Search Results (4,316)

Agricultural credit rationing remains a persistent systemic friction driven by information opacity and limited collateral. This study develops a credit risk early-warning system by fusing multi-source institutional digital footprints (tax compliance signals, judicial enforcement records, and credit history indicators) for 1021 agricultural enterprises in China. Methodologically, we propose a Default Event Isolation protocol to enforce strict ex ante validity by discarding observations at and after the event month, and implement a two-step feature optimization pipeline that reduces 138 predictors to a parsimonious set of 50 features. Empirically, the optimized LightGBM (version 4.6.0) model achieves an AUC = 0.9345 (95% bootstrap CI: 0.8745–0.9563) and PR-AUC = 0.4421, representing a 47× lift over the random baseline under extreme class imbalance (0.94% event rate), and captures 87.4% of early-warning events by monitoring only the top 10% highest-risk firms. The interpretability analysis consistently highlights judicial boundary constraints and tax stability signals as dominant predictors, forming a “judicial baseline + tax stability” dual-core structure. A strict credit-only robustness check using bank-recorded NPL labels maintains strong predictive performance (AUC = 0.9089, 95% bootstrap CI: 0.8255–0.9591), mitigating concerns that the model’s signal is driven by label overlap. These findings suggest that integrating institutional records into automated screening pipelines can enable the earlier and more targeted identification of distressed borrowers in rural lending, even when traditional financial statements are unavailable. Full article

This study empirically examines the impact of the Areas subject to permission of transaction (ASPLT) implemented by Seoul City on local real estate markets. Focusing on the case of the Seoul International District (MICE project area), where the regulated area (the geo-graphical districts subject to ASPLT) was initially designated, lifted, and later re-imposed, the analysis employs a modified repeat sales Difference-in-Differences (DID) methodology to assess its policy effect on housing price stabilization. The results indicate that the regulated areas experienced more subdued transaction volumes and price increases compared to non-regulated areas, suggesting the policy was effective in curbing short-term speculative demand. Additionally, neighboring areas exhibited signs of spillover effects due to displaced investment interest. The findings highlight both the utility and limitations of localized real estate controls and offer empirical insights for future policy design. Full article

The oxidative stability of meat products is a crucial factor determining quality, shelf life, and consumer acceptance, as lipid and protein oxidation promote undesirable changes in sensory attributes and nutritional content. Antioxidant capacity (AOC) assays such as total phenolic content (TPC), ferric reducing antioxidant power (FRAP), 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS^•+), and 2,2-diphenyl-1-picrylhydrazyl (DPPH^•) are commonly applied in meat systems to assess the AOC associated with both intrinsic muscle components (endogenous) and the protective effects of natural ingredients (exogenous added compounds), i.e., antioxidants. Although differences in analytical methodologies limit direct comparisons among studies, it has been demonstrated that meat products inherently contain compounds that modulate oxidative reactions, with their effectiveness influenced by meat type, processing, and storage conditions. Within this framework, natural ingredients, including plant- and fungal-derived ingredients and their by-products, have gained attention as sources of natural antioxidants, whose capacity depends on the extraction method, the solvent used, and their behavior during gastrointestinal digestion, as evaluated using simulated gastrointestinal digestion (sGD) models. Numerous studies have shown that incorporating natural extracts or powders into meat products enhances AOC during refrigerated storage, with the effect generally depending on the concentration used. Moreover, several natural antioxidant treatments maintain or even enhance their AOC when assessed under sGD conditions. Full article

Terminal lakes in arid regions face severe degradation due to the dual pressures of climate change and anthropogenic water consumption. Traditional single-threshold methods for defining ecological water requirements often fail to balance ecosystem stability with water scarcity. To address this, this study constructs a comprehensive framework coupling “Morphometric Stability–Ecological Security Reliability–Resource Use Efficiency” to delineate the suitable ecological interval for Ebinur Lake, the largest saltwater lake in Xinjiang. Using multi-source remote sensing data (Landsat, Sentinel, ICESat, CryoSat), we reconstruct the long-term hydrological dynamics from 2001 to 2023. Results indicate a significant shrinking trend in the lake area, driven primarily by reduced inflow. We jointly consider the lake morphometric breakpoint, the ecological security baseline, and the lower bound of ecosystem service water use efficiency (ESWUE) to determine a minimum suitable ecological area of 500 km²; the regulation upper limit is set at 740 km² based on the marginal peak of ESWUE. However, monitoring data reveal that the lake falls below the minimum 500 km² baseline in approximately 40% of months, highlighting a severe ecological deficit risk. Furthermore, ESWUE analysis shows a peak in April (10 CNY/m³), suggesting that, under current climate conditions, a “Spring Surplus and Autumn Deficit” regulation strategy—advancing the replenishment window to the spring windy season—can maximize dust suppression benefits at a lower evaporative cost. This study provides a theoretical basis and methodological paradigm that will contribute to the sustainable management of shrinking terminal lakes globally. Full article

This research investigates the colonnade of Tomb 7 at the UNESCO World Heritage site of the Tombs of the Kings in Paphos, Cyprus. Specifically, a multi-drum column located at the south-east corner of the tomb is examined from both geometric and structural perspectives. Being the only standing element to support the entablature on that side of the tomb, the column is crucial for maintaining the structural stability of the monument. Numerical structural analyses are performed on the column via the finite element method (FEM), supported by close-range recording techniques—particularly terrestrial laser scanning (TLS)—to generate finite element (FE) models. Several modelling strategies capable of converting point cloud data into reliable structural models are developed and compared with the aim of identifying the most effective and cost-efficient approach. Each method is analyzed in detail to evaluate its workflow, assumptions, strengths, and limitations in the context of heritage structures with complex irregular geometries. Linear static and dynamic analyses are performed on five different FE models to assess the column’s mechanical response and to understand how differences in geometric representation affect the structural behaviour. The results indicate that all approaches adequately capture the general structural response. The comparison of the different modelling strategies highlights the trade-offs between geometric accuracy, computational efficiency, and practical usability. These outcomes indicate the potential and the current limitations of exploiting point cloud data for structural analysis and contribute to the development of more robust and accurate scan-to-FEM methodologies for the conservation and assessment of cultural heritage structures. Full article

The global expansion of continuous GNSS networks has generated large-scale spatiotemporal datasets whose analysis requires robust mathematical and statistical tools. This study introduces a geospatial, multivariate statistical framework for classifying 21,548 GNSS stations from the University of Nevada repository. The methodology integrates harmonic regression, stochastic noise modeling, quality assessment, and slope estimation into a unified feature space suitable for high-dimensional analysis. Using unsupervised learning clustering computed with our custom-developed code, based entirely on free and open-source software, we identify homogeneous station groups that reflect dominant signal properties—periodicity, noise structure, data quality, and long-term velocity—together with their spatial context. The resulting clusters exhibit strong mathematical coherence and reveal continental-scale patterns driven by seasonal forcing, tectonic regime, climatic variability, and monument stability. By grouping stations with similar statistical behavior, the proposed framework improves reference-site selection, enhances deformation-field interpretation, and supports the detection of anomalous or hazard-related behavior. Overall, this approach provides a scalable, data-driven mathematical tool for analyzing complex spatiotemporal signals and contributes to more reliable deformation modeling and environmental risk assessment. Full article

This study presents the use of a Battery Energy Storage System (BESS) and a thermal power plant to enhance Primary Frequency Regulation (PFR) in a power system. This integration seeks to mitigate operational challenges, such as the reduction in system inertia and frequency regulation, which are heightened when increasing renewable energy use in power grids with high hydroelectric generation. The proposed solution enables thermal generators to operate at optimal capacity, while the BESS provides a rapid frequency response, thereby enhancing operational efficiency and compliance with national standards. The process was structured in five stages: criteria definition, analysis, design, models, and evaluation. A comprehensive methodological approach was adopted, including dynamic system modeling and BESS sizing based on regulatory parameters. The method was tested with real data from a thermal plant under the conditions of the Colombian electricity market. The simulation results highlight the effectiveness of the proposed BESS, with a response time of approximately 0.6 s and regulation maintenance for over 30 s, reducing mechanical stress and preventing frequency overshoot. The control strategy was designed to maintain the energy neutrality of the BESS, thereby stabilizing its state of charge over the operational horizon. The results show that the BESS targets high-frequency transients and the generator focuses on low-frequency adjustments, managed by an Energy Management System (EMS) with a unified control approach. Full article

Tea plantation ecosystems, as typical human–natural integrated systems, rely on biodiversity to sustain yield, quality, and ecological sustainability. With the global popularization of ecological agriculture concepts, eco-oriented tea production has emerged as a core development direction for the tea industry. However, a systematic elucidation of the mechanisms by which tea plantation biodiversity modulates tea quality, alongside standardized assessment methodologies for this biodiversity, remains inadequate. This paper comprehensively synthesizes how genetic, species, and ecosystem diversity regulate the accumulation of tea polyphenols, amino acids, and aromatic compounds—key determinants of tea quality. It evaluates mainstream assessment frameworks and identifies DPSIR (Driving Forces-Pressure-State-Impact-Response) as the most comprehensive and practical option. This paper further dissects the impacts of genetic, ecosystem, and species diversity (the three core dimensions of tea garden biodiversity) on tea quality formation. Genetic diversity shapes metabolic traits; ecosystem diversity modulates secondary metabolism via microclimate and soil; and species diversity (plants, animals, microbes) exerts synergistic effects on nutrient cycling and pest control. All these collectively improve tea sensory quality, safety, and stability. Future research should focus on plant–microbe interactions, quantitative biodiversity–quality models, and precision ecological management, laying a theoretical foundation for sustainable, high-quality tea production. Full article

In recent years, there has been a significant negative impact on the sustainability of supply chains for the delivery of small batch cargo, caused by crisis situations. Therefore, it is important to develop a modern methodology to reduce uncertainty in the delivery of small batch cargo, especially when considering a flexible inventory management system. This study proposes an integrated approach to inventory management, consisting of three elements: an updated ABC-XYZ structure of inventory formation analysis with criteria that determine stability; an additive mathematical model for calculating inventory management costs; and the development of a regression model for operational forecasting of inventory management costs, based on the number of end customers, unit cost and batch size. A comparison of regressions showed the advantage of the power model over the linear one. The main advantage of the study is the proposed mathematical and regression models for the operational calculation of inventory management costs, considering the uncertainty factors that determine the sustainability of the supply chain. This approach will be of interest to trading enterprises, allowing them to make flexible decisions in inventory management in the event of various disruptions in small batch cargo supply chains. Full article

Direct collocation transcription is a dominant technique for solving complex optimal control problems, converting continuous dynamics into large-scale, sparse nonlinear programming problems. The computational efficiency of this approach is fundamentally limited by the evaluation of first- and second-order derivatives required by modern optimization algorithms. While general-purpose automatic differentiation tools exist, they often fail to fully exploit the repetitive substructure inherent in trajectory discretization. This paper presents a vectorized, sparse, second-order forward automatic differentiation framework specifically tailored for direct collocation methods. By explicitly distinguishing between scalar and vector nodes within the expression graph, the proposed method leverages the independence of mesh point evaluations to enable Single Instruction, Multiple Data (SIMD) execution and optimize memory access patterns. This structure-aware approach ensures linear time complexity with respect to the number of discretization nodes while maintaining the flexibility to handle complex dependencies. The methodology is implemented in the open-source software package pockit and is validated through three distinct engineering case studies: the aggressive stabilization of a nano-quadrotor, the powered descent guidance of a reusable launch vehicle, and a low-thrust heliocentric orbital transfer. These applications demonstrate the framework’s capability to deliver high-performance derivative computation for large-scale, nonlinear dynamical systems. Full article

The 2025 fire season in Portugal was marked by large fires, underscoring the vulnerability of the forested areas to fire. The study analyzes the main meteorological conditions during a critical period of fire activity and addresses the following question: Why can the northeast (NE) weather pattern be so critical for fire danger in Portugal? Fire severity in the Arouca wildfire, the largest fire of the period, was estimated using a methodology that integrates foundation vision models with computer vision algorithms. ECMWF analyses and convection-permitting Meso-NH simulations are used to examine large-scale circulation and the mesoscale environment, respectively. Synoptic-scale analysis revealed the Azores anticyclone centered slightly northwest of the Iberian Peninsula (IP), with its eastern sector directly affecting the northern IP under north/northeast winds. The hectometric-scale simulation demonstrated that orographically enhanced wind gusts over the northern Portuguese mountains substantially intensified near-surface fire-weather conditions when the winds were nearly easterly. Furthermore, strong low-level winds and atmospheric stability constrained vertical plume growth, favoring horizontal smoke transport. In addition, the study highlights that Arouca’s fire had 88% of its area affected with moderate to high severity. Overall, the results demonstrate that the interaction between large-scale NE circulation and local orography plays a decisive role in amplifying fire danger in northern Portugal, emphasizing the need for high-resolution atmospheric modeling to identify fire-prone regions under specific synoptic patterns. Full article

Accurate estimation of wireless propagation characteristics is essential for guiding the design and deployment of fifth-generation (5G) communication systems. As network demand increases and 5G infrastructure is introduced in progressive phases, reliable path loss (PL) prediction models are required to refine deployment strategies and improve network efficiency. Conventional propagation models frequently display limited flexibility when applied to diverse environmental conditions and often entail considerable computational expense, reducing their practicality for large-scale 5G planning. Recent developments in data-centric artificial intelligence (AI) have enabled more adaptive and analytically powerful approaches to propagation modeling, resulting in notable gains in PL prediction accuracyThis study employs a comprehensive dataset produced using the NYUSIM channel simulator, integrating a wide spectrum of atmospheric parameters and seasonal variations within South Asian urban microcell environments, complemented by broad empirical observations. The core objective is to construct, optimize, and evaluate four machine learning (ML) models capable of accurately predicting PL at high-frequency bands critical to 5G performance. A fully automated hyperparameter tuning pipeline, based on the Optuna framework, is applied to twelve regression algorithms, including advanced ensemble methods, regularized linear techniques, and classical baseline models. Performance assessment emphasizes predictive reliability, stability, and cross-model generalization. Furthermore, statistical analysis utilizing bootstrap confidence intervals and paired t-tests indicates that all ML methods perform equivalently (p > 0.4), while SHapley Additive exPlanations (SHAP) analysis across all models supports a consistent feature importance distribution, supporting the statistical analysis results. To showcase the superiority of the ML approaches, a comparison with conventional free-space PL modeling methods is presented, with the AI methodology demonstrating robust performance across seasonal variations and a 95.3% improvement. Full article

In this paper, the coordinate transformation method is applied to the Navier–Stokes equations expressed in terms of the stream function and vorticity formulation. An elliptical grid generator is used to construct an orthogonal curvilinear grid within an irregular domain of complex geometry, mapping the physical region onto a computational square domain. The developed algorithm is capable of generating both orthogonal and general curvilinear grids. The finite-difference scheme of the Navier–Stokes system in arbitrary orthogonal curvilinear coordinates is then solved numerically on this grid using the alternating direction method. Numerical simulations of the Roach problem are conducted at low Reynolds numbers and on grids of varying resolutions. The obtained results are compared with the reference studies of Napolitano and Orlandi, showing satisfactory agreement with the data reported by 16 other research groups. Overall, the proposed method enables efficient numerical simulation of laminar flows in domains with complex geometry. The developed approach provides high accuracy and stability and can be effectively used for the numerical analysis of applied fluid dynamics problems. Furthermore, the methodology described in this work may serve as a foundation for future studies focused on improving computational efficiency and expanding the applicability of curvilinear grid techniques in modern fluid dynamics. Full article

The Folin–Ciocalteu method remains the standard approach for quantifying total phenolics in plant extracts; however, matrix-specific optimization is essential for obtaining accurate results for chemically complex botanical materials. The Peruvian Amazon harbors extensive botanical biodiversity, including numerous medicinal species with uncharacterized phenolic profiles. This study developed and validated a Folin–Ciocalteu method specifically optimized for twelve ethnomedicinal plants representing eleven families from the Peruvian Amazon, following ICH Q2(R2) guidelines. Method optimization established optimal analytical conditions: 765 nm wavelength, 60 min reaction time, 14.05% sodium carbonate, and gallic acid as the reference standard. Comprehensive validation demonstrated excellent linearity (R² = 0.995–1.000), specificity confirmed through parallel standard addition curves (slope differences < 3%), precision with relative standard deviations below 2.63% for both repeatability and intermediate precision, and accuracy with recovery of 89.43 ± 2.76% meeting AOAC guidelines for complex matrices (80–120%). Robustness testing via response surface methodology confirmed method stability across variations in sodium carbonate concentration (7.50–14.05%), Folin–Ciocalteu reagent dilution (50–100%), and reaction time (30–90 min). Limits of detection and quantification were 4.43 and 13.44 μg/mL, respectively. Application to the twelve species revealed 10-fold variation in total phenolic content (24.6 ± 2.1 to 256.8 ± 4.3 mg gallic acid equivalents per gram dry extract), with Aspidosperma schultesii leaves exhibiting the highest concentration. This validated methodology provides a reliable analytical framework for the quality control and standardization of Amazonian medicinal plants, supporting bioprospecting efforts and therapeutic development. Full article

Fusarium head blight (FHB) is one of the most significant diseases in wheat globally, affecting about 200 million tons of grain per year through mycotoxin contamination. Besides yield losses, mycotoxin contamination is a major concern. FHB resistance in wheat is partial and polygenic, and since the efficacy of plant protection measures is generally weak-to-moderate, an integrated approach is needed for successful control. We evaluated a more comprehensive methodology for improved protection; in this two-year study, five registered organic products and six conventional products were compared under artificial and natural infection conditions. The disease index (DI), Fusarium-damaged kernels (FDKs) and deoxynivalenol (DON) contamination were evaluated. The stability of the fungicides was also evaluated based on 10 epidemic conditions. The organic fungicides showed much lower efficacy than the conventional ones, although significant reductions in symptoms and DON contamination were observed. In each group, significant variability was detected. The best fungicides for DON contamination showed the lowest variance (highest stability) between 10 and 20 (Verben, Prosaro, Ascra Xpro). The organic fungicides were much less stable; the least stable showed a variance of 141 (Fusarium control: 264). The best organic fungicide was the Bordeaux mixture supported by sulfur addition (variance: 54). The DI and FDK values presented very similar trends. For the more resistant cultivar GK Pilis, the combined DON reduction exceeded 90% for all fungicides. For the most susceptible cultivar, GK Békés, the values were between 30 and 83%, respectively. High resistance to FHB and toxin contamination is the key to controlling FHB in both organic and conventional production. For efficient fungicide control, stable resistance to disease and toxin accumulation are equally required. Principal component analysis (PCA) verified the importance of considering all traits to identify the fungicidal “fingerprint” and demonstrated the differences between fungicides regardless of their organic or conventional nature. PC response differs for traits and fungicides, supporting the complex evaluation of plant and fungicide behavior. Knowledge of resistance levels, in addition to improving mycotoxin control, aids in disease forecasting and epidemic management. The results are applicable to both organic and conventional production systems. Due to the variability in resistance and fungicidal effects, there is an opportunity to improve food safety in both organic and conventional wheat production. Full article