Search Results (190)

Background: Obstructive sleep apnea (OSA) is one of the most prevalent yet underdiagnosed sleep disorders. We evaluated the diagnostic accuracy of radar-based systems and ranked frequency bands for the non-contact detection of OSA. Methods: A systematic search of six databases was conducted from inception to May 23, 2025. Eligible studies included adults assessed for OSA using radar-based systems compared to polysomnography. Hierarchical SROC modeling, threshold-based meta-analyses, and frequency band-stratified network meta-analysis were performed. Certainty of evidence was assessed using GRADE. The PROSPERO registration number is CRD420251059236. Results: We identified 23,906 records and included 20 studies involving 1540 participants. The primary outcome included a high area under the curve (AUC) of approximately 0.91, an optimal apnea–hypopnea index (AHI) cutoff of ≥22 with a sensitivity of 0.8155 (95% confidence interval (CI): 0.6862–0.8993) and specificity of 0.8819 (95% CI: 0.7799–0.9402). At an AHI threshold of 30, X-band dual radar performed the best, followed by K-band, which yielded significant but more variable results. C-bands consistently showed lower diagnostic values. Conclusions: This study provides a novel radar band comparison for OSA detection, highlighting clinically relevant thresholds. Key limitations are indirect comparisons and limited, varied samples. Radar-based systems show high sensitivity for OSA detection, optimized by frequency, radar type, artificial intelligence support, and dual sensors within 0.2–1.5 m. Future work should expand the frequency analysis, standardize AHI thresholds, and validate results in specific subgroups. Full article

Digital transformation (DT) has become a strategic priority for public administrations, particularly due to the need to deliver more efficient and citizen-centered services and respond to societal expectations, ESG (Environmental, Social, and Governance) criteria, and the United Nations Sustainable Development Goals (UN SDGs). In this context, the main objective of this study is to propose an innovative methodology to automatically evaluate the level of digital transformation (DT) in public sector organizations. The proposed approach combines traditional assessment methods with Artificial Intelligence (AI) techniques. The methodology follows a dual approach: on the one hand, surveys are conducted using specialized staff from various public entities; on the other, AI-based models (including neural networks and transformer architectures) are used to estimate the DT level of the organizations automatically. Our approach has been applied to a real-world case study involving local public administrations in the Valencian Community (Spain) and shown effective performance in assessing DT. While the proposed methodology has been validated in a specific local context, its modular structure and dual-source data foundation support its international scalability, acknowledging that administrative, regulatory, and DT maturity factors may condition its broader applicability. The experiments carried out in this work include (i) the creation of a domain-specific corpus derived from the surveys and websites of several organizations, used to train the proposed models; (ii) the use and comparison of diverse AI methods; and (iii) the validation of our approach using real data. Based on the deficiencies identified, the study concludes that the integration of technologies such as the Internet of Things (IoT), sensor networks, and AI-based analytics can significantly support resilient, agile urban environments and the transition towards more effective and sustainable Smart City models. Full article

Conventional conductive hydrogels are susceptible to swelling in aquatic environments; which compromises their mechanical integrity; a limitation that poses a potential challenge to their long-term stability and application. In this study, a zwitterionic ion-conductive hydrogel was fabricated from polyvinyl alcohol (PVA), acrylic acid (AA), and [2-(methacryloyloxy)ethyl]dimethyl-(3-sulfopropyl)ammonium hydroxide (SMBA), forming a dual-network structure. A copolymer of zwitterionic SBMA and AA formed the first network, and PVA formed the second network by repeated freeze–thawing. The equilibrium state of zwitterionic SBMA was modulated by AA to protonate the SBMA, which resulted in the conversion of -SO₃⁻ to -SO₃H; thus, hydrogels had the anti-swelling property driven by electrostatic repulsion. In addition, the prepared hydrogels possessed excellent mechanical properties (tensile strength of 0.76 MPa, elongation at break of 322%, and compressive strength of 0.97 MPa at 75% compressive strain) and remarkable anti-swelling properties (80% swelling after 120 h of immersion). Owing to the zwitterionic nature of SBMA, the hydrogel also showed inherent antimicrobial properties and high electrical conductivity, which could be capable of monitoring human movement and physiological signals. This work provides a facile strategy for designing hydrogels with remarkable mechanical properties and anti-swelling characteristics, expanding the application environment of hydrogels in flexible sensing Full article

Flexible hydrogel sensors demonstrate emerging applications, such as wearable electronics, soft robots, and humidity smart devices, but their further application is limited due to their single-responsive behavior and unstable, low-sensitivity signal output. This study develops a dual-responsive starch-based conductive hydrogel via a facile “one-pot” strategy, achieving mechanically robust pressure sensing and ultra-sensitive humidity detection. The starch-Poly (2,3-dihydrothieno-1,4-dioxin)-poly (styrenesulfonate) (PEDOT:PSS)-glutaraldehyde (SPG) hydrogel integrates physical entanglement and covalent crosslinking to form a porous dual-network architecture, exhibiting high compressive fracture stress (266 kPa), and stable electromechanical sensitivity (ΔI/I₀, ~2.3) with rapid response (0.1 s). In its dried state (D-SPG), the film leverages the starch’s hygroscopicity for humidity sensing, detecting minute moisture changes (ΔRH = 6.6%) within 120 ms and outputting 0.4~0.5 (ΔI/I₀) signal amplitudes. The distinct state-dependent responsiveness enables tailored applications: SPG monitors physiological motions (e.g., pulse waves and joint movements) via conformal skin attachment, while D-SPG integrated into masks quantifies respiratory intensity with 3× signal enhancement during exercise. This work pioneers a sustainable candidate for biodegradable flexible electronics, overcoming trade-off limitations between mechanical integrity, signal stability, and dual responsiveness in starch hydrogels through synergistic network design. Full article

In this study, we propose a bidirectional chemical sensor platform based on a reconfigurable ion-sensitive field-effect transistor (R-ISFET) architecture. The device incorporates Ni-silicide Schottky barrier source/drain (S/D) contacts, enabling ambipolar conduction and bidirectional turn-on behavior for both p-type and n-type configurations. Channel polarity is dynamically controlled via the program gate (PG), while the control gate (CG) suppresses leakage current, enhancing operational stability and energy efficiency. A dual-control-gate (DCG) structure enhances capacitive coupling, enabling sensitivity beyond the Nernst limit without external amplification. The extended-gate (EG) architecture physically separates the transistor and sensing regions, improving durability and long-term reliability. Electrical characteristics were evaluated through transfer and output curves, and carrier transport mechanisms were analyzed using band diagrams. Sensor performance—including sensitivity, hysteresis, and drift—was assessed under various pH conditions and external noise up to 5 V_pp (i.e., peak-to-peak voltage). The n-type configuration exhibited high mobility and fast response, while the p-type configuration demonstrated excellent noise immunity and low drift. Both modes showed consistent sensitivity trends, confirming the feasibility of complementary sensing. These results indicate that the proposed R-ISFET sensor enables selective mode switching for high sensitivity and robust operation, offering strong potential for next-generation biosensing and chemical detection. Full article

Time domain reflectometry (TDR) has been validated for monitoring water level evolution and riverbed scouring in the laboratory. Previous studies have also validated the feasibility of field-based single hydrological parameter monitoring using TDR. However, the current research focuses on developing separated TDR sensing systems, and integrated measurements of multiple hydrological parameters from a single reflected waveform have not been reported. This study presents an improved helical probe sensor specifically designed for implementation in geologically hard soils, together with an improved data interpreting methodology to simultaneously determine water surface level, bed elevation, and suspended sediment concentration from a single reflection signal. Experimental comparisons were conducted in the laboratory to evaluate the measuring performance between the traditional dual-needle probe and the novel spiral probe under the same scouring conditions. The experiments confirmed the reliability and superior performance of spiral probe in accurately capturing multiple hydrological parameters. The measurement errors for the spiral probe across multiple hydrological parameters were all within ±10%, and the accuracy further improved with increased probe embedding depth in the sand medium. Across all tested parameters, the spiral probe showed enhanced measurement precision with a particularly significant improvement in suspended sediment concentration detection. Full article

Developing stretchable strain sensors that combine both high sensitivity and a wide linear range is a critical requirement for health electronics, yet it remains challenging to meet the practical demands of daily health monitoring. This study proposes a novel heterogeneous surface strategy by in situ silver deposition on modified PDMS followed by MXene spray coating, constructing a multilevel microcrack strain sensor (MAP) using silver nanoparticles and MXene. This innovative multilevel heterogeneous microcrack structure forms a dual conductive network, which demonstrates excellent detection performance within GF_max = 487.3 and response time ≈65 ms across various deformation variables. And the seamless integration of the sensor arrays was designed and employed for the detection of human activities without sacrificing biocompatibility and comfort. Furthermore, by adopting advanced deep learning technology, these sensor arrays could identify different joint movements with an accuracy of up to 95%. These results provide a promising example for designing high-performance stretchable strain sensors and intelligent recognition systems. Full article

The development of multifunctional conductive hydrogels with rapid self-healing capabilities and powerful sensing functions is crucial for advancing wearable electronics. This study designed and prepared a polyvinyl alcohol (PVA)–borax hydrogel incorporating carbon nanotubes (CNTs) and biomass carbon nanospheres (CNPs) as dual-carbon fillers. This hydrogel exhibits excellent conductivity, mechanical flexibility, and self-recovery properties. Serving as a highly sensitive piezoresistive sensor, it efficiently converts mechanical stimuli into reliable electrical signals. Sensing tests demonstrate that the CNT/CNP/PVA–borax hydrogel sensor possesses an extremely fast response time (88 ms) and rapid recovery time (88 ms), enabling the detection of subtle and rapid human motions. Furthermore, the hydrogel sensor also exhibits outstanding cyclic stability, maintaining stable signal output throughout continuous loading–unloading cycles exceeding 3200 repetitions. The hydrogel sensor’s characteristics, including rapid self-healing, fast-sensing response/recovery, and high fatigue resistance, make the CNT/CNP/PVA–borax conductive hydrogel an ideal choice for multifunctional wearable sensors. It successfully monitored various human motions. This study provides a promising strategy for high-performance self-healing sensing devices, suitable for next-generation wearable health monitoring and human–machine interaction systems. Full article

Barcodes are ubiquitous in manufacturing and logistics, but defects can reduce decoding efficiency and disrupt the supply chain. Existing studies primarily focus on a single barcode type or rely on small-scale datasets, limiting generalizability. We propose Y8-LiBAR Net, a lightweight two-stage framework for multi-type barcode defect detection. In stage 1, a YOLOv8n backbone localizes 1D and 2D barcodes in real time. In stage 2, a dual-branch network integrating ResNet50 and ViT-B/16 via hierarchical attention performs three-class classification on cropped regions of interest (ROIs): intact, defective, and non-barcode. Experiments conducted on the public BarBeR dataset, covering planar/non-planar surfaces, varying illumination, and sensor noise, show that Y8-LiBARNet achieves a detection-stage mAP@0.5 = 0.984 (1D: 0.992; 2D: 0.977) with a peak F1 score of 0.970. Subsequent defect classification attains 0.925 accuracy, 0.925 recall, and a 0.919 F1 score. Compared with single-branch baselines, our framework improves overall accuracy by 1.8–3.4% and enhances defective barcode recall by 8.9%. A Cohen’s kappa of 0.920 indicates strong label consistency and model robustness. These results demonstrate that Y8-LiBARNet delivers high-precision real-time performance, providing a practical solution for industrial barcode quality inspection. Full article

In complex marine environments, ramp-based recovery systems for autonomous underwater vehicles (AUVs) often encounter engineering challenges such as reduced docking accuracy and success rate due to disturbances in the capture window attitude. In this study, a desktop-scale physical experimental platform for recovery compensation was designed and constructed. The system integrates attitude feedback provided by an attitude sensor and dual-motor actuation to achieve active roll and pitch compensation of the capture window. Based on the structural and geometric characteristics of the platform, a dual-channel closed-loop control strategy was proposed utilizing midpoint tracking of the capture window, accompanied by multi-level software limit protection and automatic centering mechanisms. The control algorithm was implemented using a discrete-time PID structure, with gain parameters optimized through experimental tuning under repeatable disturbance conditions. A first-order system approximation was adopted to model the actuator dynamics. Experiments were conducted under various disturbance scenarios and multiple control parameter configurations to evaluate the attitude tracking performance, dynamic response, and repeatability of the system. The results show that, compared to the uncompensated case, the proposed compensation mechanism reduces the $MSE$ by up to 76.4% and the $MaxAE$ by 73.5%, significantly improving the tracking accuracy and dynamic stability of the recovery window. The study also discusses the platform’s limitations and future optimization directions, providing theoretical and engineering references for practical AUV recovery operations. Full article

Conductive polymer composites (CPCs) filled with anisotropic materials such as carbon nanotubes (CNTs) exhibit electrical behavior governed by percolation through filler networks. While filler volume and shape are commonly studied, the influence of orientation and alignment remains underexplored. This study uses Monte Carlo simulations to examine how the mean orientation angle and angular dispersion of CNTs affect conductive network formation. The results demonstrate that electrical connectivity is highly sensitive to orientation. Contrary to conventional assumptions, maximum connectivity occurred not at 45° but at around 55–60°. A Gaussian-based orientation probability function was proposed to model this behavior. Additionally, increased orientation dispersion enhanced conductivity in cases where alignment initially hindered connection, highlighting the dual role of alignment and randomness. These findings position orientation as a critical design parameter—beyond filler content or geometry—for engineering CPCs with optimized electrical performance. The framework provides guidance for processing strategies that control alignment and supports applications such as stretchable electronics, directional sensors, and multifunctional materials. Future research will incorporate full 3D orientation modeling to reflect complex manufacturing conditions. Full article

This study presents a one-step electrospinning method to fabricate polyacrylonitrile (PAN) nanofibers embedded with green-synthesized silver nanoparticles (AgNPs) for efficient catalytic dye reduction and real-time monitoring. Utilizing avocado seed extract for AgNP synthesis, the resulting composite nanofibers exhibit uniform nanoparticle dispersion and enhanced surface area, significantly improving adsorption and catalytic properties. The membranes demonstrated outstanding catalytic activity, achieving over 95% degradation of methyl orange within 45 min when paired with sodium borohydride, and maintained structural integrity and performance over ten reuse cycles. The integration of a novel 3D-printed support enabled scalability, allowing a 60-fold increase in treatment volume without compromising efficiency. Additionally, the composite’s electrical conductivity changes enabled the real-time monitoring of the dye reduction process, highlighting its dual functionality as both catalyst and sensor. These results encourage the potential of PAN/AgNPs supported on a 3D-printed structure nanofiber membranes for scalable, sustainable wastewater treatment and in situ reaction monitoring. Full article

This study advances the development of self-sensing concrete through functionalization with carbon nanotubes (CNTs) for structural health monitoring. Through experimental analyses, it relies on its dual responsiveness to mechanical and thermal stimuli. Three-point bending and thermal tests were systematically conducted on concrete samples with CNT concentrations ranging from 0 to 0.05 wt.% of cement, evaluated at 7- and 28-day curing periods. Mechanical testing demonstrated curing-dependent behavior: At 7 days, mechanical strength and electrical current response exhibited pronounced variability across CNTs loadings, with optimal balance achieved at 0.01% CNTs. At 28 days, the tests show that the mechanical properties are relatively stabilized, reaching the highest value at 0.006 wt.% CNTs and achieving the best electrical sensitivity at 0.01 wt.% CNTs. The thermal experiments revealed faster current modulation in the 7-day samples than in the 28-day counterparts, with intermediate CNT concentrations (e.g., 0.01 wt.%) showing a more sensitive response. The sensitivity was analyzed for both mechanical and thermal changes to further evaluate the feasibility of using CNT-reinforced concrete as a sensor material. Conductivity measurements on fully cured samples indicated that all samples exhibited electrical conductivities in the 10⁻⁴ S/m range, suggesting semiconductive behavior, while 0.006 wt.% CNTs yielded the highest conductivity. Higher CNT content did not further improve conductivity, likely due to agglomeration disrupting the network. These findings confirm CNT-modified concrete’s dual electromechanical and thermal responsiveness and support its potential as a multifunctional sensing material. Full article

This study aims to mitigate slope-collapse hazards that threaten life and property at the Lujiawan resettlement site in Wanbi Town, Dayao County, Yunnan Province, within the Guanyinyan hydropower reservoir. It integrates centimeter-level point-cloud data collected by a DJI Matrice 350 RTK equipped with a Zenmuse L2 airborne LiDAR (Light Detection And Ranging) sensor with detailed structural-joint survey data. First, qualitative structural interpretation is conducted with stereographic projection. Next, safety factors are quantified using the limit-equilibrium method, establishing a dual qualitative–quantitative diagnostic framework. This framework delineates six hazardous rock zones (WY1–WY6), dominated by toppling and free-fall failure modes, and evaluates their stability under combined rainfall infiltration, seismic loading, and ambient conditions. Subsequently, six-degree-of-freedom Monte Carlo simulations incorporating realistic three-dimensional terrain and block geometry are performed in RAMMS::ROCKFALL (Rapid Mass Movements Simulation—Rockfall). The resulting spatial patterns of rockfall velocity, kinetic energy, and rebound height elucidate their evolution coupled with slope height, surface morphology, and block shape. Results show peak velocities ranging from 20 to 42 m s⁻¹ and maximum kinetic energies between 0.16 and 1.4 MJ. Most rockfall trajectories terminate within 0–80 m of the cliff base. All six identified hazardous rock masses pose varying levels of threat to residential structures at the slope foot, highlighting substantial spatial variability in hazard distribution. Drawing on the preceding diagnostic results and dynamic simulations, we recommend a three-tier “zonal defense with in situ energy dissipation” scheme: (i) install 500–2000 kJ flexible barriers along the crest and upper slope to rapidly attenuate rockfall energy; (ii) place guiding or deflection structures at mid-slope to steer blocks and dissipate momentum; and (iii) deploy high-capacity flexible nets combined with a catchment basin at the slope foot to intercept residual blocks. This staged arrangement maximizes energy attenuation and overall risk reduction. This study shows that integrating high-resolution 3D point clouds with rigid-body contact dynamics overcomes the spatial discontinuities of conventional surveys. The approach substantially improves the accuracy and efficiency of hazardous rock stability assessments and rockfall trajectory predictions, offering a quantifiable, reproducible mitigation framework for long slopes, large rock volumes, and densely fractured cliff faces. Full article

Soft sensors are designed to be flexible, making them ideal for wearable devices as they can conform to the human body during motion, capturing pertinent information effectively. However, once these wearable sensors are constructed, modifying them is not straightforward without undergoing a re-prototyping process. In this study, we introduced a novel design for a modular soft sensor unit (M2SU) that incorporates a short, wire-shaped sensory structure made of eutectogel, with magnetic blocks at both ends. This design facilitates the easy assembly and reversible integration of the sensor directly onto a wearable device in situ. Leveraging the piezoresistive properties of eutectogel and the dual conductive and magnetic characteristics of neodymium magnets, our sensor unit acts as both a sensing element and a modular component. To explore the practical application of M2SUs in wearable sensing, we equipped a glove with 8 M2SUs. We evaluated its performance across three common gesture recognition tasks: numeric keypad typing (Task 1), symbol drawing (Task 2), and uppercase letter writing (Task 3). Employing a 1D convolutional neural network to analyze the collected data, we achieved task-specific accuracies of 80.43% (Top 3: 97.68%) for Task 1, 88.58% (Top 3: 96.13%) for Task 2, and 79.87% (Top 3: 91.59%) for Task 3. These results confirm that our modular soft sensor design can facilitate high-accuracy gesture recognition on wearable devices through straightforward, in situ assembly. Full article