Particulate Matter Emissions from Household Cooking Methods and Associated Respiratory Health Risks: A Systematic Review

Abstract

Indoor air quality remains largely unregulated although people spend an average of 21.6 hours indoors daily, compared to only 2.4 hours outdoors. While ambient air pollution has been extensively studied, there is a significant gap in research on household and indoor air pollution, particularly regarding emissions from everyday cooking practices. This study explores commonly used cooking methods, their associated particulate matter (PM) emissions, their potential impacts on respiratory health, and gaps in current literature. A total of 416 unique studies were screened, with 354 excluded based on title and abstract. Sixty-two reports were sought for retrieval, and ultimately 13 studies were included in the review. The results revealed that pan-frying generated a mean PM1 concentration of 139.55 µg/ m³, while toasting produced 58.8 µg/m³. Deep-frying yielded the highest mean PM2.5 concentration at 841 µg/m³, followed by stewing, stir-frying, roasting, pan-frying, boiling, toasting, and steaming, in descending order. Similarly, deep-frying produced the highest PM10 levels (1192 µg/m³) with roasting, pan-frying and toasting also contributing substantially. Interestingly, when comparing frying and non frying methods, no statistically significant differences in emission levels were found. However, this analysis is limited by the generalization of data across regions and cooking cultures, as variations in oils and fats may influence emission profiles. Despite these limitations, this study establishes a foundational understanding of PM emissions by cooking method and their potential links to respiratory and cardiovascular health outcomes, lung function, and carcinogen exposure. Further research is warranted to evaluate indoor air pollution associated with specific appliances and cooking methods, particularly emerging technologies such as air fryers.

The Value of the Veto: Analysis of Resolution Content and Veto Reasoning on the Palestinian Question (2000-2025)

Abstract

The veto is a controversial part of the United Nations’ procedural rules, defended by the Permanent Members as a quality-control mechanism for resolutions and viewed as a symbol of the Council’s oligarchical system and obstacle to meaningful action in politically strife questions such as Israel-Palestine. Previous research on Security Council resolutions indicates that wording has an impact on whether or not a resolution is passed, as well as the existence of two clear, consistent narratives within the body. This paper uses mixed methodology – qualitative research is used to compare how often vetoed resolutions use strong terms compared to passed ones, as well as word count and Regional Group patterns in resolution sponsorship. Vetoed resolutions were found to have more average occurrences of strong terms and a greater average word count, as well as the pattern that US vetoes were almost exclusively on draft resolutions with a majority-Arab authorship. In qualitative analysis, the paper found that China and Russia had the same veto reasons and vetoed with each other. The US was found to have generally consistent reasoning throughout the time periods examined. Some veto reasoning observed across the three countries directly countered each other. These findings provide insight into the writing and veto habits of Council members and reveal the importance of pursuing solutions in the United Nations system beyond the Security Council, as well as the potential current value of the veto in conflict prevention.

Evaluation of Vector Representations of Lipid Nanoparticles in Cheminformatic Predictions of Transfection Efficiency

Abstract

Lipid nanoparticles (LNPs) are a revolutionary drug delivery system for RNA-based therapeutics, as they are difficult to degrade and can efficiently transport their contents to distant target cells. Optimizing LNP formulations is essential for improving therapies, yet the best method to computationally represent these formulations for predictive modeling remains unexplored. This study analyzes different strategies for constructing the formulation vector of LNPs to evaluate their impact on predicting transfection efficiency. Specifically, three approaches were examined: fully describing all lipid components using molecular descriptors, fully describing only the cationic lipid while incorporating molar ratios for other components, and fully describing both the ionizable and helper lipids while using molar ratios for rest. Machine learning models were trained using each formulation representation, revealing minimal differences in prediction accuracy. The results suggest that the structures of the cationic and helper lipids are most critical, and including molecular descriptors for the PEGylated (PEG) lipid and cholesterol may introduce excess noise in the neural network without improving its performance. This can streamline LNP formulation research, which traditionally takes years of testing to design specific LNPs. By identifying effective strategies to represent LNP formulations, this work contributes to optimizing RNA-based drug delivery systems. revolutionary drug delivery system for RNA-based therapeutics, as they are difficult to degrade and can efficiently transport their contents to distant target cells. Optimizing LNP formulations is essential for improving therapies, yet the best method to computationally represent these formulations for predictive modeling remains unexplored. This study analyzes different strategies for constructing the formulation vector of LNPs to evaluate their impact on predicting transfection efficiency. Specifically, three approaches were examined: (1) fully describing all lipid components using molecular descriptors, (2) fully describing only the cationic lipid while incorporating molar ratios for other components, and (3) fully describing both the ionizable and helper lipids while using molar ratios for rest. Machine learning models were trained using each formulation representation, revealing minimal differences in prediction accuracy. The results suggest that the structures of the cationic and helper lipids are most critical, and including molecular descriptors for the PEGylated (PEG) lipid and cholesterol may introduce excess noise in the neural network without improving its performance. This can streamline LNP formulation research, which traditionally takes years of testing to design specific LNPs. By identifying effective strategies to represent LNP formulations, this work contributes to optimizing RNA-based drug delivery systems.

Genetic Mechanisms and Heritable Risk Factors in Food Allergy Development

Abstract

Food allergies are a growing public health issue both in the United States and worldwide. Immune responses to food allergies include the immune system producing antigen-specific IgE, which can lead to food sensitization. Reexposure of the allergen causes the allergen to bind to specific antibodies, causing the release of mediators, such as histamine. Histamine is what triggers reactions throughout the body. This is known as food allergic reactions. Essentially, when an allergen the body sees as an intruder or threat is consumed, the body produces IgE antibodies, which is specific for each allergen. These IgE antibodies bind to the allergen whenever consumed, causing the release of histamine, and therefore, an allergy reaction. Such reactivity can range from hives and rashes to anaphylactic means. A combination of genetic and environmental influences are known to affect food allergy development. It is difficult to separate genetic and environmental influences completely from one another to observe individually, as one’s life is greatly influenced by both simultaneously. However, while the environmental effects are more obvious (early introductions versus late introductions, tolerance-building, food processing types, etc.), genetic influences are not as much. This review explores how specific genetic mutations can influence one’s development of food allergies throughout their lifespan. Through the analysis of various candidate gene studies, it was found that such mutations, specifically polymorphisms, can have a critical role in food allergy development overtime, and in a population, can be considered heritable.

Motherhood, Matriliny, and Language: Power and Collectivism in Mosuo Society

Abstract

Motherhood under the patriarchal lens is always correlated with the identity of domestic labor and caregiver without power in their hands. However, this essay uses a contemporary model of matrilineal society, the Mosuo people are mostly categorised in the Chinese ethnic group Na, to present a different perspective on the role of the mother, with similar domestic roles but different living situations, and have power, authority, and respect. This essay will investigate how motherhood is constructed and experienced in matriarchal societies, which differ from patriarchal societies and traditional families, particularly in China, focusing on the societal construction and values reflected in their daily behaviors. This essay combines primary and secondary research methods, including an analysis of an unstructured interview and a video recording, as well as a review of literature on matriarchal societies, offering a broad and unique insight into mothers in a Matrilineal society. Mothers in a matrilineal society are more collective and well-respected, with great freedom due to the different family construction, societal values, and also subtly reflected in their unique language. The concept of language control, combined with social and family construction, offers a new insight into the Mosuo matriarchal family and society, reflecting the culture and beliefs underlying their language, which highlights their respect for mothers and leads to the honor of female identity.

Understanding Adolescent Suicide at the Population Level: The Role of Education, Inequality, and Health Indicators Across Nations

Abstract

Adolescent suicide is a leading cause of death among young people worldwide, yet limited research has explored how country level socioeconomic, technological, and health indicators collectively influence suicide rates. This study aimed to examine the associations between adolescent suicide rate (ASR) and multiple national level factors including income inequality, education, internet access, urbanization, and health-related behaviors. A cross-country analysis was based on publicly accessible data from the Global Burden of Disease Database and World Bank Databank. Pearson correlation models, multiple linear regression models and stepwise models were employed to determine significant predictors of suicide rates in adolescents in countries. Increased literacy and urban population proportions were uniformly related inversely to ASR, reflecting protective effects of education and urban infrastructure. Tobacco use had an unexpected inverse relationship with suicide rates, possibly due to confounding factors or cultural heterogeneity. GDP per capita was positively related to adolescent suicide, reflecting complicated interactions between economic condition and youth mental health. The findings suggest that higher literacy rate may serve as a protective factor in reducing adolescent suicide risk at the population level. Future research should explore individual-level data and cultural contexts to further clarify these associations and guide targeted interventions.

The Economics of Memecoins and the Breakdown of Financial Legitimacy

Abstract

This paper investigates the extent to which memecoin price movements follow social sentiment trends and how their extreme, hype-driven surges challenge economic theories of value, financial regulation, and cryptocurrency legitimacy. Through a randomized controlled trial simulating exposure to viral memecoin propaganda, the study finds that younger participants significantly underestimated risk and overestimated returns after viewing promotional content, even for a fictional asset with no fundamentals. These behavioral shifts demonstrate how memecoins leverage narrative framing and emotional appeal, particularly among younger investors. The paper then explores how memecoins exist in a regulatory void, using case studies and policy responses to demonstrate the dangers of unregulated attention-based assets. The findings present that memecoins pose systemic risks, not just to individual investors, but to the integrity of financial markets, particularly by eroding public trust in the crypto landscape.

Psychological Tactics in Marketing: How Color, Urgency, and Anchoring Bias Influence Consumer Behavior

Abstract

Psychological tactics are deeply ingrained in everyday marketing, subtly influencing consumer decisions without their conscious awareness. This paper aims to explore specific psychological tactics used in marketing to influence consumer behavior and drive sales. Key strategies investigated include color psychology, urgency and scarcity, and anchoring bias. The content for this paper was gathered from academic sources and includes real-world case studies, ranging from the significant uses of color in fast food chains to luxury brands, as well as the BOGO (Buy one, get one free) offer, which anchors the value of two products for one. These examples demonstrate the effectiveness of marketing techniques across global populations and the universal efficacy of these tactics across diverse markets. While the techniques explored in this paper have been proven to yield clear results and drive profits for brands, the applications of these strategies must carefully consider the population to whom they are marketing to ensure appropriateness, addressing variables such as gender, culture, and age. Furthermore, the effectiveness of these techniques depends on their remaining hidden from the consumer’s conscious awareness to act effectively upon the subconscious mind. To this end, the paper also investigates the potential pitfalls of these tactics. It considers how the misuse of these techniques could damage brand identity in the long term, which would outweigh short-term increases in consumer sales. By examining both the benefits and drawbacks of these psychological tactics, this paper provides valuable insight for marketers to seek a balance between healthy persuasion and ethical responsibility.

A Comparative Performance of U-net and Mask R-CNN for Lung Segmentation Across Public Chest X-ray Datasets

Abstract

Lung segmentation is critical for detecting and monitoring respiratory conditions and abnormalities in the lungs, making it a useful diagnostic tool. This study compares the performance of two deep learning models, U-net convolutional neural network (U-net) and Mask Region-based Convolutional Neural Network (R-CNN), for segmenting lungs using publicly available datasets. The U-net model was trained and validated on the Montgomery dataset, while the Mask R-CNN model was evaluated after being pre-trained without fine-tuning. Both models were tested on both the Montgomery and Shenzhen datasets to assess generalizability. Mask R-CNN was found to have the best performance with a Dice Coefficient of 0.9302 and IoU of 0.8696 on the Shenzhen dataset. Although Mask R-CNN showed stronger performance on the unseen Shenzhen dataset, the comparison is limited since the two models are trained on different datasets. This study highlights strengths and limitations of each model and outlines future work to make the study more fair.

How did Humanism during the Renaissance Impact Economic Growth in Italy?

Abstract

This paper explores the significance of Humanism - a philosophy that emphasises human efforts, skills and development often over religious or supernatural beliefs - and its effect on countries and their economies. This paper focuses on the Renaissance era in Italy when Humanism emerged. The paper determines that Humanism was a factor in the development of the country itself; economically, socially and politically. However; when viewed from a greater lens, it is understood that Humanism did not have a significant impact on the expected state of the country’s economy compared to other countries of the same period. These countries were not as greatly influenced by Humanism during the time and had economic states similar to Italy’s if not better. This paper also discusses the various other economic factors that Humanism contributed to or assisted in such as employment of supply side policies, increases in production, GDP per capita and literacy rate: all factors that further led to economic growth.

Evaluating the Viability of Electrolyzer Technologies for Integration with Diverse Renewable Energy Sources

Abstract

As the demand for renewable energy grows, efficient methods for storing excess energy are crucial to ensuring reliable and sustainable power systems. Hydrogen production through water electrolysis provides a promising solution for long-term energy storage. This paper explores the integration of electrolyzers with renewable energy sources, focusing on the advantages and limitations of three key electrolyzer technologies: Proton Exchange Membrane (PEM), Anion Exchange Membrane (AEM), and Alkaline Water Electrolyzers. While PEM and AEM electrolyzers offer high efficiency, rapid response times, and compatibility with intermittent renewables, they face challenges in scaling and cost. Alkaline electrolyzers, the most established technology, excel in large-scale industrial applications but struggle with dynamic operations and hydrogen crossover. The analysis highlights the need for continued advancements in electrolyzer technology to maximize the potential of green hydrogen in grid balancing, decarbonizing industry, sustainable transportation, and distributed hydrogen production. Recommendations for improving electrolyzer performance, reducing costs, and enabling seamless integration with renewable energy are provided to support a cleaner, hydrogen-powered future. Advancing these technologies will be pivotal in unlocking hydrogen’s role as a cornerstone of a resilient, low-carbon energy economy.

Artificial Intelligence and Justice in Family Law: Addressing Bias and Promoting Fairness

Abstract

Artificial Intelligence (AI) plays a crucial role in the legal field today, carrying out processes such as predictive analysis, data interpretation, and decision making. AI is valued for its efficiency and accuracy along with its affordability. However, one problem that arises in the legal field is that AI systems sometimes make flawed decisions, partly because of their inability to recognize human emotions and the fact that they misinterpret data. This is a major drawback because the responsibility of the legal f ield is to make fair and equal decisions that aim at justice. If utilizing AI poses potential threats to maintaining justice and equality, the positive attributes to AI will lose value. This paper argues that there are various tools and features to AI that will benefit and enhance the legal profession despite potential harms that may appear. In addition, the paper will explore key factors that impede an impartial decision-making process and discuss possible solutions to maximize tools to ensure a fair and equitable verdict. My argument is to use and develop tools working alongside AI to ensure impartiality and instill justice in every legal decision. By addressing these challenges, the legal system will be better suited to use AI with equity and justice in mind to its full potential.

How Unemployment Trends in California Vary by Scale: Where Linear Modeling Simplicity Obscures Structural Complexity

Abstract

This study explores long-term unemployment trends in California across two geographic scales: county and state levels. While state-wide unemployment rates are often used as an indicator for economic health, this research paper highlights how such indicators can hide local and regional variations. Using California labor force and unemployment data from 1990 to 2025, this paper critiques the limitations of linear models that assume uniform trends across different geographical scales. The findings reveal that several counties were not representative of the state’s averages, deviating significantly from state-wide averages. Counties, such as Imperial County and Colusa County, significantly deviated from California state-wide averages with Imperial County having the most significant deviation, with a mean deviation of 15.59 percentage points throughout the period of 35 years. The study also found that simplistic models like linear regression models were not accurate at predictions for both the state and county levels, with R-squared values of less than 0.14. The findings highlight the importance of economic policies that account for local conditions to ensure more equitable regional support. The results also show that simplistic models can risk misinformed economic decision-making.

Market Failures in Women’s Health: Systemic Undervaluation and Paths to Reform

Abstract

Background: Despite medical advances, persistent disparities in women’s health remain under addressed across healthcare systems, research priorities, and innovation pathways. This paper argues that many of these gaps reflect market failures–instances where economic systems fail to allocate resources fairly or efficiently. Methods: A narrative literature review was conducted using structured searches across major databases and reference lists. Seventeen empirical studies published between 2003 and 2025 were included based on relevance to gender-specific health disparities and structural or economic explanations. Results: This review identifies major drivers of underinvestment in women’s health: information asymmetries, the public good nature of foundational research, stigma that distorts demand signals, and biased investment patterns in venture capital and insurance markets. Across the included articles, we analyzed how these failures manifest in clinical settings, such as the underdiagnosis of conditions like chronic pelvic pain and insomnia, and the neglect of high-burden issues such as endometriosis, fibromyalgia, and menopause related disorders. Furthermore, we found that structural factors —such as gender bias in medical training, exclusion from clinical trials, and lack of women in leadership—compound market dysfunction. Conclusion: The market failure framework was adopted to discuss the findings across studies, but it has limitations. Some health needs lack profit incentives or do not fit easily into economic models. Issues rooted in stigma, sexism, or cultural silence often require solutions beyond market reform. As such, this article proposes recommendations that combine economic and policy responses. By identifying how systemic neglect in women’s health stems from identifiable economic failures, this paper offers a novel lens for shaping health policy and resource allocation to advance gender equity.

The GameStop Short Squeeze: Retail Investors, Market Mechanics, and the Decline of a Legacy Business Model

Abstract

The GameStop (GME) short squeeze permanently affected the financial world. On July 20, 2020, GME stock closed at around $7.47 per share. More than 140% of GME’s public float was sold short, making it one of the most heavily shorted stocks on the market. In late 2020, GME experienced a decline in profit from its traditional retail model for video games, stemming from the shift to digital video game markets. Many institutional investors anticipated a decrease in the stock. This caused GME to be one of the most short-sold stocks in history. A central figure in this phenomenon was Keith Patrick Gill, also known as “Roaring Kitty,” who posted his views on GME on the subreddit r/wallstreetbets. His commentary encouraged many retail investors to buy GME shares. This social media group chat triggered a short squeeze, resulting in substantial losses for major hedge funds that had accumulated extensive short positions. The outcome highlights an unexpected shift in the history of the financial world, where social media creates an opportunity for retail investors to influence outcomes previously determined by institutional investors. This research paper argues that while the GME short squeeze demonstrated the growing influence of retail investors and exposed flaws in financial markets, ultimately, it could not save GME from the inevitable collapse of its outdated business model in a digital economy.

Natural Killer Cells in Health and Disease: Mechanisms of Dysfunction and Emerging Therapeutic Targets

Abstract

Natural Killer (NK) cells are a key component of the innate immune system, responsible for the rapid detection and elimination of virally infected and malignant cells. As our knowledge of immune cells including NK cells continues to expand, recent research revealed a complex network of intrinsic and extrinsic factors that modulate NK cell function in both health and disease. This review explores diverse pathological contexts in which NK cell activity is impaired, including severe viral infections (e.g., COVID-19), cancers such as multiple myeloma, immune evasion via checkpoint molecules like IGSF8, and the stage of immunosenescence due to aging. We also highlight metabolic exhaustion observed in Myalgic Encephalomyelitis / Chronic Fatigue Syndrome (ME/CFS) and novel epigenetic suppression mechanisms mediated by the SUPT16H–BRD4 axis. Transcriptional regulators T-BET and EOMES are shown to be indispensable for maintaining NK cell identity and cytotoxic integrity. Through these case studies, we identify shared patterns of dysfunction, such as impaired cytokine signaling, defective immune synapse formation, and energy metabolism failure, and discuss the emerging therapeutic strategies aimed at reversing them. A comprehensive list of molecular markers discussed across these cases is provided in Supplementary Table 1. Together, these findings emphasize the critical role of NK cells in immune surveillance and emphasize their potential as targets for innovative immunotherapies in infection, cancer, and chronic immune disorders.

Hybrid Paris Physics-Informed Neural Network for Predicting CFM56-7B Engine Fatigue Failure

Abstract

Existing methodologies for predictive maintenance in aviation engines have largely diverged into data-centric and physics-centric models, each constrained by their unchangeable limitations. In this study, a hybrid framework integrating both perspectives was developed to address fatigue-induced failures in the CFM56-7B engine. Specifically, a Bayesian Physics-Informed Neural Network (B-PINN) was constructed, embedding Paris’ Law within a deep learning structure and modeling key fatigue parameters as probabilistic distributions. Selected sensor data from NASA’s CMAPSS FD004 dataset was employed to assume latent stress signals and simulate fatigue crack propagation. The results show that the proposed model has advantages on interpretability and reliability of fatigue predictions but also quantifies uncertainty through variational inference and Monte Carlo dropout.

The Impact of Cigarette Smoking and Vaping on Acute Chest Syndrome in Adolescents and Young Adults with Sickle Cell Disease

Abstract

Sickle Cell Disease (SCD) is a hereditary blood disorder that distorts the shape of red blood cells, increasing the risk of blocked blood vessels and damage to organs. Sickled red blood cells may lead to acute chest syndrome (ACS), which presents with the detection of a lung abnormality during chest imaging accompanied by fever or respiratory symptoms. ACS is a leading cause of premature death in youth with SCD, accounting for one-quarter of SCD-related deaths. For many years, tobacco smoke exposure has been suspected to worsen the frequency or severity of ACS. However, much of the earlier evidence combined children, adolescents, and adults together and relied on self-reported smoking habits. Biomarkers of smoke exposure are now available and e-cigarette use (vaping) has become widespread. The transition period to adulthood is marked by increasing autonomy, experimentation with nicotine products, and peak SCD complications. Therefore, a review of literature discussing the effects of cigarette smoking and vaping in adolescents and young adults with SCD was conducted. Publications were systematically reviewed for the impact of combustible cigarette smoke, second-hand smoke, or vaping in SCD patients ≤24 years old. Across these studies, any tobacco smoke exposure was associated with an increase in ACS- and SCD-related hospitalizations, ACS frequency, ER visits, and overall mortality. This review demonstrates the increased risk for ACS and poor outcomes in the younger SCD population who are at higher risk for experimentation with cigarette smoking and vaping and highlights critical research gaps needed to inform targeted prevention, counseling, and policy interventions.

Assessment of the Practicality of LLMs in the Field of Cybersecurity and Detection of Malicious Code

Abstract

Large Language Models (LLMs) hold significant promises to revolutionize cybersecurity. Unlike traditional machine learning (ML) models, LLMs can be fine-tuned for specific tasks such as identifying malicious code, analyzing software vulnerabilities, and detecting phishing attacks. However, challenges remain, including the high computational cost of development, potential biases in training data, and the risk of adversarial attacks against these models. This review examines the practicality of using LLMs in cybersecurity with current technologies over the next five years. It evaluates their real-world applicability and draws on recent literature to highlight potential security threats, benefits, and implementations of LLMs in this domain. These studies provide examples where LLM-based tools have been both effective and flawed, helping establish precedents for LLM use. Additionally, this review discusses the history and rapid development of LLMs, comparing current advances to past technological growth, and explores future research directions for integrating LLMs into cybersecurity.

Iconicity in Rimsky-Korsakov’s Scheherazade: A Musical Semiotic and Experimental Approach

Abstract

This paper investigates the semiotic mechanisms through which Nikolai Rimsky-Korsakov’s Scheherazade communicates narrative meaning in instrumental music. Combining a detailed musical semiotic analysis with an experimental listener survey involving 38 participants, this study explores how the piece utilizes both primary iconicity (direct musical imitation) and secondary iconicity (symbolic emotional expression) to convey the stories and emotions inspired by One Thousand and One Nights. Participants listened to musical excerpts and evaluated their correspondence to narrative summaries, allowing for analysis of how listeners interpret musical cues. The survey results show that listeners are able to identify and interpret key musical icons regardless of formal musical training or paratextual cues such as movement titles. Ultimately, this research affirms Scheherazade as a powerful example of storytelling through sound and offers a broader model for understanding how music communicates across cultural boundaries.

Who Cares? Unpaid Labor, Human Capital and Inequality In India

Abstract

India’s hidden economy with unpaid care, childcare, eldercare and any household maintenance is what shapes human capital formation yet continues to remain invisible in national statistics. This paper describes the tension between the static neoclassical view where households outsource care to minimize private costs and the feminist critique which emphasizes care work’s foundational role and the inequalities it reinforces. Using India’s 2014 female-to-male ratio of unpaid care work and the 2019- 20 Time Use Survey, we are able to document that the poorest households shoulder on average 53.9 more minutes of unpaid care per day than the richest households. Afterwards, the paper develops a simple two-period model where period-1 care inputs determine period-2 human capital, that helps show that neglecting the feedback of care can have a consequence of socially mediocre outcomes. Finally, it highlights policy interventions that include paid stipends, expanded public early childhood services and conditional cash transfers that all serve to realign the private incentives of individuals with long term efficiency in mind. The framework of the paper highlights how integrating feminist concerns into a much more dynamic neoclassical model now can guide policies to value and redistribute care and ultimately narrow international inequality.

Optimizing a Flexible Packaging Plant using Building Automation Systems (BAS)

Abstract

The flexible packaging industry, driven by demands for customization, sustainability, and efficiency, faces significant operational challenges including high energy consumption, process variability, equipment downtime, and waste generation. This research explores the integration of Building Automation Systems (BAS) into flexible packaging plants as a solution to these issues. BAS, comprising interconnected hardware and software systems, offers real-time monitoring, intelligent control, and predictive maintenance across various subsystems such as HVAC (Heating, Ventilation, and Air Conditioning), lighting, and machinery. The study outlines how BAS can significantly optimize energy use by dynamically adjusting environmental controls and production loads, thus reducing peak energy charges and supporting renewable energy integration. Furthermore, BAS enables precise control of material usage and minimizes production waste through advanced sensors, machine learning analytics, and system interoperability. Safety and maintenance protocols are enhanced via predictive risk management and automated response systems, mitigating hazards and ensuring regulatory compliance. Despite notable implementation challenges—such as legacy equipment compatibility, workforce resistance, and integration complexity—BAS emerges as a pivotal enabler of Industry 4.0 for packaging operations. The paper concludes by emphasizing the need for customized BAS solutions and further research to document successful case studies, ultimately fostering smarter, safer, and more sustainable manufacturing ecosystems in the flexible packaging sector.

Adapting Cybersecurity Maturity Models for Online Startups and Small Businesses

Abstract

This research examines the understanding and application of cybersecurity practices by internet companies. Due to a lack of understanding and limited resources, online startups and small businesses are more susceptible to cyber threats, including ransomware, malware, phishing, and non-social engineering attacks, such as system vulnerabilities. This study analyzes current models of cybersecurity maturity and modifies them to fit the specific requirements and limitations of online startups and small businesses. As part of the process, a comparison of current models and suggestions for a customized model suitable for new digital enterprises is presented.

Margins To Mainstream: Analyzing The Rise of American Labor Unions (1929-1945)

Abstract

On October 24, 1929, a stock market crash put an end to a decade of economic prosperity that doubled the United States’ total wealth (1). The crash destroyed banking systems. Factories went bankrupt and shut down. Workers went hungry and stood in breadlines that stretched for blocks. The unemployment rate rose to 25%, leaving millions of Americans homeless. This paper illustrates how labor unions took advantage of different opportunities amidst the Great Depression, New Deal, and World War II to end their previously marginalized status, rising to become major political and institutional forces. The impact of these changes on American workers is also analyzed. Poor humanitarian conditions accelerated calls for improved worker protection measures. This caused union membership to surge. A combination of political advocacy and collective bargaining with social safety net measures allowed labor unions to become powerful political forces. President Franklin D. Roosevelt’s revolutionary New Deal policies made this possible through the National Labor Relations Act of 1935. The act granted unions government permission to help workers collectively bargain.

Advances in Lightweighting Strategies for Aerospace: Materials, Structures, and Fuel Innovations

Abstract

This paper explores the practice of lightweighting in aerospace, which involves reducing a vehicle’s weight without compromising its functionality. Lightweighting is a rapidly advancing field with significant impact on commercial, private, and federal air/spacecraft. It is a top priority in aerospace research due to its broad benefits, including improved performance, reduced environmental impact, and increased operational efficiency. Additionally, lightweighting offers value to industries beyond aerospace, such as manufacturing and transportation. This paper delves into three main approaches to lightweighting: material innovations, structural optimization, and fuel advancements. Lightweight materials like alloys, composites, and polymers each have distinct advantages and drawbacks. Alloys are cost-effective and have a strong strength-to-weight ratio, while composites offer higher strength at lower weight but are more expensive and complex. Polymers can be tailored for specific functions, making them versatile in their applications. Structural optimization involves balancing durability and weight through advanced designs like ribbed fuselage shells and hybrid wing-body structures. Meta-heuristic algorithms help find optimal solutions to the structural problem of size, shape, and topology. Lastly, alternative fuel sources are explored for their energy density and potential to support lightweighting. Overcoming the challenges presented by new fuel sources and their characteristics proves an ongoing endeavor, however. Some radical and new ideas exist to reduce energy consumption uncorrelated with fuel source, also progressing lightweighting. Together, these methods contribute to significant advancements in aerospace technology.

The Measurement Problem and the Sebens-Carroll Derivation of the Born Rule

Abstract

The measurement problem remains one of the most profound puzzles in the foundation of quantum mechanics. It contains two distinct aspects: the collapse of the wavefunction and the origin of probabilities. While Everettian Quantum Mechanics (EQM) and the Many Worlds Interpretation (MWI) work around the collapse aspect, by positing that the universe branches through pure unitary evolution, it leaves unresolved the question of why measurement outcomes occur with frequencies given by the Born Rule. This paper explores a recent derivation of the Born Rule by Carroll and Sebens based on principles of Self-Locating Uncertainty (SLU) and the Epistemic Separability Principle (ESP). By examining this framework in detail, beginning with an equal amplitude case and extending to an unequal amplitude case, we show how these principles account for the probabilities given by the Born Rule, hence showing that SLU and the ESP, within the scope of EQM, offer a solution to the measurement problem. We further show the significance and implications of deriving the Born Rule and highlight the relevance of the measurement problem to more recent developments like quantum gravity.

Analyzing the Impact of Space Debris and Reviewing Potential Solutions

Abstract

Space debris, composed of used rocket parts, defunct satellites, and collision fragments, present a growing threat to active spacecraft and the future of space exploration. As satellite launches increase, so does the risk of collisions, potentially creating more debris in a dangerous cycle known as the Kessler Syndrome. This accumulation of space junk jeopardizes satellites, requiring costly maneuvers to avoid damage and risking critical services like communication and navigation. Current solutions, including tracking systems and self-deorbiting satellites, offer some mitigation but face challenges due to technological limitations, high costs, and lack of global coordination. Advanced debris removal technologies, such as autonomous vehicles and lasers, show promise; however, further research is needed. Effective space debris management will require international cooperation, improved tracking, debris mitigation designs, and investment in innovative removal techniques. Addressing space debris now is essential to ensuring a safe and sustainable space environment for future generations and preserving vital satellite functions on Earth.

Exploring Mouse Models and Optogenetics to Probe Bipolar Disorder

Abstract

Bipolar disorder is a neuropsychiatric disease affecting 1-4% of the worldwide population; characterized by spontaneous depressive and manic episodes, very little is known about the pathology of bipolar disorder, and there is no known cure. Theorized pathologies of bipolar disorder include genetic and environmental factors, the dopamine hypothesis, and the role of gut microbiota. This paper reviews several pieces of scientific literature pertaining to these theories, integrating optogenetics as a research tool to more accurately examine and manipulate relevant cellular activity. The reviewed literature draws clear connections from the HPA axis, gut microbiota, circadian rhythms, and dopaminergic expression to the pathology and manifestation of bipolar disorder. However, despite this, there is simply not enough information about the disease itself for researchers to come to a sound conclusion. The disparity is mentioned at least once in each piece of literature and is attributed to both the ambiguity of the disease, and the variability in symptoms between patients. Not only does this affect the current theories surrounding bipolar disorder, but also makes studying the disease incredibly difficult, as without a pathology, ideal animal models are nearly impossible to identify. Literature rooted in the use of optogenetics present results which express increased specificity in both data and conclusions, cementing its potential in future studies concerning bipolar disorder. From this, this paper proposes a new behavioral experimental design in mouse models, integrating optogenetics with the most prevalent theories of the pathology of bipolar disorder.

A Comparative Analysis of Machine Learning Models on Predicting GDP Based on Greenhouse Gas Emissions

Abstract

As climate change intensifies, it is increasingly important to understand how greenhouse gas (GHG) emissions affect economic performance. Using machine learning, this study examined the potential of using GHG emission data to predict gross domestic product (GDP) across 161 nations. Linear Regression, Decision Tree, Random Forest, and Multi-layer Perceptron (MLP) were trained and optimized individually per country, with their hyperparameters tuned by Optuna. The results show that MLP significantly reduced its mean MAPE from 27.583% to 5.265% and mean RMSE from 154.221 to 34.051 billion USD, while also significantly suppressing outliers (with its maximum error dropping from 117.955% to 22.661%). Random Forest also showed strong improvement, with mean MAPE decreasing to 5.477% and a notable reduction in large errors. Decision Tree showed some small-scale improvement, while Linear Regression was relatively poor with a mean MAPE of 11.244%, which highlighted the nonlinearity of GHG-GDP relationship. Statistically significant improvements were found in all analyses (p<0.001) using Wilcoxon signed-rank tests, indicating consistent gains across different countries. Findings indicate that GHG emissions can serve as a source of meaningful predictive signal for non-linear models, making them valuable tools for estimating economic trends in areas with limited data or policy constraints, where emissions data may be more readily available than GDP reports. Hyperparameter optimization is a key factor in improving model accuracy and reliability, as highlighted in this study. Future work should expand the feature set, incorporate time series forecasting techniques, and improve model interpretability to support real-world policy applications.

Framing the Imperfect Victim: Gender, Voice, and Visibility in China’s Media Discourse

Abstract

This research investigates how gendered expectations shape public perceptions of victimhood in contemporary Chinese media narrative. Focusing on the concept of the “imperfect victim”—a figure disqualified from sympathy due to deviations from behavioral norms—the study analyzes three real world cases: the Xi’an Metro clothing removal incident, the Alibaba sexual assault case, and the Yang Li JD.com commercial partnership controversy. Using a qualitative case study method, I conducted close textual and content analysis of both state media reports and civic responses on Chinese social media platforms such as Weibo and Rednote. Drawing from framing theory and the concept of state media censorship, the project examines how victims’ behaviors, speech, and emotional displays are framed as either credible or illegitimate. The analysis shows that loudness, confidence, and expressiveness, especially when exhibited by women, are often reframed as social disruption, thus undermining their authority and legitimacy. Across all three cases, visibility does not guarantee validation; instead, camera surveillance, textual evidence, and visual testimony become fields of tension where interpretation is constantly debated. These findings challenge the assumption that evidence or exposure alone ensures justice. Instead, victimhood emerges as an earned status based on institutionalized gender and social roles. The study thus concludes that the “imperfect victim” is not an isolated phenomenon, but a recurring figure produced at the intersection of state censorship, social media platform dynamics, and public values.

AI Image Detection through Human-Centered Training Methods

Abstract

As AI-generated images become increasingly prevalent in digital media, the ability to distinguish between real and manipulated content is essential for combating misinformation. Our study investigates whether targeted training can significantly improve individuals’ ability to detect AI-generated images. Somoray and Miller (2023) found that deepfake detection accuracy remained low, averaging around 55%, regardless of whether participants were given a list of detection strategies [1]. Our study builds on this by implementing a structured training program, which includes video demonstrations and interactive practice with feedback. We investigate whether detection accuracy improves after participants view videos explaining how to identify deepfakes for AI-image identification instead of just a list of strategies. The experiment began with a pre-test to assess participants’ baseline ability to distinguish between real and AI-generated images. Next, two-thirds of the participants received targeted training on identifying inconsistencies, while one-third served as a control group with no training. Finally, we administered a post-test to measure any improvements in their detection skills after training. Demographic and experiential factors such as age, sleep, AI experience, and screen time did not significantly impact detection accuracy. A paired t-test was performed to evaluate the impact of training on detection accuracy, and the results show a statistically significant improvement in detection accuracy post-training (p=0.009). A statistically significant positive correlation was found between the time spent analyzing images and detection accuracy (p < 0.0001), indicating that more thorough analysis improves performance.

What Are the Long-Term Effects of Concussions on Autistic Youth?

Abstract

Concussions, a type of mild traumatic brain injury (mTBI), are infamous for their instant effects of dizziness, fatigue, and mental confusion. While it is known that concussions can cause lasting neurological effects, including impacts on cognitive function, behavior, and focus, the specific long term effects on the brain, particularly among autistic youth, are not yet fully understood. Autistic youth may experience these effects differently from neurotypical populations, necessitating specialized care. Inherent differences in the neuropsychology and neurological structure of autistic individuals mean that diverse parts of the brain will be affected resulting in various behaviors. This review paper answers the following questions: What regions of the brain do concussions affect the most in neurotypical populations? How is the development of each of these regions affected by concussions in neurotypical populations? Are these trajectories different for autistic youth? By evaluating findings from existing studies, this paper enhances science’s current understanding of the interplay between concussions and autism among the developing population.

Ramifications and Impacts: Private Equity Acquisitions in Healthcare in United States

Abstract

In recent years, private equity firms have had a specific investment focus in the healthcare sector in USA. Their acquisitions play a significant role in the current healthcare industry, ultimately impacting insurance companies, healthcare providers, and most importantly, patients. This article investigates the outcomes resulting from the acquisition of medical practices by private equity firms in USA. The focus is on key variables such as patient care, quality, and costs as a result of these transactions. Research shows that the implications are negative when analyzing variables of cost, patient care, employee structure, and long-term sustainability. Private equity firms aim to maximize short-term gains by utilizing practices such as raising the cost of services and reducing staffing, both of which affect patient care. To evaluate this issue, online research and journal articles were analyzed. Metrics used to quantify patient care and quality include patient satisfaction scores, clinical outcome success rates, patient-provider ratios, and operational metrics. The most significant finding is that private equity acquisitions in USA are shifting healthcare from a patient-centered model to a profit-driven one, raising major concerns about systemic impacts.

Pluripotent Stem Cell-Derived Cell Therapy for the Treatment of Type 1 Diabetes

Abstract

Type 1 diabetes (T1D) is a chronic autoimmune disease affecting millions of people worldwide. The condition results from the immune-mediated destruction of pancreatic β-cells, leading to insulin deficiency and lifelong dependency on insulin therapy. Despite advances in insulin delivery and glucose monitoring technologies, many patients struggle to maintain optimal glycemic control, and severe hypoglycemia remains a persistent risk. Islet transplantation offers a potential alternative but is limited by donor shortages and the need for lifelong immunosuppression. Pluripotent stem cells (PSCs), including embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs), offer a renewable and scalable source of insulin-producing β-cells. These cells can be differentiated in vitro and have demonstrated glucose-responsive insulin secretion and the ability to reverse diabetes in animal models. Recent years have seen the translation of PSC-derived β-cell therapies into clinical trials, with several promising candidates including VX-880, VC-02, CTX-211 and OZTx-410 under investigation. These approaches include both allogeneic and autologous strategies as well as gene-edited and encapsulated cell delivery systems designed to enhance cell survival and minimize immune rejection. This article reviews the latest advances in PSC-based cell therapies for T1D with a focus on differentiation protocols, preclinical studies and ongoing clinical trials. It also discusses current challenges including immune protection, vascular integration, and scalability and outlines future directions for achieving a functional cure through regenerative medicine.

Disentangling Fiction from Frameworks: A Critical Review of Instrumental Convergence in AI Ethics Risk

Abstract

As Artificial Intelligence continues to evolve at a rapid pace, the concerns regarding its capabilities circling around existential risk have increased at a similar rate. The concept of instrumental convergence primarily – the theory that intelligence AI agents may become indifferent to the programming and commands of humans and prioritize self-preservation – have alerted individuals, as this could endanger humanity. However, this paper critically evaluates the extent of the validity of instrumental convergence as a real-world AI safety concern by arguing that it overextends AI’s capabilities and is driven by anthropogenic assumptions. AI agents lack the fundamental characteristics such as intrinsic motivation, agency, and the capacity for self-generated terminal goals which would otherwise drive the theory of instrumental convergence. Moreover, other barriers to AI alignment are discussed in this paper, specifically the orthogonality thesis and the tricky notion of aligning complex human values with agents. Counterarguments to popular X-risk narratives are presented to support the conclusion that instrumental convergence is overstated. My findings suggest that a more accurate public and professional understanding of AI’s limitations and capabilities would bring us closer to achieving a safe and effective relationship between humanity and AI.

Feasibility and Management of Residential Direct Current Microgrids

Abstract

Renewable energy sources, such as rooftop solar panels, are becoming increasingly widespread as the world transitions toward a more sustainable future. While traditional alternating current (AC) grids are well-established, the prospect of direct current (DC) microgrids, which can accommodate advanced battery storage systems and widespread DC loads, becomes more favorable in the context of growing global energy demand. Offering potential efficiency gains from reducing conversion losses, DC microgrids are a promising alternative for residential power delivery. This paper aims to contribute to the understanding of how residential DC microgrids can aid the global shift toward renewable energy by exploring practical considerations, advantages, and areas for further development.

CAR-T Cell Therapy for Malignant Melanoma: Current Landscape and Future Directions

Abstract

Chimeric Antigen Receptor (CAR) T cells are modified T cells that have been genetically engineered to produce chimeric antigen receptors. Chimeric antigen receptors allow the T cells to identify and destroy specific cancer cells more efficiently, thus making CAR-T cell therapy a transformative immunotherapy. CAR-T cells have been tested and used as a treatment for leukemia and lymphomas and have shown high success and remission rates for decades. In this review, the modification and use of this therapy for solid cancers, specifically malignant melanomas, as well as its effectiveness, is explored. Studies work on discovering the most effective modifications of CAR-T cells, including inhibiting certain growth factors and testing numerous generations of the intracellular signaling domain. CAR-T cells are shown to be partially effective in certain studies and have been attributed to reduced recurrence rates in patients. The heterogeneity of melanoma cancers, antigen loss, and toxicities associated with the therapy limit the viability and effectiveness of the current treatment model. As such, the effectiveness, feasibility, and safety of CAR-T cell therapies in melanoma treatment is inconclusive as of now. More research is needed in overcoming the limitations of this therapy in order to determine its effectiveness as a treatment for melanoma cancers. Additionally, future research should focus on testing combinations of modifications of CAR-T cells, instead of testing the modification of only one component, in order to accelerate the understanding of whether this therapy is effective for treating melanomas, and if so, how it could best be used to increase efficiency while combating toxicities and limitations.

Economic Drivers and Impacts of Interstate Migration: The California to Texas Exodus

Abstract

This paper examines the economic determinants behind the recently observed migration flow from California to Texas. This paper is motivated by the recent rapid increase in migration from California to Texas, a trend that has sparked significant debate among politicians and media members and raised questions about the underlying economic factors driving this shift. While interstate migration has been broadly studied, few analyses have focused specifically on the recent California-to-Texas migration flow. By analyzing key economic indicators between 2010 and 2020, including outflow migration, unemployment, housing costs, crime rates, median household income, and labor participation—this study employs data on these factors in a two-way fixed effects regression model to uncover the factors that drive relocation decisions. Notably, the findings of the model suggest that median household income is a critical predictor, with higher income levels associated with a reduction in outflow migration, while other variables, such as housing costs and unemployment, exhibited limited statistical significance as predictors in the model. The results highlight the differences in economic environments in California and Texas in issues like housing affordability and income disparities. Limitations of the study include a relatively small dataset and potential model misspecification, suggesting the need for further research incorporating additional variables and possibly a more specialized model.

Challenges Women Face in American Politics

Abstract

Despite recent gains, women continue to be underrepresented in Congress and other elected offices. Although American society has become more accepting of women candidates, politics is still widely viewed as a “men’s arena.” This paper reviews existing literature to examine the obstacles female politicians face across all stages of their political careers—deciding to run, campaigning, navigating voter behavior, and serving in Congress. Literature suggests women’s underrepresentation stems from multiple, interconnected factors, including lower political ambition, biased party recruitment, gendered double binds regarding femininity and masculinity, more competitive candidate fields, and reduced influence in legislative processes. Partisanship further shapes these challenges, with Republican women facing greater barriers due to preferences of party elites, donors, and voters. Additionally, through two experiments, I find that Asian American women are not only underrepresented in Congress but also overlooked in academic research. Addressing the unique barriers faced by Asian female politicians is crucial for enhancing their representation in higher office and fostering a more inclusive democracy in the United States.

Beyond Insulin: How CRISPR-Cas9 is Reshaping the Future of Diabetes Treatment

Abstract

Diabetes mellitus remains one of the most pressing global health challenges, with existing treatments largely focused on managing symptoms rather than addressing root causes. CRISPR-Cas9 genome editing technology offers an opportunity to reshape therapeutic strategies for both Type 1 and Type 2 diabetes. In Type 1 diabetes, CRISPR has been employed to enhance the survival and function of insulin-producing cells and to reduce harmful immune responses. For Type 2 diabetes, researchers are focusing on how genetic interventions can help regulate blood sugar levels and reduce inflammation. This review highlights current clinical applications and various ethical considerations related to germline editing, equitable access, and off-target risks. Through the explanation of cutting-edge research, this review underscores CRISPR-Cas9’s role in redefining the future of diabetes therapy.

Morality in Intergroup Relations: Exploring the Role of Moral Foundations and Perceptions of Outgroup Morality

Abstract

Unspoken moral codes form the basis of social interaction, prescribing how individuals and groups conduct themselves and relate to one another. Given that society today is characterized by high levels of intergroup conflict and polarization over differing notions of right and wrong, examining groups’ underlying moral values is imperative to understanding why countries, political parties, and ethnic or religious groups are unable to see eye-to-eye. The purpose of this paper is to explore how morality factors into intergroup dynamics and how moral values dictate how other groups are treated and perceived. We utilize Moral Foundations Theory as a psychological framework to effectively parse apart the abstract notions of right and wrong. Our main findings are that each of the five core moral principles—Care/Harm, Fairness/Injustice, Ingroup/Loyalty, Authority/Respect, and Purity/Disgust—is responsible for driving specific attitudes and behaviors, both positive and negative, toward other groups. Specifically, these principles drive prejudice-reduction through empathy, define equality and reparations amongst competing groups, and shape group allegiances. On a darker note, these moral principles also beget prejudicial attitudes about social change and contribute to dehumanization. After dissecting the role of each moral foundation, we move to a broader investigation of how judgments of other groups’ morality drive intergroup attitudes, finding that stereotypes and social norms are critical factors. Throughout the paper, we provide actionable recommendations for how to use moral principles to induce groups to behave in more virtuous, harmonious ways. Key limitations of Moral Foundations Theory in intergroup contexts are discussed.

Entrepreneurial Capital Dependence and Firm Ownership

Abstract

As companies scale, their founders often lose control of them, particularly after receiving investments from private equity or selling stock ownership to private investors. This research examines why founders are forced to leave their companies and how they can keep their positions. The study compares four case studies: McDonald’s, Instagram, WeWork, and OpenAI. It analyzes how founders lost their authority or used resources to keep their power through various forms of capital, such as financial, governance, organizational, and social capital. After analyzing and exploring each case study, it is discovered that the most common dependencies for founders are financial and governance capital, depending on how decisions are made in an organization. The study also reveals that founders can counteract dependencies by utilizing capital types such as social capital. As shown in the OpenAI case study where a founder successfully maintains their position, the strongest method to offset founder dependencies is through social capital built from long-term relationships with employees. Founders play crucial roles in the growth of their companies and are at higher risk of being removed as they expand. It is crucial that founders understand how capital functions to avoid being left behind. There are many studies and articles on founder removal, but few focus on how they can remain competitive. This study advances the discussion of how founder influence and responsibility may last by proving that capital is a resource that can be leveraged in order to sustain their company status.

Timothy Syndrome, A CACNA1C Gene Genetic Mutation, Results In Varying Symptomatic Disease Progressions

Abstract

Timothy Syndrome is a rare, multisystem genetic disorder originating from mutations in the CACNA1C gene, which encodes the a1C subunit of the L-type voltage-gated calcium channel. Distinct mutations give rise to multiple clinical variants, leading to heterogeneity in presentation, which can include cardiac arrhythmias, neurodevelopmental abnormalities, and congenital defects. This review synthesizes current knowledge of the molecular and pathophysiological mechanisms linking CACNA1C dysfunction to the syndrome’s clinical manifestations. Insights gained from studying Timothy Syndrome extend beyond the rare disorder, offering a framework for understanding other calcium channel disorders and more prevalent genetic diseases.

Identifying Genetic Correlates of Colorectal Cancer Through Bioinformatics Analysis

Abstract

Colorectal cancer (CRC) is a leading cause of cancer-related mortality worldwide, with genetic mutations playing a significant role in tumor progression and overall survival. This study investigates the impact of mutations in APC, KRAS, BRAF, and RNF43 on survival time using the analysis of patient data sourced from Cercek et al. [1] through cBioPortal. The results revealed that APC mutations were positively correlated with survival time, while KRAS, BRAF, and RNF43 were negatively correlated with survival time. These findings are consistent with previous literature and the roles of these genes in tumorigenesis and cancer progression. Further research on the importance of genetic mutations and prognostic biomarkers can improve targeted therapies and personalized treatments for colorectal cancer patients.

Ride-Hailing and Class Restructuring: Didi’s Role in China’s Digital Economy

Abstract

This article explores how digital platform economies influence labor organization and social mobility in urban China, using Didi Chuxing as an example. Through a combination of literature review and 5 interviews with Didi drivers in five megacities, the article examines how the platform has reshaped working conditions, employment opportunities, and class relations. Although Didi has provided many job opportunities and promoted the modernization of urban transportation, it has also brought many new forms of instability to society and individuals, such as income instability, lack of social protection, and the phenomenon of highly educated talents being forced to engage in low-skilled work. This paper also explores the work experiences of Didi’s tech employees, revealing a dual reality: behind innovation-driven progress lies a high-pressure work environment and job insecurity. Finally, it explores the evolving consequences of the Chinese regime on platforms like Didi, between economic growth and digital innovation, and regulatory concerns over labor rights and data security. The study concludes that Didi is both a part of China’s entrepreneurial aspiration and its emerging social-economic contradictions, providing significant implications for the sustainability and equity of the platform-based economy.

Evaluating the Predictive Power of Candlestick Patterns in the Real Estate Financial Market

Abstract

This study examines the predictive reliability of four candlestick patterns—Bullish Engulfing, Bearish Engulfing, Hammer, and Hanging Man—in the real estate financial market using historical price data from the Vanguard Real Estate ETF (VNQ) from 2008 to 2024. Given real estate’s lower liquidity and slower price adjustments compared to stock markets, bullish patterns were expected to demonstrate higher predictive accuracy, while bearish patterns were anticipated to be less reliable due to the gradual nature of real estate price declines. The results show that the Hammer pattern had the highest accuracy (59.86%), followed by Bullish Engulfing (54.35%), while Bearish Engulfing (42.39%) and Hanging Man (41.56%) exhibited lower reliability. Further analysis segmented by TNX (10-Year Treasury Yield) trends revealed that the Hammer pattern was more effective during declining interest rates, while the Bullish Engulfing pattern performed better when rates were rising. These findings suggest that macroeconomic conditions play a significant role in the reliability of candlestick signals, underscoring the value of tracking interest rate trends. Although candlestick patterns offer some predictive insight, their effectiveness is closely tied to broader economic conditions. Incorporating macroeconomic indicators, such as interest rate trends, alongside technical analysis can enhance accuracy and reliability. These insights can help investors and analysts make better-timed decisions by showing when certain candlestick patterns have a greater reliability. By paying attention to interest rate trends alongside technical signals, the real estate market can be approached with a greater sense of confidence.

An Introduction to Nanotechnology and Its Role in Shaping Healthcare

Abstract

This paper examines the impact of nanotechnology as a multidisciplinary field that bridges life sciences. By focusing on the manipulation of matter at the nanoscale, nanotechnology has opened new pathways for innovation in material design and functional systems. There has been special attention given to its applications in medicine, where nanoscale tools are driving advances in diagnostics, targeted drug delivery, and therapeutic techniques. The research also explores various synthesis methods and classification frameworks, providing a broad foundation for understanding nanotechnology’s expanding role in science and healthcare.

To What Degree is Spider Silk a Better Alternative to a Conventional Graft in Ligament Reconstruction Surgery?

Abstract

The purpose of this review is to evaluate synthetic spider silk as a potential ligament in anterior cruciate ligament (ACL) reconstruction surgery. Synthetic spider silk, produced using ion-doped and twisted hydrogel fibers, has high capabilities in tensile strength, damping capacity, toughness, stretchability, and Young’s modulus, along with biocompatible properties. It surpasses the commonly used bone-tendon-bone (BTB) graft in tensile strength and stiffness, and the synthetic ligament augmentation reconstruction system (LARS) graft in tensile strength. Clinical trials are needed to fully assess the synthetic spider silk’s viability as a reliable graft in ACL reconstruction. The lack of data on biomechanical properties is apparent in the current literature surrounding ACL grafts. Further research and trials need to be done on varying biomechanical properties (Stretchability, damping capacity, toughness, stiffness) in order to determine the best graft for ACL reconstruction surgery.

Assessing the Impact of Endocrine-Disrupting Chemicals in Makeup Products on Female Reproductive Health: Exposure Levels and Regulatory Improvements

Abstract

This report discusses the common endocrine disrupting chemicals present in makeup products and their effects on female reproductive health. It also reviews original data collected to understand the prevalence of these identified chemicals in the products of popular makeup brands. The chemicals surveyed were lead, butylated compounds, phthalates, parabens, formaldehyde and formaldehyde releasing preservatives, titanium dioxide, and per and polyfluoroalkyl substances. Three types of makeup products (lip gloss, concealer, and mascara) from both affordable (L’Oreal Paris, Pacifica Beauty, and Revlon) and luxury (Anastasia Beverly Hills, Dior Beauty, and Benefit Cosmetics) brands were surveyed for the identified endocrine disrupting chemicals using the Environmental Working Group database. 133 products were surveyed in total with every identified chemical except per or polyfluoroalkyl substances being present in at least 1 product. Since the United States does not regulate ingredients in cosmetic products as strictly as the European Union, more stringent regulations should be put in place. Public education and outreach should also be conducted to spread awareness to makeup consumers, especially young girls, on the importance of avoiding the use of endocrine-disrupting chemicals.

Beyond the Threshold: How Categorizing Air Quality Metrics Alters Statistical Inference and Model Performance

Abstract

Air pollution remains a critical environmental and public health issue, with harmful pollutants such as nitrogen oxides, sulfur dioxide, carbon monoxide, and volatile organic compounds posing significant risks to human health and ecosystems. This study investigates key factors influencing air pollution and evaluates how converting continuous pollutant measurements into categorical Air Quality Index (AQI) labels affects statistical inference and model performance. Using a global dataset of air quality measurements, the analysis incorporates a combination of statistical techniques— including correlation analysis and multiple linear regression—to examine pollutant sources and data transformation impacts. The results show that continuous data produced stronger correlations, higher R² values, and better prediction accuracy than categorical data. However, categorical models offered clearer interpretability and may still be useful in settings with limited data or for effective public communication. The findings offer valuable insights into the trade-offs between data accessibility and analytical precision, informing policymakers in the development of targeted mitigation strategies and sustainable air quality management practices.