Search Results (70)

The accurate measurement of dynamic forces is pivotal for advancing manufacturing process monitoring and enhancing equipment intelligence. To address the challenges of contact interface force field nonlinearity in existing PVDF piezoelectric film force sensors and the inability of a monolithic PVDF piezoelectric film to measure multi-dimensional forces, this study designs a uniform-load double-bossed elastic force-transmitting diaphragm to achieve contact interface force field reconstruction between the sensor’s elastic sensing structure and the sensitive element group. Building upon the load-bearing surface domain segmentation technique, the silver ink electrode on the front surface of a complete circular PVDF piezoelectric film is segmented into four independent sector-shaped rings. Each sector ring, together with its underlying PVDF piezoelectric film, constitutes a sensitive element, and these four sensitive elements are integrated to form the sensitive element group. The three-dimensional force measurement method of this sensitive element group in the Cartesian coordinate system is investigated. The measurement of three-dimensional force is realized by leveraging the tensile-compressive piezoelectric effect of each sensitive element in conjunction with a pre-stressed assembly structure. Quasi-static calibration test results indicate that the charge sensitivities of the force sensor in the X-, Y-, and Z-directions are 52.63 pC/N, 55.96 pC/N, and 9.02 pC/N, respectively, with a linearity ≤4.6%. Dynamic calibration test results reveal that the force measurement module exhibits a natural frequency of 4675.5 Hz. Experimental investigations into the response of triaxial cutting forces to variations in cutting speed, feed rate, and cutting depth were conducted, which verified the sensor’s ability to capture dynamic three-dimensional cutting forces. This study provides an effective solution for the structural design and three-dimensional force measurement methodology of PVDF piezoelectric film force sensors. Full article

Marine fish communities in the Yangtze River Estuary and Adjacent East China Sea (YRE-ECS) are subject to complex environmental gradients; however, their multidimensional assembly mechanisms remain insufficiently resolved. Here, we integrated environmental DNA (eDNA) metabarcoding, co-occurrence network analysis, and environmental profiling to examine fish community structure across vertical layers, hydrographic zones, and seasons. Vertically, surface communities dominated by pelagic-associated Perciformes and Clupeiformes showed more variable assembly patterns, whereas bottom communities enriched in Gobiiformes and Pleuronectiformes were more strongly associated with temperature and dissolved oxygen. Horizontally, among three zones delineated by salinity and hydrographic characteristics, the Mixed Transitional Water (MTW) supported the most diverse and interactive assemblages and functioned as an ecological connector between estuarine (EHSW) and offshore (OWSW) waters. Seasonally, community structure shifted markedly: spring communities exhibited higher diversity and denser trophic networks supported by zooplankton-rich, phototrophic plankton (e.g., Arthropoda, Bacillariophyta), whereas autumn communities were simpler, dominated by Chlorophyta and microbial taxa, with fish assemblages showing increased modularity and reliance on fewer planktonic groups. This seasonal pattern suggests a transition from diversified energy pathways to more constrained trophic coupling. βNTI and Mantel analyses jointly revealed a stratified environment-response-feedback framework driving community differentiation through combined stochastic and deterministic mechanisms. These findings highlight the importance of integrated spatial-temporal monitoring and suggest that protecting transitional zones and spring food-web integrity is critical for ecosystem resilience in the YRE-ECS. Full article

This study proposes an integrated façade design framework that harmonizes the creative divergence of Generative AI with the economic efficiency of Design for Manufacturing and Assembly (DfMA). To address low productivity in the construction industry, a stepwise pipeline is developed, synthesizing image generation via Midjourney, automated coding using ChatGPT, and quantitative optimization. Central to this process is the Hamming Distance algorithm, which evaluates image similarity to implement core DfMA principles: standardization and simplification. The study introduces a multidimensional decision-making model utilizing Grid Size (GS), Replacement Rate (RR), and Hamming Threshold (HT) indices to visualize the trade-off between component minimization and design fidelity. This process transforms abstract 2D patterns into manufacturable geometric panels, bridging the gap between conceptual design and constructability. The results demonstrate that algorithmic optimization significantly reduces component count, contributing to potential cost savings and schedule reduction. Ultimately, this research establishes a collaborative model where architects’ qualitative insights complement AI’s quantitative analysis, enabling designers to regain agency over digital tools and realize creative visions within technical constraints. Full article

Dam construction can significantly alter local habitat characteristics and the distribution patterns of aquatic organisms. However, the variations in the multidimensional diversity of fish assemblages in reservoirs, as well as the relative significance of potential community assembly rules, remains poorly understood. This study elucidated the patterns of taxonomic and phylogenetic α- and β-diversity with its decomposition components (i.e., turnover and nestedness) of fish assemblages in the Dongfeng Reservoir, situated in the karst basin of southwest China. Additionally, we evaluated the relative importance of environmental heterogeneity and spatial structure. We found significant nonlinear relationships (p < 0.05) between taxonomic and phylogenetic richness. Both fish taxonomic and phylogenetic β-diversity values were low (<0.33) with high turnover patterns (72.23% and 67.42%), underscoring the necessity for local managers to protect entire water areas to maintain or enhance community diversity. Only taxonomic and phylogenetic richness are significantly positively (e.g., water depth) and negatively (e.g., turbidity) affected by different environmental variables. Environmental heterogeneity was the dominant factor influencing both total β-diversity and turnover processes at the taxonomic and phylogenetic levels, while spatial distance primarily influenced the nestedness process. These findings are critical for elucidating changes in patterns of fish community diversity and their driving factors in the context of dam construction, providing a foundation for the conservation and management of aquatic organisms in other rivers, lakes, and reservoirs. Full article

Intensive greenhouse management profoundly alters soil biogeochemical processes and biotic interactions, distinguishing greenhouse soils from open-field systems. Understanding the drivers of soil fauna assembly is essential for sustaining soil health and productivity. In this study, we examined nematode community drivers in greenhouse melon systems under 2- and 10-year rotations using environmental DNA sequencing. Plant phenology, more than rotation, shaped nematode communities, particularly omnivore predators and bacterivores. This driver was mirrored by a shift in nematode faunal indices from an enriched, bacterial-dominated state at seedling stages to a structured state at maturity. LDA Effect Size and random forest identified key genera (Prismatolaimus, Acrobeloides, and Ceramonema), demonstrating multidimensional drivers of community assembly. Redundancy analysis showed soil organic matter (SOM) and acid phosphatase as major drivers. Mantel tests indicated that the microbial biomass carbon and nitrogen ratio (MBC/MBN) consistently explained community variation (relative abundance: r = 0.229; functional diversity: r = 0.321). Structural equation modeling linked available phosphorus to microbial carbon cycling via cumulative carbon mineralization (CCM, 0.41) and MBC (0.40). SOM increased MBN (0.62) but suppressed Chao1 (−0.76). MBN had the strongest positive effect on Pielou_e (0.49). pH negatively affected functional diversity (−0.33), while nitrate nitrogen (0.35) and CCM (0.32) had positive effects. Our results indicate that MBC and MBN act as microbial bridges linking soil properties to nematode diversity, providing a mechanistic basis for optimizing greenhouse soil management and ecosystem functioning. Full article

Rural settlements are the fundamental socio-economic units of China’s countryside. In line with national strategies that emphasize place-based and category-specific pathways for rural revitalization, accurate classification of rural settlements is essential for differentiated planning and policy delivery. However, given the sheer number of settlements, manual classification is time-consuming and resource-intensive, limiting scalability. This study proposes an AI-driven, multi-model framework to automate rural settlement classification with high stability and accuracy. First, informed by a rigorous literature review, we construct a multidimensional indicator system that integrates natural conditions, socio-economic attributes, and land-use factors to capture spatial and functional characteristics at the settlement scale. Using Gaoqing County (Shandong Province) as the study area, we collect and curate survey data and apply outlier detection for preprocessing. We then benchmark multiple machine learning models and find that algorithms with native handling of missing values perform markedly better—a critical advantage given the prevalence of missingness in survey-based datasets. Finally, we assemble the three best-performing models—LightGBM, CatBoost, and XGBoost—into a weighted-voting ensemble, achieving an overall classification accuracy of approximately 88%. The results demonstrate that the refined indicator system, coupled with a multi-model ensemble, substantially improves both accuracy and robustness. This work provides a methodological foundation and empirical evidence to support differentiated planning and targeted rural revitalization at the settlement level, offering a scalable blueprint for broader regional and national implementation. Full article

The severe degradation of alpine grasslands on the Qinghai–Tibet Plateau poses a significant threat to regional ecological security. While insects are critical for ecosystem functions, their responses to restoration measures in these fragile habitats are poorly documented. This study assessed the initial impacts of four restoration approaches—grazing exclusion fencing (FE), no-till reseeding (FR), planting grass (GC), and grazing control (CK)—on insect trophic guilds (herbivores, predators, saprophagous, and omnivores) in the Qilian Mountains. Using a multi-dimensional indicator (alpha, zeta, and dark diversity), we systematically assessed community assembly and recovery potential. The results revealed the following: (1) FE supported the highest insect abundance, dominated by phytophagous insects. FR significantly enhanced species’ richness and diversity across multiple functional groups (p < 0.05). GC significantly increased the richness of omnivorous insects, but caused a significant decrease in the Shannon–Wiener index for saprophagous insects (p < 0.05). (2) Zeta diversity revealed stable, widespread-species-dominated communities under FR and FE, while CK and GC favored rare-species-driven succession. Dark diversity analysis indicated high recovery potential for phytophagous insects under FR and FE, while GC enhanced saprophagous latent diversity. However, we emphasize that mechanistic interpretations require further validation. Our findings highlight no-till reseeding as a promising initial strategy, though longer-term studies are essential to evaluate successional trajectories and establish definitive management protocols for alpine grassland restoration. Full article

Three-dimensional fragment assembly technology has significant application value in fields such as cultural relic restoration, medical image analysis, and industrial quality inspection. To address the common challenges of limited feature representation ability and insufficient assembling accuracy in existing methods, this paper proposes a geometry-aware hierarchical fragment assembly framework (GeoAssemble). The core contributions of our work are threefold: first, the framework utilizes DGCNN to extract local geometric features while integrating centroid relative positions to construct a multi-dimensional feature representation, thereby enhancing the identification quality of fracture points; secondly, it designs a two-stage matching strategy that combines global shape similarity coarse matching with local geometric affinity fine matching to effectively reduce matching ambiguity; finally, we propose an auxiliary transformation estimation mechanism based on the geometric center of fracture point clouds to robustly initialize pose parameters, thereby improving both alignment accuracy and convergence stability. Experiments conducted on both synthetic and real-world fragment datasets demonstrate that this method significantly outperforms baseline methods in matching accuracy and exhibits higher robustness in multi-fragment scenarios. Full article

Against the backdrop of global climate change intensifying seasonal freeze–thaw cycles, deteriorating soil conditions in farmland within seasonal frost zones constrain agricultural sustainability. This study employed an in situ field experiment during seasonal freeze–thaw periods in the black soil zone of Northeast China to investigate the joint regulatory effects of seasonal freeze–thaw processes and tillage practices on multidimensional features of soil bacterial communities. Key results demonstrate that soil bacterial communities possess self-reorganization capacity. α-diversity exhibited cyclical fluctuations: an initial decline followed by a rebound, ultimately approaching pre-freeze–thaw levels. Significant compositional shifts occurred throughout this process, with the frozen period (FP) representing the phase of maximal differentiation. Actinomycetota and Acidobacteriota consistently dominated as the predominant phyla, collectively accounting for 33.4–49% of relative abundance. Bacterial co-occurrence networks underwent dynamic topological restructuring in response to freeze–thaw stress. Period-specific response patterns supported sustained soil ecological functionality. Furthermore, NCM and NST analyses revealed that stochastic processes dominated community assembly during freeze–thaw (NCM R² > 0.75). Tillage practices modulated this stochastic–deterministic balance: no-tillage with straw mulching (NTS) shifted toward determinism (NST = 0.608 ± 0.224) during the thawed period (TP). Across the seasonal freeze–thaw process, soil temperature emerged as the primary driver of temporal community variations, while soil water content governed treatment-specific differences. This work provides a theoretical framework for exploring agricultural soil ecological evolution in seasonal frost zones. Full article

The severe damage caused by the fall webworm Hyphantria cunea is closely related to its internal microbiota. However, due to the widespread use of antibiotics and their environmental persistence, the specific effects of various antibiotics on the microbiome and fitness of H. cunea larvae remain ambiguous. This study investigated the impacts of three antibiotics (tetracycline, rifampicin, and kanamycin) on microbiome assembly, functional traits, and host fitness. Our findings revealed that each antibiotic distinctly altered the microbial community: tetracycline primarily decreased bacterial diversity (e.g., reduced abundance of Actinomycetota) and suppressed host fecundity; kanamycin lowered microbial evenness (e.g., decreased Bacillota) and diminished pupal weight; whereas rifampicin significantly restructured the community (e.g., increased Pseudomonas and decreased Bacillota), enhanced functional traits such as biofilm formation and stress tolerance, and imposed multidimensional adverse effects on fitness (prolonged developmental duration, reduced pupal weight, and decreased hatching rate). Alterations in microbiome diversity, structure, and function were tightly correlated with the differential impacts of antibiotics on host fitness. This research elucidates the mechanisms by which antibiotics disrupt host–microbe interactions in H. cunea, offering a theoretical foundation for understanding antibiotic ecological repercussions and devising microbe-based green pest control strategies. Full article

As the most unstable crystalline form of calcium carbonate, vaterite is rarely found in nature due to being highly prone to phase transitions. However, its high specific surface area, excellent biocompatibility, and high solubility properties have led to a research boom and the following breakthroughs in the last two decades: (1) From primitive calculations and spectroscopic analyses to modern multidimensional research methods combining calculations and experiments, the crystal structure of vaterite has turned from early identifications in orthorhombic and hexagonal crystal systems to a complex polymorphic structure within the monoclinic crystal system. (2) The formation process of vaterite not only conforms to the classical crystal growth theory but also encompasses the nanoparticle aggregation theory, which incorporates the concepts of oriented nanoparticle assembly and mesoscale transformation. (3) Regardless of the conditions, the formation of vaterite depends on an excess of CO₃²⁻ relative to Ca²⁺, and its stability duration relates to preservation conditions. (4) Vaterite demonstrates significant value in biomedical applications—including bone repair scaffolds, targeted drug carriers, and antibacterial coating materials—leveraging its porous structure, high specific surface area, and exceptional biocompatibility. While it also shows utility in environmental pollutant adsorption and general coating technologies, the current research remains predominantly concentrated on its medical applications. Currently, the rapid transformation of vaterite presents the primary limitation for its industrial application. Future research should prioritize investigating its formation kinetics and stability. Full article

Background/Objectives: Wolf–Hirschhorn syndrome (WHS; OMIM #194190) is a rare neurodevelopmental disorder, caused by deletions in the distal short arm of chromosome 4. It is characterized by developmental delay, epilepsy, intellectual disability, and distinctive facial dysmorphism. Clinical presentation varies widely, complicating prognosis and individualized care. Methods: We assembled a cohort of 140 individuals with genetically confirmed WHS from Spain and Latin-America, and developed and validated a multidimensional, Clinician-Reported Outcome Assessment (ClinRO) based on the Global Functional Assessment of the Patient (GFAP), derived from standardized clinical questionnaires and weighted by HPO (Human Phenotype Ontology) term frequencies. The GFAP score quantitatively captures key functional domains in WHS, including neurodevelopment, epilepsy, comorbidities, and age-corrected developmental milestones (selected based on clinical experience and disease burden). Results: Higher GFAP scores are associated with worse clinical outcomes. GFAP showed strong correlations with deletion size, presence of additional genomic rearrangements, sex, and epilepsy severity. Ward’s clustering and discriminant analyses confirmed GFAP’s discriminative power, classifying over 90% of patients into clinically meaningful groups with different prognoses. Conclusions: Our findings support GFAP as a robust, WHS-specific ClinRO that may aid in stratification, prognosis, and clinical management. This tool may also serve future interventional studies as a standardized outcome measure. Beyond its clinical utility, GFAP also revealed substantial social implications. This underscores the broader socioeconomic burden of WHS and the potential value of GFAP in identifying high-support families that may benefit from targeted resources and services. Full article

Medical consumable orders are characterized by diverse product types, small batch sizes, frequent orders, and high customization requirements, often leading to inefficient workshop scheduling and difficulties in meeting multiple production constraints. To address these challenges, this study proposes a bi-level optimization model for order splitting and reorganization considering multi-dimensional and multi-scale characteristics. The multi-dimensional characteristics encompass materials, processes, equipment, and work efficiency, while the multi-scale aspects involve finished products, components, assemblies, and parts. At the upper level, the model optimizes order task splitting by refining splitting strategies and preprocessing constraints to generate high-quality input for the reorganization phase. The lower level optimizes sub-task prioritization, batch sizes, and resource scheduling to develop a production plan that balances cost and efficiency. Subsequently, to solve this bi-level optimization problem, a hybrid bi-objective optimization algorithm is designed, integrating a collaborative iterative strategy to enhance solution efficiency and quality. Finally, a case study and comparative experiments validate the practicality and effectiveness of the proposed model and algorithm. Full article

In tolerance design for complex mechanical systems, 3D dimension chain analyses are crucial for assembly accuracy. The current methods (e.g., worst-case analysis, statistical tolerance analysis) face limitations from oversimplified assumptions—treating datum features as ideal geometries while ignoring manufacturing-induced spatial distribution of form errors and failing to characterize 3D coupled error constraints. This study proposes a six-dimensional spatial dimension chain (SDC) model based on transfer matrix theory. The key innovations include (1) a six-dimensional model integrating position and orientation vectors, incorporating geometric error propagation constraints for high-fidelity error prediction and tolerance optimization, (2) the characterization of spatially distributed form errors and 3D coupled errors of spatial dimension chain-based multiple mating-surface constraint (SDC-MMSC) using six-degree-of-freedom (6-DoF) geometric error components, reducing the assembly topology complexity while improving the efficiency, and (3) a 6-DoF error characterization method for non-mating-constrained data, providing the theoretical basis for SDC modeling. The experimental validation on an aero-engine casing assembly shows that the SDC model captures multidimensional closed-loop spatial errors, with absolute errors of max–min closed-loop distances below 9.3 μm and coaxiality prediction errors under 8.3%. The SDC-MMSC method demonstrates superiority, yielding normal vector angular errors <0.008° and envelope surface RMSE values <0.006 mm. This method overcomes traditional simplified assumptions, establishing a high-precision, multidimensional distributed-form-error-driven SDC model for complex mechanical systems. Full article

As a crucial component of the Belt and Road Initiative (BRI), China Railway Express (CR Express) plays a pivotal role in enhancing regional connectivity and economic integration. However, the systematic evaluation of CR Express node cities remains understudied, hindering the optimization of logistics networks and sustainable development goals. This study pioneers a data-driven approach by integrating multi-source geospatial data and advanced machine learning algorithms to develop a comprehensive evaluation framework spanning five critical dimensions: economic vitality, ecological sustainability, logistics capacity, network connectivity, and policy support. By comparing the evaluation performance of six machine learning models, an optimal decision-making model is identified, and the evaluation indicators are rigorously screened to provide robust decision-support for the establishment of CR Express assembly centers. The Random Forest model outperformed comparative algorithms with 99.5% prediction accuracy (8.33% higher than conventional classification models), particularly in handling multi-dimensional interactions between urban development factors. Feature importance analysis identified 11 decisive indicators from node city evaluation empirical indicators, where CR Express trade volume (weight = 0.1269), logistics hub classification (weight = 0.1091), and operational frequency (weight = 0.0980) emerged as the top three predictors. Spatial predictions highlight five strategic cities (Changsha, Wuhan, Shenyang, Jinan, Hefei) as prime candidates for CR Express assembly centers, providing actionable insights for national logistics planning under the BRI framework. Full article