Search Results (6,625)

Decarbonisation is crucial for mitigating climate change, with ground source heat pumps (GSHPs) playing a key role in reducing reliance on gas for space heating. This study investigates the potential of an open-loop GSHP system in the Carboniferous Millstone Grit of Ilkley, West Yorkshire, using Ilkley Lido and surrounding sports facilities as an example heating demand. The feasibility of systems installed at depths less than 150 m is examined, considering both shallow and deeper geological conditions. Available data on subsurface geology, hydrogeology, and geothermal gradients are utilised to characterise the formations and target depths, with cross-sectional models developed to assess the potential. The Marchup Grit aquifer is identified as the primary target due to its relatively shallow depth (~90 m), expected subsurface temperature (~14.5 °C), and moderate transmissivity. Additional geothermal potential is also considered in the Warley Wise Grit and Pendleside Limestone. The study contrasts borehole and field data with literature findings, including measurements at outcrop level of the Marchup Grit. To assess the feasibility of an open-loop GSHP system, a doublet configuration is simulated, matching the estimated heat demand of the facilities. The results demonstrate that an open-loop doublet system is conditionally feasible within the Marchup Grit aquifer; however, long-term operational performance remains sensitive to thermal feedback (estimated at ~22 years analytically for a 300 m well spacing and 8–10 years numerically under a 130 m minimum spacing constraint at peak abstraction rates). By reducing abstraction to 70% of the peak heating demand, long-term sustainability can be improved. Overall, while the system exhibits preliminary potential, commercial implementation remains subject to confirmatory site-specific borehole drilling, hydrochemical sampling, and long-duration pumping tests to validate reservoir capacity and optimise system longevity. Full article

A double lattice Boltzmann method (LBM) based on the D2Q9 model was developed to simulate natural convection melting and heat transfer in energy systems, including nuclear systems. Fluid flow in the liquid region was solved using the multi-relaxation-time LBM (MRT-LBM), whereas the energy equation was treated with the single-relaxation-time LBM (SRT-LBM) over the entire domain. Phase change was modeled through an enthalpy–porosity formulation, with the liquid fraction and solid–liquid interface determined from local enthalpy, while explicit mushy-region resolution was avoided to improve computational efficiency. Bounce-back treatment represented solid boundaries and the evolving interface. The model was validated against gallium-melting experiments and previous finite element and finite volume computations. The predicted melt-front evolution and convection behavior agreed well with benchmark data, confirming that the proposed framework can capture coupled fluid motion and heat transfer during melting without adaptive meshes or level-set methods. Full article

Large regulated rivers are increasingly affected by combined main-stem dam regulation and tributary disturbances, which reshape sediment connectivity and alter downstream sediment delivery. Existing studies have established that large reservoirs reduce sediment load, but how intensive main-stem regulation and tributary-scale human activities jointly reshape the runoff–sediment regime entering the Three Gorges Reservoir (TGR) remains insufficiently clarified. Based on monthly runoff and sediment data from 1957 to 2024 at major stations in the upper Yangtze River, this study examines the stage-wise alteration, main-stem–tributary contrast, intra-annual redistribution, and statistical explanatory factors of TGR inflow sediment. Results show that annual runoff changed weakly, whereas sediment load decreased significantly at all stations, with major breakpoints in 1998 and 2013 for the main stem and TGR inflow. After the lower Jinsha River cascade entered operation, main-stem sediment supply was strongly disrupted, and TGR inflow sediment decreased by 84.1% compared with the pre-1998 period. In contrast, tributaries with limited sediment-retention capacity retained strong episodic sediment-supply potential during wet years. Reservoir regulation altered the intra-annual delivery pattern, reducing flood-season runoff proportions and markedly weakening flood-season sediment load from Xiangjiaba. The statistical explanatory analysis indicates that reservoir capacity and the normalized difference vegetation index (NDVI) are strongly associated with the long-term sediment decline, whereas precipitation accounts for a larger share of the interannual variability. These findings suggest that the present TGR inflow boundary is no longer a simple reduced-sediment condition, but a reorganized regime controlled by main-stem sediment supply disruption and tributary residual sediment pulses. This study provides a scientific basis for sediment management and future assessments of cascade-reservoir joint operation. Full article

This paper presents a six-year assessment of the baseline comparability of a Brillouin optical time-domain analysis (BOTDA)-based sensing textile installed in a composite bridge girder under unloaded field conditions. Six monitoring campaigns collected from 2020 to 2025 were analyzed using campaign-temperature-corrected strain profiles. The known physical segmentation of the sensing path was used to evaluate cross-campaign behavior through segment-wise mean deviation, root-mean-square deviation (RMSD), percentile-based deviation, and position-wise drift-rate metrics. The results show clear segment-dependent variability, with larger deviations occurring mainly in the non-structural lead and return segments than in the bonded structural sensing sections. In the 2025 campaign, the combined bonded structural segments exhibited an RMSD of 292 µε and a 95th-percentile absolute deviation of 508 µε. Their mean drift rate was approximately −11 µε/year, with a 95th-percentile absolute drift rate of approximately 101 µε/year. These results indicate better long-term baseline repeatability within the bonded sensing path than in the non-structural portions of the measurement chain. However, because temperature correction was based on campaign-average surface measurements rather than a distributed temperature profile, residual thermal effects cannot be fully separated from the observed long-term variation. Full article

Voice and speech are increasingly studied as indicators of mental health, but the acoustic features of sustained phonation in schizophrenia remain underexplored. This feasibility study examined whether a 500 ms sustained vowel /a/ contains meaningful information for distinguishing patients with schizophrenia from healthy controls. Recordings from 84 participants (41 patients, 43 controls) were analyzed using features from eight acoustic domains. Random Forest, XGBoost, and logistic regression were evaluated under four train–test configurations with data augmentation and participant-grouped cross-validation. Feature importance with Random Forest Gini index, supported by SHAP analysis, produced a compact data-driven 15-feature set (DD15). Random Forest trained with fixed DD15 on original and augmented recordings, and evaluated on original recordings, achieved an AUC of 0.887 ± 0.078, while fold-wise feature selection produced a more conservative 15-feature estimate of 0.837 ± 0.086. Amplitude skewness, harmonic-to-noise ratio, and shimmer measures were the most discriminative features. The conservative DD15 estimate was broadly comparable with the stronger alternative representations while retaining interpretability. Additional sensitivity analyses showed that adjustment for measured recording condition variables reduced, but did not eliminate, internal patient-control discrimination. These findings support the feasibility of sustained-vowel acoustic analysis for schizophrenia classification and emphasize the need for standardized, balanced multi-site validation. Full article

Accurate and robust detection of pear leaf diseases and pests is important for early warning in orchards, precision control, and intelligent disease and pest management. In natural environments, this detection task is still difficult because target scales vary greatly, lesion boundaries are often blurred, disease symptoms may look similar, small-target features are usually weak, and cluttered backgrounds can reduce detection performance. We develop AMET-YOLO as a task-specific extension of YOLOv11n. ADown is a downsampling operator borrowed from YOLOv9 that replaces selected stride-convolution layers and thereby preserves local details during scale reduction. The Memory-guided Sparse Expert Compensation Module (MSECM) is a feature-enhancement design which couples learnable memory retrieval with four dilation-specific experts and input-dependent Top-2 routing. In the neck, the Efficient Multi-scale Aggregation Fusion module (EMAFuse) substitutes four concatenation nodes and performs fixed-width channel alignment, element-wise aggregation, and lightweight depthwise–pointwise mixing. The Task-Aligned Detection Head (TAHead) is a modified decoupled head that retains the YOLOv11n prediction structure while it routes localization responses through a one-way gating path to modulate intermediate classification features. Experiments on the six-class PearLeaf-DP6 dataset show that AMET-YOLO achieves a precision of 88.5 ± 1.3%, a recall of 83.1 ± 0.9%, an mAP@50 of 88.7 ± 0.7%, and an mAP@50:95 of 51.8 ± 0.5%. Compared with the YOLOv11n baseline, AMET-YOLO improves these four metrics by 4.8, 3.9, 3.3, and 2.3 percentage points, respectively. Experiments on a second public tea leaf disease dataset further show that the proposed model stays effective when it is trained and evaluated independently on a different crop disease detection task. AMET-YOLO is a model with 6.32 M parameters and 10.2 GFLOPs, which constitutes a moderate rise in complexity relative to YOLOv11n while it remains far more compact than RT-DETR-ResNet50. These workstation-based results position AMET-YOLO as an accuracy-oriented image-based detector under the evaluated protocol, and real-time deployment on resource-limited orchard devices is left for future validation. Full article

Accurate multi-step streamflow forecasting requires models to preserve antecedent streamflow memory and account for delayed dependence between gauges. This study proposes a prior-informed directed-lag graph neural residual model, termed PI-DLGNR. The framework decomposes prediction into a dominant linear memory component and a nonlinear residual correction regularized by soft routing priors. A multivariate VAR-Ridge backbone captures autoregressive persistence and cross-station streamflow memory. A directed-lag graph residual branch learns additional corrections using an inferred directed graph with trainable weights, learnable lag kernels, and station-wise temporal encoders. Three penalties regularize downstream ordering, hydrograph curvature, and delay structure without enforcing water balance. In the original benchmark on the GloFAS reanalysis series in the Yangtze River Basin, PI-DLGNR achieves NSE values of 0.998, 0.975, and 0.900 at Steps 1, 3, and 7, respectively. Relative to the isolated VAR-Ridge backbone, MAE decreases by 1.47%, 0.84%, and 0.72%, while RMSE decreases by 0.34%, 0.03%, and 0.01%. VAR-Ridge retains higher KGE at all three steps. In a separate three-seed comparison selected using chronological validation, the MAE-difference confidence intervals span zero at all three steps. The residual advantage is therefore not robust to the revised selection protocol. Tests with independent retraining in the Pearl and Yellow River mainstreams assess the robustness of the modeling strategy, not parameter transferability. Despite an overall Step 7 NSE of 0.900, PI-DLGNR has a Flood-NSE of −0.514, with negative Flood-NSE for every evaluated model. The evidence is stronger for general streamflow variation and selected low-flow conditions than for extremes one week ahead, although low-flow gains are not consistent across refits. Full article

Cold Metal Transfer wire arc additive manufacturing (CMT-WAAM) efficiently fabricates medium and large metallic components, yet deposited wall cumulative height varies spatially along the build direction and deposition path due to heat accumulation, inter-layer cooling, travel direction and inherited layer geometry. This study establishes a multi-factor prediction framework for single-bead multi-layer walls and evaluates CAFi-TCN, a temporal convolutional network enhanced with feature-wise linear modulation and causal attention. Height profiles were extracted from registered point clouds under 2–4 mininter-layerr cooling; the model uses deposition position, layer number, cooling time, travel direction, prior height increment and cumulative height to predict current-layer cumulative height. On the tenth-layer test set, CAFi-TCN achieved the lowest mean absolute error (MAE = 0.1836 mm) among the evaluated direct-height models, reducing MAE by 74.3%, 40.4% and 53.9% versus standard TCN, polynomial ridge regression and MLP, respectively. A Random Forest model trained on the height-increment target (RF-Δ) produced slightly higher MAE but lower RMSE and maximum absolute error, showing a trade-off between average-error control and extreme-error suppression. Additional no-PreDH, simple increment-baseline, path-block bootstrap and rolling-layer analyses show that prediction performance depends on both process-state variables and inherited geometry rather than simple copying of the previous layer. The results support bounded, layer-wise height forecasting for single-bead WAAM walls and provide a basis for pre-adjustment error identification. Full article

When autonomous mobile robots (AMRs) travel in formation, mutual occlusion reduces the map features available to adaptive Monte Carlo localization (AMCL) and introduces robot body returns. This study proposes a Virtual-AMR LiDAR fusion method that transforms synchronized 2D scans into a common frame, tracks robot contours, estimates relative pose by KD-tree overlap matching, removes robot returns, and fuses environmental measurements for AMCL. The method was evaluated using two AMRs in a 3.1 m × 10 m indoor area with 4 m straight-line and turning trajectories. Four branch-wise configurations were tested in five repeated trials. Against an internal reference trajectory constructed from the planned path and motor encoder measurements rather than an independent ground truth system, the complete method achieved trial-level 2D RMSE values of 0.0412 ± 0.0083 m for the straight-line trajectory and 0.0511 ± 0.0104 m for the turning trajectory (mean ± sample standard deviation across five trials), while reducing localization drift and preventing the AMCL divergence observed under the tested mutual occlusion conditions. Full article

Electroencephalography (EEG) is widely used to study cognitive states and clinical biomarkers, yet the contribution of stable between-subject differences to common EEG feature representations is rarely quantified directly or incorporated into evaluation design. Here, we examine subject-linked structure in spectral EEG features using a longitudinal four-session dataset spanning 199 days and replicate key findings across four public datasets covering 39 to 395 participants, multiple paradigms, and recording systems ranging from low-channel consumer devices to research-grade EEG. In spectral band-power representations, subject identity accounted for substantially more variance than session-related drift and supported extremely strong individual discriminability under subject-wise held-out protocols, with high verification performance and robust cross-session recognition over months. However, this identity structure was not fully invariant across paradigms, showing stronger transfer in richer, higher-channel recordings than in low-channel consumer EEG under larger task shifts. We further show that the same subject-linked structure can inflate downstream clinical classification when evaluation is performed with naive segment-level splits, demonstrating that apparent diagnostic performance can partly reflect identity leakage rather than biomarker learning. These findings highlight subject identity as a measurable and persistent source of structured variance in spectral EEG features and support routine use of subject-wise evaluation and explicit confound diagnostics in EEG machine-learning studies. Full article

In multi-mission air operations, area-patrol missions and long-range missions typically share a limited tanker fleet while imposing different constraint structures: patrol refueling is bounded by hard time windows, whereas long-range missions are governed by restricted-zone avoidance, detour tolerances, and multi-segment fuel verification. Conventional scenario-wise independent planning splits resources and wastes cross-region ferry mileage. This paper adapts the established paradigms of the location–routing problem (LRP) and the vehicle routing problem with time windows (VRPTW) to this joint refueling scenario: a joint planning model prioritizes the number of tanker sorties over total system flight distance, and a decoder-coupled adaptive large neighborhood search (ALNS) integrates airspace selection, task assignment, tanker routing, and dual-timeline rendezvous decoding, with all mission hard constraints embedded in a deterministic, reproducible evaluator that adjudicates feasibility at every search iteration. Experiments at three scales (17, 42, and 100 tasks) show 100% mission coverage and 100% patrol time-window satisfaction: relative to scenario-wise independent planning, tanker sorties decrease by 16.2–19.7% and tanker flight distance by 14.6–15.5% (significant after Bonferroni correction on 90 paired replicates per scale); against genetic algorithm (GA) and ant colony optimization (ACO) baselines—and against a route-encoding GA under an equal solution-space representation—the method is superior in solution quality and runtime ($p<0.001$), and the separation persists when the baselines receive a 25-fold evaluation budget. Monte Carlo simulations characterize how plan feasibility degrades under execution-time disturbances. Within the studied instance families, the framework yields executable joint refueling plans within operational runtimes. Full article

This paper focuses on quantifying overall lifecycle carbon emissions of a case-study structure, identifying key emission stages, and proposing carbon reduction strategies. The lifecycle assessment (LCA) framework combined with BIM technology was used to calculate stage-wise carbon outputs across the materials, assembly, occupancy, and decommissioning phases. The research proposes strategies such as optimizing the localized supply of materials, promoting recyclable building materials, improving building thermodynamic performance, and using clean power applications. The results offer empirical references for lifecycle carbon mitigation across the built environment and critical guidance to accelerate a sectoral low-carbon transition toward carbon peaking and carbon neutrality. Full article

Dynamic stability during human movement reflects interactions among body configuration, support conditions, mechanical state, and short-term state evolution. This study proposes a frame-wise stability score, S(t), integrating three dimensionless instability components: a height-adaptive extrapolated center of mass (XCoM)–base of support (BoS) geometric term, mechanical-energy deviation, and a Lyapunov-type state-trend term. The framework was evaluated using 24 HuMoD walking, running, kicking, and jumping trials from two participants, 26 HuMoD trials for sensitivity analysis, and 73 GAITEX trials. Task means followed the predefined biomechanical ranking (Spearman and Kendall = 1.000), which served as a consistency reference rather than an independent ground truth. Trial-level associations with margin of stability (MoS) and signed CoM–BoS distance were weak, whereas S(t) showed a negative association with center-of-pressure (COP) velocity (r = −0.667); COP velocity showed greater task discrimination (effect size 0.814 vs. 0.350). Ablation analysis showed the largest overall numerical effect for geometry, while trend contributed up to 28.77% of total instability locally. Tested parameter perturbations retained similar qualitative response patterns. These results support S(t) as an interpretable framework for organizing support geometry, mechanical-state deviation, and short-term state evolution on a common frame-wise scale; clinical and predictive validity require independent validation. Full article

Physiological-signal-based emotion recognition has received attention in human–computer interaction. Electrocardiograms (ECGs) are readily acquired, and short windows contain repeated beat morphology and beat-wise variations that single-window encoding struggles to separate. Multiscale morphology modeling and training-sample diversity remain limited. We therefore propose Complementary Feature Fusion Dual-Path (CFF-DP), an ECG emotion recognition framework using beat-level complementary feature fusion, with three components: (1) a dual-path framework, where the morphology-stable path constructs representative beats with window-adaptive Gaussian weights, while the morphology-difference path combines beat-wise encoding, positional encoding, and additive attention; gated fusion integrates representations; (2) adaptive dilated convolution (ADConv), which extracts multiscale beat-morphology features using shared kernels and input-dependent scale weights; and (3) deviation-based beat-oriented augmentation (DBOA), which adjusts real-noise injection probability and target signal-to-noise ratio according to morphological deviation. CFF-DP achieved 43.39% mean Macro-F1, close to the best comparator, with the fewest multiply–accumulate operations in five-seed WESAD three-class leave-one-subject-out (LOSO) evaluation, although recognition mainly distinguishes stress, with limited amusement discrimination. With short-gap calibration and testing within the same recording, fine-tuning using 40 s per class achieved 76.72% Macro-F1, exceeding six comparators. Binary DREAMER LOSO retained WESAD hyperparameters: valence Macro-F1 exceeded six comparators, whereas arousal fell below four; both remained below uniform random baselines, indicating limited recognition under current experimental conditions. The framework combines computational efficiency with within-record personalization advantages, although cross-subject recognition remains limited. Full article

Background: Individuals vary in aggression traits. Here, we investigated how individual aggression is associated with altered reward and punishment processing in the brain and the neural bases of sex differences. Methods: Aggression was assessed by the Achenbach Adult Self-Report. We curated the Human Connectome Project dataset and modeled the BOLD signals to identify regional responses to reward and punishment blocks in the gambling task (n = 981, 473 men) as well as responses to the identification of negative emotional face in a target-matching task (n = 885, 436 men). Whole-brain statistical significance was assessed at the voxel-level p < 0.001 (uncorrected) with cluster-level family-wise error (FWE) correction at p < 0.05. For regional responses identified in men or women alone, we conducted slope tests to examine the sex differences in their relationships with individual aggression. Results: Whole-brain regression showed higher activation of the left superior temporal gyrus (STG) during gambling loss in men. We extracted the left STG response to reward versus baseline (STG-win β) and to loss versus baseline (STG-loss β), where β is the GLM contrast estimate. Left STG-loss β was significantly correlated with aggression score in men but not in women, and a slope test confirmed the sex difference (Z = 3.54, p < 0.001). STG-loss β but not STG-win β was significantly correlated with aggression score in men, and a slope test confirmed the specificity of this valence-related correlation (Z = 3.66, p < 0.001). Further, the correlations of STG-loss β and of STG response to negative emotional faces versus shapes with aggression score differed in men (Z = 2.70, p = 0.007). Conclusions: STG response to loss represents a neural correlate of aggression in men but not in women. The STG-loss response did not reflect a broader relationship between aggression and negative emotion. Full article