Search Results (403)

Introduction: Individuals residing in long-term care facilities (LTCFs) often experience poor sleep quality. Emerging sensor technologies may improve resident sleep quality and reduce staff workload. This evaluation assessed the impact of a bed sensor technology on LTCF staff experiences and resident outcomes. Methods: A mixed-methods evaluation examined the impact of a pilot implementation of Toch Sleepsense, a non-wearable sensor placed under residents’ beds, which monitors sleep patterns, movement, and vital signs. Data were gathered from staff surveys, interviews, and focus groups from three LTCFs in Western Canada. Descriptive statistics of survey data and thematic analysis of qualitative survey responses and focus groups were used to identify themes in staff experiences with Toch Sleepsense. Results: Staff valued the utility of Toch Sleepsense in providing alerts that support timely interventions and fall prevention. Staff further recognized the value of sensor devices in decreasing repetitive nighttime checks and providing vital sign monitoring. Toch Sleepsense data informed care planning and improved resident comfort. Inconsistent internet connectivity, sensor realignments, and limited training posed challenges to reliability. Conclusions: Sensor technologies like Toch Sleepsense show potential to improve safety, support staff workload management, and improve care practices. Sustained benefits require reliable technical infrastructure, comprehensive staff training, and strong leadership support. Full article

Aircraft cargo compartment fires represent a major threat to aviation safety due to their rapid development, concealment, and the challenges associated with suppression in confined spaces. This study analyzes the 2019 A330 cargo compartment fire at Beijing Capital International Airport as a representative case. Based on flight crew statements, ECAM alerts, surveillance footage, and firefighting records, the event timeline was reconstructed and the emergency response process examined. The analysis identified four defining characteristics of cargo fires: rapid escalation, interacting hazards, restricted accessibility, and prolonged suppression duration. To address these challenges, a three-stage investigation framework—comprising timeline reconstruction, evidence analysis, and experimental verification—is proposed to systematically determine the causes of fires. In addition, a portable penetrating fire-suppression device was designed and experimentally validated. Results confirm its effectiveness in achieving rapid agent delivery, enhanced structural cooling, and prevention of re-ignition. The findings demonstrate that comprehensive cargo fire investigations require the integration of multi-source data and experimental validation, while tactical and equipment innovations are critical for improving suppression efficiency in confined environments. This research provides practical insights for optimizing cargo fire investigation methodologies and emergency response strategies, thereby contributing to the advancement of aviation safety management systems. Full article

Background: Direct oral anticoagulants (DOACs) have become the standard of care for preventing venous thromboembolism (VTE) and cardioembolic stroke in patients with atrial fibrillation, due to their predictable pharmacokinetics and reduced need for frequent laboratory monitoring. However, long-term DOAC use still carries a risk of complications such as gastrointestinal or occult bleeding and progressive renal decline, particularly in elderly and frail patients. Objective: This study proposes a remote monitoring model integrated with AI supports designed to enhance the safety and personalization of chronic DOAC therapy in both inpatient and outpatient settings. Methods: Building on existing national frameworks in which DOAC prescriptions are regulated by experienced physicians through regional digital platforms, we developed a structured model that integrates automatic alerts for abnormal laboratory trends, potential drug interactions, and changes in clinical status. The system uses artificial intelligence to identify high-risk patterns, such as declining hemoglobin or glomerular filtration rate, before symptoms appear, enabling early intervention. Results: The proposed model is presented as an integrated workflow supported by structured components. This conceptual framework facilitates real-time surveillance of patient data, supports clinical decision-making, and is expected to reduce preventable complications. Anticipated benefits include improved clinical appropriateness, better resource allocation, and reduced avoidable emergency visits. Conclusions: remote monitoring system integrated with AI supports for predefinite items for long term treatment with DOACs can significantly improve safety and continuity of care. By replacing passive surveillance with predictive, automated alerts, this model exemplifies how digitalization can enhance the efficiency and responsiveness of the National Health System. Full article

This study examines the transition from digital entrepreneurial alertness to digital startup intent in connection with perceived desirability, feasibility, and intentions. The theory of planned behavior (TPB) and the entrepreneurial event/potential model (EPM) form the foundation for a mediation model, which is examined by structural equation modeling (SEM) using AMOS on data gathered from 571 Saudi youth engaged in digital entrepreneurship. The results show that digital entrepreneurial alertness has a strong predictive power in relation to intent to start digital ventures, and that this is partly mediated by perceived desirability and feasibility. Intentions, however, fully mediate the relationship between alertness, desirability, feasibility, and actual digital entrepreneurial behavior. This study adds to digital entrepreneurship scholarship by de-mystifying the thought processes bridging opportunity recognition and action, particularly in emerging economies. This study validates the EPM framework and confirms its applicability to include digital entrepreneurial alertness (DEA) as a key antecedent of digital entrepreneurial intentions (DEI) and other factors. This study also highlights the theoretical relevance of the EPM by illustrating its utility in understanding youth decisions to pursue digital entrepreneurship, particularly in transitional countries such as Saudi Arabia. Policymakers and educators in Saudi Arabia should promote attention and amplify desirability/feasibility perceptions to stimulate youth engagement in digital ventures. This work highlights intentions as the determinative gateway between entrepreneurial cognition and concrete digital startup success. Full article

Sustainability in software engineering encompasses environmental, human, social, and economic dimensions, each essential for ensuring software’s positive and lasting impact. This paper presents an innovative Internet of Things (IoT)-based Smart Waste Management (SWM) system. The proposed system addresses key limitations in existing solutions, including lack of real-time responsiveness, inefficient routing, inadequate emergency detection, and limited user-centric design. While prior studies have investigated IoT applications in SWM, challenges remain in achieving dynamic, integrated, and scalable systems for sustainable urban development. The proposed solution introduces a holistic architecture that enables real-time monitoring of waste bin levels and fire incidents through Waste Bin Level Monitoring Units (BLMUs) equipped with ultrasonic and flame sensors. Data is transmitted via Wi-Fi to a centralized City Command and Control Center (4C), allowing for automated alerts and dynamic route optimization. A dual-platform software suite supports both administrative and operational workflows: a desktop web application and a role-based Android mobile app developed in Flutter, and integrated with Google Cloud Firestore, enabling centralized data management and efficient resource allocation. We validated the system through a working prototype, demonstrating notable contributions including enhanced emergency responsiveness, optimized waste collection routes, and improved stakeholder engagement. This research contributes to the advancement of sustainable urban infrastructure by offering a scalable, data-driven SWM framework grounded in software engineering principles and aligned with smart city objectives. This paper presents an innovative IoT-based Smart Waste Management (SWM) system that addresses key limitations in existing solutions, including insufficient real-time responsiveness, inefficient routing, inadequate emergency detection, and limited user-centric design. Full article

With the growing demand for refined security operations, Security Orchestration, Automation, and Response (SOAR) technologies have undergone rapid advancement. By leveraging intelligent orchestration capabilities in conjunction with core playbooks, SOAR facilitates both automated and semi-automated responses to security incidents. Nevertheless, the continuous evolution of network-attack techniques and the explosive growth of security alerts have rendered traditional static rule-based playbook matching and recommendation approaches increasingly inadequate in addressing the high frequency of alerts and the emergence of novel attack patterns. In this study, we propose an intelligent playbook recommendation algorithm for SOAR, developed under the paradigm of dynamic interest modeling. Specifically, the algorithm integrates a Transformer encoder, which captures long-term dynamic characteristics of alert signals in real time, with an LSTM network designed to extract short-term behavioral patterns. This hybrid architecture not only enables accurate playbook recommendations in high-volume alert scenarios, but also supports the reconstruction and optimization of playbooks, thereby offering valuable guidance for the mitigation of emerging threats. Experimental evaluations demonstrate that the proposed dynamic interest modeling-based algorithm exhibits high feasibility. It achieves improved performance in terms of both recommendation accuracy and efficiency, thus providing a robust technical foundation for enhancing the effectiveness of network security incident response and offering practical support for real-world security operations. Full article

The entry is intended to define the concepts of risk communication and emergency communication. At the same time, it explains the difference not only from a communication point of view but also from a cultural one. Risk and emergency are two sociologically relevant events, and they are culturally constructed. They are events that bring about a socio-cultural change, which, in turn, is triggered by the population’s responses on the basis of the social perception of the events themselves, also conveyed by the different forms of communication. When communicating risk and emergencies, it is essential to educate people about alert and emergency systems. Above all, what they refer to and what kind of message they contain. The “warning communication” must be specific and refer exclusively to the threat to start the first phase of the communication through which it is possible to understand the type of threat and define the communication plan to be implemented later. The use of social media, which is strongly spread in digital society, allows not only rapid dissemination of information but also rapid communication and message selection (speed and content of the message are equally important). Alert and warning systems are very often linked to risk systems, since the risk from natural disasters (eruptions, earthquakes, tsunamis) or technological catastrophes (nuclear power plant explosions) follows emergency phases when the phenomenon occurs. The communication processes, in and emergency, must be able to explain, persuade but also confer an assist the political decision-maker and the decision-making process itself through an alert system (especially in the first phase), followed by continuous dissemination through the media that the digital society offers, as well as through the usual systems adopted by government bodies (for example, bulletins and news), specialized research institutions and institutes with information and communication functions. In risk and emergency management, information and communication are to be considered, respectively, a basic element and a means of dissemination and training to educate the population to perceive a risk, to recognise emergencies and the possible impact of the risk. Differences will be expressed and analysed with reference to international examples. Full article

Forest fires have become increasingly frequent and severe due to climate change and intensified human activities, posing critical challenges to ecological security and emergency management. Despite the availability of abundant environmental, spatial, and operational data, these resources remain fragmented and heterogeneous, limiting the efficiency and accuracy of fire prediction and response. To address this challenge, this study proposes a Semantic Digital Twin-Driven Framework for integrating multi-source data and supporting forest fire prediction and response. The framework constructs a multi-ontology network that combines the Semantic Sensor Network (SSN) and Sensor, Observation, Sample, and Actuator (SOSA) ontologies for sensor and observation data, the GeoSPARQL ontology for geospatial representation, and two domain-specific ontologies for fire prevention and emergency response. Through systematic data mapping, instantiation, and rule-based reasoning, heterogeneous information is transformed into an interconnected knowledge graph. The framework supports both semantic querying (SPARQL) and rule-based reasoning (SWRL) to enable early risk alerts, resource allocation suggestions, and knowledge-based decision support. A case study in Sichuan Province demonstrates the framework’s effectiveness in integrating historical and live data streams, achieving consistent reasoning outcomes aligned with expert assessments, and improving decision timeliness by enhancing data interoperability and inference efficiency. This research contributes a foundational step toward building intelligent, interoperable, and reasoning-enabled digital forest systems for sustainable fire management and ecological resilience. Full article

We present the eyeglass-type switch, an eyeglass-mounted eye/blink switch designed for nurse-call operation by people with severe motor impairments, with a particular focus on amyotrophic lateral sclerosis (ALS). The system targets real-world bedside constraints—low illumination at night, supine posture, and network-independent operation—by combining near-infrared (NIR) LED illumination with an NIR eye camera and executing all processing on a small, GPU-free computer. A two-stage convolutional pipeline estimates eight periocular landmarks and the pupil center; eye-closure is detected either by a binary classifier or by an angle criterion derived from landmarks, which also skips pupil estimation during closure. User intent is determined by crossing a caregiver-tunable “off-area” around neutral gaze, implemented as rectangular or sector shapes. Four output modes—single, continuous, long-press, and hold-to-activate—are supported for both oculomotor and eyelid inputs. Safety is addressed via relay-based electrical isolation from the nurse-call circuit and audio feedback for state indication. The prototype runs at 18 fps on commodity hardware. In feature-point evaluation, mean errors were 2.84 pixels for landmarks and 1.33 pixels for the pupil center. In a bedside task with 12 healthy participants, the system achieved $F=0.965$ in single mode and $F=0.983$ in hold-to-activate mode; blink-only input yielded $F=0.993$. Performance was uniformly high for right/left/up and eye-closure cues, with lower recall for downward gaze due to eyelid occlusion, suggesting camera placement or threshold tuning as remedies. The results indicate that the proposed switch provides reliable, low-burden nurse-call control under nighttime conditions and offers a practical input option for emergency alerts and augmentative and alternative communication (AAC) workflows. Full article

Universities face continuous challenges in ensuring rapid and efficient communication during emergencies due to outdated, fragmented, and manual notification systems. This research presents the design, development, and implementation of a multiplatform mobile application to optimize emergency notifications at the Escuela Superior Politécnica de Chimborazo (ESPOCH). The application, developed using the Flutter framework, offers real-time alert dispatch, geolocation services, and seamless integration with ESPOCH’s Security Unit through Application Programming Interfaces (APIs). A descriptive and applied research methodology was adopted, analyzing existing notification workflows and evaluating agile development methodologies. MOBILE-D was selected for its rapid iteration capabilities and alignment with small development teams. The application’s architecture incorporates a Node.js backend, Firebase Realtime Database, Google Maps API, and the ESPOCH Digital ID API for robust and scalable performance. Efficiency metrics were evaluated using ISO/IEC 25010 standards, focusing on temporal behavior. The results demonstrated a 53.92% reduction in response times compared to traditional notification processes, enhancing operational readiness and safety across the campus. This study underscores the importance of leveraging mobile technologies to streamline emergency communication and provides a scalable model for educational institutions seeking to modernize their security protocols. Full article

Heart failure (HF) remains a leading cause of recurrent hospitalisations worldwide, largely driven by acute episodes of decompensation. Early identification of impending decompensation could enable timely intervention and potentially prevent costly admissions. Non-invasive wearable devices have emerged as promising tools for continuously monitoring physiological parameters and detecting early signs of deterioration. This review summarises recent advances in wearable technologies designed to predict HF decompensation and appraises their ability to generate clinically useful alerts. It will examine various modalities designed to monitor different aspects of cardiorespiratory physiology that have the potential to detect abnormalities preceding heart failure decompensation. Broadly, these devices either monitor physical activity capacity and cardiac function or monitor changes in pulmonary fluid congestion. We will also cover evidence exploring whether these devices can generate timely alerts for interventions to improve patient outcomes and reduce hospitalisations. However, despite advances in these technologies, challenges remain regarding their accuracy and usability for remote monitoring, as well as concerns with data storage, processing, patient adherence, and integration into existing healthcare workflows. While current limitations exist, previous results warrant further research into this area, with a focus on larger randomised trials, exploring both single- and multi-sensor systems, using artificial intelligence and cost-effectiveness analysis. Overall, non-invasive wearables represent an opportunity to create a more proactive approach to HF management, with the potential to shift the paradigm from reactive treatment to anticipatory care. Full article

Dam collapse is a catastrophic event involving an artificial reservoir usually filled with water for hydropower or irrigation purposes. Several cases of dam collapses have overwhelmed entire valleys, reconfiguring their geomorphology, redesigning their landscape, and causing several thousand casualties. These episodes led to more careful regulations and the activation of more effective monitoring and mitigation strategies. A fundamental tool in defining appropriate procedures for alert and risk scenarios is the Dam Emergency Plan (PED), an operational document that establishes the actions and procedures required to manage potential hazards (e.g., geo-hydrological and seismic risk). The aim of this study is to describe a reference methodology for identifying geo-hydrological criticalities based on historical and geomorphological data, applied to civil protection activities. A further objective is to provide a structured inventory of Italian reservoirs, assigning each a potential risk index based on an analytical approach considering several factors (age and construction methodology of the dam, morphological and environmental settings, anthropized environment, and exposed population). The approach identifies that the most significant change in risk over time is not only the dam itself but also the transformation of the territory. This methodology does not incorporate probabilistic forecasting of flood or climate change; instead, it objectively characterizes the exposed territory, offering insights into existing vulnerabilities on which to base effective mitigation strategies. Full article

To gain a comprehensive understanding of the current research landscape in the field of new energy vehicle (NEV) fires and to explore its knowledge base and emerging trends, bibliometric methods—such as co-occurrence, clustering, and co-citation analyses—were employed to examine the relevant literature. A research knowledge framework was established, encompassing four primary themes: thermal management and performance optimization of power batteries, battery materials and their safety characteristics, thermal runaway (TR) and fire risk assessment, and fire prevention and control strategies. The key research frontiers in this domain could be classified into five categories: mechanisms and propagation of TR, development of high-safety battery materials and flame-retardant technologies, thermal management and thermal safety control, intelligent early warning and fault diagnosis, and fire suppression and firefighting techniques. The focus of research has gradually shifted from passive identification of causes and failure mechanisms to proactive approaches involving thermal control, predictive alerts, and integrated system-level fire safety solutions. As the field advances, increasing complexity and interdisciplinary integration have emerged as defining trends. Future research is expected to benefit from broader cross-disciplinary collaboration. These findings provide a valuable reference for researchers seeking a rapid overview of the evolving landscape of NEV fire-related studies. Full article

Background: Healthcare 5.0 represents a shift toward intelligent, human-centric care systems. Intensive care units generate vast amounts of data that require real-time decisions, but current decision support systems lack comprehensive frameworks for safe integration of artificial intelligence. Objective: We developed and validated the Learn–Predict–Monitor–Detect–Correct (LPMDC) framework as a methodology for systematic artificial intelligence integration across the critical care workflow. The framework improves predictive analytics, continuous patient monitoring, intelligent alerting, and therapeutic decision support while maintaining essential human clinical oversight. Methods: Framework development employed systematic theoretical modeling integrating Healthcare 5.0 principles, comprehensive literature synthesis covering 2020–2024, clinical workflow analysis across 15 international ICU sites, technology assessment of mature and emerging AI applications, and multi-round expert validation by 24 intensive care physicians and medical informaticists. Each LPMDC phase was designed with specific integration requirements, performance metrics, and safety protocols. Results: LPMDC implementation and aggregated evidence from prior studies demonstrated significant clinical improvements: 30% mortality reduction, 18% ICU length-of-stay decrease (7.5 to 6.1 days), 45% clinician cognitive load reduction, and 85% sepsis bundle compliance improvement. Machine learning algorithms achieved an 80% sensitivity for sepsis prediction three hours before clinical onset, with false-positive rates below 15%. Additional applications demonstrated effectiveness in predicting respiratory failure, preventing cardiovascular crises, and automating ventilator management. Digital twins technology enabled personalized treatment simulations, while the integration of the Internet of Medical Things provided comprehensive patient and environmental surveillance. Implementation challenges were systematically addressed through phased deployment strategies, staff training programs, and regulatory compliance frameworks. Conclusions: The Healthcare 5.0-enabled LPMDC framework provides the first comprehensive theoretical foundation for systematic AI integration in critical care while preserving human oversight and clinical safety. The cyclical five-phase architecture enables processing beyond traditional cognitive limits through continuous feedback loops and system optimization. Clinical validation demonstrates measurable improvements in patient outcomes, operational efficiency, and clinician satisfaction. Future developments incorporating quantum computing, federated learning, and explainable AI technologies offer additional advancement opportunities for next-generation critical care systems. Full article

Unrecognized mechanical faults and emergency sounds in vehicles can compromise safety, particularly for individuals with hearing impairments and in sound-insulated or autonomous driving environments. As intelligent transportation systems (ITSs) evolve, there is a growing need for inclusive, non-intrusive, and real-time diagnostic solutions that enhance situational awareness and accessibility. This study introduces an interpretable, sound-based machine learning framework to detect vehicle faults and emergency sound events using acoustic signals as a scalable diagnostic source. Three purpose-built datasets were developed: one for vehicular fault detection, another for emergency and environmental sounds, and a third integrating both to reflect real-world ITS acoustic scenarios. Audio data were preprocessed through normalization, resampling, and segmentation and transformed into numerical vectors using Mel-Frequency Cepstral Coefficients (MFCCs), Mel spectrograms, and Chroma features. To ensure performance and interpretability, feature selection was conducted using SHAP (explainability), Boruta (relevance), and ANOVA (statistical significance). A two-phase experimental workflow was implemented: Phase 1 evaluated 15 classical models, identifying ensemble classifiers and multi-layer perceptrons (MLPs) as top performers; Phase 2 applied advanced feature selection to refine model accuracy and transparency. Ensemble models such as Extra Trees, LightGBM, and XGBoost achieved over 91% accuracy and AUC scores exceeding 0.99. SHAP provided model transparency without performance loss, while ANOVA achieved high accuracy with fewer features. The proposed framework enhances accessibility by translating auditory alarms into visual/haptic alerts for hearing-impaired drivers and can be integrated into smart city ITS platforms via roadside monitoring systems. Full article