Search Results (2,512)

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

Biodiesel is an alternative, sustainable, renewable, and environmentally friendly energy source, which has generated interest from the scientific community due to its low toxicity, rapid biodegradability, and zero carbon footprint. Biodiesel is a biofuel produced by the transesterification of triglycerides or the esterification of free fatty acids (FFA). Both reactions require catalysts with numerous active sites (basic, acidic, bifunctional, or enzymatic) for efficient biodiesel production. On the other hand, since the late 1990s, metal–organic frameworks (MOFs) have emerged as a new class of porous materials and have been successfully used in various fields due to their multiple properties. For this reason, MOFs have been used as heterogeneous catalysts or as a platform for designing active sites, thus improving stability and reusability. This literature review presents a comprehensive analysis of using MOFs as heterogeneous catalysts or supports for biodiesel production. The optimal parameters for transesterification/esterification are detailed, such as the alcohol/feedstock molar ratio, catalyst amount, reaction time and temperature, conversion percentage, biodiesel yield, fatty acid and water content, etc. Additionally, novel methodologies such as ultrasound and microwave irradiation for obtaining MOF-based catalysts are described. It is important to note that most studies have shown biodiesel yields >90% and multiple reuse cycles with minimal activity loss. The bibliographic analysis was conducted using the American Chemical Society (ACS) Scifinder^® database, the Elsevier B.V. Scopus^® database, and the Clarivate Analytics Web of Science^® database, under the institutional license of the Universidad Veracruzana. Keywords were searched for each section, generally limiting the document type to “reviews” and “journals,” and the language to English, and published between 2000 and 2025. Full article

We report a scalable route to hybrid aluminum matrix composites (AMCs) based on Al6060 (as-fabricated condition) reinforced with 2 wt.% TiB₂ and 1 wt.% microsilica, fabricated by ultrasonically assisted stir casting (UASC) followed by radial-shear rolling (RSR). Premixing and preheating of powders combined with acoustic cavitation/streaming during UASC ensured uniform, non-sedimentary particle dispersion and low-defect cast billets. X-ray diffraction of the as-cast composite shows fcc-Al with weak TiB₂ reflections and no reaction products; microsilica remains amorphous. Electron microscopy and EBSD after RSR reveal full erasure of cast dendrites, fine equiaxed grains, weakened texture, and a high fraction of high-angle boundaries due to the concurrent action of particle-stimulated nucleation (micron-scale TiB₂) and Zener pinning/Orowan strengthening (50–350 nm microsilica). Mechanical testing shows that, in the cast state—comparing cast monolithic Al6060 to the cast hybrid-reinforced composite—yield strength (YS) increases from 61.7 to 77.2 MPa and ultimate tensile strength (UTS) from 103.4 to 130.7 MPa, without loss of ductility. After RSR to Ø16 mm (cumulated true strain ≈ 0.893), the hybrid attains YS 101.2 MPa, UTS 150.6 MPa, and elongation ≈ 22.0%, i.e., comparable strength to rolled Al6060 (UTS 145.1 MPa) while restoring/raising ductility by ~9.7 percentage points. Microhardness follows the same trend, increasing from 50.2 HV0.2 to 73.1 HV0.2 when comparing the base cast condition with the rolled hybrid. The route from UASC to RSR thus achieves a favorable mechanical strength–ductility balance using an economical, eco-friendly oxide/boride hybrid reinforcement, making it attractive for formable AMC bar and rod products. Full article

This study employs an integrated framework that combines field-based measurements, remote sensing, and Geographic Information Systems (GISs) to monitor vegetation dynamics and assess the suitability of a steppe range reserve for livestock grazing. Forty-three surface and subsurface soil samples were collected in April and November 2021 to capture seasonal variations. Above-ground biomass (AGB) measurements were recorded at five sampling locations across the reserve. Six Sentinel-2 satellite imageries, acquired around mid-March 2016–2021, were processed to derive time-series Normalized Difference Vegetation Index (NDVI) data, capturing temporal shifts in vegetation cover and density. The GIS-based Multi-Criteria Decision Analysis (MCDA) was employed to model the suitability of the reserve for livestock grazing. The results showed higher salinity, total dissolved solids (TDSs), and nitrate (NO₃) values in April. However, the percentage of organic matter increased from approximately 7% in April to over 15% in November. The dry forage productivity ranged from 111 to 964 kg/ha/year. On average, the reserve’s dry yield was 395 kg/ha/year, suggesting moderate productivity typical of steppe rangelands in this region. The time-series NDVI analyses showed significant fluctuations in vegetation cover, with lower NDVI values prevailing in 2016 and 2018, and higher values estimated in 2019 and 2020. The grazing suitability analysis showed that 13.8% of the range reserve was highly suitable, while 24.4% was moderately suitable. These findings underscore the importance of tailoring grazing practices to enhance forage availability and ecological resilience in steppe rangelands. By integrating satellite-derived metrics with in situ vegetation and soil measurements, this study provides a replicable methodological framework for assessing and monitoring rangelands in semi-arid regions. Full article

Nitrogen fertilization is essential for balancing maize yield, grain composition, and environmental sustainability. This study aimed to evaluate the relationship between nitrogen (N) supply, grain quality traits, and yield potential using UAV-based Normalized Difference Vegetation Index (NDVI) monitoring in a long-term fertilization field experiment in Eastern Hungary. Six N levels (0–300 kg ha⁻¹) were tested during two consecutive growing seasons (2023–2024) under varying climatic conditions. The obtained results showed that moderate N doses (120–180 kg ha⁻¹) provided the optimal nutrition level for maize, significantly increasing yield compared to the control (+5.086 t ha⁻¹ in 2024), while excessive fertilization above 180 kg ha⁻¹ did not result in any substantial yield gains; however, it significantly modified grain composition. Higher N supply enhanced protein content (+0.95% between 0 and 300 kg ha⁻¹) and reduced starch percentage, confirming the protein–starch trade-off, whereas oil content was less affected by nitrogen fertilization, similarly to previous results. The strongest correlation between NDVI values and yield was measured at the post-silking stage (112 DAS; R = 0.638 in 2023, R = 0.634 in 2024), indicating the suitability of NDVI monitoring for in-season yield prediction. Overall, NDVI-based monitoring proved effective not just for optimizing N management but also for supporting site specific fertilization strategies to enhance maize productivity and nutrient use efficiency. Full article

Monthly runoff prediction plays a crucial role in water resource management, flood prevention, and disaster reduction. This study proposed a novel hybrid model for predicting monthly runoff by combining variational modal decomposition (VMD) with an extreme learning machine (ELM) and adaptive boosting (AdaBoost) algorithm. First, VMD is used to decompose the monthly runoff data, simplifying it and addressing its non-stationarity by extracting subsequences at different frequency scales. Next, the ELM model is applied to each subsequence within the AdaBoost algorithm to enhance prediction accuracy and stability. To contextualise its performance, the proposed model was systematically compared with four representative comparable models (VMD-ELM, ELM-AdaBoost, LSTM, and VMD-TPE-LSTM) using the same training/validation datasets (80% for training and 20% for validation) and evaluation metrics (root mean square error, RMSE; mean absolute percentage error, MAPE). The results indicate that the VMD-ELM-AdaBoost model outperforms all comparative models: at Yanshan Station, it achieves an RMSE of 2.521 mm and MAPE of 8.56% (34.8–45.1% lower RMSE than VMD-ELM, ELM-AdaBoost, and LSTM); at Baiguishan Station, it yields an RMSE of 2.906 mm and MAPE of 9.02% (22.3–42.6% lower RMSE than VMD-TPE-LSTM and other alternatives). This study demonstrates that the VMD-ELM-AdaBoost model balances accuracy, efficiency, and data adaptability, providing a practical tool for monthly runoff prediction in data-limited basins. Full article

This study proposes a complete data-processing framework for Geiger-mode avalanche photodiode (GM-APD) light detection and ranging (LiDAR) echo signals. It investigates the feasibility of classifying target and background noise using machine learning. Four feature processing schemes were first compared, among which the PNT strategy (Principal Component Analysis without tail features) was identified as the most effective and adopted for subsequent analysis. Based on this framework, nine models derived from six baseline algorithms—Decision Trees (DTs), Support Vector Machines (SVMs), Backpropagation Neural Networks (NN-BPs), Linear Discriminant Analysis (LDA), Logistic Regression (LR), and k-Nearest Neighbors (KNN)—were systematically assessed under Monte Carlo simulations with varying echo signal-to-noise ratio (ESNR) and statistical frame number (SFN) conditions. Model performance was evaluated using eight metrics: accuracy, precision, recall, FPR, FNR, F1-score, Kappa coefficient, and relative change percentage (RCP). Monte Carlo simulations were employed to generate datasets, and Principal Component Analysis (PCA) was applied for feature extraction in the machine learning training process. The results show that LDA achieves the shortest training time (0.38 s at SFN = 20,000), DT maintains stable accuracy (0.7171–0.8247) across different SFNs, and NN-BP models perform optimally under low-SNR conditions. Specifically, NN-BP-3 achieves the highest test accuracy of 0.9213 at SFN = 20,000, while NN-BP-2 records the highest training accuracy of 0.9137. Regarding stability, NN-BP-3 exhibits the smallest RCP value (0.0111), whereas SVM-3 yields the largest (0.1937) at the same frame count. In conclusion, NN-BP-based models demonstrate clear advantages in classifying sky-background noise. Building on this, we design a ResNet based on NN-BP, which achieves further accuracy gains over the best baseline at 400, 2000, and 20,000 frames—12.5% (400), 9.16% (2000), and 2.79% (20,000)—clearly demonstrating the advantage of NN-BP for GM-APD LiDAR signal classification. This research thus establishes a novel framework for GM-APD LiDAR signal classification, provides the first systematic comparison of multiple machine learning models, and highlights the trade-off between accuracy and computational efficiency. The findings confirm the feasibility of applying machine learning to GM-APD data and offer practical guidance for balancing detection performance with real-time requirements in field applications. Full article

The HN pig, indigenous to Henan Province, is distinguished by its reduced lean meat yield and slower growth rates relative to commercial foreign breeds. To address these limitations, three hybrid combinations were generated through the crossbreeding of Huainan sows with Yorkshire, Landrace, and Berkshire sires. In this study, extensive transcriptomic and metabolomic analyses of the LD muscle were carried out for the first time, and carcass and meat quality characteristics were compared between hybrid and HN pigs. Slaughter and muscle quality assessments revealed that the lean meat percentage of LH and YH was significantly lower than that of HN, with YH exhibiting the lowest intramuscular fat level, indicating that this breed possesses enhanced lean meat production efficiency. Transcriptomic profiling revealed markedly increased expression of SLIT2, CH25H, NR4A2, NR4A1, FOSB, CRABP2, GDF10, and MRAP2 in all three hybrid groups compared to HN. Gene Ontology enrichment analysis identified that the skeletal muscle cell differentiation (GO:0035914) and transforming growth factor beta receptor signaling pathway (GO:0007179) were exclusively enriched in the YH vs. HN comparison. Non-targeted metabolomic analysis identified 31, 36, and 12 DAMs in BH vs. HN, LH vs. HN, and YH vs. HN comparisons, with pyruvate metabolism being the sole pathway common to all groups. An integrated multi-omics analysis revealed significant correlations between phytosphingosine levels and DEGs across all three comparisons. In summary, these results indicate that crossbreeding substantially improves lean meat yield in HN pigs while providing novel molecular insights into the underlying genetic and metabolic mechanisms. Full article

We forecast short time series iteratively using a model based on stochastic differential equations. The recorded process is assumed to be consistent with an $\alpha$-stable Lévy motion. The generalized moments method provides the values of the scaling exponent and the parameter $\alpha$, which determine the form of the stochastic term at each iteration. Seven weekly recorded economic time series—the DAX, CAC, FTSE100, MIB, AEX, IBEX, and STOXX600—were examined for the period from 2020 to 2025. The parameter $\alpha$ is always 2 for the four of them, FTSE100, AEX, IBEX, and STOXX600, indicating quasi-Gaussian processes. For FTSE100, IBEX, and STOXX600, the processes are anti-persistent (H < 0.5).The rest of the examined markets show characteristics of uncorrelated processes whose values are drawn from either a log-normal or a log-Lévy distribution. Further, all processes are multifractal, as the non-zero value of the mean intermittency indicates. The model’s forecasts, with the time horizon always one-step-ahead, are compared to the forecasts of a properly chosen ARIMA model combined with Monte Carlo simulations. The low values of the absolute percentage error indicate that both models function well. The model’s outcomes are further compared to ARIMA forecasts by using the Diebold–Mariano test, which yields a better forecast ability for the proposed model since it has less average loss. The ability and accuracy of the model to forecast even small time series is further supported by the low value of the absolute percentage error; the value of 4 serves as an upper limit for the majority of the forecasts. Full article

Trends in electrical energy efficiency are key factors influencing production costs in food industry plants, as all production equipment relies on electricity. Accurate forecasting is essential for predicting future consumption and enabling effective energy management. This study aims to analyze and forecast trends in electrical energy efficiency in the food industry. Production and electricity consumption data from January 2022 to December 2023 were used to calculate the difference in electrical energy (DIFF) and the cumulative sum of electrical energy differences (CUSUM), which served as the basis for forecasting. The Long Short-Term Memory (LSTM) model, based on the deep learning approach, was employed to simulate the algorithmic patterns of electrical energy data in the food industry. Its forecasting performance was then compared with two alternative models, namely decomposition and logistic regression, using evaluation data from January to December 2024. Model accuracy was assessed using the Mean Absolute Percentage Error (MAPE) criterion. The results revealed that the decomposition model achieved lower MAPE values for both DIFF (14.47%) and CUSUM (24.13%), while the logistic regression model yielded higher MAPE values of 73.70% and 66.85%, respectively. Therefore, the decomposition model was identified as the most suitable method for forecasting electrical energy consumption trends in the food industry, providing higher accuracy and reliability than logistic regression. Forecasting energy consumption trends using the decomposition model can support strategic energy planning to enhance efficiency, reduce costs, and promote the sustainable development of the food industry in the future. Full article

This study investigated the effects of different water management practices on the growth, yield, and grain quality of rice grown under environmentally friendly farming methods in Apgok-Ri, Gungnyu-Myeon, Uiryeong-Gun, from 2022 to 2024. Treatments included mid-season drainage for 2, 3, or 4 weeks (2MD, 3MD, 4MD), followed by either low-level water management (MD-1) or alternate wetting and drying (MD-2), with continuous flooding (CF) as the control. The rice variety was machine-transplanted on 9–10 June, and organic fertilizer (90 kg N/ha) was applied as a basal dressing. Water treatments were initiated in mid-July each year. The highest yield was consistently recorded in the 2MD-2 treatment, with 5.85, 5.74, and 5.38 tons/ha from 2022 to 2024, representing 15.0%, 14.5%, and 7.8% increases over CF, respectively. On average, alternate irrigation (MD-2) resulted in higher yields than low-level water management (MD-1) by 1.19–5.90%. Grain quality was also highest in 2MD-2, showing the greatest percentage of ripened grains each year, whereas CF had the highest proportion of immature and unripe grains. Crude protein content in brown rice was lowest in 3MD-2 (6.12%), followed by 2MD-2 (7.51%). Incidences of major diseases such as sheath blight, rice blast, panicle blight, and bacterial grain blight were highest in the CF treatment. Rice leaf blight was not significantly different in 2022, but was most prevalent in CF in 2023 and 2024. There were no major differences in brown planthopper and false smut incidence, although false smut peaked in CF in 2024. These findings suggest that 2-week mid-season drainage followed by alternate irrigation (2MD-2) is an effective strategy to improve yield, grain quality, and disease resistance in sustainable rice farming systems. Full article

Canephora coffee genotypes developed in other growing regions, with traits of interest such as drought tolerance and high coffee bean yield, need to be introduced and characterized in other locations to check adaptability. The aim of this study was to check the agronomic performance and determine the genetic parameters of the clonal canephora coffee cultivar Marilândia ES 8143, composed by twelve genotypes, developed by the Capixaba Institute of Research, Technical Assistance and Rural Extension (Instituto Capixaba de Pesquisa, Assistência Técnica e Extensão Rural—Incaper), in an irrigated system of the Central Cerrado region of Brazil. The study was conducted in the experimental areas of Embrapa Cerrados at 1050 m altitude in a center pivot irrigation system using a management system with water stress controlled for around 65 days. A randomized block experimental design was used with three replications, and each plot consisted of eight plants. The clones were planted in February 2019 and in 2021 and 2022. Phenotyping was carried out to evaluate the following traits: coffee bean yields, sieve retention percentages, plant height, canopy projection, number of pairs of plagiotropic branches, and frost damage using a scoring scale. Clone 5 stood out in mean value in the two years evaluated for bean yield. Clones 5, 6, 7, 8, and 9 had higher mean values for flat-type coffee beans in both years. Clones 1 and 5 exhibited mean values indicating good vegetative development. Clones 5 and 12 showed no visible symptoms for low air temperatures and frost effects. Highly significant differences were observed among the genotypes for all the morphoagronomic traits evaluated, and high values of heritability, genetic coefficients of variation, and selective accuracy showed conditions favorable to the selection of clones for the agronomic traits analyzed. Clones 1, 2 and 6 have values in lower groups for chlorogenic acids and caffeine, and in higher groups for protein and soluble solids, thus showing greater potential for obtaining quality beverages. Full article

The centrifuge method serves as an efficient and rapid approach for determining the soil–water characteristic curve (SWCC). However, soil shrinkage during centrifugation remains overlooked and prior modified methods may suffer from complex operations, high costs, time consumption, and limited applicability. To address these issues, this study introduces a simple correction scheme (G3) for determining drying SWCCs using the centrifuge method based on high matric suction calibration points. The performance of the proposed G3 method was systematically evaluated against a modified method considering soil shrinkage (G1) and the conventional uncorrected method (G2). Results revealed significant soil linear shrinkage post-centrifugation, accompanied by a reduction in total soil porosity and an increase in soil bulk density. SWCCs from all methods exhibited strong consistency at low matric suction ranges but diverged markedly at high matric suction segments. High matric suction data dominated the SWCC fitting. The G1 method achieved the highest fitting accuracy, while the G3 method performed the worst yet maintained acceptable reliability. The G2 method yielded optimal SWCC for simulating saturated soil water content, field capacity, and permanent wilting point. Conversely, Hydrus-1D simulations revealed superior performance of the G3 method in simulating farmland soil moisture dynamics during the dehumidification process. Values of R² across methods followed G3 > G1 > G2, while mean absolute error, mean absolute percentage error, and root mean square error exhibited the opposite trend. These findings highlight that the previous modified approaches are more suitable for low and medium matric suction ranges. The proposed correction method enhances drying SWCC performance across the full matric suction range, offering a practical refinement for the centrifuge method. This advancement could enhance the reliability in soil hydraulic characterization and contribute to a better understanding of the hydraulic–mechanical–chemical behavior in soils. Full article

Beetroot-based additives are interesting for enriching bread in terms of bioactive compounds. The objective of this study was to determine the effect of the following beetroot-based additives: a beetroot lyophilizate powder (wheat–oat baking mix flour was replaced in proportions of 2.5, 5.0, 7.5, 10%), a beetroot juice (water was replaced with juice in proportions of 25, 50, 75, 100%) and a by-product of beetroot juice production, i.e., pomace (wheat–oat baking mix flour was replaced in proportions of 2.5, 5.0, 7.5, 10%) on the quality of wheat–oat bread and the content of bioactive components in this type of bread. The properties of the dough were also assessed. The type and percentage level of partially replacing wheat–oat baking mix flour or water with beetroot-based additives had a significant impact on water absorption, dough development, and stability time of the tested dough. The beetroot juice (BJ) and powder (BLP) had the most significant impact on the rheological properties of the dough, whereas the pomace (BP) had the smallest effect. Beetroot-based additives, especially powder and juice, reduced the volume of bread (from 199 to 148 cm³/100 g of bread) but did not change oven loss [%] and bread crumb porosity index. Breads with these additives showed higher increased values for dough yield [%] and bread yield [%] (for beetroot powder—by 10% compared to the control sample (133.37% and 113.83%)). Tested additives had an impact on the crust and crumb color of the tested wheat–oat breads. The proposed additives significantly increased the antioxidant activity, total phenolic content, and betalain content in the bread samples. The above results showed that, from a technological point of view, replacing water or flour in the wheat–oat bread recipe with beetroot-based additives with a maximum concentration of 5% for BP or BLP and 50% for BJ allows for obtaining a product of good quality. Full article

This paper presents a novel numerical–experimental workflow to evaluate the sealability of casing connections in geothermal and underground gas storage wells, where cyclic thermal and pressure loads challenge conventional qualification methods. The approach combines experimental make-up and cyclic loading tests with finite element analysis by explicitly modeling the connection geometry and the contact conditions. Validation against experimental data shows good agreement in seal ovality, roughness, and wear, confirming the predictive reliability of the model. Results indicate that initial geothermal discharge and seasonal storage cycles generate the highest von Mises stresses, expressed as a percentage of the material’s yield strength (%VMS), mainly under combined tensile and internal pressure loading. After the first make-up, subsequent cycles reduced seal contact pressure and length, increasing leakage risk; however, repeated loading improved tribological behavior, enhancing sealability despite occasional galling. The proposed framework enables accurate prediction of connection integrity under extreme cyclic conditions, offering a novel tool to optimize design and streamline qualification testing. Full article