Health Problems Heat Up: Climate Change and the Public's Health

Examines the health effects of climate change, the needed public health response, concerns for communities at high risk, and state planning and funding for climate change assessments and strategies. Makes federal, state, and local policy recommendations

Effects of Preprocessing Techniques on Cough Based Machine Learning Diagnosis

COVID-19 pandemic outbreak has taken the world by storm in the 18 months and the ramifications are by no means curtailing. The need of the hour with COVID-19 and other pulmonary diseases is a quick online diagnosis by handheld devices. In the light of these constraints, scientists are relying on audio based automated techniques since clinicians routinely use audio cues from the human body (e.g. vascular murmurs, respiration, pulse, bowel sounds etc.) as markers for diagnoses of diseases or the development of ailments. Until recently, such signals have been commonly obtained during scheduled visits via manual auscultation. Research has also begun to use digital technologies to collect body sounds for cardiovascular or respiratory tests, e.g. from stethoscopes, which can then be used for automated artificial intelligence- based analysis. An early study has promised to detect COVID-19 from cough and speech diagnostic signals. This research work describes how preprocessing techniques can enhance the performance of a methodology established over a large-scale crowd-sourced dataset of respiratory audios and in what ways preprocessing techniques ameliorate the performance of cough based diagnosis. Our findings demonstrate that a machine learning classifier will better distinguish a healthy individual from individual with cough due to bronchitis, pertussis or COVID-19 by applying preprocessing techniques. Robust results have been procured by user-based data split-up for the K-fold learning methodology. The results show a noticeable increase in the efficacy of the application of preprocessing techniques in an algorithmic pipeline. These results are rudimentary and only the tip of the iceberg of the potential of cough and audio-based machine learning. The research opens the door for enhancing the performance of lightweight machine algorithms to be comparable with their more complicated and resource-consuming counterparts. Such advancements can be of paramount significance in the practical field of application deployment

Portable Ventilator

The current COVID-19 pandemic has heavily impacted the healthcare system in the United States and elsewhere. The need for patients to have access to a hospital with a ventilator along with a shortage of ventilators for recovery and at-home care as a result of minimal hospital vacancy for patients has been greatly stressed. The presented problem is both an unmet demand and supply of portable and effective ventilators. Existing ventilators have many shortcomings that should be addressed: size, weight, cost, and complexity of current ventilators confines users to stay in a medical facility whilst being monitored by professionals. This both increases the risk the user has of contracting other diseases and increases the cost to operate a ventilator. Therefore, there is a need to create a ventilator that is both smaller and more accessible than what is currently available. The solution is to design a ventilator which can meet the symptoms and strains which COVID-19 can put on various individuals, should they not have access to a commercial ventilator as an economic constraint, or have restricted access to a medical facility attributed to the influx of patients. This means that a portable ventilator targeted for lower risks patients to be the ideal ventilator design. This ventilator would allow for short term out-patient care of low risk patients, thus allowing hospitals to focus on high risk patients. The lower risk patients would then be allowed to be at home and away from hospitals where both the expenses and risks of catching diseases would be higher. This would also mean that there would be greater access to care and less people denied care. While the focus is currently on COVID-19 patients, the use of the ventilator will extend to other patients who also need help breathing. The ventilator would include wireless communication, smartphone integration, and a portable power system which would allow consumers to relocate the ventilator as needed and have back-up power in the event of a power outage

WHEN IS IT OKAY TO BE HIGH? A GUIDE FOR LAW ENFORCEMENT OFF-DUTY USE OF CANNABIS

This thesis explores policy options for allowing off-duty law enforcement to use cannabis, considering the potential decriminalization and legalization of recreational cannabis use and the high stress levels of first responders. Utilizing Bardach and Patashnik’s eight-step method, this thesis discusses the mental and physical health problems that law enforcement officers face and then provides research on cannabis to determine whether cannabis could be a useful tool in curbing the stress of this occupation. Finally, policies on recreational use for other occupations and a sample from Canada are examined. Research shows that stress levels in the law enforcement occupation cause substantial mental and physical ailments for officers. Moreover, for those retired or outside law enforcement, cannabis has been used as treatment for some of these conditions. This thesis finds that given the potential of federal legalization, fit-for-duty policies that allow recreational, off-duty use are powerful and beneficial tools to mitigate stress-related ailments.Civilian, Prospect Heights Police DepartmentApproved for public release. Distribution is unlimited

The Role of the Internet of Things in Health Care: A Systematic and Comprehensive Study

The Internet of Things (IoT) is becoming an emerging trend and has significant potential to replace other technologies, where researchers consider it as the future of the internet. It has given tremendous support and become the building blocks in the development of important cyber-physical systems and it is being severed in a variety of application domains, including healthcare. A methodological evolution of the Internet of Things, enabled it to extend to the physical world beyond the electronic world by connecting miscellaneous devices through the internet, thus making everything is connected. In recent years it has gained higher attention for its potential to alleviate the strain on the healthcare sector caused by the rising and aging population along with the increase in chronic diseases and global pandemics. This paper surveys about various usages of IoT healthcare technologies and reviews the state of the art services and applications, recent trends in IoT based healthcare solutions, and various challenges posed including security and privacy issues, which researchers, service providers and end users need to pay higher attention. Further, this paper discusses how innovative IoT enabled technologies like cloud computing, fog computing, blockchain, and big data can be used to leverage modern healthcare facilities and mitigate the burden on healthcare resources