Search Results (1,580)

The effective management of energy interactions in multi-island microgrid systems presents a significant challenge due to the geographical dispersion of islands. To address this, this paper proposes a hierarchical distributed optimization strategy based on the alternating direction method of multipliers (ADMM). The strategy features a two-layer architecture: the upper layer employs the ADMM to solve the system-level optimal power flow problem and generates distributed node marginal electricity prices (DLMPs) as clear economic coordination signals. The lower layer consists of individual island microgrids, which independently and in parallel solve their internal security-constrained economic dispatch (SCED) problems upon receiving the converged DLMP signals. This layered decoupling design functionally separates system-level coordination from microgrid-level optimization and enhances privacy protection by preventing the exposure of internal cost functions and operational constraints during upper-layer iterations. Case studies demonstrate that the proposed strategy reduces total operating costs by 10.3% compared to a centralized approach, while also significantly decreasing communication data volume by 83% and ensuring robust privacy protection. The algorithm exhibits good scalability with sublinear growth in iteration counts as the system scales, validating its effectiveness and practical potential for enhancing energy management in multi-island microgrid systems. Full article

Based on five years of data (2017–2021) from the China National Lightning Detection Network (CNLDN), this study compares and analyzes the temporal and spatial distribution characteristics of cloud-to-ground (CG) lightning activities in the Hengduan Mountain region and its surroundings. It explores the relationship between CG lightning occurrences and altitude, topography, and various meteorological elements. Our findings reveal a stark east–west divide: high lightning density in the Sichuan Basin and the central Yungui Plateau contrasts sharply with lower densities over the eastern Tibetan Plateau and Hengduan Mountains. This geographical dichotomy extends to the diurnal cycle, where positive cloud-to-ground (PCG) lightning activities are more prevalent in the western part of the study area, while significant nocturnal activity defines the eastern basin and plateau. The study also finds that the relationship between CG lightning activities in the four sub-regions and 2 m temperature, precipitation, convective available potential energy, and Bowen ratio (the ratio of sensible heat flux to latent heat flux) exhibits similarities. Furthermore, we show that the relationship between lightning frequency and altitude is highly region-specific, with each area displaying a unique signature reflecting its underlying topography: a normal distribution over the eastern Tibetan Plateau, a bimodal pattern in the Hengduan Mountains, a sharp low-altitude peak in the Sichuan Basin, and a complex trimodal structure on the Yungui Plateau. These distinct regional patterns highlight the intricate interplay between large-scale circulation, complex terrain, and local meteorology in modulating lightning activity. Full article

The rational utilization of cultivated land resources is central to ensuring both ecological and food security in the Yangtze River Economic Belt (YREB), holding strategic significance for regional sustainable development. Using panel data from 2010 to 2023 for 130 cities in the YREB, this study examines a spatial correlation network (SCN) for non-grain land use (NGLU) and its driving forces via a modified gravity model, social network analysis (SNA), and quadratic assignment procedure regression. The results show the following: (1) The risk of NGLU continues to increase, with the spatial pattern evolving from a “single-peak right deviation” pattern to a “multi-peak coexistence” pattern featuring three-level polarization and gradient transmission, primarily driven by economic potential disparities. (2) The SCN has increased in density, but its pathways are relatively singular. Node functions exhibit significant differentiation, with high-degree nodes forming “control poles”, high-intermediate nodes dominating cross-regional risk transmission, and low-proximity nodes experiencing “protective marginalization”. Node centrality distribution is highly connected with the regional development gradient. (3) The formation of the spatial network is jointly driven by multiple factors. Geographical proximity, economic potential differences, comparative benefit differences, non-agricultural employment differences, and factor mobility all positively contribute to the spillover effect. Conversely, implementing cultivated land protection policies and the regional imbalance in local industrial development path dependence significantly inhibit the non-grain trend. This study further reveals that a synergistic governance system characterized by “axial management, node classification, and edge support” should be recommended to prevent the gradient risk transmission induced by economic disparities, providing a scientific basis for achieving sustainable use of regional cultivated land resources and coordinated governance of food security. Full article

Efficient wind power forecasting is critical in achieving large-scale integration of wind energy in modern electricity systems. On the other hand, limited availability of wealthy, long-term historical data of wind power generation for many sites of interest often challenges the training of tailored forecasting models, which, in turn, introduces uncertainty concerning the anticipated operational status of similar early-life, or even prospective, wind farm projects. To that end, this study puts forward a spatiotemporal, national-level forecasting exercise as a means of addressing wind power data scarcity in Greece. It does so by developing a hybrid wavelet-enhanced deep learning model that leverages long-term historical data from a reference site located in central Greece. The model is optimized for 24-h day-ahead forecasting, using a hybrid architecture that incorporates discrete wavelet transform for feature extraction, with deep neural networks for spatiotemporal learning. Accordingly, the model’s generalization is evaluated across a number of geographically distributed sites of different quality wind potential, each constrained to only one year of available data. The analysis compares forecasting performance between the original and target sites to assess spatiotemporal robustness of the model without site-specific retraining. Our results demonstrate that the developed model maintains competitive accuracy across data-scarce locations for the first 12 h of the day-ahead forecasting horizon, designating, at the same time, distinct performance patterns, dependent on the geographical and wind potential quality dimensions of the examined areas. Overall, this work underscores the feasibility of leveraging data-rich regions to inform forecasting in under-instrumented areas and contributes to the broader discourse on spatial generalization in renewable energy modeling and planning. Full article

This review investigates and analyzes the state of the art on scientific evidence related to educational interventions to improve air quality indoors and outdoors through a mapping review. The review followed proposed guidelines for mapping reviews in environmental sciences and the steps described in the Template for a Mapping Study Protocol. The search was conducted in PubMed, Web of Science, Embase, Cinahl, and Google Scholar with no language restrictions, and was completed in January 2025. Three filters were applied: search, selection with inclusion and exclusion criteria (PECOS strategy), and data extraction. Two independent reviewers assessed article eligibility, and disagreements were resolved by a third researcher. Twenty-four studies that met the eligibility criteria were included. Five research questions were answered. Studies published between 1977 and 2024 were included, totaling 7289 participants aged 12 to 85. The geographic distribution was concentrated in China (five studies) and the United States (four studies), followed by South Korea, India, Australia, and other countries, with fewer publications. The methodological predominance was experimental studies; observational studies were also analyzed, although less frequently. The period with the greatest increase in the number of publications was between 2020 and 2024. The educational methods most commonly used in the studies were lectures and the delivery of information leaflets. Particulate matter with diameters of 2.5 μm and 10 μm (PM_2.5 and PM₁₀) were the most widely investigated pollutants in the studies. From our analyses, it was observed that the educational interventions to improve air quality, adopted in the selected studies, resulted in the acquisition of knowledge about the environmental effects and the importance of individual actions. The changes in behavior included the adoption of more sustainable practices and an improvement in air quality in the environment, with a significant reduction in pollutant emissions. We conclude that interventions through environmental education demonstrate great potential to improve air quality. Based on the mapped evidence, governments and global policymakers can use this information to develop new strategies or improve existing ones to reduce air pollution in affected environments and regions. Full article

In the context of climate change and grassland degradation, the Inner Mongolia Autonomous Region, a key ecological barrier in Northern China, has faced recurrent outbreaks of the pest beetle Galeruca daurica. This study aims to project its potential geographic distribution under current and future climate scenarios to support risk assessment and management strategies. Using the Maximum Entropy (MaxEnt) model with 122 occurrence records and environmental variables (climatic, topographic, and edaphic), we simulated habitat suitability under present conditions and future scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5 for the 2050s and 2070s). The model performed excellently (AUC > 0.9), with key predictors being precipitation of the wettest month (39.6%), annual precipitation (24.0%), and annual temperature range (8.2%). Currently, about 44.9% of the region is suitable habitat, mainly in central–western arid and semi-arid areas. Future projections indicate a contraction in suitability, which is most pronounced under SSP2-4.5 (declining to 23.56% by the 2070s), along with a northward shift in the distribution centroid. These findings suggest that climate change will likely reduce and shift the suitable range of G. daurica, providing a scientific basis for early warning and targeted control in vulnerable grassland ecosystems. Full article

Artificial intelligence applications in histopathological diagnostics are rapidly expanding, with particular promise in complex hematological malignancies where diagnostic accuracy remains challenging and subjective. This study undertakes a scoping review to systematically map the extent of research on artificial intelligence applications in histopathological diagnostics of leukemias, examine geographic distribution and methodological approaches, and assess the current state of AI model performance and clinical readiness. A comprehensive search was conducted in the Scopus database covering publications from 2018 to 2025 (as of 12 July 2025), using five targeted search strategies combining AI, histopathology, and leukemia-related terms. Following a three-stage screening protocol, 418 publications were selected from an initial pool of over 75,000 records across multiple countries and research domains. The analysis revealed a marked increase in research output, peaking in 2024 with substantial contributions from India (26.3%), China (17.9%), USA (13.8%), and Saudi Arabia (11.1%). Among 43 documented datasets ranging from 80 to 42,386 images, studies predominantly utilized convolutional neural networks and deep learning approaches. AI models demonstrated high diagnostic accuracy, with 25 end-to-end models achieving an average accuracy of 97.72% compared to 96.34% for 20 classical machine learning approaches. Most studies focused on acute lymphoblastic leukemia detection and subtype classification using blood smear and bone marrow specimens. Despite promising diagnostic performance, significant gaps remain in clinical translation, standardization, and regulatory approval, with none of the reviewed AI systems currently FDA-approved for routine leukemia diagnostics. Future research should prioritize clinical validation studies, standardized datasets, and integration with existing diagnostic workflows to realize the potential of AI in hematopathological practice. Full article

Infections caused by avian orthoreovirus represent an emerging problem with a major impact on the global poultry industry, especially in the intensive rearing of broilers. This article addresses, in a complex manner, the etiology of some clinical syndromes of interest in poultry farming: malabsorption syndrome and arthritis/tenosynovitis syndrome. Data are presented, starting from the development and physiology of the digestive tract in broiler chickens in the post-hatch period, epidemiological data, clinical signs, morphopathological changes in the intestine, and diagnostic methods in orthoreovirus infections. The development of the digestive tract is influenced by factors such as diet, digestive enzymes, intestinal pH, and intestinal microbiome/virome. Avian orthoreoviruses, belonging to the Reoviridae family, are double-stranded RNA viruses with multiple tropism. Phylogenetic analysis revealed the existence of at least six major genotypes, with a heterogeneous geographical distribution and genetic diversity that complicates control measures with vaccination. Characterization of the intestinal virome of broilers highlights many other enteric viruses, in addition to reoviruses, with pathogenic potential in triggering malabsorption syndrome. Thus, we can state that the etiology of malabsorption syndrome is not unitary, with the association of several viruses with intestinal tropism aggravating the clinical signs. The article describes viral identification methods, including classical techniques and advanced next-generation sequencing (NGS) approaches, used to characterize the intestinal virome and emerging pathogens. Finally, for prophylaxis, autogenous vaccines adapted to local circulating strains are recommended. Frequent genetic recombinations and high antigenic variation require continuous monitoring and constant adaptation of immunization schedules to control the disease. Full article

Freshwater mussels in the genera Fusconaia, Pleurobema, and Pleuronaia are similar in their external shell morphology, which has made the identification and classification of species within these genera difficult and led to many taxonomic revisions. Large samples (N = 464) of select mussel species in these genera were collected from 2012 through 2014, primarily in the upper Tennessee River basin of Tennessee and Virginia, USA. Mitochondrial ND1 and nuclear ITS1 DNA sequences were analyzed to assess phylogenetic relationships among taxa. Ten species were verified as phylogenetically distinct at ND1, two of which were cryptic and previously unrecognized species. Described herein as Pleurobema parmaleei and Pleuronaia estabrookianus, each species clade was diverged at this gene region by ~3.0% from the respective closest congener. The nuclear ITS1 gene region’s nucleotide-site insertion/deletion (indel) patterns were analyzed as single mutational events rather than as fifth character states or missing data. Most species, including these two, were phylogenetically distinct at the ITS1 region when incorporating indels into analyses, but some estimated interspecific pairwise distances were lower than corresponding intraspecific estimates. Among morphological traits assessed for each species, differences in foot color and gravidity characteristics illustrated differences between phylogenetically recognized species and their closest congeners. Due to the limited known geographical distributions of these two cryptic species, each may require protection under the U.S. Endangered Species Act. While this study collected large sample sizes for each species, many streams in the basin remain unsampled and could potentially contain populations of these species or additional cryptic species. Full article

Corn rootworms of Diabrotica virgifera virgifera Le Conte, 1868; Diabrotica undecimpunctata howardi Barber, 1947, and Diabrotica barberi R.F. Smith & Lawrence, 1967 are important pests of corn crops that natively occur in America and have a potential risk of spreading into China through natural spreading or anthropogenic invasion. In this study, the potential geographic distribution and suitable area of these three Diabrotica species based on their global distribution samples and relevant bioclimatic variables were estimated, and an overlay analysis was further carried out in combination with the actual distribution of corn-growing regions, especially in China, in order to assess the potential invasion risks of these Diabrotica beetles, especially in the corn-planting regions of China. The results indicated that six bioclimatic variables (i.e., bio2 (mean diurnal range), bio4 (temperature seasonality), bio5 (max temperature of the warmest month), bio6 (min temperature of coldest month), bio13 (precipitation of wettest month), and bio14 (precipitation of driest month)) were selected for the analysis of the potential geographic distribution and suitable areas of these Diabrotica beetles. The suitable area ranges of D. undecimpunctata and D. virgifera virgifera are relatively large in China, i.e., 21.01–48.46° N and 74.01–131.26° E for D. undecimpunctata and 21.58–41.42° N and 78.71–124.43° E for D. virgifera virgifera, respectively, while D. barberi occupies only a small area in China, i.e., 34.21–46.81° N and 108.80–133.75° E. Based on the overlay analysis of the potential geographic distribution of these three Diabrotica species and the actual distribution of corn-growing regions in China, D. undecimpunctata and D. virgifera virgifera have the largest potential geographic distribution areas, totaling 2.618 × 10⁷ ha and 1.814 × 10⁷ ha in 22 and 20 provinces respectively, while D. barberi has the lowest potential geographic distribution area just in 8 provinces, totaling 44.37 × 10⁴ ha, indicating a low-suitability area. Moreover, under the four climate scenarios (i.e., SSP1_2.6, SSP2_4.5, SSP3_7.0, and SSP5_8.5) in the 2030s and 2050s, these Diabrotica beetles have the potential for sporadic increases or decreases surrounding the potential suitable areas under the current scenario. However, it is worth noting that the high-suitability areas of D. undecimpunctata and D. virgifera virgifera decreased, and their medium- and low-suitability areas increased accordingly. It is presumed that Diabrotica beetles, especially D. virgifera virgifera and D. undecimpunctata, have a high risk of potential invasion into China because there is a large potentially suitable area distribution for their possible occurrence in the maize-planting regions of China. Full article

Climate change and human activities profoundly impact forest biodiversity, with effects projected to intensify. The Symplocos genus, a diverse assemblage of flowering plants prevalent in the subtropical and tropical forests of the Yangtze River in China, holds substantial economic and medicinal value. However, the impacts of climate change and human activities on the habitat ranges of Symplocos species in China remain unclear. This study employed an optimized Maxent model to predict potential habitats for four key Symplocos species—Symplocos setchuensis, Symplocos chinensis, Symplocos groffii, and Symplocos sumuntia under current and multiple future climate scenarios (SSP1-2.6 and SSP5-8.5 during the 2070s and 2090s). Moreover, we assessed the relative importance of various predictors, including climatic, topographic, soil, and anthropogenic factors, in shaping their habitat range patterns. Currently, the habitat ranges of the four Symplocos species are mainly concentrated in southern China, exhibiting notable differences in areas of high habitat suitability. Furthermore, the habitat ranges of S. setchuensis, S. chinensis, S. groffii, and S. sumuntia were primarily influenced by the mean temperature of the driest quarter (bio9), the minimum temperature of the coldest month (bio6), the temperature annual range (bio7), and precipitation seasonality (bio15), respectively. Notably, the habitat suitability of S. setchuensis, and S. sumuntia increased at a progressively slower rate with human footprint. Under future climate scenarios, S. groffii and S. sumuntia are projected to expand their ranges significantly northward, while S. chinensis is expected to maintain stable habitat, and S. setchuensis may face considerable contractions. Our results underscore the importance of climate and human activities in shaping the habitat ranges of Symplocos species, revealing distinct adaptive responses among the four species under future climate change. Full article

Tuna species, as highly migratory apex predators of major commercial significance, play a vital role as biological indicators of microplastics (MPs) contamination due to their trophic position and wide geographic distribution. Current systematic review aims to analyze the occurrence, characteristics, and concentrations of MPs in various tuna species. Data from 19 studies were compiled, focusing on the presence of MPs in different organs (gills, muscles, gastrointestinal tracts). High concentrations of MPs were found in tuna species from the Bay of Bengal (42.13 ± 13.58 MPs/individual in Thunnus obesus) and the Persian Gulf (5.71 MPs/individual in Thunnus tonggol), indicating significant contamination in these regions. Polyethylene (PE) and Polypropylene (PP) were the most commonly detected polymers, suggesting their widespread presence in marine environments. The dominant size range of MPs was 0.5–2.5 mm, with fibers and fragments being the most common shapes. The presence of MPs in edible tissues raises concerns about potential health risks for both marine life and human consumers. Future research should focus on expanding geographical coverage and investigating the ecological and health impacts of MPs ingestion. Long-term monitoring and international collaboration are essential to address this global environmental challenge effectively. Full article

Background: Cardiovascular diseases remain a leading cause of death worldwide, yet the prevalence of pathogenic and likely pathogenic genetic variants associated with them is still underassessed in some populations. This study aimed to assess the frequency and geographic distribution of such variants within a representative sample of the Russian population. Additionally, it explored potential links between genotype and phenotype in a cohort of long-lived adults. Methods: We analyzed whole-genome sequencing data from 75,144 adults and 2,872 individuals aged 90 and older. Variants within 37 ACMG v3.1 genes were examined using InterVar, focusing on nonsynonymous variants and indels across exons and splicing sites. Variants were grouped based on ClinVar (as of 24 April 2023) annotations, with most subjected to manual review to confirm their significance. Results: Among the adult participants, 3,817 (5.1%) carried at least one of the variants under consideration. Of these, 141 (0.19%) carried pathogenic, 580 (0.77%) likely pathogenic, and 3,127 (4.16%) variants of uncertain significance. Variants not registered in ClinVar were found in 1,782 individuals (2.37%). Notably, one participant with cardiomyopathy carried a heterozygous TTN variant. In the long-lived cohort, 15 variants were classified as pathogenic or likely pathogenic, alongside 72 uncertain variants; overall, 19 individuals (0.66%) carried pathogenic or likely pathogenic variants. No significant difference was observed in variant frequency between the adult and long-lived groups. Conclusions: This study provided essential insights into the prevalence and geographic distribution of cardiovascular disease-related variants in Russia, laying the foundation for targeted genetic screening disease prevention strategies within this population. Full article

Wind energy is a critical renewable resource in the global effort toward sustainable development and climate change mitigation. This paper introduces a hybrid forecasting framework that integrates multistep gated recurrent unit (GRU) modeling with Weibull distribution analysis to assess wind energy potential and predict long-term wind speed dynamics. The approach combines deterministic and probabilistic components, improving robustness against seasonal variability and uncertainties. To demonstrate its effectiveness, the framework was applied to hourly wind data collected from multiple stations across diverse geographical regions in Turkey. Weibull parameters, wind power density, capacity factor, and annual energy production were estimated, while five machine learning models were compared for forecasting accuracy. The GRU model outperformed alternative methods, and the hybrid GRU–Weibull approach produced highly consistent forecasts aligned with historical patterns. Results highlight that the proposed framework offers a reliable and transferable methodology for evaluating wind energy resources, with applicability beyond the case study region. Full article

Water demand has increased due to population growth and rapid socioeconomic development, creating conflicts between human activities and water resources and having a substantial impact on the balance between blue and green water supplies. Existing study lacks a spatial perspective to examine the inherent relationship between blue and green water supply and demand, particularly in terms of geographical differentiation characteristics and rational allocation of blue and green water supply–demand balance in inland river basins. Using the Taolai River Basin as a case study, this research uses the distributed hydrological model SWAT from a blue–green water resources viewpoint to simulate the spatiotemporal distribution features of blue and green water resources at the sub-basin scale from 2002 to 2021. The supply and demand balance relationship of blue and green water resources within the basin was investigated, an assessment index system for water resource security was developed, and the realizable potential of blue water resources was quantified using various indicators. The findings show that during the study period, the average annual green water resources in the Taolai River Basin were 1.95 times greater than blue water resources, making green water the most abundant component of regional water resources. Spatially, both blue and green water resources showed considerable latitudinal zonality, with a declining tendency from south to north and very consistent distribution patterns. Blue water resources showed high geographic variability, with a safety index more than one, suggesting that supply–demand imbalances were most concentrated in the upper and intermediate ranges of the irrigated region, as well as the desert zone, where safety levels were relatively low. In contrast, green water resources had a safety score ranging from 0.7 to 1.0, indicating great overall safety and negligible regional variability. During the research period, the average annual theoretical transferable blue water resources were 4.06 × 108 m³, based on cross-regional water resource allocation potential analysis. This reveals tremendous potential for enhancing regional water resource allocation, hence providing substantial support for effective water consumption within the Taolai River Basin and regional economic growth. In conclusion, the assessment method developed in this work provides a solid foundation for improving water resource allocation and sustainable management in river basins. It provides technical assistance in the construction of water network systems in inland river basins, which is critical in establishing reasonable water resource distribution across various areas within these basins. Full article