Search Results (223)

This study develops a unified multiscale–machine learning framework to interpret and predict thermo-mechanical wear regime transitions in MWCNT- and nanoclay-reinforced bio-based epoxy composites. A physics-informed master wear formulation integrating real contact mechanics, geometry-dependent shear transfer, interfacial adhesion energetics, and fracture-controlled matrix detachment was combined with interpretable machine learning analytics on a unified tribological dataset. In the CNT system, increasing loading from 0.1 to 0.4 wt.% enhanced interfacial adhesion energy density from 0.00813 to 0.01906 J/m², resulting in a monotonic reduction in the wear rate from 0.00918 to 0.00613 mm³/N·m (~33% reduction). In contrast, nanoclay exhibited an optimum behavior, with a minimum wear at 0.25 wt.% (0.000093 mm³/N·m; 7.9% reduction vs. neat clay baseline), followed by deterioration at a higher loading due to dispersion loss. The unified probabilistic regime classification of low-wear conditions (k < 0.007 mm³/N·m) achieved an ROC − AUC = 0.9256 and balanced accuracy = 94.3%, with thermo-mechanical severity identified as the dominant regime-switching driver. Reinforcement identity significantly modulated regime stability, confirming distinct shear transfer (Carbon Nano Tubes(CNT)) and confinement/tribofilm (clay) mechanisms within a common mathematical framework. By enabling the durability-oriented design of bio-based tribological systems and extending component service life through predictive stability mapping, this work contributes to resource-efficient materials engineering and reduced lifecycle waste, supporting Sustainable Development Goals SDG 9 (Industry, Innovation and Infrastructure), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action). Full article

Precise regulation of the postharvest storage environment is critical for reducing losses and maintaining potato quality. Semi-underground storage facilities are widely used in major potato-producing regions of northern China; however, pronounced spatiotemporal heterogeneity in the internal temperature field often leads to localized quality deterioration. To enable accurate sensing and proactive prediction of temperature dynamics in such facilities, this study investigated a typical semi-underground potato storage cellar in Wuchuan County, Inner Mongolia. A high-density sensor network was deployed to collect temperature data, and the spatiotemporal variation patterns of the internal temperature field were systematically analyzed. The results indicate that, at the same vertical height, spatial temperature gradually increases from the entrance toward the interior of the cellar. Both the maximum and minimum temperatures in the entrance zone are lower than those in other regions, while the highest temperatures are observed near the rear wall. Based on the collected data, hierarchical clustering was employed to partition the internal temperature field into three spatiotemporal pattern clusters with significant differences. Key representative monitoring locations were then identified using the Spearman correlation coefficient. An AdaBoost-based prediction model was subsequently developed to estimate the temperatures at other test locations within each cluster using measurements from the representative points. The results demonstrate that the proposed model maintains high prediction accuracy while substantially reducing dependence on a dense sensor network. The overall MAE ranges from 0.075 to 0.373 °C, and the sensor reduction ratio reaches 87%. This approach provides a paradigm for low-cost intelligent monitoring and offers theoretical support and decision-making guidance for the smart regulation of potato storage environments. By optimizing the monitoring of potato storage environments, this study can reduce monitoring system costs and resource consumption, providing technical support for building a sustainable potato supply chain and delivering significant economic benefits in promoting the development of a resource-conserving potato industry. Full article

Traditional methods for geostressfield inversion face issues such as weak physical interpretability and insufficient generalization ability. This study pioneers the application of Physics-Informed Neural Network (PINN) to this problem, developing a data- and physics-driven inversion algorithm. The framework incorporates a constitutive-equation-based regularized loss function as a hard constraint during training to ensure physical consistency. To address boundary load uncertainty, two quantification approaches—Bayesian linear regression and surrogate model optimization—are proposed to establish 95% confidence intervals for boundary coefficients. Verification based on simple three-dimensional models and actual geological models of mines shows that PINN inversion achieves a mean absolute relative error as low as 0.0772%, with an error of 15.67% under sparse sampling conditions—significantly lower than the 31.07% error of the traditional Back propagation neural network. This demonstrates excellent robustness and data efficiency. In the practical engineering application of complex geological bodies, the average error of principal stress inversion is 9.35% with a minimum error of 0.137%. All inversion results fall within the permissible accuracy range of engineering, and the stress distribution conforms to basic laws, with an average error of 0.453 in the constitutive relation. Compared with BP neural network and multiple linear regression methods, it shows obvious accuracy advantages. This method provides a new solution for intelligent ground stress prediction with high accuracy, high efficiency, and strong physical interpretability, and also lays the foundation for early identification of geological disasters. Full article

MXene-based microwave absorbers have received extensive attention owing to their high electrical conductivity, abundant interfacial polarization sites, and tunable surface terminations. However, the structure–property relationship of MXene composites remains highly nonlinear, and the design of high-efficiency absorbers still relies heavily on trial-and-error experiments. Herein, multidimensional magnetic components, including zero-dimensional (0D) Fe₃O₄ nanoparticles, one-dimensional (1D) Fe₃O₄/Co₃O₄ nanowires, and two-dimensional (2D) Fe₃O₄-based heterostructures, were rationally integrated with Fe/MXene and Fe/Co/MXene nanosheets to engineer synergistic dielectric and magnetic losses. Comprehensive electromagnetic characterization and loss mechanism analysis reveal that the structural dimensionality strongly impacts impedance matching and attenuation capability. To further enable predictive and data-driven optimization, a machine learning framework was established to correlate the microstructure, component ratio, thickness, and electromagnetic parameters with the microwave absorption performance (e.g., minimum reflection loss (RL_min), effective absorption bandwidth (EAB)). The optimized multidimensional composite achieves an RL_min of −56.4 dB at 10.2 GHz with an EAB of 8.4 GHz (9.6–18.0 GHz) at a thin matching thickness of 1.8 mm. The machine learning model demonstrates excellent accuracy (R² = 0.947) and enables the inverse design of absorber geometries to target specific operational frequencies. This work provides a generalizable paradigm for the intelligent design of MXene-based microwave absorbers and opens up broader opportunities for the AI-accelerated discovery of advanced electromagnetic functional materials. Full article

In the era of big data, data is key to the accuracy of analytical models, and cloud computing services are often used to efficiently process large volumes of data. However, outsourcing sensitive data to a third-party cloud service provider results in a loss of direct control over the data, raising serious security concerns. The target of this study is to propose highly efficient and privacy-preserving protocols that compute the maximum/minimum value in large-scale data. To achieve the improvements in efficiency, the proposed protocols reuse the intermediate results generated in independent subprotocols. Existing privacy-preserving maximum/minimum protocols are based on approximation methods that sacrifice accuracy or reveal information during execution. They use costly comparison operations that are proportional to the size of the input data and are not suitable for large-scale data applications. In contrast, the proposed protocols theoretically reduce the number of communication rounds by 25%, the communication size by 50%, and the computational cost by 42% compared to the existing protocols. Nevertheless, the accuracy and privacy are fully maintained. In order to demonstrate these efficiency improvements concretely, we conducted experiments and demonstrated that the proposed protocols reduce the communication volume by half and the execution time by 22%. Because the proposed protocols support parallel execution, their performance can be substantially enhanced in cloud environments that provide large-scale parallel processing resources. Even data owners with restricted computational capabilities can use the protocols without exposing their information. Under the secure version, even cloud servers executing the protocol learn nothing about the input data or the computation results. Full article

To address challenges such as low contrast, complex backgrounds, and discontinuous crack distribution in coating surface microcrack detection, a detection method combining circular neighborhood features with an improved U-net is proposed. In the preprocessing stage, a background template is constructed via median filtering, and crack contrast is enhanced through a combination of difference operations and Gaussian smoothing. Based on the spatial aggregation and directionality of crack pixels, multi-scale and multi-directional circular scanning filters were constructed to generate neighborhood difference maps for quantifying the crack distribution probability. The ImF-Att-DO-U-net was designed by utilizing a dual-channel input consisting of the original image and the crack probability map. The encoder embeds lightweight CBAMs to strengthen crack features, while the decoder introduces DO-Conv and Leaky ReLU to enhance detail capture capabilities. A hybrid loss function combining Binary Cross-Entropy and Dice loss was employed to optimize class imbalance. Algorithm testing results demonstrate that the proposed method achieved a Dice coefficient of 0.884, an SSIM of 0.893, and an accuracy of 0.911, outperforming comparative models such as DO-U-net. The extraction rate for cracks ≥10 μm reached 98%, with a minimum detectable crack size at the 7 μm level. The method exhibited excellent robustness under noise and blur testing, demonstrating superior environmental adaptability. Full article

Integrated optical devices based on lithium niobate (LN) are pivotal in modern navigation systems, telecommunications, and sensing technologies. However, their practical implementation is critically limited by temperature-dependent and long-term operational instability, primarily attributed to the pyroelectric effect inherent in LN. This study addresses this challenge by investigating thermally reduced lithium niobate as a material platform to enhance the stability of integrated optical circuits, with a focus on integrated optical electric field sensors (IOES). We present the fabrication and comprehensive characterization of an IOES based on a Michelson interferometer design. Key performance metrics including optical loss, free spectral range, electro-optical sensitivity, and optical path difference were systematically evaluated. Notably, under normal climatic conditions, the optical path difference of the IOES demonstrated exceptional stability when subjected to an applied voltage ranging from 0 to 5 V, with no observable drift over time. Calibration of the IOES revealed a predominantly linear response, although a third-degree polynomial model provided a more precise fit to the experimental data. The minimum relative error achieved during calibration was 0.47%, underscoring the high accuracy of the device. Our results establish thermally reduced LN as a promising material platform for next-generation integrated optical devices. By mitigating the pyroelectric effect, this approach enables significant improvements in the long-term stability of IOES and other LN-based photonic components. These findings open avenues for the reliable deployment of integrated optical systems in demanding applications where environmental stability is paramount. Full article

Real-time object detection in soccer videos presents significant challenges due to the dynamic nature of matches, varying object scales, and the stringent requirement for efficient processing. In this work, we define real-time detection as that which achieves inference speeds of at least 30 frames per second (FPS), which is the minimum requirement for smooth video processing and live broadcast applications. While transformer-based detectors have achieved remarkable accuracy, their quadratic computational complexity limits their real-time applications. In this paper, we propose SoccerDETR, a novel real-time detection framework that integrates MobileMamba-based visual state space models with an efficient transformer encoder for soccer object detection. Our approach introduces four key innovations: (1) a MobileMamba backbone leveraging selective state space modeling to achieve linear computational complexity while maintaining global receptive fields; (2) a Semantic-aware Dynamic Feature Fusion Module (SDFM) that adaptively aggregates multi-scale features through progressive semantic injection; (3) a Spatial-Channel Synergistic Attention (SCSA) mechanism that explores the synergistic effects between spatial and channel attention for enhanced feature representation; and (4) a Separable Dynamic Decoder that employs dynamic convolution attention to replace traditional cross-attention, significantly reducing computational overhead. Additionally, we design a Scale-Aware Focal Loss (SAFL) that addresses the class imbalance and scale variation problems inherent in soccer scenarios. Extensive experiments on the Soccana and SoccerNet datasets demonstrate that SoccerDETR achieves state-of-the-art performance with 94.2% mAP@50 on Soccana and 91.8% mAP@50 on SoccerNet, while maintaining real-time inference speed of 78 FPS on a single NVIDIA RTX 4090 GPU with a batch size of 1 and an input resolution 640 × 640. Our method outperforms existing approaches by 2.3–5.7% in mAP while being 1.5–3.2× faster, demonstrating the effectiveness of state space models for efficient sports video object detection. Comprehensive ablation studies validate the effectiveness of each proposed component, and cross-dataset experiments demonstrate strong generalization capability. Full article

Trajectory prediction is critical for safe robot navigation, yet standard deep learning models predominantly rely on the Mean Squared Error (MSE) criterion. While effective under ideal conditions, MSE-based optimization is inherently fragile to non-Gaussian impulsive noise—such as sensor glitches and occlusions—common in real-world deployment. To address this limitation, this paper proposes MEE-LSTM, a robust forecasting framework that integrates Long Short-Term Memory networks with the Minimum Error Entropy (MEE) criterion. By minimizing Renyi’s quadratic entropy of the prediction error, our loss function introduces an intrinsic “gradient clipping” mechanism that effectively suppresses the influence of outliers. Furthermore, to overcome the convergence challenges of fixed-kernel information theoretic learning, we introduce a Silverman-based Adaptive Annealing (SAA) strategy that dynamically regulates the kernel bandwidth. Extensive evaluations on the ETH and UCY datasets demonstrate that MEE-LSTM maintains competitive accuracy on clean benchmarks while exhibiting superior resilience in degraded sensing environments. Notably, we identify a “Scissor Plot” phenomenon under stress testing: in the presence of 20% impulsive noise, the proposed model maintains a stable Average Displacement Error (ADE “≈” 0.51 m), whereas MSE baselines suffer catastrophic degradation (ADE > 2.1 m), representing a 75.7% improvement in robustness. This work provides a statistically grounded paradigm for reliable causal inference in hostile robotic perception. Full article

Phishing and spam emails continue to pose a serious cybersecurity threat, leading to financial loss, information leakage, and reputational damage. Traditional email filtering approaches struggle to keep pace with increasingly sophisticated attack strategies, particularly those involving malicious content and deceptive attachments. This study proposes a dual-layer deep learning architecture designed to enhance email security by improving the detection of phishing and spam messages. The first layer employs deep learning models, including LSTM- and transformer-based classifiers, to analyze email content and structural features across legitimate, phishing, and spam emails. The second layer focuses on spam emails containing attachments and applies advanced transformer models, such as GPT-2 and XLM-RoBERTa, to assess contextual and semantic patterns associated with malicious attachments. By integrating textual analysis with attachment-level inspection, the proposed architecture overcomes limitations of single-layer approaches that rely solely on email body content. Experimental evaluation using accuracy and F1-score demonstrates that the dual-layer framework achieves a minimum F1-score of 98.75 percent in spam–ham classification and attains an attachment detection accuracy of up to 99.46 percent. These results indicate that the proposed approach offers a reliable and scalable solution for enhancing real-world email security systems. Full article

A Smart campus increasingly operates on the basis of data-driven operations, but an increasing demand for energy puts their control over costs and sustainability at risk. This study addresses the challenge of anticipating and managing energy consumption peaks in multi-campus environments by proposing a hybrid framework that combines advanced time-series forecasting models with a large language model (LLM)-driven multi-agent system. Based on the UNICON dataset, LSTM, CNN, GRU, and a combination architecture are trained and compared in terms of MAE and RMSE. The hybrid configuration achieves the greatest forecasting results by returning the minimum loss values. For the identification of critical periods, we employed a strategy based on median thresholding, which offers a categorization into low, normal, and extreme category, allowing the targeting of peak mitigation actions. We also introduce a multi-agent system based on the LLM, including the data aggregator, the forecaster, and the policy advisor, which create actionable policies informed by context. We also compare LLMs (Qwen-2.5, Gemma-2, Phi-4, Mistral, Llama-3.3) in terms of context accuracy, response relevance, semantic similarity, and retrieval/recall accuracy and fidelity, with Llama-3.3 achieving the best overall results. This framework has shown great potential, not only for energy consumption forecasting but also for developing precise policies on how to effectively manage energy consumption peaks. Full article

Predicting core loss under high-frequency non-sinusoidal excitation is crucial for power electronics equipment design. Temperature significantly affects core loss, and traditional core loss prediction models typically incorporate temperature corrections to enable accurate loss estimation across varying temperatures. Based on the Modified Steinmetz Equation (nonT-MSE) model, this study considers the temperature effect by employing a combination of the Tanh function and a linear term to modify the three empirical parameters, with the Tanh function capturing the nonlinear saturation of the loss coefficient k with increasing temperature. This leads to the establishment of the temperature-corrected non-TMSE (T-MSE) model for predicting magnetic core loss under high-frequency non-sinusoidal excitation. During model derivation, training data undergo logarithmic transformation processing. Subsequently, with T-MSE empirical parameters as variables and the minimum mean squared error between T-MSE predicted values and experimental values as the objective function, a single-objective optimization model is established. Finally, the empirical parameters of T-MSE are calculated using the training data and the single-objective optimization model. Comparing the core loss experimental results of the four materials, the average MSE values for the T-MSE model, the nonT-MSE model, and the square-root temperature-corrected non-TMSE model proposed by Zeng et al. (Zeng) are 0.0082, 0.0459, and 0.0110, respectively; with average MAPE of 1.57%, 1.87%, and 2.17%, respectively; and average R² of 0.9862, 0.9807, and 0.9731. Compared to the nonT-MSE model and the Zeng model, the T-MSE model demonstrated higher prediction accuracy. Full article

Deep and ultra-deep drilling operations commonly encounter fractured and fracture-vuggy formations, where weak wellbore strength and well-developed fracture networks lead to frequent lost circulation, presenting a key challenge to safe and efficient drilling. Existing diagnostic practices mostly rely on drilling fluid loss dynamic models of single fractures or simplified discrete fractures to invert fracture geometry, which cannot capture the spatiotemporal evolution of loss in complex fracture networks, resulting in limited inversion accuracy and a lack of quantitative, fracture-network-based loss-dynamics support for bridge-plugging design. In this study, a geologically realistic wellbore–fracture-network coupled loss dynamic model is constructed to overcome the limitations of single- or simplified-fracture descriptions. Within a unified computational fluid dynamics (CFD) framework, solid–liquid two-phase flow and Herschel–Bulkley rheology are incorporated to quantitatively characterise fracture connectivity. This approach reveals how instantaneous and steady losses are controlled by key geometrical factors, thereby providing a computable physical basis for loss-zone inversion and bridge-plugging design. Validation against experiments shows a maximum relative error of 7.26% in pressure and loss rate, indicating that the model can reasonably reproduce actual loss behaviour. Different encounter positions and node types lead to systematic variations in loss intensity and flow partitioning. Compared with a single fracture, a fracture network significantly amplifies loss intensity through branch-induced capacity enhancement, superposition of shortest paths, and shortening of loss paths. In a typical network, the shortest path accounts for only about 20% of the total length, but contributes 40–55% of the total loss, while extending branch length from 300 mm to 1500 mm reduces the steady loss rate by 40–60%. Correlation analysis shows that the instantaneous loss rate is mainly controlled by the maximum width and height of fractures connected to the wellbore, whereas the steady loss rate has a correlation coefficient of about 0.7 with minimum width and effective path length, and decreases monotonically with the number of connected fractures under a fixed total width, indicating that the shortest path and bottleneck width are the key geometrical factors governing long-term loss in complex fracture networks. This work refines the understanding of fractured-loss dynamics and proposes the concept of coupling hydraulic deviation codes with deep learning to build a mapping model from mud-logging curves to fracture geometrical parameters, thereby providing support for lost-circulation diagnosis and bridge-plugging optimisation in complex fractured formations. Full article

Visual SLAM systems face significant performance degradation under dynamic lighting conditions, where traditional feature extraction methods suffer from reduced keypoint detection and unstable matching. This paper presents LDFE-SLAM, a novel visual SLAM framework that addresses illumination challenges through a Light-Aware Deep Front-End (LDFE) architecture. Our key insight is that low-light degradation in SLAM is fundamentally a geometric feature distribution problem rather than merely a visibility issue. The proposed system integrates three synergistic components: (1) an illumination-adaptive enhancement module based on EnlightenGAN with geometric consistency loss that restores gradient structures for downstream feature extraction, (2) SuperPoint-based deep feature detection that provides illumination-invariant keypoints, and (3) LightGlue attention-based matching that filters enhancement-induced noise while maintaining geometric consistency. Through systematic evaluation of five method configurations (M1–M5), we demonstrate that enhancement, deep features, and learned matching must be co-designed rather than independently optimized. Experiments on EuRoC and TUM sequences under synthetic illumination degradation show that LDFE-SLAM maintains stable localization accuracy (∼1.2 m ATE) across all brightness levels, while baseline methods degrade significantly (up to 3.7 m). Our method operates normally down to severe lighting conditions (30% ambient brightness and 20–50 lux—equivalent to underground parking or night-time streetlight illumination), representing a 4–6× lower illumination threshold compared to ORB-SLAM3 (200–300 lux minimum). Under severe (25% brightness) conditions, our method achieves a 62% tracking success rate, compared to 12% for ORB-SLAM3, with keypoint detection remaining above the critical 100-point threshold, even under extreme degradation. Full article

This study evaluates the impact of high ambient temperature (HT) on milk production in Tunisian local goats using both fixed and random regression models with quadratic and cubic Legendre polynomials. Daily minimum (Tmin), maximum (Tmax), and average (Tavg) temperatures were tested as heat load indicators, measured on the milking day and averaged over the 1–3 preceding days. The deviance information criterion (DIC) consistently showed that models including temperature effects provided a better fit than a baseline model without heat load. Cubic polynomials showed superior accuracy compared with quadratic models, even if the differences were relatively small. The best model was obtained with Tavg on the milking day, followed closely by Tmax averaged across one or two preceding days. The population response showed a thermoneutral plateau at lower temperatures, followed by declines beyond the HT thresholds. For Tmax, moderate and severe thresholds were detected at 20–23 °C and 25–27 °C, respectively, while for Tavg, thresholds occurred at 11–13 °C and 16–19 °C. Milk losses ranged from 22 to 85 g/°C depending on the temperature indicator, representing an average 4–5% decline in daily yield per degree above thermoneutrality. High variability in individual responses was observed. Some goats maintained stable production, while others showed steep declines under HT, with slope differences reaching over 150 g/°C. Correlations of milk yield across contrasting thermal environments were low, indicating that animal ranking changes with temperature. High-producing goats are more affected by heat, showing the need for a balance between production and heat tolerance. Full article