Search Results (5,051)

Electrotactile feedback is a compact approach for providing tactile cues in robotic teleoperation, but personalized calibration remains time-consuming because tactile perception varies across users. To address this problem, this study develops a subject-informed multi-layer finite element model of fingertip electric-field distribution coupled with a neural-response model and proposes a simulation-derived configuration-ranking method termed the Perceived Correctness Score (PCS). A gradient boosting regression model is then used to recommend among 36 candidate electrode diameter–spacing combinations. Validation was conducted using a custom-developed $3\times 2$ multi-channel fingertip electrotactile stimulation system in a shape/area recognition task involving six healthy subjects. The predicted PCS showed a moderate positive correlation with the measured mean recognition accuracy across configurations (Pearson $r=0.48$, $p<0.05$). The model achieved Top-1 exact matching for three of six subjects and Top-5 coverage for five of six subjects. Compared with conventional exhaustive psychophysical calibration, the proposed method reduced the average configuration time from 122.7 min to 16.0 min, corresponding to an efficiency improvement of 87.0%. These results show that model-guided ranking can substantially reduce the burden of individualized electrotactile configuration. Full article

Harnessing low-grade thermal energy from industrial processes and the environment represents an attractive route toward sustainable chemical production. In this work, we report a thermoelectrocatalytic (TE-Catal) system capable of converting small temperature gradients into chemical energy for hydrogen peroxide (H₂O₂) generation. A hybrid catalyst composed of nickel-based metal–organic frameworks (Ni-MOFs) nanoparticles integrated with carbon nanotubes (CNTs), Ni-MOFs/CNTs, was synthesized through a facile one-pot strategy. Under a temperature gradient, the thermoelectric response of the Ni-MOFs induces charge carrier generation through the Seebeck effect, enabling interfacial redox reactions that produce H₂O₂. However, rapid recombination of thermally generated carriers typically limits catalytic efficiency. By coupling Ni-MOFs with conductive CNTs networks, charge separation and transport are significantly enhanced due to the strong interfacial interaction and the high electrical conductivity of CNTs. As a result, the Ni-MOFs/CNTs nanohybrids exhibit greatly improved H₂O₂ generation rate of ~111.7 µmol g⁻¹ h⁻¹ compared with pristine Ni-MOFs (31.8 µmol g⁻¹ h⁻¹). Thermoelectric electrochemical measurements confirm that the CNT incorporation effectively promotes carrier migration and suppresses recombination. This study demonstrates the potential of MOF-based thermoelectric nanostructures for transforming waste heat into valuable chemical products. Full article

Microplastics in freshwater environments provide persistent substrates for microbial colonization, forming the plastisphere. However, how trophic conditions shape plastisphere bacterial communities in drinking-water source reservoirs remains poorly understood. In this study, nine major drinking-water source reservoirs in Longyan City, Fujian Province, China, were investigated. Water quality measurements, trophic state assessment, and 16S rRNA gene amplicon sequencing were combined to characterize plastisphere bacterial communities across oligotrophic and mesotrophic reservoirs. The comprehensive trophic level index classified four reservoirs as mesotrophic and five as oligotrophic. Bacterial alpha diversity indices showed no significant trophic-dependent pattern, whereas PERMANOVA revealed significant compositional divergence between trophic groups (p < 0.01). Electrical conductivity, pH, and dissolved oxygen were the strongest correlates of community variation. Mesotrophic reservoirs were enriched in Bacillota and Bacteroidota, with biomarkers mainly affiliated with Comamonadaceae, while oligotrophic reservoirs harbored more diverse biomarkers dominated by Pseudomonadota and Cyanobacteriota. Functional prediction indicated that only aliphatic non-methane hydrocarbon degradation differed significantly between trophic groups, whereas nitrogen-cycling functions showed no significant divergence. These findings demonstrate that trophic status acts as a significant environmental filter shaping plastisphere community structure in drinking-water source reservoirs, even within a narrow oligotrophic-to-mesotrophic gradient, providing new insights for ecological risk assessment of microplastics in source-water ecosystems. Full article

Soil erosion and nutrient loss degrade the soil resource base and water quality in dryland agricultural landscapes, yet optimal design of vegetation buffers for soil conservation under intensifying rainfall remains poorly quantified, particularly for nutrient retention. This study is novel in integrating event-scale rainfall-simulation experiments, Bayesian hierarchical modelling, Causal Forest analysis, and WEPP simulations to quantify how the sequential addition of biocrusts and grasses to shrub buffers shifts density thresholds for runoff, soil loss, and nutrient export across varying rainfall intensities. Experiments were conducted across a continuous shrub-density gradient (0–11,429 plants ha⁻¹) representing three configurations: shrub monoculture, shrub-biocrust, and shrub-biocrust-grass on agricultural hillslopes of the Chinese Loess Plateau. Runoff, soil loss, and exports of total nitrogen (TN) and total phosphorus (TP) were measured. Results demonstrate three main findings. First, multilayer shrub–biocrust–grass buffers exhibited lower soil loss than monocultures. Posterior estimates indicate reductions from approximately 3.8 t ha⁻¹ at moderate monoculture density to below 1.0 t ha⁻¹ at lower planting densities, with 94% of the highest-density intervals reflecting uncertainty in these estimates. Second, Causal Forest analysis reveals a functional separation of controls: rainfall intensity dominates soil loss (88% importance) and runoff (84%), whereas nutrient retention responds more strongly to buffer structure and density management. Third, WEPP simulations across rainfall intensities (50–180 mm h⁻¹) and slopes (10–30%) identify an optimal multilayer buffer density of 3800–5700 plants ha⁻¹, which delivers robust multifunctional benefits with 50–67% lower planting requirements than conventional high-density monocultures. These findings demonstrate that multilayer vegetation buffers enhance soil retention and reduce nitrogen and phosphorus losses from hillslopes, sustaining the soil resource base and protecting water quality in dryland agricultural landscapes. The integrated modelling framework provides transferable, evidence-based density recommendations for climate-resilient soil conservation in similar dryland regions. Full article

Flying Ad Hoc Networks (FANETs) composed of multiple unmanned aerial vehicles (UAVs) are a promising solution for emergency wireless communications when terrestrial infrastructure is unavailable. However, ensuring reliable Quality of Service (QoS) in these highly dynamic networks remains challenging due to topology changes, varying propagation conditions, and congestion. This work proposes a lightweight machine learning-based QoS optimization framework for multi-UAV emergency communications that combines realistic mobility modeling, empirical channel measurements, and adaptive traffic prioritization. UAV mobility patterns are generated with ArduSim, while LoS/NLoS propagation models are derived from real UAV flight experiments and integrated into ns-3. Multiple supervised machine learning algorithms—including Decision Trees, Random Forest, Support Vector Machines, k-NN, Gradient Boosting, and CatBoost—are trained using four input features derived from the network state: ${\mathrm{CBR}}_{\mathrm{src}}$, ${\mathrm{QP}}_{\mathrm{src}}$, ${\mathrm{CBR}}_{\mathrm{dst}}$, and ${\mathrm{QP}}_{\mathrm{dst}}$. Simulation results show that the proposed AI SMOTE EMERGENCY scheme, based on CatBoost, improves the Packet Delivery Ratio (PDR) by approximately 43% over the No-QoS baseline, achieving 89–93% delivery across all four application ports. Compared with EDCA, the proposed scheme maintains reliable delivery for all services, increases emergency throughput by 34–36%, and reduces end-to-end delay by about 70%. In addition, the higher delivery reliability translates into clear communication energy benefits, reducing energy waste across all evaluated topologies when compared with the No-QoS baseline. The inference time remains below 0.002 s, supporting real-time QoS adaptation in resource-constrained UAV networks. Full article

In sediment-laden irrigation channels, sediment deposition upstream of hydraulic measuring structures can degrade hydraulic performance, reduce measurement reliability, and increase maintenance demand. To clarify the effects of structural optimization on sediment transport and siltation resistance, physical model experiments were conducted on an airfoil weir-orifice facility under different discharges, structural angles, and sediment concentrations. The analysis focused on sediment deposition patterns, longitudinal water surface profiles, sediment concentration, suspended sediment transport rate, cross-sectional velocity distribution, vertical velocity gradient, and Froude number. The results showed that the optimized configuration produced a flatter and more uniform upstream bed morphology, and the average deposition thickness decreased from 4.83 cm to 4.31 cm, corresponding to a reduction of 10.58%. Under all tested conditions, the optimized configuration reduced upstream backwater, increased local flow velocity, and shifted the hydraulic jump closer to the facility outlet. Sediment concentration and suspended sediment transport rate were consistently higher after optimization, indicating enhanced sediment carrying capacity. In addition, the optimized configuration increased the vertical velocity gradient and Froude number, while all cases remained within the subcritical-flow regime. These findings demonstrate that structural optimization can simultaneously improve hydraulic regulation and siltation resistance, and provide an experimental basis for the application of streamlined hydraulic measuring structures in sediment-laden irrigation channels. Full article

The preparation for next-generation Earth Observation missions, such as the European Space Agency’s (ESA) Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) and Land Surface Temperature Monitoring (LSTM), requires robust pre-launch proxy datasets. Because current simulation methodologies frequently rely on isolated, platform-specific approaches, this study proposes a comprehensive, unified multisensor framework capable of dynamically generating operationally realistic CHIME and LSTM datasets from diverse airborne and satellite sources. Three distinct processing pipelines were established. For hyperspectral data simulation, precursor satellite imagery (PRISMA and EnMAP) and high-resolution airborne measurements (HySpex) were harmonized to CHIME’s 30 m specifications utilizing Spectral Response Function (SRF) adjustments, Point Spread Function (PSF) spatial resampling, and 6S atmospheric radiative transfer modeling. For thermal data simulation, archive Landsat 8/9 and ASTER imagery were transformed into LSTM’s target 50 m, 5-band configuration using a synergistic two-step approach: a physics-based Spectral Super-Resolution (SSR) module followed by an AI-driven Spatial Super-Resolution (SpSR) transformer network. Evaluated across highly diverse inland, coastal, and riverine testbeds in Italy, the simulated products demonstrated high spectral, spatial, and radiometric fidelity. While inherently constrained by the native spectral ranges of the input sensors and by the current lack of absolute on-orbit mission data for validation, the downscaled images closely reproduced complex thermal patterns and water-quality gradients. Ultimately, this scalable framework provides the remote sensing community with early access to representative datasets and mission performance assessments, while accelerating pre-launch algorithm development and testing for environmental monitoring applications—particularly those focused on water discharges. Full article

This study investigates the spatial distribution of ambient dose equivalent rates (ADER) on Avala and Kosmaj mountains, two protected landscapes located within the territory of the City of Belgrade, Serbia. Both sites, characterized by rich biodiversity and cultural heritage, were analyzed to assess their radiological safety and suitability for outdoor recreation. In mid-October 2025, in situ measurements were conducted at 42 sampling points using the Radex RD1503+ GM counter. The recorded values ranged from 0.085 to 0.2 µSv/h, remaining below the recommended safety threshold of 0.2 µSv/h. To visualize the gamma dose spatial variability, all field data were georeferenced and processed in QGIS 3.28.10 using the Inverse Distance Weighting (IDW) interpolation method. Integration of GIS and Remote Sensing techniques enabled the correlation between gamma radiation patterns, land cover, and elevation gradients derived from digital elevation models (DEMs). The comprehensive GIS-based approach confirms that Avala and Kosmaj maintain low natural background radiation levels comparable to global averages for similar geomorphological settings, and therefore are safe and suitable for sports, tourism and recreation. The applied combination of field dosimetry, Remote Sensing, and geostatistical modeling provides a valuable framework for continuous environmental monitoring and sustainable landscape management in protected mountainous landscapes in Central Serbia. Full article

Chemotaxis enables eukaryotic cells to detect and migrate along extracellular chemoattractant gradients spanning several orders of magnitude. This remarkable dynamic range relies on adaptation, a process that allows cells to reset their signaling machinery while preserving sensitivity to incremental changes in stimulus intensity. Although numerous actin-dependent feedback mechanisms have been characterized, the molecular basis of adaptation within an actin-independent core gradient-sensing module remains poorly understood. Here, we identify the Ras GTPase-activating protein, C2GAP1, as a critical F-actin-independent effector of the heterotrimeric G protein, Gα2, in Dictyostelium discoideum. Using cytoskeleton-free gradient-sensing cells, quantitative imaging, biochemical assays, FRET-based G-protein activation measurements, and structural modeling, we demonstrate that C2GAP1 controls concentration-dependent adaptation during gradient sensing. Mechanistically, C2GAP1 directly associates with Gα2 in both GDP- and GTP-bound states, with preferential binding to activated Gα2, thereby sustaining membrane recruitment and locally attenuating Ras and downstream signaling. Loss of C2GAP1 enhances G-protein activation, disrupts local inhibition, and impairs rapid reorientation in dynamic gradients. These findings define a direct coupling between heterotrimeric G proteins and the RasGAP, C2GAP1, as a core adaptive module that enables gradient sensing across a wide concentration range. Full article

X-band copper resonating cavities are key components of future pulsed GHz normal-conductive multi-TeV accelerators. High electric field gradients are required for emerging applications; however, as gradients increase, components’ lifetime decreases, primarily due to radiofrequency (RF) breakdown. Coating technologies are being investigated in several laboratories to improve RF structure, performance and lifetime. To this end, we investigated the feasibility of fabricating nanometer-periodic Cu/Mo metallic multilayers on three-dimensional (3D) aluminum mandrels designed to replicate X-band copper resonating cavities. These nanometer-period multilayers are proposed to mitigate surface degradation due to electric breakdown at high accelerating gradients by stabilizing inner cavity surfaces against dislocation evolution and roughening caused by thermo-mechanical fatigue. High-Power Impulse Magnetron Sputtering (HiPIMS) in a bias-controlled dual closed-field magnetron configuration was employed to deposit alternating Mo and Cu nano-layers onto the 3D geometries. Given the complexity of HiPIMS technology, plasma pulse evolution was studied by combining time-resolved optical emission spectroscopy with electrical measurements of the pulse discharge. The influence of the process parameters, particularly the applied DC bias, on film growth was studied using non-destructive microprobe α-particle elastic backscattering spectrometry (µEBS) and scanning transmission electron microscopy (STEM). STEM and µEBS analyses confirmed that Mo layers with thicknesses of approximately 5–35 nm were successfully deposited repeatedly on thicker Cu layers (30–150 nm), preserving individual layer properties with minimal interdiffusion and alloying. The layers were deposited inside trenches with an aspect ratio of 5:1 representative of X-band irises. This technology, coupled with the replica process, could be applied to highly engineered nanostructured coatings for X-band cavity treatment in compact particle accelerator prototypes, as it may improve electrical breakdown lifetime under high accelerating fields, at least for degradation processes driven by the high mobility of copper dislocations. Full article

This paper introduces a policy-search-based reinforcement learning algorithm aimed at generating optimal set-points of wind turbines in wind farms. The proposed approach addresses the problem of multivariable optimization in systems where the objective function is unknown or difficult to model. The algorithm is a model-free framework and relies solely on measured performance of the system. Namely, it does not require gradient information of the objective function or an explicit model of the aerodynamic interaction between wind turbines. The proposed scheme utilizes stochastic policy perturbations to explore the search space and update the policy parameters directly based on the observed reward signal. In this way, the algorithm progressively drives the control variables toward optimal operating conditions. The proposed policy-search reinforcement learning framework is analyzed to establish its connection with gradient-free optimization methods. The proposed method is applied to wind farm power optimization, where multiple turbine control variables must be adjusted in the presence of wake interactions cooperatively. The performance of the proposed approach is evaluated through extensive simulations under both steady-state and time-varying wind conditions. The proposed algorithm is compared with an extremum-seeking control method that was previously suggested for the same problem. The results demonstrate that the proposed approach is able to effectively maximize power production in wind farms while maintaining a simple and model-free optimization structure. Full article

Metal(loid) contamination is a persistent environmental stressor in terrestrial ecosystems, yet field-based evidence linking cumulative soil contamination to physiological responses in social insects remains limited. In this study, we investigated an industrial pollution gradient by measuring soil concentrations of potentially toxic elements across multiple sites and integrating multi-element exposure into a cumulative pollution index. Two ant taxa, Lasius niger (Linnaeus, 1758) and Tetramorium cf. caespitum (Linnaeus, 1758), were sampled using a standardized field design, and biochemical endpoints were assessed to characterize antioxidant defense, thiol-based redox status, oxidative damage, and neuroenzymatic responses. Ant homogenates were analyzed spectrophotometrically for antioxidant enzymes, reduced glutathione, lipid peroxidation, protein oxidation, and acetylcholinesterase activity compared with the local low-contamination reference site. In addition, PLI showed positive site-level associations with multiple biomarkers, suggesting coordinated covariation between cumulative soil contamination and biochemical responses. Because these analyses were based on site-level mean values and direct tissue metal burdens were not measured, the findings should be interpreted as field-based associations rather than evidence of direct internal dose–response or metal-specific causality. These findings suggest that cumulative soil metal(loid) contamination is linked to integrated oxidative and neuroenzymatic stress responses in ants and support the use of ant-based biomarkers as informative tools for ecological biomonitoring under field conditions. Full article

We analyse pebble RFID tracing observations to investigate sediment transport dynamics in gravel-bed rivers using statistical modelling. This study examines a dataset of nearly 3500 tracer displacement measurements collected during 27 sediment-mobilizing events in a pre-Alpine reach in Italy. Our analysis follows three main steps, addressing tracer mobility patterns, event-scale transport dynamics, and reach-scale bedload inference. First, using Markov Chain analysis of state transitions on typical and high-magnitude transport events, we demonstrate that pebbles tend to maintain their mobility state between events, characterizing the between-event intermittency of bedload transport. A subsequent analysis of flow characteristics reveals that consecutive floods of similar magnitude exhibit increasing movement probability while maintaining similar virtual velocities. Finally, we train Gradient Boosting regression models to estimate distributions of pebble displacements and virtual velocities (defined, following common usage, as the ratio between the distance a tracer travels during a mobilising event and the duration of that event). Together with Monte Carlo propagation, these models are used to derive reach-scale volume estimates. The models identify flow rate and event duration as primary controls, while grain size has minimal influence within the sampled range of tracer dimensions. To strengthen our approach, we implement an extensive multi-stage validation process aimed at both single-tracer predictions and overall basin-scale movement estimates. The results indicate that high-magnitude transport events (12% of observations) contribute similar bedload volumes as typical events (88% of observations), highlighting the significant role of extreme events in total sediment transport. Model predictions yield bedload volume estimates that align well with independent measurements from a downstream sediment retention basin. Full article

Skewness (S) and kurtosis (K) are statistical measures that provide insights into the characteristics of turbulence. This paper investigates the effects of adverse pressure gradients (APG) on S and K for mean and fluctuating velocities in the turbulent boundary layer (TBL), using the probability distribution function (PDF) and cumulative distribution function (CDF). The velocity distributions in the TBL are obtained experimentally. The experiments are conducted at Re ~ 1.12 × 10⁵. According to the Clauser criterion, the APG parameter is β = 0.62. Two test sections are examined: a straight duct (zero pressure gradient) and a straight diffuser with a divergence angle of 6° and a cross-sectional area ratio of 1:4. Measurements are performed at five streamwise stations (x/c = 1, 1.5, 2, 3, and 4, where c = 100 mm). The results show that the APG does not influence the maximum or minimum values of the PDFs for mean and fluctuating velocities. Compared to the third and fourth moments, variations in the first and second moments are minimal. It is found that S values for the straight duct are lower than those for the straight diffuser. The largest difference is observed in the fourth moment of the PDF, i.e., K. Additionally, four PDF curve-fitting equations are presented for the mean velocity and velocity fluctuations in the TBL for both the straight duct and the straight diffuser. Differential entropy analysis indicates that the decrease in entropy resulting from wall shear and the turbulent boundary layer in the straight channel is more pronounced than the reduction in mean velocity entropy caused by the APG in the diffuser channel. Full article

Dry soil spectral reflectance provides a stable baseline for characterizing soil optical properties and supporting the retrieval of soil attributes from remote sensing. However, despite the large number of studies on soil spectral reflectance, most existing research primarily focuses on empirical relationships between spectra and soil properties. The representation and prediction of dry soil reflectance as a baseline condition, particularly under the influence of environmental factors, remain insufficiently explored, and the generalizability of existing models still needs improvement. Therefore, this study collects 700 dry soil samples with laboratory-measured spectral reflectance from Northeast China and quantitatively analyzes the contribution of environmental covariates (soil properties, parent material, and geographical location) using the SHAP method. Then, an environmental and edaphic-factor-driven smooth dry soil reflectance model (EEDSR) model covering 400–2500 nm is developed based on gradient boosting regression (GBR), and its accuracy is evaluated using global ISRIC soil datasets. Our results indicate the following: (1) the reflectance of dry soil is closely related to the soil properties in the VIS to SWIR range. The reflectance of dry soil of 400–2500 nm is positively correlated with clay percentage, longitude, and parent material but negatively correlated with latitude, sand percentage and silt percentage. And its correlation with other variables (such as soil organic matter, pH, and EC) varies with wavelength. (2) The EEDSR model exhibited high predictive accuracy across the 400–2500 nm spectral range (R² = 0.93, RMSE = 0.018). Additionally, incorporating parent material (PM) and geographical factors into the predictor set enhanced the accuracy of dry soil reflectance prediction by 13.4%. (3) The spatial consistency between the predicted soil reflectance in Northeast China and the satellite observations indicates that the EEDSR model has good performance in predicting soil reflectance, as the bias of reflectance gradually increasing from west to east is consistent with the precipitation distribution in Northeast China. (4) The generalization ability of the EEDSR model was confirmed by global ISRIC datasets (R = 0.94), outperforming the deep learning-based Soil Optical Generative Model (SOGM) (R = 0.27). Overall, this study presents an efficient and interpretable framework for modeling dry soil spectral reflectance, providing a robust reference for soil reflectance prediction and remote sensing-based soil property retrieval. Full article