Search Results (626)

The rapid advancement of unmanned aerial vehicles (UAVs) has greatly increased the application of various swarm intelligence algorithms in UAV path planning. To address the potential issues with the dung beetle optimizer (DBO) in UAV trajectory planning, such as low convergence accuracy, tendency to get trapped in local optima, and imbalance between global search and local exploration, a hybrid algorithm termed DBO-PSO is proposed by integrating DBO with particle swarm optimization (PSO) to solve the UAV path planning model. The Kent chaotic map is introduced to enhance population diversity and distribution uniformity, and the velocity–position update mechanism of PSO is incorporated into DBO to strengthen its global search capability. Comparative experiments are conducted on CEC2022 benchmark functions, and multiple classical swarm intelligence algorithms are selected for comparison using six evaluation metrics, along with Wilcoxon rank-sum and Friedman statistical tests. An ablation study is also performed to evaluate the contribution of each improvement component. The path planning experimental results demonstrate that compared to DBO, PSO, IDBO, and ECFDBO under the population size of 50, DBO-PSO reduces the total path cost by 44.2%, 17.3%, 8.9%, and 45.1%, respectively. The ablation study verifies that both improvement components contribute positively, which demonstrates its competitive performance and practical applicability in UAV three-dimensional path planning. The source codes to support the presented results are publicly available on GitHub. Full article

Achieving climate-neutral aviation requires propulsion systems capable of reducing emissions and noise while maintaining high aerodynamic efficiency. Distributed Electric Propulsion (DEP) represents a promising solution; however, accurately predicting the unsteady aerodynamic interactions between multiple propellers and lifting surfaces remains challenging. This work investigates the aerodynamic performance of two Distributed Propulsion (DP) configurations using FLOWUnsteady, a meshless Computational Fluid Dynamics (CFD) solver based on the reformulated Vortex Particle Method (rVPM) within a Large-Eddy Simulation (LES) framework. The Lagrangian particle formulation eliminates mesh generation and limits numerical dissipation. Two layouts—a twin wingtip-mounted arrangement and a four-propeller configuration including inboard units are analyzed and compared with a clean wing baseline as functions of propeller position, inflow speed (20 and 33 m/s), and angle of attack. Beyond global aerodynamic performance metrics, the rVPM–LES framework provides a time-resolved and spatially resolved characterization of local propeller–wing interference in multi-propulsor configurations, highlighting differences in loading and torque demand between inboard and wingtip propellers that are not typically captured by low- to mid-fidelity modeling approaches. The results show that distributed propulsion increases lift and reduces drag relative to the clean wing by accelerating the local flow, delaying separation, and enhancing wing circulation. Thrust and torque coefficients exhibit a clear dependence on rotational speed and angle of attack: inboard propellers experience stronger aerodynamic interference and higher torque demand, whereas wingtip propellers maintain more uniform loading. These findings confirm the capability of the meshless rVPM approach to accurately and efficiently capture unsteady interactions in distributed propulsion systems, supporting its application to the analysis and design of future DEP aircraft. Full article

Cell membranes exhibit specific structural and chirality properties influencing their biological behavior and functionality. Artemia salina endothelial-like cell membranes, structurally simpler, provide insights into fundamental cellular structures, whereas human endothelial cell membranes represent complex, specialized tissues essential for understanding advanced vascular functions. This study aims to compare the structural and chiral properties of Artemia salina endothelial-like cell membranes and human endothelial cell membranes through computational molecular-level modeling, evaluating potential histological and biological implications. Membrane models for Artemia salina and human endothelial cells were developed using Protein Data Bank (PDB) structures. Computational descriptors, including radius of gyration (Rg), solvent-accessible surface area (SASA), geometric asymmetry index (GAI), chiral moment (CM), fractal dimension (FD), and additional chirality indices (SOC, HCI, ACI, CAI, ME, RDF) were calculated to assess membrane complexity, structural asymmetry, and chirality. Significant structural divergences between Artemia salina and human endothelial membranes were identified. Artemia membranes exhibited lower values of Rg, SASA, and chirality metrics, indicating simpler, more symmetrical structures. In contrast, human endothelial membranes displayed elevated structural complexity, pronounced asymmetry, higher chirality indices, and more significant structural heterogeneity, consistent with their specialized physiological functions. Principal Component Analysis (PCA) further highlighted clear structural clustering distinctions between the two models. The comparative analysis underscores fundamental structural and functional divergences between Artemia salina and human endothelial cell membranes. Artemia membranes represent simplified, uniform cellular arrangements optimized for fundamental physiological roles, while human endothelial membranes exhibit complex architectures, structural specialization, and significant chirality essential for dynamic vascular functionalities. These computational descriptors offer potential diagnostic biomarkers for evaluating endothelial functionality and pathological states. Full article

Transition finance has emerged as a critical instrument for facilitating brown firms’ sustainable transformation, yet its heterogeneous effects across different stages of corporate development remain underexplored. This study develops a novel green value metric using a regression coefficient weighting approach and employs a difference-in-differences (DID) model to investigate how transition finance influences corporate green value through innovation persistency, based on a sample of Chinese listed brown firms from 2011 to 2022. The empirical results show that transition finance is significantly associated with an enhancement in corporate green value. Specifically, brown firms receiving transition finance exhibit a 61.6% higher green value than non-recipient firms. This effect is most pronounced during the maturity stage, where the additional green value premium for mature-stage firms is approximately 15.3% higher than for decline-stage firms. Mechanism analysis reveals that innovation persistency serves as the fundamental channel; mature-stage firms exhibit superior capacity to sustain consistent R&D investments and translate these persistent efforts into market-recognized green value premiums. These findings provide actionable insights for policymakers: transition finance frameworks should incorporate lifecycle-sensitive mechanisms rather than applying uniform standards, and incentive structures should prioritize sustained innovation commitment over one-off technological upgrades to maximize long-term sustainability outcomes. Full article

Start-ups have established themselves as drivers of dynamism and economic growth. However, they face many and varied challenges, with one of them being managing their communication strategy. This study aims to analyse the digital communication strategy of start-ups in the Agrotech and Foodtech sectors. For that purpose, the annual rankings published by El Referente between 2023 and 2025 were reviewed, and 17 companies were selected, 11 from the Agrotech sector and six from the Foodtech sector. Based on this sample, a mixed methodology was used, combining content analysis with social media metrics analysis. The results show an uneven use of communication strategies by start-ups. Almost all of them have their own communication channels, such as websites or social media profiles, but their use is not uniform, and in many cases, the potential offered by these tools could be improved. The findings contribute to the literature on strategic communication in start-ups by evidencing the gap between declared identity and substantive legitimacy in sectors of high social scrutiny, such as the agri-food sector. Full article

Sloshing in partially filled tanks generates significant impact pressures that threaten the structural integrity of LNG cargo containment systems, and accurate scaling of these impacts remains a critical issue. Although Froude-based scaling has been widely applied, its validity may be limited under conditions where multiple impact mechanisms coexist. In this study, sloshing impact pressures measured across different scales were analyzed based on individual impact events. Distribution-based representative metrics, including mean and upper-percentile values, were introduced, and scale dependency was quantified using a power-law relationship. The results show that under low filling conditions, impact responses exhibit relatively consistent distributions, and gravity-based scaling yields nearly scale-independent results. In contrast, high filling conditions lead to increased variability and a pronounced expansion of the upper tail, resulting in stronger scale dependency, particularly for high-intensity events. The increase in the power-law exponent indicates that extreme impacts are more sensitive to scale variation. These findings demonstrate that sloshing impact scaling is governed not by a uniform change in pressure magnitude, but by a redistribution of impact intensity across events. Consequently, reliable scaling requires consideration of both distribution characteristics and underlying impact mechanisms. Full article

As a typical mountain ecosystem in the western Tianshan Mountains, the Ili River Valley possesses abundant vegetation resources. Soil nematodes are effective biological indicators for evaluating soil micro-food webs. Nevertheless, the response mechanisms of nematode community structure to distinct vegetation types, especially native trees and forest-edge shrubs, remain poorly understood in this region. In this study, two dominant tree species (Picea schrenkiana and Malus sieversii) and two forest-edge shrub species (Berberis heteropoda and Berberis sibirica) were investigated. We analyzed the composition, diversity, and energy structure of rhizosphere soil nematodes and further compared their differences among plant species. The results indicated that tree rhizospheres had significantly higher amounts of nitrate nitrogen ($\mathrm{N}{\mathrm{O}}_3^{-}$-N and microbial biomass carbon (MBC), along with a lower amount of extractable organic carbon/extractable total nitrogen (EOC:ETN) than shrub rhizospheres (p < 0.05). Picea schrenkiana (PS) exhibited greater root carbon storage, higher root biomass, and a higher root carbon-to-nitrogen ratio (RC:RN) than Berberis heteropoda (BH) and Berberis sibirica (BS) (p < 0.05). The genus Chiloplacus dominated the nematode community across all four woody plants. The relative abundance of omnivore-predatory nematodes was markedly higher in shrubs (BH and BS) than in trees (PS and MS). The soil food webs of PS and MS were degraded, whereas shrub food webs were in a transitional state between structured and degraded habitats. Shrubs presented a higher maturity index, structural metabolic footprint, and energy flux of omnivore-predatory nematodes, but a lower energy flux of bacterivorous nematodes. Additionally, PS had the highest nematode carbon use efficiency (NCUE) and the lowest energy flux uniformity (U). $\mathrm{N}{\mathrm{O}}_3^{-}$-N extractable total nitrogen (ETN), soil organic carbon (SOC), and root traits were the primary factors driving variations in nematode communities and carbon indicators. Therefore, nematode carbon indicators closely associated with soil carbon and nitrogen cycling have the potential to serve as sensitive auxiliary biological metrics for evaluating material cycling and energy flow in pure forests and shrub ecosystems. This study provides empirical support for the assessment of regional ecosystem stability. Full article

Understanding how staple-crop geography aligns with climate resources is important for food-security planning under climate change. Focusing on rice, winter wheat and maize in China from 2001 to 2020, this study constructed 1 km crop-abundance grids from annual 30 m crop-distribution data and integrated weighted centres of gravity (COGs), Standard Deviational Ellipses (SDEs), effective accumulated temperature and a Climate Resource Matching Index (CRMI) to evaluate crop migration, spatial-form change and matching with thermal, pluvial and radiant resource centres. Results show that rice exhibited the strongest northeastward migration, with a cumulative COG path of 448.9 km, but its CRMI declined markedly, indicating that thermal relaxation did not translate into coordinated multi-resource improvement. Winter wheat remained anchored in the Huang-Huai-Hai Plain, with adjustment mainly occurring through internal concentration and persistent moisture constraints. Maize showed expansion before 2015 followed by partial correction, and its CRMI trough in 2015 was robust under alternative weighting schemes. Overall, China’s staple-crop change represents a differentiated spatial reconfiguration rather than a uniform northward shift. Because these metrics are national-scale, the findings should inform crop zoning as broad spatial signals rather than direct local yield responses. Full article

To address the issues associated with traditional multi-point surveying processes in the dead-end cutting for oil and gas pipelines—such as cumbersome procedures, high error rates, lengthy emergency repair cycles, and difficulties in ensuring welding precision—an infrared line drawing device has been developed that enables rapid positioning, long-distance high-precision alignment, and accurate marking of cutting locations. This paper establishes mathematical models for the centering deflection mechanism and the marking mechanism, and derives theoretical solutions for key structural parameters. Thirteen finite element models were constructed using Abaqus to simulate operating conditions involving different pipe diameters and link lengths. A variance-based uniformity metric was employed to quantify structural stress stability, and optimal parameters were determined based on the principle that smaller variance indicates more uniform stress distribution and closer to ideal component service life. The results indicate that the optimal length of the three mounting bolts is 85 mm, with a maximum deflection angle of 9.25°, which meets the requirements. A spring extension of 5 mm for the marking pen can accommodate the compensation needs for marking on DN300 to DN500 pipes. An optimal set of connecting rod parameters across pipe diameters has been determined, with a 240 mm connecting rod capable of covering more than 75% of operating conditions. This device and its parameters are expected to contribute to first-pass compliance and reduce downtime, providing efficient and precise technical support for the maintenance and emergency repair of oil and gas pipelines. Full article

In arid inland watersheds, the compounding impacts of climate change and intensive human activities have severely altered hydrological regimes and accelerated landscape degradation. However, conventional spatial planning often overlooks the critical coupling between subsurface hydrological processes and surface landscape dynamics. Taking the Manas River Watershed in northwestern China as a representative case, this research investigates the multi-scale dynamics of landscape patterns and their underlying spatial determinants. Integrating multi-period land-use data (2000–2020), landscape metrics, and the GeoDetector model, we diverge from conventional uniform buffer approaches by redefining riparian boundaries utilizing four distinct River–Groundwater Transformation (RGT) patterns. This methodological shift reveals critical eco-hydrological heterogeneities previously masked by fixed-width approaches. Our multi-scale analyses demonstrate that watershed-level landscapes exhibited a trajectory of declining diversity, transient recovery, and ultimately, intensified fragmentation, while riparian patches concurrently expanded and became increasingly homogenized. GeoDetector assessments indicate a fundamental shift in driving forces: early-stage variations were constrained by natural factors, whereas post-2010 dynamics became overwhelmingly dominated by socio-economic determinants, particularly agricultural expansion and GDP growth. Crucially, our RGT-coupled spatial analysis reveals a strong spatial association between agricultural sprawl and landscape risk hotspots concentrated within groundwater overflow zones—a pattern consistent with, but not directly demonstrating, disrupted vertical hydrological connectivity. Direct verification of subsurface mechanisms would require continuous piezometric monitoring beyond the scope of this study. Consequently, rather than generic zoning, we propose a multi-scale “hydro-spatial” governance framework featuring targeted interventions. By establishing strict agricultural redlines in vulnerable overflow zones and implementing eco-hydrological restoration tailored to specific RGT regimes, this paradigm delivers robust methodological insights for advancing precision spatial planning in fragile arid ecosystems. Full article

Background/Objectives: The aim of this study was to investigate the effects of intraocular pressure (IOP)-lowering drops on the sublayers of the human tear film as assessed by a novel nanometer-resolution Tear Film Imager (TFI, AdOM, Israel). Methods: In a prospective, cross-sectional study, 98 eyes from 56 adult human subjects were imaged using the TFI. The dataset included data from 18 eyes from 12 subjects treated with preserved IOP-lowering drops and 80 eyes from 44 control subjects not under ocular hypotensive therapy. Subjects in the IOP treatment group used a variety of IOP-lowering medications, including prostaglandin analogs, beta-blockers, carbonic anhydrase inhibitors, alpha agonists, and combination drops. A linear mixed effects model was used to assess the association between IOP-lowering therapy and tear film (TF) metrics, controlling for age and intra-individual correlation. The following parameters were measured: muco-aqueous layer thickness (MALT), muco-aqueous layer thinning rate (MALTR), lipid layer thickness (LLT), lipid map uniformity (LMU), inter-blink intervals (IBI), and lipid break-up time (LBUT). Results: Average ages significantly differed (p = 0.013) between the treatment group (66.5 years) and control group (average age 51.5 years), and thus results were adjusted for age accordingly. IOP was 17.1 mmHg in the treatment group and 16.1 mmHg in the control group. When analyzing the sublayers of the TF, MALTR had a significant association with IOP-lowering therapy after adjusting for age, with a difference of −52.68 nm/s; 95% confidence interval [−96.87, −8.48]; p-value = 0.020. Additionally, IBI was significantly associated with IOP-lowering therapy after log transformation (p = 0.049), with shorter IBI in the treatment group. All other metrics (MALT, LLT, LMU, and LBUT) were statistically insignificant (p > 0.05). Conclusions: These pilot results suggest that IOP-lowering drops may accelerate thinning of the TF, specifically the muco-aqueous layer. Longitudinal studies with significantly larger samples are needed to specify the differential impact of various ocular hypotensive therapies on the human TF and the clinical implications of these findings. Full article

Operating room ventilation is a key engineering factor in maintaining clean air environments. This study presents an integrated three-part methodology combining Computational Fluid Dynamics parametric analysis, performance assessment with effect size analysis and multi-criteria decision analysis using quantitative engineering metrics, and surrogate modeling for thermal effect propagation in an orthopedic operating room. Simulations were conducted in ANSYS Fluent 2020 R₂, benchmarking an existing local operating room against an ASHRAE 170-2021 compliant model, followed by parametric evaluation of four ceiling inlet configurations. The existing system exhibited critically low velocities (0.05–0.10 m/s) with a coefficient of variation of 0.73, indicating severe flow non-uniformity. The proposed Multi-Velocity Ceiling Diffuser—featuring a high-velocity core (0.40 m/s) over the surgical area and a low-velocity peripheral frame (0.20 m/s)—achieved 85% coverage of the ASHRAE-recommended velocity range (0.20–0.30 m/s), a coefficient of variation of 0.14 (81% improvement), and 62 air changes per hour, representing an 86% reduction in supply airflow compared to a full-ceiling system. Effect size analysis confirmed that MVCD performance shows large practical differences from smaller inlet designs (Cohen’s d ≥ 0.41) and negligible difference from full-ceiling systems (Cohen’s d = 0.05). Multi-criteria decision analysis—with feasibility and cost quantified using engineering estimates (ductwork area, downtime days, standardized cost data)—ranked MVCD as optimal under the modeled assumptions (composite score = 0.84), outperforming the existing system (0.59) and full-ceiling design (0.51). To address the isothermal assumption limitation, a Random Forest surrogate model was implemented as a differentiable approximation strategy for parametric uncertainty propagation. Under non-isothermal conditions, the MVCD is predicted to maintain a spatial median velocity of 0.19 m/s (5th–95th percentile range: 0.17–0.21 m/s) and 71% ASHRAE compliance (parameter sampling range across literature-derived distributions: 63–78%). Achieving ASHRAE velocity criteria is an engineering surrogate for ventilation effectiveness; the relationship between these metrics and clinical infection outcomes depends on multiple factors beyond airflow, including surgical technique, patient factors, and antimicrobial prophylaxis. No clinical inference is permitted from the present results. Experimental measurement in a physical MVCD-equipped operating room is required to validate these predictions prior to clinical implementation. Full article

Lightweighting Building Information Modeling (BIM) models for digital-twin applications requires balancing aggressive geometric reduction with component-level engineering tolerances and mesh usability. Most geometric simplification pipelines apply uniform ratios or hand-tuned heuristics, which struggle to accommodate the strong heterogeneity of BIM components in functional role, geometric complexity, and detail distribution. End-to-end learning-based simplification can be adaptive, but it often entangles decision-making with geometric editing, making engineering constraints difficult to enforce and audit. We present Semantic-Geometric Co-driven Adaptive Budget Estimation and Reduction for BIM (SABER-BIM), which formulates lightweighting as a component-level face-budget allocation problem. Conditioned on Industry Foundation Classes (IFC) types and structure-sensitive geometric descriptors, SABER-BIM predicts target face counts for individual components and then meets a user-specified global budget through global scaling. The predicted budgets are executed by a robust geometric backend (e.g., quadric error metrics, QEM), yielding an auditable and easily deployable pipeline. To address the absence of direct supervision, we introduce an offline pseudo-ground-truth procedure that searches for the minimum feasible target face count for each component under semantic-aware tolerance and mesh-validity constraints. Experiments on the IFCNet dataset show that SABER-BIM allocates budgets more effectively under identical global constraints, improving stability in both geometric error control and engineering usability. Full article

This paper addresses the challenge of WSN topology optimisation through the development and implementation of a modified genetic algorithm (MGA). Unlike classical approaches, the proposed method is based on the assessment of sensor node distribution density, employing an adaptive penalty system and considering the minimum inter-node distance to determine optimal configurations during the evolutionary selection process. A software module has been developed in Python (version 3.12.1) for the simulation of WSN functionality, accounting for dynamic topology changes and limited network resources. A comparative analysis of the proposed approach’s effectiveness was conducted against greedy, random, and uniform algorithms, varying sensor ranges (20, 30 and 40 m) and minimum inter-node distance constraints. Simulation results for scenarios involving 25 and 100 sensor nodes demonstrate that the proposed MGA consistently outperforms traditional approaches, including uniform (mesh), greedy, and random search algorithms. Unlike these methods, which either result in significant overlap (up to 13.23%) or fail to deploy all nodes, the MGA achieves 100% node placement with near-zero overlap. Furthermore, the proposed method exhibits stable convergence and high reliability, maintaining consistent performance across multiple runs with diverse initial conditions. The proposed Integrated Energy Efficiency Metric (IEEM) establishes a relationship between the spatial distribution of sensor nodes and the overall energy consumption of a WSN. By linking topology formation with energy costs, this metric enables a comprehensive assessment of deployment efficiency. Simulation results across various deployment scenarios demonstrate that the proposed MGA consistently achieves the lowest IEEM values compared to Mesh, Greedy, and Random placement strategies. The observed improvements range from 4.76% to 31.38%, confirming a substantial reduction in total energy losses. The proposed approach is particularly well-suited for dense deployments and resource-constrained environments, where effective coverage and minimal energy consumption are critical. Full article

Two wild Saccharomyces cerevisiae strains, 9-3 and 10-2, isolated from nectarine and apple leaves, show high fermentation performance in standard breadmaking but their utility in enriched-dough products remained untested. This study evaluated their performance in kouglof production against a commercial baker’s yeast as a control. Fermentation was monitored by weight loss (reflecting CO₂ production) and pH changes. Finished loaves were evaluated for macroscopic traits (weight, height, appearance), crust/crumb color, texture, and microstructure via 3D/2D analysis of gas-cell morphology. Sensory acceptance was also assessed. While size-related traits were similar across all samples, strain 10-2 significantly outperformed 9-3. Kouglof made with 10-2 exhibited commercial-grade softness, characterized by lower hardness and gumminess, lower residual sugar content, and a finer, more uniform gas-cell structure with a darker, well-developed crust, whereas 9-3 yielded a firmer crumb with fewer, larger gas cells. Sensory acceptance was comparable across variants, with all samples rated as highly acceptable. Strain 10-2 demonstrated high sucrose tolerance and performed comparably to or better than the control across multiple product-quality metrics in enriched-dough kouglof. These findings support the potential of wild yeast 10-2 for sweet bread production with high-sucrose and high-fat formulations, though further optimization is warranted. Full article