Search Results (153)

Complex urban scenarios impose high demands on the environmental perception capabilities of unmanned systems, which serve as a prerequisite for executing autonomous missions such as disaster response, infrastructure inspection, and smart city operations. UAVs, leveraging their high mobility, can provide accurate prior maps and wide-area aerial observation for unmanned ground vehicles. However, their long-range perception accuracy is limited. Conversely, UGVs can achieve high-precision environmental perception along their navigation paths using prior maps, but suffer from a constrained field of view. The collaboration between the two platforms complements their respective strengths, thereby enhancing 3D object perception and mapping accuracy in complex scenarios. To address the aforementioned challenges, this study proposes a cross-platform feature fusion method for 3D object perception and an incremental map updating approach for UAVs and UGVs. First, a dynamic SLAM method that integrates an optimized YOLOv8 with ORB-SLAM3 is employed to mitigate map blurring caused by dynamic noise, providing prior map information for UGVs. Second, a multimodal fusion perception model is constructed for UGVs, utilizing attention mechanisms to achieve deep fusion of multimodal Bird’s-Eye-View (BEV) features. This overcomes issues such as diminishing complementarity between modalities and weak temporal feature associations. Finally, an air ground fusion model based on a cross-attention mechanism is developed to fuse aerial view features with ground-based fused BEV features across platforms, yielding a unified feature representation for 3D object detection and generating a fused high-precision map. Experimental results demonstrate that under complex occlusion scenarios in a simulated dataset, the proposed collaborative perception system improves the mean Average Precision (mAP) by 12.7% and 15.7% compared to using a single UAV or a single UGV, respectively, while increasing the map accuracy F1-score by 0.21. This study provides technical support for achieving real-time and accurate air ground collaborative perception in complex dynamic environments. Full article

Communities worldwide are increasingly required to support populations spanning multiple generations while maintaining social cohesion in the context of rapid demographic ageing and urban transformation. Although frameworks for age-friendly or inclusive environments have gained international traction, existing evaluation methods seldom integrate the environmental qualities necessary for all-age-friendliness with the spatial and social conditions that enable cross-generational interaction. This study addresses this gap by developing a dual-lens evaluation framework that quantifies both fundamental environmental attributes and the interaction potential embedded within community spaces. Grounded in field investigations, spatial analysis, expert consultation, and user surveys, the study establishes a hierarchical indicator system comprising nineteen prerequisite indicators and sixteen enhancement indicators across five dimensions: site accessibility, spatial integration, environmental comfort, safety and health, and participation and inclusion. To operationalize the framework, a combined Fuzzy Analytic Hierarchy Process and multi-objective optimization model was employed, enabling the representation of interdependencies between essential conditions and value-enhancing features. Application of the framework to 24 community cases in Shanghai and Florence reveals both shared structural patterns and distinctive cultural influences: Shanghai demonstrates strengths in walkability and health-supportive infrastructure, whereas Florence excels in natural contact and environmentally integrated spatial typologies. The findings underscore the necessity of balanced environmental performance for achieving high-quality, all-age-friendly community spaces with strong cross-generational engagement potential. The proposed framework provides a replicable and analytically rigorous tool for environmental and social impact assessment, offering guidance for planners, policymakers, and designers seeking to promote inclusive, resilient, and socially cohesive community environments. Full article

Accurate and efficient rock mass quality classification is a prerequisite for assessing slope stability, designing support schemes, and ensuring mining safety in open-pit mines. However, traditional empirical classification methods rely heavily on expert judgment and often struggle to capture the complex, nonlinear relationships among factors influencing slope stability. Existing intelligent classification models also suffer from limitations, including sensitivity to incomplete data, insufficient feature interaction learning, and unstable performance on small-scale datasets. To address these issues, this study develops a deep forest (DeepForest) model optimized by three metaheuristic algorithms—brown bear optimizer (BBO), tuna swarm optimizer (TSO), and sparrow search algorithm (SSA)—to intelligently classify slope rock mass quality. A rock mass quality dataset containing 204 groups of slope and non-slope cases was established to train and evaluate the classification performance of the DeepForest models. Six influencing factors were set as input parameters: uniaxial compressive strength (UCS) of rock, rock quality designation (RQD), spacing of discontinuities (Sd), rock mass integrity coefficient (Kv), groundwater conditions (W), and site type (St). Multivariate imputation by chained equations (MICE), isolation forest (IsoForest), and synthetic minority over-sampling technique (SMOTE) were used to handle missing values, outliers, and imbalance in the dataset, respectively. The performance of the proposed models was evaluated using five metrics: accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). The experimental results indicate that the BBO-DeepForest model performed best on the independent test set, with accuracy, precision, recall, F1-score, and average AUC values of 0.878, 0.682, 0.678, 0.678, and 0.961, respectively. A comparison with seven well-known imputation algorithms revealed the superiority of the selected imputation algorithm in recovering incomplete rock mass quality datasets. Model interpretation results showed that RQD and UCS are critical feature parameters for classifying slope rock mass quality. At last, the proposed BBO-DeepForest model was employed to verify the rock mass quality of three slopes at the Luming molybdenum mine, resulting in classifications consistent with on-site observations. It demonstrates that combining DeepForest with metaheuristic optimization algorithms is a feasible and accurate approach for intelligently classifying the rock mass quality of slopes. Full article

With increasing demand for fine-scale ecological management under carbon neutrality frameworks, multi-temporal assessment of carbon stock change (ΔC) at the individual-plant scale has become essential for understanding plant-level carbon dynamics and supporting management decisions. However, methodologies for repeated monitoring at this scale remain fragmented, showing limited cross-temporal comparability, weak cross-scale consistency, and insufficient integration across methods. Existing approaches can be grouped into three pathways: (i) process-based methods derived from CO₂ exchange measurements, (ii) state-based approaches estimating biomass and ΔC, and (iii) sensing-based approaches using structural, spectral, thermal, and fluorescence signals. These approaches offer complementary strengths, yet none simultaneously achieve high accuracy, temporal continuity, and operational scalability for multi-temporal ΔC estimation. Among these, stock-based and structural approaches form the primary estimation pathways, while flux-based and functional sensing methods provide complementary constraints. This review synthesizes and compares these approaches in terms of their theoretical basis, spatial support, temporal characteristics, and uncertainty structures. To address the lack of methodological integration, we propose a structure–function–scale framework that links heterogeneous observations across spatial and temporal domains and emphasizes cross-scale consistency as a prerequisite for reliable ΔC estimation. Within this framework, we further examine how multi-source integration can connect structural and functional observations through segmentation, co-registration, scaling, temporal alignment, and uncertainty propagation. By integrating traditional measurement logic with emerging remote sensing technologies, this review provides a unified methodological framework for ΔC estimation and identifies key directions for advancing fine-scale carbon monitoring, spatiotemporally consistent data fusion, uncertainty-aware inference, and MRV-oriented verification systems. Full article

Background: Suicide among emerging adults has become a significant global public health concern. Suicidal ideation is the prerequisite for suicide, and social support is recognized as a key protective factor against suicidal ideation. However, the relationship between the strength and consistency of social support and suicidal ideation among undergraduate students remains unclear. This study synthesized empirical studies to quantify the relationship between social support and suicidal ideation among undergraduate students and determine the different correlations between various sources of social support and suicidal ideation. Methods: A systematic review and meta-analysis was conducted following PRISMA 2020 guidelines. Five electronic databases (Web of Science, Scopus, PubMed, ProQuest, and ScienceDirect) were searched for studies published from 2016 to 2025. Eligible studies reported quantitative associations between social support and suicidal ideation among undergraduate students. Correlation coefficients were transformed using Fisher’s z and pooled using a random-effects model. Heterogeneity was evaluated using Cochran’s Q and I² statistics. Risk of bias assessments, moderator analysis, sensitivity analysis, subgroup analysis, and publication bias assessments were conducted. Results: Fifteen studies with sixteen independent effect sizes and more than 26,000 participants were included. The meta-analysis showed a moderate negative association between social support and suicidal ideation (pooled r = −0.33, 95% CI [−0.40, −0.25]) under a random-effects model. A high heterogeneity was observed among studies (I² = 97%, p < 0.001). There are no studies classified as having a high risk of bias. The standardized sample size demonstrated a significant moderating effect (β = 0.2568, p = 0.0022). Sensitivity analysis confirmed the stability of the pooled effect. Subgroup analysis indicated that the strength of the association between social support and suicidal ideation did not differ significantly between Asian and non-Asian studies. No significant publication bias was detected (Egger’s p = 0.19). Narrative synthesis further suggested that family support showed the most consistent protective association compared with friends’ support and support from others. Conclusions: Social support is moderately and consistently associated with reduced suicidal ideation among undergraduate students. These findings highlight social connectedness, particularly family support, as a central interpersonal protective factor and strengthen social support’s role in university suicide prevention initiatives. Full article

Accurate quantification of cotton content is a key prerequisite for efficient textile recycling. However, it remains challenging due to material heterogeneity and technical limitations. Near-infrared spectroscopy (NIR) combined with advanced data analysis offers a rapid, non-destructive approach. However, systematic evaluations across instrument classes and analysis strategies for industrial textile sorting remain limited. In this study, a unique set of cotton/polyester blends from the same starting material with varying cotton content was analyzed using three NIR systems representing laboratory, handheld, and industrial sensor-based applications. Multiple spectral preprocessing strategies were systematically combined with partial least squares regression and advanced machine learning models. Model performance was evaluated using cross-validation and independent test sets. The benchtop NIR system delivered the highest and most consistent performance, achieving RMSEP values below 1.0% with advanced regression models. The handheld and imaging sensor system exhibited higher RMSEP values (1.2–1.6%), reflecting not only differences in preprocessing and model selection, but also intrinsic instrumental limitations. Overall, the results demonstrate that each NIR instrument class exhibits distinct strengths and limitations with respect to accuracy, sensitivity, and robustness. Consequently, instrument-specific preprocessing, models, and hyperparameters are required, and no universally transferable pipeline was identified. Full article

For projects such as tailings ponds, slopes, and foundations, loose materials such as rock, slag, and sand, which are composed of particles, often have low cohesion and rely mainly on friction to maintain stability. The shear strength parameters, namely, the internal friction angle and cohesion, are the core parameters that describe the mechanical properties of materials and are directly related to the engineering stability of the above projects. The shear strength properties of loose media are related to the geometric morphological characteristics of particles. Particles with high irregularity will increase the bite and friction of the contact interface between particles, thereby affecting the overall peak shear strength of the material. This study takes sand as the research object. Based on the Mask R-CNN algorithm in deep learning, a sand particle image dataset consisting of single, contact, and sand surface particles is established. An image segmentation model that can identify particles on the surface of the sand layer and obtain the corresponding particle mask is trained; a Python 3.11.4 program is written to automatically calculate seven characteristic parameters of particle morphological characteristics parameters, including the Feret major diameter, the particle Feret minor diameter, the particle aspect ratio, the particle roundness, the comprehensive shape coefficient, the roughness, and the convexity through the particle mask. This method can obtain the overall morphological characteristics of sand particles in real time and is a particle processing method that is a prerequisite for the subsequent rapid prediction of the strength properties of granular materials. Full article

This study examines religion as a potent pre-socialisation factor in modern military socialisation, exploring how sacred roots and transcendent anchors influence the formation of military identity. By synthesising Ecological Systems Theory, the Religion–Military Model, and an Integrative Model, the analysis frames religiosity as a multidimensional construct that shapes integration across macro (societal), meso (organisational), and micro (individual) levels. The research reveals the dualistic nature of religious influence. On the one hand, religious pre-socialisation instils a habitus defined by normative commitment, sacrificial ethics, and ritual familiarity. These elements facilitate Person–Organisation fit and act as catalysts for identity fusion, where personal agency is united with the group’s strength. On the other hand, the study identifies a critical theological and psychological vulnerability: moral injury. When absolute religious commandments—such as the sanctity of life—collide with the lethal demands of combat, an irresolvable normative conflict arises, mirroring historical tensions between the Christian conscience and the sacramentum. By identifying strategic intervention points for chaplaincy and leadership, the study demonstrates that integrating the religious dimension is not only an ethical duty but a prerequisite for maintaining triadic equilibrium, resilience, and institutional stability. Full article

Intelligent vehicles represent a frontier in technological innovation. Effectively identifying their key core technologies and primary competitors is a crucial prerequisite for overcoming industrial technological bottlenecks, playing a pivotal role in promoting sustainable industrial development and enhancing global market competitiveness. This study is based on 46,373 authorized invention patents in the field of intelligent vehicles from 1950 to 2024 and based on four core characteristics of key core technologies: technological centrality, technological value, economic value, and competitive monopoly. Combining the entropy weight method and gray correlation analysis method, it effectively identifies 15 key core technologies in the field of intelligent vehicles, including G05D1, B60W30, G08G1, etc. These technologies cover four core domains: autonomous driving and vehicle control, intelligent transportation and vehicle–road coordination, onboard computing and data processing, and powertrain system integration and optimization. Building on this foundation, the study analyzes the technological competitive landscape from both national and corporate perspectives. The results show that the United States and Japan, with their profound technological accumulation, demonstrate strong competitive strength. China leads globally with 25.56% of worldwide patents, exhibiting rapid growth in R&D scale. However, the technological influence of key core technology patents held by major Chinese enterprises still lags significantly behind that of the United States and Japan, indicating room for improvement in R&D quality. By precisely identifying core R&D directions for intelligent vehicles, this study provides strategic guidance and practical references for optimizing green innovation resource allocation within the industry. It aims to overcome key technological bottlenecks in low-carbon intelligent vehicles, thereby achieving breakthroughs in key core technologies and enabling high-quality, sustainable industrial development. Full article

The escalation of thermal runaway in lithium-ion batteries presents severe safety hazards that necessitate advanced monitoring protocols to ensure early warning of potential failures. Carbon dioxide (CO₂) is released during preliminary decomposition well before catastrophic failure occurs, thereby providing a strategic advantage for early-stage warning. Consequently, identifying materials with high-selective CO₂ recognition is an essential prerequisite for developing reliable sensing platforms. This study integrates Grand Canonical Monte Carlo simulations with Random Forest (RF) models to systematically screen 1470 MOFs from the CoRE-MOF 2019 database. The screening process evaluates selective CO₂ recognition under multicomponent competitive adsorption conditions involving CO₂, C₂H₄, and O₂. The performance evaluation is based on working capacity, selectivity, and the trade-off between working capacity and selectivity (TSN). The RF model achieves high predictive accuracy, with tested R² exceeding 0.92 on the test samples. Shapley Additive Explanations (SHAP) interpretability analysis identifies Q⁰_st(CO₂), Q⁰_st(C₂H₄), WEPA, K_H(C₂H₄), and ETR as key performance drivers. The results indicate that CO₂ selectivity is constrained by the binding strength of competing C₂H₄. Optimal materials tend to have hard Lewis acid centers and polar inorganic clusters to minimize non-specific π-interactions with interfering species. Top-performing MOFs require balanced structural features, concentrating in moderate surface areas (965–1975 m²/g), narrow pore windows (PLD ≈ 4–7 Å, LCD ≈ 5.5–9.6 Å), high void fractions above 0.6, and low densities below 1.3 g/cm³. AJOTEY emerges as the optimal candidate with a TSN of 6.43 mol/kg, combining substantial working capacity (4.57 mol/kg) with strong selectivity (25.52). These results will accelerate the discovery of sensing materials and provide a practical pathway for MOF-based CO₂ sensor development to enhance lithium-ion battery safety. Full article

The canopy temperature of winter jujube serves as a direct indicator of plant water status and transpiration efficiency, making its accurate prediction a critical prerequisite for effective water management and optimized growth conditions in greenhouse environments. This study developed a data-driven model to forecast canopy temperature. The model serially integrates a Long Short-Term Memory (LSTM) network and a Random Forest (RF) algorithm, leveraging their complementary strengths in capturing temporal dependencies and robust nonlinear fitting. A three-stage framework comprising temporal feature extraction, multi-source feature fusion, and direct prediction was implemented to enable reliable nowcasting. Data acquisition and preprocessing were tailored to the greenhouse environment, involving multi-sensor data and thermal imagery processed with Robust Principal Component Analysis (RPCA) for dimensionality reduction. Key environmental variables were selected through Spearman correlation analysis. Experimental results demonstrated that the proposed LSTM–RF model achieved superior performance, with a determination coefficient (R²) of 0.974, mean absolute error (MAE) of 0.844 °C, and root mean square error (RMSE) of 1.155 °C, outperforming benchmark models including standalone LSTM, RF, Transformer, and TimesNet. SHAP (SHapley Additive exPlanations)-based interpretability analysis further quantified the influence of key factors, including the “thermodynamic state of air” driver group and latent temporal features, offering actionable insights for irrigation management. The model establishes a reliable, interpretable foundation for real-time water stress monitoring and precision irrigation control in protected winter jujube production systems. Full article

Graphene, owing to its unique atomic structure, exhibits a set of outstanding physical and chemical properties, including ultrahigh carrier mobility, excellent thermal conductivity, superior mechanical strength, and high optical transparency. However, the atomic-thickness nature of graphene limits its ability to form self-supporting structures, making substrate integration a prerequisite for practical applications. Graphene-skinned materials, constructed by in situ deposition of continuous graphene films on conventional substrates, have recently emerged as a promising solution. This strategy effectively integrates graphene with conventional engineering materials, harnessing its superior properties while avoiding the structural defects and contamination typical of transfer processes. Consequently, graphene-skinned materials have rapidly become a rapidly developing area of research in materials science. This review systematically summarizes recent advances in graphene-skinned materials. Particular attention is given to coating methods and chemical vapor deposition (CVD) routes, followed by a discussion of commonly employed characterization tools for evaluating graphene quality and interface integrity. Applications in electromagnetic shielding, thermal management, sensors, and multifunctional composites are critically examined. Finally, future perspectives are needed regarding the key challenges and opportunities for engineering and industrial-scale deployment of graphene-skinned materials. Full article

We investigate Snyder spacetime and its generalizations, including Yang and Snyder–de Sitter spaces, which constitute manifestly Lorentz-invariant noncommutative geometries. This work initiates a systematic study of gauge theory on such spaces in the semi-classical regime, formulated as Poisson gauge theory. As a first step, we construct the symplectic realizations of the relevant noncommutative spaces, a prerequisite for defining Poisson gauge transformations and field strengths. We present a general method for representing the Snyder algebra and its extensions in terms of canonical phase-space variables, enabling both the reproduction of known representations and the derivation of novel ones. These canonical constructions are employed to obtain explicit symplectic realizations for the Snyder–de Sitter space and to construct the deformed partial derivative which differentiates the underlying Poisson structure. Furthermore, we analyze the motion of freely falling particles in these backgrounds and comment on the geometry of the associated spaces. Full article

With the increasing application of manufactured sand, as one of the uncertain factors affecting the properties and performance of ready-mixed concrete proportioning with commonly used manufactured sand, residual flocculants in water-washed manufactured sand (WWMS) have received increased attention. Under certain prerequisites, a rapid detecting method for residual flocculants in WWMS was presented based on the pre-calibrated relationship between the Stormer viscosity of cement paste and the concentration of flocculants. Multi-dimensional and multi-factorial experiments were performed on cement paste, mortar and concrete orderly to explore the effects of flocculant content on the rheological (workability) and mechanical properties (compressive strength) of concrete. The results showed a good quantitative relationship between the Stormer viscosity and the flocculant content, and its mathematical formula depended on the type, molecular weight and content range of the flocculant. The residual flocculant contents in WWMS not only affected the workability of fresh concrete, but also the strength of hardened concrete to some extent. Full article

Excellent mechanical properties are a prerequisite for the widespread application of different types of concrete in practical engineering. However, when coal gangue (CG) is used as coarse aggregate (CA) and geopolymer cement is used as auxiliary cementitious material, while reducing the demand for ordinary cement and industrial waste emissions, it has a negative impact on mechanical performance. Therefore, in response to the data gap in the study of mechanical properties of coal gangue coarse aggregate-fly ash geopolymer concrete (CG-FA-GPC), inspired by a large number of research results on fiber-reinforced concrete, this study uses basalt fiber (BF) as a reinforcing material to investigate the enhancing effect of BF on the mechanical properties of CG-FA-GPC. We selected compressive strength, flexural strength, splitting tensile strength, and stress–strain curve as evaluation indicators to compare and analyze the mechanical properties of ordinary concrete, CG-FA-GPC, and basalt fiber-reinforced coal gangue coarse aggregate-fly ash geopolymer concrete (BF-CG-FA-GPC), and to explore the reinforcement effect of BF. The results showed that with the increase in CG substitution rate, the compressive strength, flexural strength, and splitting tensile strength of CG-FA-GPC significantly decreased. A 100% CG substitution reduced the compressive strength, flexural strength, and splitting tensile strength of CG-FA-GPC by 34.5%, 43.4%, and 31.8%, respectively. The stress–strain curve reveals the dual effects of BF on the strength enhancement and deformation modification of CG-FA-GPC. With the increase in BF content, the three mechanical strengths of CG-FA-GPC show a pattern of first increasing and then decreasing, and the optimal BF content is 0.4% (volume fraction). This experiment lays the foundation for promoting research on the mechanical properties and durability of different fiber-reinforced CG-FA-GPC, advancing the feasibility of its large-scale engineering applications. Full article