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
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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
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
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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
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
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