Mercy University

Ocean Literacy Principle #6 states: “The Ocean and humans are inextricably interconnected.” Concept 6G within this principle emphasizes that “we are all responsible for caring for the Ocean” (NOAA, Ocean literacy: the essential principles and fundamental concepts of Ocean sciences for learners of all ages. NOAA, Washington, DC, 2020, p. 11). Yet, marine education and access to Ocean Literacy (OL) opportunities remain limited, at best, for Black and Brown and/or low-income communities. Children in these communities, as a result, may feel little sense of connection to the Ocean and even less sense of agency when it comes to enacting change to improve Ocean health. Thus, if we wish for everyone to become Ocean literate, we must purposefully reach audiences who have been historically marginalized, including low-income students and those of color. This mixed-method study explores how students in predominantly Black, Brown, and low-income urban communities developed shifting perspectives of their local waterways and gained a deeper connection and care for their Ocean. Findings suggest OL opportunities need to: discuss the importance of local and distant waterways, provide opportunities to make deeper connections to local waterways, and develop a call to action that empowers students to think critically about their local waterways. In addition, researchers call attention to the lack of change after just one round of OL opportunities, recommending that OL become a consistent and on-going part of students’ education to ensure sustained shifts in OL, contributing to a diversified Ocean workforce and marine education field.

The potential benefits of family-focused practice for parents with mental illness have received increasing attention in the last decade, given the significant relationship between parenting status and mental health. It is important to place the experience of motherhood in a cultural context, an approach that is likely to enhance the effectiveness of interventions, services, and supports for mothers and their families. In this Perspective article, we propose that the relationship between parenting self-efficacy and maternal mental health is moderated by a woman’s cultural lens (perceptions of motherhood) and cultural context (i.e., family support and community resources). We consider these concepts as they reflect the experiences of Hispanic/Latina women living in the US as an example, informed by a scoping review of the related literature. The literature underscores the notion that these may change over time or vary, given shifts in acculturation and enculturation, family circumstances and needs, and children’s characteristics and stage of development. Consideration of these key concepts suggests implications for family-focused practice approaches, and the importance of relevant research measures and methods to demonstrate effectiveness.

The U.S. companies and government suppliers witness the ongoing shocks, including geopolitical conflicts and the natural disasters, which indicate weak interdependencies in the areas of planning, procurement, production, and logistics. This article focuses on the degree to which the deep integration of the enterprise resource planning (ERP) and supply-chain management (SCM) systems with the artificial intelligence (AI) solutions can significantly improve economic and supply-chain resilience. It is suggested that a reference framework can be developed integrating master data, transactional signals and external risk indicators into common, real-time pipelines which enable demand sensing, probabilistic forecasting, dynamically tuned safety-stock, supplier-risk scoring and adaptive production and transportation planning. The aspects of selection of models (gradient-boosting, long short-term memory networks, transformers, graph-based learning), human-in-the-loop exception management and multi-layered decision processes that maximize service levels under disruption and control working capital are discussed. A resilience-adjusted ratio of return-on-investment (ROI) is proposed to measure benefits that go beyond the traditional cost savings and include improvements in lead-time variance, backorders, expediting costs, and on-time-in-full (OTIF) performance and recovery of the revenue at risk. Introduction directions include interoperability standards (EDI/APIs), data management, change management and cybersecurity control that best suit regulated U.S. settings. Examples of mini-scenarios that use semiconductors, pharmaceuticals, and food/agriculture involve the AI-mediated ERP-SCM coordination that reduces the time-to-decision sensing, and balances the throughput under the threat of an upstream or logistics shock. It is shown in the analysis that organizations with high-quality data bases and modular AI services, strong machine-learn operations, and cross-functional governance are capable of achieving long-term resilience benefits which result in macro-level stability by means of increasing employment, service reliability, and faster recovery to disruption.

This essay describes a series of assignments that I have used in Writing and Social Issues, a first-year writing course that features service-learning. These assignments should prove useful to those interested in the relationship between community-based writing instruction and first-year courses that focus on the student’s transition from high school to college.

To evaluate the progression rate of Barrett esophagus (BE) to esophageal adenocarcinoma (EAC) using a prospectively maintained national registry, quality-assured endoscopy, and expert pathology. BE is the sole pathologic precursor of EAC. Targeting prevention and early diagnosis through quality-assured BE programs has a compelling rationale. A Barrett's Registry and Bioresource was founded in 2011, and data to November 2024 were prospectively documented in a web-based system (Dendrite, UK). Endoscopy and pathology (of specialized intestinal metaplasia) were strictly quality assured per current guidelines. Expert gastrointestinal pathologists classified non-dysplastic BE (NDBE), indefinite for dysplasia (IND), low-grade dysplasia (LGD), and high-grade dysplasia (HGD). Endoscopic eradication therapies were monitored. Multivariable regression models evaluated risk factors for progression, and Kaplan-Meier curves were constructed for overall progression, and progression excluding the first year after the index biopsy. Nine thousand four hundred thirty-six patients were registered, with a median follow-up of 4.4 years, and 5331 had at least one follow-up endoscopy. Overall, 252 cases (4.7%, 95% CI: 1.70-2.18) of HGD and 255 cases (4.7%, 95% CI: 1.72-2.20) of EAC were diagnosed. Among these, 150 cases (2.8%. 95% CI: 1.05-1.44) of HGD and 148 (2.7%, 95% CI: 1.05-1.44) of EAC were diagnosed more than 1 year after the index endoscopy. The overall incidence of HGD/EAC combined was 2.42% (95% CI: 2.14-2.73), 6.59% (95% CI: 5.14-8.46), and 13.79% (95% CI: 11.94-15.93) per year in NDBE, IND, and LGD, respectively. Independent risk factors include male sex [hazard ratio (HR): 0.655, 95% CI: 0.56-0.896, P <0.004], age (HR: 1.027, 95% CI: 1.02-1.04, P <0.001) and Barrett's length (HR: 1.635, 95% CI: 1.33-2.01, P <0.001). 604 (6.4%) patients underwent RFA, with a complete eradication of SIM in 80.5% and 10 (1%) patients required resectional surgery. Cancer-specific survival in the total cohort was 100%. A structured high-volume Barrett's program, underpinned by quality assurance, provides data that highlights a strategy that provides proof of concept in targeting prevention and early detection, and is anticipated to reduce mortality.

Purpose Online awareness, or the ability to detect errors and monitor performance during activities, is a component of functional cognition essential for safety and independence within everyday activities. There is a lack of assessment methods that target online awareness for individuals with neurological conditions. This study aimed to make recommendations for a clinically useful method for assessing online awareness in rehabilitation. Materials and methods The methods included a conceptual analysis to define online awareness and identify existing assessment approaches; three rounds of Delphi surveys involving an international expert panel to develop an online awareness assessment; and pilot testing of the assessment with consumers. Results The conceptual analysis generated an online awareness definition framework that included four key elements: appraisal, anticipation/prediction, monitoring, and self-evaluation. This framework guided survey questions posed to the expert panel. The Delphi process supported development of the ‘Online Awareness Behaviours Scale’, an assessment that can be used in clinical practice and research. Consumer feedback indicated high levels of usability and acceptability of the assessment. Conclusions The Online Awareness Behaviours Scale is a new, clinically acceptable tool for rehabilitation clinicians that assesses online awareness of individuals with neurological conditions.

Abstract Third-party payment libraries (TPLs) are widely used in Android applications to facilitate in-app transactions, yet their security implications remain largely underexplored. In this paper, we present a novel approach for automated detection and security analysis of payment libraries in Android applications. Our tool, PayScan, employs byte-pattern analysis and heuristic scanning techniques to identify TPLs and then it assesses their security posture. Additionally, the tool integrates three independent security scanners. We analyzed a dataset of 10,553 Android applications, detecting 18 payment libraries and evaluating their security and privacy risks. Our findings indicate that 71.7% of applications use outdated payment libraries, with some SDK versions being over four years old. Additionally, we identified 397 private key leaks across 212 applications. The security scanners detected over 20,000 vulnerabilities, including critical issues such as SSL misconfigurations, WebView XSS, and weak cryptographic implementations. We compare our detection approach against LibScout and LibRadar, demonstrating its practical performance in detecting payment libraries, including in obfuscated applications. This study reveals important security risks in mobile payment ecosystems and emphasizes the value of continued monitoring of third-party payment libraries. The proposed tool offers a scalable solution for detection and analysis, providing practical utility for researchers, developers, and auditors focused on financial application security.

Aims This study examined the impact of an eight-week Healthy Aging Wellness (HAW) group- based standardized program delivered online and in-person to improve hope and well-being in community-dwelling older adults. Methods Mix-methods, two groups design, and secondary qualitative approach. Twenty-five, eligible (independent in daily living, MMSE >24), older adults aged 65-85, assigned into in- person (n = 12) or online group (n = 13). Measures included demographic and health questionnaire, MMSE, Integrative Hope Scale, Personal Wellbeing Index, and post-program semi-structured interview. Results The HAW was delivered with high fidelity. There were no significant differences between or within groups, although both showed changes in sense of hope and wellbeing. Qualitatively, participants expressed improvements in their mental and physical well-being and satisfaction with the program. Conclusions Participants in the HAW program experienced perceived improvements in mental and physical health. Application of HAW program with larger, diverse samples is recommended to determine carryover benefits with larger diverse older adults.

Stroke continues to rank among the top killers and a major source of lasting disability around the globe. Because of that, spotting warning signs early and calculating who is at highest risk have moved to the front lines of medical care. This paper looks at how several machine-learning models can flag future strokes by sifting through patient records and teasing out which factors matter most. Researchers feed a broad mix of data-demographics, habits, lab numbers, and history of illness-into algorithms such as logistic regression, random forests, boosting trees, and deep networks. Careful feature selection lifts forward familiar red flags-age, high blood pressure, heart trouble-while techniques such as oversampling and bagging help tame skewed class counts. Results show that the smarter models reach very high accuracy, opening the door to tailored treatment plans and on-the-spot risk alerts. In short, the study paints machine learning as a game-changing ally in stroke prevention, one able to reshape big healthcare systems through clear, evidence-driven insight.