personality traits (Machiavellianism, Narcissism, and Psychopathy) would influence decision-making on risk taking. Decision making was measured with the Iowa Gambling task which assesses the risk taking vs the risk aversive behaviour in terms of gaining financial rewards and paying penalties for wrong decisions. The findings revealed that there were no relationships between Dark Triad personality traits and risky decision-making in the present sample.

The results are different from previous findings, which have consistently found that the Dark Triad personality traits are influencing decision-making. One previous study indicates that people with the Dark Triad personality traits (indicating higher levels of Machiavellianism, higher levels of Narcissism, and higher levels of Psychopathy) are more likely to make decisions that are in favour of themselves (D’Souza & Lima, 2015). However, the present study shows no relationship between these personality traits and decisions that make them better off (i.e. gaining more money and paying lower penalties). One explanation for the null findings is that the sample had relatively low scores within each of the personality sub-scales and low variability in the sample in those personality traits reduces statistical power and hence impedes potential findings.

Previous studies focus on adults, while the present study has been conducted using high school students, a sample in which potentially financial risk taking behaviour is rather low (as they experience these risk taking behaviour in daily life to a smaller extent). A previous study had shown that personality influences acceptors’ decisions in the Ultimate Game but not proposers’ decisions, and more honest people gained more and more introverted people accepted more often. The study was conducted on a sample aged 18 – 44 (Fiori et al., 2013), suggesting that these relationships might occur in a wider age range, including adults but not in younger samples. One study of 91 undergraduate students (aged 18-28) from Ohio University consisting of 90% Caucasian explored the Iowa Decision-making Task and found a relationship between personality and deck selection, however, personality traits and mood have been shown to impact the performance of the participants (Buelow & Suhr, 2013). The BIS/BAS scale was used for measuring personality and also mood measurements were implemented. The results show that participants with high level of drive and impulsivity tend to focus on short-run gain by choosing more from deck A and B. It also found that individuals with high negative mood selected more Deck B than Deck C (Buelow & Suhr, 2013). However, the present study showed no impact of mood on decision making, which might attribute to the low variability in mood.

One limitation of the study was the sample. The sample was small and only included female students. If the study included both females and males, the result might have been different. The sampling was done in only one high school due to inaccessibility to other high schools in other locations. This led to sample bias as the sample of female students could not represent the whole population of this age group. Moreover, the sampling method might have led to a sample bias; the questionnaire was sent through the school email, and might have neglected those who do not frequently check their email.

Second, the presentation of the Iowa Decision Making Task in the questionnaire is a limitation of the study. The data collected showed that some students randomly choose the card decks (A, B, C, D) or choose them in a pattern, which might be attributed to their misunderstanding of the Iowa Decision Making Task or to their impatience in the whole study data collection period. The study was observational and could not control for many confounding variables including the participants’ environment that might influence their performance in the task. Some students might perceive the whole decision-making game as too long so they randomly choose the deck instead of trying to gain more money. The introduction of the game might not be conspicuous or clear enough for some students to pay close attention to and understand the game. The Iowa Decision Making Task was not the first choice for measuring decision-making and the initial intention was to measure economic decision-making. However, due to inaccessibility of online simulation for other economic games and time limitation, the Iowa Decision Making Task was chosen. The task could not measure specifically economic decision making and is largely used to investigate participants’ cognitive skills and mental health (Bechara et al., 1994). The 50%-chance payment on deck A and B is $250 while money gained is $100 and payment on deck C and D is $50 while money gained is $50, so clearly the better way to ensure gaining money is to always choose deck C and D. Moreover, the participants were untold about the amount of payment, so they needed to figure this out by themselves from the first few trials they engaged in (Bechara et al., 1994). As a result, the Iowa Decision Making task might not be the best measurement for risk-taking in this context. One cross-section study has shown that as age increases, people are more likely to choose deck C and D to ensure money gained. Childred and adolescents are more likely to choose risky deck A and B (Buelow & Suhr, 2009). The present study agrees with these findings as the study showed that young participants are more willing to take risk. Some previous studies have used different versions of the Iowa Decision Making task specifically adapted to a younger adult sample, which might be more useful to explore their decision making behaviour (Buelow & Suhr, 2009).

Future research should aim to explore the Dark Triad personality traits in relation to economic decision-making. Economic decision-making would be measured for example with the prisoner’s dilemma or the Ultimatum game, which were tasks used in various previous research regarding decision-making, and these studies found correlations between the Big Five personality traits and economic decision-making (with e.g. higher levels of neuroticism relating to lower risk taking). If risk-taking is to be measured, the study could apply the lottery game utilized in the previous study (Anderson et al., 2011). The lottery game is designed to incur gains or losses in different trials, with the amount of incurred losses varying in different trials, which makes it a better for measuring risk-taking as it considers more aspects (Anderson et al., 2011), while the Iowa Decision Making Task only incurs fixed losses. The sample should include both female and male high school students in a larger range of locations and also a wider age width. The study could take place physically instead of providing questionnaires/ tasks online, in order to control some external factors that might influence participants’ performance.

The study investigated the relationship between personality and decision making. The study was conducted in high school female students with the use of an online questionnaire and a decision making task. Personality was measured by the Dark Triad personality test by Paulhus and Williams (2002) and decision-making in a risk-taking context was measured with the online simulation Iowa Decision Making Task (Stoet, 2010; Stoet, 2017). The results showed no relationships between the Dark Triad personality traits (Machiavellianism, Narcissism, and Psychopathy) and decision making in female high school students, which provides no support for the present hypothesis that Dark Triad personality traits would influence decision-making. Other variables that were assessed in the present study included math-liking, math-ability, previous knowledge, and mood also no relationships emerged between these and decision-making. The study is subject to several limitations, which include the sample and the choice of the decision-making task. Future research should apply different economic games (e.g. the prisoner’s dilemma or the Ultimatum Game) and should be physically conducted on a larger sample in order to provide more reliable findings.

References

Mentor: Dr. Bianca Serwinski, Northeastern University

Anderson, J., Burks, S., DeYoung, C., & Rustichini, A. (2011, January). Toward the integration of personality theory and decision theory in the explanation of economic behavior. In Unpublished manuscript. Presented at the IZA workshop: Cognitive and non-cognitive skills.

Bechara, A., Damasio A.R., Damasio H., Anderson S.W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition, 50, 7-15.

Buelow, M. T., & Suhr, J. A. (2009). Construct validity of the Iowa gambling task. Neuropsychology review, 19(1), 102-114.

Buelow, M. T., & Suhr, J. A. (2013). Personality characteristics and state mood influence individual deck selections on the Iowa Gambling Task. Personality and Individual Differences, 54(5), 593-597.

D’Souza, M., & Lima, G. A. S. F. D. (2015). The dark side of power: the dark triad in opportunistic decision-making. Advances in Scientific and Applied Accounting, São Paulo, 8(2), 135-156.

Fiori, M., Lintas, A., Mesrobian, S., & Villa, A. E. (2013). Effect of emotion and personality on deviation from purely rational decision-making. In Decision making and imperfection (pp. 129-161). Springer, Berlin, Heidelberg.

Jones, D. N., & Paulhus, D. L. (2014). Introducing the Short Dark Triad (SD3): A brief measure of dark personality traits. Assessment, 21, 28-41.

McCrae, R. R., & Costa, P. T., Jr. (2008). The Five-Factor Theory of personality. In O. P. John, R. W., Robins, & L. A. Pervin (eds.), Handbook of personality: Theory and research (3rd. ed.). New York: Guilford.

Stoet, G. (2010). PsyToolkit – A software package for programming psychological experiments using Linux. Behavior Research Methods, 42(4), 1096-1104.

Stoet, G. (2017). PsyToolkit: A novel web-based method for running online questionnaires and reaction-time experiments. Teaching of Psychology, 44(1), 24-31.

About the author

Sijia Dong

The post The Relationship Between the Dark Triad Personality Traits and Decision Making appeared first on Exploratio Journal.

Estimating the Age of the Universe Using Globular Clusters

Gracia Gaspar Arulraj — Mon, 10 Aug 2020 08:39:13 +0000

Author: Gracia Gaspar Arulraj
Notre Dame of Holy Cross School, CBSE
July 9, 2020

Introduction

Ever since mankind started gazing at the night sky, the heavens have never ceased to fascinate our human minds with its massive and luminous creations – Stars. Stars and groupings of stars have been an integral part of Cosmology for many years. Star clusters have played an important role in defining the age and the state of our universe.

The goal of this project is to calculate an estimate of the age of our universe using estimates of the ages of star clusters: NGC 2401, NGC 2420, Pal 5, and M15. This project will also provide an insight into the tools and methods used for finding the estimate.

What are Star Clusters?

Star clusters are groups of stars that share a common origin from a giant molecular gas cloud and are gravitationally bound together. They are of two distinct types: Open clusters and Globular clusters.

Open Clusters are loose groupings of stars. They are much smaller and younger than globular clusters.
Globular Clusters are densely packed spherical collections of ancient stars that orbit a galactic core.

Studying star clusters helps astronomers to model and study stellar evolution. It also helps astronomers to figure out an estimate of the age of the universe.

What is an HR Diagram?

The Hertzsprung-Russell diagram (HR diagram) is a scatter plot of stars showing the relationship between the stars’ absolute magnitudes or luminosities versus their spectral class or effective temperatures. The HR Diagram helps us to infer different properties of stars such as luminosity, spectral class, absolute magnitudes, and mass. It classifies the stars into four groups namely: Main Sequence, White Dwarfs, Giants, and Supergiants based on the mass of a star and its stage in the stellar evolution.

Stars in the stable phase of hydrogen burning lie along the main sequence. After the exhaustion of the main fuel, a star moves into the red giant branch.

HR Diagram and the age of a star cluster:

We can find the age of a star cluster with the help of an HR Diagram by analyzing the turnoff point in the main sequence. Turnoff point refers to a point on the HR Diagram where a star leaves the main sequence after the exhaustion of the main fuel.

Tools and Procedure

Data from Sloan Digital Sky Survey or SSDS was used for plotting an HR Diagram. Inverted images of globular clusters were taken from The Image Finding Tool of SSDS.

Excel, a spreadsheet program, was used to create the HR Diagram for the globular clusters.

The book ‘Introducing the Stars’ by Martin Beech was used as a reference for estimating the ages of the star clusters.

Results of the Project

Globular Cluster: NGC 2401

Inverted image of NGC 2401

HR Diagram of NGC 2401

Globular Cluster: NGC 2420

Inverted image of NGC 2420

HR Diagram of NGC 2420

Globular Cluster: Pal 5

Inverted image of Pal 5

HR Diagram of Pal 5

Globular Cluster: M15

Inverted image of M15

HR Diagram of M15

The HR Diagrams of the open clusters (NGC 2401, NGC 2420) show that they are much younger compared to the globular clusters (Pal 5, M15).

Cluster ages based upon the G-R color and luminosity of the stars at the turnoff

Scatter plot showing the relationship between the age of a cluster and star color.

Using the HR Diagrams, a G-R Color range is chosen for each of the star clusters. This range is inputted in the scatterplot to arrive at these results.

Conclusion

The average from two of the oldest globular clusters Pal 5 (11.4±0.6 Gyr) and M15 (12±0.6 Gyr) is calculated to be 11.7 Gyr. Therefore it can be reasonably estimated that the universe is at least 12 billion years old.

Acknowledgements

I would like to express my sincere gratitude to my tutor Dr. Nigel Sanitt, a former lecturer at University College London, who gave me the golden opportunity to do this project. I’m extremely grateful for his constant help and support throughout this project.

About the author

Gracia Gaspar Arulraj

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