Search Results (1,611)

This paper presents a data-driven educational framework for teaching psychrometry and air quality control in hospitals, developed within an international pilot project involving universities and hospitals in Spain, Romania, and Turkey. The objective of this pilot study is to examine a multidisciplinary framework that equips engineering students with essential technical skills for managing hospital infrastructure, particularly in critical areas like operating rooms and intensive care units. The methodology integrates theoretical instruction, analogue instruments, and digital technologies, including Arduino-based sensing and AI tools, to facilitate data interpretation and critical thinking. By bridging manual measurements with digital monitoring, the framework aims to equalize proficiency among students from diverse engineering backgrounds. Quantitative results from 23 participants provide preliminary evidence of academic growth, consistent with the hypothesis that this integrated approach may facilitate conceptual mastery. This work offers preliminary insights into the advancement of data-driven modelling in engineering education, emphasizing the significance of multidisciplinary training and international collaboration in preparing future professionals for the oversight, operational management, and maintenance of modern healthcare facilities. Full article

Home enteral nutrition (HEN) is essential for patients with a functioning gastrointestinal tract who cannot meet nutritional needs orally, yet outpatient management remains complex, resource-intensive, and supported by a limited evidence base. Artificial intelligence (AI) may augment HEN care by extending monitoring, education, risk assessment, documentation support, and operational coordination into the home environment. Consistent with the narrative review format, this article uses a pragmatic, transparent synthesis of influential HEN-specific literature, relevant clinical nutrition evidence, and background knowledge from adjacent fields, including home healthcare, chronic disease management, oncology nutrition, telehealth, and software regulation. Direct evidence in established HEN populations remains scarce; therefore, most AI applications should be considered hypotheses or early implementation opportunities rather than proven standards of care. The strongest near-term opportunities are clinician-supervised patient education, symptom triage, adherence support, remote monitoring, and workflow automation. Predictive analytics, smart pumps, and precision enteral prescription tools are promising but require prospective HEN-specific validation, interoperability with electronic health records and home-infusion systems, reimbursement pathways, and governance safeguards. Key barriers include dataset bias, limited external validation, alert fatigue, privacy and regulatory concerns, unclear accountability, digital equity, cost uncertainty, and the risk of dehumanizing care. AI should be viewed as a complement to multidisciplinary HEN expertise. Priorities for the near future include HEN registries, standardized outcomes, prospective validation, pragmatic implementation trials, health-economic evaluation, and transparent oversight that preserves clinician accountability and patient-centered care. Full article

Continuous monitoring of Activities of daily living (ADLs) requires sensing systems that are privacy-preserving, low-power, and robust to environmental variation. Ambient sensing technologies provide an alternative to RGB video and wearable devices, but individual sensing modalities exhibit characteristic limitations. Sparse mmWave radar provides strong motion sensitivity but limited posture detail, low-resolution thermal sensing preserves posture-related spatial information, and smart plug telemetry captures only appliance-mediated behavioural interaction. To address these limitations, this paper proposes a layered multimodal ambient-sensing framework comprising a sparse-track 24-GHz FMCW radar, a $32\times 24$ low-resolution thermal sensor, and a Moko smart plug. It experimentally evaluates a radar–thermal HAR branch together with a separate smart plug appliance-recognition branch. The framework proposes three streams to enable continuous non-wearable monitoring while maintaining redundancy and reduced privacy exposure for intelligent-building and ambient assisted living environments. Radar and thermal streams are jointly evaluated on binary motion and four-class posture and activity recognition tasks collected across multiple environmental configurations using recording-grouped cross-validation, while the appliance stream is evaluated using per-plug telemetry from residential-grade appliances. The radar–thermal streams use a single-subject, fixed-placement dataset of binary-motion windows and four-class posture and motion windows collected across six furniture configurations. The separate intrusive load monitoring stream utilises smart plugs to classify appliances. Regarding binary motion recognition, radar $(F_{1,\mathrm{Transformer}}=0.882\pm 0.034)$ and thermal $(F_{1,\mathrm{XGBoost}}=0.870\pm 0.069)$ pipelines achieved similar macro F1 performance. On the four-class posture and activity recognition task, thermal features $(F_{1,\mathrm{thermal}}=0.775\pm 0.053)$ substantially outperformed radar $(F_{1,\mathrm{radar}}=0.609\pm 0.110)$. Weighted late fusion produced only modest descriptive gains. Separately, smart plug telemetry demonstrated strong appliance recognition performance using lightweight tree-based models suitable for constrained edge deployment. The results support a scoped redundancy argument. Sparse track-level radar carries gross motion, while low-resolution thermal sensing carries posture. The smart plug appliance monitoring extends the framework toward appliance-mediated instrumental activity of daily living (IADL) monitoring, with lightweight tree-based models achieving strong recognition performance under constrained edge deployment conditions. The findings support a layered multimodal sensing architecture for privacy-preserving ADL monitoring, where radar contributes motion-sensitive coverage, thermal sensing contributes posture-aware spatial context, and smart plug telemetry contributes appliance-level behavioural evidence within intelligent healthcare and ambient assisted living environments. Full article

Wearable technology and the Internet of Things have increased access to personal data, enabling applications that deliver individualized treatment and therapy within clinical pathways. To optimize coaching and interventions within such pathways, it is essential to identify all relevant factors in the available data. Feature selection can be a useful tool for achieving this, but with small, high-dimensional datasets, common in healthcare, it can be challenging. The goal of this study is to develop a method for identifying the most relevant features in small, high-dimensional datasets and to introduce a new ordering index that measures the quality of the orderings produced by feature selection methods. This novel index is sensitive to the quality of feature ordering and to the prediction model’s performance metrics when combined with a feature selection method. The index reaches its maximum when the number-of-features-versus-accuracy graph has an ideal concave-downward shape, reflecting increasing accuracy with each informative feature added and decreasing accuracy with each confounding feature added. Using this index, we define six feature orderings derived from the results of four standard feature selection methods. Using the performance metrics and our new ordering index, we show that the resulting orderings can identify more relevant features and improve the overall performance of the classification models, and that the ordering index is a useful contribution to feature selection techniques. Full article

Artificial intelligence (AI) is transforming healthcare and the built environment, yet its application to healthcare real estate (HRE) remains fragmented and poorly understood. This study systematically reviews AI applications across the HRE asset lifecycle to identify evidence gaps and evaluate their potential to improve decision-making, operational performance, and sustainable healthcare infrastructure. Following the Joanna Briggs Institute methodology and PRISMA-ScR guidelines, the search identified 2881 records, of which 87 studies met the inclusion criteria. Building on the evidence gaps identified through this mapping, this study develops conceptual contributions, including a lifecycle maturity index, the Algorithm-to-Asset-Value Translation Chain, and the AI-HREDF, that serve as theoretically grounded, testable proposals for future empirical investigation. Each study was classified by lifecycle stage, evidence directness, and evidence strength. Only 14 studies (16%) provided direct evidence linking AI to HRE decisions, while most focused on operations and facility management, leaving major gaps in site selection, planning, construction, and investment. This review identifies three evidence translation gaps that prevent AI advances from becoming measurable improvements in asset performance and financial value. To address these challenges, we propose the AI-Integrated Healthcare Real Estate Decision Framework (AI-HREDF), the Algorithm-to-Asset-Value Translation Chain, and a research agenda for future work. The findings provide a foundation for integrating AI into healthcare infrastructure planning, management, and investment while supporting more resilient, resource-efficient, and sustainable healthcare facilities. Full article

In recent years, the rapid development of flexible electronics, smart materials, and micro/nanofabrication technologies has greatly promoted the advancement of flexible pressure sensors. These sensors have achieved significant improvements in sensitivity, detection range, stability, and functional integration, demonstrating great potential for applications in wearable electronics, smart healthcare, and human–machine interaction. This review summarizes recent progress in flexible pressure sensors in terms of sensing mechanisms, functional materials, structural designs, and intelligent applications. First, the working principles and performance characteristics of typical sensing mechanisms, including piezoresistive, capacitive, piezoelectric, triboelectric, iontronic, self-powered, and electrochemical sensing, are introduced and compared. Then, the development of key materials, such as flexible substrates, carbon-based nanomaterials, metal nanostructures, conductive hydrogels, and MXenes, is summarized. The effects of structural designs, including serpentine, three-dimensional porous, crack, wrinkle, and Kirigami structures, on flexibility, stretchability, sensitivity, detection range, and cycling stability are also discussed. Furthermore, the applications of flexible pressure sensors in pulse monitoring, blood pressure monitoring, human motion detection, cardiovascular health assessment, disease diagnosis, gesture recognition, human–machine interaction, and electronic skin are reviewed. Finally, the major challenges and future perspectives of flexible pressure sensors are discussed. Full article

Durable medical devices (DMD) are essential healthcare assets, yet their management in public hospitals is constrained by fragmentation, limited traceability, reactive maintenance, and weak lifecycle integration. This study proposes a framework for digitally enabled, sustainable, and circular DMD management. A mixed-methods design integrated a literature review, expert consultation using the Best–Worst Method, weighted technology nominations, and case-based process mapping in Brazilian hospitals. Eleven experts assessed the criteria guiding Industry 4.0 technology selection for DMD management and the technologies best responding to these priorities; nine consistent judgments were aggregated. Patient-Centered Care, Operational Efficiency, and Resource Efficiency and Cost Reduction emerged as the leading influences on technology selection. Big Data and Analytics, Artificial Intelligence, the Internet of Things, Cloud Computing, Cyber-Physical Systems, Smart Sensors, and Machine Learning formed the priority portfolio, accounting for 84% of the weighted score. The cases contextualized these priorities by revealing discontinuous information flows, limited asset visibility, corrective maintenance, fragmented governance, and weak end-of-life practices. By connecting decision priorities and technological capabilities with observed gaps, the TO-BE framework organizes sustainable procurement, traceable use, predictive maintenance, redeployment, refurbishment, and responsible disposal through material and information flows, providing a pathway for digital and circular transformation in resource-constrained healthcare systems. Full article

Background: Dental caries is a highly prevalent, biofilm-mediated disease characterized by microbial dysbiosis, excessive acid production, and progressive enamel demineralization. Although traditionally managed through restorative treatment, increasing attention has shifted toward preventive strategies focused on modulation of the oral microbiota and maintenance of ecological balance within the oral cavity. Methods: This narrative review summarizes current evidence regarding the ecological and mechanistic basis of dental caries and microbiota-centered prevention strategies. Literature published between January 2000 and March 2026 was retrieved from PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar using keywords related to dental caries, oral microbiota, cariogenic bacteria, biofilms, probiotics, prebiotics, salivary diagnostics, metabolomics, quorum sensing, and artificial intelligence. Results: Current evidence demonstrates that dental caries is driven by ecological shifts favoring acidogenic and aciduric microorganisms within cariogenic biofilms. Emerging preventive approaches include dietary modification, oral hygiene optimization, probiotics, prebiotics, synbiotics, and functional dietary agents aimed at restoring microbial homeostasis and inhibiting cariogenic biofilm maturation. In addition, advances in salivary microbiome profiling, metabolomics, artificial intelligence-assisted predictive modeling, and smart responsive materials have shown promising potential for improving early diagnosis, risk assessment, and personalized prevention strategies. Conclusions: Microbiota-based approaches represent a promising paradigm shift in dental caries prevention by emphasizing ecological modulation rather than pathogen eradication alone. Continued interdisciplinary research integrating microbial ecology, diagnostics, biomaterials, and digital technologies may facilitate the development of personalized and preventive oral healthcare strategies. Full article

Smart cities (SCs) use the Internet of Things (IoT) to collect and process data to communicate with their infrastructure and assets in real time. A great deal of techniques, such as encryption protocols, Random Forest-based AI-driven threat detection, and blockchain architectures, have been developed to address cybersecurity challenges in smart cities (SCs). These techniques, however, have limitations such as their scalability, high computational expenses, and energy inefficiency. Therefore, in this study, to overcome these challenges, we propose a blockchain-based infrastructure called BlockSafeNet. This uses artificial intelligence, big data, and blockchain to enhance cybersecurity in SCs. The effectiveness of the proposed BlockSafeNet framework was evaluated using responsiveness, computational time, encryption quality score, detection rate, false positive rate, latency, throughput, and energy consumption as the primary cybersecurity performance metrics. These metrics were selected to assess communication efficiency, threat detection capability, privacy preservation, scalability, and overall security performance within smart-city IoT environments. To ensure secure data transactions, robust threat detection, and efficient communication. The system’s high calculation speed and detection rate show potential for managing sensitive maternal health data collected by IoT devices. The platform also shows how IoT may be used by healthcare services to monitor public health in real time, allowing hospitals, emergency services, and public health agencies to securely share data. This aids in resource optimization, improving service delivery, and preserving data privacy and trust in SCs. Data was obtained from the UCI Machine Learning Repository on Kaggle to validate the developed framework. By evaluating the effectiveness of BlockSafeNet in tackling cybersecurity challenges, we establish its practical relevance and usability in SCs. The proposed BlockSafeNet framework achieved a responsiveness of 24 s, an encryption quality score of 0.89, computational time of 85 s, and a detection rate of 91%, demonstrating significant improvements in secure IoT communication, privacy preservation, and AI-driven cyber threat detection within smart city infrastructures. shows that SC IoT security has significantly improved through the adoption of new data protection methods and better measures of security, providing a positive impact on the SC ecosystem. Full article

The rapid proliferation of Internet of Things (IoT) technologies has transformed the modern home into a complex cyber–physical ecosystem encompassing hundreds of millions of connected devices globally. Smart homes support automation, energy management, and healthcare monitoring, but they also introduce a broad and evolving range of security and privacy challenges. This review examines 233 sources published between 2018 and May 2025, selected through a PRISMA-informed process covering five major academic databases and relevant standards and technical reports. It discusses communication protocols, including Matter, develops a Threat-Layer-Defense synthesis matrix covering ten attack categories; examines the practical limitations of AI-based anomaly detection and blockchain-based trust management; and derives recommendations for manufacturers, platform providers, users, and regulators. Privacy challenges, regulatory frameworks, and user behavior are considered alongside technical threats. The findings suggest that scalable smart home security requires coordinated progress in protocol standardization, enforceable device update lifecycles, gateway-level anomaly detection, and privacy-preserving local analytics rather than reliance on a single technical solution. Full article

The rapid deployment of Internet of Things (IoT)-enabled video surveillance systems has expanded the capabilities of real-time monitoring in smart cities, healthcare facilities, industrial environments and critical infrastructure. However, integrating resource-constrained cameras, heterogeneous communication protocols, edge/cloud analytics, and sensitive video data creates a complex cybersecurity landscape. This systematic review synthesizes recent evidence on security challenges and mitigation strategies in IoT-enabled video surveillance systems. Following the PRISMA 2020 guidelines, four bibliographic databases (Scopus, IEEE Xplore, Web of Science, and Google Scholar) were searched for peer-reviewed journal articles and conference papers published between January 2021 and July 2025. After duplicate removal, title/abstract screening, full-text assessment, and quality appraisal, 21 studies were included for qualitative synthesis. The findings show that vulnerabilities occur across three interdependent architectural layers: device/perception, network/communication, and application/cloud. The frequently reported weaknesses were default credentials, insecure firmware, unencrypted video streams, weak protocol configuration, metadata leakage, and inadequate cloud access control. Existing mitigation strategies, including multi-factor authentication, role-based access control, TLS/DTLS, lightweight encryption, intrusion detection systems, and secure boot, provide partial protection but remain constrained by latency, computational overhead, energy consumption, scalability, cost and legacy device compatibility. This review further identifies a persistent research–practice gap: only a small subset of studies provides evidence of real-world deployments, while most solutions remain evaluated in simulations, testbeds, or conceptual frameworks. This review contributes a domain-specific taxonomy of IoT video surveillance security, a comparative evaluation of mitigation strategies using technical, operational, and economic criteria, and deployment-oriented recommendations for smart city, industrial, healthcare, residential, and critical infrastructure settings. The study highlights the need for cross-layer security architectures, lightweight and post-quantum-ready cryptography, privacy preservation, edge AI, federated learning, zero-trust access control, and standardized security baselines. Full article

Real-world home monitoring requires sensing systems that can capture daily behaviour without continuous raw-video retention or excessive user burden. However, domestic environments present irregular activity timing, fragmented human presence, asynchronous multimodal events, and privacy-sensitive data management. This study proposes an event-driven multimodal sensing and computing framework for context-aware home monitoring using stereo vision and dietary event anchoring. The framework integrates stereo RGB-based three-dimensional human motion sensing, dining-zone-triggered meal image acquisition, runtime event orchestration, timestamp-based cross-modal synchronization, privacy-aware local storage, and large-language-model-assisted dietary context interpretation. Instead of continuously recording all sensor streams, the system activates and organizes sensing through human presence detection, debounce logic, cooldown-based session control, and dining-zone occupancy events. Meal-related events are used as contextual anchors to associate motion sessions and dietary observations into synchronized behavioural episodes. The prototype was deployed for 11 consecutive days in a real kitchen–dining environment, with the stabilized real-time monitoring phase evaluated from 11 to 14 February 2026. During this phase, the system generated 26 event-driven motion sessions and 51,165 captured pose frames, of which 25,925 were valid. Sustained active sessions accounted for 30.8% of all sessions but contributed 81.5% of captured pose frames, indicating that event-driven orchestration concentrated motion data within behaviourally meaningful activity windows. Eight meal-related records were obtained, seven of which overlapped with motion sessions, resulting in 87.5% meal-event overlap coverage. Structured pose outputs required approximately 550 kB/min, corresponding to about 33 MB/h of recorded pose data. LLM-assisted meal-image interpretation achieved a mean absolute percentage error of 25.44%, supporting its use for coarse dietary-context description rather than precise nutritional quantification. However, this result is interpreted only as evidence for coarse dietary-context description and not as validation of a precise nutritional or clinical dietary assessment method. These results demonstrate the system-level feasibility of transforming irregular domestic observations into structured, temporally indexed, and privacy-aware multimodal behavioural records for future home monitoring applications. Full article

Artificial intelligence (AI)-driven positioning and tracking systems combine geometric trajectories with behavior understanding in smart-city, healthcare, and autonomous environments. Skeleton sequences provide compact, privacy-preserving motion geometry, but labeled data are costly, and coordinate-only self-supervision cannot recover object, scene, or interaction cues. Vision-language transfer can supply these cues, but instance-level targets remain sensitive to noisy crops, incomplete descriptions, and ambiguous actions. We propose CrossVLS, which is a cross-modal vision-language-guided framework that transfers semantic knowledge from red, green, and blue (RGB) frames and generated language descriptions to a skeleton encoder during pretraining while retaining skeleton-only inference. CrossVLS replaces noisy instance-level transfer with a shared prototype space: skeleton, RGB, and language features are softly assigned to a common prototype bank through balanced optimal transport, and the resulting assignments define semantic soft targets for contrastive learning. A full-batch progressive training schedule gradually increases cross-modal guidance without splitting the physical batch, preserving the support set used to construct semantic targets. Experiments on NTU RGB+D 60, NTU RGB+D 120, and PKU-MMD demonstrate strong performance under linear and semi-supervised evaluation using only the pretrained skeleton encoder at inference. These results show that prototype-mediated vision-language transfer can improve skeleton representations for the behavior-interpretation stage of positioning and tracking pipelines. Full article

Minimizing computational overhead in healthcare time-series classification remains a critical challenge, particularly for deep learning models operating on high-dimensional sequential data under resource and latency constraints. This challenge is further exacerbated by adversarial attacks, which introduce evolving threats and necessitate robust yet efficient mechanisms for maintaining reliable model performance—a requirement of paramount importance in healthcare, where wearable and smart devices have brought continuous monitoring outside clinical settings and model failures can directly compromise patient safety. In this paper, we propose ReLATE+, a unified framework for adversarially robust and computationally efficient time-series classification. ReLATE+ integrates three key capabilities: (i) detection and classification of adversarial inputs, (ii) dataset-level similarity analysis, and (iii) adaptive model selection. Upon receiving new data, the framework first determines whether the input is adversarial and identifies the attack type. It then leverages this information to retrieve a similar dataset from a repository and identify the corresponding high-performing models and trains only a small set of selected candidates instead of exhaustively retraining all models. This approach ensures strong performance while reducing the need for retraining, and it generalizes well across different domains with varying data distributions and feature spaces. Experiments show that ReLATE+ reduces computational overhead by an average of 77.68%, enhancing adversarial resilience and streamlining robust model selection, all without sacrificing performance, within 2.02% of Oracle. Full article

This paper proposes a digital twin (DT)-enabled edge computing framework for healthcare task offloading in smart medical environments. The DT maintains a continuously synchronized virtual representation of the physical edge system, capturing queue states, resource utilization, and energy dynamics. Based on this virtual state, a deterministic optimization model is formulated to support real-time offloading and scheduling decisions under latency, energy, and fairness constraints. Unlike prediction-only approaches, the proposed DT operates in a closed-loop manner, where state estimation and synchronization directly influence scheduling feasibility and system performance. The offloading problem is formulated as a mixed-integer linear programming (MILP) model to jointly optimize task allocation, delay minimization, and energy efficiency. The simulated workload includes 10,000 heterogeneous healthcare applications. At each time slot, one to five tasks are generated and uniformly selected from ECG, video-processing, or medical-imaging workloads, with average task sizes of 90 KB, 280 KB, and 150 KB, respectively. This setup aims to emulate diverse real-world healthcare edge workloads with varying communication and computation demands. The proposed approach significantly reduces average latency by up to 57%, eliminates task drops in all evaluated scenarios, and improves load balancing compared with hospital-only and round-robin baselines. Although total energy consumption increases moderately, energy efficiency per completed task improves due to more effective scheduling by stability, reducing deadline violations and enhancing resource utilization. We further analyze the impact of DT freshness and updated frequency and show that outdated or misaligned DT updates can degrade performance by increasing delay and leading to suboptimal decisions, while overly frequent updates introduce additional coordination overhead. These results highlight the importance of jointly designing DT synchronization mechanisms and optimization-based scheduling strategies for reliable and cost-efficient healthcare edge systems. Full article