Search Results (6,617)

To assess the treatment efficiency and spatio-temporal variation characteristics of urban wastewater treatment plants, this study analyzed influent and effluent water quality data, including pH, COD, BOD₅, SS, NH₃–N, TN, and TP, as well as treatment volume data from 19 plants in Changsha from 2020 to 2024. The results revealed significant fluctuations in influent water quality across different plants, though effluent quality generally complied with discharge standards. Removal rates of SS, NH₃–N, and BOD₅ all exceeded 80%, while that of TN ranged between 63% and 79%. The COD/BOD₅ ratios in the influent mostly exceeded 0.3, indicating generally good biodegradability of the municipal wastewater. However, 79% of the plants exhibited SS/BOD₅ > 1.5, and 83.2% had BOD₅/TN < 4, suggesting a widespread carbon deficiency for denitrification. Principal component analysis (PCA) demonstrated that both influent and effluent quality indicators were suitable for dimensionality reduction, with pH, COD, NH₃–N, and TN identified as core evaluation factors. Cluster analysis (CA) indicated phased increases in influent concentrations, while effluent quality showed progressive annual improvement from 2020 to 2024. Urban WWTPs’ influent pollution loads were hydrological period-dependent, with high-flow effluent fluctuations and controllable low-flow loads. This study provides data support for operational optimization of wastewater treatment plants in Changsha. Full article

Efficient aero-acoustic regression is critical for unmanned aerial vehicle (UAV) design and urban air mobility operations, where noise mitigation is essential for regulatory compliance and public acceptance. While data-driven fuzzy Takagi–Sugeno–Kang (TSK) systems have shown potential for modeling complex aero-acoustic behaviors in UAV applications, their performance is strongly affected by input dimensionality and rule-base complexity. This work extends previous research on dimensionality reduction for genetic algorithm-optimized fuzzy systems by conducting a comparative benchmark on an aero-acoustic database regression task relevant to drone propulsion noise prediction. Several TSK architectures are evaluated, including zero- and first-order models, different membership function granularities, and clustering-based rule-generation strategies. In addition, a physics-based heuristic TSK rule system incorporating aero-acoustic knowledge is introduced and compared against data-driven fuzzy configurations. Model performance is primarily assessed through graphical regression analysis and optimization convergence behavior, with a focus on computational efficiency, structural complexity, and qualitative prediction trends—critical considerations for onboard UAV systems and real-time acoustic monitoring. The results highlight the trade-offs between data-driven learning and physics-informed rule construction, demonstrating that physics-based heuristics can reduce optimization complexity while preserving physically consistent behavior. This study provides practical insights into the design of interpretable and efficient fuzzy regression models for UAV aero-acoustic applications, supporting next-generation drone acoustic signature management. Full article

Metal–organic frameworks (MOFs), a class of porous crystalline materials, consists of metal ions or clusters coordinated to organic linkers. The unique features of MOFs such as exceptionally high surface area, chemical versatility, and tunable porosity make them highly suitable for several applications, including gas storage, drug delivery, catalysis, and sensing. Various synthesis techniques, including solvothermal, hydrothermal, microwave-assisted, mechanochemical, electrochemical, and sonochemical methods, have been used for the fabrication of MOFs. The selection and optimization of synthesis technique significantly influence the fundamental framework structure, the existence of defects, the available active sites, and the effectiveness of MOFs in special applications. This study focuses on advances in MOF fabrication techniques and examines their role in tuning the key properties of MOFs for targeted applications. The insights of this work may guide researchers in selecting or designing appropriate fabrication strategies for application-specific development of MOFs. Full article

Winter fallow fields (WFF) are widespread across humid subtropical croplands in the Yangtze River Economic Belt, exerting direct implications for annual land-use efficiency and winter production potential. However, acquiring fine-scale, year-to-year WFF information remains challenging due to frequent cloud contamination and the high fragmentation of agricultural parcels. Here, we mapped the annual 10 m WFF distribution in the Wanjiang Plain for six winter seasons (2019–2024). We employed a hierarchical mapping framework that integrates winter-stage Sentinel-1/2 composites with a Random Forest (RF) pre-classifier and a Fine Resolution Network (FR-Net) refinement module. Parcel-wise validation demonstrated robust and consistent performance across years (pooled OA = 0.969, F1-score = 0.969, MCC = 0.938). Spatiotemporal analyses revealed that WFF persistently occupied 52.3–65.6% of the regional cropland (7.59 × 10³–9.52 × 10³ km²), exhibiting a pronounced “hot-north, cold-south” spatial clustering. Approximately 52% of the cropland experienced high fallow recurrence (>67% frequency), forming stable high-recurrence cores. Furthermore, our MaxEnt association model (AUC = 0.739) identified relief amplitude, slope, and silt content as the most influential biophysical constraints. While these correlational variables act as proxies for underlying drainage and workability constraints rather than deterministic drivers, our high-fidelity 10-m WFF layers provide a consistent, policy-relevant baseline for hotspot-oriented screening and targeted winter-cropping optimization. Full article

Plane segmentation of real-world 3D point clouds captured by LiDAR or depth sensors remains challenging due to data sparsity, noise, and complex geometric configurations such as stepwise and intersecting non-coplanar structures. To address these issues inherent in sensor-acquired data, this paper proposes a geometry-aware plane segmentation method that leverages supervoxel boundary adjacency, normal coherence, and projection-line fitting constraints. Supervoxels were generated using the toward better boundary preserved supervoxel segmentation (TBBS) algorithm, and their natural adjacency relationships were constructed based on boundary points. Subsequently, the supervoxels were initially clustered according to their normal information. Finally, the projected point clouds of adjacent supervoxel were fitted with straight lines, and the fitting errors were calculated to optimize the clustering results. Experimental results demonstrate that this method performs excellently in handling stepwise non-coplanar structures, effectively segmenting planar regions with significant geometric features. It shows particular advantages in cases involving stepwise non-coplanar and intersecting planes. On benchmark datasets, the method achieves precision and recall rates of (97.7%, 94.4%, 91.2%, 80.4%, 92.3%) and (98.9%, 95.7%, 93.7%, 84.8%, 96.0%), respectively, highlighting its effectiveness and robustness for practical 3D sensing applications. Full article

This study investigates the typology of the pastoral farming systems in the arid region of southern Tunisia, with a particular focus on the governorate of Tataouine. A field survey was conducted among 111 livestock farmers distributed across different agro-ecological zones. The typology of breeding systems was established using a Factor Analysis of Mixed Data (FAMD), which identified eleven dimensions explaining 69.74% of the total data variance. The first three dimensions accounted for 15.91%, 8.79%, and 7.67% of the variability, respectively, and were defined by herd composition, resource availability, and management strategies, including variables such as the number of goats, sheep, and camels, distance to water sources, infrastructure, reproductive practices, and workforce availability. Hierarchical clustering revealed three distinct systems: System 1, regrouping “Small Urban Farmers”, defined by small-scale operations relying on family labor, localized feed resources, and market-driven production targeting urban consumers; System 2, representing large livestock, composed of professionalized operations with improved infrastructure, hired labor, and transhumance practices to optimize resource use and productivity; and System 3, for herds with camels, characterized by extensive systems utilizing collective rangelands and camels to adapt to arid conditions and ensure ecological resilience. The results emphasize how ecological constraints, infrastructure, and spatial organization shape the diversity of these systems. This typology provides critical insights into the challenges and potential of livestock farming in arid environments and offers a foundation for designing targeted interventions to support the sustainability of pastoral systems under increasing environmental and economic pressures. Full article

Identifying real-world saturation points and grid-hosting capacity in mixed-use urban Renewable Energy Communities (RECs) requires dynamic spatial evaluation. To address this, this paper introduces a novel simulation framework that integrates GIS spatial analysis with an iterative heuristic selection algorithm. The proposed method evaluates the energetic interaction between a primary generation node and surrounding consumers, utilizing a dynamic function to calculate the collective Self-Consumption Rate (SCR). Applied to the Flaminio Stadium in Rome, the model incrementally aggregates users to determine the optimal cluster size for economic feasibility. The results demonstrate that the heuristic selection algorithm successfully refined the community from an initial pool of 854 buildings to an optimal cluster of 734. This targeted selection eliminated energy surplus and achieved a near-perfect collective SCR of 99.8%. Furthermore, by strategically reducing the required installed PV capacity by 52.6%, the initial capital investment dropped from € 89.9 million to € 42.6 million, significantly de-risking the project while maintaining a competitive payback period of approximately 13 years. Ultimately, this study presents a scalable spatial optimization tool that empowers decision makers to transform large-scale urban infrastructure into the energetic and economic engines of district wide decarbonization Full article

Background: Cartilage defects remain a clinical challenge due to the limited intrinsic repair capacity of hyaline cartilage, driving increasing interest in blood-derived products, including platelet-rich plasma (PRP). Variability in PRP preparation and activation protocols limits reproducibility and clinical translation, particularly in large animal models where species-specific differences are an additional cue. This study aimed to standardize and optimize in pigs a protocol for plasma rich in growth factors (PRGF), a leukocyte-poor PRP, aligned with current human clinical practice. Methods: Whole blood from six female pigs was processed via three centrifugation protocols and activated with varying CaCl₂ concentrations to evaluate gelation and morphology. PRGF was characterized through hematological analysis, ELISA-based quantification of soluble factors, and structural imaging of fibrin gel via histology and scanning electron microscopy. Data were further analyzed using protein–protein interaction networks, hierarchical clustering, and comparative human PRGF proteomic profiles. Results: Protocol with 400× g centrifugation followed by 13.3 mM CaCl₂ activation achieved the most favorable performance, yielding the highest platelet recovery, effective leukocyte clearance, and consistent formation of a well-organized fibrin network. Porcine activated PRGF showed substantial overlap in detected factors and concentration ranges with human activated PRGF prepared with the same protocol. Conclusions: These findings establish a robust, clinically aligned porcine PRGF protocol and support the pig as a relevant translational model for PRP-based regenerative strategies, providing a reliable platform for preclinical evaluation of cartilage therapies. Full article

This study used bibliometric methods to systematically analyze the development trend, knowledge structure and evolution path of the field of “quantitative research on agricultural soil respiration based on machine learning” from 2021 to 2025, and further explored its implications for agricultural soil carbon sinks. Based on 966 articles included in the core collection of Web of Science, this paper comprehensively uses tools such as Biblioshiny, CiteSpace and VOSviewer to carry out multi-dimensional analysis from the aspects of annual publication trends, international and institutional cooperation networks, keyword clustering and emergent evolution. It is found that this field has shown phased evolution characteristics of “technology-driven mechanism deepening–application expansion” in the past five years. At the beginning of the 5-year period of research, the introduction of machine learning methods and model verification were the core, then gradually expanding to multi-algorithm comparison, environmental factor coupling mechanisms and multi-source data fusion. Recently, the field has focused on regional-scale simulation, uncertainty quantification and model interpretability research. Keyword clustering identifies three thematic clusters—machine learning algorithm and model optimization, environmental driving factors and process mechanism, and remote sensing fusion and regional application—which form a knowledge system of “method–mechanism–application” collaborative evolution. The national cooperation network presents a pattern of “Asia-led, China–US dual-core, and European connectivity”. China dominates in scientific research output, and the United States plays a key role in international cooperation. This study further points out that the development of this field provides important methodological support and a scientific basis for accurate assessment, intelligent management and carbon neutralization decision-making for agricultural soil carbon sinks. Based on the above findings, future research should focus on the development of intelligent models of mechanisms and data fusion, the construction of multi-source data assimilation and uncertainty assessment frameworks, the expansion of global diversified agricultural system cases, and the promotion of an open and shared international scientific research cooperation ecology. This study provides empirical evidence and a direction reference for academic development, scientific research layout, carbon sink management and international collaboration in this field. Full article

Quantitative evaluation of public transit networks (PTNs) with complex-network models informs route optimization and operational adjustments. Prior studies emphasize large cities and pay limited attention to small-sized urban systems. This study examines the bus network of Cangzhou City, Hebei Province, China, to broaden the empirical scope and characterize PTNs in smaller cities. The dataset for this study comprises route and stop records, passenger boarding logs, and bus GPS traces. We develop a general workflow for bus data cleaning and completion. To characterize the dynamic bus network and compare it with the static network, we construct a static network and Directed Weighted Dynamic Network I (DWDN I) using the L-space method, and we construct Directed Weighted Dynamic Network II (DWDN II) using the P-space method. We calculated network metrics including degree, weighted degree, clustering coefficient, path length, network diameter, network efficiency, and small-world coefficient. The principal results show that: (1) at the macroscopic level, the dynamic PTN tracks passenger demand, as the average degree, weighted average degree, and clustering coefficient fluctuate in concert with passenger flows; (2) key stations concentrate in the urban core, and stations with high weighted degree display pronounced spatial autocorrelation; (3) the exponential form of the weighted-degree distribution indicates that the examined bus network is not scale-free, while the dynamic network’s small-world coefficient exceeds that of the static network across time periods, reflecting stronger small-world characteristics. This study integrates network and spatial attributes of the PTN to offer an exploratory case for investigating public transit networks in third-tier cities. The findings can inform comparable studies and offer practical guidance for bus operators. Full article

Crop residues can harbor pathogens, making winter sanitation essential for sustainable viticulture. The grass–sheep–grape system could improve vineyard health through microbial optimization. To evaluate this, we assessed the effects of sheep feeding on fallen leaves on the occurrence of grape diseases through greenhouse experiments and used high-throughput-sequencing to compare microbial communities in grape fallen leaves and sheep feces, aiming to determine whether winter grazing reduces residue-borne pathogens. The results revealed that sheep grazing in vineyards significantly reduces the occurrence of grape leaf and cluster diseases, as well as a fundamental difference in microbial structures between leaves and feces, with no fungal taxa detected in the feces. The number of shared bacterial OTUs was minimal, while feces contained significantly more unique bacterial OTUs than fallen leaves. Additionally, bacterial diversity was significantly higher in feces than in fallen leaves. Sheep feces harbored a substantial number of highly efficient cellulose-degrading anaerobic bacteria, which may enhance organic matter conversion efficiency, and promote nutrient cycling in vineyards. Moreover, the grazing process directly reduced several pathogenic fungi associated with grape leaf, fruit, and root diseases. Functional analysis further indicated that fecal bacterial communities were primarily enriched in core metabolic and genetic processing functions, while leaf microbes were more involved in microbial interactions and secondary metabolism. More importantly, no function guilds of plant pathogenic fungi were present in feces. Overall, winter sheep grazing in vineyards can remove fallen leaves, not only reducing the risk of pathogen transmission but also potentially introducing beneficial bacterial communities. This study provides a feasible strategy for organic vineyard management in winter, and offers important insights for promoting sustainable vineyard production. Full article

This study explores a novel class of polynuclear superhalogen anions featuring heterovalent central atoms from groups 13 (B, Al) and 15 (P, As). The investigated species follow a modified general formula, (X_nY_n’F_{{(3n+5n’)+1}})⁻ where X = B and/or Al, Y = P and/or As, and n + n′ = 2–4. Low-energy isomers were identified using the Coalescence Kick method and subsequently optimized at the MP2/aug-cc-pVDZ level of theory. Electronic stability was assessed via the outer valence Green’s function (OVGF) approach with the same aug-cc-pVDZ basis set. All examined anions exhibit exceptional electronic stability, with vertical electron detachment energies (VDEs) ranging from 10.70 to 12.37 eV, significantly exceeding the superhalogen threshold of 3.65 eV. Thermodynamic analyses indicate that aluminum atoms play a crucial role in stabilizing larger clusters by acting as a structural “glue”, thereby suppressing fragmentation through the loss of neutral XF₃ or YF₅ units. In contrast, larger non-metallic analogs show an increased propensity toward dissociation. The potential of the heterovalent polynuclear superhalogen anions as weakly coordinating anions (WCAs) was further evaluated through molecular electrostatic potential (ESP) analysis. The results demonstrate that combining different central atoms within boron-based frameworks leads to a more homogeneous charge distribution, enhancing weakly coordinating behavior. Full article

Industrial Quality Inspection (IQI) is a pivotal part of intelligent manufacturing, critical to ensuring product quality. Deep learning-based methods have attracted growing attention for their excellent feature extraction ability, outperforming traditional detection approaches. However, existing methods still face issues of insufficient efficiency and poor transferability, and this paper proposes a Fine-tuned Lightweight Faster RCNN (FLF-RCNN) framework designed to address key challenges in IQI, including the trade-off between accuracy and computational efficiency, and the insufficient adaptability of preset anchor box ratios. FLF-RCNN introduces a lightweight backbone network, LSNet, which enhances the receptive field through architectural optimization. Specifically, it uses a collaborative mechanism that combines large kernel convolutions for extracting contextual information and small kernel convolutions for capturing fine-grained details. This mechanism enables the model to efficiently and precisely represent defects. To enhance generalization in data-scarce industrial scenarios, the framework leverages transfer learning with pretrained weights. Furthermore, an Adaptive Anchor Box-Adjustment Module (AAB-AM) based on K-means clustering is introduced to improve detection across varied defect scales. Extensive experiments conducted on the Tianchi dataset show that FLF-RCNN achieves a mAP50 of 43.6%, outperforming detectors using MobileNet and EfficientNet backbones and surpassing the baseline Faster R-CNN by 7.9% in mAP50. Meanwhile, the proposed method reduces computational complexity by approximately 40%, reaching 98.65 GFLOPs, and decreases parameter count by around 30% to 28.2M. These results demonstrate that FLF-RCNN offers a feasibility and practical solution for IQI, achieving a superior accuracy-efficiency balance within the two-stage detection paradigm. Full article

Against the backdrop of the accelerating global energy transition toward clean and low-carbon sources, hydrogen energy is emerging as a vital component of future energy systems due to its zero-carbon emissions, high energy density, and renewable nature. The safe and efficient transportation of hydrogen is a critical link in the hydrogen energy industry chain. As core connecting components in hydrogen transmission systems, the sealing integrity, hydrogen embrittlement resistance, and long-term service reliability of hydrogen pipeline joints directly impact the stable operation of entire hydrogen transmission systems and the feasibility of large-scale application. This study systematically reviews the research literature on hydrogen pipeline joints from 2014 to 2025 using bibliometric and knowledge graph analysis methods based on the Web of Science Core Collection database. It constructs co-occurrence networks and clustering graphs of keywords to identify core research themes in this field, including hydrogen embrittlement failure mechanisms, degradation of sealing material properties, structural design optimization of joints, and intelligent monitoring and fault diagnosis. Furthermore, this study highlights existing research gaps in evaluating joints’ long-term service performance, developing low-cost and efficient manufacturing technologies, and verifying reliability under complex operating conditions. This study provides a systematic bibliometric perspective on hydrogen pipeline joint technology development, aiding in identifying research frontiers and technological evolution pathways. It offers theoretical support and decision-making references for the safe construction and standardized development of hydrogen energy infrastructure. Full article

Fast and reliable aerodynamic predictions are crucial in the early phases of aircraft design, where a quick assessment of various configurations is required. In this context, the Vortex Lattice Method (VLM) is widely adopted due to its computational efficiency; however, its predictive accuracy is highly sensitive to panel discretization strategies, which are often determined heuristically. This study proposes a bio-inspired optimization framework for VLM panel discretization and evaluates it through a systematic case study on a representative wing geometry. A grid-convergence analysis was initially carried out to ensure solution independence across various spanwise-to-chordwise panel ratios. Subsequently, a novel Hybrid Response Surface Methodology (HRSM), integrating Box–Behnken and Central Composite experimental designs, was employed to enable a more comprehensive exploration of the factor space while quantifying the effects of clustering parameters at the leading-edge, trailing-edge, root, and tip regions of the wing. The HRSM dataset was further utilized to train Ensemble Machine-Learning surrogate models, which were coupled with bio-inspired Crayfish and Aquila optimization algorithms, alongside a classical Genetic Algorithm (GA) as a performance benchmark, to identify the optimal discretization strategy and to enable a comparative assessment of their convergence behavior and robustness against the numerical noise of the ensemble-based landscape. Compared to base (i.e., uniform) panel distribution, the optimally clustered discretization enhanced overall aerodynamic prediction accuracy by approximately 33%, particularly at low angles of attack, while maintaining robust performance at higher angles. Both algorithms converged to similar minima; however, the Aquila algorithm achieved higher solution consistency, whereas the Crayfish algorithm exhibited greater dispersion despite faster convergence, revealing a multimodal optimization landscape. The variance decomposition revealed that trailing-edge clustering dominated aerodynamic accuracy at low angles of attack, contributing up to 90% of the total variance, whereas tip clustering became increasingly influential at higher angles, exceeding 30%, highlighting the need for adaptive discretization strategies to ensure reliable VLM-based aerodynamic analyses. Full article