Search Results (456)

This paper introduces an iceberg-based digital risk twin (DRT) framework for the health monitoring of complex engineering systems. The proposed model transforms multidimensional sensor and contextual data into a structured, interpretable three-dimensional geometry that captures both observable and latent risk components. Each monitored parameter is represented as a vertical geometric sheet whose height encodes a normalized risk level, producing an evolving iceberg structure in which the visible and submerged regions distinguish emergent anomalies from latent degradation. A formal mathematical formulation is developed, defining the mappings from the risk vector to geometric height functions, spatial layout, and surface composition. The resulting parametric representation provides both analytical tractability and intuitive visualization. A case study involving an aircraft fuel system demonstrates the capacity of the DRT to reveal dominant risk drivers, parameter asymmetries, and temporal trends not easily observable in traditional time-series analysis. The model is shown to integrate naturally into AI-enabled health management pipelines, providing an interpretable intermediary layer between raw data streams and advanced diagnostic or predictive algorithms. Owing to its modular structure and domain-agnostic formulation, the DRT approach is applicable beyond aviation, including power grids, rail systems, and industrial equipment monitoring. The results indicate that the iceberg representation offers a promising foundation for enhancing explainability, situational awareness, and decision support in the monitoring of complex engineering systems. Full article

Thermoelectric generators (TEGs) based on single-walled carbon nanotubes (SWCNTs) offer a promising approach for powering sensors in wearable systems. However, achieving high performance remains challenging because the high thermal conductivity of SWCNTs limits the temperature gradient within the device. We previously developed flexible SWCNT-TEGs with enhanced heat dissipation by dip-coating SWCNTs onto mesh sheets; however, their performance in real wearable environments had not been evaluated. In this study, we demonstrate the practical operation of these SWCNT-TEGs under conditions such as fingertip contact and cap-based wear. The output voltage increased proportionally with the number of fingers touching the device, and a stable voltage of 6.1 mV was obtained when the TEG was mounted on a cap and worn outdoors at 7 °C. These findings highlight the promising potential of flexible SWCNT-TEGs as power sources for next-generation wearable technologies, including human–computer interaction and health monitoring. Full article

Intrinsically disordered regions enable transcription factors (TFs) to undergo structural changes upon ligand binding, facilitating the transduction of environmental signals into gene expression. In this study, we applied molecular modeling methods to explore the hypothesis that unstructured inter-domain and subdomain linkers in bacterial TFs can function as sensors for carbohydrate signaling molecules. We combined molecular dynamics simulations and carbohydrate docking to analyze six repressors with GntR-type DNA-binding domains, including UxuR, GntR and FarR from Escherichia coli, as well as AraR, NagR and YydK from Bacillus subtilis. Protein models obtained from different time points of the dynamic simulations were subjected to sequential carbohydrate docking. We found that the inter-domain linker of the UxuR monomer binds D-fructuronate, D-galacturonate, D-glucose, and D-glucuronate with an affinity comparable to nonspecific interactions. However, these ligands formed multimolecular clusters, a feature absent in the UxuR dimer, suggesting that protein dimerization may depend on linker occupancy by cellular carbohydrates. D-glucose interacted with linkers connecting subdomains of the LacI/GalR-type E-domains in GntR and AraR, forming hydrogen bonds that connected distant structural modules of the proteins, while in NagR, FarR and YydK, it bridged the inter-domain linkers and a β-sheet within the HutC-type E-domains. Hence, our results establish flexible linkers as pivotal metabolic sensors that directly integrate nutritional cues to alter gene expression in bacteria. Full article

Transparent conductive oxides (TCOs), such as indium tin oxide (ITO), are essential for flexible electronics; however, conventional vacuum-based deposition is costly and thermally aggressive for polymers. This study investigated the surface functionalization of PET substrates with ITO thin film-based forced hydrolysis as a low-cost, reproducible alternative. $\mathrm{S}\mathrm{n}{\mathrm{O}}_2$ nanoparticles were synthesized by forced hydrolysis at 180 °C for 3 h and 6 h, yielding crystalline nanoparticles with a cassiterite phase and an average crystallite size of 20.34 nm. The process showed high reproducibility, enabling consistent structural properties without complex equipment or high-temperature treatments. The $\mathrm{S}\mathrm{n}{\mathrm{O}}_2$ sample obtained at 3 h was incorporated into commercial ${\mathrm{I}\mathrm{n}}_2{\mathrm{O}}_3$ to form a mixed In–Sn–O oxide, which was subsequently deposited onto PET substrates by spin coating onto UV-activated PET. The resulting 1.1 µm ITO films demonstrated good adhesion (4B according to ASTM D3359), a low resistivity of 1.27 × 10⁻⁶ Ω·m, and an average optical transmittance of 80% in the visible range. Although their resistivity is higher than vacuum-processed films, this route provides a superior balance of mechanical robustness, featuring a hardness of (H) of 3.8 GPa and an elastic modulus (E) of 110 GPa. These results highlight forced hydrolysis as a reproducible route for producing ITO/PET thin films. The thickness was strategically optimized to act as a structural buffer, preventing crack propagation during bending. Forced hydrolysis-driven PET sheet functionalization is an effective route for producing durable ITO/PET electrodes that are suitable for flexible sensors and solar cells. Full article

Understanding short-term glacier motion is vital for assessing ice sheet dynamics in a warming climate. This study investigates the tidal and diurnal influences on the flow of Helheim Glacier, one of Greenland’s fastest-flowing marine-terminating glaciers, using data from 18 high-frequency GPS sensors and a regional tide gauge collected during summer 2013. A Kalman filter was applied to separate and quantify glacier velocity, tidal admittance, and diurnal melt-driven acceleration. Results reveal a high level of tidal admittance affecting the horizontal flow speed of the glacier, especially at the centre of the glacier, which is propagated upstream. This admittance corresponds to a 0.38–0.68 m/day reduction from the mean at high spring tide and a comparable increase at low tide. The glacier’s vertical motion showed strong tidal control close to the terminus, of 0.6–1.05 m during high spring tides, but this was significantly reduced more than 1 km from the terminus. Diurnal variations in horizontal speed are less spatially and temporally variable, with most nodes experiencing changes from a mean speed of ±0.1–0.3 m/day. These findings demonstrate that both tidal forcing and meltwater input to the basal system exert a significant, and potentially spatially variable, control on glacier dynamics, highlighting the need to incorporate short-period external forcing into predictive models of marine-terminating glacier behaviour. Full article

rGO/ZnO composites have been widely studied for use as toxic gas sensors due to the synergistic effect between the materials and the reduction in sensor operating temperature promoted by rGO. However, few studies have employed rGO/ZnO sensors for ozone detection, as graphene materials are oxidized and/or degraded when exposed to ozone. This paper reports on a study of ZnO/rGO/ZnO-based sensors with different ZnO NP morphologies for ozone sensing. ZnO nanoparticles with needle-like and donut-like morphologies were synthesized by the precipitation method, and bare ZnO and ZnO/rGO/ZnO composite sensors were fabricated by layer-deposition of ZnO and/or rGO via drop-casting, forming a “sandwiched” structure that protects the rGO sheets. Bare ZnO and ZnO/rGO/ZnO composites were analyzed by varying the temperature from 200 to 300 °C. The ZnO/rGO/ZnO sensor provided a high 13.3 response (R_gas/R_air) and recovery times of 442 s and 253 s, respectively, for 50 ppb of O₃, as well as high selectivity to ozone gas compared to CO, NH₃, and NO₂ gases. No oxidation or degradation of the sensor was observed during ozone detection measurements, indicating that the adopted manufacturing methodology was successful. Full article

Leaks in ponds are a problem due to the loss of water resources, although the problem is greater when the ponds store livestock or agricultural waste (slurry or wastewater), in which case there is a risk of hydrogeological contamination of the environment. The proposed leak detection system is based on distributed temperature sensing (DTS) with hybrid fiber optics using the Raman effect. Using active detection techniques, i.e., applying a specific amount of electrical power to the copper wires that form part of the hybrid cable, it is possible to increase the temperature along the fiber and measure the thermal increments along it, detecting and locating the point of leakage. To validate the system, a full-scale prototype reservoir (25 m × 10 m × 3.5 m) was built, equipped with mechanisms to simulate leaks under the impermeable sheet that retains the reservoir’s contents. For environmental reasons, the tests were carried out with clean water. The results of the leak simulation showed significant differences in temperature increases due to the electrical pulse in the areas affected by the simulated leak (1 °C increase) and the areas not affected (5 °C increase). This technology, which uses hybrid fiber optics and a low-cost sensor, can be applied not only to ponds, but also to other types of infrastructure that store or retain liquids, such as dams, where it has already been tested, to measure groundwater flow, etc. Full article

Transparent conductive materials (TCMs) are essential for optoelectrical devices ranging from smart windows and defogging films to soft sensors, display technologies, and flexible electronics. Materials, such as indium tin oxide (ITO) and silver nanowires (AgNWs), are commonly used and offer high optical transmittance and electrical conductivity, but suffer from brittleness, oxidation susceptibility, and require high-cost materials, greatly limiting their use. Carbon nanotube (CNT) networks provide a promising alternative, featuring mechanical compliance, chemical robustness, and scalable processing. This study reports an aqueous ink formulation composed of ultra-long mix-walled carbon nanotubes (UL-CNTs), compatible with the flow coating process, yielding uniform transparent conductive films (TCFs) on polyethylene terephthalate (PET), glass, and polycarbonate (PC). The resulting films exhibit tunable transmittance (85%–88% for single layers; ~57% for three layers at 550 nm) and sheet resistance of 7.5 kΩ/□ to 1.5 kΩ/□ accordingly. These TCFs maintain stable sheet resistance for over 5000 bending cycles and show excellent mechanical durability with negligible effects on heating performance. Post-deposition treatments, including nitric acid vapor doping or flash photonic heating (FPH), further reduce sheet resistance by up to 80% (7.5 kΩ/□ to 1.2 kΩ/□). X-ray photoelectron spectroscopy (XPS) results in reduced surface oxygen content after FPH. The photonic-treated heaters attain ~100 °C within 20 s at 100 V. This scalable, water-based process provides a pathway toward low-cost, flexible, and stretchable devices in a variety of fields, including printed electronics, optoelectronics, and thermal actuators. Full article

We investigate crosstalk effects in a dual-modality tactile–proximity sensing system based on Time-of-Flight (ToF) technology integrated within a flat poly(methyl methacrylate) (PMMA) light guide. Building on the OptoSkin framework, we employ two commercially available TMF8828 multi-zone ToF sensors, one configured for tactile detection via frustrated total internal reflection (FTIR) and the other for external proximity measurements through the same transparent substrate. Controlled experiments were conducted using a 2 cm² silicone pad for tactile interaction and an A4-sized diffuse white target for proximity detection. Additional measurements with a movable PMMA sheet were performed to quantify signal attenuation, peak broadening, and confidence degradation under transparent-substrate conditions. The results demonstrate that the TMF8828 can simultaneously resolve both contact-induced scattering and distant reflections, but that localized interference zones occur when sensor fields of view overlap within the substrate. Histogram analysis reveals the underlying multi-path contributions, providing diagnostic insight not available from black-box ToF devices. These findings highlight both the opportunities and limitations of integrating multiple ToF sensors into transparent waveguides and inform design strategies for scalable robotic skins, wearable interfaces, and multi-modal human–machine interaction systems. Full article

Objectives: Evidence regarding the impacts of systemic inflammation on nocturnal sleep in advanced cancer patients is limited. We determined the association of serum C-reactive protein (CRP) levels with sleep in patients with non-imminent and those with imminent death. Methods: This was a secondary analysis of an observational study conducted in patients newly referred to a palliative care unit. Nocturnal sleep was assessed based on the “sleeping time” measured using a sheet-type non-wearable sensor. Patients were divided into long-survival and short-survival groups depending on the median survival (11 days), and within each group, the patients were categorized according to CRP levels: low (<1 mg/dL), moderate (1–10 mg/dL), and high (≥10 mg/dL). To evaluate correlations between CRP levels and sleeping time, binomial logistic analysis was performed. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. Results: A total of 535 patients were included in the main analysis. In the long-survival group (n = 273), the high-CRP patients had significantly longer sleeping time than the low-CRP patients (OR 2.81, 95% CI 1.19–6.65, p-value 0.019), whereas there were no significant correlations in the short-survival group (n = 262). Conclusions: Higher CRP levels were associated with longer sleeping time in patients with non-imminent death, whereas there were no correlations in patients whose death was imminent. The clinical implications of serum CRP levels appear to vary with life expectancy in terminally ill patients with cancer. Further research is necessary to verify the present findings. Full article

Conductive hydrogels show great promise for wearable sensors but suffer from low sensitivity in small strain ranges. In this study, we developed a micropatterned composite hydrogel sheet (thickness: 1.2 ± 0.1 mm) by constructing a continuous electronic conductive network of carbon nanotubes (CNTs) on a highly crosslinked micropatterned hydrogel sheet. The sheet was fabricated via a two-step synthesis of a polyvinyl alcohol/polyacrylic acid polymer network—crosslinked by Zr⁴⁺ in a glycerol-water system—using sandpaper as the template. The first step ensured tight conformity to the template, while the second step preserved the micropattern’s integrity and precision. The reverse sandpaper micropattern enables secure bonding of CNTs to the hydrogel and induces localized stress concentration during stretching. This triggers controllable cracking in the conductive network, allowing the sensor to maintain high sensitivity even in small strain ranges. Consequently, the sensor exhibits ultra-high sensitivity, with gauge factors of 76.1 (0–30% strain) and 203.5 (30–100% strain), alongside a comfortable user experience. It can detect diverse activities, from subtle physiological signals and joint bending to complex hand gestures and athletic postures. Additionally, the micropatterned composite hydrogel sheet also demonstrates self-healing ability, adhesiveness, and conformability, while performing effectively under extreme temperatures and sweaty conditions. This innovative structure and sensing mechanism—leveraging stress concentration and controlled crack formation—provides a strategy for designing wearable electronics with enhanced performance. Full article

Ballistocardiography (BCG) is a noninvasive modality for detecting cardiac activity. This study developed a robust atrial fibrillation (AF) detection algorithm using multiple BCG sensors at different locations and evaluated the improvement in accuracy by combining data from multiple sensors. We recorded the BCG using a piezoelectric rubber sheet sensor and an electrocardiogram in 84 participants (29 with AF and 55 without AF) in the supine position. Four BCGs (BCG1–4) were obtained using sensors placed from the head to the lumbar region (0, 25, 45, and 65 cm from the head). The BCG signals were divided into 32 s blocks and analyzed. After applying fast Fourier transform, we input the power spectrum, focusing on frequencies below 10 Hz, into machine learning (ML) classifiers to distinguish between AF and non-AF with parameter tuning. The AdaBoost classifier for BCG2 exhibited the highest accuracy (0.88) among the ML models for each sensor. When we applied the classifier to other BCGs, it achieved accuracies of 0.92, 0.73, and 0.78 for BCG1, 3, and 4, respectively. The combined model using multiple sensors exhibited an accuracy of 0.92. The optimized model for BCG2 was robust against shifts in the sensor toward the head and lumbar directions. A combined assessment using multiple sensors improved performance. Full article

This study presents an integrated experimental and numerical investigation of the Refill Friction Stir Spot Welding (RFSSW) process applied to dissimilar aluminum alloys. The primary objective is to evaluate the mechanical and thermal behavior of the joints and to identify key process parameters influencing weld quality. Experimental welding trials were performed on aluminum alloy sheets using RFSSW, followed by shear testing and metallographic analysis to assess joint integrity, microstructure evolution, and fracture behavior. Infrared thermography and temperature sensors were employed to monitor heat distribution during welding. In parallel, a finite element model was developed to simulate the thermal cycle and stress distribution within the welded region. The numerical results showed good agreement with the experimental data, particularly regarding peak temperature and cooling trends at specific distances from the tool center. The findings demonstrate that RFSSW can successfully join dissimilar aluminum alloys with minimal defects when optimized parameters are applied. The combination of experimental observations and FEM simulation provides valuable insights into the underlying thermomechanical phenomena and offers a foundation for further process optimization. Full article

With the breakthroughs in quantum theory and the rapid advancement of quantum precision measurement sensor technologies, atomic magnetometers based on the spin-exchange relaxation-free (SERF) mechanism have played an increasingly important role in ultra-weak biomagnetic field detection, inertial navigation, and fundamental physics research. To achieve high-precision measurements, SERF magnetometers must operate in an extremely weak magnetic field environment, while the detection of ultra-weak magnetic signals relies on a low-noise background. Therefore, accurate measurement, modeling, and analysis of magnetic noise in shielding materials are of critical importance. In this study, the magnetic noise of permalloy sheets was modeled, separated, and analyzed based on their measured magnetic properties, providing essential theoretical and experimental support for magnetic noise evaluation in shielding devices. First, a single-sheet tester (SST) was modeled via finite element analysis to investigate magnetization uniformity, and its structure was optimized by adding a supporting connection plate. Second, an experimental platform was established to verify magnetization uniformity and to perform accurate low-frequency measurements of hysteresis loops under different frequencies and field amplitudes while ensuring measurement precision. Finally, the Bertotti loss separation method combined with a PSO optimization algorithm was employed to accurately fit and analyze the three types of losses, thereby enabling precise separation and calculation of hysteresis loss. This provides essential theoretical foundations and primary data for magnetic noise evaluation in shielding devices. Full article

Tool wear is a major challenge in sheet-metal forming, as it directly affects product quality and process stability. Reliable monitoring of tool-wear conditions is therefore essential, yet it remains challenging due to limited data availability and uncertainties in manufacturing conditions. To this end, this study evaluates different strategies for developing robust machine learning models under data scarcity for fluctuating manufacturing conditions: a 1D-CNN using time-series data (baseline model), a 1D-CNN with signal fusion of force and acceleration signals, and a 2D-CNN based on Gramian Angular Field (GAF) transformation. Experiments are conducted using inline data from a blanking process with varying material thicknesses and varying availability of training data. The results show that the fusion model achieved the highest improvement (up to 93.2% with the least training data) compared to the baseline model (78.3%). While the average accuracy of the 2D-CNN was comparable to that of the baseline model, its performance was more consistent, with a reduced standard deviation of 5.4% compared to 9.2%. The findings underscore the benefits of sensor fusion and structured signal representation in enhancing classification robustness. Full article