Search Results (11,302)

In recent years, UHV AC/DC transmission projects in China have developed rapidly, and transmission line corridors have become increasingly limited. As a result, UHV AC transmission lines are increasingly located near areas where residents live and work, and the electric field distribution around houses near transmission lines has become a major public concern. To further investigate the electric field around houses near UHV transmission lines, a full-scale house platform was constructed near an actual operating 1000 kV AC transmission line. The electric field distribution above the house platform and around the house was systematically measured and analyzed. The effects of house height and the distance between the house and the transmission line on the electric field distribution were discussed, with emphasis on the electric field distribution on the two-story platform. In addition, the shielding effect of shielding wires installed near the house on the electric field of the house platform was analyzed. The results show that the electric field on the two-story platform is significantly higher than that on the single-story platform, and the electric field decreases approximately linearly with distance. Installing shielding wires can effectively reduce the power-frequency electric field intensity on the house platform. A relatively optimal balance between shielding performance and installation economy can be achieved when the shielding wire is installed along the edge of the house, with a length 3 m longer than the house edge and a height 1.5 m higher than the position to be shielded. A reasonable combination of multiple shielding wires provides a much better shielding effect than a single shielding wire. Full article

The Extended Kalman Filter (EKF) serves as a widely utilized approach to evaluate the state of charge (SOC) of energy storage batteries. However, the conventional EKF is commonly adversely affected by ambient temperature variations, uncertain noise matrices, and inaccurate parameter estimation in practice. Therefore, hybrid elephant herding and golden eagle optimization based EKF (HEGO) is introduced to enhance the precision and effectiveness of battery SOC estimation. The local contraction capability of elephant herding optimization is utilized to narrow the search range within a predefined search space and accurately locate the region of the optimal solution. Within the narrowed search range provided by EHO, golden eagle optimization (GEO) is then employed to accurately identify the noise matrix and equivalent circuit parameters appropriate for the current state, thereby increasing the precision and resilience of SOC estimations against environmental disturbances. Data for an 18650-battery evaluated with the Federal Urban Driving Schedule (FUDS), Dynamic Stress Test (DST), and Hybrid Pulse Power Characterization (HPPC) conditions were collected using an experimental platform, and the proposed algorithm was experimentally validated. The results demonstrate that, across different temperatures and operating conditions, the proposed algorithm consistently achieves optimal performance, with a mean absolute error below 0.7% and strong generalization, thereby providing stable and reliable technical support for battery SOC estimation. Full article

Background: The allocation of non-divisible inbound deliveries across multiple warehouses requires the simultaneous consideration of capacity constraints, category-specific restrictions, workload balance, and long-term allocation consistency. Methods: This study proposes a hierarchical two-level allocation framework combining strategic category-rotation policies, normalized marginal scoring, and signed historical feedback. The algorithm is executed once per day to generate warehouse assignments for the following operational day. Historical correction is based on a rolling window covering the preceding 30 daily planning periods. Results: The framework was evaluated using daily simulation instances ranging from 100 to 1500 pallets, with an average of approximately 130 lots per pallet. Across all evaluated instances, the complete allocation procedure was completed in less than 5 s on the specified test system. The results indicate balanced warehouse utilization, progressive reductions in category–location imbalance, stable historical correction, and preservation of hard operational constraints. Conclusions: The framework provides an interpretable and computationally efficient approach for next-day inbound allocation. By combining explicit feasibility filtering, strategic policy signals, and a 30-day historical correction mechanism, it supports both short-term operational decisions and longer-term allocation balance. Full article

Fused deposition modeling (FDM) is one of the most widely deployed additive manufacturing methods, and the mechanical performance of FDM-printed parts is governed by a small set of strongly coupled process parameters. Silk PLA, a PLA-based filament engineered to deliver a high-gloss finish with improved mechanical performance, has received far less attention than commodity PLA, and its parameter–property relationships remain incompletely characterized. In this work, the effects of three FDM printing parameters: (i) layer height (0.10, 0.15, and 0.20 mm), (ii) extrusion temperature (200, 210, and 220 °C), and (iii) print speed (100, 120, and 140 mm/s) on the tensile characteristic of Silk PLA were investigated through a full-factorial design consisting of 27 parameter combinations and 135 ASTM D638 Type-I specimens. Tensile tests were performed on an MTS E42 universal testing frame, while Digital Image Correlation (DIC) was employed to obtain full-field longitudinal and transverse strain distributions and to identify the onset and location of necking. Analysis of variance (ANOVA) was used to assess the statistical significance, while an interpretable machine learning (ML) pipeline combining extreme gradient boosting (XGBoost), Shapley additive explanations (SHAP) values, and partial dependence plots (PDPs) was employed to quantify the relative influence of each parameter and elucidate its effect on the tensile response. Both analyses identified extrusion temperature as the dominant factor governing ultimate tensile strength and Young’s modulus. Partial dependence analysis further revealed that strength gains saturate above 210 °C and are maximized at an intermediate print speed of 120 mm/s, providing actionable guidance for process optimization. The highest tensile strength, 40.68 MPa, was achieved at a layer height of 0.20 mm, an extrusion temperature of 220 °C, and a print speed of 120 mm/s. DIC measurements showed that thinner layers (0.10 mm) produced higher breaking strains and necking that initiated at the gauge-section edges, whereas thicker layers (0.20 mm) shifted necking toward the mid-gauge. Together, the experimental, statistical, and ML results provide a consistent, mechanistically interpretable framework for optimizing FDM process parameters for Silk PLA functional parts. Full article

The retreating roadway is a critical passage for the safe and efficient retreat of equipment from a fully mechanized longwall face. Its roof stability directly affects the transportation safety of large equipment such as hydraulic supports and the shearer. However, the structural evolution of the overlying strata of the retreating roadway and the roof stability under monorail crane loading have not been systematically investigated. Therefore, taking the retreating roadway of the 1093 fully mechanized longwall face in a coal mine in Anhui Province as the engineering background, this study combined physical similarity simulation, digital image correlation (DIC), and theoretical analysis to investigate the evolution of overlying strata fracture, caving, displacement, and stress fields during face extraction and retreating roadway formation. An analytical model was established to calculate bed separation between the immediate roof and the main roof under an equivalent static concentrated monorail crane load. The results indicate that the vertical displacement field of the overlying strata exhibits an overall trapezoidal distribution and continuously extends toward the higher overlying strata as the longwall face advances. In the physical model, no further propagation of fractures or bed separation toward the retreating roadway was observed after either roof-cutting operation. The withdrawal of hydraulic supports caused no significant changes in the stress or displacement of the overlying strata of the retreating roadway, indicating that the integrity of the overlying strata structure was well maintained. The model predicted a maximum bed separation of 6.39 mm between the immediate roof and the main roof at the gob-side end. The model can assist in identifying critical roof locations susceptible to bed separation and in prioritizing roof monitoring and support optimization during monorail-assisted equipment withdrawal. Full article

Most Transmission System Operators (TSOs) rely on seasonally static line rating models based on extreme weather conditions to determine the transmission capacity of power lines. These conservative rating approaches constrain grid capacity, limiting the integration of new renewable energy sources and delaying the transition to a more sustainable power system. Furthermore, they restrict cross-border transmission capacity between market zones, leading to “false” congestion and unnecessary market splitting. Market splitting can result in economic losses for market participants due to price differences between market zones and the potential curtailment of renewable generation. The adoption of Dynamic Line Rating (DLR) models can help avoid the need for new transmission infrastructure, reduce market splitting and false congestion, and mitigate line degradation in a cost-effective manner. The OptiRES.Lines tool integrates several DLR models, enabling their simulation and visualization through a Geographic Information System (GIS) interface. These dynamic rating models are combined with an Optimal Power Flow (OPF) model to assess: (1) the long-term potential for integrating new power plants at different grid locations; (2) the available cross-border transmission capacity between market zones; and (3) short-term grid congestion. The tool was tested in two regions of Portugal and demonstrated a significant increase in the grid’s capacity to accommodate additional renewable generation, thereby contributing to a more sustainable power system. The results showed that, although DLR alone indicated an increase in transmission line capacity during approximately 70% of the analysed period, the inclusion of OPF analysis revealed that DLR reduced line loading factors during 95% of the time analysed, highlighting its broader system-level benefits. Full article

Biomass-based bioenergy plants are commonly compared using aggregate indices that locate a system on a utilization scale but conceal the dimensional structure of its remaining deficit. This study extends the Biopolygeneration Diagnostic Index (BDI) by defining the biopolygeneration gap as a weighted multidimensional distance between each plant profile and a synthetic componentwise best-demonstrated reference constructed exclusively from real operating plants. Each reference coordinate has been demonstrated independently; simultaneous feasibility of the complete vector is not assumed. The squared Euclidean metric is decomposed into criterion-level contributions to identify the dominant diagnostic leverage, without interpreting that leverage as a cost-optimal retrofit. The framework is evaluated using 34 literature-derived cases (21 real plants and 13 models) covering 11 conversion technologies and 16 countries. Relative gaps range from 0.198 to 0.827, and energy efficiency and exergetic output quality provide the dominant leverage in 31 of 34 baseline cases. Incremental information beyond the aggregate BDI is demonstrated by three real plants with nearly identical BDI values (0.606–0.629) but distinct dominant deficits: energy efficiency, exergetic quality, and coproduct valorization. Rank ordering remains stable under 90th-percentile and top-three-median references ($\rho =0.984$–$0.992$), alternative distance norms, local weight perturbations, and correction for the shared C1/C2 source. A bounded input-uncertainty scenario yields a mean rank correlation of 0.893 and shows that leverage stability is case-specific. The framework therefore supports transparent dimension-level diagnostic prioritization while preserving a clear boundary with techno-economic, environmental, and implementation decisions. Full article

This study addresses the Traffic Sensor Location Problem for complete link-flow observability under non-uniform sensor measurement uncertainty. The proposed framework minimizes the accumulated error propagated from observed link flows to inferred unobserved link flows while preserving the structural conditions required for complete network observability. Its methodological novelty lies in combining a structured new-link selection procedure with a fuzzy-enhanced Binary Particle Swarm Optimization (FBPSO) algorithm that adaptively balances exploration and exploitation. An ILU-preconditioned GMRES procedure is also incorporated to efficiently solve the sparse linear systems generated during the evaluation of candidate sensor configurations. The proposed framework is evaluated using the Fishbone and Sioux Falls benchmark networks and the large-scale Austin transportation network, which contains 7388 non-centroid nodes and 18,961 directed links. Its performance is compared with standard Binary Particle Swarm Optimization (BPSO) and the Binary Bat Algorithm (BBAT) under uniform and non-uniform measurement-error conditions. For the Fishbone network, all three methods reach the same minimum accumulated inference error of 89.21, indicating agreement on the best solution for this small test case. For the Sioux Falls network, FBPSO obtains an inference error of 634.46, compared with 641.78 for BPSO and 640.94 for BBAT. For the Austin network, FBPSO achieves the lowest final inference error and continues improving after the comparison methods reach prolonged plateaus. These findings demonstrate that the proposed framework provides an effective and scalable approach for uncertainty-aware traffic-sensor placement and reliable network-wide link-flow inference. Full article

The Cretaceous shallow oil sands in the Dagang area, located on the western slope of the Songliao Basin, are characterized by a burial depth of ≤182 m, an average reservoir temperature of 11.8 °C, an extremely high crude oil viscosity of 1,750,000 mPa·s at 15 °C, and water-bearing layers in both the roof and floor. Conventional thermal recovery methods such as SAGD and CSS are geologically unsuitable for this deposit and suffer from high energy consumption and carbon emissions. As microbial oil recovery is a technically advanced enhanced oil recovery technology that leverages microbial growth, reproduction and metabolism in the reservoir to alter the properties of oil, rock, gas and water through interaction with these components, and petroleum biotechnology research confirms that microorganisms can degrade high-molecular-weight petroleum hydrocarbons to reduce crude oil viscosity and improve its fluidity, this study explores the technical feasibility of microbial cold recovery for in situ extraction of such low-temperature, high-viscosity oil sands. The study adopts a five-well pilot pattern (one injector and four producers) with an integrated approach combining reservoir unblocking, microbial viscosity reduction, and vibration-assisted production. Systematic screening identified Pseudomonas, Chryseobacterium, and Citrobacter as the most efficient indigenous microbial strains. Pseudomonas exhibited a crude oil degradation rate of 32.17%, reducing asphaltene content from 7.47% to 3.56%, and achieved large-scale proliferation (2.5 × 10⁸ cfu/mL) at 15 °C. It also achieved a 40.8% reduction in crude oil viscosity and a desulfurization rate, alongside 56.6% denitrification. With the optimal activator No. 3, the viscosity reduction rate reached 45.18%, and the viable cell count exceeded 9.45 × 10⁸ cfu/mL. The synergistic action of Pseudomonas and an A-type nano-microemulsion surfactant reduced the oil–water interfacial tension from 49.56 to 1.25 mN/m (a 97.48% reduction) and lowered the crude oil viscosity at 25 °C from 302,000 to 11,023 mPa·s (a 96.35% reduction). Core flooding tests demonstrated an incremental oil recovery of 7.38% compared with the water-flooded control, with interfacial tension dropping from 48.21 to 1.18 mN/m. In the field trial, composite perforation (32 shots/m, 1610 mm penetration) and two cycles of oil-based fermentation fluid huff-n-puff reduced injection pressure from 2.0 to 2.5 MPa to 1.0–1.8 MPa. A total of 1489 m³ of microbial agent was injected into five wells, followed by a 125-day shut-in period. Nano-microemulsion single-well huff-n-puff (579 m³ over 87 days) further decreased injection pressure to 0 MPa. A downhole harmonic vibration source (≤20 Hz) was also applied during the trial. During the production phase, Pseudomonas was found to dominate the produced fluid, with its peak relative abundance exceeding 70%. Cumulative fluid production reached 4114 m³, yielding 21 m³ of oil sand oil. Wells with vibration assistance showed significantly higher oil content and better emulsification performance than wells without vibration assistance. Full article

Reliable state of health (SOH) estimation provides important support for safety management and efficient operation of lithium-ion battery energy storage systems. To address the limitations of existing SOH estimation methods in terms of feature interpretability, joint modeling of local variations and overall morphological characteristics within each feature segment, and the rationality of model hyperparameter configuration, this study proposes a hybrid data-driven SOH estimation method integrating interpretable feature construction, an improved Transformer, and Bayesian optimization (BO). Specifically, raw charging data are first converted into incremental capacity (IC) curves based on incremental capacity analysis, and IC peaks are dynamically located in different cycles. Local voltage–capacity segments around the IC peak voltage are then extracted as interpretable input features closely related to battery aging. Subsequently, the Transformer encoder is improved by incorporating patch embedding and a multi-head differential self-attention mechanism, thereby enhancing the model’s ability to jointly capture local variations and overall morphological characteristics within each cycle-wise feature segment. BO is further employed to adaptively optimize key model hyperparameters. Experimental results on the CALCE-CS2 and CALCE-CX2 battery datasets show that the proposed BO-PatchDiffFormer model can provide accurate and stable SOH estimation in both comparative and generalization experimental scenarios. Compared with the best-performing baseline model, the maximum reductions in RMSE and MAE on the four CS2 test batteries reach 31.81% and 23.76%, respectively. In the generalization experiments, the average RMSE and MAE on the two CX2 test batteries are 1.6039% and 1.3081%, respectively. In addition, the maximum model size is only 2.07 MB, and the single-sample inference time remains within 0.84–1.18 ms, indicating good potential for practical deployment and application. Full article

Flooding remains a major hydro-meteorological hazard in KwaZulu-Natal, yet province-wide susceptibility assessments incorporating modelling uncertainty are limited. This study develops an ensemble multi-criteria flood susceptibility framework integrating Analytical Hierarchy Process (AHP), fuzzy logic transformation, and frequency ratio (FR) modelling within a cloud-based geospatial environment. Twelve hydro-geomorphological and environmental conditioning factors, including topography, rainfall, land cover, hydrology, and soil proxies, were normalized using percentile scaling. Three independent flood susceptibility models were generated and combined using ensemble mean aggregation, while pixel-wise standard deviation quantified spatial uncertainty. Model validation employed a 10-year historical flood inventory (2015–2025) comprising 65 documented flood locations. The ensemble flood susceptibility index (FSI) ranged from 0.05 to 1.00, with moderate susceptibility zones covering 51.08% of the province. High and very high susceptibility classes occupied 8.36%, indicating spatially concentrated but hydrologically significant risk hotspots. Uncertainty analysis showed low inter-model variability (0.00–0.11), demonstrating strong methodological stability. Validation results confirmed that 73.85% of historical flood points were located within high susceptibility zones, with over 90% captured within overall susceptible classes. The study introduces a hybrid deterministic–fuzzy–probabilistic ensemble modelling approach combined with pixel-level uncertainty mapping and scalable cloud computation. Findings support disaster risk reduction, urban and catchment planning, and early warning system optimization in flood-prone regions. The framework provides a transferable methodology for data-limited environments requiring reliable and uncertainty-aware flood hazard assessment. Full article

Synthetic aperture radar (SAR)–optical image fusion and object detection are two closely related tasks in remote sensing. Fusion can provide richer texture and structural cues for downstream detection, while detection can, in turn, provide object-level location and semantic information to improve fusion. However, effectively integrating these two tasks within a unified training framework remains challenging. Their optimization objectives are inherently different: fusion emphasizes cross-modal information preservation and structural fidelity, whereas detection focuses more on discriminative target representation. As a result, direct joint training often leads to mutual interference rather than mutual reinforcement. In addition, most existing joint frameworks remain serial or unidirectional, limiting effective bidirectional knowledge transfer between fusion and detection. To address these issues, we propose PCFD-Net (Parallel Collaborative Fusion-Detection Network), which consists of a fusion branch, a detection branch, and a bidirectional interaction branch, and unifies fused image generation and oriented object detection within a single training framework through explicit bidirectional interaction. The fusion branch employs dual ResNet-50 encoders, a multi-scale attention fusion module, and a progressive decoder, while the detection branch is built on YOLOv8. The key component of the proposed framework is the bidirectional interaction branch. On the one hand, the multi-scale fused features generated by the fusion branch are injected into the detection backbone to enhance the exploitation of cross-modal intermediate representations. On the other hand, we develop CSMDE (Category Semantic–Modality Disentangled Embedding), which disentangles category-discriminative and modality-preference semantics to map detector category outputs into instance-level semantic embeddings. These embeddings, together with object locations, are further fed into a dual-discriminator mechanism to reversely constrain the fusion branch, thereby strengthening SAR-discriminative target preservation and optical background structure consistency. Experiments on the M4-SAR and OGSOD1.0 datasets demonstrate that PCFD-Net consistently outperforms representative fusion and detection methods, achieving superior fusion quality and stronger downstream detection performance. Full article

Large language models have become routine participants in everyday cognition. Their role has widened from retrieval and text generation to helping users define problems, organize arguments, make judgments, and interpret themselves. Yet their cognitive consequences are strikingly divergent. For some users, generative AI appears to reduce critical engagement, independent judgment, and tolerance for difficulty. For others, the same class of systems becomes a medium for conceptual expansion, reflective questioning, and higher-order learning. This divergence cannot be explained by model capability alone. Mental effort is often treated as a cost to be reduced. Yet repeated delegation may also reduce opportunities to practice the processes required for independent judgment. The key issue is developmental: how sustained AI use changes users’ cognitive capacities over time. This perspective proposes cognitive entanglement as a framework for understanding the developmental consequences of sustained human-AI coupling. Cognitive entanglement refers to a relation in which human and AI activity become mutually shaping, irreducible to either party alone and organized across different developmental levels. The framework examines how repeated interaction with AI changes the ways users formulate problems, evaluate reasons, and make judgments. Unlike theories that locate the boundaries of cognition (the extended mind, enactivism) or explain the mechanisms of consciousness (global workspace, higher-order, predictive-processing, and integrated-information theories), cognitive entanglement examines whether sustained AI use preserves, weakens, or reorganizes users’ cognitive capacities. The article argues that current AI systems are often optimized for fluency, immediacy, and user satisfaction, and this may reduce the productive difficulty that supports higher-order cognitive development. If AI is to support human cognitive growth, design must move beyond answer provision and efficiency maximization toward the organization of productive human-AI relations: relations that challenge users’ initial assumptions while providing support appropriate to the task and the user’s level of expertise. The argument draws on philosophy of mind, cognitive science, and learning science, and compares divergent approaches to coupling in order to specify which forms of relation carry which developmental consequences. The concept shifts attention from AI as a tool or automation system to the developmental consequences of sustained human-AI interaction. Full article

Background/Objectives: Patients on maintenance hemodialysis (MHD) have an increased risk of catheter-related right atrial thrombosis (CRAT) due to long-term central venous catheterization. Transthoracic echocardiography (TTE) and digital subtraction angiography (DSA) are commonly used to evaluate right atrial thrombi, yet their diagnostic consistency in this patient population remains unclear. This study aimed to compare TTE and DSA diagnostic agreement for CRAT and explore its independent risk factors. Methods: We retrospectively enrolled 254 MHD patients who underwent paired same-day TTE and DSA from November 2021 to May 2025. Cohen’s kappa, intra-class correlation coefficient (ICC), and Bland–Altman analysis were used to evaluate diagnostic consistency. Univariable and multivariable logistic regression screened independent CRAT risk factors. Results: The thrombus detection rates of TTE and DSA were 17.3% and 14.6%, respectively, with moderate diagnostic consistency (κ = 0.487, p < 0.001). The ICC values were 0.871 for thrombus length and 0.824 for thrombus width between TTE and DSA, indicating excellent consistency in thrombus size measurement between the imaging modalities. Catheter tip located in the mid-right atrium, decreased central venous catheter flow, prolonged catheterization, and male sex were independent risk factors for CRAT. The predictive model exhibited good discrimination (area under the curve = 0.788) and acceptable calibration (Hosmer–Lemeshow, p = 0.429). The optimal cutoff derived from the Youden index was 0.191, with a corresponding sensitivity of 81.0% and specificity of 67.3%. Conclusions: TTE is recommended as the first-line CRAT screening tool for MHD patients. Combined TTE and DSA use improves diagnostic yield. Regular targeted surveillance for high-risk individuals reduces CRAT incidence. Full article

In the single-depot multi-traveling salesman problem, traditional depot location methods often overlook task balance among traveling salesmen, leading to excessive load on certain units and compromising overall operational efficiency. To address this issue, this paper proposes an optimized depot location method based on clustering and multi-task balancing. The core contribution lies in the design of a multi-weight adaptive depot optimization method. This approach clusters city nodes into multiple groups through cluster analysis and dynamically synthesizes direction vectors using information such as the number of samples within each cluster and the convex perimeter. It iteratively optimizes depot locations, minimizing the total path length while enhancing workload balance across all traveling salesman routes. Additionally, a “divide-and-conquer” strategy decomposes the complex MTSP into multiple parallel TSP subproblems, which are then efficiently solved using Or-Tools. A comprehensive evaluation framework is introduced, incorporating Total-Sum distance, Min-Max distance, Workload Balance, Cluster separability, Robustness, and Running time. Experimental results on the TSPLIB standard dataset demonstrate that the proposed method exhibits significant advantages over various traditional clustering algorithms in both route optimization and route balancing, validating its effectiveness and practicality. The method’s robust performance provides a reliable solution for real-world applications such as logistics distribution, further highlighting its practical value. Experimental results show that the proposed method reduces the total travel distance and improves workload balance on multiple TSPLIB instances compared with conventional depot selection baselines. Full article