Search Results (4,483)

Informal food trading is a cornerstone of urban livelihoods and food security in Namibia, yet traders operate under fragile conditions marked by limited capital, policy exclusion, and exposure to shocks such as COVID-19. Despite this vulnerability, traders exhibit resilience through everyday forms of social innovation. This study investigates how adaptive pricing, customer credit, and digital communication and e-payment practices function as pathways of resilience among 470 informal food traders in Windhoek, using Structural Equation Modelling to assess gender-differentiated determinants and outcomes. The analysis reveals that women’s adoption of adaptive pricing and digital tools is driven primarily by education and startup capital, while men’s innovation practices are shaped by vendor type and access to financing. Social innovations mediate the effects of these structural factors on enterprise growth, demonstrating that innovation acts as a critical mechanism linking resources and resilience. The study concludes that enhancing informal traders’ resilience requires policies that strengthen human and financial capital, improve digital inclusion, and recognize gendered differences in access to opportunity. It recommends targeted support for women’s entrepreneurial training, affordable credit, and digital infrastructure to transform the informal food sector into a more equitable and sustainable component of Namibia’s urban economy. Full article

The rapid digital transformation of healthcare has improved clinical efficiency, patient engagement, and data accessibility, but it has also introduced significant cyber security and data privacy challenges. Healthcare IT systems increasingly rely on interconnected networks, electronic health records (EHRs), tele-medicine platforms, cloud infrastructures, and Internet of Medical Things (IoMT) devices, which collectively expand the attack surface for cyber threats. This scoping review maps and synthesizes recent evidence on cyber security risks in healthcare, including ransomware, data breaches, insider threats, and vulnerabilities in legacy systems, and examines key data privacy concerns related to patient confidentiality, regulatory compliance, and secure data governance. We also review contemporary security strategies, including encryption, multi-factor authentication, zero-trust architecture, blockchain-based approaches, AI-enabled threat detection, and compliance frameworks such as HIPAA and GDPR. Persistent challenges include integrating robust security with clinical usability, protecting resource-limited hospital environments, and managing human factors such as staff awareness and policy adherence. Overall, the findings suggest that effective healthcare cyber security requires a multi-layered defense combining technical controls, continuous monitoring, governance and regulatory alignment, and sustained organizational commitment to security culture. Future research should prioritize adaptive security models, improved standardization, and privacy-preserving analytics to protect patient data in increasingly complex healthcare ecosystems. Full article

Urban sustainability is increasingly defined by the resilience of the built environment against hazards. While Agent-Based Models (ABMs) are commonly used to simulate these dynamics, their predictive capacity is often limited by a lack of empirical behavioral data. This study addresses this gap by introducing a Human-Centric Digital Twin framework that integrates procedural generation with immersive Virtual Reality (VR) to quantify ‘spatial sustainability’, defined as the capacity of an urban form to support life safety without compromising its morphological identity. In this framework, VR serves as a controlled environment for observing navigation under stress, while procedural generation creates structurally distinct urban morphologies (orthogonal vs. organic) to enable universal calibration. The approach was validated through evacuation experiments with 37 participants under varying visibility conditions. Results reveal that while performance was similar in daylight, significant behavioral divergence emerged at night; the organic layout (Type A) exhibited greater variability and longer evacuation times compared to the orthogonal grid (Type B). These findings confirm that spatial configuration dictates resilience when sensory inputs degrade. Consequently, this study offers a transferable, data-independent protocol for measuring and monitoring urban resilience in data-scarce environments. Full article

As universities expand their e-learning systems, it becomes increasingly important to understand how the use of information and communication technologies (ICTs) changes the skills needed for effective formative assessment. This study uses the principles of human–computer interaction (HCI) to create a framework for examining how digital tools, interfaces, and modes of interaction influence the way teachers assess students in higher education. The research relies on the information provided by 115 Mohammed V University teachers, who filled out a competency-based assessment grid regarding online assessment practices. The results remain exploratory and context-dependent and do not make claims of statistical representativeness beyond the studied institutional context. The findings attest to the virtues of digital technology in improving methodological and techno-pedagogical skills, without excluding the existence of serious shortcomings in semio-ethical and evaluative skills. It is certainly useful to leverage feedback to correct imperfections in evaluation practices and make them more responsive to digital interfaces. It is becoming imperative to rethink professional skills as the regulatory halo of the online formative assessment system, in order to evaluate a more synergistic framework that can give better visibility to virtual classrooms. Full article

This paper presents Nexus, a proof-of-concept low-cost, modular, and reprogrammable multichannel data logger aimed at validating the architectural feasibility of an open and scalable acquisition platform for scientific instrumentation. The system was conceived to address common limitations of commercial data loggers, such as high cost, restricted configurability, and limited autonomy, by relying exclusively on widely available components and open hardware/software resources, thereby facilitating reproducibility and adoption in resource-constrained academic and industrial environments. The proposed architecture supports up to six interchangeable acquisition modules, enabling the integration of up to 20 analog channels with heterogeneous resolutions (24-bit, 12-bit, and 10-bit ADCs), as well as digital acquisition through multiple communication interfaces, including I²C (two independent buses), SPI (two buses), and UART (three interfaces). Quantitative validation was performed using representative acquisition configurations, including a 24-bit ADS1256 stage operating at sampling rates of up to 30 kSPS, 12-bit microcontroller-based stages operating at approximately 1 kSPS, and 10-bit operating at 100 SPS, consistent with stable real-time acquisition and visualization under proof-of-concept constraints. SPI communication was configured with an effective clock frequency of 2 MHz, ensuring deterministic data transfer across the tested acquisition modules. A hybrid data management strategy is implemented, combining high-capacity local storage via USB 3.0 solid-state drives, optional cloud synchronization, and a 7-inch touchscreen human–machine interface based on Raspberry Pi OS for system control and visualization. Power continuity is addressed through an integrated smart uninterruptible power supply, which provides telemetry, automatic source switching, and limited backup operation during power interruptions. As a proof of concept, the system was functionally validated through architectural and interface-level tests, demonstrating stable communication across all supported protocols and reliable acquisition of synthetic and biosignal-like waveforms. The results confirm the feasibility of the proposed modular architecture and its ability to integrate heterogeneous acquisition, storage, and interface subsystems within a unified open-source platform. While not intended as a finalized commercial product, Nexus establishes a validated foundation for future developments in modular data logging, embedded intelligence, and application-specific instrumentation. Full article

Seafood processing environments represent some of the most demanding hygienic settings in the global food sector. High humidity, variable temperatures, and heavy organic residues promote the persistence of Listeria monocytogenes, Vibrio spp., and Salmonella spp., making sanitation both critical and inherently complex. This review synthesizes recent advances in hygienic design, sanitation technologies, and workforce safety as interconnected elements of a single “hygiene continuum.” Building upon Codex, FDA, and European hygiene frameworks (2020–2024), the review examines how engineering design, Sanitation Standard Operating Procedures (SSOPs) and Good Manufacturing Practices (GMPs) systems, and occupational hygiene jointly determine microbial control, sustainability, and workforce well-being. Particular focus is given to biofilm dynamics, emerging disinfection technologies, and automation through cleaning-in-place (CIP) and cleaning-out-of-place (COP) systems. Recent trends—including digital monitoring, eco-efficient cleaning, and human-centered facility design—are discussed as drivers of next-generation hygiene management. Collectively, these insights demonstrate that hygienic performance in seafood processing is not a fixed endpoint but a living system linking design, management, and human behavior toward safe, sustainable, and resilient seafood production. Full article

The growing prevalence of AI-powered chatbots in digital service environments has raised user expectations from mere functional efficiency to emotionally satisfying interactions. Drawing on Language Expectancy Theory (LET), this study investigates the impact of AI chatbot language style (namely, elaborate vs. succinct language) on customer service satisfaction. Across three studies, we demonstrate that customers exhibit higher satisfaction when interacting with chatbots employing elaborate language as opposed to succinct language. Furthermore, this effect is mediated by warmth and moderated by customer relationship norm orientation. The influence of elaborate language is more pronounced among customers with communal relationship norms, whereas those with exchange relationship norms respond more favorably to succinct language. Theoretically, this study enriches the literature on language style in human–computer interaction by introducing elaborateness as a pivotal communicative dimension. Practically, our results offer strategic guidance that can help service providers and developers to strategically tailor chatbot language styles to distinct customer segments, consequently enhancing service quality, fostering emotional engagement, and cultivating long-term customer loyalty within automated service systems. Full article

Antonopoulou and Dare’s ongoing collaborative projects (Phi Books 2008: ongoing; Digital Dreamhacker 2013: ongoing) enact an open-ended, experimental set of slow ‘Fictioning’ practices and actions that involve performing, diagramming, or assembling to create or anticipate new modes of existence. In this paper, the authors use the visual essay form to evidence how their daily practices of drawing, writing, and exchanging, position art and the artist. These practices unfold without, in this case, the utilitarian, economic, and epistemic priorities and systems of reductive representation which underpin the extractive models of Generative AI and other ‘innovative’ intermediaries, systems which expedite content and regulate consumption in the cultural and creative industries and in ‘arts and humanities’ education. Focusing on their creative practices, Antonopoulou and Dare reposition commodified notions of productivity, creativity, and innovation, seeking what Haraway describes as a way ‘of making, thinking and worlding’ beyond the neoliberal imperatives of extracting profit from labour. Positioned within an era of escalating precarity combined with ecological and political instability driven by extractive colonialism, the temporality of collaboration and drawing over decades is proposed as an act of material resistance to art’s subsumption into the venture capitalist hype cycles. Such cycles are associated with an accelerating array of crises, discussed here. Full article

The incorporation of artificial intelligence (AI) and digital technology in healthcare has revolutionized service delivery, improving diagnostic precision, patient outcomes, and operational efficacy. Nonetheless, despite considerable progress, numerous problems persist that impede the realization of full potential. Current reviews predominantly emphasize the advantages of AI in disease detection and health guidance, neglecting significant concerns such as social opposition, regulatory frameworks, and geographical discrepancies. This SLR, executed in accordance with PRISMA principles, examined 21 publications from 2020 to 2025 to assess the present condition of AI and digital technologies inside Saudi Arabia’s healthcare industry. Initially, 863 publications were obtained, from which 21 were chosen for comprehensive examination. Significant discoveries encompass the extensive utilization of telemedicine, data analytics, mobile health applications, Internet of Things, electronic health records, blockchain technology, online platforms, cloud computing, and encryption methods. These technologies augment diagnostic precision, boost patient outcomes, optimize administrative procedures, and foster preventative medicine, contributing to cost-effectiveness, environmental sustainability, and enduring service provision. Nonetheless, issues include data privacy concerns, elevated implementation expenses, opposition to change, interoperability challenge, and regulatory issues persist as substantial barriers. Subsequent investigations must concentrate on the development of culturally relevant AI algorithms, the enhancement of Arabic natural language processing, and the establishment of AI-driven mental health systems. By confronting these challenges and utilizing emerging technologies, Saudi Arabia has the potential to establish its status as a leading nation in medical services innovation, guaranteeing patient-centered, efficient, and accessible healthcare delivery. Recommendations must include augmenting data privacy and security, minimizing implementation expenses, surmounting resistance to change, enhancing interoperability, fortifying regulatory frameworks, addressing regional inequities, and investing in nascent technologies. Full article

Ongoing global disruptions, including pandemics, geopolitical tensions, and climate-driven events, have exposed vulnerabilities in healthcare supply chains (HSCs). This study examines how artificial intelligence (AI) is reshaping HSCs to improve agility, resilience, and sustainable performance. Using a systematic literature review with PRISMA-style screening across Scopus and Web of Science, the study is complemented by bibliometric analysis and latent Dirichlet allocation topic modeling to analyze peer-reviewed articles. The results indicate an exponential increase in AI-enabled HSC research, concentrated in a small number of journals and spanning a globally diverse author community. Three dominant thematic clusters emerged: (1) sustainability-oriented supply chain design, (2) disruption and resilience management, and (3) healthcare-focused digital transformation. Across these themes, AI, digital twins, Internet of Things, and simulation are evolving from efficiency tools to strategic enablers of decision intelligence, supporting real-time sensing, scenario analysis, and proactive risk mitigation. The study highlights a convergence of “triple transformation” in which digitalization, resilience, and sustainability are increasingly co-dependent capabilities in HSCs. However, persistent barriers exist, including data quality issues, legacy systems, workforce skill gaps, limited model interpretability, and incomplete governance frameworks, which constrain large-scale adoption. The findings indicate a need for longitudinal and multi-method studies on human–AI collaboration, trust calibration, and leadership in AI-enabled HSCs. This study provides practical guidance for healthcare organizations looking to leverage AI in developing agile, resilient, and sustainable supply chain ecosystems. Full article

Digital mental health resources have expanded rapidly in the wake of the COVID-19 pandemic, offering new opportunities to improve access to mental healthcare through technologies such as AI chatbots, mobile apps, and online platforms. Despite this growth, significant challenges persist, including low user retention, limited digital literacy, unclear privacy regulations, and insufficient evidence of clinical effectiveness and safety. AI chatbots, which act as virtual therapists or companions, provide counseling and personalized support, but raise concerns about user dependence, emotional outcomes, privacy, ethical risks, and bias. User experiences are mixed: while some report enhanced social health and reduced loneliness, others question the safety, crisis response, and overall reliability of these tools, particularly in unregulated settings. Vulnerable and underserved populations may face heightened risks, highlighting the need for engagement with individuals with lived experience to define safe and supportive interactions. This review critically examines the empirical and grey literature on AI chatbot use in mental healthcare, evaluating their benefits and limitations in terms of access, user engagement, risk management, and clinical integration. Key findings indicate that AI chatbots can complement traditional care and bridge service gaps. However, current evidence is constrained by short-term studies and a lack of diverse, long-term outcome data. The review underscores the importance of transparent operations, ethical governance, and hybrid care models combining technological and human oversight. Recommendations include stakeholder-driven deployment approaches, rigorous evaluation standards, and ongoing real-world validation to ensure equitable, safe, and effective use of AI chatbots in mental healthcare. Full article

Geomorphological change is a fundamental consequence of high-magnitude flood events, as extreme hydraulic forcing can rapidly reshape river channels, redistribute sediment, and alter floodplain connectivity. This study applies multi-temporal UAS-based Structure-from-Motion (SfM) photogrammetry to quantify flood-induced geomorphological changes along two representative reaches of the Lilas River (Evia Island, Central Greece) affected by the extreme August 2020 flash flood. High-resolution aerial surveys were conducted prior to the event (June 2018) and shortly after the flood (September 2020), producing Digital Surface Models (DSMs) and orthomosaics with a ground sampling distance of ~2.5 cm. Differential DSM analysis reveals pronounced spatial heterogeneity in erosion and deposition, with net erosional lowering locally exceeding 7 m and depositional aggradation reaching up to ~5 m after accounting for vegetation effects. Channel widening was the dominant response, with cross-sectional widths increasing by a factor of three to nine at selected locations, driven primarily by lateral bank erosion. The results highlight the strong interaction between extreme hydrological forcing, loose alluvial sediments, vegetation removal, and human interventions such as roads and engineered terraces. The study demonstrates how repeatable UAS–SfM workflows can provide quantitative evidence to support post-flood assessment, guide infrastructure adaptation, and inform river restoration and flood risk management in Mediterranean catchments prone to extreme events. Full article

In vitro models of the human nasal cavity are crucial for understanding the deposition dynamics of nasally administered drugs. Three-dimensional (3D) printing offers a powerful method for creating patient-specific, anatomically precise models for such experimental purposes. Background/Objectives: This study details the complete workflow for the development, design, and fabrication of a sectioned nasal cavity model intended for droplet deposition analysis of nasal sprays. Methods: A digital nasal cavity model was derived from medical imaging data and optimized for computer-aided design (CAD) operations. It was segmented into five therapeutically relevant regions: nasal vestibule, olfactory area, middle and upper turbinates, lower turbinate, and nasopharynx. Sections were 3D-printed in polypropylene for chemical compatibility, and a carbon fiber-reinforced fixation frame ensured precise alignment and airtight assembly. Results: Functional validation confirmed the model’s functional relevance through comparative deposition studies using automated actuation and high-performance liquid chromatography (HPLC) based regional quantification. Two devices with distinct spray characteristics (characterized separately by laser diffraction, plume geometry, and spray pattern imaging) were tested under varied administration conditions. The study demonstrated the model’s ability to discriminate between products, establishing a solid foundation for future investigations incorporating additional variables. Conclusions: Overall, the developed methodology provides a cost-effective and replicable platform for producing anatomically accurate, sectioned nasal cavity models. The newly developed in vitro system is well suited for detailed, region-specific analysis of nasal spray deposition, offering a valuable tool for pharmaceutical research and development. Full article

Small and medium-sized enterprises (SMEs) increasingly rely on digital technologies in everyday operations, often without having sufficient resources or structured mechanisms to manage the cyber risks that accompany this dependence. As digitalization deepens, cyber incidents in SMEs are shaped not only by technical vulnerabilities but also by human behavior and organizational practices. However, much of the existing research still approaches cyber resilience through fragmented technological or managerial lenses. This study takes a conceptual and theory-driven approach to examine cyber resilience in SMEs as a socio-technical system. Building on systems theory and adaptive management, the analysis draws on a structured synthesis of interdisciplinary literature to develop a systemic model of adaptive digital risk management. The model is developed through a structured conceptual process combining systematic exploration of interdisciplinary literature, analytical synthesis of recurring conceptual patterns, and system-level model construction informed by systems theory and adaptive management principles. Cyber resilience is therefore interpreted as a dynamic capability that develops over time, especially in digital environments characterized by increasing automation and evolving forms of human–technology interaction. The study contributes to cyber resilience research by offering a system-oriented perspective and provides SMEs with a conceptual basis for strengthening adaptive approaches to digital risk management. Full article

Artificial intelligence (AI) is rapidly reshaping gastrointestinal (GI) surgery by enhancing decision-making, intraoperative performance, and postoperative management. The integration of AI-driven systems is enabling more precise, data-informed, and personalized surgical interventions. This review provides a state-of-the-art overview of AI applications in GI surgery, organized into four key domains: surgical simulation, surgical computer vision, surgical data science, and surgical robot autonomy. A comprehensive narrative review of the literature was conducted, identifying relevant studies of technological developments in this field. In the domain of surgical simulation, AI enables virtual surgical planning and patient-specific digital twins for training and preoperative strategy. Surgical computer vision leverages AI to improve intraoperative scene understanding, anatomical segmentation, and workflow recognition. Surgical data science translates multimodal surgical data into predictive analytics and real-time decision support, enhancing safety and efficiency. Finally, surgical robot autonomy explores the progressive integration of AI for intelligent assistance and autonomous functions to augment human performance in minimally invasive and robotic procedures. Surgical AI has demonstrated significant potential across different domains, fostering precision, reproducibility, and personalization in GI surgery. Nevertheless, challenges remain in data quality, model generalizability, ethical governance, and clinical validation. Continued interdisciplinary collaboration will be crucial to translating AI from promising prototypes to routine, safe, and equitable surgical practice. Full article