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AI-Enhanced Biodegradable Sensors for Environmental Monitoring
Full text linkIn an era where electronic waste is a growing global concern, the development of biodegradable sensors represents a crucial step towards sustainable environmental monitoring. Traditional sensors, composed of non-biodegradable materials, contribute significantly to the mounting problem of electronic waste. This paper explores the integration of artificial intelligence (AI) with biodegradable sensors, which not only mitigates the environmental impact of electronic waste but also enhances the precision, real-time decision-making, and efficiency of environmental monitoring systems. While these AI-enhanced sensors offer promising advances, challenges such as data privacy, infrastructure costs, and the ecological impact of their deployment remain. Furthermore, the paper addresses the critical issue of AI ethics and bias mitigation, emphasizing the need for transparent, inclusive, and interdisciplinary approaches in developing AI-driven technologies. The discussion provides insights into future possibilities for AI-enhanced biodegradable sensors, including expanded applications, advancements in biodegradable materials, and the ethical deployment of these technologies. The paper underscores the necessity of interdisciplinary collaboration to fully harness the potential of these innovations while ensuring their alignment with sustainability and ethical goals.
Key Words: biodegradable sensors; artificial intelligence; environmental monitoring (EM); electronic waste; smart environment; sustainable technology; bias mitigatio
How do Women Entrepreneurs in Uzbekistan Overcome Obstacles in Setting up and Expanding Businesses?
Full text linkEntrepreneurship in Uzbekistan is a male-dominated field. The underrepresentation of women in business inspired organizations such as The USAID, World Bank, WTO and others to initiate projects to support and develop women entrepreneurship. In this paper, I qualitatively examine the existing data featuring potential obstacles faced by women, and identify some gaps in the previous works of research scholars. I analyze primary data collected through interviews with women entrepreneurs based in Uzbekistan, as well as secondary data collected from archival sources. Based on my analysis of primary and secondary data, I discuss obstacles faced by Uzbek women entrepreneurs, and identify strategies used by them to overcome these obstacles. The findings of my study offer insights on strategies that may assist women who are looking to pursue entrepreneurship in this region and opportunities for their career development
An Analysis of Potential Impacts of the Proposed Vehicle Miles Traveled Tax Bill in California
Full text linkShould a vehicle miles traveled (VMT) tax be implemented in California? I perform a literature review covering all papers that study VMT tax systems in other contexts and discuss how effective these are at reducing miles traveled and air pollution. I use California public infrastructure and vehicle sales data to show what a VMT in California would cost denizens in the state. I conclude with a discussion on the global applicability of this tax structure based on my findings. A VMT tax can be effective at solving a revenue deficit provided incomes are high enough, there are trusted institutions, and other transit options for consumers to substitute.
Keywords: Climate Change, Emissions, Greenhouse Gases, Externalities, Pigouvian ta
A Novel Multimodal Deep Learning-Based Approach to the Recognition and Analysis of Pro-Eating Disorder Content on Social Media
Full text linkOver the last decade, there has been a vast increase in eating disorder diagnoses and eating disorder-attributed deaths, reaching their zenith during the Covid-19 pandemic. This immense growth derived in part from the stressors of the pandemic but also from increased exposure to social media, which is rife with content that promotes eating disorders. This study aimed to create a multimodal deep learning model that can determine if a given social media post promotes eating disorders based on a combination of visual and textual data. A labeled dataset of Tweets was collected from Twitter, recently rebranded as X, upon which twelve deep-learning models were trained and evaluated. Based on model performance, the most effective deep learning model was the multimodal fusion of the RoBERTa natural language processing model and the MaxViT image classification model, attaining accuracy and F1 scores of 95.9% and 0.959, respectively. The RoBERTa and MaxViT fusion model, deployed to classify an unlabeled dataset of posts from the social media sites Tumblr and Reddit, generated results akin to those of previous studies that did not employ artificial intelligence-based techniques, indicating that deep learning models can develop insights congruent to those of researchers. Additionally, the model was used to conduct a time-series analysis of yet unseen Tweets from eight Twitter hashtags, uncovering that, since 2014, the relative abundance of content that promotes eating disorders has decreased drastically within those communities. Despite this reduction, by 2018, content that promotes eating disorders had either stopped declining or increased in ampleness anew on those hashtags
The Fabrication of Colored Cellulose-Based Hydrogels For Solar Water Purification
Full text linkPollution and the depletion of clean drinking water sources have made maintaining freshwater supplies a subject of constant concern. Industrial waste dumping within aquatic ecosystems has further compounded this problem and contributed to a rise in infectious waterborne diseases, such as botulism and cholera (Schooner 2015). Therefore, environmentally responsible water purification technology development is essential to ensure a reliable supply of fresh, potable water. One of the most promising emerging technologies for water purification is the hydrogel. Recent studies have shown that hydrogels lower the amount of solar energy needed to evaporate water, thus increasing the efficiency of evaporation-based solar water purification systems (Weerasundara et al. 2021). Although evaporation efficiency rates may vary with the structure and composition of the hydrogel, no study has yet ascertained how hydrogel color affects evaporation efficiency rates. Therefore, this study investigates the impact of different hydrogel colors on water evaporation rates in an experimental solar water purification system utilizing xenon light. The hydrogel colors tested included red, green, and translucent. Consistent with the assumption that hydrogels with the darkest dye would promote the fastest evaporation, red hydrogels performed the fastest, followed by green and transparent hues. This effect may be due to different dyes absorbing different wavelengths of light at unequal rates. Overall, this study provides new insights into the future direction of hydrogels and can help address remaining challenges in the field of water purification
Prevalence of the Diffusion Collision Model of Protein Folding In Vivo: A Mechanistic Analysis of the Acceleration of Protein Folding by Peptidyl-Prolyl Isomerase and the GroEL/ES Chaperonin System
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This review assesses the differences between the diffusion-collision model and the extended nucleation condensation model of protein folding and attempts to determine; by analyzing the mechanisms through which peptidyl-prolyl isomerase and the GroEL/ES chaperonin system accelerate the rate of folding of their respective substrate proteins, which model of protein folding prevails in vivo. The difference in the kinetics between the two protein folding models was assessed in the introduction using free energy profiles which led to the identification of conditions that would favor one model over the other. Following the justification of the choice of chaperones used for analysis, the mechanism through which both chaperones accelerated the rate of folding of their respective proteins was investigated to determine whether the conditions developed by the chaperones were consistent with one model of protein folding over another. The review concludes with a summary of the key findings gleaned from mechanistic analysis of chaperone function and highlights its relevance to the biochemical and medical fields
Histories of Privatization: Examining Culture, Legal Conflict, and Economic Transformation at Adams Morgan Plaza in Washington, DC
Full text linkPublic spaces are disappearing at alarming rates as cities face mounting housing, economic, and social issues. Adams Morgan Plaza in Washington, DC is no different. Over the past several years, the Plaza has experienced numerous changes that have radically altered the neighborhood. It sparked community outrage and an ensuing lawsuit that wounds way, slowly, through the DC courts. This study uses census data and primary source documents to understand why Adams Morgan Plaza was privatized and how these changes are challenged. The results shed new light on how legal battles and economic pressures shaped the development of a central neighborhood landmark. It’s history as a theatre and communal epicenter are obscured by its liminality. The Plaza today contradicts Adams Morgan’s legacy of diversity and artistic culture, but also finds itself on a new path: to create affordable housing. These findings provide insights into an ongoing history of change, cultural expression, and community within a vibrant neighborhood of the nation’s capital
A.I. In Law: Adversary or Ally? Addressing the Possible Implications of A.I. Technology in Law and the Necessity of Regulation
Full text linkThis paper asserts that the progression of Artificial Intelligence technology is an incredible feat and holds momentous potential to grant significant efficiency to individuals involved in the study and practice of law. Nonetheless, such benefits do not transpire without subsequent harms; this paper means to present the adverse effects that could befall historically marginalized and disadvantaged groups and promote the motion of constructing infrastructure to regulate the utilization of this revolutionary technology in the legal field. Identifying the promises that the techno-solutionist mindset preaches and contrasting it with the current capabilities of Artificial Intelligence, the aim is to have a clear understanding of how an integration of this magnitude could benefit students of and practitioners in law. By focusing on past incidents regarding A.I. integration in media, business, and finance, this paper will bring credibility to the concerns previously outlined. Brief and preliminary data gathered from individuals involved in the identified discipline will illustrate the imperative for cautionary advancement of another integration and further progression of A.I. technology in the legal field. Contrary to what business moguls and wealthy proprietors would like people to believe, this technology is still in its infancy and has faults that prevent it from executing the extraordinary feats that it is credited with being able to accomplish. Such unmerited promotion can lead to unfavorable outcomes that will not only impact utilizers of this technology, but also the clients they serve, and thus regulation is demanded. By regulation, we mean to incur internal regulation by legal institutions (e.g., law schools and law firms) combined with government policy to hold these agent conglomerates accountable and protect common people, specifically the most vulnerable of society, from harm. This paper concludes with the perspective to think critically regarding the development and integration of advanced technology within all fields, and not solely the legal sector
Serving Whom? Ethical and Practical Limits of AI Mental Health Chatbots for Marginalized Communities
Full text linkAI mental health chatbots could offer a promising solution to the care gap faced by underserved communities, especially during times of social isolation and health crisis. These tools provide round-the-clock, low-cost, and stigma-free support. Yet, this paper explores how current implementations face challenges that may limit their long-term efficacy and equitable impact. Drawing on recent empirical studies and ethical scholarship, I evaluate the capabilities and constraints of AI chatbots as short-term mental health supports and propose safeguards that promote inclusive, culturally relevant, and ethically responsible deployment. This study presents new empirical insights from a qualitative interview study with 18 low-income, first-generation community college students of color, whose reflections on chatbot use underscore the importance of trust, cultural resonance, and long-term engagement. Rather than comparing or critiquing specific products, this research centers community voices and advocates for collaborative, community-informed improvement. It also draws from an ethically sourced and protected dataset created by Dr. Harriett Jernigan at Stanford University, which provides a compelling counterexample of culturally specific chatbot design
Behind Our Masks: The Impact of Intersectional Identity on Neurodivergent Masking
Full text linkThis research paper examines how gender and ethnocultural identity intersections impact when, why, and how individuals with Attention-Deficit/Hyperactivity Disorder (ADHD), autism, and Sensory Processing Disorder (SPD) mask nonverbally. A qualitative study combining autoethnography, interpretative phenomenological analysis (IPA), and thematic analysis (TA) was conducted with 40 primary written and digital accounts of masking, focusing on women and non-binary folks and individuals with non-Western ethnocultural backgrounds. Results reveal the diversity of masking and identity formation experiences in the intersectional neurodivergent community and identify directions for future exploration such as Asian American and indigenous neurodivergent identity formation and conceptualization, conflation of sensitivity with sexist language and stereotypes, and legacies of racism and colonization. A broader, unifying theme of catering to external social expectations (i.e., gender roles, racial and ethnocultural stereotypes) resulting in estrangement from one’s own body, needs, and sense also emerges. A shift towards more integrative, holistic, and body-based medical and psychological frameworks is thus critical for the effective recognition and treatment of intersectional neurodivergent brains and bodies. Overall, this generative research reveals how we must move towards viewing neurodivergent identities as inherently personal and cultural such that we can accurately capture the interconnected, multifaceted nature of intersectional neurodivergent identities