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MEDICAL SURVEILLANCE IN CUSTODIAL JSAs: PREVENTING BLOODBORNE PATHOGEN EXPOSURE
Get PDFCustodians play an important role in maintaining our surroundings safe and sanitary. However, in doing so, they are their exposure to health hazards that put them at risk, yet these exposures are often overlooked. One major risk they face is contact with bloodborne pathogens, which put custodial staff at risk for infections such as hepatitis B. While Job Safety Analyses (JSA’s) outline various protective measures, many fail to incorporate medical surveillance as part of their risk management program. Overlooking this important detail leaves custodians vulnerable to health risks that can be prevented.
Adding a medical surveillance section to custodial JSA’s, with a specific focus on hepatitis B vaccination as a preventative measure is essential in protecting these custodians. Incorporating vaccination standards and routine health monitoring into the JSA would create a more proactive approach to occupation safety which would ensure that custodians receive the necessary protection before being exposed.
After thorough research of occupational health policies and other studies, including medical surveillance can help reduce long-term health complications. Protecting custodians means going beyond the standard safety measures to establish a workplace culture that prioritizes prevention. Implementing the medical surveillance component not only enhances compliance with safety regulations, but it also leads to fewer work-related illnesses, lower healthcare expenses, and an improvement in overall health
Predicting Music Origin with Deep Learning
Get PDFThis project explores the usage of a late fusion deep learning architecture to predict the geographic origin of music. Mel-Frequency Cepstral Coefficients (MFCCs) and the language of the music sample are used as features. MFCCs were extracted from audio files to capture sound features. The language was identified using OpenAI’s Whisper model to provide additional context. A late fusion neural network architecture combining Long Short-Term Memory (LSTM) layers for sequential MFCC input and dense layers for non-sequential language features were employed to support both classification and regression tasks. The classification model achieved an accuracy of 33.03% across 56 countries or territories, substantially outperforming the random baseline (1.79%). The regression model produced a Mean Great-Circle Error (MGCE) of 2,754.83 km. While regression offers more flexibility for geographic estimation, classification demonstrated a more promising performance with the current dataset. This work highlights the potential of multimodal learning in music-origin prediction
EXPLORING THE USE OF FORENSIC CANINES IN THE DETECTION OF LATE PRE-CONTACT (1000–300 B.P.) CREMAINS FOR APPLICATIONS IN ARCHAEOLOGY
No full textForensic canines are tremendously effective at detecting the presence of human remains in modern and historic contexts. However, less evidence exists of the ability of historic human remains detection (HHRD) dogs to detect cremated human remains of Pre-contact age, due to the limited opportunities to excavate within highly sensitive and protected sites. Cultural resource management (CRM) projects often encounter buried human cremains during excavation within or adjacent to cultural sites. Archaeologists use several remote sensing methods to locate intact burials, such as ground penetrating radar (GPR) or aerial photography, but these methods cannot identify scattered and fragmented cremains. HHRD dogs from the Institute of Canine Forensics (ICF) can noninvasively detect the scent of human cremains, but pinpointing the exact location of the bone fragments is not always possible. In this study, I invited the ICF to blind search areas positive for human cremains, as confirmed by archaeological site records, excavation during a CRM project, ethnographic accounts, and prior ICF dog alerts from 2020. I investigate how the dogs’ alert behaviors and the handlers’ interpretations of those behaviors can help us visually depict scent patterns in areas containing cremains. Overlapping dog alerts suggest a high likelihood of accurate scent detection, even when archaeologists fail to identify individual bone fragments. Combined with additional lines of evidence, overlapping alerts from multiple dogs and surveys aid in the identification of cremation sites and their general context. Results of this study will allow archaeologists to increase the probability of identifying and avoiding sensitive Native American cremation sites when incorporating forensic canine surveys into CRM project designs
Ethical Dilemmas and Solutions in The Adoption of AI Technologies in Supply Chain
Get PDFAs organizations increasingly adopt artificial intelligence (AI) technologies to optimize their supply chain operations, they face a growing set of ethical challenges that demand careful consideration. This study explores the key ethical dilemmas and potential solutions in implementing AI within the supply chain context, drawing insights from in-depth interviews with a diverse group of experts. The thematic analysis of the interview data revealed four primary ethical dilemmas: algorithmic bias and discrimination, lack of transparency and explainability, privacy and data ethics issues, and the socioeconomic impacts of AI-driven automation. To address the dilemmas, this study identified various mitigation strategies, including techniques for bias detection and correction, methods for improving the interpretability of AI-driven decisions, comprehensive data governance frameworks, and approaches to responsible automation that prioritize human-AI collaboration. The findings of this research are grounded in various philosophical principles, such as fairness and distributive justice, and the ethical treatment of workers in the face of technological change.
By contextualizing the practical challenges within these broader philosophical considerations, the study provides a holistic understanding of the ethical implications of AI adoption in supply chain management.
The proposed ethical AI governance framework for supply chain organizations offers a systematic approach to navigating the complex terrain of responsible innovation, fostering a culture of ethical decision-making, and ensuring the long-term sustainability of AI-powered supply chain operations.
This research contributes to the growing body of literature on the ethical dimensions of emerging technologies and their implementation within operational settings
GETMYGROCERY
No full textThe GetMyGrocery application is an innovative digital platform designed to simplify and streamline the grocery shopping process by connecting customers with dedicated shoppers. The system integrates three user roles, Customer, Shopper, and Company to create a transparent, convenient, and efficient ecosystem for grocery order management, billing, and subscription handling.
At its core, the system provides essential functionalities such as customer registration, subscription-based access, grocery list management, real-time order notifications, and secure payment options. Customers can select shoppers based on proximity or reviews, share their grocery lists, and receive notifications when their orders are shopped or ready for pickup. Shoppers, in turn, can manage orders, send detailed receipts, and track their commissions, while the company oversees subscription revenue and platform analytics.
By combining user-centered design with technologies for account management, real-time notifications, and integrated payment processing, GetMyGrocery demonstrates how digital transformation can improve everyday consumer tasks. The application not only enhances convenience for users but also introduces a scalable model for micro-entrepreneurship through shopper engagement. This project highlights the potential for technology-driven platforms to bridge efficiency gaps in the retail and service sectors, offering a more connected and seamless grocery shopping experience
Potential Adverse Effects on Lifestyle and Food Intake Pattern in Night Shift Workers
No full textAbstract
Night shift work is prevalent in healthcare and other industries, yet its effects on lifestyle factors such as food intake, sleep quality, and mental health remain underexplored. This study investigates the impact of night shift schedules on nurses’ wellness, focusing on eating patterns, sleep, physical activity, and mood. A sample of 13 nurses were divided into two shift groups: day (n=5) and night (n=8); and they completed a questionnaire to assess the variables related to their behavior, food choices, eating habits, sleep patterns.
The results of previous research studies indicate that night shift nurses are more likely to adopt irregular eating patterns, with increased consumption of calorie-dense foods and a tendency to skip breakfast, potentially contributing to imbalances to metabolism (Centers for Disease Control and Prevention [CDC], n.d.). Additionally, night shift nurses reported lower sleep quality and higher levels of stress compared to their day shift counterparts, suggesting a link between circadian disruption and adverse mental health (Centers for Disease Control and Prevention [CDC], n.d.).
These results emphasize the need for targeted workplace interventions, such as structured meal breaks, availability of healthier food options, and sleep hygiene support, to improve wellness among night shift workers. Future research should examine these associations in larger, diverse samples; and explore mechanisms linking night shift work and to provide interventions to reduce health risks. This study underscores the importance of organizational policies to support the health of night shift workers
Adoption of Blockchain Technology in Beverage Supply Chains: Drivers, Benefits, and Challenges in the Coca-Cola and BODYARMOR Sport Water Recall Case
Get PDFThe beverage industry faces growing scrutiny as demands for transparency and accountability intensify. In today’s digital landscape, companies must prioritize enhanced traceability to ensure product safety, comply with regulations, maintain consumer trust, and safeguard brand reputation. This research investigates blockchain technology as a potential solution to these challenges, emphasizing its capacity to decentralize data, improve traceability, and accelerate response times during safety recalls.
The study traces the evolution of food safety regulations and analyzes current traceability practices and technological innovations within the beverage sector. Using the Coca-Cola and BODYARMOR Sport Water recall as a case study, this study examines drivers, benefits, and challenges of the adoption of blockchain in beverage supply chains.
Key findings highlight the importance of integrating blockchain with legacy systems, ensuring accurate data input, and addressing concerns around scalability and privacy. This research bridges academic theory and industry application, offering practical strategies for strengthening supply chain integrity through blockchain adoption in the beverage industry. Several promising future research directions related to the adoption of blockchain in the food and beverage industry are also suggested
SKILL EVOLUTION IN THE AGE OF AI-UTILIZING TEXT ANALYTICS FOR SKILL GAP ANALYSIS TO PREPARE WOMEN FOR LEADERSHIP ROLES
Get PDFABSTRACT The rapid advancement of Artificial Intelligence (AI) is transforming the global workforce, presenting both opportunities and challenges for leadership development, particularly for women. As AI automates routine tasks and redefines skill requirements, there is a growing demand for uniquely human capabilities such as emotional intelligence, creativity, and strategic thinking, qualities that are inherently strong and often highly associated with women. Research indicates that women typically score higher in emotional intelligence, particularly in areas such as empathy and relationship management, which are critical for effective leadership (Goleman, 2020). Furthermore, studies by McKinsey & Company (2022) highlight that gender-diverse leadership teams, benefiting from women\u27s creativity and strategic thinking, drive higher innovation and improved business performance. This alignment between women\u27s natural strengths and the evolving needs of the AI-driven workplace presents a pivotal opportunity to bridge gender gaps in leadership roles. This shift offers a pivotal opportunity to bridge gender gaps in leadership roles by equipping women with the necessary skills to thrive in an AI-driven world. This study explores the Skill Evolution in the Age of AI, focusing on how targeted education, mentorship, and policy interventions can empower women for leadership positions. Utilizing text analytics techniques, the research employs topic modeling on training materials, leadership programs, and professional development content. The analysis aims to identify existing and emerging skill gaps, evaluate the effectiveness of current programs, and propose actionable strategies for inclusive leadership development. A key aspect of this research is examining how AI technologies can be leveraged to reduce the gender gap in leadership roles while addressing the biases in hiring and promotion processes, while also addressing the risks of perpetuating inequalities if gender considerations are overlooked. The findings highlight essential competencies that women need to lead effectively across various disciplines especially in AI-integrated workplaces. Moreover, the study provides practical recommendations for organizations, educational institutions, and policymakers to create environments that support women\u27s advancement into leadership roles.Ultimately, this research underscores the potential of AI as a catalyst for gender equity in leadership, advocating for systemic change that leads to more diverse, innovative, and resilient organizations in the digital era