Search Results (1,078)

With the increasing embeddedness of AI robots and other intelligent technologies in organizational workplaces, leader AI-focused attention has emerged as an important reference point for employees as they use and adapt to AI-related technologies. Drawing on work-related rumination theory, this study develops and tests an integrated mediation model to examine how leader AI-focused attention is related to employee proactive behavior through two parallel pathways: problem-solving pondering and affective rumination. It further investigates the moderating role of AI job role clarity. Based on structural equation modeling of multi-wave survey data from 514 employees, the results show that leader AI-focused attention positively predicts employees’ problem-solving pondering and affective rumination. Problem-solving pondering is positively related to employee proactive behavior, whereas affective rumination is negatively related to employee proactive behavior. In addition, AI job role clarity positively moderates the relationship between leader AI-focused attention and problem-solving pondering; specifically, this positive relationship is stronger when employees report higher AI job role clarity. From the perspective of work-related rumination, this study extends the explanation of the psychological mechanisms linking leader AI-focused attention to employee proactive behavior. It also provides theoretical insights and practical implications for understanding the boundary condition of leaders’ attentional signals in AI-related work contexts and for supporting employee proactive behavior. Full article

Heated crude oil pipelines transporting high-pour-point, high-viscosity, and high-wax-content crude oil are increasingly operated under small-batch and multi-condition scenarios. Under such conditions, fixed-parameter models and experience-based operating strategies may fail to accurately describe the evolving thermo-hydraulic state, resulting in inaccurate temperature-safety assessment and conservative energy use. To address this problem, this study develops a hybrid modelling and simulation framework for the energy-efficient operation of heated crude oil pipelines. The framework integrates operating-state perception, online parameter inversion, transient thermo-hydraulic simulation, data assimilation, and rolling optimization. First, an online parameter inversion method based on inverse problem solving is established to dynamically identify the overall heat-transfer coefficient and friction correction factor from Supervisory Control and Data Acquisition (SCADA) measurements. Second, a transient thermo-hydraulic simulation and data-assimilation model is constructed to predict pressure, temperature, and safety margins under changing boundary conditions. Third, a constraint-aware rolling optimization strategy is introduced to coordinate heating and pumping operations while satisfying temperature and pressure constraints. The proposed framework is validated using a practical crude oil pipeline. Under a representative low-flow-rate condition, online parameter inversion corrects the overestimation of the thermo-hydraulic state by the fixed-parameter model: the total temperature drop along the pipeline is revised from 33.12 °C to 35.65 °C, and the minimum station-inlet oil temperature is revised from 24.77 °C to 21.61 °C. After optimization is introduced, the total operating energy consumption decreases from 11,715.65 kW to 11,287.43 kW, corresponding to a reduction of 3.66%, while all temperature and pressure constraints remain satisfied. Under time-varying boundary conditions, the rolling optimization strategy further adjusts heating-furnace operation according to variations in inlet flow rate, inlet oil temperature, and ambient temperature, thereby reducing cumulative heating energy consumption while maintaining safe operation. The results demonstrate that the proposed framework provides an implementable modelling and simulation approach for online state assessment, transient prediction, and energy-efficient operation of heated crude oil pipelines under variable operating conditions. Full article

Fault location in power transmission lines (PTLs) relies on impedance or traveling wave (TW) principles. TW approaches offer superior accuracy and high robustness against fault resistance. While multi-ended methods require precise terminal synchronization, single-ended TW (SETW) methods utilize measurements from one terminal, requiring accurate distinction of reflected waves. This study employs the computationally efficient approximate derivative (AD)—the difference between consecutive samples—for SETW fault detection and location. Normally near zero, the AD of modal signals produces sharp transitions during faults. Comparing AD output to a threshold achieves fault detection. The AD then identifies arrival times of the incident and reflected TWs. When using TW theory to distinguish reflections from the fault point and remote end, the fault distance is calculated from their arrival time difference. Validated through 293 diverse ATP simulated fault scenarios, the approach delivered highly accurate results despite using a lower sampling rate than established methods, utilizing an exceptionally short data window—only 2.03 ms for a 300 km line. Finally, operational boundaries for the signal-to-noise ratio (SNR) in noisy conditions are established. Full article

Wire arc additive manufacturing of aluminum generates coarse, anisotropic solidification microstructures that limit mechanical performance, and interlayer friction stir processing (FSP) is increasingly applied to refine them. This study reports the layer-resolved grain morphology and the recrystallized crystal evolution in MIG + FSP-fabricated aluminum alloy 4043 walls, pairing the FSP spindle torque recorded from the CNC controller with multi-descriptor grain morphology in a coupling that, to the authors’ knowledge, has not been previously reported in the WAAM + FSP literature. Methodologically, two four-bead, three-layer walls were co-fabricated under identical deposition conditions on a HAAS VF-3 CNC platform, one by MIG deposition alone and one by the complete MIG + FSP route; the FSP spindle torque was measured at three positions per layer (118 ± 6 N·m at 600 RPM for L1, and 19.1 ± 1.0 and 26.6 ± 1.3 N·m at 1200 RPM for L2 and L3), and quantitative image analysis of 10,091 grains provided the layer-resolved mean grain area, equivalent diameter, aspect ratio, perimeter-to-area ratio, and circularity. The results show that the mean grain area increased from 8.55 μm² (L1) to 12.96 μm² (L3) while the aspect ratio decreased monotonically (1.389 to 1.323), indicating progressive grain equiaxiality with build height; the P/A ratio followed a non-monotonic layer dependence (2.54 to 2.11 to 2.50 μm⁻¹), with the L2 minimum consistent with reduced boundary line density under the combined thermal influence of two adjacent FSP events. The MIG + FSP route produced grain areas 29–48× smaller per layer than the MIG wall and a 45.8% higher hardness (75.8 ± 7.7 versus 52.0 ± 1.3 HV; n = 6; p = 0.0027). In conclusion, the L3 torque exceeds the L2 torque at equal 1200 RPM, qualitatively consistent with the d^−p term in the grain-size-explicit creep framework $\stackrel{.}{\gamma }$ = C·(τⁿ/d^p)·exp(−Q/RT), although temperature, strain rate, and grain size cannot be fully decoupled from the present three-layer dataset. The morphology and the distributional evidence are consistent with dynamic recrystallization (DRX); discrimination between continuous and discontinuous DRX requires EBSD. Full article

Non-Hermitian photonic lattices offer unconventional control over light evolution owing to modal non-orthogonality and the resulting non-normal dynamical response. In this work, we show that a uniform passive waveguide lattice with dissipation confined to one or a few sites near an edge can exhibit an algebraic(nearly linear) decay of optical power—an absorption law forbidden in orthogonal (normal-mode) dissipative systems, where any superposition of eigenmodes yields purely multi-exponential attenuation. We demonstrate that algebraic absorption arises when the input excitation is appropriately tailored to exploit non-orthogonal modal interference, effectively channeling energy toward the dissipative boundary. In particular, under the condition of coherent perfect absorption (CPA) associated with a spectral singularity of the semi-infinite lattice, nearly complete light absorption accompanied by algebraic decay of the optical power can be achieved. Starting from the minimal configuration of a single lossy edge site, we derive compact analytical expressions for the dynamics and identify the conditions under which linear-like absorption emerges. We then extend the analysis to multiple edge-proximal lossy sites. Our results show that simple dissipative photonic lattices, when driven by suitably prepared input states, enable robust sculpting of absorption laws through non-normal dynamics, providing a new route to programmable attenuation. Full article

To address the issues of strong inter-well interference during multi-well fracturing in shale reservoirs, low efficiency of conventional numerical simulation, and the tendency of machine learning models to overfit and lack interpretability under small-sample conditions, this paper constructs an explainable ensemble learning framework for predicting hydraulic fracture asymmetry. A geology–engineering integrated numerical simulation is adopted to quantify the fracture asymmetry index η as an interference metric, and an initial dataset is constructed comprising natural fracture orientation, well spacing, and injection rate. Subsequently, Jensen–Shannon (JS) divergence-constrained Gaussian data augmentation and second-order interaction features are introduced, and the GBRT model parameters are optimized using particle swarm optimization (PSO). Furthermore, random forest and ridge regression are incorporated, and ensemble weights are determined via cross-validation to build a weighted ensemble prediction model. The results show that the proposed model achieves good predictive performance in repeated validation, with an average coefficient of determination R² of 0.8484 and a 95% confidence interval of 0.8179–0.8790, while also demonstrating favorable overall accuracy in multiple baseline model comparisons and regularization-controlled experiments. Through leave-one-simulation-scenario validation, prediction interval analysis, and interpretability robustness testing, the model’s generalization boundary, prediction uncertainty, and explanation reliability under small-sample conditions are further evaluated. SHAP analysis and grouped permutation importance results indicate that the natural fracture angle is the dominant factor controlling asymmetric fracture response, while the interaction between well spacing and the natural fracture angle also significantly affects the predictions, suggesting that asymmetric fracture propagation is primarily governed by the combined effects of natural fracture steering and inter-well stress interference. The proposed framework can serve as a fast surrogate model for evaluating inter-well interference and screening fracturing designs within a given simulation parameter space, providing an interpretable data-driven approach for fracturing design optimization in shale reservoirs under small-sample conditions. Full article

A fundamental aspect in the design of modern utility-scale wind turbines is predicting the vibrational response of their blades when excited by gust pulses of various amplitudes and frequencies in atmospheric flow. Improved designs based on accurate blade-response predictions can prevent extreme oscillations, reduce fatigue stress, and extend turbine’s operational life. In previously published works, the authors introduced and applied a novel technique that provided an energy-based Reduced-Order Characterization (ROC) for the oscillatory response of wind turbine rotors, when excited by wind gust pulses with different combinations of timespan and amplitude under various operational conditions. Those studies established the universal nature of the ROC by expressing the turbine aeroelastic response as a vibrational Stability Map, plotted in terms of non-dimensional quantities, which could be applied to turbines of any size that share a similar blade construction. In the present paper, the authors will expand the ROC technique beyond the scope of their previously published studies, to analyze the Multi-Modal Response observed in regions located at the external boundaries of the stable zones of the Stability Map. This will provide valuable information about rotor stability behavior in extreme turbine operational conditions which were previously unexplored. Full article

Hyperspectral images (HSIs) and Light Detection and Ranging (LiDAR) data offer complementary spectral and spatial information and are extensively applied to land cover classification. Nevertheless, current fusion–classification approaches frequently suffer from cross-modal feature entanglement and insufficient exploitation of LiDAR physical priors, particularly the Digital Surface Model (DSM), which limits the interpretability of learned features and restricts classification accuracy. To address these issues, this study presents a Physics-Guided Adaptive Decoupling and Collaborative Enhancement Network (ADCE-Net) that embeds explicit geometric guidance into multimodal feature learning. In ADCE-Net, the DSM serves as an explicit geometric conditioning signal to guide feature decoupling, decomposing input representations into modality-shared semantic features (SSF) and modality-specific discriminative features (MSF), thereby mitigating cross-modal interference at an early stage. Based on this decomposition, an adaptive collaborative enhancement mechanism is designed using bidirectional cross-attention and dynamic gating to achieve context-aware mutual refinement between SSF and MSF, facilitating more effective utilization of cross-modal complementary information. Furthermore, a multi-level collaborative classification architecture is constructed to integrate multi-scale contextual representations, enhancing spatial consistency and boundary delineation. Extensive experiments on three benchmark datasets—Trento, Houston 2013, and Muufl Gulfport—demonstrate that ADCE-Net achieves overall accuracies of 99.69%, 97.37%, and 94.90%, respectively, surpassing multiple representative methods including support vector machines, 3D convolutional neural networks, transformer-based models, and recurrent neural networks. Noticeable improvements are also achieved for minority classes and classes with highly similar spectral signatures. The DSM-driven physics guidance boosts both classification performance and feature interpretability, providing a reliable and explainable paradigm for multimodal remote sensing classification. Full article

Large language models (LLMs) have achieved strong performance in many linguistic tasks, but their ability to process discourse-level information structure remains insufficiently understood. In particular, current models may identify locally coherent spans while failing to determine the minimal constituent that carries informational prominence in context. Chinese “shi…de” cleft constructions provide a theoretically important testing ground for this problem because they combine a stable formal pattern with context-dependent focus interpretation, exhaustivity, and discourse-sensitive boundary variation. This study investigates whether current LLMs can identify the minimal focus domain in Chinese “shi…de” clefts and whether their performance goes beyond simple surface-form heuristics. Based on 105 human-validated gold-standard samples, we compared three API-accessible models, ChatGPT 5.4, Claude Opus 4.6, and DeepSeek-V4-Pro, with two rule-based baselines. Baseline 1, which extracted the full span between “shi” and “de”, achieved only 2.86% accuracy, while Baseline 2, a stronger minimal-cue heuristic, reached 46.67%. Under the main prompt condition, DeepSeek-V4-Pro achieved the highest accuracy (65.71%), followed by Claude Opus 4.6 (60.00%), whereas ChatGPT 5.4 (41.90%) did not outperform Baseline 2. A prompt-level QUD ablation showed no stable or statistically significant improvement, indicating that explicit discourse-question guidance alone is insufficient for minimal focus-boundary identification. Performance across focus types further showed that topical focus was relatively easier than informational and contrastive focus, suggesting the importance of topic continuity. Overall, the findings reveal both symmetry and asymmetry in LLM focus processing: models share certain task-level constraints, but differ in cue weighting and boundary-compression strategies. The study argues that Chinese “shi…de” focus identification is better modeled as a multi-cue focus-span ranking problem rather than as direct QUD-answer matching. Future research should extend the dataset and further test whether explicit multi-cue ranking methods can improve focus-boundary identification across models and languages. Full article

Mass transfer is central to food processing but remains difficult to quantify because food materials are heterogeneous, multiphase, porous, biologically structured, and dynamically changing. Under these conditions, experiments alone cannot fully capture the spatiotemporal complexity of transport behavior, making modeling and simulation essential for mechanism interpretation, process prediction, and engineering optimization. Existing reviews mainly address specific operations or numerical methods, with limited synthesis of governing equations, simulation workflows, application implementation, and practical applicability. This review examines food mass transfer by linking coupled momentum, heat, and mass transfer laws with governing equation selection, simulation workflow, and representative food processing applications. Governing formulations for Fickian diffusion, conservation-based transport, heat–mass coupling, multicomponent transfer, Darcy-type porous-medium flow, and related model extensions are summarized, together with their assumptions, geometric applicability, and dimensionless criteria. A unified simulation workflow is then organized, covering transport type identification, governing equation and physical model selection, geometric representation, parameter determination, initial and boundary condition specifications, numerical method and simulation tool selection, numerical implementation, validation, and transferability assessment. Representative applications are discussed for drying, heat–mass coupled processes, multicomponent transfer, transport in porous foods, and redistribution in multi-ingredient or multilayer foods. Overall, future progress requires more integrated, structure-aware, experimentally validated, transferable, and application-oriented simulation frameworks. Full article

Wind farm layout optimization (WFLO) plays a key role in reducing wake effect energy losses and increasing annual energy production (AEP). This paper proposes a double-stage optimization approach that incorporates staggered grid-based optimization with coordinate-based local optimization. In the first stage, staggered grid-based optimization is performed to determine optimal turbine locations within predefined grid boundaries. In the second stage, turbine positions are locally optimized within bounded regions to improve AEP efficiently without extending the search across the entire wind farm. The modified electric charged particle optimization (MECPO) algorithm is applied to evaluate five optimization approaches, including two double-stage and three single-stage approaches. The framework is tested on a wind farm covering an area of 2000 m by 2000 m with 20 turbines under single-direction, uniform multi-directional, and spatially varying wind conditions. The proposed double-stage optimization approach achieves comparable or improved net AEP while significantly reducing computational cost across different wind conditions. The method provides up to 0.36% improvement in net AEP, reduces wake losses by up to 6.84%, and decreases computational time by up to 90% compared with the coordinate-based approach. These results confirm that the proposed approach significantly enhances computational efficiency while maintaining comparable energy performance. The findings indicate that integrating staggered grid-based optimization with coordinate-based local optimization provides an effective balance between solution quality and computational efficiency, offering a practical and scalable approach for WFLO. Full article

Smartphone-based proximal sensing has emerged as a promising low-cost approach for pasture monitoring. A critical component of this methodology is accurate leaf segmentation, as it directly affects the reliability of subsequent image-based analyses. Despite advances in computer vision, the role of preprocessing strategies in segmentation performance remains insufficiently explored, particularly under resource-constrained conditions. This study presents a systematic comparative evaluation of three preprocessing pipelines based on HSV and CIELab color spaces for the segmentation of Urochloa grass leaves (Urochloa hybrid Mavuno and Urochloa decumbens) using smartphone imagery acquired field conditions. The pipelines were assessed using a multi-criteria framework, including the Fisher Discriminant Ratio (FDR), Intersection over Union (IoU), Overlap Error (OE), Structural Similarity Index (SSIM), and Edge Preservation Index (EPI), complemented by discordance map analysis. The results demonstrate that preprocessing design significantly influences segmentation stability, boundary preservation, and robustness to illumination variability. Pipelines based on HSV channels showed high sensitivity to shadows and non-uniform lighting, leading to reduced segmentation consistency. In contrast, the CIELab-based pipeline relying on the a* channel achieved superior performance, with higher discriminative capacity, improved edge preservation, and lower computational cost. These findings highlight that carefully designed classical preprocessing strategies remain highly effective for low-cost, real-time applications, even in the absence of computationally intensive models. This work establishes a robust segmentation foundation for future integration with advanced analytical methods, including machine learning approaches, and supports the development of scalable smartphone-based tools for pasture monitoring. Full article

Satellite remote sensing imagery has become an essential resource for large-scale surface water monitoring. Nevertheless, in karst regions, the elongated and fragmented morphology of water bodies, along with terrain shadows and vegetation interference, still leads to limitations in existing methods for small water body detection and accurate boundary delineation. To overcome the aforementioned issues, this paper proposes MESA-Net, a CNN–Mamba hybrid segmentation network for water body extraction in complex karst terrain. The network employs ResNet-18 as an encoder to extract shallow-level features. The decoder primarily consists of three modules: the Cross-Scale Adaptive Feature Fusion (CAFF) module, the Directional Gradient Histogram Edge-Guided Fusion (DGHEF) module, and the Omni-directional Global-Local Mamba Block (OGLMB). Among these, the CAFF module enhances the detection capability for small-scale water bodies by performing cross-scale feature fusion and dynamic weight allocation on the feature outputs from each level of the encoder. The OGLMB integrates an omnidirectional state space model with an 8-directional scanning mechanism and cross-attention guidance, effectively enhancing the ability to represent the structural continuity and global consistency of water bodies. The DGHEF utilizes directional gradient histograms to explicitly model multi-directional boundary information of water bodies, and combines this with a boundary guidance mechanism to enhance the representation of water body boundary features whilst suppressing spurious responses. In addition, the LJ-Water dataset has been constructed for the Lijiang River Basin in Guangxi, which is based on Sentinel-2 imagery. To validate the effectiveness and generalization capability of the method, comparative experiments were conducted on the self-built LJ-Water dataset as well as the publicly available Water-CD and LoveDA datasets. Experimental results demonstrate that MESA-Net consistently outperforms representative CNN-based, Transformer-based, and Mamba-based segmentation networks. On the LJ-Water dataset, it achieves 84.59% IoU and 91.65% F1, whilst on the Water-CD dataset, it attains 92.15% IoU and 95.91% F1, and 69.83% IoU and 82.24% F1 on the LoveDA dataset. Relative to the strongest baseline method, the proposed model achieved IoU gains of 1.51%, 2.34%, and 1.73% on the three datasets, respectively. In summary, MESA-Net demonstrates superior water segmentation performance under complex background conditions. Full article

The success of DNA amplification in Polymerase Chain Reaction (PCR) devices inherently depends on the rapid and absolute zero-overshoot temperature control of thermoelectric cooler (TEC) systems. In the literature, metaheuristic algorithms employed for proportional–integral–derivative (PID) tuning typically operate within unconstrained search spaces, relying exclusively on time-domain error metrics like ITAE. This conventional approach causes ‘gradient blindness’ and neglects frequency-domain robustness, resulting in excessive temperature overshoots that violate biological safety limits and lead to enzyme denaturation. To solve this problem, we propose a hybrid frequency-time domain optimization framework. Utilizing a first order plus dead-time (FOPDT) model for TEC dynamics, the PID search space is analytically restricted via Ziegler–Nichol’s stability boundaries. Furthermore, Phase Margin (PM ≥ 45°) and absolute zero-overshoot conditions are integrated into the objective function as a strict penalty mechanism. Evaluations conducted with five distinct metaheuristic algorithms (PSO, GWO, WOA, ABC, and ACO) prove that while traditional unconstrained methods yield overshoots up to 19.04%, the proposed architecture successfully confines all optimization agents to a globally stable region, enabling specific algorithms like ABC, PSO, and WOA to achieve exactly 0.00% overshoot. Validated across a realistic multi-step PCR cycle (95–55–75 °C), the developed robust controller settles into the denaturation phase with a 0.00 °C peak error, guaranteeing biological sample safety and delivering a reliable control framework for rapid-cycle PCR platforms. Full article

This paper focuses on the characterization of the micro-leakage channel geometry in the flange-gasket rough contact interface of hazardous chemicals transport vehicles. This work represents the first step in a multi-physics simulation framework for optical-fiber-based micro-leakage monitoring. Directly establishing a full-scale contact model from micron-scale rough peaks and valleys to the decimeter-scale flange structure would lead to extremely high computational costs; a nonlinear contact model based on quasi-representative volume element (quasi-RVE) and quasi-periodic boundary condition (quasi-PBC) is proposed in this paper. Quasi-RVE refers to a local region selected from the overall rough surface. Unlike a traditional RVE that requires strict geometric periodicity, the quasi-RVE is only approximately consistent with the overall surface with respect to key morphological parameters and volume parameters. Quasi-PBC only imposes in-plane displacement compatibility constraint on the relative side boundary without imposing periodic constraints in the peak-valley height direction. In this paper, the average interface gap and its distribution are selected as the geometric descriptors of the micro-leakage channel, and the reliability of the contact model is verified by comparing with the existing experimental and numerical results. On this basis, the influences of surface roughness, gasket material and loading conditions on the geometric characteristics of the micro-leakage channel are further analyzed. The results show that the lower stiffness gasket is easier to fit with the rough flange surface under the same load conditions, so as to obtain a larger contact area and a smaller average gap. The quasi-RVE contact model established in this paper can effectively reduce the computational scale of contact analysis of the rough sealing interface, and provide reliable channel geometric information for subsequent micro-leakage fluid simulation and optical fiber signal response simulation. Full article