Search Results (1,718)

Power systems with high renewable penetration impose multi-dimensional demands on energy storage (ES) regulation. Short-duration ES is required for power balance and frequency support, while medium- and long-duration ES is essential for daily, weekly, and seasonal peak shaving and energy time-shifting. Aiming at the challenge of multi-timescale configuration of hybrid energy storage (HES) in the initial planning stage of carbon-neutral transition, this paper proposes an optimal configuration strategy combining STL-PDM-VMD. First, the seasonal and trend decomposition using Loess (STL) is used to extract quarterly trends of annual net power for seasonal ES configuration. Then, the Past Decomposable Mixing (PDM) module in the time-mixer model is applied to decouple and mix multi-scale features of the detrended power curve for monthly and weekly configurations. Finally, an improved Variational Mode Decomposition (VMD) is adopted to decompose daily net power fluctuations and optimize intra-day energy storage schemes. Based on actual data from a carbon-neutral transition region, simulations are carried out and compared with the VMD method with decomposition layers optimized by Gurobi. The results show that the proposed STL-PDM-VMD multi-timescale hybrid energy storage configuration strategy can effectively capture the multi-timescale fluctuation characteristics of net load, significantly improve the Renewable Energy (RE) penetration rate, and ensure the power and energy balance of the new power system at multiple timescales. penetration, and maintain power and energy balance in the new-type power system. Full article

Background: Plantar fasciitis is a leading cause of heel pain, affecting approximately 10% of the population. Despite its prevalence, treatments may result in symptom recurrence and chronicity, which can significantly increase patient dissatisfaction. Google Trends provides insights into public interest through search volume analysis. This study examines global and USA trends in plantar fasciitis, focusing on temporal, seasonal, and income-based variations. Methods: Google Trends data for “Plantar Fasciitis” (2004–2024) were analyzed for both global and USA search trends. Monthly and seasonal search volumes were grouped by time and location. Regression and post hoc tests were conducted to identify significant patterns. Comparisons were made between high- and low-income states in the USA. Results: Public interest in plantar fasciitis increased significantly over time, both globally (R² = 0.871, p < 0.001) and in the USA (R² = 0.854, p < 0.001). Interest peaked in summer and declined in winter, with seasonal differences significant worldwide (p < 0.05). Monthly variations were significant only in the USA. No significant difference was found between high- and low-income states (p > 0.05). Conclusions: Interest in plantar fasciitis has grown steadily, reflecting its prevalence and impact. The findings emphasize the need for accessible, high-quality information to address public demand. These insights can guide healthcare professionals and policymakers in developing targeted resources. Full article

The accurate anticipation of passenger demand across specific origin–destination (OD) airport routes is a cornerstone of strategic and operational decision-making within the global aviation sector, including airlines optimizing fleet and route management, airports planning infrastructure development, and regulatory bodies overseeing airspace efficiency. However, conventional forecasting techniques frequently encounter limitations when confronted with the inherent complexities and non-linear interdependencies that characterize air travel demand patterns. These patterns are shaped by an array of dynamic variables, including macroeconomic trends, population dynamics, distinct seasonal variations, and emergent phenomena. This investigation evaluates the utility of Artificial Intelligence (AI) paradigms in constructing predictive models for monthly passenger volumes between international OD airport pairs. This work highlights the ongoing transformative impact of AI methodologies on forecasting tasks within the aviation industry. Full article

Understanding the genetic architecture underlying growth variation is central to improving aquaculture species through genomic selection. Here, we performed a genome-wide association study (GWAS) on 303 individuals from a G₂ breeding population of channel catfish (Ictalurus punctatus) using whole-genome resequencing data. After stringent quality control, 5.64 million high-confidence single nucleotide polymorphisms (SNPs) were retained for association analyses of two key growth traits—monthly weight gain (MWG) and body depth (BH). We identified 15 and 28 loci significantly associated with MWG and BH, respectively, with the majority concentrated on chromosome 20. Two SNPs (Chr20:14,657,971 and Chr20:14,658,012) located in exon 9 of the rrp44 gene were significantly associated with both traits. Functional annotation and enrichment analyses revealed that the rrp44 gene, encoding an exoribonuclease subunit of the RNA exosome complex, participates in mitotic spindle regulation and post-transcriptional RNA decay, processes critical for cellular growth and metabolic homeostasis. We propose that rrp44 may influence growth through the modulation of feeding rhythm and circadian regulation, providing a potential molecular basis for growth heterogeneity in channel catfish. These findings enrich our understanding of growth-related genomic variation and offer valuable molecular markers for precision breeding and genetic improvement of catfish. Full article

The increasing penetration of wind generation requires performance evaluation methods that extend beyond average annual energy production. Temporal delivery characteristics, such as monthly dispersion and exposure to low-production periods, can influence both technical robustness and economic sensitivity. Building upon a previously developed probabilistic and entropy-based assessment framework, this study evaluates the robustness of delivery-oriented performance metrics for onshore and offshore wind units under parametric and economic uncertainty. Using high-resolution operational data from four wind units (three onshore and one offshore), the analysis incorporates percentile sensitivity, threshold variation in low-production exposure, bootstrap-based uncertainty intervals, and Monte Carlo simulation of economic inputs including CAPEX, operation and maintenance costs, and discount rate. The results indicate that variations in percentile definitions and stochastic economic assumptions modify absolute performance values but do not substantially alter the relative positioning between offshore and onshore units. Averaged over 2022–2024, the analyzed offshore unit exhibited a lower monthly energy dispersion coefficient ($C{V}_E=0.255$) than the analyzed onshore units ($C{V}_E=0.368$), corresponding to an approximate 30% reduction in relative variability. The offshore unit also showed lower mean low-production exposure ($LPE=0.526$ versus $0.581$ for onshore units) and consistently lower amplification of robustness-adjusted LCOE under conservative delivery assumptions. These results indicate that the analyzed offshore unit retains stronger delivery robustness and lower economic sensitivity across the tested parameter ranges. The proposed robustness-validation framework complements conventional yield-based assessments and provides additional insight for risk-aware evaluation of wind generation assets in renewable-dominated power systems. Full article

Accurately characterizing the spatiotemporal variations in precipitation in China’s drylands is important for solving water scarcity in the region, guaranteeing security in the ecological environment, and conducting precise drought disaster management. To reduce the uncertainty in the existing precipitation products, we developed a two-stage machine-learning framework combining extreme gradient boosting (XGBoost) and random forest (RF) residual corrections. Based on the ground-based observation data from 1030 meteorological stations and numerous high-precision precipitation products (GPM IMERG Final V6, MSWEP V2, CMFD 2.0, TerraClimate), a monthly fused precipitation dataset (XGB-RF) for China’s drylands was produced during the 2001–2020 period at the 0.1° resolution. The validation results showed that the XGB-RF had a monthly Kling–Gupta Efficiency (KGE) of 0.941, and it improved 20.6–62.2% relatively with that of input individual products. For the dataset as a whole, we found very consistent, reliable performance in all seasons and topography, in particular in winter time and data-scarce western areas where individual products have large biases. More importantly, the XGB-RF was employed for drought monitoring based on the 1-month Standardized Precipitation Index that calculated the median KGE of 0.888, which made good drought trend tracking and drought features possible. Notably, the KGE for the mean drought intensity was 0.757, which was higher than that of independent original products. This study provides a high-resolution precipitation forcing dataset and demonstrates the effectiveness of two-stage machine learning strategies in enhancing hydroclimatic monitoring and drought risk assessment in arid and semi-arid regions. Full article

Water distribution networks are part of critical infrastructure, and ensuring a rapid return to service after failures is vital for public health and economic resilience. While numerous studies have quantified failure rates and examined factors that influence the duration of repairs, the seasonal variability of repair time itself has received little attention. This study analyses 264 monthly observations (January 2004–December 2025) from a large urban water supply system in south-eastern Poland. We evaluate the seasonality of failure counts, average repair time per event, and the total labour hours needed to restore service. Methods include descriptive statistics, seasonal indices, non-parametric tests, kernel density estimation, parametric distribution fitting, empirical exceedance curves of monthly mean repair duration, and time-series decomposition. The results show a pronounced seasonal pattern in the number of failures and total labour hours, with peaks in winter and minima in spring, whereas the monthly mean repair duration remained stable at approximately 8 h and showed no significant seasonal variation. Among the positive-support candidate distributions, the log-normal model provided a slightly better fit than the Weibull model. Empirical exceedance analysis and non-parametric tests confirmed no significant differences in monthly mean repair duration between seasons or calendar months. Decomposition reveals a small downward trend in total repair hours after 2010. These findings provide new insights for maintenance planning and indicate that winter workload peaks are driven primarily by higher failure counts rather than by longer mean repair duration. Full article

Natural ventilation is widely regarded as an energy-saving strategy in buildings; however, under continuous mechanical ventilation in Japanese residential buildings, its performance remains insufficiently understood. This study evaluates the performance of different natural ventilation strategies for a typical two-story detached house across eight climate zones in Japan using dynamic building energy simulation. Four ventilation strategies are examined, including baseline mechanical ventilation (S0), shoulder-season natural ventilation (S1), summer night ventilation (S2), and an adaptive natural ventilation strategy with humidity constraints (S3). Annual HVAC loads, monthly variations, and the structure of cooling loads are analyzed. Results show that shoulder-season natural ventilation (S1) does not lead to energy savings and may result in a slight increase in annual HVAC loads in most climate zones. In contrast, summer night ventilation (S2) reduces annual HVAC loads by approximately 8–10% in transitional climates (CZ3–CZ5), while its effect is weaker in hot and humid regions. The adaptive strategy (S3) achieves moderate reductions of up to about 2–3% and significantly decreases the proportion of latent cooling loads. Overall, the effectiveness of natural ventilation is governed by the trade-off between sensible load reduction and latent load increase and is strongly climate-dependent. These findings provide a basis for optimizing hybrid ventilation strategies under continuous mechanical ventilation conditions. Full article

High spatio-temporal resolution satellite imagery has become increasingly accessible thanks to advancements in the aerospace industry which, combined with a growing computational power, has enabled the spring of novel techniques regarding recognition in remote sensing (RS) images. However, there is still a lack of studies around 3D convolutions for spatio-temporal data applied to classification problems in RS. Hence, this study investigates the feasibility of 3D convolutional neural networks (3DCNNs) within a spatio-temporal perspective for scene classification and semantic segmentation in RS images, focusing on the identification of mining sites. We firstly developed a dataset covering several parts of Brazil based on MapBiomas products and Planet imagery, then we evaluated the effectiveness of 3DCNNs in capturing temporal information from a sequence of monthly captured images. Moreover, not only for scene classification but also for semantic segmentation, we compared 3D and 2D approaches. As for scene classification, a 3DCNN was better than the corresponding 2D model, while a 2D U-Net was better than a U-Net3D for semantic segmentation. The main explanation for this lies in the fact that a less costly annotation and training time strategy was adopted, but this may have harmed spatio-temporal approaches for semantic segmentation but not for scene classification. However, U-Net3D presented the highest Precision of all models, meaning that it is highly accurate when it predicts a positive. Moreover, 3DCNN (U-Net3D) presented significantly better performance with respect to semantic segmentation compared to other spatio-temporal approaches like ConvLSTM+U-Net and TempCNN. Sensitivity analysis revealed that the near-infrared (NIR) band played a decisive role in distinguishing mining areas, emphasizing its importance in highlighting subtle spectral variations associated with land-cover disturbances. Full article

Climate change poses increasing challenges to livestock production in tropical regions, where rising temperatures, rainfall variability, and feed cost fluctuations affect productivity and economic stability. However, few studies have jointly quantified the effects of climatic and economic variables on swine production in tropical production systems, particularly in Brazil. This study examined the effects of maximum temperature, precipitation, and feed price on swine production density in Brazil’s main producing states. The analysis included Paraná and Rio Grande do Sul as the principal empirical base, while Mato Grosso was retained because of its strategic relevance but contributed only limited observations and was therefore interpreted more cautiously. Using monthly observations and a multiple linear regression model with heteroskedasticity- and autocorrelation-consistent (HAC, Newey–West) standard errors, we found that higher maximum mean temperatures were associated with lower production density: a 1 °C increase corresponded to an estimated decline of 1.34 × 10⁶ kg/km². Precipitation showed a positive association, with each additional millimeter corresponding to an increase of approximately 1.82 × 10⁵ kg/km², whereas a 0.173 USD/kg increase in feed price was associated with a reduction of about 6.2 × 10⁶ kg/km². Although the model explained only a modest share of monthly variation (R² = 0.162), the results suggest that climatic exposure and feed-cost pressure are relevant components of swine production dynamics in Brazil and should be considered in future climate-risk and agricultural planning. Full article

The castor bean tick (Ixodes ricinus) is a widespread European ectoparasite and vector of multiple diseases that can impair cattle health and productivity. This study evaluated whether a single application of a gel microencapsulated acaricide (α-cypermethrin and permethrin) reduces I. ricinus infestation in dairy cows and affects milk yield. Twenty cows were allocated to treated and control groups; treated animals received one spray at the start of the grazing season, and tick counts were recorded daily for 196 days with monthly milk-yield measurements. Two activity peaks were observed (June and September–October) with reduced abundance in July–August; all control cows were parasitised, with daily counts of 1–18 ticks (median = 2). During the first month after treatment, the acaricide reduced tick numbers by around 80% versus controls (p < 0.001); efficacy declined over the ensuing months, and differences were negligible after about five months. No adverse effect on milk yield was detected (p = 0.38), and seasonal variation (p < 0.0001) and lactation stage (p < 0.001) were the primary determinants of production. Primiparous and young cows, as well as cows in mid-to-late lactation, tended to show higher levels of tick infestation. A single application of the gel microencapsulated product provided a significant reduction in tick burden during the first month after treatment in grazing dairy cows, without a negative impact on milk production, supporting its use in endemic areas. Full article

Lakes on the Inner Mongolia–Xinjiang Plateau (IMXP) are increasingly vulnerable to eutrophication under climate change and human pressure, yet long-term monitoring remains limited by sparse field sampling. Here, we reconstruct multi-decadal trophic dynamics across the IMXP using Landsat time series and temporally transferable machine-learning models and further quantify the underlying natural and anthropogenic drivers. We compiled monthly in situ water-quality observations (chlorophyll-a, Chl-a; total phosphorus, TP; total nitrogen, TN; Secchi depth, SD; and permanganate index, COD_Mn;) and calculated the trophic level index (TLI). After rigorous quality control and monthly aggregation, we compiled a dataset of 1345 matched lake–month samples spanning 2000–2024, and divided it into a training set (n = 1076; ≤2019) and an independent test set (n = 269; 2020–2024) to evaluate temporal transferability. We utilized Google Earth Engine to generate monthly surface reflectance composites from Landsat 7 ETM+, Landsat 8 OLI, and Landsat 9 OLI-2. Four supervised regression algorithms—ridge regression (RR), support vector regression (SVR), random forest (RF), and eXtreme Gradient Boosting (XGBoost)—were trained to estimate TLI. On the independent test period, XGBoost performed best (R² = 0.780, RMSE = 3.290, MAE = 1.779), followed by RF (R² = 0.770, RMSE = 3.364), SVR (R² = 0.700, RMSE = 3.842), and RR (R² = 0.630, RMSE = 4.267); we then used XGBoost to reconstruct monthly and yearly TLI for 610 perennial grassland lakes from 2000 to 2024. From 2000 to 2024, the annual mean TLI (48–49) across the IMXP exhibited a statistically significant upward trend (slope = 0.0158 TLI yr⁻¹; 95% confidence interval (CI) = 0.0050–0.0267; p = 0.006). Meanwhile, spatial heterogeneity was distinct (TLI: 41.51–59.70). High values concentrated in endorheic and desert–oasis basins (e.g., Eastern Inner Mongolia Plateau, >51), whereas lower values characterized high-altitude regions (e.g., Yarkant River, <45). Overall, trends ranged from −0.49 to 0.51 yr⁻¹, increasing in 54% of lakes (15.6% significantly) and decreasing in 46% (15.4% significantly). Attribution analyses identified NDVI (33.92%) and temperature (21.67%) as dominant drivers (55.59% combined), followed by precipitation (13.99%) and human proxies (30.42% combined: population 10.66%, grazing 10.31%, built-up 9.45%). Across 53 sub-basins, NDVI was the primary driver in 28, followed by temperature (11), population (7), precipitation (3), grazing (3), and built-up land (1); notably, the top two drivers explained 56.6–87.1% of variations. TWFE estimates revealed bidirectional NDVI effects (significant in 31/53): positive associations in 22 basins were linked to nutrient retention, contrasting with negative effects in nine basins associated with agricultural return flows. Temperature effects were significant in 15 basins and predominantly negative (14/15), except for the Qiangtang Plateau. Overall, eutrophication risk across the IMXP lake region reflects the combined influences of climatic conditions, vegetation conditions, and human activities, with their relative contributions varying among basins. Full article

The inhomogeneity of the evaporation duct significantly influences electromagnetic propagation. Based on observation data from four buoy stations in the northern South China Sea (SCS) and European Centre for Medium-Range Weather Forecasts (ECMWF) data, the Naval Postgraduate School (NPS) model is employed to calculate the evaporation duct height (EDH). The concept of information entropy is used to assess the horizontal inhomogeneity of the evaporation duct and the evaporation duct height entropy (EDHE) is defined as the assessment index. The research findings are as follows: (1) The probability of EDH differences based on statistical methods between stations falling within the range of −2 m to 2 m remains above 60%, with uniformity characteristics showing minimal variation throughout the day. (2) The EDHE can better quantify the horizontal inhomogeneous characteristics of EDH between buoy stations compared to statistical methods. (3) The monthly variation characteristics of EDHE between buoy stations based on ECMWF reanalysis data are quite consistent with actual observations, but it overestimates the EDHE values. Therefore, the EDH derived from ECMWF data leads to an overestimation of inhomogeneity characteristics compared to buoy observations. Full article

This study utilized a high-resolution, three-dimensional hydrodynamic model with improved model evaluation to investigate seasonal variations in key hydrographic conditions, including sea level, water temperature, salinity, current speed, and circulation in the Gulf of Thailand (GoT), as well as its interaction with the South China Sea (SCS). The analysis focuses on a climatological year calculated from a 15-year average for 2006–2020, which is categorized into four seasons: northeast monsoon, the first inter-monsoon, southwest monsoon, and the second inter-monsoon. Evaluation of model performance, based on observational data with temporal resolutions ranging from 30 min to monthly average with a duration from 10 months to 5 years, demonstrated good accuracy through high coefficients of determination and low root mean square errors. Results clearly depicted seasonal variability in hydrographic properties, characterized by alternating patterns of high and low sea level, high and low water temperatures, saline and fresh water, along with a persistent anticyclonic gyre in the central area of GoT and a smaller anticyclonic gyre in the southern area. Seasonal exchange flows between the SCS and the GoT were also evident, with the strongest outflow in northeast monsoon and the weakest in the second inter-monsoon. Full article

(1) Brucellosis is a zoonotic infection that remains a significant public health concern in endemic regions. This study aimed to determine the seroprevalence of brucellosis in a tertiary care hospital, analyze associated risk factors, and evaluate the diagnostic performance of commonly used serological tests. (2) The study was based on the serological test results of 24,545 samples collected between 2020 and 2023. Rose Bengal, standard tube agglutination, and Brucellacapt tests were used for the diagnosis of brucellosis. Data were analyzed according to age, sex, clinical department, and seasonal distribution using SPSS version 25.0. (3) Overall, 367 cases (1.5%) tested positive. When the 367 seropositive cases were evaluated by year, the annual distribution showed a declining trend, decreasing from 2.5% in 2020 to 1.2% in 2023. Among the positive cases, 57.8% were female, and 36% were aged between 41 and 64 years. The infectious diseases department had the highest positivity rate (37.1%). Brucellacapt showed the highest positivity rate (90.2%), followed by Rose Bengal (76.2%). The highest monthly positivity rate was observed in October (11.4%), and seasonally in autumn (31.3%). (4) The Brucellacapt test has demonstrated high sensitivity and serves as a valuable supplementary diagnostic tool in the evaluation of brucellosis. However, its low specificity underscores the necessity for careful interpretation of positive results and supports its use in conjunction with other serological tests to enhance diagnostic accuracy. Considering seasonal and departmental variations, a combined testing approach may improve overall diagnostic accuracy. Full article