Search Results (3,158)

Methane source-rate inversion from remote-sensing plume imagery is essential for emissions monitoring, but its accuracy is often limited by uncertainty in ancillary wind information. This study examines how physical knowledge can be integrated into a deep-learning inversion model when the available wind input is imperfect. Using a controlled large-eddy-simulation (LES) benchmark designed for EnMAP/PRISMA-style imaging-spectrometer methane quantification, we compare six models that span image-only regression, flexible wind conditioning, simplified hard integrated-mass-enhancement (IME) coupling, and soft physics-guided learning under clean inputs, deterministic wind bias, stochastic Gaussian wind noise, and source-rate-stratified tests. Under clean benchmark conditions, flexible wind conditioning provides the best scalar accuracy, with FiLM reaching a mean absolute percentage error (MAPE) of 6.19% and a root mean squared error (RMSE) of 1323.36, followed closely by Concat (MAPE 6.37%, RMSE 1325.69). The simplified hard-coupling model is sensitive to wind perturbations: DIN-hard rises from MAPE 8.44% under clean inputs to 31.39% and 26.89% under deterministic wind-bias multipliers α = 0.7 and α = 1.3, respectively, and becomes unstable under stronger Gaussian wind noise in the tested protocol. By contrast, DIN-soft-v2 remains competitive under clean conditions (MAPE 6.39%, RMSE 1360.94), follows smoother degradation under biased or noisy wind, and improves plume spatial diagnostics relative to DIN-soft (center-of-mass shift 3.92 versus 4.07 pixels; plume alignment degree 2.60 versus 2.72 degrees). The calibrated IME-style physical baseline reaches a clean MAPE 24.45%, indicating that the learning-based models substantially outperform this benchmark physical proxy. Within this LES-based benchmark and the tested wind-perturbation protocols, the results suggest that IME-inspired physical knowledge is more robustly incorporated as a calibratable soft prior than as the simplified hard log-additive forward coupling considered here; however, transfer to real satellite scenes still requires validation. Full article

Polyurethane synthetic leather is a widely used covering material in automotive interiors, and its surface coating characteristics directly determine the occupant experience. However, the underlying mechanisms by which these characteristics influence visuo-tactile perception in the context of new energy vehicles (NEVs) require further investigation. In this study, a composite experimental matrix was constructed by combining surface textures with distinct roughness gradients and representative colors extracted via data mining within the HSV color space. Targeting these two surface coating characteristics—color and texture—systematic evaluations were conducted across three independent perception stages: purely visual, purely tactile, and combined visuo-tactile. Eye-tracking metrics, specifically pupil diameter and total fixation duration, were extracted and cross-analyzed alongside multidimensional subjective evaluations. The results indicate that surface texture exerts a significant main effect on both perceived tactile softness and pleasantness, whereas the impact of color variation is remarkably weak. Furthermore, highly complex surface textures lead to prolonged fixation durations, reflecting increased exploratory interest and the high perceptual salience of intricate details rather than mere cognitive workload. Moreover, significant differences in pupil diameter were observed across texture conditions, potentially reflecting the combined influence of low-level image properties and higher-order texture perception. Concurrently, an interference effect of visual features on tactile perception was observed; specifically, the introduction of visual cues (encompassing color and texture) significantly diminished the pleasantness experienced during tactile interaction. These findings elucidate the intrinsic connections between surface coating characteristics and users’ visuo-tactile perception, offering important theoretical guidance and practical implications for optimizing the surface design of automotive polyurethane synthetic leather and enhancing the overall occupant experience. Full article

To improve the path-following performance of an underactuated autonomous underwater vehicle (AUV) under varying path geometries and desired velocities, this study proposes a direct X-rudder control method based on Task-Informed Inductive-Bias Conservative Soft Actor–Critic (TIB-CSAC). The proposed method directly learns the X-rudder control policy from the path-following information of the current and subsequent path segments in a data-driven way, thereby avoiding the complex design and manual tuning of guidance laws and attitude controllers for rudder command generation. To support such two-segment policy learning, a task-informed inductive-bias encoder is proposed to construct structured and conditioned state representations, thereby improving sample efficiency and overall training quality. In addition, given the long-tail characteristics of task difficulty in agent training, a multi-head conservative value evaluation mechanism is incorporated to mitigate return drawdowns induced by challenging tasks in the tail stage of training and to enhance tail-stage convergence stability. The path-following performance is validated in three representative scenarios with different path pitch, path heading variations, and desired surge velocity conditions. The results show that, compared with the baseline soft actor–critic (SAC) method, TIB-CSAC improves multiple vertical and horizontal error metrics, including maximum absolute error, mean absolute error, tail error, and error threshold exceedance ratio. These results indicate that TIB-CSAC not only improves overall adherence to the reference path, but also more effectively suppresses extreme errors and tail errors, thereby demonstrating stronger path-following robustness and reliability. Full article

Driven by the growing demand for functional textiles featuring excellent waterproofness, moisture permeability and mechanical robustness in outdoor sportswear, medical protection and technical apparel, traditional pongee—despite its desirable softness, high wrinkle resistance and good stability as an ideal substrate fabric—is severely restricted in further application by its intrinsically poor hydrostatic pressure resistance in extremely wet environments. Accordingly, we developed a modified waterborne polyurethane (WPU) coating for pongee substrates to fabricate functional textiles that maintain high hydrostatic pressure resistance while possessing good mechanical properties and increased UV absorption. In this study, by using the sol–gel method, an amorphous silicon dioxide (SiO₂) coating layer was constructed on the surface of titanium dioxide (TiO₂) particles, forming silica-modified titania particles (SiO₂/TiO₂). These SiO₂-modified particles were subsequently physically blended with an anionic waterborne polyurethane system that had been previously modified with a polyester-type modifier A to enhance its hydrostatic pressure resistance. The resulting composite coating was designed to combine the high hydrostatic pressure resistance inherited from the modified WPU matrix, the mechanical reinforcement and increased UV absorption contributed by SiO₂/TiO₂, and satisfactory water repellency on fabric substrates. The results indicate that the incorporation of an appropriate amount of modifier A into the prepolymer system significantly enhances hydrostatic pressure resistance while maintaining high elongation at break. At a SiO₂/TiO₂ loading of 0.2 wt%, the composite film exhibits optimal comprehensive performance, characterized by superior mechanical properties, low water absorption, and static water contact angles exceeding 100° for coated fabrics. SiO₂/TiO₂ composite WPU coatings substantially improve hydrostatic pressure resistance across various fabrics, with 380T polyester taffeta demonstrating the best performance. This resistance remains remarkably stable after standard washing, indicating excellent wash fastness and practical applicability. Full article

This research investigates atmospheric neutron-induced single event effects (SEEs) on advanced artificial intelligence (AI) chips during natural environment operation. The RK3588 neural processing unit (NPU) is the evaluated target chip, and its SEE is assessed through a combination of irradiation testing and software fault injection. During the irradiation test, the chip was exposed to a spectrum neutron at the China Spallation Neutron Source. Upon reaching a cumulative fluence of 8.25 × 10⁹ n·cm², a total of 14,018 soft errors were detected, of which 99.97% manifested as variations in target recognition accuracy and network inference latency. Among these variations, both detrimental effects (reduced target recognition accuracy or prolonged network inference time) and beneficial effects (enhanced target recognition accuracy or shortened network inference time) caused by single event effects were observed. In addition, atmospheric neutron single event effects were found to cause NPU operation suspension and system crashes. Based on the irradiation test results, failure predictions for neural processing units in real-world environments were estimated, and mitigation recommendations were proposed. Furthermore, software fault injections were employed to conduct in-depth analysis of detected soft errors during irradiation testing. This research provides support and references for the reliable application of artificial intelligence chips in natural environments. Full article

This paper investigates the magnetization reversal processes in spring-exchange magnetic composites featuring irregular, dendritic structures. A disorder-based cluster Monte Carlo method combined with a Diffusion-Limited Aggregation (DLA) algorithm was used to model a fractal-like soft magnetic phase (Fe) embedded in a high-coercivity hard matrix (Fe-Nb-B-Dy). A multiparameter analysis was performed by varying the soft phase volume fraction (10–30%), intergrain exchange coupling via contact bridges (25–100%), system scale factors (1–20), surface-to-volume anisotropy ratios (K_S/K_V = 1–20), and the degree of random anisotropy contribution (RAC = 0–100%). The simulations reveal that highly branched fractal structures enhance the interfacial contact area, which accelerates the nucleation of domain reversal driven by the soft phase, paradoxically lowering the overall coercivity compared to compact morphologies. Furthermore, a lack of easy magnetization axis coherent alignment triggers a cascading reversal mechanism through local “weak links”, severely degrading the coercive field from approximately 4.2 T to below 0.4 T in extreme cases (at 30% Fe, 25% coupling and high K_S/K_V ratio). These findings suggest potentially the most important factors and their impact that should be taken into account in the design and optimization of next-generation powder-sintered permanent magnets. Full article

Balancing of macronutrient intake assumes that animals change their food preferences to increase consumption of the deficient nutrients and/or decrease consumption of nutrients in excess. Harvestmen are generalist predators that consume mostly soft-bodied insects, but they supplement this with plant-derived food such as berries (omnivory). In spite of this, they are often carbohydrate-limited in their natural habitats. As aphids have higher sugar content than most other insect prey, they are a potential source of sugar. We hypothesized that sugar-deficient harvestmen have increased preference for aphids relative to other insect prey (fruit flies) and consume more aphids than sugar-satiated harvestmen. Likewise, we hypothesized that protein-deficient harvestmen would show increased consumption of aphids relative to a pure sugar source (dried grape pulp). The former hypothesis was confirmed but the latter was not. Carbohydrate-deprived harvestmen (Leiobunum gracile) consumed 1.9 times more aphids than nutritionally balanced ones (p = 0.0004). Consumption of dried grape was increased in carbohydrate-deficient harvestmen, while protein deficiency did not increase consumption of aphids. These results indicate that aphids may be used as a carbohydrate source if no better alternative is available, but they are unable to relieve a deficiency in protein. We suggest that carbohydrate deprivation in predators may enhance aphid control. Full article

Accurate analysis of lesions in colonoscopic images is essential for computer-aided diagnosis. However, most existing methods are designed for single-lesion segmentation and assume a predefined lesion category, limiting their applicability in real-world scenarios where multiple lesion types exhibit similar visual characteristics. To address this issue, we propose a unified framework for the joint classification and segmentation of dual-type lesions in colonoscopic images, enabling simultaneous identification and localization of submucosal lesions and polyps/adenomas. The proposed method integrates joint supervision, context-aware feature enhancement, and ambiguity-aware optimization to improve consistency between semantic recognition and spatial delineation. In particular, a soft-label supervision strategy is introduced to alleviate semantic ambiguity, while an imbalance-aware loss design enhances segmentation accuracy and reduces false negative predictions. Extensive experiments on both private and public datasets demonstrate that the proposed method achieves superior performance compared with representative CNN- and transformer-based approaches. Notably, the method shows clear advantages in segmentation accuracy, localization precision, and robustness under challenging conditions. Ablation studies further confirm the effectiveness of each component in the proposed framework. These results indicate that the proposed approach provides an effective solution for dual-type lesion analysis and has the potential to assist clinical decision-making in gastrointestinal endoscopy. Full article

Digital technologies provide effective tools for formulating sustainable, evidence-based policies; however, this field has so far lacked a cohesive and practical framework to guide their application. Providing comprehensive answers to six primary research questions, this study aims to address this critical gap concerning the prerequisites, challenges, opportunities, key technologies, policy areas, and critical success factors (CSFs) for applying digital technologies in environmental sustainability policymaking. In this study, 39 articles were analyzed from 293 documents indexed in the Web of Science as of 19 August 2025, in accordance with the PRISMA 2020 guidelines. The prerequisites are categorized into the following themes: fiscal incentives, a culture of innovation and sustainability, effective regulations, robust digital infrastructures, participation, and reliable and accessible data. We identified significant challenges, including financial constraints, human resource deficits, infrastructural and regulatory gaps, and the adverse environmental impacts of digital technologies themselves. Opportunities emerged under two main domains: effective policymaking and enhanced environmental management. Our study indicates that pioneering technologies at the core of this transformation include artificial intelligence, big data, blockchain, the Internet of Things, machine learning, and robots. Their applications are predominant in key policy areas, including the environment, energy, climate change, urban sustainability, agriculture, industry, and food security. The analysis identifies four CSFs: the policy–digital–sustainability nexus, fundamental processes, soft capacities, and hard capacities. Full article

In recent years, short video platforms have rapidly developed into important media for cultural dissemination. The interactions of netizens in short video comment sections not only reflect their focus on cultural content but also contain rich emotional attitudes. However, given the vast and fragmented nature of comment data, accurately extracting keywords, identifying cultural themes, and analyzing sentiment tendencies pose significant challenges in understanding netizens’ cultural perceptions. To address these challenges, this study proposes a text analysis framework that integrates keyword extraction, clustering analysis, and sentiment analysis to explore the core topics and emotional characteristics of cultural dissemination in short video comment sections. Firstly, to address the challenge of balancing statistical information and semantic understanding in short-text keyword extraction, this paper proposes the TF-IDF-KeyBERT Integrated Algorithm (TKIA) keyword extraction algorithm, which integrates Term Frequency–Inverse Document Frequency (TF-IDF) and Key Bidirectional Encoder Representations from Transformers (BERT). Experiments on the CSL dataset demonstrate improvement in the F1@5 metric, showing its potential to enhance keyword extraction performance for short texts. Secondly, to address the difficulty of simultaneously considering semantic representation capability and clustering flexibility in short-text clustering analysis, this paper designs the Self-Supervised Contrastive Enhanced Clustering (SCEC) algorithm by integrating self-supervised contrastive learning with a soft clustering strategy. Compared to baseline methods, SCEC improves clustering accuracy (ACC) by 17.5% on AGNews and 6.8% on THUCNews, suggesting a more effective way to reveal the underlying structure of cultural topics. Finally, to address the challenge of effectively leveraging both text structural information and global semantic features in short-text sentiment analysis, this paper develops the BERT-GCN Cross-Attention (BGC) Model, integrating BERT embeddings and Graph Convolutional Network (GCN)-based structural features via a Cross-Attention mechanism. On the My_weibo_senti_100k dataset, the BGC model achieves a 2.45% increase in Macro-F1 and a 2.41% improvement in accuracy over strong baselines, offering its ability for high-precision modeling of user sentiment. This study offers effective data support and technical pathways for applications such as cultural content understanding, personalized recommendation, and user emotion guidance. Full article

Background: Continuous Positive Airway Pressure (CPAP) therapy is the standard treatment for obstructive sleep apnea–hypopnea syndrome (OSAHS), and photoplethysmography (PPG) sensors are commonly used in wearable devices for home sleep apnea testing. The recorded airflow and PPG signals from both sensors capture rich physiological patterns. We hypothesize that by combining information from these signals, we can efficiently estimate sleep dynamics of patients receiving CPAP treatment. Methods: The airflow signals were obtained from CPAP titration devices, denoted as CPAP-airflow, while the PPG signals were collected using the PranaQ TipTraQ (TTQ001), a fingertip-worn wearable device. We separately trained one-dimensional convolutional neural networks for CPAP-airflow and PPG signals and fused their outputs through probabilistic ensembling to predict sleep stages. The ensemble method is a late-fusion soft-voting scheme that computes a linearly weighted combination of synchronized softmax probability vectors from the modality-specific models. Results: For three-stage classification (Wake, REM, NREM), the PPG-based and CPAP-airflow-based models achieved overall Cohen’s kappa scores of 0.511 and 0.452, respectively, while the ensembled model improved the overall kappa to 0.587. The F1-score for the REM stage improved to 0.706 using the ensemble method, compared to 0.685 and 0.532 achieved by the individual models, respectively. In the four-stage classification (Wake, REM, Light, Deep) task, a deep sleep sensitivity of 0.596 was attained through the application of probabilistic ensembling. Conclusions: A fusion scheme of complementary information from the CPAP and PPG enhances the accuracy of sleep stage detection and hence enables more precise sleep monitoring, especially with an improved REM identification. Clinical implications include applying the proposed algorithm to improve in-home auto-CPAP titration by capturing REM-related respiratory instability and avoiding under-titration in REM-dominant OSAHS, better reflecting the patient’s true nocturnal respiratory needs. Full article

The growing demand for high-strength and low-core-loss soft magnetic materials in high-efficiency energy conversion devices necessitates the development of novel alloys that combine excellent mechanical and soft magnetic properties. This work investigated the effect of Ta content on the microstructure and properties of as-cast (Fe₇Co₆Ni₆)_93-xTa_xAl₇ (x = 3, 5, 7) multiprincipal element alloys (MPEAs). Microstructural characterization and mechanical and magnetic testing were conducted using scanning transmission electron microscopy (STEM), tensile testing, and vibrating sample magnetometry (VSM). The alloys featured an FCC matrix, in which Ta addition led to the precipitation of a Ta-rich Laves phase and significant grain refinement. The Ta5 alloy demonstrated an optimal balance of properties, with a yield strength approaching 992 MPa, an elongation of 10%, a saturation magnetization (M_s) of 94.16 emu/g, and a coercivity of 6.69 Oe, indicating a good balance of strength, ductility, and soft magnetic performance. An appropriate amount of Ta enhanced strength via precipitation and grain-boundary strengthening, while the M_s showed only a moderate reduction. Full article

In this article, the authors have experimentally investigated the changes in four key properties of six non-edible low-impact energy carries based on rapeseed oil quality grade HM and viscosity grade VG46, which were used as a filling in the hydraulic system of a round wood sorting and transporting trolley. These oils were enriched with thermo-oxidizing, extreme-pressure additives, anti-foaming, and lubricating additives to enhance performance. Three supervised machine learning prediction algorithms were used to predict key parameters essential for optimizing their performance and RUL (remaining useful life), namely support vector regression (SVR), generalized additive model (GAM), and Gaussian process regression (GPR). The model’s performance was scored from multiple perspectives using metrics such as root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R²) to state actual values, thereby demonstrating the validity of the models in predicting lubricant lifespan. Based on the collected data, this study demonstrated that it is possible to predict the degradation of hydraulic oil factors to the limit state, integrate these parameters into a comprehensive metric for more accurate remaining useful life (RUL) estimation, and obtain actual operating trends. A negative correlation was found between the remaining useful life (RUL) and parameters such as acid number, kinematic viscosity, peroxide number, and water content. The comparison of modeling algorithms showed that all three algorithms adequately described the degradation patterns. By using these performance criteria, we defined the most accurate and reliable soft-computing model for predicting hydraulic fluid parameters, providing valuable insights into optimizing machine learning models for practical applications. Full article

Tungsten carbides are important materials for various application fields. Their unique combination of mechanical properties makes them a good choice for applications demanding high hardness and moderate fracture toughness, such as cutting tools, oil and gas, mining, or machining industries. The microstructure is composed of a hard phase embedded in a soft, ductile binder. Cobalt, which provides the best compatibility with the tungsten carbide phase, is the main binder. However, some issues have been addressed to cobalt during the last decades, including a classification as a critical raw material by the European Commission, a fluctuation of its price due to intense use in batteries, and health and ethical problems. Nickel-based binders are thus a good alternative to cobalt. Nevertheless, their processing requires a higher sintering temperature to achieve full density, which leads to abnormal grain growth and thus reduces mechanical properties. The proposed solution is to use a small amount of boron, which is added during the milling of the powders, to reduce the sintering temperature. After vacuum sintering, the results show that the sintering temperature can be decreased to reach full density. Mechanical properties show enhanced hardness with moderately decreased fracture toughness compared to the parts without boron additions (hardness around 1450 to 1515 HV₃₀ and fracture toughness around 10 to 12 MPa√m). Those results provide a good hardness-to-toughness ratio. Full article

Background/Objectives: Malignant transformation of soft tissue lesions is uncommon but represents a significant diagnostic challenge with substantial clinical consequences. This spectrum encompasses four interrelated processes but biologically distinct processes: (1) true malignant transformation of benign lesions; (2) dedifferentiation of low-grade or intermediate malignancies; (3) secondary malignancy arising in chronic inflammatory or non-neoplastic conditions; and (4) apparent progression related to tumor heterogeneity and sampling error. Although these four entities involve biologically distinct mechanisms, they are grouped under “malignant progression” for conceptual clarity. While this umbrella approach has limitations due to biological heterogeneity, this unified radiologic framework aims to supplement, rather than oversimplify, their distinct biological behaviors. Representative examples include neurofibroma and epidermal inclusion cyst among benign lesions; atypical lipomatous tumor/well-differentiated liposarcoma, dermatofibrosarcoma protuberans, and solitary fibrous tumor among lesions showing dedifferentiation or malignant progression; and chronic inflammatory or scar-related conditions and previously irradiated tissue associated with secondary malignancy. Some lesions that appear to progress during follow-up may represent initial underdiagnosis rather than true biologic progression. Methods: This narrative review summarizes current imaging features, underlying pathologic mechanisms, and clinical risk factors associated with these processes in soft tissue lesions. Particular emphasis is placed on radiologic–pathologic correlation and conditions prone to histopathologic misinterpretation. Results: Imaging red flags—including interval or rapid growth, deep fascial invasion, heterogeneous enhancement, perilesional edema, and necrosis—should raise concern for malignant progression across these categories. However, overlapping imaging features and sampling errors may result in pathologic misdiagnosis and delayed treatment. Particularly, atypical lipomatous tumors are frequently misdiagnosed as simple lipomas, while fibrosarcomas may be erroneously interpreted as aggressive fibromatosis. Advanced imaging and multidisciplinary review may help reduce diagnostic errors. Patients with predisposing factors such as genetic syndromes, chronic inflammation, prior burns, or previous radiation exposure warrant close surveillance. Conclusions: Accurate diagnosis of soft tissue lesions with true malignant transformation, dedifferentiation, or secondary malignancy—as well as recognition of diagnostic pitfalls—is essential for appropriate management. Integrated radiologic–pathologic assessment may help improve diagnostic accuracy and clinical decision-making in soft tissue oncology. Full article