Search Results (941)

Part surface topography is characterized by complex multi-scale and multi-feature coupling, and accurate topography modeling is essential for predicting assembly precision in high-performance mechanical systems. Gaussian Process Regression (GPR) offers a principled, probabilistic framework for surface modeling from sparse measurements, but its performance depends critically on kernel function selection. A fixed single kernel lacks the flexibility to represent surfaces that simultaneously exhibit smooth trends, periodic textures, and linear drift. To address this limitation, an adaptive composite kernel method is proposed. Initial GPR residuals are analyzed through statistical hypothesis tests and spectral decomposition to identify which geometric features are present; matching base kernels—Squared Exponential (SE), Periodic (PER), and Linear (LIN)—are then selected and combined additively or multiplicatively. Experiments on three representative synthetic surfaces show that the composite kernels reduce RMSE by up to 95.09% relative to the single SE kernel. Validation on a machined part confirms that the method successfully transfers to real measured data, achieving a 30.65% RMSE reduction and raising R² from 0.9536 to 0.9777. The results demonstrate that residual-analysis-driven kernel selection yields physically interpretable models with substantially improved reconstruction accuracy. Full article

Accurate estimation of internal battery temperature is critical for the safety and state-of-health assessment of lithium-ion batteries, yet it remains challenging due to the trade-off between model accuracy and computational feasibility on resource-constrained edge hardware. This work targets stationary large-scale battery energy storage stations (BESS), where ambient temperatures are actively regulated within a narrow range (typically 15–35 °C), and is developed and validated on large-format prismatic LFP cells. We propose ThermaPhysLite, a lightweight physics-informed neural network (PINN) framework with three innovations: (i) a lightweight PINN architecture tailored for edge devices; (ii) integration of a simplified electro–thermal model—a lumped-parameter thermal circuit coupled with the Bernardi heat generation equation—into a multi-scale temporal convolutional network (MS-TCN) through the PINN paradigm; and (iii) real-time online deployment on the ESP32-S3 embedded platform. Ground-truth internal temperatures were obtained via side-drilled thermocouple embedding in disassembled cells. Offline validation under three operating conditions demonstrates RMSE values of 0.15–0.20 °C. Following INT8 quantization (compressed to 84.29 KB), online deployment yields RMSE values of 0.17–0.24 °C with single-cell inference latency of 120 ms, demonstrating practical viability for BMS in large-scale energy storage systems. Full article

Accurate forecasting of oil well production via field monitoring systems is significantly restricted by a structural conflict in modeling, where temporal dependency learning and nonlinear feature representation are closely coupled. Such coupling forces a trade-off between capturing long-term temporal dependencies and retaining sensitivity to short-term sensor fluctuations, while amplified local sensitivity easily increases noise interference and weakens model robustness under complex non-stationary sensor dynamics. To solve this problem, this study proposes a novel sensor-driven hybrid framework named Temporal Augmented Residual Network (TAR-Net), which adopts a decoupled paradigm to separate global temporal modeling and local fluctuation compensation explicitly. A multi-scale dilated Temporal Convolutional Network (TCN) extracts long-range temporal patterns from multi-source sensor data, and a LightGBM-based residual module conducts targeted error correction. Meanwhile, multi-scale temporal features and adaptive multi-fidelity Bayesian optimization are applied to enhance model adaptability. Validated on real sensor data from the Volve oilfield, TAR-Net surpasses 13 benchmark models with an R² of 0.9832 and a MAPE of 7.8%. Residual and trajectory analyses verify its balance between global trend consistency and local fluctuation sensitivity. This framework offers a robust sensor-aware solution for complex multi-scale temporal modeling in industrial production systems. Full article

Linear heritage corridors are increasingly exposed to spatially heterogeneous pressures from climate change and human activities, yet integrated geospatial frameworks for corridor-scale risk identification remain limited. Taking the Beijing–Hangzhou Grand Canal as a representative linear World Heritage corridor, this study developed a GIS-based spatiotemporal assessment framework to quantify natural risk, anthropogenic pressure, and their coupled patterns during 1995–2024. Approximately 350 canal segments were constructed as comparable assessment units and linked with 49 heritage sites and 18 World Heritage canal sections through a multi-scale spatial framework integrating canal sections, buffer zones, and heritage sites. Natural risk was characterized using extreme temperature, precipitation, and drought indices, while anthropogenic pressure was represented by nighttime lights, population density, impervious surface, and road density. The results reveal a clear north–south gradient in integrated natural risk, with higher values concentrated in the southern canal sections. Among the three natural-risk modules, temperature, precipitation, and drought contributed weights of 0.594, 0.242, and 0.164, respectively, indicating the dominant role of heat-related processes. The first two principal components of anthropogenic pressure explained 80.8% of the total variance. Four dominant coupling types were identified, among which the dual high-pressure type was concentrated mainly in the southern canal and marked the most critical areas of compound risk. This study provides a geospatial approach for hotspot detection and spatial decision support for the conservation of large linear heritage systems. Full article

Karst regions are commonly characterized by highly interwoven bare rock–bare soil–vegetation mosaics, strong coupling between surface and subsurface processes, and pronounced geomorphic fragmentation. Conventional land cover classification systems, which are primarily organized around land use patterns or generic ecological types, are often unable to accurately represent these key surface components and their roles in ecological processes. From the perspective of reconstructing classification semantics, this study proposes a Natural-Attribute-Based Karst Land Cover Classification framework (NALCC). The framework takes bare rock, bare soil, vegetation, water bodies, and impervious surfaces as primary classes, and further develops a hierarchical system consisting of subclasses, attribute labels, hierarchical coding, multi-scale organization, and parameter mapping with ecosystem service models. Compared with conventional land cover classification systems, the innovation of this framework lies not in increasing the number of categories, but in reconstructing the semantic organization of classification units, so that land cover classification can move beyond surface-type description toward the expression of process-sensitive information. The classification objective of NALCC is not to develop a universal land cover classification system, but to establish a process-oriented classification framework for ecosystem service monitoring, rocky desertification diagnosis, and governance zoning in karst regions, which can directly represent key surface components and their ecological-process significance. However, its regional transferability and mapping performance still need to be further validated through case studies in representative areas. Full article

Surface stain contamination poses a critical barrier to the automated, high-precision fiber identification required for industrial-scale waste textile recycling. In this study, a dataset comprising 120 physical specimens (yielding 1200 regions of interest, ROIs) across 12 contamination categories was constructed by contaminating cotton, polyester, and poly-cotton blend textiles with carbon black, protein, and oil stains. The spectral interference effects of stains—including baseline drift and spectral overlapping induced by physical shielding and chemical absorption—were systematically analyzed. To identify the optimal classification pipeline, three mathematical preprocessing methods (First Derivative, FD; Standard Normal Variate, SNV; and Multiplicative Scatter Correction, MSC) were evaluated alongside Support Vector Machine (SVM) and One-Dimensional Convolutional Neural Network (1D-CNN) models. Results show that among the SVM-based pipelines, the FD-SVM model effectively resolves overlapping absorption peaks, achieved an average accuracy of 98.17% ± 1.33%, but remains highly dependent on mathematical preprocessing. In contrast, the 1D-CNN model employing a progressive stacking architecture of multi-scale convolutional kernels attains a highly robust mean accuracy of 99.58% ± 0.56% under a strict specimen-level 10-fold cross-validation. It achieves this by directly utilizing radiometrically calibrated raw spectra, thereby effectively bypassing manual spectral feature engineering. These findings demonstrate that Hyperspectral Imaging coupled with end-to-end deep learning provides a feasible and industrially deployable solution for simultaneous stain detection and fiber identification in waste textile sorting. Full article

Ultra-high-performance concrete incorporating coarse aggregates (UHPC-CA) exhibits pronounced multi-scale heterogeneity and staged damage evolution. However, existing single-scale reinforcement strategies often fail to address the complete micro-to-macro fracture process, leaving a critical research gap in achieving full-stage crack control. To address this, this study introduces a novel cross-scale toughening strategy using hybrid steel fibers (SF) and calcium carbonate whiskers (CCW), and decouples the coupled influences of water-to-binder (W/B) ratio, coarse aggregate (CA), and multi-scale fibers via an orthogonal design. Mechanical properties, fiber dispersion, and pore structure are jointly characterized to establish structure–property relationships. An optimal composition (W/B = 0.32, CA = 18%, SF = 2%, CCW = 1%) is identified, achieving a balanced enhancement of strength and ductility. Results indicate that matrix densification is primarily controlled by W/B via pore refinement, while mechanical performance is governed by the interplay between fiber spatial uniformity and interfacial integrity; the roles of CA and CCW are clearly stress-state dependent. Furthermore, a novel cross-scale synergistic mechanism is revealed, in which micro-scale CCW regulates microcrack initiation and stabilizes the pre-peak response, whereas macro-scale SF dominates post-peak behavior through crack bridging and pull-out energy dissipation. This sequential activation enables a full-stage enhancement of tensile performance, shifting failure from brittle localization to pseudo-ductile multiple cracking. The findings provide a correlative framework for tailoring UHPC-CA through multi-scale hybrid reinforcement. Full article

For the Vacuum Induction Gas Atomization (VIGA) powder preparation process, a multi-scale coupled numerical simulation and experimental validation were employed to systematically reveal the influence mechanisms of process parameters on the primary atomization flow field structure, secondary atomization droplet breakup behavior, and powder particle size distribution Using Computational Fluid Dynamics (CFD) methods combined with the VOF (Volume of Fluid) multiphase flow model, the fragmentation morphology of the melt during primary atomization was simulated, capturing the dynamic characteristics of liquid film thinning and the reduction in initial droplet area. Concurrently, the DPM (Discrete Phase Model) coupled with the TAB (Taylor Analogy Breakup) model was applied to predict the droplet size distribution in secondary atomization. The results indicate that increasing atomization pressure (2.5–4.5 MPa) significantly enhances secondary fragmentation intensity, reducing the median particle size (D50) from 42.1 μm to 37.5 μm. Experimental studies on Ni-based superalloys, validated by laser particle size analysis, confirmed that higher atomization pressure improves gas velocity and gas–liquid energy conversion efficiency, optimizes turbulent flow structures, and refines powder particles. The study concludes that the multi-scale coupled model effectively predicts atomization dynamics. By optimizing atomization pressure, powder particle size can be significantly refined, providing a theoretical basis for process control of high-performance spherical powders used in additive manufacturing. Full article

As industrial parks face increasing pressure to balance economic development with environmental sustainability, optimizing emission strategies becomes critical for achieving sustainable development goals. In this study, a pollutant dispersion module is coupled with the WRF-FDDA-LES (Weather Research and Forecasting four-dimensional data assimilation and large-eddy simulation) to establish a multiscale air quality model for the Pengzhou Industrial Park, Sichuan, China, hereafter referred to as PZ-LESTD. Using PZ-LESTD, the study conducts refined large-eddy simulations of pollutant dispersion from elevated sources in the industrial park on 23 August 2022. The capability of the model in simulating large-scale weather conditions and pollutant transport, together with its performance in refined-grid LES of elevated emission dispersion, is evaluated. Sensitivity experiments with different pollutant emission heights are also carried out. The results demonstrate that the model can satisfactorily reproduce large-scale meteorological variables and pollutant distributions over China and achieve high accuracy in the refined LES simulations. Analysis of the simulated dispersion processes of elevated sources indicates that the current elevated emission strategy in the Pengzhou Industrial Park is effective in mitigating the impact of industrial exhaust on surface air quality in the park and surrounding areas. Sensitivity tests of emission heights reveal that source heights of 20 m to 50 m can significantly reduce impacts on nearby ambient air quality, whereas increasing the source height from 50 m to 160 m results in only minor differences in surface-level pollution, although higher emission sources lead to greater horizontal transport of pollutants. This study provides scientific evidence for sustainable industrial planning and emission management strategies, supporting the transition towards environmentally sustainable industrial parks. The findings contribute to evidence-based policymaking for air pollution prevention and control, facilitating the achievement of sustainable development goals through optimized industrial emission layouts and green industrial transformation. Full article

Fusarium graminearum infection of maize induces complex transcriptional reprogramming, yet existing differential-expression and local graph convolutional approaches struggle to capture long-range and multi-scale regulatory dependencies. We propose DC-FusionGNN, a dual-channel fusion graph neural network for key resistance-gene identification. Based on the transcriptome dataset GSE174508, we first construct a comprehensive gene interaction network by integrating a WGCNA co-expression network with a STRING-based interaction network. The left channel combines structure-aware propagation with a Transformer-based global self-attention mechanism to model long-range cross-module dependencies, while the right channel couples GraphSAGE with a GCN to capture local topology and neighborhood heterogeneity. Embeddings from the two channels are concatenated to form a unified gene representation, trained via self-supervised link prediction. Compared with baseline graph neural networks, DC-FusionGNN achieves competitive and overall improved performance across multiple metrics, and robustness and independent cross-species (rice, GSE39635) experiments further confirm its stability and generalization ability. GO and KEGG enrichment analyses show that the top-ranked candidate genes are significantly enriched in plant defense responses, hormone signaling, and secondary metabolism, supporting the biological relevance of the model’s predictions. Full article

Glioblastoma multiforme (GBM) remains the most aggressive and therapeutically intractable primary brain tumor, with many patients experiencing rapid relapse despite maximal surgical resection followed by standard chemoradiation. This persistent failure reflects the convergence of profound tumor-intrinsic genetic heterogeneity and a highly dynamic, spatially structured, and immunosuppressive tumor microenvironment (TME). Together, these forces create strong selective pressures that fuel tumor evolution, intratumoral diversity, phenotype plasticity, diffuse invasion, and robust resistance to therapy. The TME of GBM is orchestrated through a complex interplay between diverse cellular constituents, including tumor-associated macrophages, reactive astrocytes, endothelial cells, pericytes, and GBM stem cells, and non-cellular components such as extracellular matrix remodeling, hypoxia, metabolic and nutrient gradients, and spatially patterned cytokine and chemokine signaling networks. Additionally, heterogeneity in blood–brain barrier (BBB) and blood–tumor barrier (BTB) complicates drug delivery and immune surveillance, reinforcing therapeutic resistance and regional tumor adaptation. Conventional two-dimensional cell cultures and animal models fail to sufficiently capture these multiscale, patient-specific interactions, limiting their translational predictive power. In this narrative review, we synthesize recent advances in GBM organoid technologies as physiologically relevant, three-dimensional platforms that more faithfully recapitulate TME for driving tumor evolution and treatment resistance. We compare complementary organoid strategies, including patient-derived GBM organoids that preserve native cytoarchitecture, cerebral organoid co-culture systems that reconstruct tumor–brain interactions, and advanced platforms incorporating immune and vascular features such as air–liquid interface cultures, microglia-enriched systems, and BBB/BTB-integrated models. Finally, we highlight emerging innovations such as spatial transcriptomics, organoid-on-a-chip systems, live imaging coupled with lineage tracing, genome engineering, and artificial intelligence integration that collectively position GBM organoids at the forefront of precision neuro-oncology, reproducing TME, enabling dynamic mapping of tumor evolution, and accelerating patient-specific therapeutic discovery. Full article

Reinjection represents a fundamental strategy for sustainable geothermal reservoir development. During reinjection, reservoirs are subjected to pronounced physicochemical disequilibrium, under which complex water–rock interactions render long–term behavior difficult to predict. This review synthesizes mineral reactions and reservoir dynamic responses from a geochemical perspective. The interplay between reaction kinetics and fluid transport is examined using the Damköhler number, elucidating the spatiotemporal evolution of reactive transport. The dissolution–precipitation behaviors of silicate, carbonate, and sulfate minerals are evaluated, highlighting their distinct roles in governing long–term structural reorganization, short–term permeability variability, and rapid clogging. The influence of mineral reactions on pore structure evolution and the development of nonlinear porosity–permeability relationships is examined, alongside commonly used constitutive models and their inherent limitations. Multiscale characterization approaches for porosity–permeability evolution and the distinct responses of different reservoir types are reviewed. The chemo–mechanical coupling induced by water–rock interactions and its implications for reservoir stability are addressed. This work establishes a unified conceptual framework linking mineral reactions, fluid transport, and reservoir evolution, providing a basis for optimizing reinjection strategies and improving long–term geothermal system performance. Full article

Optical and Synthetic Aperture Radar (SAR) sensors capture complementary and consistent information, and their fusion enhances remote sensing image quality. Existing pyramid decomposition-based methods suffer from insufficient texture–edge discrimination. Additionally, the manual setting of parameters during pyramid decomposition introduces uncertainty in the fusion results. To address this problem, we propose an optical and SAR image fusion framework based on local extrema adaptive pyramid decomposition (LEAPFusion), which enhances edge preservation and improves parameter adaptability. Specifically, by leveraging the edge-preserving properties of local extrema, we introduce them into the image pyramid decomposition framework to construct complementary local extrema and Laplacian pyramids. Then, we introduce an explicit parameter adaptation strategy in which the decomposition levels and local extrema kernel sizes are automatically determined from image size and pyramid scale, enabling consistent multi-scale representation and reducing parameter sensitivity compared to empirically tuned settings. Finally, by exploiting the complementary properties of the two pyramids, we implement a multi-type fusion strategy: weighted averaging for low-frequency components and parameter-adaptive pulse-coupled neural network (PAPCNN) for high-frequency details. Our decomposition framework seamlessly integrates three representative edge-preserving filters—a median filter, a guided filter, and a rolling guidance filter—demonstrating strong generalization capability across different filtering paradigms. Extensive experiments on two benchmark datasets demonstrate that our method outperforms seven state-of-the-art algorithms, achieving the best results across diverse scenes with improvements of up to 13.38% in SF and 18.90% in SCD compared to the second-best methods. Full article

We construct a connection-theoretic framework for parallel transport of spectral components along parameter families of signals on the similarity group $\tilde{G}=\mathbb{R}\times \mathrm{SO}\left(2\right)$. Let ${\left\{f_t\right\}}_{t\in I}$ be a signal family that evolves under a $C^1$ group trajectory. The frequency support of the associated scale-rotation transforms produces three Hilbert subbundles over the parameter interval, and the trajectory velocity induces a covariant derivative on each subbundle. The standard spectral viewpoint treats transformation behavior at individual parameter values. Our formulation instead organizes the propagation of spectral components along the entire parameter path and provides closed-form transport operators together with error bounds on each subbundle. We derive three explicit parallel transport formulas. On the equivariant subbundle the transport is an exact isometric translation. On the coupled subbundle, the transport combines log-scale translation with a phase factor $e^{i{n}_0\Delta \theta }$. On the invariant subbundle, the transport is approximate, with the quantitative bound $\parallel {\mathsf{\Pi }}^{\mathrm{inv}}F-F\parallel \le \epsilon |\Delta \tau |\parallel F\parallel$, where ${\mathsf{\Pi }}^{\mathrm{inv}}$ denotes the parallel transport operator on that subbundle. We introduce the notion of non-parallelism rate as a pointwise measure of deviation from parallel evolution, and we prove that cumulative deviation along the path is bounded by the path integral of this quantity. The bound separates into two parts. One part is controlled by trajectory estimation error and reflects geometric mismatch. The other part is controlled by intrinsic appearance variation and reflects non-geometric drift. We also show that regularity transfers from the signal family to the spectral sections, and we establish a discrete transport theorem whose finite-sum error bounds recover the continuous estimates in the small-step limit. The framework provides a quantitative geometric tool for multi-scale feature evolution under continuous scale-rotation transformations. Full article

Mechanized harvesting in the industrial tomato sector is currently bottlenecked by excessive mechanical injuries and elevated levels of foreign materials generated during electro-mechanical combine harvesting operations. To combat these limitations, this comprehensive review explores recent breakthroughs in harvester-mounted smart grading systems engineered specifically for complex, open-field conditions. Rather than relying solely on conventional optical inspection, the study examines the transition toward advanced, heterogeneous edge-computing frameworks—incorporating FPGAs and embedded GPUs—deployed within electro-mechanical harvesting platforms. This architectural evolution plays a crucial role in mitigating unpredictable processing delays caused by intense operational vibrations, although achieving absolute real-time stability under extreme field conditions remains an ongoing challenge. To minimize bruising and physical deterioration, our analysis synthesizes findings from multi-scale explicit dynamic finite element simulations, unpacking the underlying microstructural failure modes of the crop. We illustrate how regulating applied forces via soft robotic effectors can help approach a ‘damage-free’ handling threshold, though empirical results vary depending on fruit maturity and dynamic operational speeds. Furthermore, coupling multi-modal sensor fusion with Convolutional Neural Networks (CNNs) shows promising potential for non-destructive internal property evaluation under the vibration, dust, and throughput constraints of electro-mechanical harvesters, pending broader validation across diverse field datasets. Ultimately, by projecting future trends in onboard electro-mechanical harvester separation and advocating for a closer synergy between agronomic practices and machine engineering, this paper delivers a comprehensive blueprint for building next-generation, highly resilient, and gentle sorting machinery. Full article