Search Results (6,393)

Cement-stabilized macadam often shows salt expansion deformation under the action of a sulfate attack, and its pore structure determines its ability to accommodate this deformation. In this paper, the influence of the pore structure of cement-stabilized macadam on its macroscopic deformation is analyzed using a single-grain salt expansion deformation test, scanning electron microscopy (SEM), and computerized tomography (CT) scanning. The results show that ettringite and sodium sulfate decahydrate crystals are key factors in salt expansion deformation. In addition, we find that when the sulfate content increases from 0% to 5%, the porosity of the mixture decreases by 1.5%, the proportion of primary pores increases by 12.1%, and the linear expansion rate increases by 0.05%. Finally, a salt expansion deformation prediction model for cement-stabilized macadam is proposed, which takes the porosity of the mixture, the proportion of graded pores, and the deformation influence factor as parameters, and the error is found to be less than 10%. Full article

Soils in alpine mining areas suffer from severe heavy metal contamination and infertility, yet little is known about the effects of different materials on soil improvement in such regions. In this study, a field experiment was conducted in farmlands contaminated by the Lanping lead–zinc mine in Yunnan, China, to compare the effects of four materials (biochar, organic fertilizer, lime, and sepiolite) on soil properties, heavy metal (lead (Pb), cadmium (Cd), copper (Cu), and zinc (Zn) fractions and their availability, and the growth of Phaseolus coccineus L. Results showed that biochar and organic fertilizer significantly enhanced soil nutrient content and enzyme activities. Lime, biochar, and sepiolite effectively reduced heavy metal bioavailability by promoting their transition to residual fractions. Notably, biochar outperformed other materials by substantially increasing grain yield (by 82%), improving nutritional quality (sugars, protein, and starch contents raised by 20–88%), and reducing heavy metal accumulation in grains (by 36–50%). A comprehensive evaluation based on subordinate function values confirmed biochar as the most effective amendment. Structural equation modeling further revealed that biochar promoted plant growth and grain quality primarily by enhancing soil available nutrients and immobilizing heavy metals. These findings demonstrate the strong potential of biochar for remediating heavy metal-contaminated farmlands in alpine lead–zinc mining regions. Full article

The distribution of grain particles within a silo influences heat and moisture transfer during stored grain ventilation, leading to grain quality losses. A study on porosity distribution analysis and ventilation tests was conducted in a pilot silo with a height of 3 m, a diameter of 1.5 m, and a conical dome height of 0.85 m. The E-B constitutive model was incorporated into the secondary development of FLAC^3D 5.0 to analyze the vertical pressure distribution in the grain bulk. An anisotropic porosity distribution model for the maize bulk was developed, accounting for both vertical pressure and segregation mechanisms. The differences in airflow and heat transfer during ventilation between isotropic and anisotropic porosity distributions were quantified. A nonlinear model was innovatively proposed to predict the temperature front curve (TFC) during ventilation as affected by porosity variation. The results indicate that friction between the maize kernel and the silo wall led to vertical pressure at the center of the bottom that was 10.7% higher than that near the wall. The average surface porosity of the maize bulk was 2.8% higher than at the bottom. This led to a minimum porosity of 0.409 at the center of the silo bottom, due to the combined effect of impact during the loading process and vertical pressure. The numerical simulation demonstrated excellent consistency with the experimental data. At a supply vent air velocity of 0.126 m/s, an increase in the maize bulk height from 0.725 m to 2.9 m resulted in reductions in airflow rate and average relative humidity of 20.3% and 9.67%. The airflow velocity near the wall was 13.4% higher than that in the center, leading to a faster cooling rate in the peripheral region compared to the center of the maize bulk. The airflow velocity based on the isotropic porosity model was higher at the center than that predicted by the anisotropic model, whereas the opposite trend was observed near the wall. The temperature front during ventilation based on the anisotropic porosity model exhibited a concave curve. A nonlinear model was developed to predict this temperature front, showing strong agreement with computational data. Full article

Smart cities offer new opportunities for urban governance and sustainable development. However, at the current stage, the construction and development of smart cities generally exhibit a technology-driven tendency, neglecting real resident demand, which contradicts the “human-centric” principle. Traditional top-down methods of demand collection struggle to capture the dynamics and heterogeneity of public demand. At the same time, government service platforms, as one dimension of smart city construction, have accumulated massive amounts of user-generated data, providing new solutions for this challenge. This paper aims to construct a big data-driven analytical framework for dynamically identifying and accurately forecasting core resident demand. The study uses Xi’an City, Shaanxi Province, China, as a case study, utilising user messages from People.cn spanning 2011 to 2023. These messages cover various domains, including urban construction, healthcare, education, and transportation, as the data source. The People.cn message board is China’s most significant nationwide online political platform. Its institutionalised feedback mechanism ensures data content focuses on highly representative specific grievances, rather than the broad emotional expressions on social media. The study employs user messages from People.cn from 2011 to 2023 as its data source, encompassing urban construction, healthcare, education, and transportation. First, a large language model (LLM) was used to preprocess and clean the raw data. Subsequently, the BERTopic model was applied to identify ten core demand themes and construct their monthly time series, thereby overcoming the limitations of traditional methods in short-text semantic recognition. Finally, by integrating variational mode decomposition (VMD) with support vector machines (SVMs), a hybrid demand forecasting model was established to mitigate the risk of overfitting in deep learning when forecasting small-sample time series. The empirical results show that the proposed LLM-BERTopic-VMD-SVM framework exhibits excellent performance, with the goodness-of-fit (R2) on various demand themes ranging from 0.93 to 0.96. This study proposes an effective analytical framework for identifying and forecasting resident demand. It provides a decision-support tool for city managers to achieve proactive and fine-grained governance, thereby offering a viable empirical pathway to promote the transformation of smart cities from technology-centric to human-centric. Full article

Rural livelihood transformation is increasingly vital for achieving agricultural modernization, reducing poverty, and promoting sustainable development in developing countries. Despite growing attention to land consolidation as a tool for improving agricultural resource allocation and productivity, its role in shaping rural livelihoods remains insufficiently understood. Addressing this gap, this study investigates the impacts of China’s High-Standard Farmland Construction (HFC), the country’s flagship land consolidation policy, on farmers’ livelihoods, focusing on both income level and income structure. Using provincial panel data from 30 regions, we adopt a continuous difference-in-differences design and mediation effect model to identify the causal effects of HFC. The results indicate that HFC significantly promotes total household income. Specifically, HFC facilitates mechanized agricultural production by consolidating fragmented plots, reducing production costs, and improving crop yields, thereby increasing agricultural income. Simultaneously, mechanization substitutes for labor and releases surplus workers, who often move to off-farm employment, diversifying income sources and stabilizing household livelihoods. Heterogeneity analysis reveals that the benefits of HFC are unevenly distributed. Low-income households, central provinces, and major grain-producing areas experience the greatest gains, and moderate-scale implementation proves more effective than either small- or excessively large-scale projects. This study highlights mechanization as a key mechanism linking land consolidation to rural livelihood transformation. The findings demonstrate that well-planned and efficiently implemented HFC policies can not only enhance agricultural productivity but also foster diversified and inclusive rural livelihoods. Full article

Understanding visitor sentiment is essential for developing effective tourism strategies, particularly as Google Maps reviews have become a key channel for public feedback on tourist attractions. Yet, the unstructured format and dialectal diversity of Arabic reviews pose significant challenges for extracting actionable insights at scale. This study evaluates the performance of traditional machine learning and transformer-based models for aspect-based sentiment analysis (ABSA) on Arabic Google Maps reviews of tourist sites across Saudi Arabia. A manually annotated dataset of more than 3500 reviews was constructed to assess model effectiveness across six tourism-related aspects: price, cleanliness, facilities, service, environment, and overall experience. Experimental results demonstrate that multi-head BERT architectures, particularly AraBERT, consistently outperform traditional classifiers in identifying aspect-level sentiment. Ara-BERT achieved an F1-score of 0.97 for the cleanliness aspect, compared with 0.91 for the best-performing classical model (LinearSVC), indicating a substantial improvement. The proposed ABSA framework facilitates automated, fine-grained analysis of visitor perceptions, enabling data-driven decision-making for tourism authorities and contributing to the strategic objectives of Saudi Vision 20300. Full article

Accurate image segmentation is a fundamental requirement for fine-grained image analysis, providing critical support for applications such as medical diagnostics, remote sensing, and industrial fault detection. However, in complex industrial environments, conventional deep learning-based methods often struggle with noisy backgrounds, blurred boundaries, and highly imbalanced class distributions, which make fine-grained fault localization particularly challenging. To address these issues, this paper proposes a Defect-Aware Fine Segmentation Framework (DAFSF) that integrates three complementary components. First, a multi-scale hybrid encoder combines convolutional neural networks for capturing local texture details with Transformer-based modules for modeling global contextual dependencies. Second, a boundary-aware refinement module explicitly learns edge features to improve segmentation accuracy in damaged or ambiguous fault regions. Third, a defect-aware adaptive loss function jointly considers boundary weighting, hard-sample reweighting, and class balance, which enables the model to focus on challenging pixels while alleviating class imbalance. The proposed framework is evaluated on public benchmarks including Aeroscapes, Magnetic Tile Defect, and MVTec AD. The proposed DAFSF achieves mF1 scores of 85.3%, 85.9%, and 87.2%, and pixel accuracy (PA) of 91.5%, 91.8%, and 92.0% on the respective datasets. These findings highlight the effectiveness of the proposed framework for advancing fine-grained fault localization in industrial applications. Full article

Molecular dynamics (MD) can dynamically reveal the structural evolution and mechanical response of Zirconium (Zr) at the atomic scale under complex service conditions such as high temperature, stress, and irradiation. However, traditional empirical potentials are limited by their fixed function forms and parameters, making it difficult to accurately describe the multi-body interactions of Zr under conditions such as multi-phase structures and strong nonlinear deformation, thereby limiting the accuracy and generalization ability of simulation results. This paper combines high-throughput first-principles calculations (DFT) with the machine learning method to develop the Deep Potential (DP) for Zr. The developed DP of Zr was verified by performing molecular dynamic simulations on lattice constants, surface energies, grain boundary energies, melting point, elastic constants, and tensile responses. The results show that the DP model achieves high consistency with DFT in predicting multiple key physical properties, such as lattice constants and melting point. Also, it can accurately capture atomic migration, local structural evolution, and crystal structural transformations of Zr under thermal excitation. In addition, the DP model can accurately capture plastic deformation and stress softening behavior in Zr under large strains, reproducing the characteristics of yielding and structural rearrangement during tensile loading, as well as the stress-induced phase transition of Zr from HCP to FCC, demonstrating its strong physical fidelity and numerical stability. Full article

We present a novel methodology for classifying code obfuscation techniques in LLVM IR program embeddings. We apply isolated and layered code obfuscations to C source code using the Tigress obfuscator, compile them to LLVM IR, and convert each IR code representation into a numerical embedding (vector representation) that captures intrinsic characteristics of the applied obfuscations. We then use two modern boost classifiers to identify which obfuscation, or layering of obfuscations, was used on the source code from the vector representation. To better analyze classifier behavior and error propagation, we employ a staged, cascading experimental design that separates the task into multiple decision levels, including obfuscation detection, single-versus-layered discrimination, and detailed technique classification. This structured evaluation allows a fine-grained view of classification uncertainty and model robustness across the inference stages. We achieve an overall accuracy of more than 90% in identifying the types of obfuscations. Our experiments show high classification accuracy for most obfuscations, including layered obfuscations, and even perfect scores for certain transformations, indicating that a vector representation of IR code preserves distinguishing features of the protections. In this article, we detail the workflow for applying obfuscations, generating embeddings, and training the model, and we discuss challenges such as obfuscation patterns covered by other obfuscations in layered protection scenarios. Full article

Fine-grained identification of crop planting structures provides key data for precision agriculture, thereby supporting scientific production and evidence-based policy making. This study selected a representative experimental farmland in Qingyang, Gansu Province, and acquired Unmanned Aerial Vehicle (UAV) multi-temporal data (six epochs) from multiple sensors (multispectral [visible–NIR], thermal infrared, and LiDAR). By fusing 59 feature indices, we achieved high-accuracy extraction of cropland and planting structures and identified the key feature combinations that discriminate among crops. The results show that (1) multi-source UAV data from April + June can effectively delineate cropland and enable accurate plot segmentation; (2) July is the optimal time window for fine-scale extraction of all planting-structure types in the area (legumes, millet, maize, buckwheat, wheat, sorghum, maize–legume intercropping, and vegetables), with a cumulative importance of 72.26% for the top ten features, while the April + June combination retains most of the separability (67.36%), enabling earlier but slightly less precise mapping; and (3) under July imagery, the SAM (Segment Anything Model) segmentation + RF (Random Forest) classification approach—using the RF-selected top 10 of the 59 features—achieved an overall accuracy of 92.66% with a Kappa of 0.9163, representing a 7.57% improvement over the contemporaneous SAM + CNN (Convolutional Neural Network) method. This work establishes a basis for UAV-based recognition of typical crops in the Qingyang sector of the Loess Plateau and, by deriving optimal recognition timelines and feature combinations from multi-epoch data, offers useful guidance for satellite-based mapping of planting structures across the Loess Plateau following multi-scale data fusion. Full article

The integrated rice–aquaculture farming system (IRAFS), which combines rice cultivation with aquaculture, is a crucial strategy for improving economic efficiency, ecological sustainability, and social welfare. This model has been widely adopted across most regions of China, recognized for its sustainability and environmental benefits. The study analyzes the economic, social and environmental benefits of the current integrated rice–aquaculture integrated farming practices while assessing its market prospects. It identifies key limitations in existing models, particularly regarding water conservation, pollution reduction and system performance. Additionally, the study highlights future research directions and offers actionable recommendations to fully leverage the development potential of IRAFS. Through comparative analysis, this study identifies shortcomings in current water-saving and emission-reduction practices. It proposes an integrated model to balance grain production, environmental benefits, and economic returns. The aim is to provide theoretical support for enhancing agricultural quality and efficiency while promoting sustainable development. Full article

3D hand pose estimation has achieved remarkable progress in human computer interaction and computer vision; however, real-world hand point clouds often suffer from structural distortions such as partial occlusions, sensor noise, and environmental interference, which significantly degrade the performance of conventional point cloud-based methods. To address these challenges, this study proposes a curve fitting-based framework for robust 3D hand pose estimation from corrupted point clouds, integrating an Adaptive Sampling (AS) module and a Hand-Curve Guide Convolution (HCGC) module. The AS module dynamically selects structurally informative key points according to local density and anatomical importance, mitigating sampling bias in distorted regions, while the HCGC module generates guided curves along fingers and employs dynamic momentum encoding and cross-suppression strategies to preserve anatomical continuity and capture fine-grained geometric features. Extensive experiments on the MSRA, ICVL, and NYU datasets demonstrate that our method consistently outperforms state-of-the-art approaches under local point removal across fixed missing-point ratios ranging from 30% to 50% and noise interference, achieving an average Robustness Curve Area (RCA) of 30.8, outperforming advanced methods such as TriHorn-Net. Notably, although optimized for corrupted point clouds, the framework also achieves competitive accuracy on intact datasets, demonstrating that enhanced robustness does not compromise general performance. These results validate that adaptive curve guided local structure modeling provides a reliable and generalizable solution for realistic 3D hand pose estimation and emphasize its potential for deployment in practical applications where point cloud quality cannot be guaranteed. Full article

Social media platforms have become a widely used medium for individuals to express complex and multifaceted emotions. Traditional single-label emotion classification methods fall short in accurately capturing the simultaneous presence of multiple emotions within these texts. To address this limitation, we propose a classification model that enhances the pre-trained Cardiff NLP transformer by integrating additional self-attention layers. Experimental results show our approach achieves a micro-F1 score of 0.7208, a macro-F1 score of 0.6192, and an average Jaccard index of 0.6066, which is an overall improvement of approximately 3.00% compared to the baseline. We apply this model to a real-world dataset of tweets related to the 2011 Christchurch earthquakes as a case study to demonstrate its ability to capture multi-category emotional expressions and detect co-occurring emotions that single-label approaches would miss. Our analysis revealed distinct emotional patterns aligned with key seismic events, including overlapping positive and negative emotions, and temporal dynamics of emotional response. This work contributes a robust method for fine-grained emotion analysis which can aid disaster response, mental health monitoring and social research. Full article

The development and use of teachers’ pedagogical content knowledge (PCK) can enhance students’ understanding of specific content. PCK occurs at three grain sizes: discipline-, topic-, and concept-specific levels. In 2013, Mavhunga and Rollnick proposed the topic-specific PCK (TSPCK) framework to describe how teachers transform topic-specific content in chemistry lessons. This systematic review brings together worldwide research on TSPCK, offering a thorough summary of the use of topic-specific knowledge in science instruction and identifying areas that most require teacher development. This review, conducted on 29 June 2025 in the Scopus database, identified 34 studies that used the TSPCK framework to investigate teachers’ TSPCK in science in the period from 2013 to 2025. An in-depth analysis of each study’s context, methodological approach, and focus was conducted. Findings revealed that studies mostly measure or improve secondary pre-service and in-service teachers’ PCK, use qualitative or mixed-methods approaches, utilise chemistry and biology topics, and are conducted in the (South) African context. Furthermore, the findings suggest that the use of the TSPCK is highly contextualised. The results also indicate a tendency for research to integrate the TSPCK framework into the Consensus Models of PCK. The review has also highlighted several gaps in PCK research, such as the limited research on pre-school, primary school, and university levels. Furthermore, there is limited research on interventions to improve in-service teachers’ PCK. Implications and opportunities of these findings for research on science teachers’ knowledge (TSPCK) are discussed. We recommend the application of the TSPCK framework to develop and evaluate teachers’ TSPCK through interventions such as workshops, lesson studies, micro-teaching and training modules. Furthermore, research may involve comparative studies with teachers having different degrees of teaching experience, including pre-service teachers, in-service teachers, and teacher educators. Full article

Weld seam detection is a fundamental prerequisite for robotic welding automation, yet it remains challenging due to the elongated shape of welds, weak contrast against metallic backgrounds, and significant environmental interference in industrial scenarios. To address these challenges, we propose a novel deep neural network architecture termed SANet (Strip-Aware Network). The model is constructed upon a U-shaped backbone and integrates strip-aware feature modeling with multistage supervision. It mainly consists of two complementary modules: the Paralleled Strip and Spatial Context-Aware (PSSCA) module and the Multistage Fusion (MF) module. The PSSCA module enhances the extraction of elongated strip-like features by combining parallel strip perception with spatial context modeling, thereby improving fine-grained weld seam representation. In addition, SANet integrates the StripPooling attention mechanism as an auxiliary component to enlarge the receptive field along strip directions and enhance feature discrimination under complex backgrounds. Meanwhile, the MF module performs cross-stage feature fusion by aggregating encoder and decoder features at multiple levels, ensuring accurate boundary recovery and robust global-to-local interaction. The weld seam detection task is formulated as a two-dimensional segmentation problem and evaluated on a self-built dataset consisting of over 4000 weld seam images covering diverse industrial scenarios such as pipe joints, trusses, elbows, and furnace structures. Experimental results show that SANet achieves an IoU of 96.23% and a Dice coefficient of 98.07%, surpassing all compared models and demonstrating its superior performance in weld seam detection. These findings validate the effectiveness of the proposed architecture and highlight its potential as a low-cost, flexible, and reliable pure vision solution for intelligent welding applications. Full article