Search Results (6,946)

Defect calculations for semiconductors demand both large supercells and accurate electronic structures, posing a significant challenge to first-principles methods. Conventional density functional theory (DFT) with local or semi-local exchange-correlation functionals severely underestimates the band gap, whereas hybrid functionals such as HSE06 provide higher accuracy but at a substantially increased computational cost. In this work, we demonstrate that shell DFT-1/2, a self-energy correction method for electronic structure calculations, may be used jointly with HSE06 to reach the optimal efficiency as well as accuracy. In particular, indium phosphide (InP) was taken as an example. The shell DFT-1/2 method was utilized to yield accurate band structures with a 1.44 eV direct gap, without any empirical parameter. Subsequently, the portion of exact exchange was tuned to match the shell DFT-1/2 electronic structure in HSE06 calculations. The charge transition levels of various point defects in InP were derived using HSE06, and HSE06 and shell DFT-1/2 may be employed alternatively to yield the density of states for the defective supercells. Their consistency proves the feasibility of the complementary employment of the two methods, and this strategy is readily extendable to other semiconductor research. Full article

With the rapid growth of social media, online news has become increasingly multimodal, combining textual and visual information, posing new challenges for fake news detection. Existing methods often suffer from redundant features, distribution differences across modalities, and insufficient modeling of semantic interactions. To address these issues, this paper proposes an Adaptive Binary Osprey Optimization Algorithm and Cross-modal Disentangled Fusion model (ABOOA-CDF). First, an Adaptive Binary Osprey Optimization Algorithm (ABOOA) is developed for multimodal feature selection by integrating chaotic initialization, adaptive search, and binary mapping strategies to identify informative feature subsets. Then, a Cross-modal Relation Disentanglement Module (CRDM) is introduced to decompose multimodal representations into shared, discrepant, and complementary components, thereby enhancing semantic relationship modeling. Furthermore, an Adaptive Semantic Fusion Module (ASFM) dynamically learns fusion weights to generate discriminative multimodal representations. Experimental results demonstrate that ABOOA-CDF effectively improves detection performance. Compared with MFO and OOA, the proposed method achieves Accuracy improvements of 1.02 and 2.66 percentage points, respectively, verifying its effectiveness in feature optimization, cross-modal relation modeling, and semantic fusion. Full article

Background/Objectives: Transcranial magnetic stimulation (TMS) initiates a cascade from intracranial electric-field exposure through neural recruitment and plasticity to distributed network responses. Neuronavigation improves the geometric reproducibility of delivery but does not guarantee equivalent cortical exposure or target engagement. This narrative review integrates these levels within an operational framework for precision TMS. Methods: Six domain-specific PubMed searches covering 1 January 1985 to 31 July 2026 were supplemented by Google Scholar and citation tracking. A documented rerun on 17 August 2026 yielded 6430 records (5617 unique after cross-query deduplication). Evidence was synthesized narratively; no quantitative synthesis or formal risk-of-bias assessment was performed. Results: Neuronavigation improves geometric precision by stabilizing target definition and coil pose, whereas individualized electric-field models estimate intracranial exposure. Neither establishes biological precision, which also depends on neuronal orientation, brain state, circuit architecture, medication, and behavior. Motor-system measures are not validated as universal biomarkers for nonmotor cortex, and no single validated biomarker captures TMS-induced plasticity. Convergent, controlled multimodal evidence may strengthen inference about target engagement; adaptive and closed-loop approaches remain experimental. Conclusions: Geometric delivery, modeled exposure, biological engagement, and durable functional or clinical benefit require separate validation. Spatial accuracy alone does not establish clinical value. Full article

The emergence of resistance-associated mutations in coronavirus main protease (Mpro) poses a significant challenge to the development of broad-spectrum antiviral therapeutics. In this study, we improved and accelerated a mutation-aware machine learning (ML) framework to predict the binding score of Nirmatrelvir analogue ligands against wild-type and mutant MERS-CoV Mpro. A library of 15,889 Nirmatrelvir derivatives generated through systematic scaffold modification was docked against the wild-type and five variants of the Mpro, producing a total of 95,334 structural and docking score datasets of these protein–ligand complexes. During the ML model development phase, ligand effects were learned from RDKit molecular descriptors and graph-based representations, and the mutation-induced effects were captured through delta-encoded physicochemical properties (hydrophobicity, charge, aromaticity, and polarity) of the active-site residues. Among the evaluated models, the CatBoost regressor tree-based algorithm achieved the lowest mean absolute error (MAE) value of 0.23 Kcal/mol and an R² of 0.87. Further improvement was achieved by creating a weighted ensemble model combining the CatBoost regressor, XGBoost and LightGBM regressor, resulting in a prediction accuracy with a MAE of 0.19 Kcal/mol and an R² of 0.90 relative to docking scores. Model robustness was further evaluated through random-, ligand group- and scaffold group- K-fold cross-validation along with their Y-randomization. Moreover, the models were also tested with a new set of 1000 structurally diverse compounds. SHAP analysis was conducted, which identified 20 molecular descriptors critical for accurate predictions. The ensemble model accurately predicted the binding affinities of Nirmatrelvir and its four analogues (E1–E4), reproducing the experimental pIC50 trend and correctly identifying the most potent inhibitors. The ensemble model also showed consistent performance across all MERS-CoV Mpro variants, S147Y, S142G, L144A, S142G/S147Y, and S142G/L144A/S147Y, demonstrating its potential for rapidly discovering mutation-resistant antiviral drugs. Full article

The slow evolution and inconspicuous nature of buffer-layer ablation in high-voltage cables pose a significant challenge for early fault diagnosis. To tackle this issue, we propose a hybrid diagnostic approach that integrates frequency-domain impedance measurement with a convolutional neural network (CNN). A cable simulation model is first established using transmission-line theory and a distributed-parameter framework. We examine the impedance and phase responses at the cable’s sending end, revealing a consistent decreasing trend with rising frequency alongside periodic resonant peaks. The simulator generates a diverse set of spectral signatures corresponding to various cable health states. The CNN then extracts discriminative features from these waveforms, and a probabilistic clustering preprocessing step further refines the input data. Experimental results on a test set of 78 samples—comprising 52 experimentally measured normal spectra and 26 experimentally calibrated simulated spectra for mild and severe ablation—demonstrate a classification accuracy of 0.95, confirming that the proposed methodology enables reliable, non-intrusive detection of buffer-layer ablation without cable disassembly. Full article

Repeated non-equidistant unmanned aerial vehicle (UAV) inspections of slope-road cracks require measurements from different distances, poses, and image scales to remain comparable and sufficiently precise for engineering-state decisions. Existing studies rarely integrate cross-view physical conversion, measurement uncertainty, and a project-defined minimum detectable change. We develop an engineering-detectability framework that defines cross-period criteria for crack width and displacement and derives equivalent widths for ideal, representative non-standard, and arbitrary viewpoints. First-order error propagation and reliability allocation convert the minimum detectable change into accuracy requirements for range, field of view, and normalized image coordinates. Crack-boundary coordinates and localization uncertainties provide a common interface for interchangeable detection and photogrammetric modules. Validation combines a controlled fixed-camera sequence with a close-range field-camera multiview test of seven physical openings under local coplanarity. All six determinate stages in the controlled sequence agreed with the digital image correlation (DIC) comparison, while one borderline stage required review. Across the seven openings, the four-view means gave a mean absolute error (MAE) of 0.196 mm and a root mean square error (RMSE) of 0.270 mm, with cross-view coefficients of variation (CVs) of 0.33–4.93%. An illustrative error budget demonstrates reverse screening of system configurations from project thresholds. The framework therefore connects viewpoint-equivalent measurements, uncertainty constraints, and engineering-state decisions in an auditable chain. Full article

Bovine tuberculosis (bTB) remains endemic in southern Italy, particularly in Sicily, posing challenges for disease control and eradication. This study evaluated the diagnostic agreement between the intradermal tuberculin test (IDT), interferon-gamma release assay (IFN-γ), and post-mortem lesion detection in 1801 cattle from 44 herds in the province of Palermo during 2024–2025. Diagnostic agreement between IDT and IFN-γ was substantial in 2024 (κ = 0.731; 95% CI: 0.64–0.82; n = 991) and decreased to moderate in 2025 (κ = 0.453; 95% CI: 0.33–0.57; n = 572), reflecting a higher proportion of IFN-γ positive and inconclusive animals. An increase in inconclusive results for IFN-γ was observed in 2025, but the available data did not allow investigation of the factors underlying this difference. Agreement between ante-mortem tests and post-mortem lesion detection was poor in a selected subgroup of slaughtered animals, potentially subject to verification bias, highlighting the limitations of single diagnostic tools and the need for a complementary approach combining immunological and pathological methods. These findings support the interpretation of the complementary roles of IDT and IFN-γ within herd-level bTB surveillance, highlight the need for standardized protocols to manage inconclusive IFN-γ results, and underline the complementary contribution of post-mortem inspection within integrated surveillance strategies in endemic settings. Official confirmation of bovine tuberculosis relies on an integrated evaluation combining diagnostic results, post-mortem findings, and epidemiological investigations. Given the absence of a validated gold standard applicable to all animals in this observational dataset, the study was designed as a diagnostic agreement study rather than a formal accuracy assessment. Full article

During the X-ray Diffraction (XRD) identification of the traditional Chinese medicine Os Draconis, the identification model often suffers from limited classification accuracy due to the difficulty in determining optimal parameters. To address this issue, this paper proposes a Hydrodynamic Improved Particle Swarm Optimization (HIIPSO) algorithm for the deep optimization of model parameters. In practical identification scenarios, the high complexity of XRD data poses severe challenges to the convergence speed and global search capability of optimization algorithms. To enhance model performance, this study introduces the interaction mechanism from fluid dynamics into the particle swarm optimization process. Specifically, HIIPSO incorporates a Voronoi neighbor topology to enhance population diversity and spatial distribution rationality. Concurrently, a hydrodynamic interaction mechanism is constructed to simulate the cooperative behavior of particles in a fluid environment, thereby effectively preventing the algorithm from falling into local optima. A theoretical analysis of the computational complexity of the HIIPSO algorithm in the parameter search task for XRD identification models was conducted, confirming that it falls within an ideal range for engineering applications. Statistical analysis of the experimental results demonstrates that, in the parameter optimization task for the Os Draconis identification model, the HIIPSO algorithm significantly outperforms traditional and other baseline algorithms across key metrics, including the optimal value, mean, standard deviation, and median of the objective function. The experimental data indicates that the HIIPSO algorithm can substantially improve the robustness and identification accuracy of the XRD-based Os Draconis identification model, making it an optimal solution for parameter optimization problems in the digital identification of complex mineral-based traditional Chinese medicines. Full article

Residual creases generated during the leather spreading process exhibit highly variable morphologies and irregular feature distributions, causing significant challenges for feature extraction and leading to low localization accuracy. To address these issues, this paper proposes the Dynamic, Cross-Calibrated, and Edge-Enhanced YOLOv8n-Pose (DCE-YOLOv8n-Pose) algorithm. Instead of providing regional approximations, this framework outputs precise spatial coordinates for robotic grasping by integrating three synergistic components in a progressive network flow. First, dynamic snake convolution adaptively perceives the continuous geometric features of elongated creases; subsequently, an efficient multi-scale attention mechanism provides cross-dimensional weight calibration to suppress highly homochromatic background interference and correct spatial misalignments; finally, an edge-enhanced content-aware reassembly of features module preserves high-frequency gradients and prevents feature fracturing during multi-scale fusion. For comprehensive evaluation, a dataset comprising 700 original laboratory images was constructed. To prevent data leakage, the dataset was partitioned into training and validation sets based on individual leather specimens, ensuring that images of the same leather piece do not appear in both sets. Additionally, an independent test set of 500 images collected from an actual processing plant was designed for industrial validation. Experimental results indicate that, at an Intersection over Union (IoU) threshold of 0.5, the DCE-YOLOv8n-Pose model achieves a bounding box mean average precision (mAP@0.5) of 91.8% and a keypoint mAP@0.5 of 85.1%, with a keypoint precision of 87.9%. The computational load is maintained at 9.2 GFLOPs, alongside an inference speed of 114.3 FPS. Furthermore, consistent convergence across four independent training runs substantiates the model’s reliability in reducing missed detection rates and localization deviations. In conclusion, the proposed algorithm demonstrates practical applicability for the visual guidance of automated leather spreading equipment by balancing detection precision and inference speed, thereby offering an effective coordinate reference for subsequent robotic stretching operations. Full article

Granular collapse-induced wave generation is a critical process in coastal engineering and natural hazards, yet its rapid and complex fluid–solid coupling mechanism poses significant challenges for numerical modeling. This paper presents a comprehensive validations and performance benchmarking study of non-spherical granular collapse-induced wave generation using a GPU-accelerated resolved SPH-DEM coupling framework. Through three benchmark cases with increasing complexity, the numerical accuracy and robustness of the model are thoroughly verified with respect to free-surface flows, multi-body collisions, and intense fluid–solid interactions. Subsequently, the influence of SPH resolution and particle shape on computational efficiency is quantitatively assessed. It is found that the total runtime is dominated by the number of SPH particles, while the GPU acceleration advantage becomes more pronounced as the number of DEM faces increases. Furthermore, in the granular collapse-induced wave case, the temporal evolution of the leading wave amplitude and the difference in granular runout distance under dry and wet conditions are analyzed, revealing from the particle scale how fluid resistance modulates the coupling between wave generation and granular motion. This study not only validates the capability of the model to capture complex particle–wave interactions, but also provides quantifiable performance benchmarks and physical insights for its engineering applications. Full article

Currently, insulation faults in the DC system of photovoltaic (PV) power stations are handled by a full shutdown strategy of inverters, and fault branch localization relies on manual inspection, resulting in low efficiency and poor accuracy, leading to prolonged unplanned outages and substantial power generation losses. This paper proposes an integrated solution combining high-precision insulation monitoring and intelligent fault line selection, which ensures the reliability of line selection criteria through improved measurement accuracy and achieves automatic fault isolation via optimized line selection strategies. The paper analyzes the mathematical essence of the ill-conditioned measurement equations of the traditional bridge method under severe single-pole grounding faults, establishes a dual-channel heteroscedastic noise model, and utilizes the inherent physical constraint that the sum of the positive and negative pole-to-ground voltages always equals the bus voltage to transform the ill-posed inverse problem into an equality-constrained optimal estimation problem, deriving an analytical solution in the sense of constrained least squares. A collaborative monitoring strategy of “balanced bridge monitoring first, unbalanced bridge precision measurement afterward” is proposed. An automatic fault line selection and isolation algorithm based on sequential branch switching is designed, which leverages the operational characteristic that PV systems allow short-term branch interruption, enabling automatic identification and isolation of faulty branches and automatic restoration of non-faulty branches without installing any leakage current sensors. Experimental results show that under severe fault conditions with a single-pole insulation resistance as low as 22 kΩ, the proposed method limits the error to within 5%; the proposed line selection strategy can complete identification and isolation of all faulty branches within at most two rounds of switching. Full article

Multi-view fringe projection profilometry systems that integrate a telecentric projector with multiple oblique-view cameras offer unique advantages for inspecting high dynamic-range surfaces featuring densely packed, intricate microstructures. Nevertheless, such systems encounter fundamental calibration challenges, namely, sign ambiguity in the rotation matrices and truncated extrinsic parameters inherent to telecentric projector models, as well as difficulties in multi-view point cloud registration. This paper introduces a novel calibration framework with three principal contributions. First, we resolve the sign ambiguity by calibrating the telecentric projector under a quasi pinhole model and directly transferring the extrinsic sign conventions, thereby obviating the need for costly precision displacement stages or elaborate virtual targets. Second, we fix the axial-gauge freedom by constraining the origin of the projector coordinate system to lie on the XY-plane of the camera coordinate system. Third, we establish precise relative poses between all cameras and a designated reference camera, enabling unified multi-view point cloud registration directly within the projector coordinate frame, which substantially reduces alignment errors and accelerates data processing. Experimental results demonstrate marked improvements in accuracy: reprojection root-mean-square errors of 0.084 pixels for the cameras and 0.106 pixels for the projector, corresponding to in-plane spatial resolutions of 0.21 µm and 0.26 µm, respectively. The proposed method offers a robust solution for micron-level inspection in semiconductor packaging and precision manufacturing. Full article

This work presents the first complete system for automated six degrees of freedom (6DOF) satellite pose estimation from spatially resolved, ground-based, adaptive optics (AO)-corrected imagery, addressing a key challenge in Space Domain Awareness (SDA). The approach mitigates the need for human labeling by directly regressing satellite orientation and position from blurry, noisy, and deeply shadowed imagery. A multi-stage deep neural network pipeline localizes the satellite, predicts pose, and optionally applies temporal filtering. Networks are trained exclusively on fully synthetic imagery generated from a CAD model, yet generalize effectively to real data, bridging the Sim2Real domain gap. On 137 real, human-labeled test images of Seasat, the model achieved a mean rotation error of $5^{°}$ and a mean image-plane translation error of 21 cm. Slant range error was quantitatively evaluated on synthetic data due to unknown real-sensor parameters. Qualitative evaluation of additional real Seasat imagery rated 177 of 199 predicted poses as “ground truth equivalent” or “high-confidence match,” with zero catastrophic failures. The system was extended to seven degrees of freedom (7DOF) for satellites with articulating components and demonstrated on real Hubble Space Telescope (HST) imagery, achieving ${5.5}^{°}$ rotation error, 51 cm image-plane translation error, and $8^{°}$ symmetry-adjusted solar array error on a 249-frame pass with causal temporal filtering. Across 586 real test images from Seasat and HST (captured over multiple decades under diverse conditions) the system consistently performed well. Full 6DOF performance was quantified on a high-fidelity wave optics (HFWO) synthetic test set of Seasat, where the model achieved ${8.4}^{°}$ mean rotation error, 34 cm image-plane translation error, and 1.4% line-of-sight range error at $r_0=6$ cm and 1031 km range. In a limited 200-image benchmark, the model demonstrated 48% lower mean rotation error than a single human labeler while operating ∼800× faster. It required <40 h and a single A100 GPU to generate data and train. The approach was also demonstrated for ARGOS, a smaller satellite with highly symmetric geometry. An exploratory General Image-Quality Equation-based image quality metric (AO-IQ) was introduced as an empirical correlate for pose accuracy. General-purpose models like GPT-4o and Depth Anything V2 failed across most SDA tasks, but rapid gains in vision-language models warrant continued monitoring. These results establish a new operational baseline for practical, real-time satellite pose estimation from AO SDA imagery. Full article

The flexible and precise design of photovoltaic (PV) skin morphologies for building facades constitutes a critical element for optimizing building energy efficiency and enhancing indoor spatial performance. However, the complex interrelationships among climatic parameters and their spatiotemporal variations pose significant challenges to the prior validity and accuracy of climate-adaptive parametric skin morphology adjustments. To address these limitations, this study proposes a morphological generative framework for climate-adaptive Building-Integrated Photovoltaics (BIPV) facades integrating Artificial Intelligence Algorithms and Bayesian prior-parameterized building envelopes. This framework is specifically designed to facilitate morphological decision-making regarding the overall climate-adaptive opening states of parametric PV skins under spatiotemporal dynamics. The proposed method integrates AI-based pattern recognition in spatiotemporal climate data with Bayesian Network-based prior probability techniques to derive optimal facade morphology schemes with the highest overall climate adaptability scores derived from weather forecasts, thereby achieving optimal transformations of the building envelope. Specifically, the model first employs an Artificial Intelligence Algorithm to generate the Bayesian Network structure required for overall climate adaptability scoring. Secondly, utilizing the Chinese Standard Weather Data (CSWD), the GRASSHOPPER algorithm is applied to implement variable parametric design on the facade skin, generating dynamic parametric skins and visual climatic data analysis cloud maps for energy benefit assessment. Finally, facade updates are executed based on the overall climate adaptability scores. The results demonstrate that the proposed framework effectively enables the real-time selection of optimal morphologies and opening states for dynamic skins based on comprehensive climatic adaptability criteria. Following model training and validation using 2025 Panjin meteorological data in the EnergyPlus Weather (EPW) format, the generated facade morphologies yielded solar radiation gains of 166.9 kWh/m²·month (peak month) for one of the optimal summer configurations and 90.5 kWh/m²·month (December) for one of the optimal winter configurations. Furthermore, by providing definitive evaluations of PV energy yields and indoor comfort levels across diverse weather scenarios, this framework offers explicit guidance for skin design, thereby reconciling the multi-objective optimization relationship between building energy conservation and occupant comfort. Full article

Fine-grained sports recognition from static images is challenging because visually similar sports often exhibit nearly identical human poses, whereas their decisive differences are encoded by small-scale equipment and subtle human–equipment interactions. Existing single-stream convolutional or Transformer-based models tend to emphasize either local appearance or global context, making them vulnerable to equipment-detail loss and background interference. To address this problem, we propose the Equipment-Primed Network (EP-Net), a heterogeneous dual-stream architecture that treats sports equipment as a primary semantic cue for action discrimination. EP-Net employs an EfficientNetV2-S-based Equipment Stream to capture localized equipment shapes and textures and a Swin-Tiny-based Behavior Stream to model the athlete’s spatial configuration and global scene context. We further introduce a Cross-Modal Channel Attention (CMCA) module that projects equipment features into the behavior-feature space and performs directional channel recalibration. Unlike simple feature concatenation, CMCA uses equipment information to enhance action-relevant channels while reducing the relative influence of background-dominated responses. Experiments on the Sports-100 dataset show that EP-Net achieves a Top-1 accuracy of 98.80%, outperforming EfficientNetV2-S and Swin-Tiny by 3.00 and 2.35 percentage points, respectively. It also improves on naive dual-stream concatenation by 0.88 percentage points. Grad-CAM visualizations further indicate that EP-Net attends more consistently to discriminative equipment and human–equipment interaction regions. These results suggest that equipment-guided local–global feature interaction provides an effective solution to pose ambiguity and background interference in static fine-grained sports recognition. Full article