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The process of producing enormous amounts of ephemeral clothing at accelerated rates, known as fast fashion, creates significant environmental and societal issues. The phenomenon of fast fashion rose due to globalization, economic factors, lack of legislation, and the advancement of technology. Governments, companies, and consumers must work together to create more sustainable retail supply chains. I have gathered information from interviews with individuals in the sustainable fashion industry, books, case studies, online reports, and newspaper articles. Based on my research, I recommend that companies should target wealthier consumers, develop a common language concerning sustainability, invest in sustainable fibers, and listen to factory employees for solutions to improve their working conditions. I also advise that the U.S governments should revise fashion copyright laws and international governments should emphasize regulations concerning the fashion industry. Lastly, consumers should adopt a price-per-wear mindset and utilize resale options. Overall, while perfect sustainability is improbable, consumers, governments, and companies should not use this as an excuse to avoid responsibility.
The purpose of this paper is to make the beyond a reasonable doubt standard in criminal trials more comprehensible for the modern juror while also increasing the modern juror’s motivation and ability to apply this standard consistent with trial proceedings. The major problems addressed include why the beyond a reasonable doubt standard is so difficult for modern jurors to understand in addition to why modern jurors lack both the motivation and ability to perform their integral function in criminal trials due to their enforced passive role. This paper traces the origins of the modern jury, delving into the centuries-long transition of the jury from an active to passive function, and the impacts historical change has had on the modern juror’s role in criminal trials. It also looks to define the beyond a reasonable doubt standard in terms of case law and pattern jury instructions and through contrast with its constituent lower civil standards of evidentiary certainty. The solution posed to remedy the aforementioned issues rests on a unique application of metaphor and imagery that can be implemented in lawyers’ rhetorical methods to instruct jurors on their paramount function in modern criminal suits.
The opioid crisis was inflamed by multiple sources, from which Purdue Pharma and other pharmaceutical companies benefited. The first is the Revolving Door, where government workers go to work for the companies they were once in charge of regulating. Existing loopholes allow former officials to immediately become lobbyists and perform consulting work. The Food and Drug Administration has close ties with lobbyists and pharmaceutical companies, which casts doubt and suspicion on its policies. Tightening and expanding current Revolving Door regulations would begin to stem this problem. Extending the cooling-off period to a minimum of five years would prevent former government workers from immediately influencing government policies. Furthermore, the laws need to be modified to include more specific language to eliminate loopholes. Banning former government employees from any counseling services or lobbying any government branch, agency, or office will make it much more difficult to circumvent the rules.
The second are “pill mills,” whereby physicians, clinics, or pharmacies prescribe prescription drugs inappropriately. There exists a web of regulation and reporting laws from federal and state governments, but pill mills still established themselves. Florida enacted laws that created stricter requirements for dispensing drugs, medical examinations, and follow-ups before and after prescribing opioids for chronic pain. These laws had positive results in stopping pill mills. Similar laws should be enacted nationally. Existing laws focusing on the pharmaceutical manufacturers, distributors, and pharmacies should be expanded to improve reporting between those agencies and the DEA and the DEA and other government agencies.
The last one is the American drug addiction rehab system. It is fraught with stigma, lax insurance information, inconsistent treatments, and poorly utilized information. The system often fails to provide care for those who need it. Increasing the scope of treatments would boost its effectiveness. States need to require insurance companies to cover mental health treatment to the same extent and degree as physical health issues and use a uniform, standardized tool to decide the necessary level of care addiction patients need. Public report cards for treatment centers would improve their long-term level of care and ease patients in finding a treatment center that fits them.
Addressing these problems has already begun at the both federal and state level. As these causes are identified and attacked, it will become easier to pass the laws needed to repair the system that allowed the opioid crisis to occur.
The goal of this project is to develop a deeper understanding of how machine learning pertains to the business world and how business professionals can capitalize on its capabilities. It explores the end-to-end process of integrating a machine and the tradeoffs and obstacles to consider. This topic is extremely pertinent today as the advent of big data increases and the use of machine learning and artificial intelligence is expanding across industries and functional roles. The approach I took was to expand on a project I championed as a Microsoft intern where I facilitated the integration of a forecasting machine learning model firsthand into the business. I supplement my findings from the experience with research on machine learning as a disruptive technology. This paper will not delve into the technical aspects of coding a machine model, but rather provide a holistic overview of developing the model from a business perspective. My findings show that, while the advantages of machine learning are large and widespread, a lack of visibility and transparency into the algorithms behind machine learning, the necessity for large amounts of data, and the overall complexity of creating accurate models are all tradeoffs to consider when deciding whether or not machine learning is suitable for a certain objective. The results of this paper are important in order to increase the understanding of any business professional on the capabilities and obstacles of integrating machine learning into their business operations.