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 goal of this research project is to create a Mathcad template file capable of statistically modelling the effects of mean and standard deviation on a microparticle batch characterized by the log normal distribution model. Such a file can be applied during manufacturing to explore tolerances and increase cost and time effectiveness. Theoretical data for the time to 60% drug release and the slope and intercept of the log-log plot were collected and subjected to statistical analysis in JMP. Since the scope of this project focuses on microparticle surface degradation drug release with no drug diffusion, the characteristic variables relating to the slope (n = diffusional release exponent) and the intercept (k = kinetic constant) do not directly apply to the distribution model within the scope of the research. However, these variables are useful for analysis when the Mathcad template is applied to other types of drug release models.

This thesis looks at the digitalization process holistically. It recognizes that for a digitalization initiative to be successful, it takes input from multiple departments and experts from diverse backgrounds. This paper will be evaluating the interconnectivity needed between the supply chain and human resources departments to spearhead the creation of a digitalization team. Both sectors must have a firm understanding of the other’s needs, in order to acquire, train, and maintain people who will have the necessary hard and soft skills to develop the digital processes. After conducting extensive research around hiring and training, the researchers identified several best practices that companies can utilize to build a successful digital logistics team. Regarding hiring, companies can improve their current practices by collaborating with universities to create synergy between enterprise needs and college curriculum, as well as utilizing talent acquisition data analytics. They must also employ targeted recruiting strategies to attract high-quality talent and create explicit and attractive job postings. In addition to hiring, companies must also continuously improve their training initiatives to ensure their team’s success. In order to do so, firms should conduct training needs analysis, personalize training using technology, offer non-traditional learning modalities, provide holistic supply chain training, and create a learning culture.

Polymer drug delivery system offers a key to a glaring issue in modern administration routes of drugs and biologics. Poly(lactic-co-glycolic acid) (PLGA) can be used to encapsulate drugs and biologics and deliver them into the patient, which allows high local concentration (compared to current treatment methods), protection of the cargo from the bodily environment, and reduction in systemic side effects. This experiment used a single emulsion technique to encapsulate L-tyrosine in PLGA microparticles and UV spectrophotometry to analyze the drug release over a period of one week. The release assay found that for the tested samples, the released amount is distinct initially, but is about the same after 4 days, and they generally follow the same normalized percent released pattern. The experiment could continue with testing more samples, test the same samples for a longer duration, and look into higher w/w concentrations such as 20% or 50%.