Search Results (1,872)

The economic resilience of agricultural enterprises is increasingly relevant for maintaining processing continuity and food quality in highly perishable agro-food chains. This study examines the associations between financial knowledge, financial management competency, business liquidity, and operational food-processing continuity in Türkiye’s tea sector. A quantitative cross-sectional design was employed, using structured survey data from 203 senior managers across 86 public and private tea-processing firms in Rize Province. The data were analysed using Ordinary Least Squares regression, mediation analysis, exploratory factor analysis, and robustness checks in accordance with OECD/INFE guidelines. Results indicate a significant deficit in theoretical financial knowledge (mean score: 4.47/10) alongside widespread overconfidence among 85% of managers. Applied financial management competency is positively associated with perceived business liquidity (β = 0.336, p < 0.001), suggesting that practical budgeting, cash-flow planning, and financial decision-making capabilities are relevant to maintaining operational funding capacity. In contrast, cash-flow difficulties are not significantly explained by firm-level financial knowledge, managerial competency, liquidity, or ownership structure (R² = 0.014, p = 0.722), indicating that these difficulties may reflect broader seasonal and sector-wide financing constraints. The findings challenge the assumption of a linear relationship between theoretical financial knowledge and managerial outcomes. They suggest a dual policy approach that combines applied financial management training with structural financing mechanisms to ensure the continuity of fresh leaf procurement and processing. While the study does not directly measure food safety, post-harvest losses, or SDG outcomes, the results have potential implications for reducing processing disruptions and supporting more resilient agro-food processing systems. Full article

Highway–rail grade crossing (HRGC) incidents remain a persistent safety concern due to repeated interactions between roadway users and rail operations under varying environmental and operational conditions. Existing studies rely on raw incident counts or partial exposure measures that can be influenced by differences in infrastructure exposure and do not account for spatial dependence, limiting consistent comparison across locations. This study developed an exposure-normalized framework to model incident intensity at the county level using accumulated incidents per crossing (AIPC), which normalizes cumulative incidents by crossing exposure. The analysis integrated statistical distribution modeling, spatial clustering, and supervised machine learning. The study combined county-level HRGC data for the contiguous United States from 1975 to 2025 with infrastructure, traffic, environmental, and accessibility variables. Results showed that AIPC was consistent with a gamma distribution, indicating a continuous representation of incident intensity without discrete risk regimes. Local Moran’s I identified statistically significant high-intensity clusters in specific regions, confirming spatial dependence in incident intensity. Machine learning models achieved strong predictive performance, with the extra trees model reaching AUC = 0.907 (F1 = 0.528) and ensemble methods consistently outperforming linear and kernel approaches. SHAP and permutation-based feature importance analysis identified temperature, train frequency, and accessibility measures as the most influential predictors, while aggregate density measures contributed the least. The results provided consistent evidence that incident intensity was associated with environmental conditions, operational exposure, and network structure. The proposed framework supports exposure-based risk assessment and enables identification of high-intensity counties for targeted intervention. This approach provides a transparent and transferable method for improving HRGC safety analysis and prioritizing resource allocation across large geographic areas. Full article

This paper investigates uncertainty-resilient stabilization of an inverted pendulum on a cart (IPOC) using an interval type-2 Takagi–Sugeno (IT2 T–S) fuzzy model and an LQR-based control framework. The IPOC dynamics are represented as a weighted combination of local linear subsystems, where interval firing strengths derived from upper and lower membership functions capture modeling uncertainties. An LQR state-feedback controller is designed for each subsystem, and the final control input is obtained by blending the local controllers according to the normalized firing strengths. To analyze stability, an LMI-based verification condition is established as a sufficient condition for the subsystem LQR controllers. Simulation results show that this condition is satisfied only in a limited operating region, while the closed-loop system can still remain stable even when the condition is violated, demonstrating the reduced conservatism and flexibility of the proposed approach. Furthermore, comparisons with the conventional PDC structure confirm that the proposed method provides greater design flexibility and enables a trade-off between robustness and transient-state performance. Full article

Accurate wheat spike detection is essential for crop phenotyping and yield estimation, but real-world field conditions—such as dense spike overlap, environmental domain shifts, and degradation-induced failures like motion blur—pose significant challenges. Achieving robust perception under these circumstances while maintaining a strict accuracy-efficiency trade-off for edge devices remains a pressing research problem. To overcome these limitations, we propose GG-YOLO, a unified lightweight detection framework specifically tailored for complex agricultural environments. Rather than a simple recombination of existing lightweight modules, GG-YOLO integrates three original structural adaptations: First, a Dual-path Attentive Ghost Mechanism (DAGM) introduces gradient-guided attention modulation to enhance feature discrimination and explicitly resolve feature confusion in dense, overlapping regions. Second, a C3Ghost module combines multi-branch aggregation with linear feature generation, mitigating parameter redundancy in the prediction head by approximately 31% compared to the standard YOLOv8s without sacrificing semantic capacity. Third, DSample, a dynamic upsampling operator featuring an original dual-mode adaptive mechanism, robustly recovers fine-grained spatial details during multi-scale feature pyramid fusion. Extensive cross-dataset experiments on the GlobalWheat2020 and HNKJXYwheat datasets validate the model’s exceptional resilience to domain shifts and varying growth stages. GG-YOLO achieves a precision of 94.35%, a recall of 91.93%, and a state-of-the-art mAP@50 of 96.47%. Furthermore, the model contains only 7.89 M parameters and requires 20.4 GFLOPs, reaching an inference speed of 165 FPS on a desktop GPU and a validated real-time speed of 64 FPS on an NVIDIA Jetson edge computing platform. These results demonstrate that GG-YOLO establishes a superior accuracy-efficiency frontier, making it highly reliable for real-time field deployment in precision agriculture. Full article

The increasing penetration of photovoltaic (PV) systems in agricultural irrigation poses significant challenges in terms of energy self-sufficiency and operational cost, particularly when installed capacity is insufficient to cover pumping demand. This comparative study evaluates the energy and economic performance of three storage-based configurations applied to a real PV-powered irrigation system, using a PV capacity of 112 kWp as a common baseline. The methodology combines hourly energy balance modelling with linear programming optimization, implemented under both a grid energy minimization objective and a net cost minimization objective, within a model predictive control framework. Three scenarios are compared against a passive reference case: battery storage integration (Scenario 1), reservoir-based hydraulic storage (Scenario 2), and a combined electrical and hydraulic storage configuration (Scenario 3). Results show that system performance is strongly conditioned by the chosen objective function. When self-sufficiency is prioritized, Scenario 3 achieves the greatest reduction in grid imports by combining intraday electrical flexibility with demand rescheduling. When net cost minimization is the primary criterion, Scenario 2 proves most competitive, exploiting pumping flexibility and surplus compensation revenues. These findings highlight that storage technology selection in PV irrigation systems should be driven by the primary operational objective rather than by a single performance indicator. Full article

Background/Objectives: Chronological age does not fully capture the heterogeneity of physiological aging among healthy individuals. Immune aging and redox imbalance are key hallmarks of biological aging, yet their interaction and relationship with circulating extracellular vesicles (EVs) remain incompletely understood. This study aimed to investigate whether endothelial- and platelet-derived EVs are associated with immune and oxidative aging processes in clinically healthy subjects. Methods: Circulating EVs were isolated and characterized by flow cytometry in a cohort of healthy volunteers spanning a wide age range. Endothelial-derived EVs (EeEVs) and platelet-derived EVs (PEVs) were quantified and analyzed in relation to chronological age, immune function parameters, redox biomarkers, ImmunolAge (an immune aging index), and OxyScore (a composite redox index). A normalized EV-Score was developed using an age- and sex-adjusted Z-score approach. Associations were assessed using correlation analyses, non-linear regression models, generalized additive models, and receiver operating characteristic (ROC) curves. Results: Both EeEVs and PEVs increased non-linearly with age, with a pronounced rise during midlife. EV concentrations were positively associated with molecular aging markers and inversely related to multiple immune function parameters. EVs were also linked to redox biomarkers, although oxidative status alone did not explain EV variability. EV-Score was strongly associated with immune aging and showed context-dependent relationships with oxidative status. Notably, high EV-Score values were observed primarily in individuals with accelerated immune aging, whereas subjects with high oxidative status but preserved immune aging exhibited low EV-Score values. ROC analyses demonstrated that the discriminative capacity of EV-Score for immune or oxidative aging depended on the combined immune–redox context. Conclusions: Circulating EVs may reflect the integrated state of immune and redox aging rather than chronological age alone. These findings suggest the potential utility of EVs as dynamic biomarkers of biological aging in healthy individuals and highlight the importance of considering immune and oxidative processes jointly to interpret EV-associated aging signatures. Full article

This study presents a field-validated, scenario-based two-layer dispatch framework for sustainable rural electrification, demonstrated at the Khlong Ruea hybrid microgrid (50 kW micro-hydro, 20 kWp PV, 48 kWh LiFePO₄ BESS, 48 kW diesel) in Chumphon Province, southern Thailand. The framework combines an offline mixed-integer linear program (MILP) with scenario-based uncertainty handling (k-medoid clustering, N = 8; CVaR penalty at α = 0.9) and an operator-assisted execution layer implementing source transitions via manual changeover switches. A Fluke 435 IEC 61000-4-30 Class-A field campaign with stationary block-bootstrap inference (B = 2000 resamples, 10 min blocks) documented substantial power quality improvements under BESS supply: the three-phase average THD-V reduced from 5.4% to 2.9% with 95% confidence intervals that do not overlap between the two supply modes; the THD-I dropped from 55.8% to 4.9% (Phase A; 91.2% reduction; three-phase average 64.0% → 7.8%); the voltage unbalance fell from 0.86% to 0.03%; and the displacement power factor improved from 0.92 to 0.95. IEEE Std 1459-2010 decomposition reveals that 93% of the non-fundamental apparent power under diesel supply is attributable to current-distortion volt-amperes (Dᵚ = 4737 VA vs. 283 VA under BESS). A composite power quality index confirms that diesel operation fails the IEEE 519-2022 current-distortion limits while BESS supply satisfies all EN 50160 and IEEE 519-2022 thresholds (PQI: 0.75 vs. 3.89). A 365-day closed-loop simulation confirmed an 18.4% reduction in annual operating cost and a 27.6% reduction in diesel runtime relative to a rule-based baseline, while maintaining LPSP at or below 0.53%. Techno-economic projection from field-verified HOMER inputs reduced the levelized cost of electricity from approximately 0.69 USD/kWh (diesel-only) to 0.36 USD/kWh for the proposed PV + BESS + Hydro + Diesel configuration, which retains diesel as a low-utilization backup at a near-100% renewable energy share. The same configuration delivered a 47.9% net present cost advantage over diesel-only operation and a 12.8 t (82%) annual CO₂ reduction. Manual source-transfer interruptions of 1–3 min are fully characterized, and a cost-estimated ATS + SCADA upgrade roadmap is defined. Full article

This study aims to develop an efficient and accurate method for identifying joint parameters in assembled structures. A novel neural network-based joint identification framework is proposed. Frequency response function (FRF) datasets are generated by combining finite element simulation with frequency-domain substructure synthesis. The Uniform Manifold Approximation and Projection (UMAP) algorithm is employed for nonlinear dimensionality reduction in FRF sequences, preserving critical characteristics. A multilayer perceptron (MLP) network is then trained to regress joint parameters from the reduced-dimension FRF data. The necessity of the nonlinear dimensionality reduction within this joint identification framework is verified through comparison with the linear dimensionality reduction technique of principal component analysis (PCA). This methodology is implemented and validated using a tool–collet–holder system. Comparative studies with the global optimization method reveal that the proposed approach maintains superior identification accuracy while achieving significant improvements in computational efficiency across varying preload conditions. Furthermore, the identified joint parameters exhibit strong predictive capability when tested under tool/holder component changes, preload variations, and when coupled with a spindle, proving robustness under complex operational scenarios. This study provides a new technical pathway for the joint identification of assembly structure. Full article

A new method has been established for determining trace amounts of platinum in water using ion liquid (IL)-dispersive liquid–liquid microextraction (DLLME) combined with graphite furnace atomic absorption spectroscopy (GFAAS). The method is based on the use of a self-prepared reagent, 5-(5-cyano-2-pyridineazo)-2,4-diaminotoluene (5-CN-PADAT), as a chelating agent, which reacts with Pt(IV) to form a hydrophobic chelate. The extraction solvent is 1-octyl-3-methylimidazolium hexafluorophosphate ([C₈mim][PF₆]), and ethyl acetate is used as the dispersive solvent. After the extraction is completed, the extraction phase formed by [C₈mim][PF₆] and ethyl acetate has a relatively low viscosity and can be directly used for the determination of GFAAS. A single-factor rotational method was employed to optimize conditions affecting DLLME extraction efficiency. The interactions among the factors affecting DLLME were analyzed using response surface optimization (RSM). Under optimal conditions, platinum concentrations exhibited good linearity within the range of 40–280 ng/mL, with a detection limit of 0.3 ng/mL. AGREEprep was used to discuss the ecological friendliness of the method, demonstrating its low cost, ease of operation, simple equipment requirements, and environmental friendliness. When applied to determining trace amounts of platinum in water samples, the results were satisfactory. Full article

The structural strength requirements for timber buildings have been significantly tightened in the second generation of Eurocodes (EN 1990:2023, EN 1991-1-7), which poses a particular challenge for solid timber frames with a beam-and-column structure, where the transfer of tensile forces via dowel connections is inherently limited. Existing multiscale frameworks for timber post-and-beam robustness lack operational detail at each scale, and no validated workflow currently bridges joint-level continuum damage mechanics and frame-level progressive failure analysis in compliance with the second-generation Eurocodes. This paper addresses this gap by proposing an effective two-scale finite element method (FEM) modelling framework for assessing the strength of such frames during column removal. Existing multiscale models describing the strength of timber structures with beam-and-column systems lack the operational details necessary to integrate failure mechanics at the joint level and progressive failure modelling at the frame level within a single, validated workflow. In this paper, this gap is addressed through three specific contributions: a physically modified quadratic Hashin-type failure criterion for timber, which eliminates the non-physical increase in shear strength under combined stress states perpendicular to the grain; a two-scale structure based on the finite element method (FEM), in which the results of continuous damage mechanics at the joint level directly parameterise non-linear joint elements with six degrees of freedom at the frame level, taking into account coupled directional wear and erosion of the elements; and quantitative validation of both scales against experimental data and the conversion factors for characteristic values of the second generation of Eurocode 5 (prEN 1995-1-1:2023). At the connection level, the simulated strength and stiffness values agree with the experiments to within an error of no more than 5%. At the frame level, the model correctly reproduces the non-linear ‘load–displacement’ relationship, the sequence of joint failure, and the axial forces in the chain line for vertical displacements up to 390 mm, which corresponds to experimental observations. Full article

Oxalic acid is extensively used in industrial chemical processes, purification systems, hydrometallurgical operations, and advanced oxidation environments where rapid and environmentally sustainable analytical methodologies are increasingly required for process monitoring and quality control. In this study, a micro-Raman spectroscopy methodology was developed for the direct quantification of oxalic acid in aqueous systems at moderate-to-high concentrations (0.079–0.793 M). The analytical strategy was based on the integrated Raman response of the carbonyl stretching region (1700–1750 cm⁻¹), selected due to its strong concentration-dependent behavior, spectral definition, and reduced interference from the aqueous matrix. The proposed methodology demonstrated excellent analytical performance, including high linearity (R² > 0.998), satisfactory precision, and reliable concentration-dependent reproducibility throughout the evaluated concentration range. To evaluate operational robustness, matrix-matched standards incorporating temperature variation (25–40 °C), turbidity (0–57 mg/L), dissolved Ca²⁺ (0–58 mg/L), and dissolved Fe³⁺ (0–7 mg/L) were prepared to simulate chemically perturbed industrial environments. Principal Component Analysis (PCA) demonstrated that the carbonyl vibrational region retained organized concentration-dependent spectral behavior despite operational perturbations. Partial Least Squares (PLS) regression models developed under these matrix-informed conditions preserved strong predictive capability (R² ≈ 0.997), while preliminary prediction of process-related samples yielded excellent agreement between predicted and reference concentrations (R² = 0.990). Although operational perturbations produced substantial attenuation of Raman intensity, particularly at lower concentration levels, the carbonyl Raman band remained spectrally detectable and analytically interpretable throughout all evaluated conditions. Electronic-structure analysis using Natural Bond Orbital (NBO) and Atoms-in-Molecules (AIM) methodologies demonstrated that the strong analytical behavior of the ν(C=O) vibrational mode is associated with enhanced electron-density localization, covalent stabilization, and favorable polarizability characteristics of the carbonyl bond. The combined experimental, chemometric, and computational results demonstrate the feasibility of matrix-informed micro-Raman spectroscopy as a rapid, reagent-free, and operationally robust methodology for oxalic acid monitoring in chemically perturbed aqueous industrial systems. Full article

In receiving-end modular multilevel converter (MMC)-based flexible high-voltage direct current (HVDC) grid-connected systems, high-frequency oscillation can significantly increase the peak values of the point of common coupling (PCC) voltage and grid current. To address this issue, this paper proposes a coordinated suppression strategy for high-frequency oscillation in receiving-end MMC grid-connected systems. First, an MMC impedance model is established based on harmonic linearization, and its frequency-domain interaction with the grid impedance is analyzed to clarify the formation mechanism of high-frequency oscillation and its main influencing factors. Then, considering the different roles of the voltage feedforward and current feedback channels in the target frequency band, a coordinated suppression strategy combining band-stop filtering in the voltage feedforward path with low-pass filtering and lead compensation in the current feedback path is designed. Hardware-in-the-loop experimental results show that the proposed method effectively identifies and suppresses high-frequency oscillation. Under the validated operating condition, the oscillation-induced peak increases in the PCC voltage and grid current are limited to within 20% and 12.5%, respectively, thereby suppressing further oscillation growth and reducing the risk of approaching the overvoltage and overcurrent protection thresholds. Full article

In this paper, we introduce and study a Szász-type family of positive linear operators generated by Euler polynomials of negative order on $[0,\infty )$. The construction is based on an explicit finite representation of these polynomials with non-negative terms, which ensures the positivity of the corresponding kernel. We prove the basic properties of the operators and show that they can be represented as finite convex combinations of shifted classical Szász operators. We also provide a probabilistic representation of the kernel as a finite mixture of Poisson distributions, which clarifies the role of the parameter k and the resulting moment structure. The corresponding algebraic and central moment identities are derived and used to establish convergence on compact intervals and to obtain quantitative estimates in terms of the modulus of continuity, Lipschitz-type classes, and Peetre’s K-functional. Furthermore, Voronovskaya-type asymptotic results are obtained, including a quantitative form and a second-order asymptotic formula. Numerical tables and a graphical illustration are presented for selected test functions and parameter values, and the results are consistent with the theoretical convergence behaviour. The paper shows that Euler polynomials of negative order provide a positive and structurally tractable framework for constructing Szász-type approximation operators on the positive real axis. Full article

This study examines the evolution of container throughput and competitive market-share dynamics in the three principal Baltic container ports—Klaipeda, Riga, and Tallinn—during the period 2005–2024 and provides baseline forecasts to 2030. The proposed analytical framework combines descriptive statistical analysis, normalized market-share assessment, growth-rate analysis, and ordinary least-squares trend estimation with prediction intervals to distinguish aggregate market fluctuations from port-specific competitive realignments. The results indicate increasing market concentration in Klaipeda, a gradual decline in Riga’s relative position, and long-term stagnation and volatility in Tallinn. Common regional shocks are observed during the 2009 global financial crisis and the COVID-19 disruption in 2020, while atypical positive deviations in Klaipeda suggest competitive redistribution effects associated with changes in regional logistics flows and shipping-network configurations. Forecast results indicate continued medium-term growth in Klaipeda and Riga, whereas Tallinn demonstrates weaker trend stability and greater forecast uncertainty. The study contributes a transparent and reproducible baseline decision-support framework that can be implemented using routinely available throughput statistics for medium-term infrastructure assessment and capacity evaluation, infrastructure prioritisation, and risk monitoring. The findings also highlight the limitations of deterministic linear forecasting in volatile port systems and support future integration with higher-frequency operational data and machine-learning forecasting approaches. Full article

Masonry retaining walls are widely used in mountainous regions but are susceptible to progressive internal damage under environmental and operational loads, which is often difficult to detect through conventional visual inspection. To address this problem, this study proposes a baseline-free vibration-based damage identification method for existing masonry retaining walls. The method combines impulse response function (IRF) estimation with wavelet packet decomposition (WPD) and introduces a scalar damage index, termed the energy ratio standard deviation (ERSD). Unlike conventional WPD energy ratio deviation (ERD) vectors, ERSD condenses multi-band energy redistribution into a single positive scalar for each sensor location, thereby facilitating spatial interpolation and field-level damage localization without modal extraction. The method was validated through four monthly impact hammer tests on a masonry retaining wall in Zhenjiang, China. The results show that non-zero ERD vectors indicate vibration energy redistribution between successive monitoring states, while the spatial peak of ERSD identifies the most likely damage zone. The ERSD maximum occurred at point 5 and was confirmed by post-test visual inspection, which revealed a local crack of approximately 0.8–1.2 mm in the adjacent mortar joint. To avoid overfitting with the limited four-test dataset, the temporal trend of ERSD was evaluated using a linear regression and finite-difference progression rates rather than a high-order polynomial. The proposed method provides a practical preliminary screening tool for field damage localization; however, its quantitative damage severity calibration requires further validation using controlled stiffness-reduction tests and environmental compensation models. Full article