Abstract: 
This study seeks to solve the lack of low-cost, accurate water quality measuring tools by creating an inexpensive spectrophotometer utilizing a smartphone for the measurement of various water contaminants. The device consists of a mount, LEDs, and a smartphone, and uses advanced software tools, including machine learning, for data processing, analysis, and contaminant concentration prediction. The machine learning models were trained on 2,282 prepared test samples and 15 gathered field samples from eight sources, which were separately tested using standard techniques. The overall device and analysis software was then evaluated using eight separate testing samples from eight different locations, and was used to conduct a proof-of-concept mini-study where samples taken at various distances from a drainage pipe were analyzed. Overall, the predictive system reached an accuracy of 83.3%, cost $29.14, and was able to be completed in under three minutes by each of three volunteers. The main conclusion of this work is that the system developed is cheap, effective, and easy to use, providing a template for future mass production and better testing of water quality worldwide.

Keywords: Water Quality Analysis, Spectrophotometry, Machine Learning,  SVM algorithm, Multivariate Multiple Regression

References

1. Chowdhary, P, Bharagava, RN, Mishra, S, Khan, N (2019). Role of Industries in Water Scarcity and Its Adverse Effects on Environment and Human Health. Environmental Concerns and Sustainable Development 1, 235-256. 

2. Lin, L, Yang, H, Xu, X (2022). Effects of Water Pollution on Human Health and Disease Heterogeneity: A Review. Frontiers in Environmental Science 10, 880246. 

3. Khatri, N, Tyangi, S (2014). Influences of natural and anthropogenic factors on surface and groundwater quality in rural and urban areas. Frontiers in Life Science 8, 23-39.

4. Boelee, E, Geerling, G, Zann, Bvd, Blauw, A, Vethaak, AD (2019). Water and health: From environmental pressures to integrated responses. Acta Tropica 193, 217-226.

5. Ella, EMAE, Elnazer, AA, Salman, SA (2017). The effect of human activities on the pollution of water in southwest Giza area, Egypt. Water Supply 17, 1368-2376.

6. Karthe, D, Chalow, S, Borchardt, D (2014). Water resources and their management in central Asia in the early twenty first century: status, challenges and future prospects. Environmental Earth Sciences 73, 487-499. 

7. Altenburger, R, Brack, W, Burgess, RM, et al (2019). Future water quality monitoring: improving the balance between exposure and toxicity assessments of real-world pollutant mixtures. Environmental Sciences Europe 31, 12. 

8. Kirschke, S, Avellán, T, Bärlund, I, et al (2020). Capacity challenges in water quality monitoring: understanding the role of human development. Environmental Monitoring Assessment 5, 192-298.

9. United Nations Sustainable Development Goals. https://www.un.org/sustainabledevelopment/sustainable-development-goals/ (accessed Dec 2022)

10. Kriss, R, Pieper, KJ, Parks, J, Edwards, MA (2021). Challenges of Detecting Lead in Drinking Water Using at-Home Test Kits. Environmental Science Technology 55, 1964-1972. 

11. Hamoudi, A, Jeuland, M, Lombardo, S et al (2012). The Effect of Water Quality Testing on Household Behavior: Evidence from an Experiment in Rural India. The American Journal of Tropical Medicine and Hygiene 87, 18-22. 

12. Norris, RH, Georges, A (1986). Design and Analysis for Assessment of Water Quality. In Limnology in Australia. (ed. Decker, PD, Williams, WD), pp. 555-572. Springer, Dordrecht, Germany. 

13. Jordan, P, Cassidy, R (2022). Perspectives on Water Quality Monitoring Approaches for Behavioral Change Research. Frontiers in Water 4, 917595.

14. Soltani, AA, Oukil, A, Boutaghane, H, et al (2021). A new methodology for assessing water quality, based on data envelopment analysis: Application to Algerian Dams. Ecological Indicators 121, 106952. 

15. Luvhimbi, N, Tshitangano, TG, Mabunda, JT, et al (2022). Water quality assessment and evaluation of human health risk of drinking water from source to point of use at Thulamela municipality, Limpopo Province. Scientific Reports 12, 6059.

Abstract: 

This paper focuses on the studies conducted on the changes in the exchange rate behavior of selected currencies such as USD, EURO, KRW, and PESO. The importance of the exchange rate is immeasurable since the movement in currency exchange affects the trade and direction of all money between countries. The studies done for this paper were used to observe the changes in this exchange rate on a global basis. Data collection provided a clear overview of the distribution of these studied rates. The exchange rate of each currency was predetermined, and the information was used to create visual histograms and other graphs for prediction. The data from the line graph provided a specific accounting of the movement of the rates over a period of time. The histograms used the information to decide which rate was standard and it was found that the rates fluctuated regularly but always peaked at a specific time. The predictions show that there are no specific patterns and the rates peaked sharply while the modeling was being conducted.  

The average range of these fluctuation patterns continued to change regularly until 2021. The predictive model was found using LSTM where it was determined that this data could be used to make significant advancements in the exchange rate volatility on economic growth. It was found that several of the studies determined that the high volatility of the exchange rate had a positive effect on international trade and economic growth. Those who support this theory feel that increased flexibility combined with these volatile exchange rates allow countries to stimulate economic growth. They also determined that as the volatility decreases, the result could present a global financial crisis. When observing these contradictions, it was clear that the impact of exchange rate volatility affects international trade. The bottom line is that economic growth continues to be a significant financial issue. 

Keywords: Exploratory Data Analysis(EDA), Exchange Rates, LSTM( Long Short-Term Memory), Neural Networks and Statistical Analysis

References

1.  Exchange Rates: What They Are, How They Work, Why They Fluctuate, Investopedia, https://www.investopedia.com/terms/e/exchangerate.asp

2. O'Sullivan, Arthur; Steven M. Sheffrin (2003). Economics: Principles in action. Upper Saddle River, New Jersey 07458: Prentice Hall. p. 458. ISBN 0-13-063085-3.

3.  Broz, J. Lawrence; Frieden, Jeffry A. (2001). "The Political Economy of International Monetary Relations". Annual Review of Political Science. 4 (1): 317–343. doi:10.1146/annurev.polisci.4.1.317. ISSN 1094-2939.

4. The Economist – Guide to the Financial Markets (pdf)

5.  "Triennial Central Bank Survey: Foreign (other countries) exchange turnover in April 2013 : preliminary global results : Monetary and Economic Department" (PDF). Bis.org. Retrieved 23 December 2017.

6. Peters, Will. "Find the Best British Pound to Euro Exchange Rate". Pound Sterling Live. Retrieved 21 March 2015.

7. Understanding foreign exchange: exchange rates Archived 2004-12-23 at the Wayback Machine

8. https://www.wsj.com/market-data/quotes/fx/USDEUR/historical-prices

9. https://www.wsj.com/market-data/quotes/fx/USDKRW/historical-prices

10.  "Mean Squared Error (MSE)". www.probabilitycourse.com. Retrieved 2020-09-12.

11. https://www.economicshelp.org/macroeconomics/exchangerate/

12. Bickel, Peter J.; Doksum, Kjell A. (2015). Mathematical Statistics: Basic Ideas and Selected Topics. Vol. I (Second ed.). p. 20. 

13.  Lehmann, E. L.; Casella, George (1998). Theory of Point Estimation (2nd ed.). New York: Springer. ISBN 78-0-387-98502-2. 

14.  Gareth, James; Witten, Daniela; Hastie, Trevor; Tibshirani, Rob (2021). An Introduction to Statistical Learning: with Applications in R. Springer. ISBN 978-1071614174.

15.  Wackerly, Dennis; Mendenhall, William; Scheaffer, Richard L. (2008). Mathematical Statistics with Applications (7 ed.). Belmont, CA, USA: Thomson Higher Education. ISBN 978-0-495-38508-0.

16. ​​How to Interpret Histograms, https://www.labxchange.org/library/items/lb:LabXchange:10d3270e:html:1

Abstract: 

Plants are an integral part of human life. Crops, especially fruits and vegetables, provide humans with energy and nutrients. What would happen if we didn’t have these foods at all? The aim for this research study was to determine if the plant ridding disease, Tobacco Mosaic Virus
(TMV), could be mitigated if treated with propolis; a substance collected from Apis mellifera (honey bees) when they pollinate flowers. The plant selected for this study was Solanum lycopersicum (tomato plant) due to its commonality with the virus itself. Due to the hardships of germination, the plants were bought and not grown and only the leaf count was measured. The plants were split into two groups, control and experimental. TMV was applied to both groups, but only the experimental group was treated with Apis mellifera propolis. Several days later, the experimental group received the propolis. With the control group, about 6 leaves were destroyed and wrinkle while in the experimental group only 2 leaves were shriveled and destroyed. The control group plant had a tilt as the stem was weakening, while the experimental group was upright. The colors didn't change all too much in either except for the leaf color. In the control group, the leaves looked black and brown, while in the experimental it looked slightly brown
rather than black. According to the data collected, the plant-ridden disease of the Tobacco Mosaic Virus can be rid of with the propolis of Apis mellifera.

Keywords: Solanum lycopersicum,  Apis mellifera Propolis,  Growth of Tumors, Tobacco Mosaic Virus
(TMV)

References

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7. Wagh V. D. (2013). Propolis: a wonder bees product and its pharmacological potentials. Advances in pharmacological sciences, 2013, 308249. https://doi.org/10.1155/2013/308249
8. Yin, Z., KOBIKI, A., & KAWADA, H. (2012). Tobacco Mosaic Virus as a New Carrier for Tumor Associated Carbohydrate Antigens. Tobacco Mosaic Virus As A New Carrier For Tumor
   Associated Carbohydrate Antigens, 2012(0), 47. https://doi.org/10.1299/jsmeintmp.2005.47_2

Abstract: 

 In this experiment  working with Chromium and Eisenia fetida studying the health and behaviors of Eisenia fetida and how Chromium will affect their behaviors when exposed to Chromium. Other researchers that have done similar research showed that their Eisenia fetida have died because of being exposed to too much Chromium or in other experiments they did not have an outcome because the Eisenia fetida was not exposed to enough Chromium. The Eisenia fetida will be exposed to Chromium for about 2 weeks. The worms  will be monitored. The habitat of the Eisenia fetida is moist soil, although some Eisenia fetida actually prefer mud, such as the mud that is found along the shores of lakes or swamps. Eisenia fetida can be found in the soil of backyards as well as near bodies of fresh and saltwater.  When the  Eisenia Fetida arrive  there will be an enclosure for them to be in. Earthworms eat soil. Their nutrition comes from things in soil, such as decaying roots and leaves. The entire surface of a worm's body absorbs oxygen and releases carbon dioxide. Moisture Eisenia Fetida moves  by squeezing muscles around their water- filled bodies. The Earthworms  will lose weight  when being exposed to Chromium. They will also shrink and the regeneration process for the earthworms will slow down. This shows how Chromium does have an effect on Eisenia fetida  and can cause the worms to have different effects.

Keywords: Eisenia fetida, Earthworms, Sublethal doses, Hexavalent chromium

References

1. Burlinson, B., Tice, R.R., Speit, G., Agurell, E., Brendler-Schwaab, S.Y., Collins, A.R., Escobar, P., Honma, M., Kumaravel, T.S., Nakajima, M., Sasaki, Y.F., Thybaud, E., Uno, Y., Vasquez, M., Hartmann, A., 2007. Fourth international workgroup on genotoxicity testing: results of in vivo comet assay workgroup. Mutat. Res.

2. Ching, E.W.K., Siu, W.H.L., Lam, P.K.S., Xu, L., Zhang, Y., Richardson, B.J., Wu, R.S.S., 2001. DNA adduct formation and DNA strand breaks in green-lipped mussels (Perna viridis) exposed to benzo[a]pyrene: dose- and time-dependent relationships. Mar. Pollut. Bull. 42, 603–610. Cotelle, S., Ferard, J.-F., 1999. Comet assay in genetic ecotoxicology: a review. Environ. Mol. Mutagen. 34, 246–255.

3. Di Marzio, W.D., Saenz, M.E., Lemière, S., Vasseur, P., 2005. Improved single-cell gel electrophoresis assay for detecting DNA damage in Eisenia foetida. Environ. Mol. Mutagen. 46, 246–252. Fourie, F., Reinecke, S.A., Reinecke, A.J., 2007. The determination of earthworm species sensitivity differences to cadmium genotoxicity using the comet assay. Ecotoxicol. Environ. Saf. 67, 361–368. 

4. Di Palma, L., Gueye, M.T., Petrucci, E., 2015. Hexavalent chromium reduction in contaminated soil : a comparison
   between ferrous sulfate and nanoscale zero-valent iron. J. Hazard Mater. 70–76.https://doi.org/10.1016/j.jhazmat.2014.07.058.

5. Dong, H., Deng, J., Xie, Y., Zhang, C., Jiang, Z., Cheng, Y., Hou, K., Zeng, G., 2017.Stabilization of nanoscale
   zero-valent iron (nZVI) with modified biochar for Cr(VI)removal from aqueous solution. Journal of Hazardous Materials.
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6. Inzunza, B., Orrego, R., Peñalosa, M., Gavilán, J.F., Barra, R., 2006. Analysis of CYP4501A1, PAHs metabolites in bile, and genotoxic damage in Oncorhynchus mykiss exposed to Biobío River sediments, Central Chile. Ecotoxicol. Environ. Saf. 65, 242–251. 

Abstract: 

Coronavirus disease -19 (COVID-19) has gained widespread interest in the field of mathematical epidemiology in order to inform the public on basic statistics surrounding COVID-19. However, the age-standardized mortality rates (ASMRs), which adjust age and population discrepancies between different regions by comparing a subpopulation to a standard population, have not been shown publicly. Usually, COVID-19 ASMRs have not been calculated due to the lengthy process required to calculate them; however, ASMRs for COVID-19 have occasionally been calculated, but their effectiveness have been hindered due to the use of a hand-written formula and graphical manual methods. My study involved the development of a deep learning algorithm to calculate ASMR and to instantly graph the ASMR of a subpopulation versus the crude mortality rate of the standard population. This algorithm was used to compare the ASMRs for COVID-19 in American states to the crude mortality rate of the standard population, America. In this study, the algorithm shows efficiency with a consistent runtime of time≤5seconds, within 95% confidence interval error bars among trials. ASMRs show statistically significant differences in expected COVID-19 deaths among most populations. There is at least 95% confidence (p≤0.05) that differences in ASMR are independent of age and population distributions. These findings suggest that there are more factors than just age discrepancy that affect COVID-19 mortality rates.

Keywords: COVID-19, Age-Standardization, Mortality Rate, Algorithm, Deep Learning

References

1. Wang, D., Li, Z., & Liu, Y. (2020). An overview of the safety, clinical application and antiviral research of the COVID-19 therapeutics. Journal of Infection and Public Health. doi:10.1016/j.jiph.2020.07.004
2. Brown, S. M., Doom, J. R., Lechuga-Peña, S., Watamura, S. E., & Koppels, T. (2020). Stress and parenting during the global COVID-19 pandemic. Child Abuse & Neglect. doi:10.1016/j.chiabu.2020.104699
3. Overton, C. E., Stage, H. B., Ahmad, S., Curran-Sebastian, J., Dark, P., Das, R., . . . Webb, L. (2020). Using statistics and mathematical modelling to understand infectious disease outbreaks: COVID-19 as an example. Infectious Disease Modelling, 5, 409-441. doi:10.1016/j.idm.2020.06.008
4. Tiirinki, H., Tynkkynen, L., Sovala, M., Atkins, S., Koivusalo, M., Rautiainen, P., . . . Keskimäki, I. (2020). COVID-19 pandemic in Finland – preliminary analysis on health system response and economic consequences. Health Policy and Technology. doi:10.1016/j.hlpt.2020.08.005
5. Russell, T. W., Hellewell, J., Jarvis, C. I., Zandvoort, K. V., Abbott, S., Ratnayake, R., . . . Kucharski, A. J. (2020). Estimating the infection and case fatality ratio for coronavirus disease (COVID-19) using age-adjusted data from the outbreak on the Diamond Princess cruise ship, February 2020. Eurosurveillance, 25(12). doi:10.2807/1560-7917.es.2020.25.12.2000256
6. Bernardino, G., Benkarim, O., Garza, M. S., Prat-Gonzàlez, S., Sepulveda-Martinez, A., Crispi, F., . . . Ballester, M. A. (2020). Handling confounding variables in statistical shape analysis - application to cardiac remodelling. Medical Image Analysis, 65. doi:10.1016/j.media.2020.101792
7. Xu, L., Polya, D. A., Li, Q., & Mondal, D. (2020). Association of low-level inorganic arsenic exposure from rice with age-standardized mortality risk of cardiovascular disease (CVD) in England and Wales. Science of The Total Environment, 743. doi:10.1016/j.scitotenv.2020.140534
8. Shende, R., Gupta, G., & Macherla, S. (2019). Determination of an inflection point for a dosimetric analysis of unflattened beam using the first principle of derivatives by python code programming. Reports of Practical Oncology & Radiotherapy, 24(5), 432-442. doi:10.1016/j.rpor.2019.07.009
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Abstract: COVID-19 has proven detrimental to the economy and changed the nature of social interactions. Governments at every level have increasingly required the use of face masks in public spaces. Evidence has shown that mandatory mask-wearing policies can effectively control the outbreak of the virus, protecting susceptible populations (i.e., individuals with preexisting conditions, and individuals 65 and older). Many communities encourage mask-wearing to reduce the chance of viral transmission. 

While mandatory mask policies appear to effectively reduce transmission of the virus, their long-term psychological effects are not yet known. In this study, we examine the association between the implementation of face mask mandates and detrimental psychological and social consequences as well as other relevant aspects. Also, this study tries to figure out if the mandatory mask policies are advisable, and if so, how it benefits the public. 

Keywords:  Mask policies, Social behavior, Psychological consequences, Covid-19, Face mask during the pandemic

References

1. Detsky, A. S. and Bogoch, I. I. (2020, August 25). The Canadian Response To COVID-19. Retrieved from https://jamanetwork.com/journals/jama/fullarticle/276943

2. Duan, L. and Zhu, G. (2020). Psychological interventions for people affected by the COVID-19 epidemic. Lancet. Psych. 7 300–302. 10.1016/s2215-0366(20)30073-

3. Greenberg, N., Docherty, M., Gnanapragasam, S. and Wessely, S. (2020). Managing mental health challenges faced by healthcare workers during covid-19 pandemic. BMJ 368:m1211. 10.1136/bmj.m121

4. Liu S., Yang L., Zhang C., Xiang Y. T., Liu Z., Hu S., et al. (2020). Online mental health services in China during the COVID-19 outbreak. Lancet. Psych. 7 E17–E18. 10.1016/S2215-0366(20)30077-

5. Maheu, M. P., McMenamin, J. and Posen, L. (2012). Future of telepsychology, telehealth, and various technologies in psychological research and practice. Profess. Psychol. Res. Prac. 43 613–621. 10.1037/a0029458

6. Parshley, L. and Zhou, Y. (2020, December 4). Why every state should adopt a mask mandate, in 4 charts. Retrieved from https://www.vox.com/science-and-health/21546014/mask-mandates-coronavirus-covid-19

7. The Economist. (2020, October 14). Tracking covid-19 excess deaths across countries. Retrieved from https://www.economist.com/graphic-detail/coronavirus-excess-deaths-tracker

8. The Economist. (2020, October 11). Covid-19 has led to a sharp increase in depression and anxiety. Retrieved from https://www.economist.com/graphic-detail/2021/10/11/covid-19-has-led-to-a-sharp-increase-in-depression-and-anxiety

9. Wang, C. J., Chun, Y. and Brook, R. H. (2020, April 14). Response to COVID-19 in Taiwan: Big Data Analytics, New Technology, and Proactive Testing. Retrieved October 18, 2020, from https://jamanetwork.com/journals/jama/fullarticle/2762689

10. Zhou X., Snoswell C. L., Harding L. E. (2020). The Role of Telehealth in Reducing the Mental Health Burden from COVID-19. Telemed. E Health. 26 377–379. 10.1089/tmj.2020.0068

Abstract: 

Current estimates reveal that approximately 1.2 billion people reside in areas susceptible to flooding. However, due to human-inflicted changes to the environment, it is predicted that within the next 30 years, this number will increase by at least 400 million. Despite the prevailing belief that the effects of flooding are diminutive, catastrophic destruction is possible, especially when victims belong to vulnerable populations. Aside from physical damage, severe flooding often prevents individuals from securing the bare necessities- water, food, shelter, and medical attention- leading to health crises and social segregation. Following Hurricane Sandy, these adverse effects devastated communities on the East Coast, namely those in New York City and Long Island. To mitigate complications during recuperation, researchers proposed updating strategies and policies to take into account factors such as social capital and economic vulnerability. Doing so may ensure that all communities have equal access to ample resources and services, regardless of demographic composition. Therefore, this study investigated the role of community support, as opposed to socioeconomic status, in the vulnerability and resiliency of New York residents to flooding from Hurricane Sandy. Those who are more engaged in politics tend to be more vigilant about the efforts of their local government. If local politicians are unjustly favoring a certain demographic and neglecting the needs of others, people who pay attention to politics are able to identify the problem and understand how it can be rectified. Furthermore, people who pay attention to the workings of their government are more inclined to address social issues. For vulnerable families, this is relevant because an unsupportive, inept government is frequently the root of problems including forced evacuation/homelessness, poverty, inaccessible resources, etc. If political attentiveness could be quantified, policymakers and community organizations would be able to ascertain which populations are less educated about flooding preparation/reconstruction and which populations can assist the former.

Keywords: Flooding, social segregation, flooding preparation/reconstruction, Hurricane Sandy, devastated communities, in New York City and Long Island.

References

1. Becker, J. S., Taylor, H. L., Doody, B. J., Wright, K. C., Gruntfest, E., & Webber, D. (2015). A Review of People's Behavior in and around Floodwater. Weather, Climate, and Society, 7(4), 321-332. https://doi.org/10.1175/WCAS-D-14-00030.1
2. Bukvic, A., Zhu, H., Lavoie, R., & Becker, A. (2018). The role of proximity to waterfront in residents' relocation decision-making post-Hurricane Sandy. Ocean & Coastal Management, 154. https://doi.org/10.1016/j.ocecoaman.2018.01.002
3. Campbell, K. A., Laurien, F., Czajkowski, J., Keating, A., Hochrainer-Stigler, S., & Montgomery, M. (2019). First insights from the Flood Resilience Measurement Tool: A large-scale community flood resilience analysis. International Journal of Disaster Risk Reduction, 40, 101257. https://doi.org/10.1016/j.ijdrr.2019.101257
4. Chakraborty, L., Rus, H., Henstra, D., Thistlethwaite, J., & Scott, D. (2020). A place-based socioeconomic status index: Measuring social vulnerability to flood hazards in the context of environmental justice. International Journal of Disaster Risk Reduction, 43, 101394. https://doi.org/10.1016/j.ijdrr.2019.101394
5. Clay, P. M., Colburn, L. L., & Seara, T. (2016). Social bonds and recovery: An analysis of Hurricane Sandy in the first year after landfall. Marine Policy, 74, 334-340. https://doi.org/10.1016/j.marpol.2016.04.049
6. Deria, A., Ghannad, P., & Lee, Y.-C. (2020). Evaluating implications of flood vulnerability factors with respect to income levels for building long-term disaster resilience of low-income communities. International Journal of Disaster Risk Reduction, 48, 101608. https://doi.org/10.1016/j.ijdrr.2020.101608
7. Flores, A. B., Collins, T. W., Grineski, S. E., & Chakraborty, J. (2020). Social vulnerability to Hurricane Harvey: Unmet needs and adverse event experiences in Greater Houston, Texas. International Journal of Disaster Risk Reduction, 46. https://doi.org/10.1016/j.ijdrr.2020.101521
8. Fujimi, T., & Fujimura, K. (2020). Testing public interventions for flash flood evacuation through environmental and social cues: The merit of virtual reality experiments. International Journal of Disaster Risk Reduction, 50, 101690. https://doi.org/10.1016/j.ijdrr.2020.101690
9. Gibbens, S. (2019, February). Hurricane Sandy, explained. In National Geographic. https://www.nationalgeographic.com/environment/natural-disasters/reference/hurricane-sandy/#close
10. Graham, L., Debucquoy, W., & Anguelovski, I. (2016). The influence of urban development dynamics on community resilience practice in New York City after Superstorm Sandy: Experiences from the Lower East Side and the Rockaways. Global Environment Change, 40. https://doi.org/10.1016/j.gloenvcha.2016.07.001
11. Hamilton, K., Demant, D., Peden, A. E., & Hagger, M. S. (2020). A systematic review of human behaviour in and around floodwater. International Journal of Disaster Risk Reduction, 47, 101561. https://doi.org/10.1016/j.ijdrr.2020.101561
12. Maantay, J., & Maroko, A. (2009). Mapping urban risk: Flood hazards, race, & environmental justice in New York. Applied Geography, 29(1). https://doi.org/10.1016/j.apgeog.2008.08.002
13. Martins, V. N., Nigg, J., Louis-Charles, H. M., & Kendra, J. M. (2019). Household preparedness in an imminent disaster threat scenario: The case of superstorm sandy in New York City. International Journal of Disaster Risk Reduction, 34, 316-325. https://doi.org/10.1016/j.ijdrr.2018.11.003
14. McGuire, A. P., Gauthier, J. M., Anderson, L. M., Hollingsworth, D. W., Tracy, M., Galea, S., & Coffey, S. F. (2018). Social Support Moderates Effects of Natural Disaster Exposure on Depression and Posttraumatic Stress Disorder Symptoms: Effects for Displaced and Nondisplaced Residents. Journal of Traumatic Stress, 31(2), 223-233. https://doi.org/10.1002/jts.22270
15. Morss, R. E., Mulder, K. J., Lazo, J. K., & Demuth, J. L. (2016). How do people perceive, understand, and anticipate responding to flash flood risks and warnings? Results from a public survey in Boulder, Colorado, USA. Journal of Hydrology, 541, 649-664. http://dx.doi.org/10.1016/j.jhydrol.2015.11.047
16. Ntontis, E., Drury, J., Amlôt, R., Rubin, G. J., & Williams, R. (2020). Endurance or decline of emergent groups following a flood disaster: Implications for community resilience. International Journal of Disaster Risk Reduction, 45, 101493. https://doi.org/10.1016/j.ijdrr.2020.101493
17. Pourebrahim, N., Sultana, S., Edwards, J., Gochanour, A., & Mohanty, S. (2019). Understanding communication dynamics on Twitter during natural disasters: A case study of Hurricane Sandy. International Journal of Disaster Risk Reduction, 37. https://doi.org/10.1016/j.ijdrr.2019.101176
18. Rezende, O. M., Ribeiro da Cruz de Franco, A. B., Beleño de Oliveira, A. K., Miranda, F. M., Pitzer Jacob, A. C., Martins de Sousa, M., & Miguez, M. G. (2020). Mapping the flood risk to Socioeconomic Recovery Capacity through a multicriteria index. Journal of Cleaner Production, 255, 120251. https://doi.org/10.1016/j.jclepro.2020.120251
19. Thistlethwaite, J., Henstra, D., Brown, C., & Scott, D. (2017). How Flood Experience and Risk Perception Influences Protective Actions and Behaviours among Canadian Homeowners. Environmental Management, 61(2), 197-208. https://doi.org/10.1007/s00267-017-0969-2
20. Wang, Z., Lam, N. S.N., Obradovich, N., & Ye, X. (2019). Are vulnerable communities digitally left behind in social responses to natural disasters? An evidence from Hurricane Sandy with Twitter data. Applied Geography, 108, 1-8. https://doi.org/10.1016/j.apgeog.2019.05.

Abstract: 

Even though Major Depressive Disorder (MDD) is the leading cause of disability worldwide impacting over 300 million individuals, early detection and intervention is hindered by the limited knowledge of its underlying mechanisms. One association found to be significant within MDD is the presence of early life stress (ELS), such as sexual abuse, emotional abuse and family conflict. However, the biological mechanism linking ELS and MDD are unknown.

To properly assess the function consequences of ELS within MDD and address these open questions, we propose an analysis of the metabolism of AMY, ACC, HIP, and DLPFC through FDG PET in addition to a structural MRI in MDD patients with and without ELS. We hypothesize that in MDD patients with prior history of ELS, compared to those without ELS, will have a smaller volume/cortical thickness as measured by MRI and decreased metabolism as measured by PET scans in the bilateral DLPFC, ACC, HIP, and AMY. This study would for the first time, assess both structure and function of critical regions of the HPA axis in MDD, while accounting for the common confounder of ELS.

Keywords: Major Depressive Disorder (MDD), early life stress (ELS), emotional abuse, family conflict. bilateral DLPFC, ACC, HIP

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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

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Abstract: 

Though sharp-wave ripples have been recorded in the EEG data of the hippocampus of mammals for years, it remains unclear how ripples can contribute to memory for different behaviors.. Sharp wave ripples are one of the most synchronous patterns in the mammalian brain. These waves are most common during non-REM sleep, although they can also be associated with consummatory behaviors. In EEG recordings, these occurrences can be seen as large amplitude negative polarity deflections (40–100 ms) in CA1 stratum radiatum that are associated with a short-lived fast oscillatory pattern of the LFP in the CA1 pyramidal layer, known as “ripples.” The purpose of this study was to investigate the distinction between sleep and awake ripples along with the connection between sharp-wave ripples and specific mammalian behaviors during memory tasks. The hypothesis tested was that SPW-Rs occur when the animal has an experience that will help guide subsequent successful task completion that results in obtaining a desired reward. To conduct the experiment electrophysiological signals were collected from a rat’s hippocampus during various tasks. The data were then analyzed using Neuroscope and compared to a visual recording of the rat’s actions. The data suggest that sharp wave ripples are more likely to occur close to a reward, most often before the reward, and do not have a higher tendency to occur early or late in learning. Future research can further clarify these results and investigate the process by which these ripples occur.

Keywords: EEG data, non-REM sleep, harp-Wave Ripples, Mammalian Behaviors

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