Monmouth University

An after-school program, run by a partnership between an elementary school and a university, helps fifth-grade students build mathematical belonging and STEM identities.

This fast development of cloud-native computing and Generative Artificial Intelligence (GenAI) has facilitated the creation of scalable and intelligent applications in various fields. But current literature and practice are full of individual studies on cloud-native machine learning pipelines and Generative AI systems, creating a fragmented architecture, haphazard governance, and more complicated operation in enterprise. Specifically, no combined architectural advice exists that combines principles of cloud-native computing with foundation model-driven GenAI processes into an integrated and consistent framework. The given paper provides a systematic literature review of cloud-native AI architectures, Generative AI models, and AWS-managed AI services to reveal major patterns of design, challenges, and gaps of the current practices. It was on this basis that we have developed a consistent cloud-native reference architecture to deploy and operate Generative AI workloads on Amazon Web Services (AWS). This work has the contribution of introducing a design-oriented AWSspecific reference architecture that applies standard cloud-native principles to unify traditional machine learning pipelines and Generative AI systems. Instead of concentrating on empirical performance measurement, the study suggests architectural guidance and practical design advice that are supposed to guide the organization in designing scalable, secure, and governance-conscious GenAI deployments on AWS. Future research will involve implementation-level verification and empirical evaluation of the proposed architecture in the real-life application contexts.

Digital screen exposure has become ubiquitous, yet its causal impact on self-regulation traits remains poorly understood due to methodological limitations in observational research. This study develops a methodological innovation by integrating Mendelian randomization (MR) with advanced multivariable modeling and machine-learning instrument selection. Unlike conventional MR applications primarily in biomedical research, our framework extends genetically informed causal inference into computational social systems. Specifically, we propose a multivariable MR approach enhanced by least absolute shrinkage and selection operator (LASSO)-based instrumental variable selection, which disentangles the independent causal effects of multiple, genetically correlated digital exposures. Using genome-wide association summary data from over 450 000 individuals from the UK Biobank, we investigate the behavioral effects of mobile phone use, computer use, computer gaming, and television viewing on self-regulation traits. The results show that mobile phone and computer use increase risk-taking tendencies, television viewing is associated with lower risk-taking scores and higher perseverance scores, possibly reflecting the passive and sequential nature of this activity, while computer use and gaming are linked to diminished endurance. These findings not only uncover causal pathways between digital activity patterns and psychological traits but also demonstrate a robust methodological advance for applying MR in complex behavioral systems. By demonstrating how screen exposure causally shapes core personality traits, this study provides actionable insights for technology designers to develop personality-aware digital interfaces, for policymakers to establish evidence-based screen time guidelines differentiated by media type, and for parents and educators to implement personalized digital engagement strategies that account for individual differences in self-regulation capacity.

Disability inclusion has become a central focus in education and social policy; however deeply rooted stigma and misconceptions continue to shape how individuals with disabilities are perceived and treated. Despite legal protections and advocacy efforts, negative attitudes continue to persist. As this influences educational access, social participation, and personal identity formation for people with disabilities. Understanding the origins and impact of these attitudes is critical for creating truly inclusive environments. Attitudes toward individuals with disabilities have historically been shaped through stigmatization and exclusion. This has influenced how people with disabilities are treated in schools, society, and even how their humanity is viewed. Understanding how these perceptions develop is essential for fostering inclusive environments. This literature review examines the historical, social, and educational roots of stigma and how these attitudes influence the self-perception of individuals with disabilities. It explores how inclusion must go beyond access to educational materials. True inclusion requires a change in mindset, language, and policy. By exploring the evolution of beliefs surrounding disability, this review connects past misconceptions with present perspectives. The literature consistently emphasizes the need for continued change. These findings offer insight into how educators, policymakers, and communities can promote acceptance of all individuals. Keywords: disability, inclusion, stigma, education, attitudes DOI: 10.7176/JEP/17-1-15 Publication date: January 30th 2026

Industry 5.0 promotes the transformation of manufacturing toward flexibility, personalization, and sustainability. As a critical component of closed-loop manufacturing systems, disassembly operations urgently require more flexible and efficient human–robot collaboration models. To this end, this work, for the first time, proposes a multihuman–robot collaborative circular disassembly line balancing problem. By allowing workers to move between robotic workstations, the proposed system enhances operational flexibility. Furthermore, a multiworker mechanism is introduced to improve fault tolerance and system stability, overcoming the limitations of fixed worker positions in existing collaborative disassembly research. To solve this problem, we formulate a discrete-time mixed-integer programming model based on product AND/OR graphs, aiming to maximize disassembly profit. The model’s correctness is verified using CPLEX. Additionally, we develop a heterogeneous graph neural network-enhanced proximal policy optimization (PPO) algorithm. By integrating product and workstation information into a heterogeneous graph, the algorithm performs two-stage feature extraction and node embedding via graph neural networks. Based on these embeddings, the agent dynamically selects multiple actions per decision step to simulate the behavior of multiple workers moving simultaneously. Experimental results show that the proposed method outperforms traditional reinforcement learning algorithms such as PPO and deep Q-network algorithm in terms of disassembly profit. Moreover, it demonstrates strong generalization capability in cross-task transfer and scalability experiments involving different task graph sizes. The improved performance is achieved with acceptable computational time.

ABSTRACT The Middle Atlantic Bight (MAB) of the eastern US differs from other offshore wind (OSW) development sites due to a unique seasonal oceanographic stratification regime. Fisheries there target migratory finfish and sedentary shellfish, the productivity and distribution of which are driven by oceanography with dynamic mesoscale features that can encompass one or more OSW leases. The regulatory environment allows competition among universities and private companies in designing and executing innovative Fisheries Monitoring Plans (FMPs) under federal guidelines but has hindered a comprehensive plan that considers all the wind farms proposed for the MAB under shifting timelines. Different FMPs reflect that OSW development itself is not unified, but FMPs could integrate and share data. Here we present a perspective on an FMP developed as several surveys implementing Before‐After‐Control‐Impact (BACI) and Before‐After‐Gradient (BAG) designs to meet the challenges of this environment. These anticipate built structures and other nonaligned leases in an “oceanography based” approach. This plan roots analysis in an ecological understanding of the MAB even if methods require resource‐by‐resource survey. It is also novel in planning around potential sampling impacts by project development, and in anticipating concerns that multiple, independent, or loosely unified campaigns would otherwise bring. It merges extractive and nonextractive methods to support development of survey strategies that anticipate structural hindrance, limit cumulative impacts, and protect sensitive resources. Finally, it fully integrates commercial fisher participation in the design and execution to utilize the sector's extensive knowledge, capable vessels, potential displaced effort, and community trust building in survey results.

Abstract We provide evidence that similar price effects occur from new multifamily rental housing on surrounding owner-occupied property values regardless of whether the development was subsidized. These effects were on average negative in higherincome communities, but became either non-distinguishable from zero or positive in higher-income communities with sufficient population density. These results imply that previous opposition to all new rental housing by homeowners is misguided as developments could raise property values in some higher-income neighborhoods.

Purpose The field of educational leadership has historically centered on formal administrative roles, often overlooking how nontraditional leaders—across identities and positions—contribute to equity-oriented change. This limited perspective constrains how leadership theories and practices are developed and applied. Addressing systemic inequities in U.S. education requires broader recognition of leadership enacted by students, educators, scholars, community advocates, and others beyond traditional hierarchies. Data/Methods We conducted narrative-based qualitative interviews with 26 educational leaders from diverse backgrounds, including those working in schools, nonprofit organizations, research institutions, and advocacy groups. Using narrative analysis, we examined how participants described navigating systemic challenges and enacting equity-oriented change. Findings Our findings illustrate that leaders who strive to promote equity appear to strategically leverage social and cultural capital to address complex educational inequities. Participants used relationships, cultural knowledge, and community-rooted practices to challenge exclusionary policies, elevate marginalized voices, and advocate for inclusive systems. In doing so, they expanded the conventional boundaries of capital by adapting it to their unique contexts and challenges. Implications This study contributes to educational leadership theories, illustrating how social and cultural capital can serve not only as tools for practice, but as evolving frameworks shaped through lived leadership experiences. Specifically, what this paper adds to the existing literature base around equity-oriented leadership is a focus on how nontraditional leaders leverage forms of capital to disrupt, instead of manage, structural challenges across education. Ideally this discussion can promote more inclusive leadership to better counter varied, contemporary challenges across education. We propose a broadened conception of educational leadership one that recognizes diverse actors, contexts, and strategies as central to advancing equity. This reconceptualization invites educators and scholars to rethink who leads, how leadership functions, and what equity requires across the educational ecosystem.