Search Results (691)

As Chinese open-pit mines progressively transition to deeper operations, challenges such as rising stripping ratios, declining slope stability, and environmental degradation have become increasingly pronounced. The sustainability of traditional open-pit mining models faces substantial challenges. Underground mining, offering higher resource recovery rates and minimal environmental disruption, is emerging as a pivotal technological pathway for the green transformation of mining. Consequently, the transition from open-pit to underground mining has emerged as a central research focus within mining engineering. This paper provides a comprehensive review of key technological advancements in this transition, emphasizing core issues such as mine development system selection, mining method choices, slope stability control, and crown pillar design. A typical case study of the Anhui Xinqiao Iron Mine is presented to analyze its engineering approaches and practical experiences in joint development, backfilling mining, and ecological restoration. The findings indicate that the mine has achieved multi-objective optimization of resource utilization, environmental coordination, and operational capacity while ensuring safety and recovery efficiency. This offers a replicable and scalable technological demonstration for the green transformation of similar mines around the world. Full article

Pancreatic cancer is frequently accompanied by cancer-associated cachexia, a debilitating metabolic syndrome marked by progressive skeletal muscle wasting and systemic metabolic dysfunction. This study presents a systems biology framework to simultaneously identify therapeutic targets for both pancreatic ductal adenocarcinoma (PDAC) and its associated cachexia (PDAC-CX), using cell-specific genome-scale metabolic models (GSMMs). The human metabolic network Recon3D was extended to include protein synthesis, degradation, and recycling pathways for key inflammatory and structural proteins. These enhancements enabled the reconstruction of cell-specific GSMMs for PDAC and PDAC-CX, and their respective healthy counterparts, based on transcriptomic datasets. Medium-independent metabolic biomarkers were identified through Parsimonious Metabolite Flow Variability Analysis and differential expression analysis across five nutritional conditions. A fuzzy multi-objective optimization framework was employed within the anticancer target discovery platform to evaluate cell viability and metabolic deviation as dual criteria for assessing therapeutic efficacy and potential side effects. While single-enzyme targets were found to be context-specific and medium-dependent, eight combinatorial targets demonstrated robust, medium-independent effects in both PDAC and PDAC-CX cells. These include the knockout of SLC29A2, SGMS1, CRLS1, and the RNF20–RNF40 complex, alongside upregulation of CERK and PIKFYVE. The proposed integrative strategy offers novel therapeutic avenues that address both tumor progression and cancer-associated cachexia, with improved specificity and reduced off-target effects, thereby contributing to translational oncology. Full article

Accurate modeling of ionospheric variability is critical for space weather forecasting and GNSS applications. While machine learning approaches have shown promise, progress is hindered by the absence of standardized benchmarking practices and narrow test periods. In this paper, we take the first step toward fostering rigorous and reproducible evaluation of AI models for ionospheric forecasting by introducing IonoBench: a benchmarking framework that employs a stratified data split, balancing solar intensity across subsets while preserving 16 high-impact geomagnetic storms (Dst ≤ $-100$ nT) for targeted stress testing. Using this framework, we benchmark a field-specific model (DCNN) against state-of-the-art spatiotemporal architectures (SwinLSTM and SimVPv2) using the climatological IRI 2020 model as a baseline reference. DCNN, though effective under quiet conditions, exhibits significant degradation during elevated solar and storm activity. SimVPv2 consistently provides the best performance, with superior evaluation metrics and stable error distributions. Compared to the C1PG baseline (the CODE 1-day forecast product), SimVPv2 achieves a notable RMSE reduction up to $32.1\%$ across various subsets under diverse solar conditions. The reported results highlight the value of cross-domain architectural transfer and comprehensive evaluation frameworks in ionospheric modeling. With IonoBench, we aim to provide an open-source foundation for reproducible comparisons, supporting more meticulous model evaluation and helping to bridge the gap between ionospheric research and modern spatiotemporal deep learning. Full article

Single-image super-resolution (SISR) plays a critical role in enhancing visual quality for real-world applications, including industrial inspection and embedded vision systems. While deep learning-based approaches have made significant progress in SR, existing lightweight SR models often fail to accurately reconstruct high-frequency textures, especially under complex degradation scenarios, resulting in blurry edges and structural artifacts. To address this challenge, we propose a Dense Residual Fused Attention Network (DRFAN), a novel lightweight hybrid architecture designed to enhance high-frequency texture recovery in challenging degradation conditions. Moreover, by coupling convolutional layers and attention mechanisms through gated interaction modules, the DRFAN enhances local details and global dependencies with linear computational complexity, enabling the efficient utilization of multi-level spatial information while effectively alleviating the loss of high-frequency texture details. To evaluate its effectiveness, we conducted ×4 super-resolution experiments on five public benchmarks. The DRFAN achieves the best performance among all compared lightweight models. Visual comparisons show that the DRFAN restores more accurate geometric structures, with up to +1.2 dB/+0.0281 SSIM gain over SwinIR-S on Urban100 samples. Additionally, on a domain-specific rice grain dataset, the DRFAN outperforms SwinIR-S by +0.19 dB in PSNR and +0.0015 in SSIM, restoring clearer textures and grain boundaries essential for industrial quality inspection. The proposed method provides a compelling balance between model complexity and image reconstruction fidelity, making it well-suited for deployment in resource-constrained visual systems and industrial applications. Full article

This review critically examines the effects of moisture absorption on the mechanical and fatigue properties of natural fiber composites (NFCs), with a focus on tensile strength, elastic modulus, and long-term durability. Moisture uptake can cause reductions in tensile strength of up to 40% and in elastic modulus by 20–30% depending on fiber type, mass fraction (typically in the range of 30–60%), and surface treatments. The review highlights Ithat while surface modifications (e.g., alkaline and silane treatments) significantly mitigate moisture-induced degradation, their effectiveness is highly sensitive to the processing conditions. Additionally, hybridization strategies and optimized fiber orientations show promise in enhancing fatigue resistance under humid environments. Despite substantial progress, major challenges remain, including the lack of standardized testing protocols and the limited understanding of multiscale aging mechanisms. Future research directions include developing predictive models that couple moisture diffusion and mechanical deterioration, implementing advanced in situ monitoring of damage evolution, and exploring novel bio-based treatments. By addressing these gaps, NFCs can become more reliable and widely adopted as sustainable alternatives in structural applications. Full article

This study focuses on the influence of prolonged ultraviolet (UV) irradiation on the mechanical properties and surface microstructure of glass fiber-reinforced plastics (GFRPs) and basalt fiber-reinforced plastics (BFRPs), which are widely used in construction and transport infrastructure. The relevance of the research lies in the need to improve the reliability of composite materials under extended exposure to harsh climatic conditions. Experimental tests were conducted in a laboratory UV chamber over 2000 h, simulating accelerated weathering. Mechanical properties were evaluated using three-point bending, while surface conditions were assessed via profilometry and microscopy. It was shown that GFRPs exhibit a significant reduction in flexural strength—down to 59–64% of their original value—accompanied by increased surface roughness and microdefect depth. The degradation mechanism of GFRPs is attributed to the photochemical breakdown of the polymer matrix, involving free radical generation, bond scission, and oxidative processes. To verify these mechanisms, FTIR spectroscopy was employed, which enabled the identification of structural changes in the polymer phase and the detection of mass loss associated with matrix decomposition. In contrast, BFRP retained up to 95% of their initial strength, demonstrating high resistance to UV-induced aging. This is attributed to the shielding effect of basalt fibers and their ability to retain moisture in microcavities, which slows the progress of photo-destructive processes. Comparison with results from natural exposure tests under extreme climatic conditions (Yakutsk) confirmed the reliability of the accelerated aging model used in the laboratory. Full article

Global context information is essential for semantic segmentation of remote sensing (RS) images. Due to their remarkable capability to capture global context information and model long-range dependencies, vision transformers have demonstrated great performance on semantic segmentation. However, the high computational complexity of vision transformers impedes their broad application in resource-constrained environments for RS image segmentation. To address this challenge, we propose multi-faceted adaptive token pruning (MATP) to reduce computational cost while maintaining relatively high accuracy. MATP is designed to prune well-learned tokens which do not have a close relation to other tokens. To quantify these two metrics, MATP employs multi-faceted scores: entropy, to evaluate the learning progression of tokens; and attention weight, to assess token correlations. Specially, MATP utilizes adaptive criteria for each score that are automatically adjusted based on specific input features. A token is pruned only when both criteria are satisfied. Overall, MATP facilitates the utilization of vision transformers in resource-constrained environments. Experiments conducted on three widely used datasets reveal that MATP reduces the computation cost about 67–70% with about 3–6% accuracy degradation, achieving a superior trade-off between accuracy and computational cost compared to the state of the art. Full article

The design of materials with programmable degradation profiles presents a fundamental challenge in pattern recognition across molecular space, requiring the identification of complex structure–property relationships within an exponentially large chemical domain. This paper introduces a novel physics-informed deep learning framework that integrates multi-scale molecular sensing data with reinforcement learning algorithms to enable intelligent characterization and prediction of polymer degradation dynamics. Our method combines three key innovations: (1) a dual-channel sensing architecture that fuses spectroscopic signatures from Graph Isomorphism Networks with temporal degradation patterns captured by transformer-based models, enabling comprehensive molecular state detection across multiple scales; (2) a physics-constrained policy network that ensures sensor measurements adhere to thermodynamic principles while optimizing the exploration of degradation pathways; and (3) a hierarchical signal processing system that balances multiple sensing modalities through adaptive weighting schemes learned from experimental feedback. The framework employs curriculum-based training that progressively increases molecular complexity, enabling robust detection of degradation markers linking polymer architectures to enzymatic breakdown kinetics. Experimental validation through automated synthesis and in situ characterization of 847 novel polymers demonstrates the framework’s sensing capabilities, achieving a 73.2% synthesis success rate and identifying 42 structures with precisely monitored degradation profiles spanning 6 to 24 months. Learned molecular patterns reveal previously undetected correlations between specific spectroscopic signatures and degradation susceptibility, validated through accelerated aging studies with continuous sensor monitoring. Our results establish that physics-informed constraints significantly improve both the validity (94.7%) and diversity (0.82 Tanimoto distance) of generated molecular structures compared with unconstrained baselines. This work advances the convergence of intelligent sensing technologies and materials science, demonstrating how physics-informed machine learning can enhance real-time monitoring capabilities for next-generation sustainable materials. Full article

Skeletal muscle atrophy is a debilitating condition characterized by the loss of muscle mass and function. It is commonly associated with aging, chronic diseases, disuse, and prolonged glucocorticoid therapy. Oxidative stress and catabolic signaling pathways play significant roles in the progression of muscle degradation. Despite its clinical relevance, few effective therapeutic options are currently available. In this study, we investigated the protective effects of an ethanolic extract of Glycine Semen Preparata (GSP), i.e., fermented black soybeans, using in vitro and in vivo models of dexamethasone (Dexa)-induced muscle atrophy. In C2C12 myoblasts and myotubes, GSP significantly attenuated both oxidative stress-induced and Dexa-induced damages by reducing reactive oxygen species levels and by suppressing the expression of the muscle-specific E3 ubiquitin ligases MuRF1 and Atrogin-1. Moreover, GSP upregulated key genes involved in muscle regeneration (Myod1 and Myog) and mitochondrial biogenesis (PGC1α), indicating its dual role in muscle protection and regeneration. Oral administration of GSP to mice with Dexa-induced muscle atrophy resulted in improved muscle fiber integrity, increased proportion of large cross-sectional area fibers, and partial recovery of motor function. Isoflavone aglycones, such as daidzein and genistein, were identified as active compounds that contribute to the beneficial effects of GSP through antioxidant activity and gene promoter enhancement. Thus, GSP is a promising nutraceutical that prevents or mitigates muscle atrophy by targeting oxidative stress and promoting myogenesis and mitochondrial function. Further studies are warranted to standardize the bioactive components and explore their clinical applications. Full article

To address traditional methods’ limitations in neglecting the interaction between random shock loads and progressive degradation, as well as failure correlations, this study proposes a dynamic reliability framework integrating Gamma processes, homogeneous Poisson processes (HPP), and mixed Copula functions. The framework develops a wind turbine gearbox reliability model under shock–degradation coupling while quantifying failure correlations. Gamma processes characterize continuous degradation, with parameters estimated from P-S-N curves. Based on stress–strength interference theory, random shocks within damage thresholds are integrated to form a coupled reliability model. A Gumbel–Clayton–Frank mixed Copula with a multi-layer nested algorithm quantifies failure correlations, with correlation parameters estimated via the RSS principle and genetic algorithms. Validation using a 2 MW gearbox’s planetary gear-stage system covers four scenarios: natural degradation, shock–degradation coupling, and both scenarios with failure correlations. The results show that compared to independent assumptions, the model accelerates reliability decline, increasing failure rates by >37%. Relative to natural degradation-only models, failure rates rise by >60%, validating the model’s effectiveness and alignment with real-world operational conditions. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder marked by dopaminergic neuronal loss, α-synuclein aggregation, and chronic neuroinflammation. Recent evidence suggests that exosomal microRNAs (miRNAs)—small, non-coding RNAs encapsulated in extracellular vesicles—are key regulators of PD pathophysiology and promising candidates for biomarker development and therapeutic intervention. Exosomes facilitate intercellular communication, cross the blood–brain barrier, and protect miRNAs from degradation, rendering them suitable for non-invasive diagnostics and targeted delivery. Specific exosomal miRNAs modulate neuroinflammatory cascades, oxidative stress, and synaptic dysfunction, and their altered expression in cerebrospinal fluid and plasma correlates with disease onset, severity, and progression. Despite their translational promise, challenges persist, including methodological variability in exosome isolation, miRNA profiling, and delivery strategies. This review integrates findings from preclinical models, patient-derived samples, and systems biology to delineate the functional impact of exosomal miRNAs in PD. We propose mechanistic hypotheses linking miRNA dysregulation to molecular pathogenesis and present an interactome model highlighting therapeutic nodes. Advancing exosomal miRNA research may transform the clinical management of PD by enabling earlier diagnosis, molecular stratification, and the development of disease-modifying therapies. Full article

Significant progress has been made in the low-temperature toughness and crack resistance of polypropylene fiber-reinforced composites. However, there is still a gap in the research on damage evolution under freeze–thaw cycles and complex stress ratios. To solve the problem of durability degradation of traditional rubble masonry in cold regions, this paper focuses on the study of polypropylene fiber-mortar-masonry blocks with different fiber contents. Using acoustic emission and digital image technology, the paper conducts a series of tests on the scaled-down polypropylene fiber-mortar-masonry structure, including uniaxial compressive tests, three-point bending tests, freeze–thaw cycle tests, and tests with different stress ratios. Based on the Kupfer criterion, a biaxial failure criterion for polypropylene fiber mortar-masonry stone (PPF-MMS) was established under different freeze–thaw cycles. A freeze–thaw damage evolution model was also developed under different stress ratios. The failure mechanism of PPF-MMS structures was analyzed using normalized average deviation (NAD), RA-AF, and other parameters. The results show that when the dosage of PPF is 0.9–1.1 kg/m³, it is the optimal content. The vertical stress shows a trend of increasing first and then decreasing with the increase in the stress ratio, and when α = 0.5, the degree of strength increase reaches the maximum. However, the freeze–thaw cycle has an adverse effect on the internal structure of the specimens. Under the same number of freeze–thaw cycles, the strength of the specimens without fiber addition decreases more rapidly than that with fiber addition. The NAD evolution rate exhibits significant fluctuations during the middle loading period and near the damage failure, which can be considered precursors to specimen cracking and failure. RA-AF results showed that the specimens mainly exhibited tensile failure, but the occurrence of tensile failure gradually decreased as the stress ratio increased. Full article

The increasing deployment of photovoltaic and energy storage systems (ESSs) in modern power grids has highlighted the critical challenge of component degradation, which significantly impacts system efficiency, operational costs, and long-term reliability. Conventional energy dispatch and optimization approaches fail to adequately mitigate the progressive efficiency loss in PV modules and battery storage, leading to suboptimal performance and reduced system longevity. To address these challenges, this paper proposes a quantum-enhanced degradation pathway optimization framework that dynamically adjusts operational strategies to extend the lifespan of PV storage systems while maintaining high efficiency. By leveraging quantum-assisted Monte Carlo simulations and hybrid quantum–classical optimization, the proposed model evaluates degradation pathways in real time and proactively optimizes energy dispatch to minimize efficiency losses due to aging effects. The framework integrates a quantum-inspired predictive maintenance algorithm, which utilizes probabilistic modeling to forecast degradation states and dynamically adjust charge–discharge cycles in storage systems. Unlike conventional optimization methods, which struggle with the complexity and stochastic nature of degradation mechanisms, the proposed approach capitalizes on quantum parallelism to assess multiple degradation scenarios simultaneously, significantly enhancing computational efficiency. A three-layer hierarchical optimization structure is introduced, ensuring real-time degradation risk assessment, periodic dispatch optimization, and long-term predictive adjustments based on PV and battery aging trends. The framework is tested on a 5 MW PV array coupled with a 2.5 MWh lithium-ion battery system, with real-world degradation models applied to reflect light-induced PV degradation (0.7% annual efficiency loss) and battery state-of-health deterioration (1.2% per 100 cycles). A hybrid quantum–classical computing environment, utilizing D-Wave’s Advantage quantum annealer alongside a classical reinforcement learning-based optimization engine, enables large-scale scenario evaluation and real-time operational adjustments. The simulation results demonstrate that the quantum-enhanced degradation optimization framework significantly reduces efficiency losses, extending the PV module’s lifespan by approximately 2.5 years and reducing battery-degradation-induced wear by 25% compared to conventional methods. The quantum-assisted predictive maintenance model ensures optimal dispatch strategies that balance energy demand with system longevity, preventing excessive degradation while maintaining grid reliability. The findings establish a novel paradigm in degradation-aware energy optimization, showcasing the potential of quantum computing in enhancing the sustainability and resilience of PV storage systems. This research paves the way for the broader integration of quantum-based decision-making in renewable energy infrastructure, enabling scalable, high-performance optimization for future energy systems. Full article

Grain boundary weakening in high-temperature environments significantly influences the fatigue crack growth mechanisms of nickel-based superalloys, introducing challenges in accurately predicting fatigue life. In this study, a dislocation-density-based crystal plasticity phase-field (CP–PF) model is developed to simulate the fatigue crack growth behavior of the GH4169 alloy under both room and elevated temperatures. Grain boundaries are explicitly modeled, enabling the competition between transgranular and intergranular cracking to be accurately captured. The grain boundary separation energy and surface energy, calculated via molecular dynamics simulations, are employed as failure criteria for grain boundary and intragranular material points, respectively. The simulation results reveal that under oxygen-free conditions, fatigue crack propagation at both room and high temperatures is governed by sustained shear slip, with crack advancement hindered by grains exhibiting low Schmid factors. When grain boundary oxidation is introduced, increasing oxidation levels progressively degrade grain boundary strength and reduce overall fatigue resistance. Specifically, at room temperature, oxidation shortens the duration of crack arrest near grain boundaries. At elevated service temperatures, intensified grain boundary degradation facilitates a transition in crack growth mode from transgranular to intergranular, thereby accelerating crack propagation and exacerbating fatigue damage. Full article

Accurate seismic performance assessment of flexure-shear critical reinforced concrete (RC) columns necessitates precise hysteresis modeling that captures their distinct cyclic characteristics—particularly pronounced strength degradation, stiffness deterioration, and pinching effects. However, existing hysteresis models for such circular RC columns fail to comprehensively characterize these coupled cyclic degradation mechanisms under repeated loading. This study develops a novel hysteresis model explicitly incorporating three key mechanisms: (1) directionally asymmetric strength degradation weighted by hysteretic energy, (2) cycle-dependent pinching governed by damage accumulation paths, and (3) amplitude-driven stiffness degradation decoupled from cycle count, calibrated and validated using 14 column tests from the Pacific Earthquake Engineering Research Center (PEER) structural performance database. Key findings reveal that significant strength degradation primarily manifests during initial loading cycles but subsequently stabilizes. Unloading stiffness degradation demonstrates negligible dependency on cycle number. Pinching effects progressively intensify with cyclic advancement. The model provides a physically rigorous framework for simulating seismic deterioration, significantly improving flexure-shear failure prediction accuracy, while parametric analysis confirms its potential adaptability beyond tested scenarios. However, applicability remains confined to specific parameter ranges with reliability decreasing near boundaries due to sparse data. Deliberate database expansion for edge cases is essential for broader generalization. Full article