Search Results (21,350)

This study characterized the spatial distribution and assessed the ecological risks of carbon, nitrogen, and phosphorus in sediments of the Dongzhai Harbor mangrove wetland, Hainan, China. Analysis of key environmental indicators (grain size, pH, TOC, TN, TP) across twenty-seven sediment cores (0–100 cm depth) revealed distinct decreasing land–sea gradients and vertical stratification of nutrient concentrations. Mangrove plant debris was identified as the primary source of sedimentary organic matter. Elemental ratio analysis indicated terrestrial inputs as the dominant phosphorus source. Significant positive correlations between TOC, TN, and TP in surface sediments suggested coupled nutrient dynamics. Vertical distribution of C/N to C/P ratios increased with depth, which may be related to increased nitrogen and phosphorus inputs due to regional human activities. Pollution assessment showed significantly higher ecological risks in surface sediments (0–50 cm), particularly near inland areas and dense mangroves, indicating co-regulation by vegetation processes and human impacts. These findings highlight significant spatial heterogeneity in ecological risks, underscoring the need for enhanced monitoring and targeted management strategies in critical land–sea transition zones. Full article

Background and Objectives: One of the most critical areas of measurement and evaluation in healthcare is pathological evaluation, especially intraoperative consultation. Studies conducted to identify sources of error in this field are usually one-sided; however, in evaluation processes with multiple sources of error, such as intraoperative consultation, generalizability theory can evaluate these sources of error simultaneously in a single analysis, thereby contributing to the field. In this study, the reliability of intraoperative and permanent histopathological evaluations of glial tumors was analyzed using generalizability theory to identify the sources of error in the observed evaluation inconsistencies. Materials and Methods: The study included 319 glial tumor cases that underwent intraoperative evaluation and were analyzed using generalizability theory. Three pathologists performed independent evaluations in two stages. Results: The reliability coefficient calculated for all cases was 0.9234 without radiological information and 0.9243 after learning the radiological information. The reliability coefficient was 0.8875 and 0.8989, respectively, in cases over 18 years of age, and 0.8845 and 0.9062 in cases under 18 years of age. These findings indicate that the addition of radiological information to the evaluation resulted in a slight increase in reliability, particularly in cases under 18 years of age. In all of our reliability assessments for different conditions, the highest variability was found to originate from the rater. Conclusions: The findings suggest that intraoperative evaluation demonstrates a high degree of reliability in the pathological assessment of glial tumors. When differences between the rater and the technique are evaluated together, it is observed that the rater has a more significant impact on reliability. While radiological information is generally considered a factor that increases reliability, it is partially more effective, especially in cases involving individuals under the age of 18, which highlights the importance of multidisciplinary data sharing in intraoperative diagnostic processes. Full article

Indoor sports facilities face distinctive indoor air quality (IAQ) challenges due to high occupant density, elevated metabolic emissions, and diverse pollutant sources associated with physical activity. This review presents a narrative synthesis of multidisciplinary evidence concerning IAQ in sports environments. It explores major pollutant categories, including carbon dioxide (CO₂), particulate matter (PM), volatile organic compounds (VOCs), and airborne microbial agents, highlighting their sources, behavior during exercise, and associated health risks. Research shows that physical activity can increase PM concentrations by up to 300%, and CO₂ levels frequently exceed 1000 ppm in inadequately ventilated spaces. The presence of semi-volatile organics and bioaerosols further complicates pollutant dynamics, especially in humid and densely occupied areas. Measurement technologies such as optical sensors, chromatographic methods, and molecular techniques are reviewed and compared for their applicability to dynamic indoor settings. Existing IAQ standards across China, the USA, the EU, the UK, and WHO are examined, revealing a lack of activity-specific thresholds and insufficient responsiveness to real-time conditions. Mitigation strategies (e.g., including demand-controlled ventilation, use of low-emission materials, liquid chalk substitutes, and integrated HEPA-UVGI purification systems) are evaluated, many demonstrating pollutant removal efficiencies over 80%. The integration of intelligent building management systems is emphasized for enabling real-time monitoring and adaptive control. This review concludes by identifying research priorities, including the development of activity-sensitive IAQ control frameworks and long-term health impact assessments for athletes and vulnerable users. Full article

Recent concerns regarding artificial intelligent (AI) technologies have spurred studies into improving wastewater treatment efficiency and identifying low-carbon processes. Treating one cubic meter of wastewater necessarily consumes a certain amount of chemicals and energy. Approximately 20% of the total chemical consumption is attributed to phosphorus and nitrogen removal, with the exact proportion varying based on treatment quality and facility size. To promote sustainability in wastewater treatment plants (WWTPs), there has been a shift from traditional control systems to AI-based strategies. Research in this area has demonstrated notable improvements in wastewater treatment efficiency. This review provides an extensive overview of the literature published over the past decades, aiming to advance the ongoing discourse on enhancing both the efficiency and sustainability of chemical dosing systems in WWTPs. It focuses on AI-based approaches utilizing algorithms such as neural networks and fuzzy logic. The review encompasses AI-based wastewater treatment processes: parameter analysis/forecasting, model development, and process optimization. Moreover, it summarizes six promising areas of AI-based chemical dosing, including acid–base regents, coagulants/flocculants, disinfectants/disinfection by-products (DBPs) management, external carbon sources, phosphorus removal regents, and adsorbents. Finally, the study concludes that significant challenges remain in deploying AI models beyond simulated environments to real-world applications. Full article

Electrical grids are undergoing a transformation driven by the increasing integration of renewable energy sources on the consumer side. This shift, alongside the electrification of consumption—particularly in areas such as electric mobility—has the potential to significantly reduce CO₂ emissions. However, it is also contributing to a rise in electricity prices due to growing demand and infrastructure costs. Paradoxically, these higher prices serve as a catalyst for further investment in renewable energy technologies by reducing the payback periods of such systems. Recent European legislation has accelerated this transformation by mandating the liberalization of energy markets. This regulatory shift enables the emergence of prosumers—consumers who are also producers of energy—by granting them the right to generate, store, and trade electricity using the existing distribution grid. In this new landscape, photovoltaic systems represent a viable and increasingly attractive investment option for both households and businesses. This study presents an economic evaluation of photovoltaic system investments across different European countries, focusing on key indicators such as payback periods and the impact of local solar irradiation on the resulting electricity price. The analysis provides insight into the varying economic feasibility of distributed solar energy deployment, offering a comparative perspective that supports both policymakers and potential investors in making informed decisions about renewable energy adoption. Full article

To elucidate the mechanisms governing hydrocarbon accumulation and phase evolution in the deep–ultradeep reservoirs of the Mo-Yong area, this study integrated 2D basin modeling and multi-component phase state simulation techniques, investigating the differences in maturity and hydrocarbon generation history between the Fengcheng Formation (P₁f) and the Lower Wuerhe Formation (P₂w) source rocks, as well as their coupling relationship with fault activity in controlling hydrocarbon migration, accumulation, and phase evolution. The results indicate that the P₁f and P₂w in the Mo-Yong area source rocks differ in thermal maturity and hydrocarbon generation evolution. The dual-source charging from both the P₁f and P₂w significantly enhances hydrocarbon accumulation number, volume, and saturation. The temporal-spatial coupling between peak hydrocarbon generation and multi-stage fault reactivation not only facilitates extra-source accumulation but also drives condensate reservoir formation through gas-oil ratio elevation and light-component enrichment. Based on these results, a model of hydrocarbon accumulation and phase evolution of deep reservoirs was proposed. The model elucidates the fundamental geological principle that source-fault spatiotemporal coupling controls hydrocarbon enrichment degree, while phase differentiation determines reservoir fluid types. Full article

This study systematically investigated water-soluble inorganic ions (WSIIs) and their sources in PM2.5 in mountainous urban areas of Chongqing City. PM2.5 monitoring was conducted throughout 2023, spanning one year. The two districts under discussion are the Liang Jiang New Area (LJ) and He Chuan District (HC). The ion chromatography (Dionex Integrion HPIC) method was utilized to quantify eight ions (Cl⁻, SO₄²⁻, NO₃⁻, Na⁺, K⁺, Mg²⁺, Ca²⁺, NH₄⁺). The results obtained were then analyzed in conjunction with the EPA PMF 5.0 source apportionment model. The following key findings are presented: the data demonstrate that there is significant seasonal fluctuation in PM2.5 concentrations. The mean winter concentration (64 ± 27 μg/m³) was found to be 3.25 times higher than the mean summer concentration (19.7 ± 2 μg/m³). These fluctuations were primarily influenced by basin topography and unfavorable meteorological conditions. The proportion of PM2.5 mass attributable to WSII ranges from 31 to 33 percent, with the majority of this mass being attributed to secondary inorganic aerosols (SNA: SO₄²⁻, NO₃⁻, NH₄⁺; accounting for 47–85% WSII). The annual NO₃⁻/SO₄²⁻ ratio (0.69–0.80, <1) indicates that fixed sources (coal/industry) dominate, but a winter ratio >1 suggests increased contributions from mobile sources under low-temperature conditions. The sulfur oxidation rate (SOR: 0.35–0.37) is significantly higher than the nitrogen oxidation rate (NOR: 0.08–0.13), reflecting the efficient conversion of SO₂ through wet, low-temperature pathways. PMF identified six sources, with secondary formation (43.8–44.3%) being the primary contributor to the overall process. In urban LJ, transportation (26.1%) and industry (13.6%) have been found to contribute significantly, while in suburban HC, combustion (15.4%) and dust (8.8%) have been determined to have notable impacts. This study recommends the implementation of synergistic control of SNA precursors (SO₂, NOx, NH₃), the strengthening of transportation and industrial management in LJ, and the enhancement of biomass combustion and dust control in HC. Full article

To address the problem of point-cloud registration accuracy degradation or even failure in traditional Voxelized GICP(VGICP) under bad initial pose due to improper voxel resolution settings, this paper proposes an Adaptive Resolution VGICP (AR-VGICP) algorithm. The algorithm first automatically estimates the initial voxel resolution based on the absolute deviations between source points outside the target voxel grid and their nearest neighbors in the target cloud, using the Median Absolute Deviation (MAD) method, and performs initial registration. Subsequently, the voxel resolution is dynamically updated according to the average nearest neighbor distance between the transformed source points and the target points, enabling progressive refined registration. The resolution update process terminates until the resolution change rate falls below a predefined threshold or the updated resolution does not exceed the density-adaptive resolution. Experimental results on both simulated and real-world datasets demonstrate that AR-VGICP achieves a 100% registration success rate, while VGICP fails in some cases due to small voxel resolution. On the KITTI dataset, AR-VGICP reduces translation error by 9.4% and rotation error by 14.8% compared to VGICP with a fixed 1 m voxel resolution, while increasing computation time by only 3%. Results from UAV LiDAR experiments show that, in residential area data, AR-VGICP achieves a maximum reduction of 33.4% in translation error and 21.4% in rotation error compared to VGICP (1.0 m). These results demonstrate that AR-VGICP attains a higher registration success rate when the initial pose between point-cloud pairs is bad, and delivers superior point-cloud registration accuracy in urban scenarios compared to VGICP. Full article

Both studies and conservation of mountain waters are essential because of the primary role of mountains as “natural water towers” for the preservation and optimized exploitation of water reserves. In particular, under climate change stresses which induce reductions in rain and snow precipitation, especially in areas with rain-snow transition zones, increasing knowledge of the geological setting and hydrogeological context of mountain springs is pivotal for their preservation and optimized exploitation. However, the complexity and remoteness of mountain waters make them difficult to conceptualize and analyse, both observationally and instrumentally. In this context, using detailed geological mapping and hydrogeological surveys, geophysical data can provide useful information on the subsurface setting. Electrical resistivity tomography (ERT) surveys are utilized in this work for the investigation of the Montellina Spring (MS), which is located in the low Dora Baltea Valley and represents a significant drinking water source in the alpine context. Geophysical surveys, complemented by specific geological and hydrogeological observations, allowed a detailed reconstruction of the water circuit that supplies the spring along an articulated buried glacial valley and a loose bedrock in a DSGSD (deep-seated gravitational slope deformation) environment. The methodological approach also provides the basis for its successful application in similar geological contexts. Full article

The energy transition is an issue of fundamental importance in the current global context, as an increasing number of countries are committed to searching for minerals and elements essential for the storage, distribution, and supply of energy derived from new renewable and sustainable sources. In some countries, these elements (such as boron, lithium, and strontium) are considered to be critical raw materials (CRMs) because of their limited occurrence within their own borders and are commonly found in minerals and geothermal–formation waters, especially in brackish to brine waters. In the Italian territory, CRM-rich waters have already been identified by previously published studies (i.e., with mean concentrations in the Salsomaggiore Terme of 390 mg/L of boron, 76 mg/L of lithium, and 414 mg/L of strontium); however, their extraction is hampered by several knowledge gaps. In particular, a comprehensive understanding of the origin, accumulation processes, and migration pathways of these CRM-rich waters is still lacking. These factors are closely linked to the geological framework and evolutionary history of each specific area. To address these gaps, we investigated the Salsomaggiore Structure that is located at the northwestern front of the Apennine in Italy by integrating geological data with hydrogeochemical results. We constructed new preliminary distribution maps of the most significant CRMs around the Salsomaggiore Structure, which can be used in the future for the National Mineral Exploration Program drawn up in accordance with the European Critical Raw Materials Act. These maps, combined with the interpretation of seismic reflection profiles calibrated with surface geology and wells, allowed us to establish a close relationship between water geochemistry/CRM contents and the geological evolution of the Salsomaggiore Structure. This structure can be considered representative of the frontal ranges of the Northwestern Apennine and other mountain chains associated with the foreland basin systems. Full article

The application of predictive and explainable artificial intelligence to bioinformatics data such as single nucleotide polymorphism (SNP) information is attracting rising attention in the diagnosis of various diseases. However, there are few reviews available on the recent progress of genetic artificial intelligence for the early diagnosis of gastrointestinal disease (GID). The purpose of this study is to complete a systematic review on the recent progress of genetic artificial intelligence in GID. The source of data was ten original studies from PubMed. The ten original studies were eligible according to the following criteria: (participants) the dependent variable of GID or associated disease; (interventions/comparisons) artificial intelligence; (outcomes) accuracy, the area under the curve (AUC), and/or variable importance; a publication year of 2010 or later; and the publication language of English. The performance outcomes reported varied within 79–100 for accuracy (%) and 63–98 for the AUC (%). Random forest was the best approach (AUC 98%) for the classification of inflammatory bowel disease with 13 single nucleotide polymorphisms (SNPs). Similarly, random forest was the best method (R-square 99%) for the regression of the gut microbiome SNP saturation number. The following SNPs were discovered to be major variables for the prediction of GID or associated disease: rs2295778, rs13337626, rs2296188, rs2114039 (esophageal adenocarcinoma); rs28785174, rs60532570, rs13056955, rs7660164 (Crohn’s disease early intestinal resection); rs4945943 (Crohn’s disease); rs316115020, rs316420452 (calcium metabolism); rs738409_G, rs2642438_A, rs58542926_T, rs72613567_TA (steatotic liver disease); rs148710154, rs75146099 (esophageal squamous cell carcinoma). The following demographic and health-related variables were found to be important predictors of GID or associated disease besides SNPs: age, body mass index, disease behavior, immune cell type, intestinal microbiome, MARCKS protein, smoking, and SNP density/number. No deep learning study was found even though deep learning was used as a search term together with machine learning. Genetic artificial intelligence is effective and non-invasive as a decision support system for GID. Full article

The black soil region of northeast China is a critical global grain production base. The dynamic variations in soil organic carbon (SOC) are directly linked to the regional food security. To accurately monitor SOC content and evaluate the potential of integrating Landsat-9 and GF-1 satellite data for SOC inversion, we developed a machine learning framework that combines data from both satellite sources to model SOC. Using the typical black soil region of northeast China in the Tongken River Basin as the study area, we compared the MLR, PLSR, RF, and XGBoost algorithms. And XGBoost demonstrated the highest performance (R² = 0.9130; RMSE = 0.3834%). Based on the optimal model, SOC in the study area was projected from 2020 to 2024. The multi-year average SOC exhibited an initial increase followed by a subsequent decline, with an overall increase of 22.78%. Spearman correlation analysis identified parent material as the dominant factor controlling SOC variation at the watershed scale (correlation coefficient = 0.38) while also modulating the influence of land use types on SOC dynamics. The “space–ground” multi-source collaborative inversion framework developed in this study offers a high-precision technical approach for the monitoring of SOC in black soil regions. Full article

Systemic lupus erythematosus (SLE) is a complex autoimmune disease that affects multiple organs and systems with a broad and heterogeneous spectrum of clinical manifestations. National disease-specific datasets and registries are crucial for clinical research since they can provide real-world and long-term data about clinical aspects, biomarkers, and treatments. Registries collect data from actual patients over time, outside the controlled environment of randomized controlled trials. This can help enhance the understanding of the natural history of a disease, provide information about how treatments work in everyday settings and elucidate potential variations in care and outcomes across different geographic areas. Here, we present a protocol for the creation of a standardized national disease-specific dataset for patients with SLE—the Systemic Lupus Erythematous Network (LUNET) Registry—which will facilitate data sharing, cross-comparison, and interoperability among centers. The LUNET registry is intended to serve as a comprehensive primary data source, capturing real-world longitudinal clinical information and the heterogeneity of patient presentations that are often underrepresented in traditional clinical trials. Ultimately, the LUNET registry will help to optimize SLE management in routine clinical practice by enabling the compilation of real-world evidence to inform clinical decision-making and health policy. Full article

The ecological restoration of degraded alpine steppe is a critical component of ecological conservation efforts on the Qinghai–Tibetan Plateau. In this study, we investigated the effects of fertilization, reseeding, and combined fertilization with reseeding restoration measures on the vegetation community, soil properties and microbial community diversity in degraded alpine steppe through field vegetation surveys, and soil microbial high-throughput sequencing at an experimental site of fertilized and reseeded grassland restoration located in the Yellow River Source area. The results demonstrated the following: (1) both reseeding and combined fertilization with reseeding restoration measures significantly affected grassland vegetation community structure and diversity; (2) fertilization and combined fertilization with reseeding restoration measures significantly affected soil pH and total phosphorus (TP) content; (3) while fertilization and combined fertilization with reseeding restoration measures markedly altered microbial community structure, reseeding alone significantly affected microbial diversity. Co-occurrence network analysis revealed that soil microbial communities were significantly influenced by fertilization restoration measures; redundancy analysis (RDA) showed that microbial communities under fertilization and combined fertilization with reseeding restoration measures were primarily governed by soil TP, whereas those in control and reseeding plots were strongly associated with soil pH and organic carbon (SOC). This study explored effective restoration measures suitable for degenerating alpine steppe in the Yellow River Source area, aiming to provide a scientific basis and technical support for the ecological protection and restoration of the Three-River Headwaters. Full article

Coal gangue, a primary solid waste by-product of coal mining and processing, constitutes approximately 10–15% of total coal output. Its accumulation poses substantial environmental challenges, including land occupation, spontaneous combustion, acid mine drainage, and heavy metal leaching. Despite its high silica and alumina content (typically exceeding 70% combined), the highly stable and crystalline structure of raw coal gangue limits its pozzolanic activity and adsorption efficiency. To address this limitation, this review emphasizes recent advances in activation strategies such as thermal (500–900 °C), mechanical (dry/wet grinding to less than 200 µm), chemical (acid/alkali treatments), microwave, and hybrid methods. The activated coal gangue resulted in an enhanced surface area (up to 55 m²/g), amorphization of kaolinite to metakaolinite, and the generation of mesoporosity under optimal conditions. This review critically examined the geotechnical applications, such as soil stabilization and mine backfill, highlighting the replacement of 50–75% of cementitious binder in backfilling and meeting the subgrade/base material strength criteria (UCS > 2 MPa). In geoenvironmental applications (adsorption of phosphate, dyes, heavy metals, and CO₂ mineralization), more than 90% of pollutant removal is attained. In construction applications, supplementary cementitious materials and sintered bricks are examined. Several critical knowledge gaps, including limited understanding of long-term durability, inconsistent activation optimization across different coal gangue sources, and insufficient assessment of environmental impacts during large-scale implementation, are clearly addressed. This review provides a roadmap for advancing sustainable coal gangue utilization and highlights emerging opportunities for cost-effective applications in the mining and construction sectors. Full article