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Development of MindBodyU—An Innovative mHealth Intervention for Youth Living with HIV in South Carolina
Get PDFTechnology-based interventions may improve care engagement and quality of life for youth living with HIV (YLHIV). This paper describes the development of MindBodyU—a mobile Health (mHealth) intervention for YLHIV in South Carolina—a rural, southern state disproportionately burdened by the HIV epidemic. Semi-structured individual interviews were conducted with YLHIV (n=16) and HIV providers (n=15). Focus groups were held with staff from HIV community-based organizations (CBOs) (n=23). Guided by theories of resilience, data were integrated to inform the selection and development of MindBodyU—a multi-component mHealth intervention that includes health education, connection to social support, behavioral health monitoring/goal setting, and linkage to mental and behavioral health resources. mHealth interventions like MindBodyU may help YLHIV better cope with HIV-related stigma and connect to needed resources. Lessons learned from the development of MindBodyU can inform other efforts to improve HIV outcomes for young people in the southern United States (US) and beyond
Hierarchical Machine Vision application for Automated Diagnosis of Dental X-Ray Images
Get PDFThis study proposes a unified machine vision framework for the automated analysis of dental panoramic X-ray images, addressing a critical intersection between artificial intelligence and healthcare operations. We developed a deep learning pipeline that integrates advanced models to perform tooth detection, segmentation, and disease classification within a single workflow. By converting medical images into a rich source of diagnostic information, this research enhances the clinical utility of existing imaging practices. From a managerial perspective, the integration of AI enables scalable, real-time diagnostics that can reduce clinician workload, support clinical decision-making, and improve resource allocation. Furthermore, the framework has the potential to expand access to dental care through telemedicine applications, particularly in underserved regions. This research lays the foundation for the development of AI-driven diagnostic tools that align with broader goals in healthcare innovation and digital transformation
Project Management for Blockchain: Strategic Guidance and Lessons from Practice
Get PDFBlockchain technology (BCT) offers powerful solutions to long-standing enterprise challenges such as inefficiencies, lack of transparency, and low stakeholder trust. By enabling secure transaction tracking and real-time data sharing, BCT can improve operational accuracy, streamline workflows, and build confidence across organizational ecosystems. However, the implementation of BCT significantly transforms conventional project management strategies and practices. We propose practical recommendations for each stage of the process group of the project life cycle- Initiating, Planning, Executing, Monitoring & Controlling, and Closing to meet the demands of decentralization, interoperability, and continuous ecosystem coordination inherent in the implementation of BCT projects. Grounded in practitioner insights, this study advances practice-based IS research by offering actionable, process-specific guidance for managing blockchain projects and contributes to project management knowledge by revealing how decentralized technologies reshape conventional lifecycle approaches
Adaptive Kernels in DCGANs
Get PDFDeep Convolutional Generative Adversarial Networks (DCGANs) is a specialized deep learning architecture tailored for image generation tasks. DCGANs have notably enhanced the field of image generation by introducing robust training techniques and architectural principles that yield high-fidelity images that closely mirror those present in the training dataset. Recent research suggests that employing a deterministic adaptive kernel postconvolution can bolster CNN’s generalization capabilities, thereby benefiting DCGANs. However, the challenge of generating the weights for these deterministic adaptive kernels remains an active area of research. In response, we propose an innovative adaptive kernel method that utilizes the convoluted data from a layer to generate a dynamic set of four Gaussian kernels. Subsequently, convolution operations are performed on the convoluted data using either a single Gaussian kernel at a time or all four kernels sequentially, with the order of application rotating after each epoch. To validate our novel adaptive kernel approach, we conducted experiments using DCGAN and one of its variants, evaluating performance on two diverse datasets (CIFAR-10 and CIFAR-100) for image generation tasks. Importantly, our method seamlessly integrates with any DCGAN variant as a plug-and-play solution, introducing no additional trainable parameters to the network. We also offer a comprehensive analysis of the impact of our proposed adaptive kernel method
Data-Driven Framework for Consumer-Centric Microgrid Positioning in Smart Cities and Communities Towards Digital Urban Transformation
No full textRapid urbanization and the pressing global pursuit of resilience and decarbonization have positioned data-driven energy services as essential catalysts for achieving the United Nations' Sustainable Development Goals in smart cities and communities. Yet, while microgrids offer a promising neighborhood-scale solution, existing positioning methodologies often privilege techno-economic or ecological-centric criteria instead of consumer equity and local demand patterns. To fill this gap, this paper introduces a data-driven framework to support consumer-centric microgrid site selection in smart cities. At its core lies the Urban Neighborhood Energy Demand (UNEED), a proposed numerical quality index that combines the city's geo-referenced indicators of renewable potential, emergency vulnerability, critical infrastructure coverage, heating-support infrastructure availability, local heating demand, and socio-economic context. This framework is applied to the metropolitan area of Porto, Portugal, and the results demonstrate that UNEED reliably highlights locations where microgrid deployment maximizes resilience, equity, and decarbonization benefits
Effects of Cybersecurity Readiness on Firm Performance: Evidence from Conference Calls
Get PDFThis study develops a novel firm-level metric, termed cybersecurity readiness, by leveraging text mining techniques on corporate conference call transcripts to measure a firm’s preparedness and commitment to cybersecurity. Analyzing the impact of cybersecurity readiness this year on financial performance next year, we employ linear regression models and demonstrate that it moderately improves key outcomes such as Return on Assets (ROA) and Earnings Before Interest and Taxes on Assets (EBITAT). By capturing the discourse between executives and external stakeholders, our approach provides a forward-looking and dynamic measure of cybersecurity effectiveness. The findings underscore the strategic importance of cybersecurity readiness, not only as a protective measure but also as a driver of superior financial performance. This research offers empirical evidence linking cybersecurity to firm success and introduces a scalable methodology for evaluating organizational cybersecurity, contributing actionable insights for corporate leaders and policymakers. The code is publicly available
Curiosity Meets Knowledge: Designing Emotionally Aware AI Through a Knowledge Management Lens
Get PDFText - based emotion- aware AI has largely been constrained to reactive classification, labeling affect without considering its contextual evolution. This paper introduces EC - WISE, a framework designed to embed emotional curiosity into conversational AI, enabling systems to proactively engage with affective signals rather than passively recognize them. Grounded in Plutchik’s Wheel of Emotions and extending the Jennex– Olfman Knowledge Management Success Model, EC - WISE conceptualizes emotion as a form of tacit knowledge that must be surfaced through context - sensitive processes. The system architecture combines lightweight preprocessing, lexicon- and embedding- based emotion scoring, and a curiosity trigger evaluator that monitors intensity variation, diversity, repetition, decline, and negation across conversational turns. When triggered, the framework poses reflective prompts tailored to the detected affect, transforming static detection into proactive inquiry. While EC - WISE remains a conceptual prototype, its design contributes theoretically by extending KM to include affective richness, and methodologically by offering an interpretable, auditable alternative to black - box approaches. Potential applications span education, healthcare, customer service, and workplace collaboration, where emotionally aware, curiosity- driven AI can foster engagement, trust, and context - sensitive interaction
Role-Aware Backbone Extraction and Visualization of Financial Transaction Networks via Asymmetric Non-negative Matrix Factorization
Get PDFThis paper proposes a novel backbone extraction framework tailored for financial transaction networks (FTNs), which are inherently directed, weighted, and often dense. Traditional backbone extraction methods typically assume undirected or symmetric structures and struggle to capture the role-specific, directional nature of financial data. To address this issue, we introduce an Asymmetric Non-negative Matrix Factorization (Asymmetric NMF) technique that decomposes FTNs into low-rank representations, preserving directional features while simplifying network complexity. This method effectively isolates the most significant inter-firm financial relationships and identifies influential firms from both buyer and seller perspectives. The model is validated using real-world industrial financial transaction data, demonstrating its superiority over existing backbone extraction methods in interpretability and structural fidelity
AI Literacy Frameworks for Educators: An Umbrella Review
Get PDFAs artificial intelligence transforms educational environments, educators require specialized literacy frameworks for effective technology integration. This umbrella review synthesizes 28 prior studies (26 systematic/scoping reviews and 2 design/resource-mapping studies) on AI literacy frameworks for educators. Our analysis examines conceptual models, ethical principles, treatment of explainability as a subdomain of ethics, implementation challenges and enablers, and assessment strategies. The results show fragmented approaches with constructivist approaches being the predominant theoretical foundation, with TPACK and AI4K12 also commonly adopted, while attention to ethics varies widely across frameworks. Key challenges include insufficient teacher preparation, resource limitations, and technological complexity, while enablers encompass structured professional development, project-based learning, and institutional support. The review identifies significant gaps in assessment methodologies, particularly the lack of standardized teacher-specific evaluation tools, and notes that explainability, despite its importance for educator trust, is explicitly addressed in only one study. This analysis informs development of robust educator-centered AI literacy frameworks balancing technical and ethical knowledge