Abstract: 

A Novel Approach Using Designed Algorithms for Long-term Injuries Caused by Fall

1                                                          

Abstract –According to the CDC, 3 million people are treated yearly for fall related  injuries. Fall has become a major public health problem and the second leading cause of unintentional deaths. Epilepsy, Parkinson’s disease, visual impairment, and neuropathy are just a few of the illnesses that can increase the risk of falling. The purpose of this experiment was to use a fall detection algorithm to create a protective mechanism. An algorithm was developed with the use of Arduino and tri-axial accelerometers and gyro sensors. After calibrating the sensors accurately and coding in the Arduino IDE, the accelerometers were placed on a CPR manikin to model the fall of a person. After recording the slant height of the manikin during its fall, the data illustrated that the tilt of 67.01 degrees and the coordinates of (7.78, -4.08, and 8.79) is when the gear must be triggered. Through the aggregation of data,  the ideal location to place the sensors was identified. Using this data, an appropriate airbag mechanism was designed. This is particularly helpful in cases where the elderly have a fall.  The expansion of this project to a global scale can save millions of lives and prevent injuries from other accidental falls. 

Keywords: Epilepsy, Algorithm, Seizures, Fall, Tonic-Clonic

References

1. Verma, Santosh K, et al. “Falls and Fall-Related Injuries among Community-Dwelling Adults in the United States.” PloS One, Public Library of Science, 15 Mar. 2016, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4792421

2.   “Bone Fractures.” Bone Fractures - Better Health Channel, https://www.betterhealth.vic.gov.au/health/conditionsandtreatments/bone-fractures. 

3.  NHS Choices, NHS, https://www.nhs.uk/conditions/epilepsy/symptoms/#:~:text=A%20tonic%2Dclonic%20seizure%2C%20previously,may%20fall%20to%20the%20floor. 

4. “Tonic-Clonic (Grand Mal) Seizures.” Johns Hopkins Medicine, https://www.hopkinsmedicine.org/health/conditions-and-diseases/epilepsy/tonic-clonic-grand-mal-seizures. 

5. “Preventing Epilepsy.” Centers for Disease Control and Prevention, Centers for Disease Control and Prevention, 30 Sept. 2020, https://www.cdc.gov/epilepsy/preventing-epilepsy.htm#:~:text=Use%20safety%20belts%2C%20child%20passenger,of%20brain%20injuries%20from%20falls.

Abstract: Pharmaceuticals are very important due to their role in helping humans in many ways. People tend to flush these pharmaceuticals once they expire. Once flushed, it ends up in water ecosystems, which affects both the water and the different organisms that inhabit those environments. One organism that pharmaceuticals can affect is Chlorophyta, or better known as Green algae. Cetirizine and Loratadine, or more commonly referred to as Zyrtec and Claritin, are medicines used for allergy purposes that will be used for this study. 

In this research study, numerous items were used. These items consisted of the Chlorophyta plant, the two pharmaceuticals (in serum form), a hood fume, pipettes, graduated cylinders, a beaker, test tubes, 1 1000mL wheaton bottle, 5 125mL wheaton bottles, water, and 5 250mL erlenmeyer flasks.

Concentrations (10%, 1%, .1%, .01%, 0%) of the pharmaceuticals were made by measuring 90mL of water and 10mL of each pharmaceutical. The Zyrtec concentrations were poured into 125mL wheaton bottles, while the Claritin concentrations were poured into 250 ml erlenmeyer flasks. 5mL of Chlorophyta was then pipetted into 45 test tubes to later have the concentration percents pipetted into them. Data was collected by using a spectrophotometer daily.

As a result, it is unclear whether the hypothesis was supported or not. For future research, it is recommended to use different pharmaceuticals, try a different type of algae, see what specific ingredients cause the medicine to affect the algae, etc.

Keywords: Chlorophyta, Cetirizine, Loratadine, Pharmaceuticals, Concentrations

References

1. Algae in Aquatic Ecosystems Office of Water Quality. In.gov. Retrieved 15 January 2022, from https://www.in.gov/idem/files/factsheet_owq_sw_algae_aquatic.pdf. 
2. Buser, H., Poiger, T., & Müller, M. (1999). Occurrence and Environmental Behavior of the Chiral Pharmaceutical Drug Ibuprofen in Surface Waters and in Wastewater. Environmental Science & Technology, 33(15), 2529-2535. https://doi.org/10.1021/es981014w       
3. Clissold, S., Sorkin, E., & Goa, K. (1989). Loratadine. Drugs, 37(1), 42-57. https://doi.org/10.2165/00003495-198937010-00003 
4. Corsico, A., Leonardi, S., Licari, A., Marseglia, G., Miraglia del Giudice, M., & Peroni, D. et al. (2019). Focus on the cetirizine use in clinical practice: a reappraisal 30 years later. Multidisciplinary Respiratory Medicine, 14(1). https://doi.org/10.1186/s40248-019-0203-6 
5. Xing, R., Ma, W., Shao, Y., Cao, X., Chen, L., & Jiang, A. (2019). Factors that affect the growth and photosynthesis of the filamentous green algae, Chaetomorpha valida, in static sea cucumber aquaculture ponds with high salinity and high pH. Peerj, 7, e6468. https://doi.org/10.7717/peerj.6468 
6. Xin, X., Huang, G., & Zhang, B. (2021). Review of aquatic toxicity of pharmaceuticals and personal care products to algae. Journal Of Hazardous Materials, 410, 124619. https://doi.org/10.1016/j.jhazmat.2020.124619 

Abstract: With the COVID-19 pandemic and other global conflicts taking over the media, the rapid dissemination of misinformation online has drawn attention to the problem of fake news. Fake news can have detrimental effects, as demonstrated by the impact of the online anti-masking advocacy in exacerbating the COVID-19 pandemic. Various solutions have been proposed regarding the detection of fake news, with one of the most promising being deep learning. This study aims to advance current deep learning solutions in the field of fake news detection with the development of a CNN-RNN (convolutional neural network-recurrent neural network) with a complementary URL classifier. In constructing the fake news classifier, datasets were run through pre-processing techniques before being used for training. The model was subsequently tested on three datasets, spanning different areas of news: ISOT (general news), ReCOVery (COVID-19 news), and FA-KES (Syrian war news). A user interface additionally facilitated public access to the fake news classifier. After training the model on the ISOT and ReCOVery datasets, the model was able to achieve overall testing accuracies of 0.9898 (ISOT), 0.8466 (ReCOVery), and 0.5441 (FA-KES). Overall, this study broadens the options with which fake news can be identified. 

Keywords – CNN-RNN (convolutional neural network-recurrent neural network), deep learning, fake news, ISOT, FA-KES, ReCOVery, UI (user interface) 

References

1. Ahmed H, Traore I, Saad S. “Detecting opinion spams and fake news using text classification”, Journal of Security and Privacy, Volume 1, Issue 1, Wiley, January/February 2018.

2. Ahmed H, Traore I, Saad S. (2017) “Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques. In: Traore I., Woungang I., Awad A. (eds) Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments. ISDDC 2017. Lecture Notes in Computer Science, vol 10618. Springer, Cham (pp. 127- 138).

3. BBC. (2020, May 24). Coronavirus: Which health claims are circulating online? BBC News.

4. Buchanan, T., & Benson, V. (2019). Spreading disinformation on Facebook: do trust in message source, risk propensity, or personality affect the organic reach of “fake news”?. Social media+ society, 5(4), 2056305119888654.

5. Desai, S., Mooney, H., & Oehrli, J. A. (2020). Research guides:“fake news,” lies and propaganda: how to sort fact from fiction: what is “fake news”. Michigan University.

6. Elhadad, M. K., Li, K. F., & Gebali, F. (2019, November). A novel approach for selecting hybrid features from online news textual metadata for fake news detection. In International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (pp. 914-925). Springer, Cham.

7. Gottfried, J. (2020). Around three-in-ten Americans are very confident they could fact-check news about COVID-19. Pew Research Center.

8. Jain, A. K., & Gupta, B. B. (2018). PHISH-SAFE: URL features-based phishing detection system using machine learning. In Cyber Security (pp. 467-474). Springer, Singapore.

9. Mazzeo, V., Rapisarda, A., & Giuffrida, G. (2021). Detection of Fake News on COVID-19 on Web Search Engines. Frontiers in Physics, 9.

10. Nasir, J. A., Khan, O. S., & Varlamis, I. (2021). Fake news detection: A hybrid CNN-RNN based deep learning approach. International Journal of Information Management Data Insights, 1(1), 100007. 

11. Newberry, C. (2022, February 28). How the Facebook algorithm works in 2022. Hootsuite. 

12. Saleh, H., Alharbi, A., & Alsamhi, S. H. (2021). OPCNN-FAKE: Optimized convolutional neural network for fake news detection. IEEE Access, 9, 129471-129489. 

13. Salem, F. K. A., Al Feel, R., Elbassuoni, S., Jaber, M., & Farah, M. (2019, July). Fa-kes: A fake news dataset around the syrian war. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 13, pp. 573-582). 

14. Sample, C., Jensen, M. J., Scott, K., McAlaney, J., Fitchpatrick, S., Brockinton, A., ... & Ormrod, A. (2020). Interdisciplinary lessons learned while researching fake news. Frontiers in Psychology, 2947. 

15. Snopes media bias rating. AllSides. (2021, August 18).

Abstract: African Americans are often viewed as a monolithic group in the United States because Black people generally have been subjected to the same racism and prejudice throughout American society. While African Americans have had many similar experiences in the United States, their opinions on the current political, social, and economic worldview may differ based on ethnic groups. The author chose to closely examine the extent to which family history and decade of one's arrival (or one's family's arrival) to the United States, and the region from which one (or one's family) originated, might influence the current political, social and economic worldview of adolescent and adult Americans who self-identify as Black. In order to study the effects of these variables, I administered surveys to 146 African American adults in suburban New York City. The online survey consisted of four parts. These parts included views on economic success, law enforcement, current events, specifically the Black Lives Matter Movement, and Black representation in American society. Ultimately the study found statistically significant differences between region/decade of arrival and societal world views. There were also gender gaps.

Keywords: African-American, representation, BLM, Afro-Caribbean, African, economic success

References

1. Bunyasi, T. L. (2019, February 6). Do All Black Lives Matter Equally to Black People? Respectability Politics and the Limitations of Linked Fate | Journal of Race, Ethnicity, and Politics. Cambridge Core. https://www.cambridge.org/core/journals/journal-of-race-ethnicity-and-politics/article/do-all-black-lives-matter-equally-to-black-people-respectability-politics-and-the-limitations-of-linked-fate/CBC842CABC6F8FAA6C892B08327B09DA
2. Chetty, R., Hendren, N., Jones, M. R., & Porter, S. R. (2019, December 26). Race and Economic Opportunity in the United States: an Intergenerational Perspective*. OUP Academic. https://academic.oup.com/qje/article/135/2/711/5687353?login=true
3. Davis, R., & Hendricks, N. (2007, January 1). Immigrants and Law Enforcement: A Comparison of Native-Born and Foreign-Born Americans’ Opinions of the Police. International Review of Victimology. https://journals.sagepub.com/doi/abs/10.1177/026975800701400105
4. Fan, Y. (2019, February 13). Gender and cultural bias in student evaluations: Why representation matters. Plos One.

Abstract: Pneumonia, a fatal lung disease, is caused by infection of Streptococcus pneumoniae; it is detected by chest x-rays that reveal inflammation of the alveoli. However, the efficiency by which it is diagnosed can be improved through the use of artificial intelligence. Convolutional neural networks (CNNs), a form of artificial intelligence, have recently demonstrated enhanced accuracy when classifying images. This study used CNNs to analyze chest x-rays and predict the probability the patient has pneumonia. Furthermore, a comprehensive investigation was conducted, examining the function of various components of the CNN, in the context of pneumonia x-rays. This study was able to achieve significantly high performance, making it viable for clinical implementation. Furthermore, the architecture of the proposed model is applicable to various other diseases, and can thus be used to optimize the disease diagnosis industry.

Keywords: artificial intelligence, disease diagnosis, pneumonia, convolutional neural networks, machine learning

References

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2. Bebis,  G., & Georgiopoulos,  M.  (1994).  Feed-forward neural networks. IEEE Potentials, 13(4), 27-31.  doi:  10.1109/45.329294
3. Bjorck,  J.,  Gomes,  C.,  Selman,  B.,  &  Weinberger,  K.  Q.   (2018).   Understanding  batch normalization. arXiv preprint arXiv:1806.02375.
4. Eckle, K., & Schmidt-Hieber, J. (2019). A comparison of deep networks with relu activation function and linear spline-type methods. Neural Networks,110, 232–242.
5. Himavathi,  S.,  Anitha,  D., & Muthuramalingam,  A.  (2007).  Feedforward neural network implementation in fpga using layer multiplexing for effective resource utilization. IEEE Transactions on Neural Networks,18(3), 880-888.  doi:  10.1109/TNN.2007.891626
6. Ho, Y., & Wookey, S.  (2019).  The real-world-weight cross-entropy loss function:  Modeling the costs of mislabeling. IEEE Access,8, 4806–4813.
7. Huss-Lederman, S., Jacobson, E. M., Johnson, J. R., Tsao, A., & Turnbull, T.  (1996).  Implementation of strassen’s algorithm for matrix multiplication.  In Supercomputing’96:Proceedings of the 1996 acm/ieee conference on supercomputing(pp. 32–32).
8. Kermany, D., Zhang, K., Goldbaum, M., et al. (2018). Labeled optical coherence tomography (oct) and chest x-ray images for classification. Mendeley data,2(2).
9. LeCun, Y., Haffner, P., Bottou, L., & Bengio, Y.  (1999).  Object recognition with gradient-based  learning. In Shape, contour and grouping in computer vision (pp.  319–345). Springer.
10. Liu, K., Kang, G., Zhang, N., & Hou, B. (2018). Breast cancer classification based on fully-connected layer first convolutional neural networks. IEEE Access,6, 23722-23732. doi:10.1109/ACCESS.2018.2817593
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Abstract: The focus of this project is the study of catalysts for the conversion of methane to methanol as a new energy source. The methanol economy may prove to solve the problems that other energy sources create. Transition metals are treasured for their ability to assist with catalyzing reactions, including those which are used in new energy sources such as methanol based. In the past, transition metals have been used for the conversion of methane to methanol. Their catalytic efficiencies of Titanium oxides are modeled and explained based on the compound’s electron structure and how the catalytic efficiency could be improved even more by forcing the catalyst to react with methane in different ways (which are much easier to study computationally than experimentally, due to economic reasons). Catalytic oxidation reactions are crucial for chemical synthesis in pharmaceutical and petrochemicals industries. Prior research results have been controversial regarding the efficiencies of each catalyst. However, the contradictory results are due to inconsistencies of the theoretical and computational models which I reconcile in my model.

References

[1]Wes, Hickman. "Peak Oil and Public Health: Political Common Ground?" ScienceDaily. ScienceDaily, 08 Aug. 2011. Web. 13 June 2013.

[2] Olah, George A., Alain Goeppert, and G. K. Surya. Prakash.Beyond Oil and Gas: The Methanol Economy. Weinheim [an Der Bergstrasse, Germany: Wiley-VCH, 2006. Print. 

[3] Kulik, Heather J., and Nicola Marzari. "Electronic Structure and Reactivity of Transition Metal Complexes."Standford.edu. Department of Education, 2010. Web. <http://www.stanford.edu/~hkulik/www/Publications_files/05c14.pdf>. 

[4] Božović, Andrea, Stefan Feil, Gregory K. Koyanagi, Albert A. Viggiano, Xinhao Zhang, Maria Schlangen, Helmut Schwarz, and Diethard K. Bohme. "Conversion of Methane to Methanol: Nickel, Palladium, and Platinum (d9) Cations as Catalysts for the Oxidation of Methane by Ozone at Room Temperature."Chemistry - A European Journal16.38 (2010): 11605-1610. Print. 

[5] Periana, R. A., D. J. Taube, S. Gamble, H. Taube, T. Satoh, and H. Fujii. "ChemInform Abstract: Platinum Catalysts for the High-Yield Oxidation of Methane to a Methanol Derivative."ChemInform29.29 (1998): No. Print. 

[6] Zhang, Rui, and Martin Newcomb. "Laser Flash Photolysis Generation of High-Valent Transition Metal−Oxo Species: Insights from Kinetic Studies in Real Time."Accounts of Chemical Research41.3 (2008): 468-77. Print. 

[7] Betley, Theodore A., Qin Wu, Troy Van Voorhis, and Daniel G. Nocera. "Electronic Design Criteria for O−O Bond Formation via Metal−Oxo Complexes."Inorganic Chemistry47.6 (2008): 1849-861. Print. 

[8] Metz, Ricardo B. "Methane-to-Methanol Conversion by Gas-Phase Transition Metal Oxide Cations: Experiment and Theory." (n.d.): n. pag. Print. 

[9] E. R. Davidson, Adv. Quantum Chem. 6, 235 (1972).

[10] Young, David C.  Computational Chemistry: A Practical Guide for Applying Techniques to Real World Problems. New York: Wiley, 2001. Print.

[11] A. D. Becke, Modern Electronic Structure Theory Part 2 D. R. Yarkony, Ed., 1022,

World Scienti®c, Singapore (1995).

[12] "Advances in electronic structure theory: GAMESS a decade later" M.S.Gordon, M.W.Schmidt pp. 1167-1189, in "Theory and Applications of Computational Chemistry: the first forty years" C.E.Dykstra, G.Frenking, K.S.Kim, G.E.Scuseria (editors), Elsevier, Amsterdam, 2005.

Abstract: The responses to the COVID-19 pandemic have varied significantly across different political systems. Numerous factors may be attributable to the differing rates of infection rates across various countries such as availability of universal healthcare and reliance on public transportation. In fact, the political system of a particular country may determine how that country has addressed the pandemic and thereby affect that country’s infection rates. This paper will compare the political systems, pandemic responses and infection rates of countries. First, each country’s political systems will be briefly described. Next, the two countries’ respective infection rates and pandemic responses will be compared. 
As part of my analysis, I will examine how the US political system may have resulted in more effective or less effective pandemic strategies. Finally, drawing from the strategies used by other countries, two specific suggestions for improving the U.S.’s response to the COVID-19 pandemic will be considered. In a vast country like the US, the best way to mitigate the crisis is to handle it region-by-region due to the vast disparity in economy and population state-by-state rather than governmental intervention. Compared to other countries, the United States is more decentralized and naturally, states have gotten more power regarding laws and quarantines during this crisis (Dziobek, 2010). That being said, although the countries of the world have indeed done much to quarantine the crisis, states must keep control of individual laws (Dziobek, 2010). Specifically, states like California, Florida, Texas, New York and Georgia have the worst second wave of cases in the country. Since the virus is affecting states in different ways than ever imagined, state governments should be moderating the virus based on their situation rather than national lockdowns like in other countries. With five states accounting for more than 40% of all COVID-19 cases, this solution shows much promise for specifically this country.

References

1. Centers for Disease Control and Prevention. (2020). Geographic Differences in COVID-19 Cases, Deaths, and Incidence — United States, February 12–April 7, 2020. Retrieved from https://www.cdc.gov/mmwr/volumes/69/wr/mm6915e4.htm#:~:text=Community%20transmission%20of%20COVID%2D,of%20COVID%2D19.
   Cirillo, P., Taleb, N.N. Tail risk of contagious diseases. Nat. Phys. 16, 606–613 (2020). https://doi.org/10.1038/s41567-020-0921-x.

2. Ding, Lei, and Alvaro Sanchez. “COVID-19 and the Philadelphia Fed.” Federal Reserve Bank of Philadelphia, Federal Reserve Bank of Philadelphia, Apr. 2020, philadelphiafed.org/covid-19/covid-19-equity-in-recovery/what-small-businesses-will-be-impacted. 

3. Dziobek, Claudia, et al. “Measuring Fiscal Decentralization – Exploring the IMF’s Databases.” International Monetary Fund, International Monetary Fund, www.imf.org/external/pubs/ft/wp/2011/wp11126.pdf.

4. Eckfeldt, Bruce. “Key Questions to Guide Your Post-Pandemic Plan.” Inc.com, Mansueto Ventures, 25 Apr. 2020, www.inc.com/bruce-eckfeldt/key-questions-to-guide-your-post-pandemic-plan.html. 

5. Fox, Michelle. “How These Small Businesses Are Surviving during the Coronavirus Pandemic.” CNBC, CNBC, 9 Aug. 2020, www.cnbc.com/2020/08/08/coronavirus-how-these-small-businesses-are-surviving-the-pandemic.html. 

6. Jiang, I. (2020). Here's the difference between an 'essential' business and a 'nonessential' business as more than 30 states have imposed restrictions. Business Insider. Retrieved from https://www.businessinsider.com/what-is-a-nonessential-business-essential-business-coronavirus-2020-3

7. Lexis Nexus. (2020). Economic Risk—What Is It and How to Effectively Manage It. Retrieved from https://www.lexisnexis.com/en-us/products/entity-insight/economic-risk.page#:~:text=%E2%80%B9%20%E2%80%BA,that%20may%20adversely%20affect%20profits.

8. Maxouris, Christina. “US Tops 5 Million Covid-19 Cases, with Five States Making up More than 40% of Tally.” CNN, Cable News Network, 9 Aug. 2020, www.cnn.com/2020/08/09/health/us-coronavirus-sunday/index.html. 

Abstract: The visually manipulative characteristics of VR technology can have many different usages. A prominent one is the usage of VR in the medical field, more specifically, surgery training. Osso VR, a virtual reality surgery training platform, is making its way into residency programs of American medical schools, including the Vanderbilt University School of Medicine and Harvard Medical School. (Fink) The usage of VR is especially effective in surgery training, as an accurate digital simulation of real surgery environments can be created, and surgeons do not have to feel the same pressure and stressful circumstances of a real surgery. 

These technologies all have a commonality: they are manipulating human perception. These technologies all evoke synesthetic experiences across multiple modalities, which means that a visual experience from VR, AR, or 4D cinema can trigger another sensory experience, such as the sense of hearing or touch. These experiences are all possible thanks to the technology that manipulates human perception, and one would not be able to have such experiences in normal daily life.

References

1. Chislock, M.F.; Doster, E.; Zitomer, R.A.; Wilson, A.E. (2013)."Eutrophication: Causes, Consequences, and Controls in Aquatic Ecosystems". Nature Education Knowledge. 4 (4): 10. Retrieved 10 March 2018.Cycleback, David Rudd. 2003. “Eye/Brain Physiology and Human Perception of External Reality.” A Look at How Humans Think and See.

2. Danieau, Fabien, et al. 2014. Toward Haptic Cinematography: Enhancing Movie Experience with Haptic E ects based on Cinematographic Camera Motions. IEEE MultiMedia, Institute of Electrical and Electronics Engineers: 1-14. 

3. Fink, Charlie. 2018. “Osso VR Surgical Training Makes Push Into Med Schools.” Forbes,                     

4. Hill, Joe. “3D Pavement Art.” Joe Hill Art.

5. Howard, Ian P., and Brian J. Rogers. 1995. Binocular Vision and Stereopsis. Oxford University Press, Inc.

6. Krevelen, D.W.F van, and R. Poelman. 2010. A Survey of Augmented Reality Technologies, Applications and Limitations. Delft University of Technology.

7. Garciá-Valle, Gonzalo, et al. 2017. Evaluation of Presence in Virtual Environments: Haptic Vest and User’s Haptic Skills. Smith, V., Tilman, G., & Nekola, J. (1999). Eutrophication: Impacts of excess nutrient inputs on freshwater, marine, and terrestrial ecosystems. ​Environmental Pollution,100(​ 1-3), 179-196. doi:10.1016/s0269-7491(99)00091-3 12     

8. Manjoo, Farhad. 2008. “A Look at Disney and Pixar’s 3-D Movie Technology.” Salon.

9. Summers, Nick. 2016. “Ikea Made a Kitchen Showroom in VR.” Engadget. 

10. Verrier, Richard. 2009. “3-D Technology Firm RealD Has Starring Role at Movie Theaters.” Los Angeles Times.

Abstract: Deep convolutional neural networks - the state-of-the-art technique in artificial intelligence for computer vision - achieve notable success rates at simple classification tasks, but are fundamentally lacking when it comes to representation.

These neural networks encode fuzzy textural patterns into vast matrices of numbers which lack the semantically structured nature of human representations (e.g. "a table is a flat horizontal surface supported by an arrangement of identical legs").

This paper takes multiple important steps towards filling in these gaps. I first propose a series of tractable milestone problems set in the abstract two dimensional ShapeWorld, thus isolating the challenge of object compositionality. Then I demonstrate the effectiveness of a new compositional representation approach based on identifying structure among the primitive elements comprising an image and representing this structure through an augmented primitive element tree and coincidence list. My approach outperforms state-of-the-art benchmark algorithms in speed and structural representation in my object representation milestone tasks, while yielding comparable classification accuracy. Finally, I present a mathematical framework for a probabilistic programming approach that can learn highly structured generative stochastic representations of compositional objects from just a handful of examples.

Keywords – Deep convolutional neural networks, state-of-the-art benchmark algorithms, two dimensional ShapeWorld, compositional objects

References

[1] A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 25, pages 1097–1105. Curran Associates, Inc., 2012.
[2] H. Wang, B. Gao, J. Bian, F. Tian, and T. Liu. Solving verbal comprehension questions in IQ test by knowledge-powered word embedding. CoRR, abs/1505.07909, 2015.
[3] J. Johnson, B. Hariharan, L. van der Maaten, L. Fei-Fei, C. L. Zitnick, and R. B. Girshick. CLEVR: A diagnostic dataset for compositional language and elementary visual reasoning. CoRR, abs/1612.06890, 2016.
[4] Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 1998.
[5] B. Lake, R. Salakhutdinov, J. Gross, and J. Tenenbaum. One shot learning of simple visual concepts. Proceedings of the Annual Meeting of the Cognitive Science Society, 33, 2011.
[6] Image of a leopard-print sofa. https://rocknrollnerd.github.io/ml/2015/05/27/leopard-sofa.html.

Abstract: The Union for Reform Judaism’s North American Federation for Temple Youth (URJ NFTY) is a nonprofit organization serving North American Reform Jewish teens. The URJ and NFTY hold strong values tied to Jewish history that are utilized to discuss and advocate for political and social issues, and to mediate teens' relationships to Judaism. URJ NFTY offers teens year-round opportunities to convene, learn, and grow together while forging lasting relationships.
Over the past 10 years, NFTY and its related programming (particularly URJ owned
summer camps) have experienced a significant decrease in participation, causing a gradual decrease in revenue. As a result, the URJ made difficult decisions, including the closure of the URJ Kutz Camp, one of their oldest standing summer programs. Recent trend brought additional financial instability to the organization, causing employment terminations throughout the URJ and across all nineteen NFTY regions. With fewer leaders to guide teens and NFTY/camp alumni, it has become difficult for NFTY to recruit and train its next generation of leaders to advocate against injustice and to preserve the Jewish spirit.

Keywords: URJ, NFTY, URJ Kutz Camp, Judaism,  North American Federation

References:

1. Arquilevich, Ruben. “With Joy, Gratitude & Love, an Update on Summer 2021.” Union for Reform Judaism, 4 Feb. 2021, urj.org/blog/joy-gratitude-love-update-summer-2021.
2. CampaignWire. “Rising Stars: 18 Members of America's Next Political Generation.”
Medium, The Campaigner, 23 Feb. 2016, medium.com/campaigner-2016/rising-stars-18-members-of-america-s-next-political-gene
3. ration-de18ab114afa#.raebue8kl. No. 14: Jeremy Cronig
4. Jacobs, Rick. “Beyond 2020: Upcoming Changes at the URJ.” Union for Reform Judaism, 1 Oct. 2020, urj.org/blog/beyond-2020-upcoming-changes-urj.
5. Jacobs, Rick. “Financials: 2019 URJ Annual Report.” URJ Annual Report, www.2019annualreport.urj.org/financials.
6. Jacobs, Rick. “Financials: URJ 2015 Annual Report.” Flipbook, media.urj.org/flipbook/index.html?page=24.
7. Jacobs, Rick. “Financials: URJ 2017 Annual Report.” Flipbook, media.urj.org/flipbook2017/index.html?page=39.
8. Jacobs, Rick. “Financials: URJ 2018 Annual Report.” URJ Annual Report, www.2018annualreport.urj.org/financials.
9. Jacobs, Rick. “Reform Jewish Movement Leadership Statement: ‘We Must All Expect
More from the President of the United States.’” Union for Reform Judaism, 3 Sept. 2019 urj.org/press-room/reform-jewish-movement-leadership-statement-we-must-all-expect-
more-president-united.

10. “Joe Biden's Plan to End Gun Violence: Joe Biden for President.” Joe Biden for President: Official Campaign Website, 12 Oct. 2020, joebiden.com/gunsafety/.
11. Levy, Maya. “Reimagining the Future of NFTY.” NFTY, 11 June 2020, nfty.org/future/
 

Abstract: Renewable, green energy is an important field of research amidst the 21st century energy crisis. Many of the researches around the world had been consistently looking for new energy sources, but not as much as on the efficient storage of energy produced from these eco-friendly sources. This research considers how to increase the capacitance through inserting various types of dielectrics to use them as a substantial tool for sustainable development. 

The research focuses on calculating the capacitances of batteries with diverse dielectrics, differing the combinations and geometrical structure of capacitors in order to figure out the capacitances of batteries that can store more energy with better efficiency. Mathematical, physical and computational analysis were employed to figure out the capacitances and stored energy. MATLAB computer programming was used to calculate potential charge distribution within capacitors, the change in the capacitance and electric field of plate capacitors. 

Using mathematical calculations, general expressions for computing the relationship between capacitance and insulation material characteristics, such as dielectric constant, plate dimensions, for n-number of plate capacitors were found. Also the relationship between capacitance, dielectric constant, capacitor dimensions for a thin-walled hollow cylinder was studied. In this work, we showed the influence of the multi-plate capacitor system taking into account the geometrical and types of combinations of the conducting plates.

Keywords: Green energy, capacitance, dielectric constant, conducting plates

References:

1. William D. Greason (1992). Electrostatic discharge in electronics. Research Studies Press. p. 48. ISBN 978-0-86380-136-5. Retrieved 4 December 2011.

2. Tipler, Paul; Mosca, Gene (2004). Physics for Scientists and Engineers (5th ed.). Macmillan. p. 752. ISBN 978-0-7167-0810-0

3. Massarini, A.; Kazimierczuk, M.K. (1997). "Self capacitance of inductors". IEEE Transactions on Power Electronics. 12 (4): 671–676. Bibcode:1997ITPE...12..671M. CiteSeerX 10.1.1.205.7356. doi:10.1109/63.602562: example of the use of the term 'self capacitance'.

4. Jackson, John David (1999). Classical Electrodynamic (3rd ed.). John Wiley & Sons. p. 43. ISBN 978-0-471-30932-1.

5. Maxwell, James (1873). "3". A treatise on electricity and magnetism. 1. Clarendon Press. p. 88ff.

6. "Capacitance : Charge as a Function of Voltage". Av8n.com. Retrieved 20 September 2010.

7. Fundamentals of Electronics. Volume 1b — Basic Electricity — Alternating Current. Bureau of Naval Personnel. 1965. p. 197.

8. Binns; Lawrenson (1973). Analysis and computation of electric and magnetic field problems. Pergamon Press. ISBN 978-0-08-016638-4.

9. Rawlins, A. D. (1985). "Note on the Capacitance of Two Closely Separated Spheres". IMA Journal of Applied Mathematics. 34 (1): 119–120. doi:10.1093/imamat/34.1.119.

10. Vainshtein, L. A. (1962). "Static boundary problems for a hollow cylinder of finite length. III Approximate formulas". Zh. Tekh. Fiz. 32: 1165–1173. 

Abstract: In the modern age of politics, political candidates use Twitter to express their ideas and connect with voters. In 2018, Twitter was used by nearly every candidate for the U.S. House of Representatives throughout their political campaign. To analyze the language used on Twitter, we used Linguistic Inquiry and Word Count (LIWC) to analyze a text file (for each candidate) of all tweets from July 1, 2018 to November 6, 2018 to produce a descriptive output of language use in the months preceding the midterm elections. Consistent with past studies, it was predicted that candidates would use words characterized by negative affect on Twitter in order to connect with voters on an emotional level and to gain votes. In-depth analysis relating linguistic variables to vote count provided insight into how politicians used language on Twitter to improve their popularity. As theorized, candidates who used more words consisting of negative emotion obtained a greater number of votes than that of their counterparts. These findings provided support for the hypothesis that words of negative affect are deemed more impactful than neutral or positive words in politics, and that such language is highly correlated, regardless of party affiliation, with vote count. These findings provide a greater understanding of linguistics in the modern age of politics and provide insight into how increasingly prevalent social media platforms are factoring into politics.

Keywords: Political candidates, natural language, vote count, midterm election

References:

[1] Burgess, Jean, and Cornelius Puschmann. Twitter and Society. Edited by Katrin Weller, Axel Bruns, and Merja Mahrt. New York: Peter Lang, 2014.

[2] Kahn, J. H., Tobin, R. M., Massey, A. E., & Anderson, J. A. (2007). Measuring Emotional Expression with the Linguistic Inquiry and Word Count. The American Journal of Psychology,120(2), 263. doi:10.2307/20445398

[3] Larsson, A. O., & Moe, H. (2012). Studying Political Microblogging: Twitter Users in the 2010 Swedish Election Campaign. SAGE Journals,14(5), 729-747. doi:10.1177/1461444811422894

[4] Lau, R. R. (1982). Negativity in Political Perception. Political Behavior, 4(4), 353–377. doi: 10.1007/bf00986969

[5] Marres, N., & Weltevrede, E. (2013). Scraping The Social? Issues in real-time social research. Journal of Cultural Economy,6(3), 313-335. doi:10.1080/17530350.2013.772070

Abstract: This project applies a new theory in the field of intergroup conflict known as "Gateway group theory," which posits that to decrease conflict between two groups, a third group with specific characteristics that appeal to both sides needs to be identified, enabling them to act as a medium. This group is known as a "Gateway group." With the background of the bitter digital divide and echo chambers plaguing the United States’ current political discourse, this paper sought to find the Gateway group between polar Democrats and Republicans on Twitter. 

This project data mined and examined the shared “likes” of these two populations using originally developed code and definitional parameters. Then, the study analyzed the profiles of the authors of these liked Tweets to compile an aggregated Gateway group profile that can be used to find Gateway group individuals on Twitter who have the ability to decrease conflict between Democrats and Republicans. The study found that Gateway group members exist. They are a group of Moderate Democrats. Every post that was liked by both a Democrat and Republican was also tagged and analyzed for similarities in content. It was found that 55% of all posts referenced “Trump” and 92% of those votes had a negative sentiment. Additional similarities in content were found, for example a keen interest in elections and certain Democratic candidates. This project develops an effective methodology that can be applied to any conflict on Twitter to find the Gateway group for that conflict to decrease polarity between polar groups.

Keywords: Gateway group theory, Democrat and Republican, political discourse, Twitter

References:

Bessi, A. (2016). Personality traits and echo chambers on facebook. Computers in Human Behavior,65, 319-324. doi:10.1016/j.chb.2016.08.016
Demszky, D., Garg, N., Voigt, R., Zou, J., Shapiro, J., Gentzkow, M., & Jurafsky, D. (2019).

Gaertner, S. L., & Dovidio, J. F. (2012). The Common Ingroup Identity Model. Handbook of Theories of Social Psychology,2, 439-457.  ttp://dx.doi.org/10.4135/9781446249222.n48

Gaertner, S. S., Dovidio, J. F., Anastasio, P. A., Bachman, B. A., & Rust, M. C. (1993). The Common Ingroup Identity Model: Recategorization and the Reduction of Intergroup Bias.

European Review of Social Psychology,4(1), 1-26. doi:https://doi.org/10.1080/14792779343000004. Goyal, S. (2005). Strong and Weak Links. Journal of the European Economic Association,
3(2/3), 608-616. Retrieved from http://www.jstor.org/stable/40005003.

Hornsey, M. J., & Hogg, M. A. (2000). Subgroup Relations: A Comparison of Mutual Intergroup Differentiation and Common Ingroup Identity Models of Prejudice Reduction. Personality and Social Psychology Bulletin,26(2), 242-256. doi:10.1177/0146167200264010

Pettigrew, T. F., Tropp, L. R., Wagner, U., & Christ, O. (2011). Recent advances in intergroup contact theory. International Journal of Intercultural Relations,(35), 271-280.

Zollo, F., Novak, P. K., Vicario, M. D., Bessi, A., Mozetič, I., Scala, A., . . . Quattrociocchi, W. (2015). Emotional Dynamics in the Age of Misinformation. Plos One,10(9). doi:10.1371/journal.pone.0138740

Abstract: Gifted children are people who are capable of high performance in cognitive, educational, scientific, creative and artistic fields compared to their peers. But there are also gifted children who have problems with cognitive, educational, social, emotional and behavioral development. They are called twice-exceptional children. Regarding these children, who have high talents and abilities while at the same time having disabilities, is an important issue for education professionals. The present study mainly aims to compare the executive functions profile of twice exceptional children with gifted ones.To this end, 30 twice-exceptional gifted children and 30 gifted children were selected from among elementary school students in district 3 of Isfahan, Iran. Then, the two groups administered The Wechsler Intelligence Scale for Children, Fourth Edition (WISC®-IV) for assessment of the Tower Test (NEPSY) to evaluate executive functions. The research results showed that the profile and average executive function of the twice-exceptional children were lower than those of gifted children in the Tower test (NEPSY). Therefore, it is suggested to consider executive functions (planning, organization, time management, problem solving, etc.) in identifying and educating these children.

Keywords: Gifted children, twice-exceptional children, executive function

References:

[1] Dawson,P & Guara,R .(2018). Executive Skills in Children and Adolescents Third Edition: A practical Guide to Assessment and Intervention.

[2] Guilford Press Major, J. (2017). A Change Plan for Underachieving Gifted Children (Doctoral dissertation). Retrieved from https://digital commons.nl.edu/diss/252. Pfeiffer, S. I. (2015).

[3] Gifted students with a coexisting disability: The twice exceptional. Estudos de Psicologia (Campinas), 32 (4), 717-727.

[4] Sterenberg,R.J & Javin,L & Grigorenko,E.L. (2011) Exploration in Giftedness . Cambridge University Press

Abstract: There have been numerous different kinds of data such as stock prices and interest rates observed and gathered in the past. The sequential nature of these data require us to account for the dynamic nature using special statistical skill and techniques. Time series analysis provide the appropriate methods necessary in order to analyze sequential data.
It may be problematic to picture the essential, underlying trend of the data if the time series has a lot of noise. To distinguish the signal and the noise from each other, various linear and nonlinear smoothers must be applied.

This paper collected a century’s worth of P/E ratio data and used the static distribution to map out the overall trend of the P/E ratio in terms of its return period. Also, the data was plotted in Matlab, and multiple fitting models were tested out to see which one fit the data the best. The P/E ratio was chosen due to its significance in the evaluation of stocks’ values, and the static distribution due to its ability to incorporate rapidly fluctuating data into statistical analysis.

Keywords – Multiple fitting models, P/E ratio, statistical analysis, lnear and nonlinear smoothers

References

[1] Peres, D. J.; Cancelliere, A. (2016-10-01). "Estimating return period of landslide triggering by Monte Carlo simulation". Journal of Hydrology. Flash floods, hydro-geomorphic response and risk management. 541: 256–271.
[2] Anonymous (2014-11-07). "Flood Estimation Handbook". UK Centre for Ecology & Hydrology. Retrieved 2019-12-21.
[3] https://en.wikipedia.org/wiki/Gumbel_distribution
[4] ASCE, Task Committee on Hydrology Handbook of Management Group D of (1996). Hydrology Handbook | Books. doi:10.1061/9780784401385. ISBN 978-0-7844-0138-5. doi:10.1016/j.jhydrol.2016.03.036.

Abstract: In our modern world, the concept of multiculturalism is not only prevalent but also encouraged. To have people from a diversity of backgrounds coexisting in one single area was an unfathomable concept nearly a century ago. Multiculturalism at its root refers to an amalgamation of different cultures and a single bounded territory; the inhabitants are protected by right to practice and enact on their views—regardless of whether they are in line with those of the majority. Everyone makes up a part of the whole. This paper discussed the effects of political and geographical isolation and cultural diversity in different eras and countries, as well as the details of successful heterogenous makeup of America together with the changes in the population of the United States and the impact of multiculturalism on the economy. The “ethnic minority” and mixed-race population is increasing every year in America, along with the increase in bilingual populations. Finally, this research states the reasons why diversity makes us smarter and more effective: racially diverse groups share information better, diversity enhances creativity, different points of views leads to broader thinking, having different points of views gives you new platforms and tactics of analyzing/solving a problem, and diversity encourages you to push the boundaries and reconsider your perception.

Keywords – Multiculturalism, cultural diversity, political and geographical aspects, and economic growth

Introduction: The United States, in particular, is a paradigm of a multicultural nation. Home to millions of immigrants, the US serves as a beacon of potential. Since the second half of the twentieth century, multiculturalism has quickly risen among various nations. Naturally, debates have also arisen regarding the productivity of such a concept; some believe there are various negatives to the rise of multiculturalism. But, research has revealed that diversity and co-existence can actually give rise to many positive events within a nation. According to Vincent Parillo, the diverse, heterogeneous makeup of the US is steadfast and integral to the nation. As a nation’s strength lies in its citizens and inhabitants, the US serves as a model for the true power of the people. The diversity of the US constitutes a large part of the American identity; from inception to modern times immigrants have contributed largely to the country’s evolution.

Some counter that diversity is a product of economic development rather than a contributor, that multicultural populations are attracted to certain locations because of affluence or gained economic success. An important new study by economists Quamrul Ashraf of Williams College and Oded Galor of Brown University, "Cultural Diversity, Geographical Isolation and the Origin of the Wealth of Nations," was recently released by the National Bureau of Economic Research. The paper carefully follows the role of geographic isolation, proximity, and cultural exchange in regard to economic development—spanning from pre-industrial times to the modern era. The study shows that "the interplay between cultural assimilation and cultural diffusion have played a significant role in giving rise to differential patterns of economic development across the globe." Diversity in fact gives way to economic growth whereas homogeneity enacts the opposite effect.

During the formative years of the United States’ industrialization, immigrants contributed greatly to the workforce. They helped create transportation systems, cities, and labor unions. Similarly, immigrants now also strengthen American economy. The United States is influential on the world stage due to the immigrants who have devoted themselves to advancement and the potential to be greater. They have brought billions of dollars with them — boosting the nation’s economy via business, consumerism, and labor.

References

[1] Clayton-Pedersen and Musil, 2008
[2]https://www.psychologytoday.com/us/blog/life-bilingual/201809/the-amazing-rise-bilingualism-in-the-united-states
[3] “Prosperity 2050.” Center for American Progress, 2011
[4] “Current Population Survey, 1968 through 2015”, Annual Social and Economic Supplements, U.S. Census Bureau, 2015
[5]“Multiculturalism: America's Competitive Advantage.” The Atlantic, Morgan Stanley Smith Barney LLC, 2016, www.theatlantic.com/sponsored/morgan-stanley-wealth-management-2016/multiculturalism-americas-competitive-advantage/1007/.
[6] https://www.nielsen.com/wp-content/uploads/sites/3/2019/04/the-multicultural-edge-rising-super-consumers-march-2015.pdf

Abstract: This study is designed to detect the expression levels of heat shock protein 72 in the forebrain, midbrain, hindbrain, and ventricles of T. scripta, when subjected to anoxia and warm and cold temperatures for various periods of time. Previous studies have shown that HSP72 is induced early in anoxia, increasing for 8 hours but then falling to normoxic levels by 12 hours of anoxia showing that HSP72 may play a key role in the initial transition to the anoxic state (Milton and Prentice 2007).  This study examined the brain in sections, rather than the previous whole brain.

Keywords – Neuroprotection, temperature, oxygen stress, turtles, and Trachemys scripta

Introduction: The freshwater turtle, Trachemys scripta, has a unique ability to survive without oxygen for prolonged periods of time.  Unlike a vast majority of vertebrates that die after a few minutes of being deprived of molecular oxygen (anoxia), anoxia- tolerant vertebrates can survive from hours to weeks (Stecyk et al., 2007).  Anoxia followed by reoxygenation produces a rapid transient increase in reactive oxygen species (ROS) that destroys cells and its contents (Hashimoto et al. 2003). The mammalian brain is susceptible to ROS; however T. scripta may employ protective mechanisms to survive anoxia, thus preventing ROS damage.  Not only is brain function protected, but heart function is, too. One protective mechanism is the over expression of heat shock proteins (HSPs).  HSPs are overexpressed when cells are stressed, acting as a molecular chaperone. The brains and hearts of T. scripta were exposed to anoxia at 21°C, normoxia at 5°C, and anoxia at 5°C, with normoxia at 21°C as the control group. Exposure times ranged from 1.5 hours to 2 weeks. Each sample, weighing at least 200mg, was homogenized and the proteins were extracted.  Protein assays were performed on the extracts to determine the respective concentrations. Western blots were done to detect the presence of heat shock protein 72. Results are expressed as ±SD.

References

[1] Hashimoto, T., Yonetani, M., Nakamura, H. 2003. Selective brain hypothermia protects against hypoxic- ischemic injury in newborn rats by reducing hydroxyl radical production. Kobe J. Med. Sci. 49(4), 83-91. 
[2] Milton, S.L., Prentice, H.M. 2007. Beyond anoxia: The physiology of the metabolic downregulation and recovery in the anoxia- tolerant turtle.  Comp. Biochem. Physiol. A 147, 277- 290.
Stecyk, J.A.W., Stensløkken, K.-O., Nilsson, G.E., Farrell, A.P. 2007. 
[3] Adenosine does not save the heart of anoxia- tolerant vertebrates 
during prolonged oxygen deprivation. Comp. Biochem. Physiol. A  
147, 961- 973.

Abstract – Thyroglobulin is a protein located in the thyroid and controls hormone production. These hormones work to modulate behavior, central nervous system function, and energy metabolism in vertebrates (Holzer et al., 2016). In addition, it is a dimeric glycoprotein with a molecular mass of 660 kDa. Specifically, bovine thyroglobulin is heavily decorated with alpha-gal and can be used to diagnose the red meat allergy (Apostolovic et al., 2017). For these reasons, the structure of bovine thyroglobulin is crucial to find and can lead to new information about the relationship between alpha-gal and the IgE antibodies.
 Keywords – bovine thyroglobulin, alpha-gal, IgE antibodies

Introduction: Alpha-gal, an oligosaccharide, is a major blood group substance in mammals such as cattle and pigs. Studies strongly suggest that bites from the Lone Star Tick Amblyomma americanum infect the human host with the carbohydrate alpha-gal (Commins & Platts-Mills, 2013). After some time, when beef or another red meat is consumed, an immune response is initiated by the IgE antibodies, that results in an immediate allergic reaction characterized by symptoms of anaphylaxis (Sim et al., 2017). Currently, the structure of bovine thyroglobulin is unresolved. The aim of this research was to determine the molecular structure of bovine thyroglobulin using Macromolecular crystallography (MX) and Small Angle X-ray Scattering (SAXS). With MX, the aim was to test whether lysozyme is a nucleation inducing reagent of thyroglobulin, and with SAXS, the aims were to obtain a low-resolution image of the structure of bovine thyroglobulin and discover the bead model of bovine thyroglobulin. It was hypothesized that lysozyme will aid in the crystallization in thyroglobulin and that the bead model will be a complex globular structure containing alpha and beta helices, factoring inflexibility. 

References

1. Apostolovic, D., Krstic, M., Mihailovic, J., Starkhammar, M., Velickovic, T. C., Hamsten, C., & van Hage, M. (2017). Peptidomics of an in vitro digested α-Gal carrying protein revealed IgE-reactive peptides. Scientific reports, 7(1), 5201. 

2. Benkert, P., Biasini, M., Schwede, T. Toward the estimation of the absolute quality of individual protein structure models. Bioinformatics 27, 343-350 (2011). 


3. Bertoni, M., Kiefer, F., Biasini, M., Bordoli, L., Schwede, T. Modeling protein quaternary structure of homo- and hetero-oligomers beyond binary interactions by homology. Scientific Reports 7 (2017). 


4. Biosis. (2013). PRIMUS (WINDOWS ONLY). Retrieved from 
http://www.bioisis.net/tutorial/4 
http://iramis.cea.fr/Phocea/Vie_des_labos/Ast/ast_sstechnique.php?id_ast=1065 


5. Bruno Di Jeso, Peter Arvan; Thyroglobulin From Molecular and Cellular Biology to Clinical Endocrinology, Endocrine Reviews, Volume 37, Issue 1, 1 February 2016, Pages 2–36, https://doi.org/10.1210/er.2015-1090 


6. Commins, S. P., & Platts-Mills, T. E. (2013). Delayed anaphylaxis to red meat in patients with IgE specific for galactose alpha-1,3-galactose (alpha-gal). Current Allergy And Asthma Reports, 13(1), 72-77. doi:10.1007/s11882-012-0315-y 


7. Edelhoch, H., J. Biol. Chem., 235, 1326 (1960). 


8. Franke, D., Petoukhov, M.V., Konarev, P.V., Panjkovich, A., Tuukkanen, A., Mertens, 
H.D.T., Kikhney, A.G., Hajizadeh, N.R., Franklin, J.M., Jeffries, C.M. and Svergun, D.I. (2017) ATSAS 2.8: a comprehensive data analysis suite for small-angle scattering from macromolecular solutions J. Appl. Cryst. 50, 1212-1225 


9. Gentile, F., Salvatore, G., & Salvatore, G. (1995). Molecular heterogeneity of covalently-linked bovine thyroglobulin dimers. Rendiconti Lincei, 6(2), 165.
Holzer, G., Lorin, T., Gillet, B., Hughes, S., Tohme, M., Laudet, V., & ... Deleage, G. (n.d). Thyroglobulin Represents a Novel Molecular Architecture of Vertebrates. Journal Of Biological Chemistry, 291(32), 16553-+. 


10. Jakoby, W. B., Labaw, L., Edelhoch, H., Pastan, I., & Rall, J. E. (1966). Thyroglobulin: Evidence for Crystallization and Association. Science, 153(3744), 1671-1672. doi:10.1126/science.153.3744.1671 


11. Leszczyszyn, O., Hydrodynamic Radius. (2018, December 11). Retrieved from
https://www.materials-talks.com/blog/2012/11/15/size-matters-rh-versus-rg/ 


Abstract: This paper addresses the problem of addiction in society. We focus on the United States specifically and limit our model to the following drugs:  nicotine, marijuana, prescription drugs, alcohol. The problem is to create a model that can accurately predict the spread of nicotine. This is followed by the creation of a model that can be applied to different drugs with inputs depending on an individual's income, education level, and race.

From the information above, we conclude that the most dangerous substances are: Tobacco, Opioid-based Unprescribed Painkillers, and Alcohol, while the least dangerous is marijuana. This is deduced from a combination of its health impacts, explicit and implicit costs of using. While marijuana is the least dangerous according to our model, it still possesses significant dangers to productivity, safety, and cognitive function. 

Our models functioned on several assumptions. We assumed that nationwide trends are directly applicable to all individual populations, which may not be the case. A study can be conducted to provide evidence of drug usage in specific areas across the country in order to pinpoint our data. The spread of nicotine abuse as well as the abuse of other drugs is on the rise throughout the country. This is especially alarming in the younger generation as model 2 suggests. The amount of high school seniors predicted to be using these substances indicates a societal issue that needs to be addressed in order to prevent damage to today's youth and lower these numbers for later generations.  The impact of these drugs, while varied between them, signifies how abuse can quickly lead to poverty and strain on the economy that must support them.

Keywords: Addictive substances, Opioid-based Unprescribed Painkillers, Computer Modeling

Introduction:  The model developed for part 1 details how the predicted growth of nicotine usage is anticipated to level off in the future as it currently is following a pattern of logistic growth. We use information provided to graph the function from 2011 to 2018. According to the data from the table, we create a logistic function (Figure 2) y = (15.1173)/(1+1111.39e^(-2.15689x)) by calculator. In order to minimize the number for y, we use 1 for 2011, 2 for 2012, 3 for 2013, and so on. Then, we plug 29 as the corresponding number for 2029 to x to find the percentage of high school students who vape for the next 10 years, which is 15.1173 percent. This number may not be correct because there is a rising number of events created dedicate to educate students to stop/prevent them from vaping.

An alteration in this model that could more accurately depict the expansion of vaping could include increased education about its dangers which would slow its growth. As seen in Figure 1, the model closely follows the data found on the high school vaping data provided in the question. The data would follow a line of best fit calculated with a logistic regression formula because the percent of users must reach a limit as it cannot exceed 100%. Figure 3 demonstrates the age demographics of the United States which we use to determine how the percentage of growth translates into sheer numbers in terms of age. For example, if 15% of individuals use nicotine for a given year, we can multiply this by the number of individuals in their age groups and get how many people use nicotine. 

References

1. Marijuana Street Prices: How Much Should You Pay For Weed? (n.d.). Retrieved from https://addictionresource.com/drugs/marijuana/marijuana-street-prices/
2. Morbidity and Mortality Weekly Report (MMWR). (2017, June 21). Retrieved from https://www.cdc.gov/mmwr/volumes/65/ss/ss6511a1.htm
3. Motor Vehicle Safety. (2017, June 16). Retrieved from https://www.cdc.gov/motorvehiclesafety/impaired _driving/impaired-drv_factsheet.html
4. National Institute on Drug Abuse. (n.d.). What is the scope of tobacco use and its cost to society? Retrieved from https://www.drugabuse.gov/publications/research-reports/tobacco- nicotine-e-cigarettes/what-scope-tobacco-use-its-cost-to-society
5. FDA. (2018, June). Youth Tobacco Use in the U.S. Retrieved March 2, 2019
6. Race/Ethnicity and Gender Differences in Drug Use and Abuse. Retrieved March 2, 2019, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2377408/

Abstract: In 2003, Hon Lik, a Chinese pharmacist and inventor, created what would become the first commercially successful e-cigarette. Hon Lik’s invention quickly swept across the continent, gaining popularity and ultimately being introduced to the European market in April 2006. From Europe, it was a quick hop across the pond to the United States. This new, “safe” form of smoking quickly spread throughout the states. This wave quickly formed a new, highly profitable industry. With such a rapid rise to popularity, governing bodies such as the Food and Drug Administration and Federal Trade Commission have not yet regulated this industry effectively. Although, steps are being taken to do so, the damage has been done. The vaping industry has successfully targeted the youth population, creating high rates of teen and adolescent addiction. Similar to the vaping epidemic plaguing the United States, in 2011, there were approximately 20.6 million people in the United States over the age of 12 with an addiction ranging from alcohol to inhalants and hallucinogens. This number has only grown in recent years. This is why it is paramount to be able to model and predict which communities are most at risk and assess the true cost of addiction. Through complex mathematical modelling and analysis, the ability to assess the prevalence and impact of alcohol, nicotine, marijuana, and nonprescription drugs is available today.

Keywords: Substance abuse, nonfinancial and financial impacts, alcohol, marijuana, and tobacco.

Introduction: While the United States is currently experiencing an opioid epidemic with over 72,000 people dying each year from overdoses, there have also been increases in the use of other drugs such as nicotine, marijuana, and alcohol throughout the country. This is especially concerning due to an increasing proportion of the demographic is middle schoolers and high schoolers. Moreover, this is the first time in the history of the United States that the leading cause of death is opioid overdose (it surpassed vehicle crashes). It is important to understand the factors that lead individuals to use these substances so that the spread can be effectively combatted.

This section addresses the problem of addiction in society. We focus on the United States specifically and limit our model to the following drugs:  nicotine, marijuana, prescription drugs, alcohol.

The problem is to create a model that can accurately predict the spread of nicotine. This is followed by the creation of a model that can be applied to different drugs with inputs depending on an individual's income, education level, and race. These factors were chosen because we determined them to be the most significant factors in terms of influencing people to do drugs. We would have also liked to include calculations involving environmental factors such as family use and ease of access but due to time and calculating restraints, we omitted these variables.

Because of the advancement in technology, people try to find an alternative for smoking cigarettes. They found this alternative in vaping. As a result, cigarette sales are reaching an all-time low (as shown in the graph below). Overall, this indicates that the growth of vaping will more than replace the decreasing usage of cigarettes.

Our models functioned on several assumptions. We assumed that nationwide trends are directly applicable to all individual populations, which may not be the case. A study can be conducted to provide evidence of drug usage in specific areas across the country in order to pinpoint our data.

The spread of nicotine abuse as well as the abuse of other drugs is on the rise throughout the country. This is especially alarming in the younger generation as model 2 suggests. The amount of high school seniors predicted to be using these substances indicates a societal issue that needs to be addressed in order to prevent damage to today's youth and lower these numbers for later generations.  The impact of these drugs, while varied between them, signifies how abuse can quickly lead to poverty and strain on the economy that must support them.

References

1. Race/Ethnicity and Gender Differences in Drug Use and Abuse. Retrieved March 2, 2019, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2377408/
2. FDA. (2018, June). Youth Tobacco Use in the U.S. Retrieved March 2, 2019
3. Marijuana Street Prices: How Much Should You Pay For Weed? (n.d.). Retrieved from https://addictionresource.com/drugs/marijuana/marijuana-street-prices/
4. Morbidity and Mortality Weekly Report (MMWR). (2017, June 21). Retrieved from https://www.cdc.gov/mmwr/volumes/65/ss/ss6511a1.htm
5. Motor Vehicle Safety. (2017, June 16). Retrieved from https://www.cdc.gov/motorvehiclesafety/impaired _driving/impaired-drv_factsheet.html
6. National Institute on Drug Abuse. (n.d.). What is the scope of tobacco use and its cost to society? Retrieved from https://www.drugabuse.gov/publications/research-reports/tobacco- nicotine-e-cigarettes/what-scope-tobacco-use-its-cost-to-society
7. The Price Paid for Automobile Accidents and Injuries. (n.d.). Retrieved from http://www.tavss.com/library/va-nc-lawyer-economic-and-comprehensive-auto-accident-costs.cfm

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