Search Results (307)

In this study, we consider monthly load forecasting, which is an essential decision for energy infrastructure planning and investment. This study focuses on the Texas power grid, where electricity consumption has surged due to rising industrial activity and the increased construction of data centers driven by growing demand for AI. Based on an extensive exploratory data analysis, we identify key characteristics of monthly electricity demand in Texas, including an accelerating upward trend, strong seasonality, and temperature sensitivity. In response, we propose a regression-based forecasting model that incorporates a carefully designed set of input features, including a nonlinear trend, lagged demand variables, a seasonality-adjusted month variable, average temperature of a representative area, and calendar-based proxies for industrial activity. We adopt a rolling forecasting approach, generating 12-month-ahead forecasts for both 2023 and 2024 using monthly data from 2013 onward. Comparative experiments against benchmarks including Holt–Winters, SARIMA, Prophet, RNN, LSTM, Transformer, Random Forest, LightGBM, and XGBoost show that the proposed model achieves superior performance with a mean absolute percentage error of approximately 2%. The results indicate that a well-designed regression approach can effectively outperform even the latest machine learning methods in monthly load forecasting. Full article

This study investigated the impact of nitrogen (N) fertilization on the yield and grain filling (GF) characteristics of two conventional japonica rice varieties with distinct panicle types: Yangchan 3501 (large-panicle: spikelets per panicle $> 150$) and Nangeng 46 (medium-panicle: $100 <$ spikelets per panicle $< 150$). Field experiments were conducted over two growing seasons (2022–2023) with three N application rates (T1: 225 kg ha⁻¹, T2: 270 kg ha⁻¹, T3: 315 kg ha⁻¹). Key measurements included tiller dynamics, panicle composition, GF parameters modeled using the Richards equation, and enzyme activities related to nitrogen metabolism (Fd-GOGAT, NR) and carbohydrate transport (α-amylase, SPS). Results showed that the yield increased with higher N levels for both varieties, with Yangchan 3501 achieving higher yields primarily through increased grains per panicle (15.65% rise under T3 vs. T1), while Nangeng 46 relied on panicle number (8.83% increase under T3 vs. T1). Nitrogen application enhanced Fd-GOGAT and NR activities, prolonging photosynthesis and improving GF rates, particularly in the inferior grains of Yangchan 3501 during middle and late stages. However, a high N reduced seed-setting rates and 1000-grain weight, with larger panicle types exhibiting a greater sensitivity to N-induced changes in branch structure and assimilate allocation. This study highlights that optimizing N management can improve nitrogen-metabolism enzyme activity and GF efficiency, especially in large-panicle rice, while medium-panicle types require higher N inputs to maximize panicle number. These findings provide actionable insights for achieving high yields and efficient nutrient use in conventional rice cultivation. Full article

Rivers in tropical semi-arid regions face increasing anthropogenic pressures yet remain critically understudied despite their global importance. This study evaluated the aquatic macroinvertebrate community structure in the Ranchería River, Colombia, across three land use conditions: conserved zones (CZs), urban/agricultural zones (UAZs), and mining influence zones (MZs). Ten sampling stations were established, and macroinvertebrate communities were assessed alongside physical, chemical, and hydromorphological variables during the dry season (January–March 2021). A total of 9288 individuals from 84 genera across 16 orders were collected. Generalized Linear Models revealed significant differences among zones for 67 genera (79.8%), indicating strong community responses to land use gradients. Conserved zones exhibited the highest diversity according to the Hill numbers and were dominated by sensitive taxa, including Simulium, Smicridea, and Leptohyphes. Urban/agricultural zones showed the lowest richness (35 genera) and were characterized by disturbance-tolerant species, particularly Melanoides. Mining zones displayed intermediate diversity but exhibited severe habitat alterations. A redundancy analysis with variance partitioning revealed that land use types constituted the primary driver of community structure (a 24.1% pure effect), exceeding the physical and chemical variables (19.5%) and land cover characteristics (19.2%). The integrated model explained 63.5% of the total compositional variation, demonstrating that landscape-scale anthropogenic disturbances exert a greater influence on aquatic communities than local environmental conditions alone. Different anthropogenic activities create distinct environmental filters affecting macroinvertebrate assemblages, emphasizing the importance of land use planning for maintaining aquatic ecosystem integrity in semi-arid watersheds. Full article

Particulate matter (PM), mainly PM_0.5, represents a significant concern for human health, particularly relating to lung homeostasis, and more research is required to ascertain its tissue tropism and the molecular pathways involved. In this study, we first focus on classical in vitro toxicological endpoints (cytotoxicity and cell growth) in human bronchial and alveolar epithelial cell lines mimicking the two pulmonary target tissues. Air samples were collected in five Italian cities (Brescia, Lecce, Perugia, Pisa, Turin) during winter and spring. To better decipher the PM_0.5 effects on pulmonary cells, a further winter sampling was performed in Brescia, and studies were extended to assess tumour promotion, oxidative stress, and the activity of Matrix metalloproteases (MMP). The results confirmed that the effect of air pollution is linked to the seasons (winter is usually more cytotoxic than spring) and is correlated with the peculiar characteristics of the cities studied (meteoclimatic conditions, economic/anthropogenic activities). Alveolar cells were often less sensitive than bronchial cells. All PM samples from Brescia inhibited intercellular communication mediated by gap junctions (GJIC), increased the total content in glutathione, and decreased the reduced form of glutathione, whereas the Reactive Oxygen Species (ROS) content was almost constant. Long-term treatments at higher doses of PM decreased MMP2 and MMP9 activity. Taken together, the results confirmed that PM is cytotoxic and can potentially act as tumour promoters, but the mechanisms involved in oxidative stress and lung homeostasis are dose- and time-dependent and quite complex. Full article

The study presents a scenario analysis of the long-term dynamics of the water level of Lake Balkhash, one of the largest closed lakes in Central Asia, taking into account climate change according to CMIP6 scenarios (SSP2-4.5 and SSP5-8.5) and socio-economic factors of water use. Based on historical data (1947–2021) and a water balance model, the contribution of surface runoff, precipitation and evaporation to the formation of the lake’s hydrological regime was assessed. It was established that the main source of water resources for the lake is the flow of the Ile River, which feeds the western part of the reservoir. The eastern part is characterized by extremely limited water inflow, while evaporation remains the main element of water consumption, having increased significantly in recent decades due to rising air temperatures. Increasing intra-seasonal and interannual fluctuations in water levels have been recorded: The amplitude of short-term fluctuations reached 0.7–0.8 m, which exceeds previously characteristic values. The results of water balance modeling up to 2050 show a trend towards a 30% reduction in surface inflow and an increase in evaporation by 25% compared to the 1981–2010 climate norm, which highlights the high sensitivity of the lake’s hydrological regime to climatic and anthropogenic influences. The results obtained justify the need for the comprehensive and adaptive management of water resources in the Balkhash Lake basin, taking into account the transboundary nature of water use and changing climatic conditions. Full article

Background: Prevention and clinical management of musculoskeletal injuries have historically focused on the assessment and training of modifiable physical factors, but perceptual decision-making has only recently been recognized as a potentially important capability. Immersive virtual reality (VR) systems can measure the speed, accuracy, and consistency of body movements corresponding to stimulus–response instructions for the completion of a forced-choice task. Methods: A cohort of 26 female college soccer players (age 19.5 ± 1.3 years) included 10 players who participated in a baseline assessment, 10 perceptual-response training (PRT) sessions, a post-training assessment that preceded the first soccer practice, and a post-season assessment. The remaining 16 players completed an assessment prior to the team’s first pre-season practice session, and a post-season assessment. The assessments and training sessions involved left- or right-directed neck rotation, arm reach, and step-lunge reactions to 40 presentations of different types of horizontally moving visual stimuli. The PRT program included 4 levels of difficulty created by changes in initial stimulus location, addition of distractor stimuli, and increased movement speed, with ≥90% response accuracy used as the criterion for training progression. Perceptual latency (PL) was defined as the time elapsed from stimulus appearance to initiation of neck rotation toward a peripheral virtual target. The speed–accuracy tradeoff was represented by Rate Correct per Second (RCS) of PL, and inconsistency across trials derived from their standard deviation for PL was represented by intra-individual variability (IIV). Perceptual Decision Efficiency (PDE) represented the ratio of RCS to IIV, which provided a single value representing speed, accuracy, and consistency. Statistical procedures included the bivariate correlation between RCS and IIV, dependent t-test comparisons of pre- and post-training metrics, repeated measures analysis of variance for group X session pre- to post-season comparisons, receiver operating characteristic analysis, and Kaplan–Meier time to injury event analysis. Results: Statistically significant (p < 0.05) results were found for pre- to post-training change, and pre-season to post-season group differences, for RCS, IIV, and PDE. An inverse logarithmic relationship was found between RCS and IIV (Spearman’s Rho = −0.795). The best discriminator between injured and non-injured statuses was PDE ≤ 21.6 (93% Sensitivity; 42% Specificity; OR = 9.29). Conclusions: The 10-session PRT program produced significant improvement in perceptual decision-making that appears to provide a transfer benefit, as the PDE metric provided good prospective prediction of musculoskeletal injury. Full article

The accurate delineation of shrub crown boundaries is critical for ecological monitoring, land management, and understanding vegetation dynamics in fragile ecosystems such as semi-arid shrublands. While traditional image processing techniques often struggle with overlapping canopies, deep learning methods, such as convolutional neural networks (CNNs), offer promising solutions for precise segmentation. This study employed high-resolution imagery captured by unmanned aircraft systems (UASs) throughout the shrub growing season and explored the effectiveness of transfer learning for both semantic segmentation (Attention U-Net) and instance segmentation (Mask R-CNN). It utilized pre-trained model weights from two previous studies that originally focused on tree crown delineation to improve shrub crown segmentation in non-forested areas. Results showed that transfer learning alone did not achieve satisfactory performance due to differences in object characteristics and environmental conditions. However, fine-tuning the pre-trained models by unfreezing additional layers improved segmentation accuracy by around 30%. Fine-tuned pre-trained models show limited sensitivity to shrubs in the early growing season (April to June) and improved performance when shrub crowns become more spectrally unique in late summer (July to September). These findings highlight the value of combining pre-trained models with targeted fine-tuning to enhance model adaptability in complex remote sensing environments. The proposed framework demonstrates a scalable solution for ecological monitoring in data-scarce regions, supporting informed land management decisions and advancing the use of deep learning for long-term environmental monitoring. Full article

A key challenge is how to effectively conserve habitats and biodiversity amid widespread habitat fragmentation and loss caused by global urbanization. Despite growing attention to this issue, knowledge of the seasonal dynamics of habitats remains limited, and conservation gaps are still inadequately identified. This study proposes a novel integrated framework, “Habitat Suitability–Risk–Quality”, to improve the assessment of the seasonal bird habitat quality and to identify priority conservation habitats in urban landscapes. The framework was implemented in Wuhan, China, a critical stopover site along the East Asian–Australasian Flyway. It combines the Maximum Entropy (MaxEnt) model to predict the seasonal habitat suitability, the Habitat Risk Assessment (HRA) model to quantify habitat sensitivity to multiple anthropogenic threats, and a refined Habitat Quality (HQ) model to evaluate the seasonal habitat quality. K-means clustering was then applied to group habitats based on seasonal quality dynamics, enabling the identification of priority areas and the development of differentiated conservation strategies. The results show significant seasonal variation in habitat suitability and quality. Wetlands provided the highest-quality habitats in autumn and winter, grasslands exhibited moderate seasonal quality, and forests showed the least seasonal fluctuation. The spatial analysis revealed that high-quality wetland habitats form an ecological belt along the urban–suburban fringe. Four habitat clusters with distinct seasonal characteristics were then identified. However, spatial mismatches were found between existing protected areas and habitats of high ecological value. Notably, Cluster 1 maintained high habitat quality year round, spanning 99.38 km², yet only 46.51% of its area is currently protected. The remaining 53.16 km², mostly situated in urban–suburban transitional zones, remain unprotected. This study provides valuable insights for identifying priority habitats and developing season-specific conservation strategies in rapidly urbanizing regions, thereby supporting the sustainable management of urban biodiversity and the development of resilient ecological systems. Full article

Ground-level ozone (O₃) pollution is a problem when managing air quality in China, and biogenic volatile organic compounds (BVOCs) are key precursors of O₃ formation. Vegetation type and temperature influence BVOC emissions, yet the differences in emissions across vegetation types and their temperature responses still exhibit significant uncertainties. This study was focused on the Sichuan Basin in China. It used the G95 model to develop a high-resolution BVOC emission inventory, allowing the analysis of emission characteristics for different vegetation types. The study also used a temperature sensitivity algorithm to assess how temperature changes affect BVOC emissions. The impact of these emissions on regional O₃ formation potential (OFP) was then quantified using the OFP method. The results show significant differences in BVOC emissions across vegetation types. Forests at the basin edges (mixed, broad-leaved, and coniferous) have much higher emission intensity (10.5 t/km²) than agricultural areas in the center of the basin (0.15 t/km²). In terms of composition, monoterpenes (MON) mainly dominate mixed and coniferous forests (42.28% and 58.37%, respectively), while isoprene (ISOP) dominates broad-leaved forests (64.02%). The study found that temperature generally increases BVOC emissions, which vary by vegetation type. Broad-leaved forests have the highest temperature sensitivity (3.94%), much higher than agricultural vegetation (0.03%). BVOC emissions exhibit a seasonal pattern of “high in summer, low in winter” and a spatial pattern of “high at the edges, low at the center”. Temperature also influences emission intensity and composition, thus driving variations in the potential for O₃ formation. Seasonally, different vegetation types show structural changes in OFP contribution. Broad-leaved forests, dominated by ISOP, show a significant increase in summer contribution (+8.0%), becoming the main source of O₃ precursors. In contrast, mixed forests, dominated by MON, show a clear decrease in summer contribution (−6.3%). Full article

Background/Objectives: Refractory community-acquired pneumonia (r-CAP) has become a thorny issue in clinical practice, especially after the COVID-19 pandemic, even in immunocompetent patients, as conventionally defined. In this study, we aimed to identify the risk factors for immunocompetent patients with r-CAP. Methods: This was a single-center retrospective study. In total, we collected clinical data from 82 patients with r-CAP in whom the first-line antibiotic therapy failed and 82 patients with general CAP (g-CAP) who recovered with first-line antibiotics, matched at a ratio of 1:1, admitted to Beijing Shijitan Hospital, Capital Medical University, from 1 January 2022, to 31 December 2023. The differences between the two groups (clinical characteristics, peripheral blood cell count, lymphocyte subsets, and regular laboratory indicators) were analyzed using paired t, paired Wilcoxon, Chi-square, or Fisher’s exact tests, and univariate and multivariate logistics regression analyses were conducted to identify the independent risk factors. A model for predicting indicators with statistical significance was established and proved with the receiver operating characteristic (ROC) curve. Results: Warm season, a history of chronic obstructive pulmonary disease, longer time from onset to admission (T_O-A), higher percentages of CD4⁺ T, CD8⁺ T, and double-negative T (DNT) lymphocytes, as well as higher levels of C-reactive protein (CRP), low-density lipoprotein cholesterin (LDL-C), serum sodium ion (Na⁺), and free-calcium ion (FCa²⁺) were regarded as independent risk factors, while T lymphocyte percentage (T%) and total cholesterol (TC) were identified as protective factors. The combined multivariate model using all the above factors proved to be sensitive and specific (AUC = 0.8711, p < 0.0001, R² = 0.4235), and thus better than the respective univariate models. Conclusions: Increased CD4⁺ T%Lym, CD8⁺ T%Lym, and DNT%Lym, warm season, a history of COPD, longer T_O-A, and increased levers of CRP, LDL-C, Na⁺, and FCa²⁺ potentially cause CAP to be refractory, while the T lymphocyte count, namely, the overall cellular immunity, was impaired in r-CAP patients, and increased TC levels could be beneficial to pneumonia recovery. Full article

Rhizosphere microorganisms mediate the material exchange and chemical cycling between plant roots and soil. However, the response mechanisms of the rhizosphere microbial community, especially its co-occurrence patterns, to thinning remain poorly understood. We investigated the rhizosphere microbial communities of Pinus massoniana under different thinning intensities, including control (CK, 0%), light-intensity thinning (LIT, 10%), moderate-intensity thinning (MIT, 30%), and high-intensity thinning (HIT, 50%). Basic taxonomic information was obtained through high-throughput sequencing, while R software was utilized to identify thinning-sensitive operational taxonomic units (tsOTUs), construct co-occurrence networks, and perform other statistical analyses. Although no discernible patterns were observed in α-diversity changes, the Kruskal–Wallis test indicated that season was the primary factor driving α-diversity variation. Meanwhile, thinning intensity significantly shaped the rhizosphere microbial community structures, with each intensity harboring a specific tsOTUs subset. Although the top three modules of the meta-co-occurrence networks in summer and winter exhibited consistent tsOTU composition, winter triggered changes in network connectivity. Regardless of summer or winter, the number of network nodes under MIT was the highest. Additionally, after thinning, the relative abundances of most keystone taxa declined; however, MIT facilitated the enrichment of certain keystone taxa. Collectively, thinning profoundly shapes microbial community composition and network characteristics. Moderate thinning intensity may represent the optimal thinning intensity for the studied P. massoniana plantations. Full article

This narrative review examines adolescents’ perceptions of sustainable dietary characteristics, including local eating, plant-based diets, organic food, and food waste, and how these influence their understanding and behavior. Evidence indicates that adolescents often have simplified conceptions of these practices, which leads to misconceptions. Local food is frequently perceived as inherently more sustainable despite complex factors such as seasonality, production methods, and transportation. Although reducing meat consumption is crucial for environmental impact, adolescents may struggle to understand sustainable protein sources and animal-based foods in various contexts. Although viewed positively, the benefits and limitations of organic food remain poorly understood. Food waste is recognized as significant; however, adolescents often focus on individuals rather than on systemic drivers. Schools play a pivotal role in the promotion of food literacy and sustainable dietary habits. Educational interventions that integrate sustainability into curricula, provide hands-on learning, and engage families can help adolescents to develop critical thinking skills and make informed food choices. Strategies such as promoting a plant-based diet, sourcing local produce, incorporating organic options, and implementing waste reduction programs can create environments that support sustainable eating habits. These efforts must be context-sensitive, culturally relevant, and grounded in understanding food systems. By empowering adolescents to question assumptions, recognize complexities, and take action, schools can cultivate a generation capable of leading the transition towards healthier and more sustainable diets. Full article

In order to explore the correlation between meteorological factors and power load changes, as well as the role of these factors in load forecasting, a hybrid load forecasting modeling framework based on quantile Granger causality test and bidirectional long short-term memory (QGCT-BiLSTM) is proposed. The Augmented Dickey–Fuller test (ADF) is used to test the smoothness of the influencing factor series and the load series, and the variables that passed the smoothness test are subjected to QGCT for identification of the characteristic variables with significant causal associations. Furthermore, the BiLSTM model is then constructed using the selected factors to generate load forecasts. Using real data from Fujian, China, we demonstrate that QGCT-based feature screening reduces forecasting errors by an average of 34.96%, where the RMSE, MAE and MAPE are 29.19%, 30.06% and 45.63%, respectively, thereby validating the necessity of causal factor selection. Additionally, single-factor perturbation analysis at seasonal scales quantifies load sensitivity to environmental changes, while geographic–climatic coupling mechanisms explain observed load variation patterns. The results confirm that QGCT-BiLSTM effectively isolates critical meteorological drivers and significantly enhances prediction accuracy compared to conventional approaches, achieving 20.3% lower RMSE and 16.8% lower MAE than LSTM. Full article

The red imported fire ant (Solenopsis invicta, RIFA) is a globally invasive species with strong sensitivity to environmental conditions. This study investigated the seasonal dynamics and colony structure of RIFA over the course of one year across two typical habitats in South China: Camellia oleifera plantations and fishponds. The results revealed clear seasonal patterns in caste composition. Worker abundance peaked during winter (December–January), while reproductive individuals (queens, males, and alates) emerged primarily in spring and early summer (March–May). Colony biomass, worker number, and individual dry weight were significantly higher in C. oleifera plantations, whereas fishpond habitats exhibited greater numbers of larvae and male alates, suggesting different reproductive allocation strategies across habitats. An analysis of caste composition indicated that adult workers were dominant in both habitats, but the proportion of pupae was notably higher in fishpond colonies, especially in spring. Significant correlations were found between colony metrics and nest characteristics, including a negative relationship between worker body length and colony biomass. Environmental factor analysis showed that air pressure positively influenced worker numbers, while temperature was negatively associated with them. Precipitation and humidity played key roles in regulating larval and pupal populations. Overall, RIFA exhibited strong seasonal patterns and ecological plasticity in response to habitat differences and environmental variables. These findings provide insights into the species’ invasion biology and inform habitat-specific monitoring and management strategies. Full article

This study assesses the projected impacts of climate change on hydrological processes in the Gersprenz catchment, a representative low mountain range basin in central Germany, under the RCP8.5 scenario. Using the SWAT+ model and a bias-corrected climate projection ensemble, it simulates the temporal and spatial dynamics of water availability, discharge and water temperature through 2100. The results indicate a substantial reduction in seasonal discharge, with summer minima decreasing by 85% and autumn minima decreasing by 38% compared to the baseline. Rising air temperatures drive substantial warming, with maximum summer water temperatures projected to exceed 28 °C, increasing thermal stress on aquatic ecosystems. Spatial analysis reveals strong variability: Southern subcatchments, located in the upstream part of the catchment, face severe water deficits, while groundwater-fed springs provide localized thermal refuges but with limited buffering capacity. Northern regions generally show higher resilience, with exceptions. The findings highlight the fine-scale sensitivity of hydrological processes to climate change, shaped by catchment characteristics and amplified by natural seasonal variations. This study presents a framework for identifying spatio-temporal hotspots of water scarcity at the subcatchment scale, providing a basis for spatially targeted adaptation strategies to mitigate the impacts of climate change on regional water resources and ecosystems. Full article