The Fundamental Role of Dietary Habit, Physical Activity, and Sleep Pattern on Amyloid-Beta and Tau Pathology.

Abstract

Alzheimer’s disease (AD) is a debilitating neurodegenerative disease that affects an estimated 24 million adults over the age of 65 worldwide, with symptoms including memory loss, cognitive decline, and behavioral changes. Currently, there is no cure, although there is a 100% mortality rate once the disease has progressed. Age is the most significant risk factor for developing AD; however, diet, physical activity, and disrupted sleep patterns contribute to disease progression. An improper diet in particular can contribute to inflammation and increase reactive oxygen species leading to AD progression. Physical activity has many benefits on overall health but has a unique positive impact on AD through improved cognitive outcomes. Sleep disturbances are regularly reported by AD patients. There is now evidence to support sleep disruption as an early marker of AD while also significantly contributing to the progression of AD. Improving diet, physical activity, and sleep patterns may have positive outcomes for AD patients and reduce the burden on their families and caretakers, promoting a healthier family environment. In this review, we will explore how diet, physical activity, and sleep patterns contribute to AD progression and highlight how changes in these areas can improve the quality of life of AD patients.

The Effects of Stress on Alzheimer’s Disease Development and Progression

Abstract

Alzheimer’s disease is a progressive neurodegenerative disorder that profoundly impacts patients’ memory, cognition, and overall quality of life. Chronic stress is a substantial contributor to Alzheimer's patients' cognitive deterioration. The effects of stress on hippocampus function and dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis can lead to higher cortisol levels and is the link between stress and memory dysregulations. Moreover, acute and chronic stress have been linked to increased production of Amyloid Beta plaques and Tau Tangles, the primary pathological hallmarks of Alzheimer’s disease. As a result, the cellular mechanisms of memory consolidation and storage are disrupted. This paper reviews ongoing research of how cellular, network and endocrine pathways in the brain are impeded by stress and links these findings to Alzheimer’s disease development and progression. Understanding the interplay between stress and the progression of Alzheimer’s disease can play a significant role in further development of research, disease-modifying treatments, and potential cures.

The Role Of Cross-Sector Collaboration In Reducing Racial And Social Disparities In Healthcare

Abstract

Despite the presence of a robust healthcare system, racial disparity and social inequalities remain prevalent in the United States. These disparities in healthcare are persistent challenges, disproportionately affecting marginalized communities and contributing to unequal health outcomes. This study investigates the role of cross-sector collaboration among healthcare providers, public health agencies, community-based organizations, and policy makers in mitigating these disparities. Using a mixed-methods approach that includes case studies, stakeholder interviews, and data analysis from collaborative health initiatives across five urban regions, the research identifies key drivers of success in cross-sector partnerships. Findings highlight the importance of community engagement, shared data systems, culturally competent care models, and sustained funding in reducing barriers to access and improving health equity. The study concludes that cross-sector collaboration is not only essential but also highly effective in addressing the root causes of health disparities, and it offers policy recommendations to support the development and scalability of such partnerships nationwide.

A Neural Network Model in Identifying Non-Small-Cell Lung Cancer through CT Scans

Abstract

Lung cancer is the leading cause of cancer-related deaths worldwide, causing approximately 1.8 million deaths in 2022 alone. Lung cancer is often detected through the use of Low-Dose Computed Tomography (LDCT) scans, which use small amounts of radiation to construct detailed pictures of regions in the body. This study aims to explore the ability of Artificial Intelligence, particularly Convolutional Neural Networks (CNNs), to detect lung cancer from CT scans. Using an online dataset consisting of 1000 images, a CNN was developed with ResNet50 as the base model used for feature extraction. The model achieved a validation accuracy of 98.78% and a testing accuracy of 97.53%. This showcases the proficiency of the model in detecting lung cancer. However, this was only when a binary classification system was implemented, where the model was made to simply determine the presence of cancer. The model faced great difficulty in distinguishing between the types of lung cancer: Adenocarcinoma, Squamous Cell Carcinoma, and Large Cell Carcinoma. Additionally, the presence of a small number of false negatives while testing shows the danger of relying on AI and demonstrates the necessity of further fine-tuning before practical use.

Exploring The Neurocognitive Effects Of Magic Intervention On Mild Cognitive Impairment And Alzheimer’s Disease

Abstract

Magic, the art of conjuring, has fascinated people around the world for millennia. Magicians have mastered manipulating attention (misdirection) and exploiting the human mind to create seemingly impossible tricks and illusions. In the past few decades, neuroscientists and psychologists have researched the methods of magic and produced a significant amount of literature relating to how magicians manipulate people. With this surge of new knowledge, it is worthwhile to investigate whether magic can be used in treatments for mental disorders and diseases. Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD) are two of the most common neurological conditions among elderly, where behavioral interventions are crucial for slowing down the progression of neurocognitive impairment. This paper analyses and proposes how magic intervention can impact neurocognitive function in patients with MCI or AD, by reviewing current literature in the related areas of magic and the science behind it. In particular, it is found that magic intervention can positively affect executive functioning, learning, and memory in individuals with MCI and AD. Furthermore, emerging areas of research in the field indicate that magic intervention may promote curiosity and engage sensory systems, further improving neurocognitive function in these individuals. Therefore, the paper shows that magic intervention is effective in enhancing neurocognitive function in patients with MCI and AD, and in slowing down the progression from MCI to AD.

Underlying Similarities Between Psychedelic and Schizophrenia Hallucinations

Abstract

Though distinctly different in their effects, hallucinations occurring from schizophrenia (SCZ) symptoms and psychedelic drug action can appear similar. To investigate how different mechanisms underlie these effects, this paper highlights similarities at the molecular and anatomical network level and identifies potential new therapeutic applications and directions for research. This paper explores the 1950s-1960s studies involving schizophrenic patients, hallucinogenic drugs, and their historical significance. Next, the paper dives into overlaps between schizophrenic and psychedelic hallucinations– from molecular to cellular levels. The paper’s overlaps section starts with Neuroplasticity overlaps between SCZ and Psychedelics; then is followed by the cortico-striatal-thalamo-cortical (CSTC) theory in psychedelics and schizophrenia’s hallucinations; serotonin (5-HT) mediated hallucinations and psychotic effects; triple network disconnectivity in psychopathology and schizophrenia pathology; and finally, synaptic disconnections role in neuropharmacology of hallucinations seen by computational modeling. The paper includes a final section of how the drug, nicotine, impacts SCZ patients. This paper aims to clarify the neuropharmacological overlap between these phenomena to call for further research in developing targeted treatments.

Classifying Classical Music Genres with Neural Networks

Abstract

Current neural network models can process and interpret music for tasks such as melody completion and genre or style classification. However, previous classification tasks do not account specifically for distinct composition styles of different classical music periods, often focusing instead on modern genres. To bridge this gap, this project investigates the use of natural language processing techniques to classify musical excerpts from the Baroque, Classical, and Romantic periods. A curated dataset of samples representative of the three eras, converted to the OctupleMIDI format, was used to train a Sentence Transformers model to complete the classification task with maximum accuracy—62.5% when trained on all three categories and 90.5% when the Classical and Romantic labels were merged. These results indicate that the model was most effective at distinguishing Baroque music, suggesting clearer stylistic separation. These findings demonstrate the feasibility of using sentence-level embeddings for symbolic music classification, offering potential applications in musicological analysis, genre tagging for recommendation systems, and quantitative exploration of musical style beyond human perception.

Impact of Recreational Substance Use On the Adolescent Brain

Abstract

Adolescence is a critical stage of brain development marked by rapid maturation of the prefrontal cortex and hippocampus, regions of the brain responsible for memory, decision-making, and emotional regulation. During this period of heightened neuroplasticity, the brain is especially sensitive to environmental influences, including the use of substances like alcohol, marijuana, and nicotine. This literature review examines how each of these substances disrupts brain development and impairs cognitive function. Alcohol use interferes with synaptic pruning and myelination in the prefrontal cortex, leading to poor impulse control and decision-making. It also causes hippocampal damage, including inflammation and volume loss, which impairs memory encoding and learning. Marijuana’s active compound, THC, disrupts cannabinoid receptor function in the hippocampus, reducing neuroplasticity and leading to structural shrinkage and long-term memory deficits. Marijuana also thins the prefrontal cortex, delaying cognitive control development. Nicotine overstimulates acetylcholine receptors, hindering normal pruning and reducing gray matter volume, especially in the prefrontal cortex and hippocampus. This contributes to attention problems, increased impulsivity, and impaired working memory. Neuroimaging studies consistently show functional and structural differences in adolescents who use these substances compared to non-users. These changes may persist even after substance use stops, increasing the risk of long-term cognitive challenges and future substance use disorders. Early prevention and intervention efforts, such as school-based education and promotion of healthy habits, are essential to reduce adolescent substance use and protect brain development. This review highlights the urgent need for continued research and public health strategies to address the risks associated with early exposure to alcohol, marijuana, and nicotine.

Recent Advances In Carbon Nanotube Devices For The Application In Food Allergen Detection

Abstract

Carbon nanotubes are cylindrical shaped structures comprising single or multi layers of graphene sheets which have distinct characteristics of high conductivity, high flexibility, and high stability across a range of scenarios. These carbon nanotube properties provide distinct sensitivity and surface area to manufacture extremely efficient biosensors. Carbon nanotube biosensors detect low concentrations of a target analyte with the aid of specific antibodies or biomolecules immobilized on the carbon nanotube surface, allowing for rapid and accurate diagnostic results. Carbon nanotube-based biosensors have been studied to detect allergens due to the rapid and accurate results provided for identifying allergenic substances in many contexts, including food safety and prevention of subject exposure to allergenic substances, which could potentially trigger allergic reactions. This review will focus on the applications of carbon nanotubes in the development of sensitive biosensors for peanut, wheat, lysozyme, and kidney bean allergens, and will summarize the performance of each biosensor as well as their limitations and promising future applications of carbon nanotubes for allergen detection.

Factors Affecting Global Video Game Sales Rankings: Analyzing the Impact of Platform, Genre, and Market Regions

Abstract

This study investigates the determinants of global video game sales rankings by examining platform types, game genres, and regional market characteristics. Using a comprehensive dataset of 16,598 entries sourced from VGChartz via Kaggle, this research employs a hybrid methodological framework combining machine learning and traditional statistical approaches. Specifically, correlation analysis, multiple linear regression, binary logit regression, and binary probit modeling are utilized. The study aims to identify the significant predictors of video game sales performance across global markets and to understand the interactions among these predictors. Results show that platform and genre are consistently influential variables, with regional market sales playing a secondary but still impactful role. The findings offer valuable implications for developers, marketers, and stakeholders seeking data-driven strategies in the video game industry.

Exploring Quantum Dot Materials And Synthesis Methods: Current Trends And Future Prospects

Abstract

Quantum dots (QDs) have numerous applications in the fields of optoelectronics and quantum physics. Their emission wavelengths can be tuned based on size, they have high quantum yields and excellent photostability. These characteristics offer advantages over previous-generation materials such as organic dyes. Unfortunately, QD synthesis requires extensive precision and relies on processes that are difficult to replicate on industrial scales. This review summarizes the different materials and methods of QD synthesis in order to examine their current limitations and potential areas of improvement. It also emphasizes potential routes to improve the scalability of existing synthesis processes. As demand for QDs in optical applications continues to grow, an understanding of their manufacturing will be necessary to enable their continued development.

A Novel MBST-Based Intervention for Palliative Care in ALS and Terminal Cancer Patients

Abstract

Palliative care patients face increased emotional and psychological suffering, including anxiety, depression, suicidal ideations, delirium, anticipatory grief, and fear of death. Hence, an intervention focused on improving the psychological and emotional well-being of terminally ill patients is necessary. This study examines the potential of a novel intervention to enhance the emotional and psychological wellbeing of terminal patients, specifically with Amyotrophic Lateral Sclerosis (ALS) and terminal cancer. This research is divided into two studies: Study 1a, a set of pilot qualitative interviews (n= 5) to inform the design of a novel mindfulness-based therapy (MBST), and Study 1b, a quantitative experimental intervention to determine the efficacy of the intervention (n=144), to improve the psychological and emotional well-being of palliative patients, along with developing a good coping mechanism. Study 1a revealed information about the problems, needs, and opinions on the intervention of palliative patients, highlighting the demand for effective treatments, through interviews. This information was used to inform the intervention task, which then tested experimentally in Study 1b using the Suffering Pictogram. The analyses, through mixed ANOVA, yielded a significant decrease in all dependent variables tested – overall suffering discomfort, worry, fear, anger, sadness, hopelessness, difficulty in acceptance, and emptiness – for participants in the experimental condition versus the control condition. Hence, study 1b demonstrated the intervention was highly effective to relieve psychological stress. This study highlights the potential of MBST as a practical and efficient tool to improve mental and emotional health in the context of terminal disease and its significant implications for clinical practice, legislation, and future research in palliative care.

Experimental Characterization of the Mechanical Properties of 3D Printed Structures with Polylactic Acid

Abstract

Additive manufacturing, also known as 3D printing, has significantly transformed the manufacturing industry by offering cost-effective, rapid, and versatile production methods. Fused Deposition Modeling (FDM), also referred to as Fused Filament Fabrication (FFF), is among the most widely used additive manufacturing techniques, particularly for processing a broad range of thermoplastics. Key printing parameters in FDM such as infill density, print orientation, and layer thickness play a critical role in determining the mechanical properties of printed structure. Polylactic acid (PLA) is one of the most extensively used biodegradable thermoplastics for FDM printing due to its ease of processing and environmental benefits. While pure PLA is dielectric, the incorporation of conductive nanomaterials such as carbon black, significantly enhances its electrical conductivity. This study investigates the influence of 3D printing parameters on the mechanical properties of structures printed with both pure PLA and conductive PLA composite containing carbon black. 3D printed samples with varying solid infill ratios of 15%, 45%, 75%, and 100% were tested under tensile loadings with two different strain rates. Results showed that increasing solid infill density from 15% to 100% led to notable improvement in ultimate tensile strength, rising from 36.9 ± 0.35 MPa to 55.7 ± 0.12 MPa. In addition, infill orientation was also found to have a significant impact on mechanical performance. In addition, while had a significant impact on mechanical properties. Furthermore, while the incorporation of carbon black improved the electrical properties, we found that it results in a reduction in mechanical properties compared to pure PLA. This study highlights the importance of optimized printing parameters and material composition on the mechanical properties of FDM printed structures.

Microfinance and Women’s Empowerment: Comparative Models from West Africa and Latin America

Abstract

Microfinance has long been promoted as a tool for advancing financial inclusion and women’s economic empowerment, particularly in low-income regions. However, its actual transformative potential remains contested due to persistent structural and cultural barriers. This comparative study examines microfinance programs in West Africa and Latin America, focusing on how they support women entrepreneurs. Through a case study analysis of Groupe Cofina in Côte d’Ivoire and BancoSol in Bolivia, the research evaluates the institutions’ outreach models, financial products, and non-financial support services. Findings reveal that while both institutions have expanded access to credit for women, the impact on long-term economic empowerment is uneven. In West Africa, Cofina’s efforts to reach low-income women are challenged by legal constraints and entrenched gender norms. In Latin America, BancoSol has facilitated a transition from informal to formal entrepreneurship but faces issues related to financial literacy gaps and high interest rates. The study highlights the importance of digital integration, financial education, and peer mentorship as complementary tools to enhance microfinance outcomes. Ultimately, the research underscores that microfinance alone cannot dismantle systemic gender inequality. It calls for context-sensitive adaptations that incorporate legal support systems, address the digital divide, and deepen non-financial services. The comparative approach offers insights for scaling inclusive microfinance models across regions with similar socio-economic conditions.

The Ecology of a Bite: Environment, Evolution and Diet in Gray Foxes

Abstract

Inhabiting the channel islands, island foxes have a basic habitat compared to mainland foxes. Environmental data (human impact, prey availability, habitat) from Google Scholar and morphometric data (trigonid & talonid length and area) from the University of California, Los Angeles’s Dickey Collection were integrated to gain a comprehensive understanding on whether and the extent determinants contribute to shaping tooth morphology and diets in gray foxes. Methodologically, logistic regression predicts binary dietary preferences, multiple linear regression exhibits the degree of the environmental variable’s influence on BTG ratio, cluster analysis groups sets of environment controls together to uncover distinguishing traits or behaviors, and hypothesis testing determines the statistical significant differences in the mean BTG ratio between foxes to examine whether habitat type has a pronounced effect on the tooth structure. Subsequently, the multiple linear regression revealed habitat and human impact to be significant, but prey availability to have a marginal role. Although predicting meat diets was high, the logistic model’s performance was poor for plant diets, and an issue of imbalance in the dataset seemed a likely cause. Representing meat and plant diet, cluster analysis corroborated the habitat and diets’ role in dictating the tooth anatomy. Ultimately, the one-way ANOVA didn’t identify significant variation among mainland and island foxes for BTG ratios (F = 2.18, p = 0.15), suggesting that habitat type isn’t a significant influencer. Concluding these findings about the evolutionary ecological mechanisms and environmental morpho-ecological traits, future studies could build on these observations through seasonal variations and genetic analysis.

Valvular Heart Diseases: Advances in Biology, Treatments and Future Technologies

Abstract

Heart valves maintain unidirectional blood flow, ensuring effective cardiac function. Valvular heart disease (VHD) is a major global cause of morbidity. Given the scale and impact of valvular heart disease, this review focuses on the structures and mechanisms of the four major valves, aortic, mitral, tricuspid, and pulmonary valves comprehending the biology and clinical aspects of both congenital and acquired valvular diseases. This manuscript reviews emerging updates in transcatheter mitral valve repair, transcatheter aortic valve replacement, and stem cells-based approaches for the management of various valvular diseases. Additionally, the article provides insights into computational models and artificial intelligence in next generation valvular therapies.

High-Performance Organic Flexible Thermoelectric Devices for Wearable Applications

Abstract

In recent years, thermoelectric (TE) devices have garnered increasing attention for their ability to convert waste heat into usable electrical energy, offering a pathway toward enhanced energy efficiency and reduced carbon emissions. Among various TE technologies, flexible thermoelectric generators stand out as promising candidates for powering wearable electronics and industrial Internet of Things applications due to their lightweight, compact, and maintenance-free design. Unlike conventional rigid devices, these solid-state systems can conform to diverse heat sources, enabling seamless integration into both personal and industrial environments. Compared to inorganic materials, organic TE materials present compelling advantages for room-temperature and flexible energy harvesting. In this study, we investigate the thermoelectric performance of films composed of poly(3,4-ethylenedioxythiophene):pol y(styrenesulfonate) blended with graphene nanoplatelets. The maximum power factor of ~12 ± 1.3 µW/ mK2 is achieved for unsintered films at room temperature. Additionally, an in-plane device prototype was developed, achieving a maximum open circuit voltage of 5.1 mV under a temperature gradient of 15 K. These findings underscore the potential of organic thermoelectric materials for next-generation f lexible and wearable energy solutions.

Evaluating Sentiment Analysis Models on Historical Texts about Black Americans

Abstract

In this research study, four sentiment analysis models were trained on a dataset consisting of primary source documents about the experiences of Black Americans in the twentieth century, specifically in regards to their migration to the northern United States and racially oppressive legislation in the South. Each model used a different algorithm for sentiment analysis: Multinomial Naive Bayes, support vector machine (SVM), Generated Pre-Trained Transformer-2 (GPT-2), and Bidirectional Encoder Representations from Transformers (BERT). The goal was to determine which algorithm was best able to classify a 20th-century document about Black Americans as having either positive or negative outlook on their experiences. The results of this research, coupled with future, more advanced studies on such algorithmic capabilities, can allow for a more streamlined, objective, and accurate approach to categorizing historical documents, enabling historians to analyze them to generate insights and support arguments with greater speed and efficiency. Among the four algorithms, BERT achieved the highest accuracy rate (100%), followed by SVM and GPT (97%), and Multinomial Naive Bayes had the lowest accuracy rate (95%). However, the imbalanced nature of the dataset in terms of the ratio of positive to negative documents raises concerns about the algorithms being more likely to identify documents as positive. Also, the seemingly overwhelming accuracy of BERT signals that overfitting may have artificially skewed the results.

Age-Related Epigenetic Dysregulation in Cancer Development: From Mechanisms to Therapeutic Targets

Abstract

In 2021, it was estimated that one in ten people were over 65. This proportion of elderly people is only expected to increase, with projections expecting one in six people to be over 65 by 2050. Consequently, as the aging population, particularly in regions like the United States, continues to grow, understanding how the process of biological aging increases susceptibility to the development of cancer is becoming increasingly important. Indeed, understanding the epigenetic changes that occur with biological aging is becoming vital for addressing the increasing prevalence of age-related diseases, especially cancer. This paper will explore the connection between epigenetic processes and aging, underlining how this affects the deterioration of epigenetic regulatory systems overall. Furthermore, this paper will subsequently explain how the deterioration of key epigenetic mechanisms, such as DNA methylation and histone acetylation processes, leads to age-related diseases, with a specific focus on cancer. Approaching age related diseases from an epigenetic perspective offers a valuable viewpoint for medical interventions. In fact, this approach to medication is presenting a promising way forward for our increasing aging population, as evidenced by Histone Deacetylase Inhibitors (HDACis) and DNA Methyltransferase Inhibitors (DNMTis) showing anti-tumor effects in clinical trials.

Combating Vaccine Hesitancy in the Philippines: Bridging the Gap Through Visual Communication and Education

Abstract

In recent years, data has shown a growth in vaccine hesitancy for families in the Philippines, specifically in regards to the Dengue and COVID-19 immunizations for children. While vaccines remain widely available, strained health systems, poverty, lack of resources, and limited access to health education and functional literacy hinder under-resourced families from understanding and trusting essential childhood vaccinations. In low-income countries where there is a high percentage of people with low literacy rates, visual aids in health education have been proven beneficial by improving health communication and decision-making. This paper examines the roots of vaccine hesitancy in the Philippines, highlighting educational shortcomings, misinformation, and cultural perceptions, and how this can be improved with the use of visual aids.

A Lifetime of Sleeplessness: A Narrative Review of Biological and Social Influences on Women’s Sleep

Abstract

Sleep plays a critical role in physical and mental health, yet many studies show that women report poorer sleep quality and more sleep-related health problems than men across the lifespan. Although existing research has explored biological and hormonal factors affecting sleep, there is limited understanding of how social roles, structural inequalities, and intersecting identities contribute to gendered sleep disparities over time. Studies also rarely include transgender and nonbinary individuals, despite likely risks. Sixteen peer-reviewed studies published between 1999 and 2024 were identified using academic databases including PubMed and Google Scholar. Sources were identified using academic databases including PubMed and Google Scholar. Given the diversity of methods and populations studied, a narrative approach was adopted to integrate findings across disciplines and life stages. The review examined both biological and social influences on sleep across five key life stages: childhood, adolescence, reproductive years, midlife, and older adulthood. Findings showed that women face unique sleep challenges at each stage of life, shaped by hormonal changes, caregiving demands, social expectations, and socioeconomic pressures. These sleep difficulties often begin early and build up over time, with greater risk for women from marginalized groups, including women of color, low income women, and those with caregiving responsibilities. Sleep disparities are not only biological but also deeply social. An intersectional, life course approach is needed to understand and address gendered sleep inequalities. More inclusive research is required to ensure sleep health strategies meet the needs of all genders, particularly those most at risk of being overlooked.

Real Estate Financing for Green and Sustainable Buildings in India

Abstract

In the present context of climate change, rising energy costs, and government regulations that push for eco-friendly practices, expanding green real estate in India demands robust financial mechanisms. This paper examines existing certification systems, financial instruments, and regulatory frameworks governing green real estate. Through policy analysis, it identifies key challenges including technical and institutional barriers, declining investor confidence due to awareness and transparency gaps, low market demand limited by insufficient incentives, and disproportionate investment in energy over real estate. To address these pitfalls, this study recommends actionable steps to diversify green investments, strengthen financing infrastructure, and improve policy enforcement such as the development of a standardized green taxonomy, a national green credit program, and a regulated green secondary market to improve liquidity. The paper also calls for stronger coordination through public-private partnerships and multi-level governance, alongside technical capacity-building initiatives for key stakeholders. Through a comparative analysis of state-wise incentives promoting green building construction in India, this study sets the stage for further research to evaluate the effectiveness of local green policies. Finally, it outlines the future scope of green real estate in India through the lens of behavioural economics.

The Impact of Music-Induced Emotions on EEG Alpha Power and Aperiodic Exponent Across Age Groups

Abstract

Many studies have been conducted on the impact of music on periodic brain activity. However, there is a lack of research into how music influences background non-oscillatory brain activity, as measured by the aperiodic exponent. Additionally, music has the power to influence emotions which may cause novel neural activity. In the current study, we analyzed alpha power (8-13 Hz) and aperiodic exponent among different age participants during active music listening in order to discover whether age or contrasting music-induced emotions could evoke different neural activity. We conducted a secondary analysis on data obtained from OpenNeuro utilizing measures of alpha power and aperiodic exponent from electroencephalography (EEG) data. The following libraries in Python were used: Fitting Oscillations & One Over F (FOOOF) and Magnetic and Electric Neuroimaging (MNE). The analysis revealed no correlation between the emotional content of music and our EEG measures. However, there were significant increases in aperiodic exponent between younger and older participants, highlighting a change in brain activity across different age groups. This reveals the possible effects of age as a cause of change in neural activity. Future research on the differences in aperiodic activity among different age adults during other creative tasks could improve our understanding of the effects on brain activity caused by aging.

Sustainable Innovation in Hospitality with a Case Study of FIVE Hotels and Resorts

Abstract

This paper reviews how FIVE Hotels and Resorts uses sustainable innovation to reduce its environmental impact without losing sight of guest experience and profits. By exploring their efforts in saving energy and water, cutting waste, and training staff, I share insights from interviews and reports. While they’ve made real progress, challenges like upfront costs and shifting company culture still exist. The case shows that with the right approach; luxury hotels can grow their business while being sustainable.

Telebot: Evaluating the Efficacy of Thought Patterns in an Electroencephalographically-Controlled Mobile Robotic Manipulator

Abstract

Telebot aims to create a mentally operated mobile robotic manipulator that bolsters human productivity by changing the way humans interact with the world. Human brainwaves recorded noninvasively through electroencephalography can be used to mentally operate a mobile robotic manipulator through machine learning. This study determines the most compatible and effective type of brain activity to use as mental commands by comparing users’ visualization of images versus actions, as well as generic visualizations versus personalized ones. There were 6 participants in this study to evaluate the efficacy. Each trial was conducted while each participant wore an EMOTIV EPOC X–14 Channel Wireless EEG Headset running the EMOTIV Brain-Computer Interface (BCI). The participants were asked to think of generic images, individualized images, generic actions, and individualized actions. Each participant established a neutral baseline and references for each mental command through five, eight-second calibrations and then performed four live tests, one for each mental command. The data recorded the thoughts that were sustained above the detectable threshold, the number of false starts, and other metrics. The result uses the main metric to analyze the general efficacy, which measures the uptime of the mental command being above the detectable threshold. The data showed that, on average, generic images are effective 58.8% of the time, individualized images 74.3%, generic actions 59.7%, and individualized actions 77.7%. These findings contribute to the development of mobile robotic manipulators, potentially transforming the way humans interact with the world.

Gen Z High School Workers: An AI-Driven Mixed-Methods Study of Labor-Law Literacy and Career Impact

Abstract

Gen Z high school students in Massachusetts work part-time in retail, food service, and other entry level roles, yet often lack awareness of their legal rights. These early employment experiences shape career trajectories both positively and negatively. This study employs an Artificial Intelligence (AI) driven mixed-methods design combining a quantitative online survey of eighty-nine students with ethnographic field observations of more than twenty teen workers. The survey measured knowledge of labor protections, attitudes toward workplace compliance, and career readiness. The ethnographic component provided contextual insights through firsthand observation and engagement. To enhance analytical rigor and efficiency, Microsoft Azure AutoML was utilized to classify survey responses, while GPT-4 was employed for thematic coding of open-ended data, enabling reduced manual effort and improved accuracy in identifying sentiment and themes. Findings reveal a significant gap in legal awareness that may hinder both student well-being and career progression. The data shows that 42 percent of teens have never received any training on workplace rights, 53 percent want instruction on labor law and youth protections, while two-thirds believe high schools should be required to provide it. This knowledge gap poses a risk to both student well-being and future career growth. Hence, the study recommends that schools incorporate a brief labor rights unit into required courses and collaborate with employers and unions to offer free or low-cost membership in youth worker councils, enabling Gen Z students to enter the workforce informed, safe, and prepared.

Beyond the Gear: Addressing Gaps in Concussion Care in Collision Sports

Abstract

The landscape of concussion mitigation technologies in contact sports has evolved significantly, with innovations such as Guardian Caps, G8RSkin, and the Q-Collar showing promising results in reducing concussion risk. These advancements demonstrate crucial steps that have been made toward addressing the prevalence of traumatic brain injuries and potentially mitigating the development of chronic traumatic encephalopathy (CTE) among athletes. Despite these technological advancements, significant challenges remain in standardizing concussion protocols across sports. The lack of consensus on diagnosis methods, treatment approaches, and return-to-play guidelines undermines holistic athlete protection. Current diagnosis methods remain largely subjective and dependent on self-reporting, while treatment protocols vary widely between institutions. Moving forward, emphasis must be placed not only on improving protective equipment but also on developing standardized diagnostic criteria for early CTE detection, establishing consistent treatment protocols, and implementing mandatory recovery periods following head trauma. Multifaceted approaches can significantly enhance athlete safety while preserving the integrity and future of contact sports. As technological innovation advances, we must consider whether the investment in concussion research has fulfilled athletic institutions’ ethical responsibility to protect athletes without detracting from the excitement of collision sports.

Social, Neuroscientific and Psychological Factors that Contribute to Alcohol Use Among Teenager

Abstract

Adolescent alcohol consumption has detrimental effects on teenage physical and mental health, potentially leading to behavior problems. The habits and attitudes of individuals in school or at home, including friends or family, can influence teenage perceptions toward drinking. Specifically, teenagers may experience peer pressure from their friends to drink alcohol, which can initiate a cycle where drinking leads to increased alcohol intake and potentially alcohol use disorder, or addiction. Furthermore, factors like social media and marketing may convince teenagers to consume large amounts of alcohol as well. Additionally, certain brain regions are implicated in the development of alcohol use disorder, including the limbic (i.e., reward) system and the prefrontal cortex, among others. Lastly, alcohol can negatively affect teenagers’ executive function and increase impulsivity. This paper will discuss the factors that lead to teenage alcohol use, the neuroscience behind it, and the psychological components.