Search Results (966)

Background/Objectives: The accuracy and reliability of automated clinical analyzers are fundamental to patient safety and effective medical decision-making. Traditional quality control (QC) methods, which rely on periodic manual calibration and reactive maintenance, are inherently limited by high latency and labor costs; furthermore, they fail to provide continuous, real-time monitoring. This paper introduces a novel hybrid ensemble learning framework for the automated quality inspection of medical devices through the analysis of time-series reaction curves. Methods: Our system integrates three heterogeneous anomaly detection paradigms: an Enhanced Dynamic Time Warping (DTW) detector for robust non-linear pattern matching, a Shape Template Matching (STM) detector that mimics expert clinical logic by analyzing morphological features in a normalized shape space, and a specialized Time-series Variational Autoencoder (TimeVAE) for deep representation learning. The outputs of these detectors are fused using a weighted ensemble strategy, which is specifically designed to prioritize the minimization of false negatives—a critical requirement in medical diagnostics. Results: We evaluate our framework on a comprehensive, multi-center real-world dataset comprising seven distinct biochemical assays. Experimental results demonstrate that our proposed method achieves superior performance, attaining a $0\%$ false negative rate on CRE and DBIL assays and outperforming all baseline methods on the other five datasets. An ablation study confirms the model’s robustness even with limited training data, and a comparative analysis against eight state-of-the-art baseline methods further validates the effectiveness of our domain-optimized ensemble approach. Conclusions: The system provides a robust, interpretable, and highly automated solution for transitioning from reactive maintenance to proactive, real-time quality assurance in clinical laboratories. Full article

Precision Livestock Farming (PLF) increasingly relies on edge-deployed sensors to monitor bovine behaviors, fostering improved welfare and management. However, behavioral data naturally expands over time and presents severe class imbalances due to animals’ predominantly sedentary routines. When continuous sequential updates are required without access to historical datasets, deep learning methods frequently succumb to catastrophic forgetting. This study introduces an ultra-lightweight (∼0.85 MB) Continual Learning (CL) architecture built upon a CNN-BiLSTM feature extractor, tailored to process multivariate Inertial Measurement Unit (IMU) streams. We exhaustively evaluated baseline Naïve Fine-Tuning against Elastic Weight Consolidation (EWC), Learning without Forgetting (LwF), and episodic Replay under three rigorous real-world paradigms: Class Incremental, Subject Incremental (domain shift), and Imbalanced Realistic scenarios. Our empirical findings expose the fragility of static paradigms: in Class Incremental expansions, Naïve Fine-Tuning collapsed to an Average Accuracy of 33.33%. Conversely, Experience Replay emerged as the most robust defense, achieving a statistically significant Average Accuracy of 74.64 ± 6.77% across multiple random seeds. Furthermore, LwF effectively mitigated structural variations across unseen animal domains (Subject Incremental) without requiring raw data buffers. Notably, under severe biological class imbalances (Imbalanced Cumulative), the architecture proved highly resilient, maintaining 98.46% Average Accuracy and retaining perfect minority class recall. This research validates the operational feasibility of deploying adaptive, privacy-preserving CL frameworks directly on low-power wearable devices for lifelong livestock monitoring. Full article

Background/Objectives: Remote monitoring (RM) of cardiac implantable electronic devices (CIEDs) has been associated with potential clinical and economic benefits; however, its adoption among patients remains limited in some healthcare settings. This study examined patients’ intention to use RM services by applying an extended Technology Acceptance Model (TAM) that incorporates perceived effectiveness (PE), perceived barriers (PB), perceived threat (PT), and economic considerations, as well as the influence of socioeconomic factors. Methods: A cross-sectional survey was conducted among 104 patients with CIEDs in Taiwan using validated questionnaires. Structural equation modeling (SEM) was employed to examine the relationships among the proposed constructs. The association between intention to use and actual service utilization was explored. The correlations between sociodemographic factors and the constructs were analyzed using analysis of variance (ANOVA). Results: SEM showed that perceived effectiveness (PE), perceived usefulness (PU) and perceived ease of use (PEOU) were significantly associated with intention to use RM services, with economic considerations also having a significant contribution. Intention to use RM services further predicted actual adoption. However, PB and PT did not moderate these relationships. Sociodemographic factors influenced RM acceptance, with younger, more educated, employed, higher-income, and professionally employed patients reporting stronger perceptions and greater intention to use RM. Conclusions: This study reinforces the TAM framework in the context of health-related technology adoption. Overall, the adoption of RM services is complex and shaped by psychological, economic, and demographic factors, highlighting the need for user-friendly design, targeted education on clinical benefits, and flexible pricing and reimbursement strategies to improve equitable and sustained use. Full article

Medical infrared thermography, which involves the use of infrared thermal cameras for the non-invasive assessment of skin surface temperature distribution, has gained increasing interest in recent years as a tool supporting diagnosis and treatment monitoring. The aim of this article is to present the historical background and critically reassess the current role of infrared thermography in medicine, with particular emphasis on standardization as a key determinant of its clinical utility. This Perspective highlights the fundamental impact of methodological variability on diagnostic performance and reproducibility. A structured framework for standardization is proposed, encompassing patient preparation, environmental conditions, device parameters and calibration, image acquisition protocols, region-of-interest definition and analysis, as well as reporting and clinical interpretation. The analysis demonstrates how inconsistencies at each of these levels reduce measurement reliability, limit inter-study comparability, and weaken clinical confidence in infrared thermography. The article also addresses the growing availability of mobile thermal imaging systems and their integration with artificial intelligence, while emphasizing the need for stronger evidence-based support across all methodological domains. The presented analysis suggests that, despite existing limitations, medical infrared thermography holds considerable potential as a supportive clinical tool. However, its broader clinical implementation remains limited by several factors, with the lack of standardized protocols constituting a major and practically addressable translational barrier. Wider adoption will require standardization efforts alongside rigorous validation studies and application-specific interpretative guidelines. Addressing these challenges through technological advances and coordinated international standardization may facilitate meaningful progress over the next decade. Full article

With the widespread adoption of intelligent terminals, edge devices, and distributed information systems in the financial domain, financial security sensing data exhibit multisource heterogeneity, dynamic temporal patterns, and high privacy sensitivity. Traditional centralized anomaly detection methods are no longer able to simultaneously satisfy the requirements of cross-institutional or cross-node collaborative modeling, client data privacy protection, and robust monitoring of transaction and system anomalies. To address this challenge, a data-local federated deep anomaly detection framework has been proposed for distributed financial security sensing systems. Initially, a local deep financial security sensing representation module is constructed to perform temporal encoding and attention-based modeling on multisource financial signals, including terminal operation status, network transaction communication, backend server operation, identity authentication, and anomaly alerts, thereby extracting representations relevant to anomalous behaviors. Subsequently, a data-local federated optimization and personalized aggregation mechanism is developed to enable cross-node knowledge sharing without transmitting raw transaction or client data, while local personalized detection heads are employed to adapt to non-independent and identically distributed (non-IID) financial institution data. Furthermore, an adversarially robust security detection and trust-aware aggregation strategy is introduced to enhance model stability under input noise, feature masking, anomaly camouflage, and potential malicious client updates. Experimental results demonstrate that the proposed method achieves an Accuracy of $92.37\%$, a Precision of $89.41\%$, a Recall of $88.26\%$, an F1-score of $88.83\%$, an AUC of $93.06\%$, and a PR-AUC of $89.15\%$ in the primary financial anomaly detection task, significantly outperforming baseline methods such as Isolation Forest, Autoencoder, LSTM, Transformer, FedAvg, FedProx, SCAFFOLD, and MOON. In robustness experiments, the method attains F1-scores of $87.95\%$, $86.42\%$, $86.88\%$, $84.57\%$, $86.73\%$, and $83.91\%$ under Gaussian noise, feature masking, temporal shift, adversarial perturbation, and $20\%$ and $30\%$ malicious client scenarios, respectively. Ablation studies further confirm the effectiveness of local representation learning, personalized federated optimization, adversarial training, and trust-aware aggregation mechanisms. Overall, the proposed approach provides an efficient intelligent anomaly detection solution for financial AI security monitoring scenarios characterized by data localization requirements, node heterogeneity, and attack perturbations. Full article

Inadequate management of municipal solid waste represents a critical challenge for the sustainability of modern cities, characterized by low citizen participation rates due to the lack of direct incentives. Unlike existing approaches that isolate hardware classification or fleet monitoring, this article presents RENOVA as a socio-technical closed-loop system based on the Internet of Things (IoT) and artificial intelligence (AI). This system integrates an IoT-enabled smart bin, a gamified mobile application for citizens, and an administrative web panel for merchant redemption, all interconnected via a REST API. The system employs computer vision through the GPT-4o (OpenAI, San Francisco, CA, USA) multimodal model for the automatic classification of recyclable materials (PET plastic and Aluminum) and integrates a gamified rewards program to incentivize citizen participation. The methodology follows an applied technological development approach under the agile Scrum framework. Prototype validation demonstrated successful real-time communication between the IoT device and the cloud platform, achieving classification accuracy exceeding 95% under controlled conditions. A diagnostic survey applied to a convenience sample of 51 participants revealed that 94.1% accepted the proposed gamification model, while user experience evaluation (n = 74; consisting primarily of university-affiliated individuals aged 15–24) yielded a mean overall satisfaction score of 4.77/5.0 (SD = 0.48), with 79.7% of participants assigning the maximum rating. These findings reflect stated user acceptance and behavioral intention under prototype conditions rather than observed long-term behavioral change, and should not be generalized to broader urban populations without further validation. The proposed solution directly contributes to Sustainable Development Goals 11 (Sustainable Cities) and 12 (Responsible Consumption), suggesting a potentially scalable framework. Full article

Introduction: The Touvedo hydropower plant, located on the Lima River 47 km from its mouth, is equipped with a fish lift (2.14 m long × 1.29 m wide × 2.85 m high) on the left bank designed to facilitate fish migration past the dam. This mechanical system attracts fish by means of a guide current, traps them in a water-filled cage, and then lifts and releases them upstream, enabling passage over the dam. Within the framework of the Sustainability Policies from the EDP Group, particularly those related to Environment and Biodiversity, and under the Eel Management Plan, a long-term video-monitoring program has been implemented since 2011 to collect data on the species using the device and to evaluate its effectiveness. Objective: This study aims to present and analyze nine years of video-monitoring data collected across three programs—the “Action Plan for the Optimization of the Fish Lift at the Touvedo Hydroelectric Facility (2011/2014)”, which aimed to diagnose and assess the effectiveness of the fish lift and to define and implement measures needed to optimize its operation; “Video Monitoring of the Touvedo Fish Lift (2017/2020)”, that was carried out as a follow-up to the Action Plan; and more recently, a new video-monitoring project (2021–2024) which was implemented to expand the dataset and validate the patterns observed in the previous studies. Methodology: The fish lift was continuously monitored using an automatic video-recording system, which consists of a video camera installed at the top of the lift to capture images of the trapping cage during the final stage of its ascent, and a server for video storage. The trapping cage is lined with 20 cm × 20 cm white tiles to increase contrast and allow estimation of fish body length. Collected data included the timing of fish passage (day and hour), the number of fish per cycle, species-level identification and the estimated total length of each individual. Results: The European eel (Anguilla anguilla) has remained the dominant species using the lift, and, consistent with observations from Video-Monitoring 1, the Iberian barbel (Luciobarbus bocagei) has become the second most representative species, replacing the northern straight-mouth nase (Pseudochondrostoma duriense), whose proportion has declined. Brown trout (Salmo trutta) showed a slight but continued increase in Video-Monitoring 2, following the decrease recorded in Video-Monitoring 1 compared to the Action Plan. Conclusions: These results highlight the importance of continuing video monitoring of the Touvedo fish lift to assess its operability, confirm the observed passage patterns, determine the success of the implemented improvements, and evaluate the possible need for additional measures. Full article

Low-cost optical sensors have emerged as promising tools for in situ freshwater quality monitoring, offering the potential to expand spatial and temporal data coverage, particularly in community-based monitoring projects. However, despite rapid technological development of low-cost optical sensors, analytical validation practices of these devices remain poorly studied. This study aims to systematically and critically assess analytical validation practices applied to low-cost optical sensors based on absorbance, fluorescence, colorimetry, and light scattering, potentially designed for community-based freshwater monitoring. A total of 40 studies were analysed to evaluate how key analytical performance parameters, including sensitivity, accuracy, precision, and repeatability, as well as comparison with reference methods or benchtop instruments, were assessed and reported in relation to established validation guidelines. The analysis revealed substantial heterogeneity and critical gaps in validation approaches. While most studies report sensitivity metrics such as limits of detection and quantification, comprehensive evaluation of key analytical parameters such as accuracy, precision, and reproducibility was often limited. The reliance on single calibration experiments and high determination coefficients ($R^2$) frequently overestimates sensor performance. The lack of open-source materials further limits reproducibility and deployment: essential information such as design files, calibration procedures, and open-source resources is often incomplete or unavailable. To address these limitations, we propose a structured framework for validation and reporting that integrates established analytical guidelines with the practicalities of low-cost sensor development. Adoption of this approach would enable more consistent performance evaluation, improving reproducibility and facilitating comparison across studies and devices. Overall, strengthening analytical validation and reporting practices is essential to support the transition of low-cost optical sensors from proof-of-concept systems to reliable analytical devices for freshwater quality monitoring. Full article

Cyber-physical systems (CPSs) based on the Internet of Things (IoT) form the backbone of modern smart infrastructures, including smart cities, healthcare monitoring, industrial automation, and intelligent transportation. However, connecting many resource-limited IoT devices makes them more vulnerable to cyber threats, particularly quantum attacks. This review comprehensively examines quantum-secure communication (QSC) frameworks for IoT-enabled CPS, focusing on Quantum Key Distribution (QKD), post-quantum cryptographic (PQC) algorithms, and hybrid quantum–classical security models suitable for constrained devices. A PRISMA-guided search of the Scopus and Google Scholar database was conducted in January 2026 using three keyword groups related to hybrid security, artificial intelligence, and cyber-physical systems. Based on the evaluation, 6008 publications have been identified between 2001 and 2026. The first-round screening was performed for 4948 articles, after excluding duplicates. During the screening stage, 348 articles were selected for abstract scrutiny, 115 records were excluded due to no direct focus on CPS/IoT applications, 52 studies were excluded because these papers relied on traditional security models, 25 studies were excluded due to insufficient relevance to the review objectives, and 15 additional non-English studies were removed. Following the screening stage, 141 studies were selected for full-text eligibility. Out of those, 86 studies were removed due to a lack of specific evaluation metrics or not being published in a peer-reviewed venue. Furthermore, the publications are classified as QKD-based secure CPS and QSC for industrial IoT, AI-Assisted Secure Communication for CPS Networks, and hybrid PQC-QKD models for CPS/IoT devices. This article investigates recent advancements in secure data transmission, verified protocols, and AI-driven anomaly detection customized to CPS/IoT environments. In addition, operational hurdles, interaction with open innovations, real-time deployment, and secure edge-cloud integration are highlighted. By analyzing recent developments and identifying research gaps, this review provides a structured roadmap for designing secure, scalable, and quantum-safe IoT-based CPS frameworks capable of withstanding next-generation cyber threats. This systematic review was performed and reported according to the PRISMA 2020 guidelines. Full article

Human sitting posture recognition is critical for smart seating, ergonomic monitoring, and healthcare systems. However, existing deep learning approaches typically rely on highly complex network architectures that are computationally expensive, hindering their lightweight deployment on edge devices. Furthermore, current methods frequently struggle with indistinct boundaries among multi-class postures and are highly prone to overfitting when constrained by small-sample pressure sensor datasets. To bridge this gap, this paper proposes a novel, lightweight posture recognition framework specifically tailored for pressure distribution maps. First, sitting pressure data is collected using a thin-film pressure array sensor and uniformly mapped into an [M × N] image representation, establishing an effective sample format for Convolutional Neural Network (CNN) inputs. Second, as our primary architectural contribution, we fundamentally optimize the classic LeNet-5 network to enhance complex feature representation without inflating model complexity. Specifically, the depth of the convolutional layers is increased with a progressively increasing channel configuration. Batch Normalization (BN) is introduced to accelerate convergence and ensure training stability, while a Dropout mechanism is embedded within the fully connected layers to strictly penalize overfitting under small-sample constraints. These architectural improvements are synergistically combined with targeted data augmentation strategies—including random translation, rotation, and intensity perturbation—to further strengthen the model’s generalization capability. Experimental results demonstrate that the proposed method achieves a classification accuracy of 95.5% in a five-class sitting posture recognition task, significantly outperforming baseline models such as the traditional LeNet-5, AlexNet-Lite, and VGG-Small. The findings indicate that this approach achieves an optimal balance among recognition accuracy, training stability, and low model complexity, providing a robust algorithmic baseline and proof-of-concept for smart healthcare perception systems, paving the way for future large-scale subject-independent validation. Full article

The increasing demand of digital technologies and their integration with wearable health devices provides an efficient trigger for next-generation wearable healthcare devices for long-term physiological monitoring. The advancement of energy harvesting mechanism, nanomaterial-based sensor fabrication and their integration with digital technologies have emerged as a promising solution for transforming future of digital health. This study provides a comprehensive summary and framework for wearable self-powered electronic devices, enabling continuous, battery-free health monitoring and advancing the development of sustainable, next-generation digital healthcare systems. This review paper presents a broad and detailed overview of current technologies and sensors advancement in developing low-power wearable, self-powered electronic devices suitable for healthcare applications. The importance and reliable use of key energy harvesting approaches including triboelectric, piezoelectric, thermoelectric, and photovoltaic approaches are systematically presented which focused on development of energy efficient wearable devices. This review further examines the low-power circuit design strategies for flexible electronics focusing personalized healthcare monitoring. Current challenges and limitations related to advanced manufacturing of wearable health devices focusing on large-scale deployment are also analyzed. Finally, the key future research directions are outlined for advancing a next-generation intelligent digital health system. Full article

Sensor-based human activity recognition (HAR) plays a fundamental role in healthcare monitoring, sports analytics, and ambient-assisted living. Although deep learning has substantially advanced HAR performance, two practical issues still limit its real-world deployment: (i) the distribution shift caused by changes in users or sensor placements can degrade generalization, and (ii) the quadratic $\mathcal{O}\left(L^2\right)$ complexity of standard self-attention hinders efficient long-sequence modeling on resource-constrained wearable devices. To address these issues, we propose DSHformer, which is an accuracy-oriented HAR framework that combines compact channel–temporal encoding with locality-sensitive hashing (LSH)-based attention. Specifically, DSHformer (i) employs a low-parameter patch-based graph-attention encoder to jointly model latent relationships among sensor channel–temporal dynamics; (ii) introduces a trainable prototype pool together with a multi-layer decomposition network to improve intra-class compactness and inter-class separability via prototype alignment; and (iii) introduces a decomposition-stable LSH-based attention mechanism tailored for HAR, whose core design couples prototype-guided feature decomposition with locality-sensitive hashing to ensure that semantically related tokens remain consistently grouped in the same hash bucket even after decomposition-induced attenuation. The mechanism thereby operates at $\mathcal{O}(LlogL)$ attention complexity on longer sensor sequences. Extensive experiments on five public benchmarks (WISDM, UCI-HAR, PAMAP2, Opportunity, and UniMiB-SHAR) show that DSHformer achieves accuracies of 98.6%, 93.7%, 98.4%, 88.5%, and 96.6%, respectively, achieving competitive or superior performance compared with both Transformer variants and HAR-specific baselines under the adopted benchmark protocols. Ablation studies further confirm the complementary contribution of each component. Full article

Bionic sensors are emerging as powerful analytical platforms driving the development of next-generation detection technologies, particularly for small molecule sensing in complex environmental and biological systems. However, accurate and selective detection of small molecules remains fundamentally challenging due to their low molecular weight, limited structural specificity, and strong interference from complex matrices. This review provides a comprehensive overview of recent advances in bionic sensor technologies, focusing on how the integration of synthetic biology, nanomaterials, and artificial intelligence (AI) addresses these limitations. Key biorecognition elements, including enzymes, antibodies, aptamers, and molecularly imprinted polymers, are examined for their suitability in small molecule sensing applications. Advances in nanomaterials such as graphene, carbon nanotubes, quantum dots, and MXenes are discussed in relation to signal transduction enhancement, sensitivity improvement, and device miniaturization. In parallel, the roles of AI and machine learning in signal denoising, adaptive calibration, and molecular fingerprinting for complex datasets are highlighted. Applications in wearable and implantable biosensors, environmental monitoring, and food safety are analyzed, emphasizing real-time detection of metabolites, pollutants, and toxins. Key challenges associated with AI-driven systems, including scalability, cost, data reliability, and ethical concerns, are also discussed. Emerging trends such as hybrid sensing platforms, self-powered biosensors, and secure data integration frameworks are presented as future directions. This review aims to provide a problem-driven perspective on how next-generation bionic sensors can overcome current limitations and enable robust small molecule detection in real-world applications. Full article

Modern power systems are becoming increasingly complex due to the rapid integration of renewable energy sources, the widespread use of nonlinear power-electronic devices, and the deployment of microgrids operating in parallel with conventional power grids. These evolving conditions intensify the occurrence of diverse and highly complex power quality disturbances (PQDs), demanding accurate and computationally efficient monitoring strategies. This paper presents a novel multi-stage hierarchical framework for PQD detection and classification, comprising an initial training stage with a dedicated 1D Convolutional Neural Network (1D-CNN), a transfer learning stage, and a subsequent fine-tuning stage. The proposed approach operates directly on raw voltage waveforms, eliminating the need for any signal preprocessing, as the CNN performs internal feature extraction. The framework is evaluated using a comprehensive dataset that includes synthetic signals, Matlab/Simulink (version R2022a) time-domain simulations, and real voltage sag events. Additionally, up to 29 types of disturbances, including complex multi-event combinations defined by the IEEE-1159 Standard, are generated using the PQ-SyDa toolbox. The proposed model achieves an F1-score of 97.8% using a three-cycle analysis window and further improves to 98.86% when five cycles are used. These results highlight the robustness and generalization capability of the proposed approach for the real-time PQD monitoring task in modern electrical networks. Full article

This study presents the development and field testing of a Pressurized Driving-Down Air Multilevel Sampler (PDA-MLS), an integrated groundwater sampling device designed for depth-discrete sampling in boreholes affected by floating non-aqueous phase liquids (NAPLs). Conventional sampling methods—such as low-flow pumps, bailers, and packer-isolated systems—often fail under these conditions due to limited accessibility, cross-contamination, or disturbance of the water column. The proposed system addresses these limitations through a controlled pressurized-gas actuation mechanism that transfers groundwater from multiple PTFE-membrane chambers installed at discrete depths. This configuration enables low-disturbance sampling below floating contaminant layers. The use of chemically inert materials (stainless steel and PTFE) minimizes sampling artifacts and ensures compatibility with volatile organic compound (VOC) analyses. A simplified hydraulic conceptual framework describing inflow, outflow, and pressure-driven displacement was developed to support purge-duration estimation and operational parameter definition. The device was tested in a 90 m deep fractured limestone aquifer contaminated by tetrachloroethylene (PCE), where floating hydrocarbons limited the applicability of conventional sampling techniques. Field testing showed stable discharge conditions (~145–160 mL/min), repeatable sampling cycles, and successful collection of depth-discrete groundwater samples under the investigated site conditions. No evidence of sampler-related hydrocarbon entrainment was observed in the collected samples within the analytical detection limits of the adopted laboratory methods. To the authors’ knowledge, the PDA-MLS represents one of the few groundwater sampling systems specifically designed to combine low-disturbance multilevel sampling with operation in wells affected by floating NAPL. These features make it a promising tool for environmental monitoring, high-resolution characterization of fractured aquifers, and long-term assessment of contaminated sites. Full article