Search Results (2,756)

Infrared small target detection (IRSTD) is a challenging task in intelligent infrared sensing and electronic imaging systems, because dim targets often occupy only a few pixels and are easily disturbed by clutter, noise, and low-contrast background structures. A practical detector should preserve pixel-level target cues while suppressing target-like false responses. This paper proposes a Pyramid Target Perception Network (PTPN) for single-frame pixel-level IRSTD. The network integrates three complementary components: an Efficient Context Modeling (ECM) encoder employing 7 × 7 depthwise separable convolution for lightweight contextual feature extraction, a multi-scale target cross-attention (MTCA) module for hierarchical feature interaction, and a small-target feature pyramid network (STFPN) for target-preserving multi-scale aggregation. In addition, a physics-constrained loss (PCL) is introduced during training to regularize predictions according to infrared imaging characteristics, including point spread consistency, target-region relative intensity consistency, and signal-to-noise-ratio-aware separability. Experiments on IRSTD-1k, NUAA-SIRST, and NUDT-SIRST demonstrate that PTPN achieves $IoU$ scores of 71.87%, 79.56%, and 86.47%, respectively, with 4.55M parameters, 4.96G FLOPs at an input resolution of 256 × 256, and an inference speed of 45.0 FPS. Although PTPN achieves competitive overall performance, it does not attain the highest $IoU$ on NUDT-SIRST, indicating that pixel-level target-region estimation under complex scenes remains an area for further improvement. Overall, PTPN provides an effective balance between target localization, false-alarm suppression, and computational efficiency, supporting its potential application in AI-driven infrared image processing and intelligent electronic sensing systems. Full article

Unmanned aerial vehicle (UAV) imagery supports intelligent surveillance, environmental monitoring, traffic management, and infrastructure inspection. Yet aerial detection is difficult when objects are small, crowded, and observed at markedly different scales. Background clutter and illumination changes further weaken target cues and impair localization. We therefore propose HD-YOLO, a lightweight multiscale detector for small objects in UAV imagery. Its Multi-Dilation Shared Convolution Kernel (DSCK) extracts local texture and contextual information with shared dilated kernels. The Hybrid Dilated Bidirectional Feature Pyramid Network (HDFPN) reconstructs global and local cues before bidirectional aggregation, enabling high-resolution evidence to reach the prediction layers. The Efficient and Slim Head (ES-Head) combines shared operations with differential convolution to reduce cost and strengthen boundary-sensitive features. A joint ShapeIoU and Normalized Wasserstein Distance loss improves regression for small, irregular objects. Together, these components reduce missed detections in dense, cluttered scenes without relying on large model capacity. On VisDrone2019, HD-YOLO improves precision, recall, mAP50, and mAP50:95 over YOLOv8n by 6.9%, 7.2%, 8.2%, and 5.2%, respectively, while reducing parameters from 3.0 M to 0.9 M. Evaluations on TinyPerson and HIT-UAV also support its utility for tiny pedestrians and infrared aerial targets. HD-YOLO therefore improves small-object detection with a compact parameter footprint, while direct hardware benchmarks remain necessary to establish deployment efficiency. Full article

The shear behavior of loess is closely linked to its microstructural evolution, and understanding this relationship is essential for deciphering the mechanisms of loess hazards. In this study, consolidated-drained (CD) triaxial tests were conducted on intact Q₃ Malan loess from the Dongzhi tableland, China, under varying water contents and confining pressures. Scanning electron microscopy (SEM) and mercury intrusion porosimetry (MIP) analyses were performed on specimens before and after shearing to quantitatively and qualitatively characterize the changes in pore and particle properties and their connection to shear deformation. The results reveal three failure modes, including shear, homogeneous, and plastic failure. They are governed by the combined effects of microstructural variation and microcrack development, depending on confining pressure and water content. Quantitatively, as water content increases from 9% to 20%, cohesion decreases by 86.8% and peak shear strength reduces by 68.4%, while the internal friction angle decreases only slightly. Water-induced strength deterioration is governed primarily by cohesion loss rather than friction angle reduction. Thus, 20% water content was identified as the critical threshold marking the transition from cohesion-dominated to friction-dominated strength degradation. A critical threshold at approximately 27% water content is identified, beyond which about 70% of mesopore and macropore volumes undergo collapse, after which the strength is almost entirely sustained by interparticle friction. Based on these findings, the water-induced strength decay mechanism is categorized into three stages: rapid cement degradation, friction-dominated transition, and slow attenuation. These macroscopic phenomena are closely linked to the continuous adjustment of the microstructure, manifested by the softening, dispersion, and disintegration of cementations, particle movement and rearrangement, and the reduction and mutual transformation of inter-aggregate pores under loading and wetting. The three-stage mechanism and threshold characteristics of loess strength degradation upon wetting revealed in this study can provide theoretical support for early slope-instability warning in loess irrigation and heavy rainfall regions, as well as engineering reinforcement prioritizing the recovery of cohesion. Full article

Binder bonding strength (BBS) is critical to aggregate retention and friction performance in high-friction surface treatment (HFST). Although epoxy resin is traditionally used, asphalt-based binders offer a sustainable alternative; however, their bonding behavior and influence on polishing performance remain insufficiently understood. This study investigated the evolution and optimization of BBS in crumb rubber-modified (CRM) asphalt for rhyolite-based HFSTs. BBS was measured under dry and wet-conditioned states for two CR contents (10% and 15%), two CR types (cryogenic and ambient), two interaction temperatures (170 and 200 °C), and interaction times from 10 to 240 min. HFST performance was assessed using the British pendulum tester (BPT), dynamic friction tester (DFT), and circular track meter (CTM) under accelerated polishing. CR modification reduced dry BBS relative to the base binder but substantially improved moisture resistance: wet conditioning reduced base-binder BBS by 23.8%, versus 5.8–9.8% for CRM binders. BBS evolution was temperature-dependent. At 170 °C, BBS decreased and gradually recovered through 240 min, while binders prepared at 200 °C peaked at 120 min before declining. Type III factorial ANOVA identified CR content as the dominant factor affecting BBS, with interaction temperature, interaction time, and their combined effects also being significant. Despite lower BBS, 15% CRM binders generally retained higher HFST friction performance than 10% binders after accelerated polishing. The moderate relationship between dry BBS and coefficient of friction (COF) loss (R² = 0.68) confirmed that BBS alone could not predict HFST durability, while a stronger BPN-COF loss correlation (R² = 0.79) confirmed consistent rankings across friction test methods. Cryogenic CRM prepared at 200 °C for 120 min provided the best balance among bonding development, rheology, and friction retention. These findings demonstrate that while bonding strength alone does not govern HFST durability across interactions, its evolution with interaction time exerts a significant effect on friction retention within a given interaction condition, highlighting interaction-time optimization as a critical parameter for developing sustainable CRM-based HFSTs. Full article

Cross-view geo-localization (CVGL) between unmanned aerial vehicle (UAV) imagery and satellite imagery is a key technique for autonomous UAV navigation in Global Navigation Satellite System (GNSS)-denied environments. However, most existing methods rely on energy-intensive Artificial Neural Networks (ANNs), making them difficult to deploy on resource-constrained edge computing platforms. Spiking Neural Networks (SNNs) provide a promising alternative for energy-efficient inference, but their application to CVGL still faces two challenges that remain insufficiently addressed. First, the isotropic computation used by existing SNN backbones is mismatched with the directional characteristics of spike activations. Spike activations tend to form oriented aggregation patterns along elongated geographic structures, and isotropic computation can therefore dilute directional signals. Second, the limited representational capacity of SNNs further increases the sensitivity during training optimization. However, the standard triplet loss adopts a static weighting strategy and assigns the same weight to all triplets that violate the margin constraint, which is unfavorable for learning from hard negatives. To address these challenges, we propose a framework with two core contributions. At the feature extraction level, the Directional Adaptive Convolution Module (DACM) processes spike feature maps by sequentially performing horizontal strip convolution and vertical strip convolution, thereby capturing a more complete geometric structure of directional spike clusters. At the training supervision level, we propose a Dual-dimensional Progressive Reweighting (DPR) loss, which jointly characterizes sample difficulty from pairwise difficulty and positive-pair quality difficulty. A learnable fusion parameter is used to adaptively balance these two types of difficulty information. Experimental results on the University-1652 and SUES-200 benchmarks show that the proposed framework, when equipped with the same representation learning head as its ANN counterparts, achieves competitive and, in many settings, superior performance. In terms of energy efficiency, its estimated theoretical energy consumption is over $8.8\times$ lower than that of published ANN methods under their original configurations. Under a more rigorous matched ANN control that shares the identical architecture, the estimated energy is reduced from 29.84 mJ to 6.36 mJ, an approximately $4.7\times$ reduction obtained at a cost of only 2.29 percentage points in R@1. Full article

Urban PV is commonly connected through individual inverters, causing low utilization, uncoordinated reverse power flow, and overvoltage at high penetration. This paper addresses where to site DC aggregation points that collect nearby PV systems on a common DC bus and connect through centralized inverters. This paper formulates a bi-level model under urban land-use constraints as follows: the upper level jointly selects point locations, capacities, and PV allocations to minimize annualized cost and maximum voltage deviation; the lower level routes collectors along roads and verifies AC/DC power flows. PV uncertainty is represented by clustered scenarios, and an improved NSGA-II is coupled with SOCP DistFlow evaluation. On a modified IEEE 33-bus feeder at 122% penetration, the method reduces annualized cost by 8.3% versus two-stage siting and 14.6% versus K-means siting, with maximum voltage deviation reduced from 5.5% to 4.8%. Against conventional AC connection, it eliminates overvoltage and reduces active loss by 40.3% from the no-PV base. Tests on a 69-bus system plus sensitivity, extreme-scenario, and reliability analyses confirm scalability and robustness. Full article

With rapid development of 5G/6G communication and high-power electronic devices, electromagnetic interference (EMI) shielding textiles are urgently required to mitigate electromagnetic pollution. Traditional metallic shielding suffered from heavy weight, poor corrosion resistance, and secondary electromagnetic reflection, while continuous basalt fibers (CBFs) exhibit excellent mechanical strength, lightweightness, thermal/chemical resistance, and electrical insulation, which makes CBFs ideal substrates for EMI devices. Herein, a multifunctional waterborne polyurethane (WPU) sizing agent (coating emulsion) integrated with Mn-Zn spinel ferrite was developed for in situ lubrication on the as-spun CBFs. The composite sizing agents consisted of a WPU matrix, water-soluble epoxy, mineral oil lubricant, CTAB surfactant, KH-570 coupling agent, and micro-sized Mn-Zn ferrites. Characterizations including particle size distribution, thermogravimetric analysis, water contact angle (WCA), water absorption, FTIR, XRD, and SEM were conducted to verify uniform anchoring of ferrites on the CBF surfaces. Increasing ferrite dosages induced slight particle aggregation, elevated surface hydrophobicity (WCA = 42.4° → 99.43°), and reduced water absorption (65% → 35%), which greatly improved the moisture resistance of the CBFs. The X-band EMI shielding tests revealed that the total shielding effectiveness (SET) of modified CBF fabrics increased from 0.11 dB (pristine fiber without ferrite) to 58.57 dB at a loading of 8.0 g/L ferrite. The absorption loss (SEA) dominated the shielding performance over reflection loss (SER). The low-to-moderate contents (1.5–3.0 g/L) of ferrite achieved ultra-high absorption, while higher ferrite loading (5.0–8.0 g/L) intensified the impedance mismatch and enhanced surface reflection. This work establishes a scalable fabrication of absorption-prioritized lightweight CBF shielding, which provides a feasible pathway for flexible EMI shielding textiles. Full article

Large-scale administrative hospital discharge data is a key resource for secondary health systems research, yet reuse is constrained by barriers of access, quality, interoperability, and semantic comparability. This paper presents and validates a reproducible, declarative, loss-aware harmonization framework for public discharge data that avoids full migration to a comprehensive common data model. The framework comprises a lightweight 14-field canonical model, versioned JSON crosswalks, a shared execution engine, a resilient file reader, schema validation, idempotency tests, value-domain checks, and an information-loss map. It was evaluated on public record-level discharge data from five jurisdictions on three continents (Korea, Brazil, Mexico, Chile, and New York State), comprising 561,966,231 harmonized records from 2001 to 2025. Validation demonstrated conformance to the declared source profiles for 82 of 99 files and full canonical conformance for 39, idempotency across all 99 files, 99.99% conformance with permitted value domains under an explicitly stated aggregation, and detection of source-level defects such as truncated files, malformed rows, and completeness anomalies. A marker-condition query for ischemic stroke (ICD-10 I63) showed that a single case definition executes consistently on the four sources retaining raw ICD-10 codes. The results show that, for heterogeneous administrative data, the key value lies not in scale alone but in the auditability of transformations, explicit loss documentation, and reproducibility of the harmonization process. Full article

To reduce aggregate gradation counting errors caused by over-segmentation and under-segmentation of stacked and clustered aggregates with mixed types and diverse spatial distributions in hydraulic concrete, this study proposes BC-GECO2, a coarse and fine aggregate segmentation and counting method. Firstly, a BAHiera feature extraction network is designed to extract multi-scale deep features through edge-aware attention. In addition, a DFG-Edge module is developed to enhance the boundary features of densely distributed aggregates by integrating wavelet transform with a gated fusion mechanism, thereby alleviating the loss of small aggregate features during downsampling. Secondly, a CSFM-GFFCA module is constructed, in which a dual-branch structure is employed to adaptively fuse adjacent-scale features, strengthen the edge responses of densely distributed small aggregates, and enhance cross-layer feature interaction. Finally, a joint optimization function combining Focal loss and counting loss is established to guide the model toward hard-to-classify pixels, especially boundary pixels, thereby improving segmentation integrity and counting accuracy. Experiments conducted on an aggregate dataset collected from practical construction sites show that, compared with the baseline GECO2 model, the proposed method improves the average segmentation IoU, Dice, and BIoU by 2.92%, 5.04%, and 2.83%, respectively, while reducing the average counting MAE and RMSE by 6.92 and 15.65, respectively. Moreover, BC-GECO2 exhibits superior robustness and generalization capability under different stacking densities and blurred-boundary scenarios, providing technical support for the intelligent development of rapid concrete gradation detection. Full article

The development of high-density cementitious composites is critical for specialized applications such as heavy-duty structural components. This study investigates the impact of replacing silica sand with barite powder on the physical, mechanical, and microstructural properties of cementitious composites. The replacement levels varied from \0% to 100% to evaluate the extent of property modification. Experimental results indicate a significant positive correlation between barite content and composite density, which increased by 17.6% to reach a maximum of 2857 kg/m³ at 100% replacement. However, this densification was accompanied by a systematic degradation in mechanical performance. At the 100% replacement level, compressive, tensile, and flexural strengths decreased by 26.4%, 30.2%, and 34.1%, respectively. Furthermore, water absorption nearly doubled, increasing from 1.9% in the control to 3.8% in the 100% barite mix. Scanning Electron Microscopy (SEM)-based microstructural observations suggest that the decline in mechanical performance and the increase in permeability are consistent with weak aggregate–matrix adhesion. The study concludes that while barite is highly effective for increasing composite density, the resulting increase in porosity and loss of cohesive strength must be carefully managed through mix optimization to ensure structural durability. Full article

Forests reduce water-driven soil erosion, yet their protective contribution has not been assessed consistently across the European Union (EU) in a framework that separates per-area service intensity from aggregate service flow. We quantified erosion regulation service (ERS) as RUSLE-based avoided sheet and rill erosion: the difference between structural soil-loss potential (C = P = 1) and loss under forest conditions. Spatially aligned European RUSLE factors were combined with the CORINE Land Cover 2018 forest mask and summarised by country, biogeographical region and elevation. Across 134.10 Mha, ERS totalled 6806.15 Mt yr⁻¹ (mean 50.75; median 12.36 t ha⁻¹ yr⁻¹), revealing strong spatial concentration. Slovenia had the highest mean intensity (257.45 t ha⁻¹ yr⁻¹) across 1.13 Mha of mapped forest, whereas Italy provided the largest national total (1437.05 Mt yr⁻¹) across 7.83 Mha of mapped forest. Alpine and Mediterranean forests supplied 64.0% of total ERS in the 27 EU Member States (EU27) while occupying 27.7% of mapped forest area. Mean intensity increased from 9.70 t ha⁻¹ yr⁻¹ below 200 m to 248.53 t ha⁻¹ yr⁻¹ at ≥2000 m, but total service peaked at 500–1000 m. This intensity–area trade-off distinguishes priority locations from major national contributions and provides a spatially consistent baseline for multifunctional forest management, soil protection and ecosystem restoration. ERS represents modelled avoided hillslope erosion, not sediment yield or a deforestation scenario. Full article

Accurate protein function prediction (PFP) is essential for understanding biological systems. However, structure-based graph neural networks often rely on fixed-distance contact maps, which may inadequately capture continuous, multi-scale spatial topologies, while the long-tail distribution of Gene Ontology (GO) labels may bias prediction toward frequent functions. We propose DHST, a deep hybrid structure–topology framework that integrates sequence semantics from a pretrained protein language model with local structural information learned by a residual graph convolutional network. DHST further introduces site-specific persistent homology to encode multi-scale topological invariants and a topology-guided residue-wise gated fusion module to modulate structure–semantics representations using local topological embeddings. The fused residue features are aggregated through dual-path pooling, and a weighted binary cross-entropy loss is used to mitigate the adverse effects of label imbalance. On the PDB dataset, DHST achieved area under the precision–recall curve (AUPR) scores of 0.779, 0.481, and 0.557 for molecular function (MF), biological process (BP), and cellular component (CC), respectively; on the AF2 dataset, the corresponding scores were 0.729, 0.390, and 0.459. The model also demonstrated robust generalization to low-homology proteins and maintained strong predictive performance across GO terms with different levels of functional specificity. Ablation results supported the contributions of the main components. Full article

Lignin-derived chemicals are important renewable building blocks for a sustainable chemical industry, and their selective hydrodeoxygenation (HDO) into cyclohexanol offers a promising route to high-value products; however, efficient C–O bond cleavage over non-noble-metal catalysts remains challenging. Herein, a series of oxygen-vacancy-regulated Co/CeO₂ catalysts was prepared by supporting Co on hydrothermally synthesized CeO₂ nanocubes, with the CeO₂ calcination temperature (400–800 °C) used to tune the defect density and interfacial structure. Low-temperature calcination preserved the nanocubic morphology, high surface area, abundant Ce³⁺–OV sites, and highly dispersed reduced Co species, whereas higher calcination temperatures promoted crystallite growth, surface-area loss, oxygen-vacancy depletion, and Co aggregation. These structural changes directly governed guaiacol HDO performance. Under optimized conditions (160 °C, 2 MPa H₂, 4 h, isopropanol), Co/CeO₂-400 achieved nearly complete guaiacol conversion, with cyclohexanol accounting for approximately 99% of the relative GC–MS product distribution. Mechanistic studies indicate that metallic Co promotes H₂ activation and aromatic-ring hydrogenation, while adjacent Ce³⁺–OV sites facilitate adsorption and cleavage of oxygen-containing groups. The resulting Co–CeO₂ interfacial synergy drives a sequential hydrogenation–deoxygenation pathway and suppresses the accumulation of partially hydrogenated intermediates. Co/CeO₂-400 also showed activity toward representative lignin-derived oxygenates and retained over 90% of its initial activity after five cycles. This work highlights oxygen-vacancy engineering as an effective strategy for designing robust non-noble-metal catalysts for selective lignin valorization. Full article

Satellite precipitation products like GPM IMERG are crucial for hydrological modeling and disaster prevention; yet, their reliability in complex mountainous monsoon regions remains challenging. While the latest IMERG V07 introduces key upgrades, including a Climatological Calibration Algorithm (CCA), its cross-scale error propagation mechanisms and performance heterogeneity in complex underlying surfaces are poorly understood. This study evaluates the daily and monthly performance of IMERG V07 and V06 (Early, Late, and Final Runs) from 2014 to 2020 against 91 rain gauges in Guangxi, China—a typical mountainous monsoon region. The evaluation employs multiple statistical metrics and a multi-dimensional stratification approach based on elevation, precipitation intensity, and seasonality to quantify error propagation and climate-topography coupling effects. The results reveal that V07, particularly the Late Run, enhances daily precipitation detection capabilities, it significantly increases the proportion of systematic positive bias from 62.3 to 64.8% (V06) to 67.2–68.9% (V07). Consequently, upon temporal aggregation to the monthly scale, this systematic overestimation is severely amplified, leading to degraded performance, with the Final Run suffering the most substantial accuracy loss. Furthermore, retrieval accuracy is heavily constrained by surface heterogeneity, with systematic overestimation surging in areas where relatively dry (mean annual precipitation < 1300 mm) and complex terrain (elevation 100–500 m) coincide. The introduced CCA effectively improved dry season estimations but failed during wet season by introducing substantial positive biases. Ultimately, while V07 better captures short-term precipitation dynamics, its structural systematic biases compromise long-term cumulative reliability, highlighting the necessity for physics-based bias correction in hydrological applications and dynamic calibration in future algorithm upgrades. Full article

Loss models of fixed capacity constitute a fundamental tool in teletraffic theory, with the classical Erlang loss model being widely used for dimensioning purposes. In practical communication systems, however, server failures and repairs introduce time-varying capacity, significantly affecting call blocking probabilities (CBPs). This paper studies an Erlang loss system where busy servers may fail. Failed servers are repaired by either a shared or a non-shared repair facility, while in-service calls are lost upon server failure. Three approaches for determining CBP in the shared and non-shared repair cases are examined. The first provides exact results by solving a 2D Markov chain but becomes computationally demanding for large systems. The second, known as the performability method, offers a simple approximation but allows failures of idle servers. The third approximate approach employs state aggregation while restricting failures to busy servers. These approximate solutions offer computational efficiency, but they cannot ensure consistently accurate performance. To circumvent this limitation, we propose a novel method for the exact and efficient determination of CBPs. Analytical comparisons show that: (1) the third approach consistently outperforms the performability method and (2) the proposed method outperforms the approximate methods in both the shared and the non-shared repair cases. Full article