Search Results (30,252)

The lightning withstand performance of transmission lines is critically affected by grounding impulse characteristics, particularly for river-crossing towers where soil conditions are complex. This study develops a coupled seepage–electric field model to evaluate these characteristics under dynamic hydrological influences. A complex soil model is constructed integrating Bernoulli’s laminar flow equation with Richards’ equation for unsaturated seepage; long-term finite-element iterations simulate seepage dynamics, yielding distributed soil conductivity parameters that vary with river flow velocity, water depth, and impermeable layers. These parameters are then coupled with an electroquasistatic Maxwell framework to model impulse current dispersion. Validation against experimental data confirms the model’s accuracy. Results show that seepage increases moisture and lowers resistivity. Increasing flow from static to 10 m/s reduces riverbed pressure from 5.61 × 10⁴ Pa to 1.86 × 10⁴ Pa, shifting the 0 Pa isobar downward by 5.1 m, weakening seepage and raising impulse resistance. A shallower impermeable layer deflects seepage laterally, reducing nearby resistivity. Raising water depth from 5 m to 10 m increases pressure from 1.96 × 10⁴ Pa to 5.61 × 10⁴ Pa, enhancing seepage and lowering resistivity. These findings indicate that grounding design must holistically account for flow velocity, water depth, and subsurface barriers to ensure reliable lightning current dissipation and stable grid operation. Full article

Type I diabetes mellitus (T1DM) is a chronic metabolic disease resulting from insufficient insulin secretion into the bloodstream‚ causing elevated blood glucose concentrations to dangerous levels․ Automated regulation of blood glucose levels in T1DM can be modeled as a nonlinear‚ uncertain‚ and disturbance-affected closed-loop control process with a time delay‚ time-varying insulin sensitivity, and imperfect glucose measurements. This paper presents the design, multi-objective tuning, and robustness evaluation of a fuzzy logic controller (FLC) for automated insulin-infusion regulation. The proposed FLC uses the glucose tracking error and its time derivative as feedback signals to determine the required insulin control action and maintain glucose within the desired range of (70–160 mg/dL). The controller parameters are optimized using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to address three competing control objectives: minimizing hypoglycemia risk, minimizing hyperglycemia risk, and reducing total insulin usage. The resulting Pareto-optimal solutions provide a set of trade-off controller designs for decision-makers based on safety, performance, and insulin-efficiency requirements. The robustness of the proposed automated control framework is evaluated under challenging operating conditions, including elevated initial glucose levels, model-parameter uncertainties, external disturbances, variations in insulin sensitivity, distorted glucose measurements, and delayed insulin infusion. A comparative study with a linear quadratic regulator-based controller (LQRC) is conducted as a benchmark. Simulation results demonstrate that the optimized FLC provides superior closed-loop performance and stronger robustness than the LQRC across all tested scenarios. The proposed fuzzy-control framework, therefore, offers a promising automation-based strategy for resilient glucose regulation under uncertainty, measurement imperfections, and actuation delays. Full article

This paper presents a short-term traction-load forecasting method that fuses optimization-driven dual-scale decomposition and multiscale information fusion (ODE-MMF) with TimeXer-Mamba to address non-stationary prediction difficulties caused by intermittent and volatile traction loads. A correlation analysis module is first constructed for adjacent feeding sections, where mutual information quantifies cross-arm load transfer induced by train operations and extracts key spatial features. In the ODE-MMF signal processing module, an improved whale migration algorithm searches for the optimal parameters of optimization-driven dual-scale decomposition, enabling multiscale decomposition of load features. Multiscale transfer entropy is then used to measure information flow among decomposed components, and highly redundant components are adaptively merged into complementary feature subsequences. In the TimeXer-Mamba prediction module, TimeXer enhances exogenous variables such as holidays, whereas Mamba captures long-range dependencies through the selective state-space model. A gated fusion mechanism integrates the two representations, after which the merged subsequences are predicted in parallel and reconstructed to obtain the final forecast. Experiments conducted on real-world traction-load data demonstrate that the proposed model consistently outperforms all evaluated baselines. Relative to the best-performing baseline, LSTM-Transformer, it achieves reductions of 9.61%, 9.32%, and 9.81% in mean absolute error, root mean square error, and mean absolute percentage error, respectively, while maintaining high computational efficiency and demonstrating strong potential for practical deployment in railway power supply systems. Full article

Cornus mas L. fruits are a valuable source of bioactive natural products with potential applications in functional foods and food preservation. In the present study, supercritical carbon dioxide (Sc-CO₂) extraction was applied for the recovery of lipophilic fractions from Cornus mas fruits under different operating conditions of pressure, temperature, and extraction time. The influence of extraction parameters on the extraction yield was investigated using a design of experiments (DoE) approach. The chemical composition of the obtained extracts was characterized via high-resolution gas chromatography (HRGC), revealing a fatty acid profile dominated by linoleic acid (58–63%) and oleic acid (22–24%), followed by palmitic acid (8–9%), stearic acid (2–4%), arachidic acid (about 2%), and behenic acid (about 1%). The extracts were further evaluated for antimicrobial activity against selected Gram-positive and Gram-negative bacteria, including Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa, as well as against the yeast Candida albicans. The results demonstrated that supercritical CO₂ extraction represents an effective green technology for the selective recovery of lipophilic bioactive compounds from Cornus mas fruits. The extracts exhibited measurable antimicrobial activity in broth microdilution assays against selected Gram-positive and Gram-negative bacteria, as well as Candida albicans, supporting their potential as natural antimicrobial agents. The combination of green extraction technology, analytical characterization, and bioactivity evaluation contributes to the valorization of Cornus mas fruits as a source of food-derived natural products. Full article

In the context of economic globalization, effective cooperation and information sharing are critical for enhancing the efficiency and competitiveness of supply chains. Fresh agricultural product supply chains face distinctive challenges arising from high perishability, rapid demand fluctuations, and information asymmetry among supply chain members. This study develops an analytical framework comprising the following three conceptually connected but independently calibrated modules: an EOQ cost module, a comparative profit module, and a two-period, two-task dynamic principal–agent module. The modules are connected through the common economic logic of value creation, value distribution, and incentive design, rather than through a one-to-one numerical mapping. Using operational and financial information from the Erli River Crab supply chain in Panshan County, the EOQ and comparative profit models are independently calibrated at different decision scales to evaluate the benchmark economic effects of information-sharing-enabled coordination. The EOQ analysis uses annual aggregate operational quantities, whereas the comparative profit analysis uses a normalized transaction-demand scale; therefore, their numerical quantity magnitudes are not intended for direct one-to-one comparison. Within the EOQ module, information-sharing-enabled coordination reduces total relevant supply chain cost by 8.41% relative to the farmer-led decentralized benchmark and by 60.75% relative to the retailer-led decentralized benchmark. The difference arises because the farmer-led batch quantity is closer to the coordinated optimum, whereas the retailer-led order quantity is substantially smaller, implying a higher modeled frequency of upstream setup activities and a larger setup-cost component under the benchmark parameterization. These results capture the joint effect of full information availability and coordinated batch optimization under two alternative decentralized decision regimes. Within the comparative profit module, over the benchmark wholesale-price interval, the coordinated full-information-sharing benchmark increases total supply chain profit by 1.38–2.30% relative to the decentralized no-information-sharing benchmark. Under the unchanged-wholesale-price comparison, the farmer’s profit increases by 13.21–17.65%, whereas the retailer’s profit decreases by 1.74–3.11%. These results identify positive aggregate value creation and an asymmetric initial allocation under the linear-demand and unchanged-wholesale-price benchmark. This asymmetric allocation is benchmark-specific rather than an unavoidable consequence of information-sharing-enabled coordination, because a negotiated transfer or wholesale-price adjustment can redistribute the additional surplus. The extended analysis derives a participation-compatible transfer interval within which both the farmer and the retailer are weakly better off than under decentralized decision-making. Within the dynamic principal–agent module, a case-motivated illustrative simulation using standardized benchmark parameters shows that more informative intertemporal performance signals strengthen the optimal second-period incentive coefficient, whereas greater risk exposure limits the appropriate intensity of performance-based compensation. Continuation–payoff and ratchet-like effects jointly shape first-period information-sharing effort. Together, the three modules show how information-sharing-enabled coordination affects operational efficiency, surplus allocation, and intertemporal incentives. Full article

Heating load is jointly affected by meteorological conditions, system operating states, and historical evolution, exhibiting nonlinear, time-varying, and locally fluctuating characteristics. To improve short-term forecasting accuracy, this study proposes a particle swarm optimization (PSO)-based Trend–convolutional neural network (CNN)–bidirectional long short-term memory (BiLSTM)–Attention model. The model constructs trend-enhanced features from meteorological variables, operating parameters, temporal periodicity, historical lags, rolling statistics, differenced features, and exponentially weighted moving averages. CNN is used to extract local temporal features, BiLSTM captures bidirectional temporal dependencies, and Attention identifies key time steps. PSO further optimizes key hyperparameters. Two datasets are constructed from hourly heating-season operating data, and the proposed model is compared with BiLSTM, CNN-BiLSTM, CNN-BiLSTM-Attention, and Trend-CNN-BiLSTM-Attention models. The proposed model achieves the best performance on both datasets. For Dataset 1, the mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R²) are 0.628 GJ, 0.928 GJ, 9.308%, and 0.851, respectively; for Dataset 2, they are 0.3987 GJ, 0.5954 GJ, 10.32%, and 0.8709. These results indicate that trend-enhanced features and PSO improve forecasting performance and can support heating system operation scheduling. Full article

This study, conducted in the Ecuadorian Andes, evaluated the anaerobic co-digestion of local crop residues (amaranth and quinoa) with llama, vicuña, and pig manure to analyze methane production kinetics. The raw materials were characterized by proximate, elemental, and structural analyses, and biogas volume and the CH₄ fraction were monitored daily. The Amaranth-vicuña and Amaranth-llama treatments reached 77.29 ± 5.63 and 64.62 ± 3.62 mL biogas/g VS and 36.20 ± 7.29 and 31.78 ± 3.62 mL CH₄/g VS, respectively; in contrast, Quinoa-vicuña and Quinoa-llama produced only 1.04 ± 0.25 and 0.24 ± 0.03 mL CH₄/g VS. Monod-model parameters were estimated using an apparent formulation based on the methane production rate, and the kinetic behavior was compared with first-order, modified Gompertz, and modified logistic models. In addition, artificial neural networks (ANNs) were evaluated to predict the methane production curve from substrate characterization. Network 44, with a 13-15-10-1 architecture, yielded an overall R² = 0.998, validation R² = 0.997, and validation MSE = 0.415. Ten-times repeated five-fold cross-validation of the same architecture yielded R² = 0.985 ± 0.007 and RMSE = 1.21 ± 0.28 mL CH₄/g VS, supporting its interpolation capability within the experimental domain, although this does not demonstrate extrapolation to new substrate combinations. Overall, the proposed approach combines interpretable kinetic parameters with ANN-based prediction, but external validation with independent datasets is still required. The reported yields correspond to the specific production achieved in a low-cost batch system operated at room temperature and should not be interpreted as standardized biochemical methane potential (BMP) values. Full article

Implementation of real-scale systems for rainwater harvesting, treatment and reuse of greywater in residential areas is challenging because several factors should be considered for full adoption and satisfaction of decision-makers, urban developers and users. Technological, construction, operational, social (acceptance), impact on water resources, regulatory, and economic factors are involved. This study presents the implementation of a full-scale hybrid system for rainwater harvesting, treatment and reuse of greywater in a residential building located in Monterrey, Nuevo León, Mexico. The study included intervening in the hydraulic infrastructure of an already constructed residential building for collecting greywater, harvesting and collecting rainwater, designing and constructing an 80 m² controlled natural soil treatment system (CNSTS) and a 65 m³ storage tank for treating and storing rain and greywater. Furthermore, the full-scale hybrid system was monitored under real operating conditions for a two-month period to assess its performance. Results showed that the CNSTS has the potential to replace up to 2835 m³ year⁻¹ of potable water, equivalent to 65% of the building’s annual water consumption. The CNSTS achieved removal efficiencies of up to ~90% for Chemical Oxygen Demand, 90% for surfactants, and 50% for total nitrogen. Most of the measured parameters complied with the corresponding limits established by the Mexican standards NOM-003-SEMARNAT-1997 for non-potable water reuse, NOM-001-SEMARNAT-2021 for wastewater discharges, and NOM-127-SSA1-2021 for potable water with the exception of methylene blue active substances (surfactants), which exceeded the permissible limit during the initial monitoring stage, highlighting the need for further optimization of the system’s vegetative cover. Based on these findings, conceptual designs and preliminary evaluations were conducted for additional buildings, resulting in potable water substitution rates above 90% with investment payback periods of 2 to 5 years, depending on the water demand and the water catchment potential. Full article

Accurate state of health (SOH) estimation of lithium-ion batteries is critical to the reliability of electric vehicles. However, under dynamic operating conditions, conventional feature extraction based on fixed time window often exhibits poor generalization, as it fails to account for the multi-timescale parameter couplings inherent in the non-stationary voltage responses. To address this issue, this paper proposes a Bayesian Adaptive Time Window Optimization (BATWO) framework for feature extraction in battery SOH estimation. Within this framework, the time window length is treated as a learnable structural parameter and is adaptively optimized via Bayesian optimization to identify the most informative observation timescale for extracting degradation-sensitive statistical features under given operating conditions. Evaluations on a cycle-aging dataset containing 69 lithium-ion battery samples subjected to distinct dynamic operating profiles show that the optimal time window lengths vary significantly, ranging from 500 s to 27,630 s. The BATWO framework achieves an average root-mean-square error (RMSE) of 2.07% and a mean absolute error (MAE) of 1.45%, outperforming the best fixed time window strategy by reducing the RMSE and MAE by 2.35% and 2.68%, respectively. Moreover, compared with LSTM- and Transformer-based models without feature extraction, the BATWO framework reduces training time by over 97%. These results highlight the superior generalization capability and computational efficiency of the BATWO framework, demonstrating its great potential for practical deployment in battery management system. Full article

Virtual synchronous generator (VSG) control is widely used to improve the frequency stability of low-inertia microgrids. However, most existing adaptive VSG strategies tune the virtual inertia and damping coefficient mainly according to local frequency deviations of the energy storage converter, while the effect of supplementary wind turbine frequency support on the admissible VSG parameter range is rarely considered. To address this limitation, this paper proposes a wind–storage coordinated frequency control strategy that combines an energy storage fuzzy VSG with active-power-frequency droop support from a doubly fed induction generator (DFIG). The scientific contribution of this study is that the DFIG droop support term is incorporated into a reduced-order wind–storage small-signal model, and an admissible scheduling region for the virtual inertia and damping coefficient is constructed according to prescribed damping ratio and natural angular frequency constraints. This region is used to constrain the online fuzzy parameter scheduling of the energy storage VSG. In addition, bell-shaped membership functions are introduced to obtain smoother parameter variation and are compared with triangular membership functions under the same operating conditions. MATLAB/Simulink simulations are conducted under grid-connected/islanded transition, load switching, and renewable-power fluctuation conditions. Compared with the benchmark strategies, the proposed method reduces the maximum and average frequency deviations to 0.181 Hz and 0.016 Hz, respectively. The maximum discharge power, RMS power, and cumulative energy throughput of the energy storage system are reduced to 236.853 kW, 155.822 kW, and 5.751 kWh, respectively. These results indicate that the proposed coordinated strategy improves frequency regulation while reducing the transient regulation burden of the energy storage system within the investigated operating conditions. Full article

Capturing reliable hyperspectral imager data at a meaningful frame rate for explosions and other fast-changing scenes is not possible in the mid-wave infrared region under traditional sensor operating configurations and processing techniques, which typically have frame rates on the order of 0.5–2.0 Hz. To combat these shortcomings, the scene acquisition parameters were tailored for explosions and a new method for processing optical signatures of fast transient scenes with Fourier-transform infrared hyperspectral imagers was developed. For this technique, the instrument was first configured to collect asymmetric interferograms while optimizing the number of measurement points on the short side of the interferogram. Additionally, pixel-wise zero path distance offset and phase corrections were applied to the interferograms, a reduced spectral resolution of 8 cm⁻¹ was selected, and the window size was narrowed to 32 × 64 pixels while using a lens with a wide field of view. The smooth offset correction for scene change artifacts was then applied in post-processing to address any remaining artifacts in the Fourier-transformed spectra. These procedures yielded a 29× increase in frame rate and significant improvements in spectra fidelity. This work makes reliable field calibrations and measurements of explosions with Fourier-transform infrared hyperspectral imagers more achievable than before. Full article

Background: Minimally invasive approaches for multivalve surgery have attracted increasing interest; however, data on combined aortic and mitral valve replacement or repair using via right anterior minithoracotomy remain quite limited. This study aimed to evaluate the feasibility, safety, and early outcomes of minimally invasive concomitant aortic and mitral valve replacement or repair using this approach. Methods: This retrospective study included 24 patients who underwent simultaneous aortic and mitral valve procedures via right anterior minithoracotomy. We collected preoperative, intraoperative, and postoperative data, assessing echocardiographic parameters. Early clinical outcomes, complications, and mortality rates were analyzed, with correlations between EuroSCORE II and outcomes explored. Results: The median follow-up was 412 days. All procedures were completed successfully without conversion to sternotomy. Postoperative echocardiography demonstrated a significant reduction in transvalvular gradients, with aortic mean pressure gradient decreasing from 51.3 ± 23.0 mmHg to 6.7 ± 1.7 mmHg (p < 0.001) and mitral mean pressure gradient from 19.3 ± 26.7 mmHg to 4.0 ± 1.4 mmHg (p < 0.001), while left ventricular ejection fraction remained unchanged (p = 0.67). During the study period, one patient died from a non-cardiac cause. EuroSCORE II showed a moderate positive correlation with intensive care unit length of stay (p = 0.011) but not with hospital stay or operative times. Conclusions: Minimally invasive aortic and mitral valve replacement or repair via right anterior minithoracotomy is feasible and was associated with favorable early hemodynamic and clinical outcomes in this single-center cohort. Full article

Manufacturing tolerances and winding-layout variations can introduce phase-to-phase mismatches in stator resistance and leakage inductance. Under such unbalanced phase impedances, conventional field-oriented control (FOC), typically designed under balanced-parameter assumptions, may produce unequal phase currents, distorted airgap MMF, reduced efficiency, increased torque ripple, and undesired vibro-acoustic behavior. This paper investigates an open-end winding (OEW) induction machine (IM) drive, in which each phase is independently driven by an H-bridge inverter fed by the same DC source. To mitigate phase–current imbalance without parameter estimation, an RMS-based phase–current-balancing controller is proposed. The controller continuously calculates the RMS value of each phase current and adaptively scales the corresponding reference-phase voltage in a low-bandwidth outer loop, while preserving the classical FOC structure. The balancing law is derived directly from the phase-impedance imbalance model; convergence of the three coupled per-phase loops is proven via a Lyapunov argument, and stability of the cascaded structure is established through an analytical bandwidth-separation analysis shown to be robust to ±30% machine-parameter variation and across the 500–1500 rev/min speed range. Simulation and experimental results across multiple operating points demonstrate effective phase–current equalization. Full article

Wildfires generate rapidly evolving hazard landscapes that disrupt ground-based logistics and render conventional disaster-response operations ineffective, motivating the use of unmanned aerial vehicles (UAVs) in civil-protection missions such as medical resupply, casualty search-and-rescue, and perimeter surveillance. Existing evaluations, however, share a common limitation: they assess performance using navigation-centric metrics—primarily success rate—without accounting for the temporal value of the mission objective. This paper characterises, within each planner family, how a single risk coefficient ρ governs the trade-off between navigation success and time-decaying mission value: holding each family’s algorithm and replan trigger fixed and sweeping only ρ isolates its effect, so that the risk setting maximising a family’s navigation success need not maximise its mission value. To study this, FLARE is introduced, a deterministic benchmark for fire-landscape adaptive risk evaluation, inspired by recent Greek wildfire events; all hazard dynamics (fire spread, structural collapse, moving obstacles, dynamic no-fly zones) are modelled as benchmark abstractions rather than incident-specific reconstructions. FLARE evaluates eight planners spanning six risk-handling paradigm families, isolating the risk coefficient by sweeping ρ within each family (algorithm fixed) with A* as the risk-blind static reference, and quantifies mission impact—medication efficacy, casualty survival, and data freshness—through a strictly time-decreasing mission-score function. The mission-value leverage of ρ is strongly family-dependent: decisive for the incremental soft-cost-inflation family—whose success-optimal ρ collapses its mission value (0.91 → 0.41)—strong for the worst-case (CVaR) family, moderate for the sampling family, and negligible for the reactive, hard-threshold and frequent-replan families. In some families the success-optimal and mission-optimal ρ diverge—a within-family ranking inversion—while in others they coincide; because the algorithm is fixed across each sweep, the divergence is attributable to ρ alone. Success of navigation is therefore a necessary but insufficient proxy for mission effectiveness, and the risk coefficient is a meaningful per-family parameter for mission-aware configuration. Full article

Accurate and real-time apple detection in complex orchard environments is essential for robotic harvesting but remains challenging because of illumination variation, foliage occlusion, and limited computational resources. This study proposes Fast-YOLO11n, a lightweight detector derived from the nano variant of You Only Look Once 11 (YOLO11n) and integrating three complementary components. A Fast-C3k2 module based on partial convolution (PConv) reduces redundant computation while preserving cross-layer feature transmission. A focal modulation (FM) mechanism enhances target-related responses and suppresses background interference under occlusion and uneven illumination. In addition, a parallel downsampling module, termed ADown, retains local geometric details and multi-scale semantic information during downsampling. Experiments were conducted on a field-collected orchard dataset comprising 2240 images and 22,673 annotated apple instances under diverse lighting, scale, and occlusion conditions. Fast-YOLO11n achieved mean average precision values of 75.76% across intersection-over-union (IoU) thresholds of 0.50–0.95 (mAP@50–95) and 91.29% at an IoU threshold of 0.50 (mAP@50), while operating at 366.19 frames per second (FPS) with 2.51 million parameters and 6.00 billion floating-point operations (FLOPs). Compared with the YOLO11n baseline, it improved mAP@50–95 and mAP@50 by 2.39 and 1.39 percentage points, respectively, while reducing the parameter count and FLOPs by 2.71% and 5.36%. Ablation experiments demonstrated the individual and combined effects of the three modules on detection performance and computational efficiency. The proposed model provides a favorable balance between detection accuracy and computational efficiency, indicating its potential for real-time orchard perception on resource-constrained platforms. Full article