Search Results (4,345)

Digital twins have become a central paradigm for cultural heritage documentation, monitoring, and preventive preservation. Yet, when cultural heritage systems promise prediction, simulation, intervention planning, and decision support, a more explicit account is needed of the computational commitments behind such claims. This position paper proposes the notion of the heritage world model as a conceptual and architectural abstraction that uses the semantic digital twin as its representational layer and extends it toward prediction, memory, uncertainty-aware reasoning, and intervention evaluation. We define a heritage world model as a structured, temporally updated, semantically grounded, and action-aware model of a heritage asset and its preservation environment, capable of integrating observations, estimating latent risk states, predicting plausible future trajectories, and evaluating interventions under uncertainty. The paper does not present a validated deployed system. Rather, it clarifies the architectural conditions under which a decision-support digital twin infrastructure could support the kind of world-model-like preservation system proposed here. It further argues that such a model becomes operationally meaningful only when it includes a human-supervised controller layer that maps semantic state, predicted risk trajectories, uncertainty, memory, and institutional constraints into preservation-relevant actions, alerts, monitoring adaptations, or requests for expert review. Sensor data, remote sensing, computational models, risk assessments, policies, and conservation actions are interpreted as possible observational, dynamic, and intervention layers of a heritage world model. The paper reviews adjacent work in heritage digital twins, semantic and reactive ontologies, risk-aware preservation, agentic AI, and modern AI world models, and proposes a research agenda for moving toward predictive, memory-bearing, and intervention-aware preservation intelligence. Full article

This study examines the relationships between intrinsic motivation factors—task autonomy, personal growth and development opportunities, self-actualization, and decision-making participation—and employee retention, as well as the mediating role of perceived organizational pride. Using a quantitative cross-sectional survey, data were collected from 154 academic staff members at Taif University, Saudi Arabia. CFA supported the measurement model, and the hypotheses were tested using Hayes’ PROCESS macro. The findings show that all intrinsic motivation factors are positively associated with employee retention. Perceived organizational pride also mediates these relationships, suggesting that intrinsically motivating work conditions may support retention by strengthening employees’ pride in institutional membership. The results further indicate that developmental and participative factors show stronger associations with retention than task autonomy. This study contributes to employee retention research by integrating intrinsic motivation and identity-based explanations in the context of Saudi higher education. However, given the cross-sectional design and single-university sample, causal interpretation and generalizability should be treated with caution. The findings highlight the importance of growth-oriented, participative, and pride-enhancing work environments for supporting academic staff retention. Full article

Driven by global population aging, long-term in-home and institutional elderly care faces challenges in delivering continuous, privacy-aware, and resource-efficient safety and health monitoring. Existing edge-based solutions struggle to jointly balance detection accuracy, privacy, and resource overhead during continuous operation, and often have limited situational awareness and inflexible management. We propose EdgeElderCare, a resource-aware, scene-adaptive edge-cloud collaborative system for continuous elderly safety and health monitoring. Its contributions are threefold: (1) a scene-adaptive multi-sensor task-sharing architecture that deploys vision-based fall detection in public areas and privacy-aware millimeter-wave radar in private spaces. Combined with edge-side task scheduling, it provides spatially complementary coverage of public and private areas, mitigates the accuracy–privacy conflict, and reduces computing and bandwidth consumption relative to data-level fusion; (2) a lightweight myocardial infarction detection module deployed on an edge platform, enabling local ECG analysis with low resource overhead; (3) a 3D digital-twin edge-cloud management platform that maps multi-source sensing data to a virtual scene in real time and supports hierarchical visual alerting. Experiments in a real nursing home environment show that the system operated stably on resource-constrained edge hardware: UWB positioning achieved centimeter-level RMSE, visual fall detection reached a recall of 0.90, millimeter-wave radar fall detection achieved accuracy, and F1 above 0.90, and myocardial infarction detection exceeded 0.99 accuracy on the public PTB/PTB-XL benchmark. These results indicate an engineering-feasible approach to intelligent elderly care. Larger-scale and longer-term validation remains the focus of future work. Full article

Artificial intelligence (AI) and digital platforms are increasingly influencing how universities manage experiential learning, interdisciplinary collaboration, and innovation-oriented educational activities. Challenge-based capstone and graduation projects play an important role in this context because they connect technical learning with teamwork, stakeholder engagement, project management, and applied innovation. However, many universities still rely on fragmented and highly manual coordination processes, which can limit scalability, transparency, and effective alignment between project requirements and participant capabilities. This study presents Smart Innovation Hub, an AI-enabled information system developed to support challenge-based innovation and capstone-project coordination in higher education. The platform brings together challenge intake, participant profiling, AI-supported recommendations, mentor coordination, workflow governance, and human review within a shared educational innovation environment. The system operationalizes an Innovation Bridge ecosystem model that connects students, faculty mentors, research centers, and external partners through a data-supported coordination framework. A Design Science Research (DSR) methodology guided the development and pilot evaluation of the platform within a public university environment. The pilot evaluation relied on several evidence sources, including platform logs, coordinator records, stakeholder surveys, milestone documentation, and partner feedback collected during implementation activities. Early pilot observations suggested an approximate 60% reduction in average team-formation cycle time, together with positive stakeholder perceptions regarding workflow usability and recommendation quality. These findings should be interpreted as preliminary implementation indicators within a single-institution pilot environment. The study contributes an AI-enabled educational innovation ecosystem architecture, a hybrid semantic-structured recommendation framework for challenge-based coordination, and a structured workflow model that integrates explainability and human oversight into educational innovation management. The findings further suggest that AI-enabled information systems may improve the transparency and coordination of challenge-based innovation workflows while preserving institutional governance and human decision-making. Full article

This paper presents an organization-managed IMAP Agent framework for extending email functionality in environments that rely on outsourced mail services. In this study, outsourced mail services refer to externally operated mailbox providers offering sufficiently scalable email infrastructures and standard IMAP interfaces, such as Gmail, Microsoft 365, and other commercial mailbox providers. In the proposed framework, IMAP Agents are operated within an organization, while user authentication continues to rely on existing institutional infrastructures such as Identity Providers (IdP) or Integrated Authentication Infrastructure (IAI). The IMAP Agent operates as a post-authentication processing component using credentials issued by these infrastructures, without modifying or intervening in the outsourced mail service itself. The framework enables organization-managed mailbox-side email processing without requiring administrative control over the mail server or dependence on provider-specific APIs. As a proof of concept, representative email-processing functions are implemented, including detection of suspicious messages based on header-level authentication information and automatic insertion of thread-consistent warning messages without altering the original email content. To evaluate the feasibility of the proposed framework, a prototype system was implemented using multiple containerized IMAP Agent instances. The experimental results showed that warning messages were typically appended within approximately 300 ms after message detection. Multi-container evaluations ranging from 1 to 100 concurrent IMAP Agent instances demonstrated low CPU overhead and approximately linear memory growth under idle-monitoring conditions, indicating the operational feasibility of deploying multiple IMAP Agent instances on a single host. These results suggest that the proposed framework can provide provider-independent and organization-managed extension of email functionality in outsourced mail environments through standard IMAP operations. Full article

Background: Flexible bronchoscopy (FB) has long been integral to pulmonology, but its bedside role in the intensive care unit (ICU) is expanding. Despite a lack of high-level evidence, FB remains a pivotal tool for airway visualization, sampling, and selected interventions in critically ill patients. Objective: This meta-narrative review critically appraises the clinical use, evolving indications, safety profile, and emerging controversies of FB in ICU settings, particularly regarding the role of non-pulmonologist intensivists. Methods: A structured literature search was conducted using PubMed, Scopus, and Google Scholar for studies published in the past 15 years. Emphasis was placed on observational studies, meta-analyses, and guidelines relevant to FB in ICU patients. Key controversies were grouped under thematic questions based on clinical relevance. Results: A total of 84 articles were retrieved, of which 47 met the predefined inclusion criteria. Seven key thematic domains were synthesized regarding the use of flexible bronchoscopy (FB) in the intensive care unit (ICU) setting. FB performed by trained intensivists was found to be safe and diagnostically effective across a range of ICU populations, including elderly and non-intubated patients. Although procedure-related hypoxemia was reported, it was largely manageable with appropriate precautions. FB demonstrated critical utility in the management of acute respiratory failure (ARF), acute respiratory distress syndrome (ARDS), and sepsis, particularly through bronchoalveolar lavage (BAL), airway secretion clearance, and, selectively, bronchoscopic lung biopsy. The adoption of disposable bronchoscopes may reduce infection risk and economic burden. Furthermore, the integration of advanced techniques such as endobronchial ultrasound (EBUS) and transbronchial cryobiopsy is emerging, although application in the critical care environment remains cautious and selective. Conclusions: With structured training and careful patient selection, FB is an adaptable and often underutilized tool in ICU medicine. Multidisciplinary competency development and institutional protocols can enhance its safe integration. Full article

Multi-cloud is becoming more challenging to secure as traditional perimeter-based security models have a hard time protecting workloads running across multiple cloud platforms, identities, and services. To address this challenge, organizations are shifting to Zero-Trust Architecture (ZTA), which focuses on constant verification and stringent access control, coupled with anomaly detection methodologies to gain better visibility and threat detection in the distributed cloud environment. This paper presents a Systematic Literature Review (SLR) of anomaly detection approaches in multi-cloud environments and how these are applied in zero-trust security models. The review is conducted according to the guidelines of the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020), and is based on studies published between 2020 and 2025 selected from the databases of the following journals: Institute of Electrical and Electronics (IEEE) Xplore, Science Direct, MDPI, Google Scholar, and the Saudi Digital Library. Studies found on benchmark datasets such as CICIDS-2017 and UNSW-NB15 are not evaluated, as none addressed real multi-cloud environments. Although zero trust is highlighted in general, very few studies have implemented basics of zero trust such as micro-segmentation, identity federation, and enforcement through policy. Overall, this review identifies gaps around cross-cloud validation, explainability, and compliance-aware security design, including lack of attention to regulations such as the GDPR and HIPAA. These findings provide helpful recommendations for future research and development on practical and security solutions for multi-cloud environments. Full article

Higher education institutions, particularly those with teaching and research laboratories, play an important role in transmitting knowledge and attitudes regarding chemical safety to their students. As such, this study aims to assess the knowledge and attitudes of higher education students across different study programs regarding laboratory chemical safety. A cross-sectional study was conducted using a questionnaire adapted and translated into Portuguese. The instrument comprised twenty-seven questions and was distributed to students enrolled in undergraduate and graduate programs that include laboratory practices in their curricula in March and July of 2025. A total of 284 students participated in the study, divided among the different study programs (CTeSP = 4.2%; Bachelor’s = 70.4%; Master’s = 21%; Doctorate = 4.2%). The results showed that, although a large percentage of students have a high level of knowledge, their attitudes are not always the most appropriate, which could jeopardize their safety and that of those around them. Our findings revealed that there is room for curriculum adjustments. Early exposure to chemical and laboratory safety concepts can promote the development of students’ awareness and future professionals’ competence. Integrating safety modules into education may enhance knowledge and skills for making informed decisions that reduce accidents/incidents in laboratory environments. Full article

Background/Objectives: Technology transfer (TT) has been identified as a global health priority due to its impact on improving access to vaccines, including for pandemic influenza preparedness and response through bilateral and multilateral mechanisms. This study aimed to (1) characterize examples of influenza vaccine TT (IVTT) and (2) identify key lessons learned that may inform future activities relevant for next-generation influenza vaccine technologies. Methods: Using a contingent effectiveness model, a convergent mixed-methods study was conducted with vaccine manufacturers and global experts to capture quantitative survey data on IVTT activities and enablers and qualitative data on successes, challenges, and opportunities for IVTT through interviews, complemented by secondary data from peer-reviewed and grey literature to characterize additional IVTT observations. Results: This study included 24 participants, including 14 representatives from 13 vaccine manufacturers and 10 experts. Interviews were conducted with representatives from eight manufacturers and seven experts. Eighteen IVTT observations were identified through the surveys and interviews, of which 15 IVTT transfers were completed and 13 resulted in an approved vaccine. Secondary data provided additional evidence on eight IVTT recipients and one supplier, expanding the range of institutional and programmatic contexts assessed. Shorter IVTT completion and vaccine approval timelines were observed in association with prior TT experience and private management structures for manufacturers, for pre-pandemic/pandemic influenza vaccines versus seasonal influenza vaccines, and among bilateral transfer mechanisms (versus multilateral mechanisms) and fill/finish transfer methods. Manufacturers also described spillover benefits, including the use of IVTT-related know-how for the development of COVID-19 and routine vaccines. Both manufacturers and experts generally agreed on a list of 17 enablers for successful IVTT and ranked government commitment to vaccine production and procurement as the top enabler. Findings from the literature-based observations were consistent with primary data and included additional public sector recipient experiences, evidence of widespread human capital development, and a commentary on the importance of the demand environment. Conclusions: Assessed IVTT activities across primary and secondary data sources yielded commercial and spillover benefits as described in the contingent effectiveness model and provided a triangulated analysis of IVTT experiences across manufacturers, experts, and documented cases. Participants agreed that effective technology transfer is contingent upon a host of determinants. Using a systematic application of the contingent effectiveness model to IVTT, this study provided an exploratory analysis of past activities among vaccine manufacturers and experts. While certain nuances for influenza were identified, the lessons learned from this study may be applicable for other TT activities, including those to support pandemic preparedness. The contingent effectiveness model is a useful tool to inform and evaluate future TT activities. Full article

This review examines the relationship among foreign direct investment (FDI), trade openness, and economic growth, with emphasis on the channels through which external integration influences development outcomes. The literature generally suggests that FDI can raise growth through capital accumulation, technology transfer, productivity gains, and stronger linkages with domestic firms, while trade openness can promote growth by expanding market access, increasing competition, and improving resource allocation. However, the evidence is not uniform: some studies report that trade openness is the main driver of growth, while others find that FDI has a stronger effect, or that both variables matter only under favorable macroeconomic, institutional, and financial conditions. This review synthesizes theoretical arguments and empirical findings, identifies major transmission mechanisms and conditional factors, and highlights the policy environment needed for FDI and trade liberalization to translate into sustained economic growth. Full article

Microfinance institutions (MFIs) are vital in promoting financial inclusion for underserved populations. However, these institutions face growing security threats, including sophisticated burglary tactics like underground tunneling. In the Philippines, notable incidents, such as the “Termite Gang” heist in Marikina City and a mall robbery in Ozamiz, highlight the limitations of conventional security systems in addressing subterranean intrusions. This study addresses the gap in existing security technologies by developing a real-time detection system that integrates a vibration sensor, a Global System for Mobile Communications (GSM) module for sending real-time SMS alerts, an audible alarm, and a solar-powered backup system for continuous operation. The system was simulated in the electrical technology laboratory to enhance classroom learning. The system’s core is an Arduino Uno microcontroller that processes inputs from the SW-420 vibration sensor, activating alarms and triggering SMS notifications via the SIM900A module when it detects unusual vibrations. Simulations A, B, and C were conducted to evaluate the system’s response time, with results showing a progressive reduction in detection time from five seconds to one second, indicating improved calibration and system efficiency. These findings also support the existing literature on user interaction with vibration alerts, demonstrating high accuracy in interpreting haptic notifications and the cognitive trade-offs involved. The proposed solution offers a proactive, energy-resilient, and cost-effective security system specifically designed to address underground burglary attempts. It applies to MFIs, pawnshops, and other high-risk financial environments. Future research should explore the application of machine learning for adaptive threat detection, expand the system’s scalability, and integrate mobile applications to enable user customization and enhance alert management. Full article

Research on migration and digital media has expanded, yet empirical knowledge about how digital network communication structures public interaction in post-migration contexts remains limited. In particular, little is known about the communicative arenas in which interaction becomes visible, the actors who gain interpretive authority, and the recurring issues and narratives that stabilize meaning. This article addresses these gaps by examining Afghanistan-linked digital publics in Germany. Eight semi-structured interviews with key informants and a qualitative content analysis of selected TikTok accounts revealed that short-video platforms can function as central arenas of attention, where repeatedly recognized communicators become orientation points for audiences through sustained interaction. Communication stabilizes around recurring issues and narratives such as migration procedures, institutional encounters, Afghanistan-related political developments, and community conflicts, which connect everyday experiences in the country of residence to political, social, and cultural debates about Afghanistan—thereby bridging local and transnational references within shared communication networks. These environments function simultaneously as spaces of practical guidance, social orientation, and public dispute. Building on these insights, the article proposes a multidimensional model of digital diaspora communication that links communicative arenas, actors, and issues and narratives. Full article

Waterfront built environments are increasingly exposed to hydrological variability and climate-related pressures that challenge conventional land-based building typologies. This systematic review examines permanently buoyant floating systems and flood-responsive amphibious systems as water-adaptive approaches to climate adaptation and regenerative waterfront development. Peer-reviewed studies indexed in Scopus and Web of Science were reviewed for January 2015–August 2025, with searches last updated on 15 August 2025. The review combines PRISMA-guided selection, bibliometric mapping of the screened publication landscape (N = 1410), and qualitative synthesis of the core evidence base (N = 63). Regenerative potential is operationalised as credible only where supported by explicit ecological, socio-spatial, governance-related, or performance-oriented evidence, including life-cycle assessment, post-occupancy evidence, ecological monitoring, habitat enhancement, blue-green infrastructure integration, or documented implementation mechanisms. The findings show that floating typologies dominate the evidence base, whereas amphibious approaches are less frequent but more directly associated with in-place flood adaptation. Persistent gaps concern regulatory frameworks, infrastructure interfaces, life-cycle assessment, ecological validation, and long-term post-occupancy monitoring. The review concludes that scalability depends on context-specific siting, institutional permission, regulatory approval, and verifiable environmental performance. Full article

This study proposes a hybrid artificial intelligence (AI) framework for graduate subject allocation that enhances fairness, transparency, and operational efficiency in higher education institutions. Traditional subject allocation processes are predominantly manual and time-consuming in increasingly complex academic environments. The proposed framework integrates a custom Python-based rule engine for institutional constraint reasoning with advanced deep learning models, including XGBoost, Wide-and-Deep Neural Networks (WDNNs), and Graph Neural Networks (GNNs), to ensure policy-compliant and data-driven subject allocation decisions. Subsequently, a systematic hyperparameter optimization strategy is applied to enhance predictive accuracy and model stability across all architectures. Experimental evaluation demonstrates that the proposed framework significantly improves predictive and ranking performance. The GNNs model achieved the highest results with Accuracy = 0.964, Precision = 0.953, Recall = 0.941, F1-score = 0.947, and AUC = 0.976, outperforming WDNN (Accuracy = 0.956, AUC = 0.972) and XGBoost (Accuracy = 0.934, AUC = 0.942). Ranking effectiveness was also validated with HR@10 = 0.784 and NDCG@10 = 0.622. Feature-importance analysis using SHAP revealed that Digital Pedagogical Competence (12.6%), Research Productivity (10.8%), and Postgraduate Supervision (9.7%) are the most influential factors in allocation decisions. To ensure institutional alignment, a multi-objective reranking mechanism was introduced to balance suitability, workload fairness, research alignment, and diversity. This approach reduced workload variance from 0.26 to 0.18 and improved research–subject alignment by 21%. Overall, the proposed framework provides a scalable, explainable, and data-driven solution for optimizing graduate subject allocation in modern higher education systems. Full article

The COVID-19 pandemic exposed critical limitations in conventional screening protocols, particularly in high-traffic environments where rapid, accurate, and contactless health assessment became essential to mitigate transmission risks. In response, this study presents the development of an Artificial Intelligence-Enhanced Contactless Screening Kiosk (AICS-K) that integrates multimodal sensing, embedded systems engineering, and machine learning into a unified workflow. Utilizing a Raspberry Pi platform with computer vision, thermal sensing, QR-based contact tracing, and intelligent control logic, the system enables efficient real-time screening while minimizing human intervention. The proposed architecture demonstrates the potential of extensible, affordable AI-driven solutions for early signs detection and institutional health resilience. Full article