Search Results (64,785)

This study combines three optimization algorithms, Tunicate Swarm Algorithm (TSA), Whale Optimization Algorithm (WOA), and Jellyfish Search Optimizer (JSO), with random forest (RF) to predict the shear bond strength of cable bolts under different types and grouting conditions. Based on the original dataset, a database of 860 samples was generated by introducing random noise around each data point. After establishing three hybrid models (RF-WOA, RF-JSO, RF-TSA) and training them, the obtained models were evaluated using six metrics: coefficient of determination (R²), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), variance account for (VAF), and A-20 index. The results indicate that the RF-JSO model exhibits superior performance compared to the other models. The RF-JSO model achieved an excellent performance on the testing set (R² = 0.981, RMSE = 11.063, MAE = 6.457, MAPE = 9, VAF = 98.168, A-20 = 0.891). In addition, Shapley Additive exPlanations (SHAP), Partial Dependence Plot (PDP), and Local Interpretable Model-agnostic Explanations (LIME) were used to analyze the interpretability of the model, and it was found that confining pressure (Stress), elastic modulus (E), and a standard cable type (cable type_standard) contributed the most to the prediction of shear bond strength. In summary, the hybrid model proposed in this study can effectively predict the shear bond strength of cable bolts. Full article

Particulate matter, particularly PM_2.5, poses a significant threat to public health due to its ability to disperse widely and its detrimental impact on the respiratory and circulatory systems upon inhalation. Consequently, it is imperative to maintain regular monitoring and assessment of particulate matter levels to anticipate air pollution events and promptly mitigate their adverse effects. However, predicting air quality is inherently complex, given the multitude of variables that influence it. Deep learning models, renowned for their ability to capture nonlinear relationships, offer a promising approach to address this challenge, with hybrid architectures demonstrating enhanced performance. This study aims to develop and evaluate a hybrid model integrating Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for forecasting PM_2.5 levels in India, Milan, and Frankfurt. A comparative analysis with established deep learning and machine learning techniques substantiates the superior predictive capabilities of the proposed CNN-RNN model. The findings underscore its potential as an effective tool for air quality prediction, with implications for informed decision-making and proactive intervention strategies to safeguard public health. Full article

Early traffic accident risk prediction in complex road environments poses significant challenges due to the heterogeneous nature and incomplete semantic alignment of multimodal data. To address this, we propose a novel Early Accident Risk Cross-modal Collaborative Perception Mechanism Network (EAR-CCPM-Net) that integrates hierarchical fusion modules and cross-modal attention mechanisms to enable semantic interaction between visual, motion, and textual modalities. The model is trained and evaluated on the newly constructed CAP-DATA dataset, incorporating advanced preprocessing techniques such as bilateral filtering and a rigorous MINI-Train-Test sampling protocol. Experimental results show that EAR-CCPM-Net achieves an AUC of 0.853, AP of 0.758, and improves the Time-to-Accident (TTA_0.5) from 3.927 s to 4.225 s, significantly outperforming baseline methods. These findings demonstrate that EAR-CCPM-Net effectively enhances early-stage semantic perception and prediction accuracy, providing an interpretable solution for real-world traffic risk anticipation. Full article

The exploitation and development of photovoltaic (PV) modules faces several technical challenges, including those related to variability in electrical performance under real conditions, such as temperature fluctuations, irradiance variability, and dust accumulation. One solution for evaluating and controlling these performances is to conduct electrical characterization under natural conditions. Many characterization techniques have been developed and proposed in the literature, with the aim of verifying manufacturer performance guarantees and better understanding the behavior of PV modules in their installation environment, where the climatic parameters, such as solar irradiation and temperature, fluctuate constantly. These techniques are based on recognized standards, including those established by the International Electrotechnical Commission (IEC) and American Society for Testing and Materials (ASTM). They are also based on methods of transposing basic electrical parameters, allowing the prediction of the performance of modules under various environmental conditions. In this work, a classification and a critical analysis of the main methods of electrical characterization were undertaken, highlighting their respective advantages and disadvantages. The experimental protocols used to evaluate the impact of environmental parameters on the performance of PV modules were examined in detail. Full article

In the context of the rapid development of e-commerce, the selection of optimal land plots for the construction of warehouse complexes that meet environmental, technical, and political requirements has become increasingly relevant. This task requires a comprehensive approach that accounts for a wide range of factors, including transportation accessibility, environmental conditions, geographic features, legal constraints, and more. Such an approach enhances the efficiency and sustainability of decision-making processes. This article presents a solution to the aforementioned problem that employs the use of land suitability maps generated by aggregating multiple evaluation criteria. These criteria represent the degree to which each land plot satisfies the requirements of various stakeholders and are expressed as suitability functions based on attribute values. Attributes describe different characteristics of the land plots and are represented as layers on a digital terrain map. The criteria and their corresponding attributes are classified as either quantitative or binary. Binary criteria are aggregated using the minimum operator, which filters out plots that violate any constraints by assigning them a suitability score of zero. Quantitative criteria are aggregated using the second-order Choquet integral, a method that accounts for interdependencies among criteria while maintaining computational simplicity. The criteria were developed based on statistical and environmental data obtained from an analysis of the Samara region in Russia. The resulting suitability maps are visualized as gradient maps, where land plots are categorized according to their degree of suitability—from completely unsuitable to highly suitable. This visual representation facilitates intuitive interpretation and comparison of different location options. These maps serve as an effective tool for planners and stakeholders, providing comprehensive and objective insights into the potential of land plots while incorporating all relevant factors. The proposed approach supports spatial analysis and land use planning by integrating mathematical modeling with modern information technologies to address pressing challenges in sustainable development. Full article

In response to the urgent global call for “dual carbon” targets, the sustainable transformation of public museums has become a focal issue in both academic research and engineering practice. This study proposes and empirically validates an integrated management framework that unites digital twin modeling, artificial intelligence, and green energy systems for next-generation green smart museums. A unified, closed-loop platform for data-driven, adaptive management is implemented and statistically validated across distinct deployment scenarios. Empirical evaluation is conducted through the comparative analysis of three representative museum cases in China, each characterized by a distinct integration pathway: (A) advanced digital twin and AI management with moderate green energy adoption; (B) large-scale renewable energy integration with basic AI and digitalization; and (C) the comprehensive integration of all three dimensions. Multi-dimensional data on energy consumption, carbon emissions, equipment reliability, and visitor satisfaction are collected and analyzed using quantitative statistical techniques and performance indicator benchmarking. The results reveal that the holistic “triple synergy” approach in Case C delivers the most balanced and significant gains, achieving up to 36.7% reductions in energy use and 41.5% in carbon emissions, alongside the highest improvements in operational reliability and visitor satisfaction. In contrast, single-focus strategies show domain-specific advantages but also trade-offs—for example, Case B achieved high energy and carbon savings but relatively limited visitor satisfaction gains. These findings highlight that only coordinated, multi-technology integration can optimize performance across both environmental and experiential dimensions. The proposed framework provides both a theoretical foundation and practical roadmap for advancing the digital and green transformation of public cultural buildings, supporting broader carbon neutrality and sustainable development objectives. Full article

This paper presents the SMALLDERS project, a European initiative aimed at transforming smallholders’ business models through an innovative technological platform. The platform functions as an e-marketplace that connects small farmers directly with consumers while simultaneously promoting environmental sustainability and collaboration across the agri-food value chain. The study evaluates the platform’s commercial viability and acceptance through a mixed-methods approach, incorporating qualitative and quantitative data. Research methods include focus group sessions, interviews with key stakeholders—such as transport companies, large distributors, and public administrations—and a consumer survey assessing intentions and attitudes toward the e-marketplace. Results indicate limited overall consumer readiness to adopt the platform; however, 48.6% of respondents expressed willingness to use it provided competitive prices and personal benefits are assured. Smallholders regard e-commerce as a promising opportunity, yet they face significant barriers, including limited resources, low digital literacy, and logistical constraints. Stakeholders generally view the platform positively, emphasizing that its success depends on achieving a critical mass of business volume. To foster adoption, SMALLDERS proposes three business models for smallholders: sustainable, cooperative, and technological. The platform includes a user-friendly feature to assist smallholders in transitioning among these models, complemented by training and support services designed to encourage more resilient and innovative agricultural practices. Full article

The contact performance of high-contact-ratio asymmetrical planetary gears is comprehensively evaluated using multiple indicators. The relationship between the indicators and modification parameters is difficult to accurately describe with a single type of surrogate model due to their varying degrees of nonlinearity. This paper proposes an optimization design method for comprehensive modification parameters based on a hybrid surrogate model to improve the optimization accuracy of comprehensive modification. Firstly, a theoretical model of comprehensive modification for high-contact-ratio asymmetrical planetary gears and a dynamically selected hybrid surrogate model are proposed based on different contact performance indicators. Then, the explicit constraints of comprehensive modification and the implicit constraints of non-edge contact are modeled for the modification parameters. Finally, a multi-objective optimization algorithm for the modification parameters based on the hybrid surrogate model is established and validated through experiments and simulations. The results show that the proposed method improves the optimization accuracy and edge contact on the tooth surface is avoided while improving the contact performance, and they provide a reference for efficient and precise optimization of high-contact-ratio asymmetrical planetary gears. Full article

This paper presents a comprehensive survey of deep learning-based methods for 3D object detection in autonomous driving, focusing on their use of diverse sensor modalities, including monocular cameras, stereo vision, LiDAR, radar, and multi-modal fusion. To systematically organize the literature, a structured taxonomy is proposed that categorizes methods by input modality. The review also outlines the chronological evolution of these approaches, highlighting major architectural developments and paradigm shifts. Furthermore, the surveyed methods are quantitatively compared using standard evaluation metrics across benchmark datasets in autonomous driving scenarios. Overall, this work provides a detailed and modality-agnostic overview of the current landscape of deep learning approaches for 3D object detection in autonomous driving. Results of this work are available in a github open repository. Full article

We address the task of point cloud denoising by leveraging a diffusion-based generative framework augmented with adversarial training. While recent diffusion models have demonstrated strong capabilities in learning complex data distributions, their effectiveness in recovering fine geometric details remains limited, especially under severe noise conditions. To mitigate this, we propose the Adversarial Diffusion Bridge Model (ADBM), a novel approach for denoising 3D point cloud data by integrating a diffusion bridge model with adversarial learning. ADBM incorporates a lightweight discriminator that guides the denoising process through adversarial supervision, encouraging sharper and more faithful reconstructions. The denoiser is trained using a denoising diffusion objective based on a Schrödinger Bridge, while the discriminator distinguishes between real, clean point clouds and generated outputs, promoting perceptual realism. Experiments are conducted on the PU-Net and PC-Net datasets, with performance evaluation employing the Chamfer distance and Point-to-Mesh metrics. The qualitative and quantitative results both highlight the effectiveness of adversarial supervision in enhancing local detail reconstruction, making our approach a promising direction for robust point cloud restoration. Full article

Hydraulic motors are increasingly pivotal in high-power drive systems for heavy-duty vehicles and industrial machinery due to their high power density. Radial piston hydraulic motors are commonly employed in heavy-load applications, while digital hydraulic motors have surfaced as a potential substitute for traditional hydraulic motors. Yet challenges such as torque pulsation and inefficient flow distribution persist in traditional designs. To improve performance and reliability, this paper proposed a digital radial piston hydraulic motor using several switching valves to distribute hydraulic oil, along with a comprehensive strategy to mitigate flow pulsation and enhance hydraulic transmission efficiency in digital hydraulic motors. The inherent torque pulsation characteristics are systematically investigated, revealing their dependence on valve actuation patterns and load dynamics. A novel torque pulsation mitigation design is introduced. Then, valve modeling and efficiency evaluation are developed; the phase-correction-based flow distribution method is conducted by optimizing valve sequencing; and simulations and experiments are carried out to demonstrate the feasibility. In conclusion, insights have been drawn to direct the design and control of radial piston digital hydraulic motors. This paper presents a potential solution for heavy-duty traction applications. Full article

Modeling risk evolution in financial networks presents both practical and theoretical challenges, particularly during periods of heightened systemic stress. This issue has gained urgency recently in China as it faces unprecedented financial strain, largely driven by structural shifts in the real estate sector and broader economic vulnerabilities. In this study, we combine Fractional-order Partial Differential Equations (FoPDEs) with network-based analysis methods, proposing a hybrid framework for capturing and modeling systemic financial risk, which is quantified using the $\Delta CoVaR$ algorithm. The FoPDEs model is formulated based on reaction–diffusion equations and discretized using the Caputo fractional derivative. Parameter estimation is conducted through a composite optimization strategy, and numerical simulations are carried out to investigate the underlying mechanisms and dynamic behavior encoded in the equations. For empirical evaluation, we utilize data from China’s financial and real estate sectors. The results demonstrate that our model achieves a Mean Relative Accuracy (MRA) of 95.5% for daily-frequency data, outperforming LSTM and XGBoost under the same conditions. For weekly-frequency data, the model attains an MRA of 91.7%, exceeding XGBoost’s performance of 90.25%. Further analysis of parameter dynamics and event studies reveals that the fractional-order parameter $\alpha$, which controls the memory effect of the model, tends to remain low when $\Delta CoVaR$ exhibits sudden surges. This suggests that the model assigns greater importance to past data during periods of financial shocks, capturing the persistence of risk dynamics more effectively. Full article

Background/Objetives: Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease with poor prognosis. Nutritional disorders, particularly cachexia, significantly impact morbidity and mortality in IPF but remain under-investigated. This study aimed to characterize cachexia phenotypes in IPF through morphofunctional assessment (MFA) and to evaluate their prognostic relevance, including the role of mitochondrial biomarkers. Methods: In this prospective bicenter study, 85 IPF patients underwent MFA including bioelectrical impedance vector analysis (BIVA), nutritional ultrasound (NU), and T12-level computed tomography (T12-CT) for body composition. Functional and strength assessments included timed up and go test (TUG) and handgrip strength (HGS), respectively. Cachexia was defined by Evans’ criteria, Martin’s CT-based criteria, and our IPF-specific proposed definition. Serum GDF-15 and PGC-1α levels were also measured. Results: Cachexia prevalence varied by definition: 24.71% (Evans), 29.5% (Martin) and 42.4% (IPF Cachexia Syndrome). Cachectic patients showed significantly lower muscle mass, function, and quality (measured by reduced muscle attenuation at T12-CT), along with higher GDF-15 and lower PGC-1α levels. The presence of IPF Cachexia syndrome (HR 2.56; 95% CI, 1.08–6.07; p = 0.033), GDF-15 > 4412.0 pg/mL (HR 3.21; 95% CI, 1.04–9.90; p = 0.042) and impaired TUG (>8 s) (HR 3.77; 95% CI, 1.63–8.71; 0.002) were all independently associated with increased 24-month mortality. Conclusions: Cachexia is prevalent in IPF and showed strong concordance between the three diagnostic criteria. The IPF Cachexia syndrome, based on comprehensive morphofunctional phenotyping, demonstrated superior discriminatory capacity. The addition of mitochondrial biomarkers may improve early detection and support personalized interventions to improve patient outcomes. Full article

Plant disease segmentation has achieved significant progress with the help of artificial intelligence. However, deploying high-accuracy segmentation models in resource-limited settings faces three key challenges, as follows: (A) Traditional dense attention mechanisms incur quadratic computational complexity growth (O($n^{2}d$)), rendering them ill-suited for low-power hardware. (B) Naturally sparse spatial distributions and large-scale variations in the lesions on leaves necessitate models that concurrently capture long-range dependencies and local details. (C) Complex backgrounds and variable lighting in field images often induce segmentation errors. To address these challenges, we propose Sparse-MoE-SAM, an efficient framework based on an enhanced Segment Anything Model (SAM). This deep learning framework integrates sparse attention mechanisms with a two-stage mixture of experts (MoE) decoder. The sparse attention dynamically activates key channels aligned with lesion sparsity patterns, reducing self-attention complexity while preserving long-range context. Stage 1 of the MoE decoder performs coarse-grained boundary localization; Stage 2 achieves fine-grained segmentation by leveraging specialized experts within the MoE, significantly enhancing edge discrimination accuracy. The expert repository—comprising standard convolutions, dilated convolutions, and depthwise separable convolutions—dynamically routes features through optimized processing paths based on input texture and lesion morphology. This enables robust segmentation across diverse leaf textures and plant developmental stages. Further, we design a sparse attention-enhanced Atrous Spatial Pyramid Pooling (ASPP) module to capture multi-scale contexts for both extensive lesions and small spots. Evaluations on three heterogeneous datasets (PlantVillage Extended, CVPPP, and our self-collected field images) show that Sparse-MoE-SAM achieves a mean Intersection-over-Union (mIoU) of 94.2%—surpassing standard SAM by 2.5 percentage points—while reducing computational costs by 23.7% compared to the original SAM baseline. The model also demonstrates balanced performance across disease classes and enhanced hardware compatibility. Our work validates that integrating sparse attention with MoE mechanisms sustains accuracy while drastically lowering computational demands, enabling the scalable deployment of plant disease segmentation models on mobile and edge devices. Full article

With the continuous expansion of power grids and the advancement of ultra-high voltage (UHV) projects, transmission lines are increasingly traversing areas characterized by micro-terrain. These localized topographic features can intensify meteorological effects, thereby increasing the risks of hazards such as conductor icing and galloping, directly threatening operational stability. Enhancing the disaster resilience of transmission lines in such environments requires accurate and efficient terrain identification. However, conventional recognition methods often neglect the spatial alignment of the transmission lines, limiting their effectiveness. This paper proposes a deep learning-based recognition framework that incorporates a dual-branch network architecture and a cross-branch spatial attention mechanism to address this limitation. The model explicitly captures the spatial correlation between transmission lines and surrounding terrain by utilizing line alignment information to guide attention along the line corridor. A semi-synthetic dataset, comprising 6495 simulated samples and 130 real-world samples, was constructed to facilitate model training and evaluation. Experimental results show that the proposed model achieves classification accuracies of 94.6% on the validation set and 92.8% on real-world test cases, significantly outperforming conventional baseline methods. These findings demonstrate that explicitly modeling the spatial relationship between transmission lines and terrain features substantially improves recognition accuracy, offering important support for hazard prevention and resilience enhancement in UHV transmission systems. Full article