Abstract: 

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

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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.