Search Results (8,963)

Schizopygopsis younghusbandi is an endemic and economically important fish in the Qinghai-Xizang Plateau, but its aquaculture is limited by harsh environmental conditions and incomplete understanding of host–microbiome–environment interactions. This study applied metagenomic sequencing to examine how different culture environments affect growth, water microbial communities, and gut microbiome network stability. Three-year-old juveniles (initial body weight 50.57 ± 1.88 g) were reared for 90 days in five systems: conventional pond (P), wetland (WL), concrete tank (G), river (R), and recirculating aquaculture system (RC). No significant differences in initial body weight or length were observed among groups (p > 0.05). Fish in the RC system achieved the highest final body weight, weight gain rate, and specific growth rate (p < 0.05), while survival rates were highest in the river and RC groups and lowest in ponds (p < 0.05). Microbial diversity and community composition differed significantly among culture modes, with bacterial and protozoan communities showing the strongest environmental responsiveness. Co-occurrence network analyses revealed that RC and G systems exhibited higher network complexity, density, and proportion of positive correlations, reflecting enhanced microbial interaction and ecological stability, whereas the WL system showed reduced network connectivity. Correlation analysis indicated that bacterial abundance was positively associated with total nitrogen, total phosphorus, and dissolved oxygen (p < 0.05), highlighting environmental regulation of microbial assemblages. Overall, the aquaculture environment shapes gut microbial networks, which closely relate to growth performance. Recirculating aquaculture systems can mitigate growth limitations in plateau fish by stabilizing the environment and reinforcing gut microbial communities, providing a sustainable strategy for high-altitude aquaculture development. Full article

Fog is a near-surface weather phenomenon with low visibility that significantly threatens transportation safety. Understanding the spatiotemporal evolution and formation mechanisms of fog is essential for improving fog forecasting and warning services to reduce related casualties and economic losses. This study examines consecutive dense fog events with long duration and high intensity that occurred along the Ma-Zhao Expressway in northeastern Yunnan from 24 to 30 October 2022. Yunnan is a typical low-latitude plateau region in southwestern China with complex terrain and diverse climates, leading to particularly complicated fog formation processes. Correlation analysis indicates that thermal and vapor factors show stronger correlations with visibility, with correlation coefficients reaching 0.68 for vertical temperature difference and −0.63 for surface relative humidity, both significant at the 99% confidence level. These values are notably higher than those of dynamic factors such as near-surface wind speed, which yields a correlation coefficient of 0.47. The results confirm the dominant role of thermal and vapor conditions in the formation and maintenance of these dense fog events, together with favorable conditions including near-surface air saturation, weak dynamic processes, and a temperature inversion in the lower troposphere. Standardized anomaly analysis reveals obvious atmospheric anomalies during the fog episodes. A strong southerly wind anomaly appears in the lower troposphere, driven by a cyclone over the Philippines and an anomalous anticyclone east of Yunnan. This southerly transport delivers warm and moist air toward the Ma-Zhao Expressway, accompanied by a positive temperature anomaly of 1.7, standard deviations near 700 hPa and a positive specific humidity anomaly of more than 2 standard deviations in the lower troposphere. These conditions favor the development of temperature inversions and atmospheric saturation, further promoting the occurrence and persistence of consecutive dense fog events. This study clarifies the key effects of thermal and vapor conditions as well as low-level southerly wind anomalies on dense fog over the Yunnan low-latitude plateau. These conclusions deepen the understanding of fog formation mechanisms in complex plateau terrain and provide a scientific reference for fog forecasting and early warning along mountain expressways in similar low-latitude plateau regions. Full article

Muskmelon (Cucumis melo L.) is an economically important crop that remains highly susceptible to destructive fungal diseases, including gummy stem blight, downy mildew, Fusarium wilt, and anthracnose. Although fungicides and resistant cultivars are widely used, reliance on chemical control raises concerns regarding environmental safety, food quality, and the emergence of fungicide-resistant pathogen populations. Consequently, microbial biopesticides, particularly Bacillus species, have attracted increasing attention as sustainable alternatives. In this study, muskmelon plants exhibiting leaf wilting, chlorosis, and stem yellowing were collected from Guangming Farm in Wufeng, Taichung, Taiwan, and associated pathogens were isolated from stem tissues and identified to determine the causal agent of these symptoms. In addition, the biocontrol efficacy of Bacillus subtilis strain MCLB2 against melon fruit rot, as well as its underlying mechanisms, was evaluated. Pathogenicity assays confirmed that isolate F01 was the causal agent. Based on morphological characteristics and internal transcribed spacer (ITS) sequence analysis, this isolate showed 99.8% identity to Fusarium pernambucanum URM 7559 (GenBank accession no. NR_163754), and phylogenetic analysis further placed it within the Fusarium incarnatum–equiseti species complex (FIESC). Antagonistic assays demonstrated that B. subtilis MCLB2 significantly inhibited mycelial growth and suppressed the spore germination of F. pernambucanum. In addition, culture filtrates of strain MCLB2 effectively reduced Fusarium-induced fruit rot in melon and disrupted fungal cellular respiration. Liquid chromatography–tandem mass spectrometry (LC–MS/MS) analysis revealed that the strain produced surfactin-family lipopeptides. In conclusion, B. subtilis MCLB2 exhibits potential as a sustainable biocontrol agent for managing Fusarium fruit rot in melon, likely through surfactin-mediated disruption of fungal cellular respiration. Full article

Shape optimization of arch dams is essential for balancing structural safety and economic efficiency, yet remains computationally intensive due to costly finite element analyses and strict geometric constraints. Conventional sampling techniques often yield infeasible designs that undermine surrogate model fidelity. This study proposes an integrated framework combining Stratified Conditional Latin Hypercube Sampling (SC-LHS), automated modeling, and Gaussian Process (GP) surrogate models. SC-LHS incorporates hierarchical constraints to eliminate infeasible samples during generation, while a Python-driven workflow automates the process from parameterization to simulation. Coupling the GP surrogate with NSGA-II enables efficient Pareto front exploration. The results indicate that SC-LHS is superior to standard LHS, Constrained LHS, and Sobol sequences with rejection in terms of feasibility rate and space-filling metrics. The optimal compromise solution reduces dam volume by 10.4% and tensile zone volume by 15.2% compared to the initial design. This framework effectively reconciles economic and safety objectives, offering a robust methodology for complex hydraulic structure design. Full article

The increasing demand for energy, rising electricity costs, and the growing need to reduce carbon emissions have driven industries toward the adoption of Renewable Energy Sources (RES) and Energy Storage Systems (ESS). However, selecting the most suitable ESS for industrial peak-shaving applications remains a complex decision involving technical, economic, and operational considerations. This paper proposes a practical and structured methodology for ESS selection that integrates conventional performance criteria with Industry 5.0 (I5.0) requirements, emphasizing sustainability, resilience, and human-centric industrial operation. Unlike existing multi-criteria decision-making approaches, the proposed framework reduces reliance on expert-based weighting, improving transparency and reproducibility. The methodology is implemented in two stages: initial KPI-based shortlisting of technologies, followed by detailed comparative performance analysis. A case study conducted in a European tire manufacturing plant compares lithium-ion batteries and flywheel energy storage systems under different peak-shaving strategies. Lithium-ion batteries demonstrated superior performance, covering approximately 80% of demand peaks compared with the 73% achieved by the flywheel system, confirming the effectiveness of the proposed methodology for practical industrial ESS selection. Full article

The replacement of synchronous generators (SGs) with inertia-less renewable energy sources (RESs) poses a significant challenge to grid stability due to the reduction of system inertia. To prevent grid instability, energy storage systems (ESSs) with frequency-derivative controls are used to emulate inertia. However, the limited lifetime of ESSs, along with their maintenance requirements, large footprint, and high cost, imposes an additional economic burden on microgrids. This paper proposes an enhanced grid-frequency support approach by coordinating two inertia-emulation mechanisms in parallel: (i) inertia support derived from DC-link capacitor dynamics and (ii) inertia support enabled by operating the PV plant with a power reserve. The proposed method enhances the grid support capacity of the PV energy system and energy sustainability through the efficient utilization of available support resources. Moreover, the DC-link voltage is restored smoothly and naturally to its rated value without the need for a complex control algorithm. The dynamic performance of the proposed system is evaluated under different disturbance conditions and different parameter settings. Simulation results using MATLAB/Simulink R2023a show that, under a 7% load increase, the proposed controller improves the frequency nadir by 0.04 Hz and decreases RoCoF by 10% compared with the baseline controller. Full article

As a crucial national ecological barrier, China’s Southern Collective Forest Region (SCFR) plays an essential role in maintaining regional ecological security and promoting sustainable development. Understanding the mechanisms driving the evolution of its ecosystem service value (ESV) is of great significance. Based on county-level data from 2000 to 2023, this study integrated the equivalent factor method, spatial autocorrelation analysis, the XGBoost-SHAP model, geographically and temporally weighted regression (GTWR), and partial least squares structural equation modeling (PLS-SEM) to examine the spatio-temporal evolution patterns and driving mechanisms of ESV in the SCFR. The results showed that ESV in the SCFR exhibited an overall downward trend, with a cumulative loss of 1973.77 × 10⁸ CNY. This was primarily due to marked reductions in hydrological and climate regulation services. The spatial distribution of ESV exhibited a significant heterogeneity—higher in the southwestern and southeastern mountainous regions, and lower in the northern plains and coastal zones, with the center of gravity shifting first to the northeast and then to the southwest. Local spatial autocorrelation revealed relatively stable “High–High” and “Low–Low” clustering characteristics, where high-value clusters were consistently distributed in core forest zones, while low-value clusters overlapped highly with urban agglomerations. Socio-economic factors exerted a significantly stronger influence on ESV than natural factors. Population density (POP), land use intensity (LUI), and gross domestic product (GDP) were identified as the dominant drivers, exhibiting distinct non-linear threshold effects and significant spatio-temporal heterogeneity. PLS-SEM analysis further quantified LUI as the dominant direct inhibitory pathway on ESV, highlighting urbanization’s indirect negative effect mediated through intensified LUI. Meanwhile, terrain effects were confirmed to positively influence ESV indirectly by constraining LUI and modulating local climate. The analytical framework of “threshold identification–spatio-temporal heterogeneity–causal pathway analysis” proposed in this study elucidated the complex driving mechanisms of ESV evolution, providing valuable guidance for ecological restoration evaluation and differentiated environmental governance. Full article

The accelerated low-carbon transition of power systems and the widespread integration of Electric Vehicles (EVs) present both severe operational challenges and substantial flexible regulation potential for Active Distribution Networks (ADNs). This paper provides an integrative review of the coordinated control and multi-market bidding mechanisms for EV-centric Technical Virtual Power Plants (TVPPs). Moving beyond descriptive surveys, this review systematically synthesizes the fragmented literature across three critical dimensions: (1) the physical-economic bidirectional mapping, which considers nonlinear power flow constraints and node voltage limits within the TVPP framework; (2) multi-market coupling mechanisms, evolving from unilateral energy bidding to coordinated participation in carbon trading and ancillary services; and (3) real-time control strategies, critically evaluating the trade-offs between optimization techniques (e.g., Model Predictive Control) and cutting-edge artificial intelligence approaches (e.g., Deep Reinforcement Learning) in mitigating battery degradation. Furthermore, a transparent review methodology is adopted to ensure literature rigor. By explicitly outlining the boundaries between TVPPs, Commercial VPPs (CVPPs), and EV aggregators, this paper identifies core unresolved trade-offs among aggregation fidelity, market complexity, and communication latency, providing evidence-backed pathways for future engineering demonstrations and V2G applications. Full article

This study systematically examines the low-carbon transition challenges faced by the Yellow River Basin, a core strategic energy base in China with a coal-dominated energy system, under the dual carbon goals. Existing studies based on traditional econometric models or single-province analyses are mostly limited to static analysis, failing to simultaneously capture the nonlinear spatiotemporal evolution, cross-regional spillover effects, and long-term changing trends of carbon emissions in the basin. To fill this gap, this study builds an Energy–Economy–Carbon (EEC) analytical framework, and develops an integrated TFT-ASTGCN deep learning framework. Specifically, we employ the Temporal Fusion Transformer (TFT) for high-precision multivariate time-series simulation and peak forecasting, while the Attention-based Spatial–Temporal Graph Convolutional Network (ASTGCN) is used to identify complex spatial dependencies of inter-provincial emissions. The empirical results confirm that: (1) Basin carbon emissions show significant coal-driven carbon lock-in, with initial decoupling between economic growth and emissions. (2) Most provinces will maintain rising emissions under the current development mode, posing severe challenges to carbon peaking. (3) Asymmetric spatial spillover effects are prominent, underscoring cross-regional collaborative governance as a critical pathway for achieving an early and stable carbon peak in the basin. Full article

Lithium is a rare metal widely used in the renewable energy industry. The Altyn region in Xinjiang, China, contains abundant granitic pegmatite-type lithium resources; however, the deeply incised and complex terrain limits the accuracy of conventional two-dimensional remote sensing approaches for dike identification and segmentation. To address this limitation, a remote sensing segmentation method incorporating terrain information was proposed. A digital elevation model (DEM) derived from LiDAR data, together with its associated topographic factors, was integrated into the Spatial–Spectral Mamba framework to enable the joint utilization of spectral and terrain features. Rather than performing explicit three-dimensional geometric modeling, the proposed approach enhances a two-dimensional segmentation framework by introducing elevation-derived information, allowing the model to capture terrain-related spatial variations of pegmatite dikes. This design enables improved representation of both the planar distribution and terrain-influenced morphological characteristics of dikes under deeply incised conditions. The Xichanggou lithium deposit in the Altyn region is a large-scale, economically valuable pegmatite-type lithium deposit, and was therefore selected as the study area for pegmatite dike segmentation. The results demonstrated that, compared with conventional two-dimensional approaches and representative machine learning methods, the proposed method achieved higher segmentation accuracy in complex terrain. Improvements were also observed in the continuity and spatial consistency of the extracted dike patterns. Field verification indicated that the major pegmatite dikes delineated by the model were highly consistent with their actual surface exposures. Sampling analyses further confirmed the validity and reliability of the identification results. Overall, the terrain-integrated remote sensing segmentation approach exhibited good applicability and robustness under deeply incised and complex geomorphological conditions. Full article

The transition from diesel to electric trucks faces a critical adoption gap despite technological maturity and favorable economics. This study identifies multi-dimensional planning complexity, spanning technical, economic, operational, and organizational dimensions, as a primary barrier that existing decision support tools fail to address. Through systematic literature review and analysis of Danish transport sector data, we develop the AI-Readiness Framework for Fleet Electrification (ARFFE), a modular decision support system adapted to different organizational readiness levels. Our secondary data analysis illustrates that two frequently overlooked factors, the CO₂-differentiated road tax savings of 430,000–465,000 DKK over five years and charging strategy decisions creating cost differences of 930,000 DKK, have greater economic impact than traditionally emphasized factors. The framework comprises five progressive modules mapped across four readiness stages and four planning dimensions, creating an integrated decision support system for evaluating an estimated 50,000+ scenarios. This research contributes theoretically by proposing AI as a “mediating technology” in socio-technical transitions and practically by providing an actionable framework illustrated through Danish transport sector analysis. Full article

This study introduces a novel Quantum-Inspired Hybrid Bald Eagle-Ukari Algorithm with Reinforcement Learning (QI-HBEUA-RL) for comprehensive optimization of conical solar distillers equipped with sand-filled copper conical fins. The proposed algorithm synergistically combines quantum computing principles (superposition and entanglement), bio-inspired metaheuristics (Bald Eagle Search and Ukari Algorithm), and reinforcement learning mechanisms to achieve unprecedented optimization performance in complex thermal-hydraulic systems. The QI-HBEUA-RL framework employs quantum-encoded population representation, enabling simultaneous exploration of multiple solution states, while reinforcement learning dynamically adjusts algorithmic parameters based on search landscape characteristics and historical performance data. Experimental validation tested seven distiller configurations in El-Oued, Algeria, under controlled conditions (7.85 kWh/m²/day solar radiation, 42.2 °C ambient temperature). The optimal configuration of copper conical fins with 14 g sand at 0 cm spacing achieved: daily productivity of 7.75 L/m²/day (+61.46% improvement over conventional design), thermal efficiency of 61.9%, exergy efficiency of 4.02%, and economic payback period of 5.8 days. Comprehensive algorithm comparison against six state-of-the-art multi-objective optimizers (NSGA-II, MOEA/D, MOPSO, MOGWO, MOHHO) across 30 independent runs demonstrated statistically significant superiority (p < 0.001, Wilcoxon test). QI-HBEUA-RL achieved 7.42% improvement in hypervolume indicator, 29.35% reduction in inverted generational distance, and 19.49% better solution spacing. Generalization validation on seven benchmark problems (ZDT1-6, DTLZ2, DTLZ7) and three renewable energy applications confirmed algorithm robustness across diverse problem types. Three real-world case studies, remote village water supply (238:1 benefit–cost), industrial facility (100% energy reduction), and emergency relief (740× cost savings) validate practical implementation viability. This research advances solar thermal desalination technology and multi-objective optimization methodologies, providing validated solutions for sustainable freshwater production in water-scarce regions. Full article

In recent decades, cruise tourism has emerged as a key economic driver for port cities, while simultaneously intensifying environmental pressures and socio-spatial inequalities. Despite growing scholarly attention, research exploring how these pressures are distributed within urban contexts and how they interact with pre-existing vulnerability patterns remains scarce. This study addresses this gap by proposing a GIS-based integrated methodological framework, the Port-city Risk Integrated Spatial Method (PRISM), applied to the Mediterranean port city of Malaga, Spain. The approach combines socio-demographic indicators and data related to spatial amenities with environmental pressures from cruise ship emissions to construct an Urban Socio-Environmental Complexity Index. Emission scenarios for peak cruise days were estimated using a bottom-up methodology and spatialized through atmospheric dispersion modeling, enabling their integration with exposure, vulnerability, and urban capacity indicators. The results reveal marked intra-urban heterogeneity and highlight the emergence of cumulative risk hotspots in areas adjacent to the port and along prevailing inland dispersion corridors. This study demonstrates the potential of integrated spatial indices as decision support tools for urban planning, offering a replicable framework for other port cities facing similar tourism-driven transformations. Full article

Prenatal whole-genome sequencing (WGS) is a comprehensive genetic test for fetal anomalies, enabling simultaneous detection of aneuploidies, copy number variants (CNVs), single-nucleotide variants (SNVs), small insertions/deletions, structural variants, and regions of absence of heterozygosity. However, its clinical performance, optimal sequencing strategies, and implementation challenges remain incompletely defined. We conducted a narrative review of PubMed-indexed studies (1966–December 2025) evaluating prenatal WGS in fetuses with structural anomalies. Across 29 studies, diagnostic yield ranged from approximately 20% to 40%, influenced by phenotype complexity, sequencing depth, and study design. Low-coverage WGS (≤5×) reliably detected large chromosomal abnormalities with a performance comparable to chromosomal microarray analysis. Moderate-coverage WGS (20–40×) additionally enabled detection of SNVs and structural variants, providing up to 30% incremental diagnostic yield after uninformative standard testing. Turnaround times were typically 14–21 days. Higher sequencing depth increases detection of variants of uncertain significance (0.6% to 35.7%) and secondary/incidental findings (1.6–30.8%). Prenatal WGS offers meaningful diagnostic value but requires careful patient selection, multidisciplinary expertise, and structured pre- and post-test genetic counseling to ensure responsible integration into routine clinical practice, with careful consideration of clinical benefit and economic feasibility. Full article

History attempts to make sense of disparate information by trying to create discourse that lays a series of events with crisp cause–effect relationships in a sequence. Epochal shifts, such as the change from Antiquity to the Middle Ages, are especially complex since they involve a large number of economic, political and even religious factors which occur over long periods and that might overlap and interact through reciprocal feedback mechanisms, making this cause–effects sequence difficult to establish. In this research we adopt a data-driven and well-established methodology to identify, with quantifiable statistical precision, the moment when this shift happened, and from there arrive at its possible causes. We will use historical coin hoard data to find out whether such a shift is detected in a peripheral part of the Roman Empire, the Iberian Peninsula. To do so, we will apply different changepoint analysis methods to a time series of trade links created from that data, and conduct a retrospective analysis based on that result, analyzing the structure of the trade networks before and after the link. Thus, we progress from identifying when the shift happened to identifying where it took place, which in turn allows us to get to investigate why it happened, namely, historical events that could have caused it. This methodology can be used to analyze epochal changes in several steps using time-stamped network data, possibly finding disregarded causes or cause–effect links that could have been overlooked by qualitative methods; in this case, we have applied it to a dataset of coin hoards either found in the Iberian Peninsula or including coins minted there, finding a changepoint in the early 5th century, which, through network analysis, has been linked to a loss of trade with the area of Britannia. Full article