Search Results (2,270)

Among a broad range of promising applications, the use of cellular solids as lightweight structural components is an important field of research that requires reliable predictions of their stiffness and strength. Predictive and general models should not depend on extensive parameter-fitting experiments and should not rely on computationally intensive numerical calculations for each new set of geometric parameters and loading conditions. An overview of models for 2D, 2.5D, and three-dimensional structures will be presented. Most 2D and 2.5D models neglect out-of-plane behaviour and the face sheets used in sandwich panels. 3D studies, mainly by finite element models (FEMs), are often limited to a narrow set of geometries and simple loading conditions. Elastic anisotropy is well covered, but calculating yield surfaces remains a challenge. Simplified models based on structural mechanics are rare and often limited in scope. They offer a flexible, computationally efficient approach for simulating truss-based materials. For more advanced designs, parameter-based FEMs must be developed for any loading condition to facilitate the generalised incorporation of 3D cellular solids in mechanical design. Artificial intelligence and machine learning are promising approaches for making optimal use of experimental and FEM results across multidimensional parameter spaces. Full article

As a Chinese national key protected medicinal fern naturally occurring in forest understories, Cibotium barometz faces severe threats of wild population degradation, while standardized large-scale artificial breeding technology for conservation purposes remains immature. To establish an efficient spore-based conservation propagation system for this endangered forest fern, this study quantified the independent and interactive effects of spore storage temperature, storage duration and sowing density on spore germination, gametophyte growth and sporophyte seedling establishment. Spores were preserved under four gradient temperature treatments with sequential sampling at multiple storage durations, followed by sowing trials with a series of density gradients; germination rate, seedling establishment rate and gametophyte–sporophyte conversion rate were dynamically recorded and statistically analyzed. The results demonstrated that appropriately extended storage significantly shortened the germination phase and simultaneously elevated both spore germination and sporophyte seedling formation rates. Among all temperature treatments, storage at −4 °C achieved the maximum germination and seedling establishment capacity, whereas ultra-low-temperature cryopreservation at −196 °C greatly promoted gametophyte–sporophyte conversion rate. The optimal sowing density balancing growth space and survival rate was determined to be 30 spores per cm². The complete dynamic developmental traits covering the full spore propagation life cycle of C. barometz were systematically summarized in this work. Our findings supply reliable technical parameters to standardize spore breeding protocols, and offer critical support for ex situ conservation, wild forest population restoration and sustainable resource utilization of C. barometz. Full article

In recent years, an essential approach for promoting and implementing efficient sustainable construction practices has been considered through the integration of Building Information Modeling (BIM) and Life-Cycle Assessment (LCA). The introduction of OpenBIM, which is characterized by its collaborative and interoperable nature, offers an ideal framework to enhance this integration. This paper conducts a systematic review of the literature concerning the practices applied to integrate BIM and LCA, focusing on the present trends, challenges, and opportunities as well as on how the concept of OpenBIM can be applied to tackle the identified issues and gaps. Based on an intense review of the literature to identify the ways currently used to exchange data, this paper proposes a robust framework to create Information Delivery Specifications (IDS) as a solution to the identified gaps to attain an effective implementation, ultimately contributing to sustainable buildings’ practices and enhancing the integration of OpenBIM and LCA. OpenBIM emphasizes interoperability and collaboration by using open standards like Industry Foundation Classes (IFCs), which, when combined with LCA, offer a powerful method for the practice of sustainable building and provide a transparent evaluation of the environmental impacts of building materials and processes. This paper explores the definitions, key concepts, types of the exchanged data, and methods of integration and therefore provides insights into their potential in addressing the gaps that the construction industry is currently facing. The framework of integrating OpenBIM and LCA will be developed as a tool; therefore, it will combine an automated validation option by using IDS, create an enriched IFC file(s), dynamically map the data to an external LCA repositories, and incorporate feedback and reporting mechanisms. All those will be combined to address the most persistent shortcomings in the reviewed studies related to the integration of BIM and LCA. The framework will promote a holistic approach covering the early design benchmark to the detailed Whole Building LCA (WBLCA), including the operational and end-of-life phases. This next-generation workflow will align closely to the principles of OpenBIM, leading to improvement in the efficiency, accuracy, and deeper understanding of the environmental impacts by stakeholders over the construction lifecycle of buildings. Full article

The growth of Intelligent Transportation Systems (ITS) has made high-quality perception data from multi-sensor setups essential. Pairing millimeter-wave (mmW) radar with a monocular camera is a common way to recover three-dimensional information about the environment, but aligning the two is difficult because sparse radar point clouds and dense camera images differ sharply in how they sense a scene. The problem grows more severe in roadside infrastructure, where the high mounting elevation introduces perspective distortion that vehicle-mounted systems rarely face. This paper presents a systematic review, conducted under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, of radar-camera extrinsic calibration for fixed roadside infrastructure, organizing existing work into a taxonomy that separates traditional two-stage pipelines from recent end-to-end learning frameworks. Because methods designed specifically for roadside units remain scarce, the review also covers vehicle- and robot-mounted methods whose static-sensor formulation carries over to fixed roadside deployment. For the two-stage pipeline, the analysis covers target-based and targetless correspondence registration along with the optimization techniques and algorithmic assumptions behind parameter estimation. The end-to-end learning literature shows a clear shift toward self-supervised and fusion-based models, some of which report real-time performance. The review also compares the metrics and procedures used to quantify calibration accuracy. Progress is evident, but robustness in cluttered urban environments remains an open challenge, and the paper closes by outlining future directions, arguing that standardized roadside benchmarks are needed before scalable, targetless calibration can mature. Full article

As the automotive industry undergoes a green transformation, digital upgrading, and increasingly intensive supply chain collaboration, the supply chain finance credit risks faced by small and medium-sized enterprises (SMEs) in the sector exhibit characteristics such as multi-source interaction, nonlinear transmission, and class imbalance. This study uses 210 SMEs in China’s A-share automotive sector from 2020 to 2024 and constructs a credit risk evaluation system covering 56 indicators across the macro environment, financing enterprises, supply chain characteristics, and core enterprise credit support. Methodologically, DE-LightGBM is employed for feature selection to reduce redundancy and noise, while TabPFGen is introduced to generate synthetic risk-class samples. Business logic constraints and a Nearest Neighbor Distance Ratio filtering mechanism are further applied to improve the plausibility and fidelity of generated samples. Empirical results show that the TabPFN model achieves superior predictive performance after feature selection and data augmentation, and the Wilcoxon signed-rank test confirms the effectiveness and stability of sample augmentation. In addition, the ablation experiment demonstrates that green-related features provide significant incremental predictive value for supply chain finance credit risk identification. The proposed framework provides a useful reference for SME credit assessment, risk early warning, and green financial resource allocation in the automotive industry. Full article

Linearly homomorphic signatures (LHSs) are widely used in scenarios such as network coding and the Internet of Things, but their security faces the serious threat of key leakage. To address this issue, this paper introduces a forward secure mechanism into LHSs, aiming to construct a linearly homomorphic signature (LHS) scheme that can resist the risk of key leakage. By combining the binary tree minimal cover set mechanism with lattice-based extension algorithms, we construct an LHS scheme that supports time-period key updates. We prove its forward secure unforgeability under the standard model (SM) by reducing it to the Short Integer Solution (SIS) problem. To the best of our knowledge, this scheme is the first provably secure lattice-based forward secure linearly homomorphic signature (FSLHS) scheme in the SM, filling a theoretical gap in existing research. Furthermore, we apply this scheme to a smart grid data acquisition system and verify its practicality through concrete performance analysis. Full article

Drawing on sport tourism, sport ecology, and environmental policy scholarship, this narrative review argues that the event schedule architecture should be positioned as a strategic tool for managing sport-tourism externalities. Evaluation against standard instrument-choice criteria shows that the schedule architecture performs strongly in regards to cost-effectiveness, uncertainty handling, and institutional compatibility, but faces serious political feasibility constraints rooted in regulatory capture, the absence of mandatory environmental impact assessment, and misalignment between organization-level commitments and league-level scheduling authority. Five research gaps are identified and matched with a research agenda covering political economy, quantitative instrument comparison, procedural reform design, demographic and geographic extension, and causal evaluation. The contribution frames the event schedule architecture as a strategic-management instrument for sport-tourism sustainability and connects it to Sustainable Development Goals 11, 12, 13, and 17. Full article

Precision feeding is an important foundation for improving production efficiency in aquaculture, reducing feed waste, mitigating water pollution, and promoting the intelligent development of aquaculture. Conventional feeding practices remain heavily dependent on operator experience and are typically executed at predetermined times or fixed ration levels. Such approaches frequently result in extensive feeding management, poor adaptability, low feed utilization efficiency, and delayed responses to environmental changes. Advances in machine vision, the Internet of Things, machine learning, deep learning, and automatic control have progressively shifted aquaculture feeding research beyond standalone automatic feeders toward integrated systems encompassing demand perception, intelligent decision-making, precise control, and equipment coordination. This paper reviews the state of the art in precision feeding technologies and equipment in aquaculture. At the technical level, it summarizes advances in feeding demand perception, intelligent feeding decision-making, and precise control and execution. At the equipment level, it reviews the main types, design features, and field application status of precision feeding equipment in intensive aquaculture, pond aquaculture, and offshore aquaculture scenarios. Despite the considerable progress achieved, the practical deployment of precision feeding still faces several limitations. Environmental disturbances, water turbidity, illumination variation, and sensor drift may compromise the reliability of feeding demand perception. Existing decision-making models frequently exhibit limited generalizability across species, growth stages, and aquaculture scenarios. Moreover, insufficient integration of sensing, decision-making, and execution restricts the development of fully closed-loop feeding systems. High initial investment, maintenance costs, and the shortage of skilled personnel further constrain the adoption of precision feeding equipment, particularly in resource-limited regions. On this basis, the main challenges including sensing accuracy, model practicability, closed-loop control, equipment reliability, and standardization, are examined. Future development trends are also discussed, covering multi-source information fusion, synergy between mechanistic models and data-driven methods, system-level closed-loop control, equipment modularization, and industrial application. This review is expected to provide a reference for subsequent research and engineering applications. Full article

Multiple factors such as global climate warming and environmental degradation have increased natural disaster frequencies, threatening the safety of citizens’ lives and properties seriously. Existing literature primarily focuses on livelihood diversification, resilience, and vulnerability in flood-prone areas, with limited research systematically examining farmers’ livelihood risks and management strategies across multiple dimensions. To address this gap, this study advances the understanding of multidimensional livelihood risks by systematically identifying the key risk perceptions and management strategy choices of farmers, thereby providing empirical evidence essential for designing targeted interventions and sustainable adaptation policies in flood-prone regions. Specifically, this study employs an unordered multinomial logistic model to examine farmers’ risk management strategy choices, drawing on a field survey of 540 farmers from floodplain areas in Sichuan Province, China. The analysis systematically covers four livelihood risk dimensions (health, environmental, financial, social) and five management strategies (expansion, adjustment-oriented, contraction, aid-oriented, dependency-based). The results indicate that: (1) The most significant livelihood risk is environmental, and the most commonly selected risk management strategy is adjustment-oriented management; (2) When farmers face health risks, they tend to adopt dependency-based management strategy; in dealing with financial and social risks, farmers perceive no significant difference in the selection of the five management strategies. Accordingly, targeted strategies are proposed: insurance and information for environmental risks, medical security for health, employment training for social, and income diversification for financial risks. Full article

Terrestrial ecosystems are facing increasingly severe threats driven by the dual pressures of climate change and anthropogenic activities. However, current remote sensing-based ecological research still exhibits notable deficiencies in the integration of multi-source data. This study develops a Critical Transition Index (CTI) for Northeast China. The CTI integrates four remotely sensed vegetation variables (LAI, NDVI, SIF, and VOD) with time series decomposition (STL), multiple early-warning signals (ar1, variance, skewness, and kurtosis), consistency scoring, and Mahalanobis distance. The framework systematically assesses vegetation resilience and its spatiotemporal responses to climatic stressors. Results reveal pronounced differences among variables: the structural indicator LAI identified the highest proportion of high-risk areas (60.8%, CTI ≥ 0.8), whereas the functional indicator SIF showed relatively high stability, with a mean CTI of 0.619 and a high-risk proportion of only 16.0%. High-risk areas are primarily concentrated in cropland–grassland mosaics, while forested regions maintain lower risk. Temporal analysis of land cover composition within high-risk areas shows a clear “structural diffusion” trend: the proportion of deciduous broadleaf forests in the high-risk category increased from being negligible in early periods (2003–2007) to approximately 20% in later periods (2013–2017) for both SIF and VOD indicators. This study underscores the necessity of multi-indicator frameworks for detecting critical transitions and provides quantitative, spatially explicit scientific insights for ecosystem early-warning and regional management strategies. Full article

Background/Objectives: Antimicrobial resistance in pediatric infections presents a worsening global public health challenge, with antimicrobial resistance (AMR) accounting for more than one million deaths annually and disproportionately affecting children younger than 5 years of age. Neonates and critically ill children face heightened risk owing to immature immunity, frequent healthcare exposures, and limited therapeutic options. This review synthesizes evidence on the epidemiology, mechanisms of resistance, clinical outcomes, and management of AMR across the full pediatric age range. Methods: PubMed/MEDLINE and Google Scholar were searched for literature from 2014 to 2026 using terms covering antibiotic resistance, pediatric populations, and key pathogens. Approximately 1840 records were screened; 69 sources met all inclusion criteria. A narrative synthesis approach was used, given heterogeneity across study designs and outcomes. Results: Extended-spectrum β-lactamase (ESBL)-producing Enterobacterales, carbapenem-resistant pathogens, and methicillin-resistant Staphylococcus aureus drive substantial morbidity and mortality in children. Approximately one in five pediatric Gram-negative bloodstream isolates are resistant to third-generation cephalosporins, a phenotype independently associated with a roughly three-fold increase in adjusted mortality. Carbapenem-resistant Klebsiella pneumoniae bacteremia carries a 30-day mortality approaching 40%, and isolates in low- and middle-income countries (LMICs) frequently harbor multiple resistance genes. Pneumococcal conjugate vaccine implementation was associated with absolute reductions of 7–11% in the proportion of pediatric pneumococcal isolates that were penicillin-non-susceptible or penicillin-resistant, largely by preventing infections caused by resistant serotypes and by reducing antibiotic selection pressure, rather than through a direct effect on resistance mechanisms; global AMR mortality in children younger than 5 years of age fell by more than 50% between 1990 and 2021. Conclusions: Pediatric AMR reflects intersecting microbiological, clinical, and health-system challenges. Priority actions include scaling antimicrobial stewardship programs, expanding access to rapid molecular diagnostics, integrating whole-genome sequencing into surveillance, conducting pediatric-inclusive randomized trials, and deploying vaccines as primary prevention tools, with particular emphasis on LMICs where the burden is greatest. Full article

Climate change and habitat loss pose severe threats to the survival of alpine butterflies worldwide. Parnassius imperator is a rare, endemic, and endangered butterfly in China, yet the spatiotemporal dynamics of its suitable habitats under climate change remain largely unknown. In this study, we applied ensemble species distribution models to simulate the shifts of its current and future suitable habitats, incorporating bioclimatic variables, elevation, normalized difference vegetation index, and human footprint. Results showed that the current suitable habitats cover 185.87 × 10⁴ km² and are concentrated in western China, mainly regulated by elevation, temperature seasonality (BIO4), precipitation of the wettest month (BIO13), precipitation of the warmest quarter (BIO18), and precipitation of the driest month (BIO14). Under future climate change scenarios, suitable habitats will shrink drastically, even to only 82.16 × 10⁴ km² under SSP585 in the 2070s, with nearly a complete loss of highly suitable habitats. In addition, centroid shift analyses reveal that the distribution centroid will shift eastward. Our findings indicate that suitable habitats will contract significantly, and P. imperator will face a sharply increasing risk of extinction in the future. Considering the overlap between suitable habitats and existing nature reserves, we recommend implementing integrated conservation strategies, including expanding protected areas, establishing long-term monitoring programs, restoring habitats, and strengthening law enforcement and public education. This study provides a scientific basis for the climate-adaptive conservation of P. imperator and other vulnerable alpine insects. Full article

Research on post-COVID tourism behaviour has expanded rapidly, yet inland UNESCO World Heritage cities remain underexamined, particularly in Mediterranean contexts. This study examines whether the pandemic produced durable changes in tourist behaviour and in willingness to pay for sustainable services in Cáceres, Spain. A structured face-to-face survey was administered to 421 visitors in March 2023, after public-health restrictions had been lifted. The analysis covered self-reported behavioural change, perceived impacts on different destination types, perceived effects on local sustainability objectives and changes in willingness to pay (WTP) for sustainable services. Descriptive statistics were complemented by an exploratory binary logistic regression predicting increased WTP. Because the model includes only sociodemographic predictors and shows modest fit, it is used to describe associations rather than to predict. Reported behavioural change was limited: mean scores for crowd avoidance, health–safety preferences, shorter stays and substitution towards rural and nature tourism ranged from 1.73 to 1.91 on a five-point scale. Respondents nevertheless perceived substantial spatial effects of the pandemic, particularly on natural parks (92.6%) and rural destinations (84.1%). Most believed that the pandemic had accelerated sustainability efforts mainly through greater institutional and business awareness (54.9%). WTP proved relatively stable, with 62.7% reporting no change and 26.1% an increase. Women and respondents with university education showed higher odds of reporting increased WTP. Because constructs such as institutional trust and pro-environmental values were not measured directly, these attitudes are interpreted—rather than demonstrated—as reflecting governance-related confidence and value orientations more than lingering health concerns. This governance-and-values reading is the study’s main interpretive contribution and requires confirmation with direct measures of the underlying constructs. Full article

Due to climate change and rapid urbanization, cities worldwide face the dual challenge of improving flood resilience and providing accessible green space within limited land resources. Sponge City parks offer a landscape-based approach for integrating stormwater management with park services. However, how park morphology structures this combined performance remains insufficiently understood. This study examines 26 Sponge City parks in Shanghai and evaluates how node-, line-, and patch-type morphologies are linked to stormwater storage and service provision. Using geospatial analysis, DEM-derived catchment delineation, land-cover interpretation, and statistical analysis, this study compares estimated stormwater storage, storage efficiency, local park availability, and land-cover composition across different park morphologies. The results show that estimated performance of stormwater management and park service provision vary across morphological types, but these differences do not follow a simple node–line–patch hierarchy. Rather, the observed patterns are jointly shaped by park morphology, catchment setting, land-cover allocation, and surrounding urban context. These findings suggest that Sponge City parks should not only be evaluated by total stormwater storage. Their contribution depends on morphology, scale, catchment setting, land-cover allocation, and urban context. The study provides a morphology–performance perspective to support more differentiated planning of multifunctional green infrastructure. Full article

Multimodal biometric systems have become essential components of modern electronic identity and authentication platforms where robustness under real-world degradation is critical. However, opaque face masks impose severe facial occlusion and attenuate high-frequency spectral components. Conversely, transparent face shields introduce complex specular reflections and act as an acoustic channel distortion source. Addressing these asymmetric degradation challenges, this paper proposes a reliability-aware Dynamic Score Fusion (DSF) for multimodal biometric identification. The proposed method performs sample-level reliability estimation for both face and voice modalities at the input stage. This enables sample-wise adaptive weighting of modality scores based on their estimated reliability. The framework integrates an ElasticFace-Arc backbone for face recognition with an Emphasized Channel Attention, Propagation and Aggregation—Time Delay Neural Network (ECAPA-TDNN) for speaker identification. The proposed approach is evaluated on the FaciaVox dataset, comprising face images and voice recordings acquired under multiple face-covering conditions. Experiments under the Standard to Cross-Condition Protocol (SCCP) and Multi-Condition Protocol (MCP) demonstrate that the proposed DSF consistently outperforms conventional score-level fusion methods, including Weighted Sum Fusion (WSF) and Logistic Regression Fusion (LRF). It achieves average Rank-1 accuracies of 89.6% (SCCP) and 93.7% (MCP), with gains of up to 9.3 percentage points over these baselines. The reliability estimators further demonstrate strong predictive capability, yielding Area Under the Curve (AUC) values above 0.95 for both modalities in distinguishing correctly and incorrectly identified samples under the closed-set identification setting. These findings confirm that sample-wise reliability modeling provides an effective mechanism for enhancing multimodal biometric performance under challenging mask and shield conditions, supporting the deployment of robust AI-driven electronic identification systems. Full article