Search Results (1,931)

To address the bottlenecks of traditional jaw crusher liner wear detection—high safety risks, insufficient precision, and limited full-range analysis—this paper proposes a non-contact, high-precision wear analysis method based on binocular vision and deep learning. At its core is the integration of the state-of-the-art FoundationStereo zero-shot stereo matching algorithm, following scenario-specific adaptations, into the 3D reconstruction of industrial liners for wear analysis. A novel wear quantification methodology and corresponding indicator system are also proposed. After calibrating the ZED2 binocular camera and fine-tuning the algorithm, FoundationStereo achieves an Endpoint Error (EPE) of 0.09, significantly outperforming traditional algorithms. To meet on-site efficiency requirements, a “single-view rapid acquisition + CUDA engineering acceleration” strategy is implemented, reducing point cloud generation latency from 165 ms to 120 ms by rewriting kernel functions and optimizing memory access patterns. Geometric accuracy verification shows a Mean Absolute Error (MAE) ≤ 0.128 mm, fully meeting industrial measurement standards. A complete process of “3D reconstruction–model registration–quantitative analysis” is constructed, utilizing three core indicators (maximum wear depth, average wear depth, and wear area ratio) to characterize liner wear. Statistical results—such as an average maximum wear depth of 55.05 mm—are highly consistent with manual inspection data, providing a safe, efficient, and precise digital solution for the predictive maintenance and intelligent operation and maintenance (O&M) of liners. Full article

Optimizing the furniture layout of middle school laboratories is crucial for improving the emergency safety, operational efficiency, and resilience of teaching buildings. This study used AnyLogic software to model and simulate pedestrian evacuation behavior in a typical middle school laboratory layout. In a standardized laboratory (90.75 m²), we constructed a behavior-oriented multi-agent evacuation model. The model incorporated key student parameters, including shoulder width (312–416 mm), walking speed (1.5–2.5 m/s), and reaction time (10–15 s). To ensure comparability between different layouts, the number of evacuees was fixed at 48. Evacuation performance was evaluated based on total evacuation time, spatial density, and detour distance. The results showed that the hybrid layout achieved the shortest evacuation time (28.0 s), which was 10.3% shorter than the island layout (31.2 s) and 34.7% shorter than the parallel layout (42.9 s). The hybrid layout also had a shorter average detour distance (9.78 m) and the lowest path variability (coefficient of variation CV = 0.33), indicating a more balanced evacuation load and a smaller bottleneck effect. Overall, these findings provide evidence-based recommendations for improving laboratory safety, space utilization, and behavioral adaptability, and provide a quantitative reference for updating educational building codes, school laboratory construction standards, and guidelines for laboratory furniture and safety facility configuration. Full article

Understanding regional disparities in Chinese modernization is essential for achieving coordinated and sustainable development. This study develops a multi-dimensional evaluation framework, integrating grey relational analysis, entropy weighting, and TOPSIS to assess provincial modernization across China from 2018 to 2023. The framework operationalizes Chinese-style modernization through five dimensions: population quality, economic strength, social development, ecological sustainability, innovation and governance, capturing both material and institutional aspects of development. Using K-Means clustering, kernel density estimation, and convergence analysis, the study examines spatial and temporal patterns of modernization. Results reveal pronounced regional heterogeneity: eastern provinces lead in overall modernization but display internal volatility, central provinces exhibit gradual convergence, and western provinces face widening disparities. Intra-regional analysis highlights uneven development even within geographic clusters, reflecting differential access to resources, governance capacity, and innovation infrastructure. These findings are interpreted through modernization theory, linking observed patterns to governance models, regional development trajectories, and policy coordination. The proposed framework offers a rigorous, data-driven tool for monitoring modernization progress, diagnosing regional bottlenecks, and informing targeted policy interventions. This study demonstrates the methodological value of integrating grey system theory with multi-criteria decision-making and clustering analysis, providing both theoretical insights and practical guidance for advancing balanced and sustainable Chinese-style modernization. Full article

As shale gas development extends into deeper formations, the unclear brittle-ductile transition (BDT) mechanism and low fracturing efficiency have emerged as critical bottlenecks, posing challenges to the sustainable and economical utilization of this clean energy resource. This study, focusing on the Liangshang Formation shale of Sichuan Basin’s Pingye-1 Well, pioneers a paradigm shift by identifying Poisson’s ratio ($\mathsf{\nu }$) as the master variable governing this transition. Triaxial tests reveal that $\mathsf{\nu }$ systematically increases with depth, directly regulating the failure mode shift from brittle fracture to ductile flow. Building on this, we innovatively propose the Poisson’s Ratio-regulated Energy-based Brittleness Index (PNE-BI) model. This model achieves a decoupled diagnosis of BDT by quantifying how ν intrinsically orchestrates the energy redistribution between elastic storage and plastic dissipation, utilizing ν as the sole governing variable to regulate energy weighting for rapid and accurate distinction between brittle, transitional, and ductile states. Experiments confirm the $\mathsf{\nu }$-dominated energy evolution: Low $\mathsf{\nu }$ rocks favor elastic energy accumulation, while high $\mathsf{\nu }$ rocks (>0.22) exhibit a dramatic 1520% surge in plastic dissipation, dominating energy consumption (35.9%) and confirming that $\mathsf{\nu }$ enhances ductility by reducing intergranular sliding barriers. Compared to traditional multi-variable models, the PNE-BI model utilizes $\mathsf{\nu }$ values readily obtained from conventional well logs, providing a transformative field-ready tool that significantly reduces the experimental footprint and promotes resource efficiency. It guides toughened fracturing fluid design in ductile zones to suppress premature closure and optimizes injection rates in brittle zones to prevent fracture runaway, thereby enhancing operational longevity and minimizing environmental impact. This work offers a groundbreaking and sustainable solution for boosting the efficiency of mid-deep shale gas development, contributing directly to more responsible and cleaner energy extraction. Full article

With the rapid development of high-resolution satellite remote sensing technology, wind turbine detection based on remote sensing imagery has emerged as a crucial research area in renewable energy. However, accurate identification of wind turbines remains challenging due to complex geographical backgrounds and their typical appearance as small objects in images, where limited features and background interference hinder detection performance. To address these issues, this paper proposes CGA-YOLO, a specialized network for detecting small targets in high-resolution remote sensing images, and constructs the SDWT dataset, containing Gaofen-2 imagery covering various terrains in Shandong Province, China. The network incorporates three key enhancements: dynamic convolution improves multi-scale feature representation for precise localization; the Convolutional Block Attention Module (CBAM) enhances feature convergence through channel and spatial attention mechanisms; and GhostBottleneck maintains high-resolution details while strengthening feature channels for small targets. Experimental results demonstrate that CGA-YOLO achieves an F1-score of 0.93 and an mAP50 of 0.938 on the SDWT dataset, and obtains an mAP50 of 0.9033 on both RSOD and VEDAI public datasets. CGA-YOLO establishes its superior accuracy over multiple mainstream detection models under identical experimental conditions, confirming its potential as a reliable technical solution for accurate wind turbine identification in complex environments. Full article

Tomatoes are an important economic crop in China, and crop diseases often lead to a decline in their yield. Deep learning-based visual recognition methods have become an approach for disease identification; however, challenges remain due to complex background interference in the field and the diversity of disease manifestations. To address these issues, this paper proposes the SXA-YOLO (an improvement based on YOLO, where S stands for the SAAPAN architecture, X represents the XIoU loss function, and A denotes the AsDDet module) symmetric perception recognition model. First, a comprehensive symmetry architecture system is established. The backbone network creates a hierarchical feature foundation through C3k2 (Cross-stage Partial Concatenated Bottleneck Convolution with Dual-kernel Design) and SPPF (the Fast Pyramid Pooling module) modules; the neck employs a SAAPAN (Symmetry-Aware Adaptive Path Aggregation Architecture) bidirectional feature pyramid architecture, utilizing multiple modules to achieve equal fusion of multi-scale features; and the detection head is based on the AsDDet (Adaptive Symmetry-aware Decoupled Detection Head) module for functional decoupling, combining dynamic label assignment and the XIoU (Extended Intersection over Union) loss function to collaboratively optimize classification, regression, and confidence prediction. Ultimately, a complete recognition framework is formed through triple symmetric optimization of “feature hierarchy, fusion path, and task functionality.” Experimental results indicate that this method effectively enhances the model’s recognition performance, achieving a P (Precision) value of 0.992 and an mAP50 (mean Average Precision at 50% IoU threshold) of 0.993. Furthermore, for ten categories of diseases, the SXA-YOLO symmetric perception recognition model outperforms other comparative models in both p value and mAP50. The improved algorithm enhances the recognition of foliar diseases in tomatoes, achieving a high level of accuracy. Full article

Digital technology is profoundly transforming the experiential landscape of tourism. However, its application does not necessarily produce cultural experiences, creating a critical bottleneck that constrains the sustainable development of the cultural tourism industry and broader societal culture. To address this gap, this study introduces psychological ownership theory as an overarching explanatory framework. It constructs and validates an integrated model that examines how digital technology characteristics (interactivity and innovativeness) influence cultural experience through three parallel mediating pathways: cognitive evaluation (perceived usefulness and ease of use), scenario construction, and flow experience. Based on 540 visitor questionnaires, structural equation modeling validated the theoretical model. Findings reveal that the interactivity and innovation of digital technology jointly stimulate visitors’ psychological ownership through three parallel pathways. Specifically, technological innovativeness exhibited the strongest effect on perceived ease of use (β = 0.387, p < 0.001), while the indirect effect via the flow experience path was also significant (effect size = 0.036). This process stimulates visitors’ psychological ownership, ultimately leading to cultural experiences. The study systematically reveals the pathways through which digital technology empowers cultural experiences across three dimensions: as a rational tool, an emotional narrative medium, and an intrinsic psychological catalyst. It highlights that strategically allocating technological resources to cultivate visitors’ psychological ownership is crucial for driving high-quality industrial development. Furthermore, the research offers significant implications for cultural sustainability, suggesting that such internally motivated identification provides a more effective foundation for the living transmission of culture and socio-cultural sustainability than external regulations or imposed norms. Full article

With the rapid growth of multimodal remote sensing (RS) data, there is an increasing demand for intelligent onboard computing to alleviate the transmission and latency bottlenecks of traditional orbit-to-ground downlinking workflows. While many lightweight AI algorithms have been widely developed and deployed for onboard inference, their limited generalization capability restricts performance under the diverse and dynamic conditions of advanced Earth observation. Recent advances in remote sensing foundation models (RSFMs) offer a promising solution by providing pretrained representations with strong adaptability across diverse tasks and modalities. However, the deployment of RSFMs onboard resource-constrained devices such as nano satellites remains a significant challenge due to strict limitations in memory, energy, computation, and radiation tolerance. To this end, this review proposes the first comprehensive survey of onboard RSFMs deployment, where a unified deployment pipeline including RSFMs development, model compression techniques, and hardware optimization is introduced and surveyed in detail. Available hardware platforms are also discussed and compared, based on which some typical case studies for low Earth orbit (LEO) CubeSats are presented to analyze the feasibility of onboard RSFMs’ deployment. To conclude, this review aims to serve as a practical roadmap for future research on the deployment of RSFMs on edge devices, bridging the gap between the large-scale RSFMs and the resource constraints of spaceborne platforms for onboard computing. Full article

Background: Large language models (LLMs) are increasingly integrated into clinical documentation, decision support, and patient-facing applications across healthcare, including plastic and reconstructive surgery. Yet, their evaluation remains bottlenecked by costly, time-consuming human review. This has given rise to LLM-as-a-judge, in which LLMs are used to evaluate the outputs of other AI systems. Methods: This review examines LLM-as-a-judge in healthcare with particular attention to judging architectures, validation strategies, and emerging applications. A narrative review of the literature was conducted, synthesizing LLM judge methodologies as well as judging paradigms, including those applied to clinical documentation, medical question-answering systems, and clinical conversation assessment. Results: Across tasks, LLM judges align most closely with clinicians on objective criteria (e.g., factuality, grammaticality, internal consistency), benefit from structured evaluation and chain-of-thought prompting, and can approach or exceed inter-clinician agreement, but remain limited for subjective or affective judgments and by dataset quality and task specificity. Conclusions: The literature indicates that LLM judges can enable efficient, standardized evaluation in controlled settings; however, their appropriate role remains supportive rather than substitutive, and their performance may not generalize to complex plastic surgery environments. Their safe use depends on rigorous human oversight and explicit governance structures. Full article

As semiconductor process nodes shrink to 3 nm and beyond, traditional optical proximity correction (OPC) and resolution enhancement technologies (RETs) can no longer meet the high patterning precision needs of advanced chip manufacturing due to the sub-wavelength lithography limits. Inverse lithography technology (ILT), a key part of computational lithography, has become a critical solution for these issues. From an EDA industry perspective, this review provides an original and systematic summary of ILT’s development and applications, which helps integrate the scattered research into a clear framework for both academic and industrial use. Compared with traditional OPC, the latest ILT has three main advantages: (1) better patterning accuracy, as a result of the precise optical models that fix complex optical issues (like diffraction and interference) in advanced lithography systems; (2) a wider process window, as it optimizes mask designs by working backwards from the target wafer patterns, making lithography more stable against process changes; and (3) stronger adaptability to new lithography scenarios, such as High-NA EUV and extended DUV nodes. This review first explains ILT’s working principles (the basic concepts, mathematical formulae, and main methods like level-set and pixelated approaches) and its development history, highlighting key events that boosted its progress. It then analyzes ILT’s current application status in the industry (such as hotspot fixing, full-chip trials, and EUV-era use) and its main bottlenecks: a high computational complexity leading to long runtime, difficulties in mask manufacturing, challenges in model calibration, and a conservative market that slows large-scale adoption. Finally, it discusses promising future directions, including hybrid ILT-OPC-SMO strategies, improving model accuracy, AI/ML-driven design, GPU acceleration, multi-beam mask writer improvements, and open-source data to solve data shortage problems. By combining the latest research and industry practices, this review fills the gap of comprehensive ILT summaries that cover the principles, progress, applications, and prospects. It helps readers fully understand ILT’s technical landscape and offers practical insights for solving the key challenges, thus promoting ILT’s industrial use in advanced chip manufacturing. Full article

Public Opinion Reports are essential tools for crisis management, yet their evaluation remains a critical bottleneck that often delays response actions. Recently, dominant Large Language Model (LLM)-based evaluators often overlook a critical challenge: highly open-ended dimensions such as “innovation” and “feasibility” require synthesizing diverse stakeholder perspectives, as different groups judge these qualities from fundamentally different perspectives. Motivated by this, we propose the Role-based Adaptive Evaluation (RAE) framework. This framework employs an adaptive mechanism leveraging multi-perspective evaluation insights through role-based analysis, and further introduces dynamically generated roles tailored to specific contexts for these dimensions. RAE further incorporates multi-role reasoning aggregation to minimize individual biases and enhance evaluation robustness. Extensive experiments demonstrate that RAE significantly improves alignment with human expert judgments, especially on challenging highly open-ended dimensions. Full article

Currently, the manual delineation of landslide anomalies from Interferometric Synthetic Aperture Radar(InSAR )deformation data is labor-intensive and time-consuming, creating a major bottleneck for operational large-scale landslide mapping. This study proposes an automated approach for large-scale landslide identification by integrating InSAR technology with an improved Faster Regional Convolutional Neural Network (Faster R-CNN). First, surface deformation over the study area was obtained using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique. An enhanced CRF-Faster R-CNN model was then developed by incorporating a Residual Network with 50 layers (ResNet-50)-based backbone, strengthened with a Convolutional Block Attention Module (CBAM), within a Feature Pyramid Network (FPN) framework. This model was applied to deformation velocity maps for the automated detection of landslide-prone areas. Preliminary results were subsequently validated and refined using optical images to produce a final landslide inventory. The proposed method was evaluated in the Ahai Reservoir area of the Jinsha River Basin using 248 ascending and descending Sentinel-1A images acquired between January 2019 and December 2021. Its performance was compared with that of the standard Faster R-CNN model. The results indicate that the CRF-Faster R-CNN model outperforms the conventional approach in terms of landslide anomaly detection, convergence speed, and overall accuracy. A total of 38 potential landslide hazards were identified in the Ahai Reservoir area, with an 84% validation accuracy confirmed through field investigations. This study provides crucial technical support for the rapid identification and operational application of large-scale potential landslide hazards. Full article

Multidrug resistance (MDR) is a central cause of chemotherapy failure and tumor recurrence and metastasis, and its mechanism involves enhanced drug efflux, target mutation, upregulation of DNA repair and remodeling of the tumor microenvironment. ABC transporter protein (P-gp, MRP, and BCRP)-mediated efflux of drugs is the most intensively researched aspect of the study, but the first three generations of small-molecule reversal agents were stopped in the clinic because of toxicity or pharmacokinetic defects. Natural products are considered as the fourth generation of MDR reversal agents due to their structural diversity, multi-targeting and low toxicity. In this paper, we systematically summarize the inhibitory activities of monoterpenes, sesquiterpenes, diterpenes and triterpenes against ABC transporter proteins in in vitro and in vivo models and focus on the new mechanism of reversing drug resistance by blocking efflux pumps, modulating signaling pathways such as PI3K-AKT, Nrf2, NF-κB and remodeling the tumor microenvironment. For example, Terpenoids possess irreplaceable core advantages over traditional multidrug resistance (MDR) reversers: Compared with the first three generations of synthetic reversers, natural/semisynthetic terpenoids integrate low toxicity (mostly derived from edible medicinal plants, half-maximal inhibitory concentration IC₅₀ > 50 μM), high target specificity (e.g., oleanolic acid specifically inhibits the ATP-binding cassette (ABC) transporter subtype ABCC1 without cross-reactivity with ABCB1), and multi-mechanistic synergistic effects (e.g., β-caryophyllene simultaneously mediates the dual effects of “ABCB1 efflux inhibition + apoptotic pathway activation”). These unique characteristics enable terpenoids to effectively circumvent key limitations of traditional synthetic reversers, such as high toxicity and severe drug–drug interactions. Among them, lupane-type derivative BBA and euphane-type sooneuphanone D (triterpenoids), as well as dihydro-β-agarofuran-type compounds and sesquiterpene lactone Conferone (sesquiterpenoids), have emerged as the core lead compounds with the greatest translational potential in current MDR reverser research, attributed to their potent in vitro and in vivo MDR reversal activity, low toxicity, and excellent druggable modifiability. At the same time, we point out bottlenecks, such as low bioavailability, insufficient in vivo evidence, and unclear structure–activity relationship and put forward a proposal to address these bottlenecks. At the same time, the bottlenecks of low bioavailability, insufficient vivo evidence and unclear structure–activity relationship have been pointed out, and future research directions such as nano-delivery, structural optimization and combination strategies have been proposed to provide theoretical foundations and potential practical pathways for the clinical translation research of terpenoid compounds, whose clinical application still requires further in vivo validation and translational research support. Full article

Against the backdrop of rapid urbanization, megacities have become crucial drivers of development. As the country with the largest number of megacities (seven in total), China is confronted with significant challenges such as population–resource–environment conflicts, which render high-quality development an imperative pursuit. This study employs a system dynamics approach to assess high-quality development in China’s megacities. It analyzes interactions among economic growth, technological innovation, environmental quality, and livelihood security under policy regulation, clarifying their evolutionary mechanisms and constructing a model to project the high-quality development index (HQDI) and coupling coordination degree (CCD) among subsystems. Findings reveal an upward trend in both HQDI and CCD across the seven megacities, with notable stratification. Beijing, Shanghai, and Shenzhen form the top echelon, leveraging financial and technological resources, driven by science and green development. Guangzhou and Chongqing constitute the second tier, supported by regional integration and industrial clusters, while Chengdu and Tianjin form the third echelon via regional strategic transformations. In coordinated development, Shanghai, Beijing, Shenzhen, and Guangzhou lead with multi-link synergy, whereas Chengdu, Chongqing, and Tianjin advance industry–ecology–livelihood coordination through regional strategies. This study offers insights for overcoming development bottlenecks, optimizing policies, and enhancing urban governance to foster a coordinated, high-quality development pattern. Full article

Industrial deployment of photocatalysis for recalcitrant wastewater treatment remains constrained by economic uncertainty and scale-up limitations. This study first reviews the current technological routes and application status of photocatalytic processes and then addresses the key obstacles through a quantitative techno-economic assessment (TEA) of a full-scale (10,000 m³/d) photocatalytic wastewater treatment plant. A process-level model integrating mass- and energy-balance calculations with equipment sizing was developed for a 280 kW UVA-LED reactor using Pt/TiO₂ as the benchmark catalyst. The framework quantifies capital (CAPEX) and operating (OPEX) expenditures and evaluates the overall economic performance of the photocatalytic treatment system. Sensitivity analysis reveals that the catalyst replacement interval and electricity tariffs are the principal economic bottlenecks, whereas improvements in catalyst performance alone provide limited cost leverage. Furthermore, the analysis indicates that supportive policy mechanisms such as carbon-credit incentives and electricity subsidies could substantially enhance economic feasibility. Overall, this work establishes a comprehensive integrated TEA framework for industrial-scale photocatalytic wastewater treatment, offering actionable design parameters and cost benchmarks to guide future commercialization. Full article