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The European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) datasets provide a rich source of climatological data. However, their Network Common Data Form (NetCDF) structure can be a barrier for researchers who are not experienced with specialized data tools or programming languages. To address this challenge, we developed ncPick, a lightweight, Windows-based application designed to make ERA5 data more accessible and easier to use. The software enables users to load NetCDF files, select points of interest manually or through shapefiles, and export the data directly to Comma-separated values (CSV) format for further processing in common tools such as Excel, R, or within ncPick itself. Additional modules allow for quick visualization, descriptive statistics, interpolation, and the generation of time-of-day heatmaps, as well as practical data handling functions such as merging and downsampling CSV files based on the time-axis. Validation tests confirmed that ncPick outputs are consistent with those from established tools (such as Panoply). The toolkit was found to be stable across different Windows systems and suitable for a range of datasets. While it has limitations with very large files and does not include automated data download for version 1 of the software, ncPick offers an accessible solution for researchers, students, and other professionals seeking a reliable and intuitive way to work with ERA5 NetCDF data. Full article

Bursera microphylla A. Gray (Burseraceae) is a medicinal plant native to Sonora, Mexico, with antioxidant, anti-inflammatory, and antiproliferative properties. However, the pharmacological potential of its ecotypes remains underexplored. This study evaluated the biological activity and chemical composition of ethanolic extracts from the fruit and stem of the Magdalena ecotype. Total phenolic content was quantified using the Folin–Ciocalteu method, and phenolic profiles were characterized by ESI-IT-MS. Antioxidant activity was assessed by DPPH and FRAP assays; anti-inflammatory activity was evaluated by measuring nitric oxide (NO) and tumor necrosis factor-alpha (TNF-α) levels in LPS-activated RAW 264.7 macrophages. Antiproliferative activity was tested against LS180, C-33 A, and ARPE-19 cell lines using the MTT assay. Fruit extract exhibited higher phenolic content (180.6 ± 22.0 mg GAE/g) and ferric-reducing power (FRAP = 2034.3 ± 89.7 μM Fe(II)/g), whereas the stem extract showed stronger DPPH scavenging capacity (IC₅₀ = 52.9 ± 0.02 μg/mL). For the first time, gallic acid glucoside, kaempferol rhamnoside, quercetin rhamnoside, and isorhamentin xyloside were identified in B. microphylla fruit extract. Furthermore, the fruit extract significantly reduced NO production (93.6 ± 4.6 μg/mL) and TNF-α levels (IC₅₀ = 101.5 ± 9.1 μg/mL). It also showed strong cytotoxicity against C-33 A (IC₅₀ = 0.6 ± 0.07 μg/mL) and LS180 (0.7 ± 0.01 μg/mL), with lower cytotoxicity in ARPE-19 cells (77.9 ± 4.3 μg/mL). These findings highlight the therapeutic potential of the Magdalena ecotype, likely associated with its phenolic and other bioactive metabolites that require further investigation. Full article

This paper considers the scheduling problem of uniform parallel machines. The objective is to minimize the makespan. This problem holds practical significance and is inherently NP-hard. Therefore, solutions of the exact formulation are limited to small-sized instances. As the problem size increases, the exact formulation struggles to find optimal solutions within a reasonable time. To address this challenge, an arc flow formulation is proposed, aiming to solve larger instances. The arc flow formulation creates a pseudo-polynomial number of variables, with its size being significantly influenced by the problem’s bounds. Therefore, bounds from the literature are utilized, and symmetry-breaking rules are applied to reduce the size of the arc flow graph. To test the effectiveness of the proposed arc flow formulation, it was compared with a mathematical formulation from the literature on small instances with up to 30 jobs. Computational results showed that the arc flow formulation outperforms the mathematical formulation from the literature, solving all cases within a few seconds. Additionally, on larger benchmark instances, the arc flow formulation solved 84.27% of the cases to optimality. The maximum optimality gap does not exceed 0.072% for the instances not solved to optimality. Full article

Background: The accurate and timely diagnosis of neurodegenerative disorders such as Parkinson’s disease, Alzheimer’s disease, and major depressive disorder critically depends on real-time monitoring and precise interpretation of authentic neurotransmitter (NT) signal dynamics in complex biological fluids (CBFs), including cerebrospinal fluid. These NT dynamics are governed by both the type and concentration of NTs present in the CBFs. However, current biosensors face significant limitations in sensitivity and selectivity, thereby hindering reliable estimation (detection and quantification) of NTs. Though nanomaterials and bioenzymes have been utilized to modify sensor interfaces for enhanced performance, issues like signal convolution, electrode fouling, and inter-NT crosstalk persist. Objectives: This review aims to evaluate and synthesize current research on the use of artificial intelligence (AI), particularly machine learning (ML), pattern recognition (PR), and deep learning (DL), to improve the automated detection and quantification of neurotransmitters from complex biological fluids. Design: A systematic review of 33 peer-reviewed studies was conducted, focusing on the integration of AI methods in neurotransmitter estimation. The review includes an analysis of commonly studied NTs, the methodologies for their detection, data acquisition techniques, and the AI algorithms applied for signal processing and interpretation. Results: The studies reviewed demonstrate that AI-based approaches have shown considerable potential in overcoming traditional biosensor limitations by effectively deconvoluting complex, multiplexed NT signals. These techniques allow for more accurate NT estimation in real-time monitoring scenarios. The review categorizes AI methodologies by their application and performance in NT signal analysis. Conclusions: AI-enhanced NT monitoring represents a promising direction for advancing diagnostic and therapeutic capabilities in neurodegenerative diseases. Despite current challenges, such as sensor stability and NT interaction complexity, AI integration, particularly in applications like closed-loop deep brain stimulation (CLDBS), offers significant potential for more effective and personalized treatments. Full article

The article presents a study of the computational complexity and efficiency of various parallel algorithms that implement the numerical solution of the equation in the hereditary $\alpha \left(t\right)$-model of radon volumetric activity (RVA) in a storage chamber. As a test example, a problem based on such a model is solved, which is a Cauchy problem for a nonlinear fractional differential equation with a Gerasimov–Caputo derivative of a variable order and variable coefficients. Such equations arise in problems of modeling anomalous RVA variations. Anomalous RVA can be considered one of the short-term precursors to earthquakes as an indicator of geological processes. However, the mechanisms of such anomalies are still poorly understood, and direct observations are impossible. This determines the importance of such mathematical modeling tasks and, therefore, of effective algorithms for their solution. This subsequently allows us to move on to inverse problems based on RVA data, where it is important to choose the most suitable algorithm for solving the direct problem in terms of computational resource costs. An analysis and an evaluation of various algorithms are based on data on the average time taken to solve a test problem in a series of computational experiments. To analyze effectiveness, the acceleration, efficiency, and cost of algorithms are determined, and the efficiency of CPU thread loading is evaluated. The results show that parallel algorithms demonstrate a significant increase in calculation speed compared to sequential analogs; hybrid parallel CPU–GPU algorithms provide a significant performance advantage when solving computationally complex problems, and it is possible to determine the optimal number of CPU threads for calculations. For sequential and parallel algorithms implementing numerical solutions, asymptotic complexity estimates are given, showing that, for most of the proposed algorithm implementations, the complexity tends to be $n^2$ in terms of both computation time and memory consumption. Full article

Valve internal leakage in thermal power stations exhibits a strong concealed nature. If it cannot be discovered and predicted of development trend in time, it will affect the safe and economical operation of plant equipment. This paper proposed an intelligent identification method for valve internal leakage that integrated an Improved Kepler Optimization Algorithm (IKOA) with Support Vector Regression (SVR). The Kepler Optimization Algorithm (KOA) was improved using the Sobol sequence and an adaptive Gaussian mutation strategy to achieve self-optimization of the key parameters in the SVR model. A multi-step sliding cross-validation method was employed to train the model, ultimately yielding the IKOA-SVR intelligent identification model for valve internal leakage quantification. Taking the main steam drain pipe valve as an example, a simulation case validation was carried out. The calculation example used Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and determination coefficient (R²) as performance evaluation metrics, and compared and analyzed the training and testing dataset using IKOA-SVR, KOA-SVR, Particle Swarm Optimization (PSO)-SVR, Random Search (RS)-SVR, Grid Search (GS)-SVR, and Bayesian Optimization (BO)-SVR methods, respectively. For the testing dataset, the MSE of IKOA-SVR is 0.65, RMSE is 0.81, MAE is 0.49, and MAPE is 0.0043, with the smallest values among the six methods. The R² of IKOA-SVR is 0.9998, with the largest value among the six methods. It indicated that IKOA-SVR can effectively solve problems such as getting stuck in local optima and overfitting during the optimization process. An Out-Of-Distribution (OOD) test was conducted for two scenarios: noise injection and Region-Holdout. The identification performance of all six methods decreased, with IKOA-SVR showing the smallest performance decline. The results show that IKOA-SVR has the strongest generalization ability and robustness, the best effect in improving fitting ability, the smallest identification error, the highest identification accuracy, and results closer to the actual value. The method presented in this paper provides an effective approach to solve the problem of intelligent identification of valve internal leakage in thermal power station. Full article

Consumers prefer snacks that are tasty, healthy, and crunchy. However, optimizing crunchiness using sensory methods is time-consuming and expensive. Therefore, this paper proposes a new approach to measuring instrumental crunchiness. Whole strawberries of the “Honeoya” variety were osmotic dehydrated in a sucrose solution or chokeberry juice concentrate for 1, 2, and 3 h before freeze-drying. Texture was analyzed using acoustic emission (AE) and a compression test. The crunchiness index was calculated taking into account the number of AE events and mechanical energy. The content of bioactive substances, water activity, and porosity of the freeze-dried products were also assessed. Freeze-dried fruits that were osmotically dehydrated in chokeberry juice concentrate were characterized by lower final water activity and higher content of bioactive substances, but their crunchiness was the lowest. The crunchiest, loudest, and least hard were freeze-dried strawberries osmotically dehydrated in the sucrose solution. The tested freeze-dried strawberries differed in the range of sound frequencies generated, which indicates a different cracking mechanism. Full article

Power outages during extreme heat events threaten occupant safety by exposing buildings to rapid indoor overheating. However, current building thermal resilience assessments rely mainly on physics-based simulations or IoT sensor data, which are computationally expensive and slow to scale. This study develops an Artificial Intelligence (AI)-driven workflow that integrates Building Information Modeling (BIM)-based residential models, automated EnergyPlus simulations, and supervised Machine Learning (ML) algorithms to predict indoor thermal trajectories and calculate thermal resilience against power failure events in hot seasons. Four representative U.S. residential building typologies were simulated across fourteen ASHRAE climate zones to generate 16,856 scenarios over 45.8 h of runtime. The resulting dataset spans diverse climates and envelopes and enables systematic AI training for energy performance and resilience assessment. It included both time-series of indoor thermal conditions and static thermal resilience metrics such as Passive Survivability Index (PSI) and Weighted Unmet Thermal Performance (WUMTP). Trained on this dataset, ensemble boosting models, notably XGBoost, achieved near-perfect accuracy with an average R² of 0.9994 and nMAE of 1.10% across time-series (indoor temperature, humidity, and cooling energy) recorded every 3 min for a 5-day simulation period with 72 h of outage. It also showed strong performance for predicting static resilience metrics, including WUMTP (R² = 0.9521) and PSI (R² = 0.9375), and required only 1148 s for training. Feature importance analysis revealed that windows contribute 74.3% of the envelope-related influence on passive thermal response. This study demonstrates that the novelty lies not in the algorithm itself, but in applying the model to resilience context of power outages, to reduce computations from days to seconds. The proposed workflow serves as a scalable and accurate tool not only to support resilience planning, but also to guide retrofit prioritization and inform building codes. Full article

In the present study, we aimed to derive analytical solutions of the homotopy analysis method (HAM) for the time-fractional Navier–Stokes equations in cylindrical coordinates in the form of a rapidly convergent series. In this work, we explore the time-fractional Navier–Stokes equations by replacing the standard time derivative with the Katugampola fractional derivative, expressed in the Caputo form. The homotopy analysis method is then employed to obtain an analytical solution for this time-fractional problem. The convergence of the proposed method to the solution is demonstrated. To validate the method’s accuracy and effectiveness, two examples of time-fractional Navier–Stokes equations modeling fluid flow in a pipe are presented. A comparison with existing results from previous studies is also provided. This method can be used as an alternative to obtain analytic and approximate solutions of different types of fractional differential equations applied in engineering mathematics. Full article

Loss of lubrication in aeronautic drivetrains can lead to catastrophic gearbox failure, and drivetrains must be tested to prove their resistance to loss of lubrication. Research led to a better understanding of the modes of action, interdependencies, and effective measures to optimize drivetrains for a loss of lubrication event. However, there are currently no calculation methods available, so gear design against loss of lubrication is mainly based on experience. This study proposes a novel calculation method that builds upon the scuffing load calculation from ISO/TS 6336-21 to allow for scuffing safety calculation for cylindrical gears facing loss of lubrication. The proposed method synthesizes existing knowledge in the context of loss of lubrication and incorporates further research results concerning the friction, temperature, and scuffing of gears. The calculation method considers relevant gear design aspects and enables estimation of the time-to-failure. A calculation study is used to compare different measures for cylindrical gears facing loss of lubrication. The results demonstrate the remarkable potential for enhancing loss of lubrication performance through increased oil share in the fluid flow, the application of coatings, the adoption of low-loss gear designs, the use of low-friction lubricants, and the incorporation of additives that increase the scuffing temperature. Full article

In vehicular networks, inter-vehicle data sharing and collaborative computing improve traffic efficiency and driving experience. However, centralized processing faces challenges with privacy, communication bottlenecks, and real-time performance. This paper proposes a trust assessment mechanism for vehicular federated learning based on graph neural network (GNN) edge weight similarity. An explainable asynchronous federated learning data sharing framework is designed, consisting of permissioned asynchronous federated learning and a locally verifiable directed acyclic graph (DAG). The GNN connection weights perform reputation assessment on edge devices through DAG-based verification, while deep reinforcement learning (DRL) enables explainable node selection to improve asynchronous federated learning efficiency. The proposed explainable incentive mechanism based on GNN edge weight similarity and DAG can not only effectively prevent malicious node attacks but also improve the fairness and explainability of federated learning. Extensive experiments across different participant scales (30–200 nodes), various asynchrony degrees ($\alpha$ = 1–5), and malicious node attack scenarios (up to 50% malicious nodes) demonstrate that our method consistently outperforms state-of-the-art approaches, achieving up to 99.2% accuracy with significant improvements of 1.3–3.1% over existing trust-based federated learning methods and maintaining 95% accuracy even under severe attack conditions. The results show that the proposed scheme performs well in terms of learning accuracy and convergence speed. Full article

Based on the principle of re-centering with low prestress and energy dissipation through sloped friction (SF) energy dissipators, this study proposes a new hysteresis concept characterized by enhanced post-stiffness and energy dissipation for self-centering prestressed concrete (SCPC) frames. The focus of this research is to compare the seismic performance of SCPC frames utilizing both traditional and novel hysteresis concepts, aiming to provide critical evidence for the advancement of seismic-resilient structures. Nonlinear dynamic time history analyses were conducted under various seismic levels to investigate the impact of the novel hysteretic concept on seismic performance indicators, including inter-story drift, residual inter-story drift, and beam-column damage. Additionally, the influence of energy dissipator configuration and prestress level on the repair costs of structures subjected to the maximum considered earthquake (MCE) was analyzed to elucidate the structural functional recovery capacity. The results indicate that the combination of low prestress and sloped friction energy dissipators significantly reduces internal forces in beams and columns compared to traditional high prestress SCPC frames with conventional friction energy dissipators. The integration of sloped friction energy dissipators and the application of low prestress to post-tensioned (PT) strands effectively dissipate the energy transmitted to the frame during an earthquake, leading to a substantial reduction in structural damage within the SCPC frame utilizing the new hysteresis concept during large earthquakes, thereby facilitating post-earthquake repairs. Full article

When designing power electronic systems, it is crucial to correctly estimate the junction temperature of semiconductor devices, particularly power MOSFETs, under actual operating conditions. Thermal resistance is a parameter that characterizes the ability of these devices to dissipate internally generated heat under steady-state conditions. Determining the value of this parameter under specific cooling conditions requires dedicated measurements. This paper considers the widely used indirect electrical method of measuring thermal resistance. The influence of the dynamic properties of the measurement system, including the A/D converter, on the measurement error of the thermal resistance of power MOSFETs was analyzed. Using the constructed measurement system, it was demonstrated that, depending on the semiconductor material of the tested transistors, different error values were obtained, even with the same system configuration. The largest errors were observed for transistors made of silicon carbide. It was further shown that, with the applied A/D converter module, the measurement error can be limited to a few percent if recording of the thermal sensitive electrical parameter (TSEP) begins soon enough after the transients caused by the switchover from heating to TSEP measurement have fully decayed. Full article

Lettuce (Lactuca sativa), pak choi (Brassica rapa), and basil (Ocimum basilicum) were grown in hydroponic NFT systems under four nitrate levels (80–180 mg L⁻¹ N). We measured natural microbial contamination by plating nutrient-solution samples and leaf washes to obtain colony-forming unit (CFU) counts of bacteria and fungi. Separately, postharvest leaves were artificially inoculated with Botrytis cinerea and stored at 22 °C or 4 °C for 7 days to assess gray mold. In lettuce, high N (180 mg L⁻¹) markedly increased culturable microbial loads in both solution and leaves, whereas pak choi microbial counts remained low at all N levels. Basil showed a non-linear response: CFU counts peaked at moderate N (120 mg L⁻¹) and were lower at both deficit and excess N. At 22 °C, gray mold severity in pak choi increased with N; leaves fertilized at N150–180 suffered about 1.5–2 times greater lesion area than those at N80. By contrast, lettuce exhibited the worst decay under N deficiency: N80 leaves developed the largest lesions by 4–7 DPI, while moderate N (120–150 mg L⁻¹) minimized disease progression. Basil was highly susceptible under warm storage: all N levels reached near-total decay by 7 days, though N120 delayed early infection slightly. Refrigeration (4 °C) greatly suppressed lesion development in lettuce and pak choi, although high-N pak choi still showed ~20–30% more infected area than low-N after 7 days. Basil, however, suffered chilling injury at 4 °C, and all refrigerated basil leaves decayed severely (regardless of N). These results indicate crop-specific nutrient and storage strategies: avoid excessive N in pak choi, maintain balanced N in lettuce, and handle basil with non-chilling methods to reduce postharvest gray mold. Full article