Search Results (12,833)

Rapid urbanization and industrialization have profoundly affected aquatic ecosystems in urban rivers, with phytoplankton taxa and functional group composition being particularly sensitive to environmental changes. Field surveys were conducted in the Nanming River, Guiyang, in October 2018 and July 2019, with 33 sampling sites evenly distributed across the upstream, midstream, and downstream reaches. The results revealed that: (1) The phytoplankton community comprised 6 phyla, 53 genera, and 61 species, dominated by Bacillariophyta, Chlorophyta, and Cyanobacteria. The community was classified into 20 functional groups, among which B, D, MP, P, and S1 were dominant and exhibited clear spatial heterogeneity along the longitudinal gradient. (2) Analysis of variance indicated that physicochemical parameters were the dominant factors explaining the variation in phytoplankton taxonomic and functional groups, with their independent contribution significantly higher than that of anthropogenic disturbance indicators and geographical factors. Redundancy analysis further identified NH₄-N, TP, and TN as key environmental factors. Spearman’s correlation analysis further indicated that human activities alter ambient environmental conditions, which are significantly correlated with dissolved oxygen and chlorophyll a levels, thereby driving the differentiation of phytoplankton niches. (3) Functional group succession followed a distinct spatial pattern: upstream areas were dominated by groups P, SN, and Y, reflecting agricultural non-point source inputs; midstream areas were dominated by groups W1, H1, and S1, characteristic of urban complex pollution; and downstream areas were dominated by groups C and X1, indicating cumulative nutrient loading. Collectively, this study elucidates the driving mechanisms of phytoplankton dynamics in karst urban rivers and provides a scientific foundation for water quality monitoring, eutrophication risk pre-warning, and aquatic ecological restoration. Full article

The pronounced heterogeneity of tight sandstone reservoirs in the Sulige Gas Field poses significant challenges to the uniform propagation of multi-cluster hydraulic fractures during horizontal well staged fracturing, often leading to uneven stimulation and compromised productivity. To address this issue, a coupled fluid–solid fracture propagation model based on the displacement discontinuity method (DDM) was developed, incorporating dynamic fluid distribution, rock deformation, and temporary plugging mechanisms. The model was validated against microseismic monitoring data from the Sulige field and subsequently employed to investigate the effects of reservoir heterogeneity—including porosity, permeability, and in situ stress—on multi-cluster fracture growth. Results indicate that permeability and stress heterogeneity exert the most significant influence on fracture non-uniformity, as reflected by increased coefficients of variation in fracture length. Engineering measures such as the use of high-viscosity guar gum fracturing fluids, variable perforation strategies (e.g., 6, 10, and 16 holes per cluster), and optimized temporary plugging parameters (timing of 0.5 with 12 balls) were shown to effectively mitigate these effects and promote more balanced fracture propagation. This study provides a quantitative framework for optimizing fracturing design in heterogeneous tight gas reservoirs and offers practical guidance for enhancing stimulation uniformity and gas recovery efficiency in the Sulige Gas Field. Full article

Hyperspectral image (HSI) and LiDAR data fusion is valuable for land-cover classification in complex surface scenes. Existing methods typically extract features from each modality independently and then consider how to fuse them, ignoring the semantic consistency between features of different modalities and across different hierarchical levels. Moreover, fully mining and exploiting the complementary information between multimodal remote sensing data remains a critical issue. To address these challenges, this paper proposes a hierarchical semantic consistency constraint (HSCC) framework for HSI and LiDAR data joint classification. The framework is co-constructed by a progressive interactive fusion network (PIFNet) and a semantic consistency constraint (SCC) strategy. Specifically, PIFNet progressively calibrates the semantic representations of multimodal features at different abstraction levels through Cross-Modal Shared Attention and Symmetric Cross-Attention mechanisms, promoting information parity in deep interactions. The SCC strategy establishes multi-level semantic associations and employs a semantic consistency constraint loss to guide the network to autonomously maintain the consistency of the same land-cover object across heterogeneous feature representations, thereby further enhancing the discriminative power of the fused features. Experiments on three public datasets, MUUFL, Houston2013, and Augsburg, demonstrate that HSCC outperforms current state-of-the-art methods, validating its effectiveness in multi-source remote sensing data fusion classification tasks. Full article

Background: Glioblastoma (GBM) is the most aggressive primary brain tumor in adults and remains associated with poor prognosis despite multimodal treatment. Ferroptosis, an iron-dependent form of regulated cell death driven by lipid peroxidation and redox imbalance, has recently emerged as a potential therapeutic vulnerability in glioma. This review summarizes current knowledge on the molecular regulation of ferroptosis in glioma and discusses its implications for tumor progression, therapeutic resistance, and translational targeting. Methods: A structured narrative review of the literature was conducted using PubMed/MEDLINE, Scopus, and Web of Science databases. Experimental, translational, and clinically relevant studies investigating ferroptosis-related mechanisms and therapeutic strategies in glioma and GBM were qualitatively analyzed. Results: Ferroptosis in glioma is regulated by interconnected pathways involving iron metabolism, phospholipid remodeling, oxidative stress, and antioxidant defense systems, particularly the SLC7A11–glutathione–GPX4 axis. Additional protective mechanisms mediated by FSP1 and DHODH, together with regulatory networks involving NRF2, ATF4, p53, and hypoxia-related signaling, contribute to adaptive resistance to ferroptosis. Increasing evidence indicates that ferroptosis interacts bidirectionally with the glioma tumor microenvironment and may exert both antitumor and immunosuppressive effects. Preclinical studies further suggest that ferroptosis induction may enhance the efficacy of temozolomide, radiotherapy, and immunotherapy, although clinical translation remains limited by tumor heterogeneity, blood–brain barrier penetration, and resistance mechanisms. Conclusions: Ferroptosis represents a biologically plausible and therapeutically promising target in glioma. Improved understanding of ferroptosis regulation, tumor microenvironment interactions, and biomarker-guided therapeutic strategies may support the future development of more effective treatments for GBM. Full article

To characterize the distinct expression profiles of microRNAs (miRNAs) in serum and tissue and to delineate the heterogeneity of their regulatory mechanisms in early-stage ovarian cancer (EOC), thereby identifying candidate biomarkers for non-invasive early diagnosis. Differentially expressed miRNAs were identified by integrating publicly available datasets of EOC tissues and serum samples from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Core miRNAs were subsequently screened through integrated differential expression analysis, weighted gene co-expression network analysis (WGCNA), and feature importance ranking derived from optimized machine learning models. Protein–protein interaction (PPI) networks and functional enrichment analyses (GO and KEGG) were performed on predicted target genes to systematically compare the functional discrepancies between serum- and tissue-derived miRNAs. No overlapping core miRNAs were observed between the two compartments. Serum miRNAs exhibited an overall up-regulated trend, whereas tissue miRNAs were predominantly down-regulated. Although the regulatory pathways demonstrated significant heterogeneity, they ultimately converged on the cell cycle and the PI3K-Akt signaling pathway, indicating high functional homology. Furthermore, serum miRNAs are not merely passive leakage products from tissues; current evidence suggests they may be selectively packaged into exosomes to participate in tumor regulation. Despite divergent expression profiles, serum and tissue miRNAs share homologous regulatory functions in EOC. These findings suggest that serum miRNAs accurately reflect the core molecular status of tumor tissues, providing a robust molecular foundation for liquid biopsy-based early detection strategies. Full article

Background: Patients with prolonged mechanical ventilation (PMV) frequently require tracheostomy due to failure to wean, yet the pathway from ventilator dependence to successful decannulation remains complex and poorly standardised. Comprehensive respiratory rehabilitation is recognised as a core strategy for improving decannulation outcomes, but no unified, evidence-based guidelines currently exist for this population. This review addresses that gap by synthesising current evidence on respiratory rehabilitation and decannulation strategies for tracheostomized PMV patients. Methods: A narrative review was conducted through a systematic search of PubMed/MEDLINE covering publications indexed from May 2019 to February 2026, supplemented by targeted searches of Embase and the Cochrane Library. The search combined free-text keywords and Medical Subject Headings (MeSH) terms across eight search string combinations. Following title and abstract screening of 830 deduplicated records, 51 studies met eligibility criteria and were included in the final narrative synthesis. Results: Six core rehabilitation intervention domains were identified: respiratory muscle training, physical rehabilitation and nutritional optimisation, sedation and delirium management, speaking valve use, airway complication management, and ventilator mode optimisation. High-intensity inspiratory muscle training at no less than 50% of maximal inspiratory pressure is currently supported by the strongest available evidence among the interventions reviewed, although this threshold derives primarily from general ICU populations and has not been specifically validated in heterogeneous tracheostomized PMV cohorts. Decannulation readiness assessment may benefit from evaluating five core domains—neurological readiness, secretion management capacity (suctioning ≤ 4 times/24 h), cough efficacy (peak cough flow > 160 L/min), safe swallowing confirmed by instrumental assessment, and upper airway patency confirmed by fiberoptic bronchoscopy—using a structured multidisciplinary framework. Conclusions: Successful decannulation in tracheostomized PMV patients requires integration of evidence-based rehabilitation interventions, structured multidisciplinary assessment, and a patient-centred outcome framework that extends beyond physiological endpoints to encompass voice restoration, psychological well-being, and social reintegration. Significant evidence gaps remain—particularly for expiratory muscle training, population-specific decannulation protocols, and adapted rehabilitation models for resource-limited settings—representing priority areas for future research. Full article

Metallic glasses (MGs) exhibit excellent mechanical properties, yet their poor tensile ductility greatly limits their practical applications as structural and functional materials. Shear banding is a typical localized rheological deformation behavior inherent to amorphous materials, which stems from heterogeneous atomic rearrangement and regional viscosity fluctuations in the glassy matrix, and fundamentally determines the macroscopic mechanical properties of MGs and their composites. This review discusses the relationship between typical toughening strategies and shear banding behavior, and proposes that deliberate suppression of shear band (SB) initiation or deceleration of their rapid propagation can effectively promote distributed plastic flow. In this review, nanosizing and metamaterial strategies are shown to hinder the formation of mature SBs, while metallic glass matrix composites (MGMCs), nanoglasses (NGs), notched design, and rejuvenation treatments contribute to restraining SB propagation. Current approaches have successfully regulated shear banding behavior and thereby realized appreciable tensile ductility in MGs. Novel design and fabrication techniques for amorphous alloys, which suppress SB initiation and retard SB propagation to achieve homogeneous plastic flow, open up new avenues for realizing controllable plasticity of MGs. Full article

The Constrained Application Protocol (CoAP) is widely adopted to ensure end-to-end reliability in resource-constrained Artificial Intelligence of Things (AIoT) and Wireless Sensor Networks (WSNs). However, CoAP’s default retransmission timeout (RTO) mechanism lacks algorithmic responsiveness under volatile channel conditions, and state-of-the-art benchmarks like CoCoA+ and FASOR often suffer from over-conservative backoff states or destabilizing retransmission storms. To overcome these operational bottlenecks, this paper proposes a novel dual-adaptive Dynamic RTO algorithm specifically engineered for heterogeneous IoT deployment scales. The proposed framework dynamically adjusts its parameter inspection cycle (N) based on instantaneous round-trip time (RTT) variance while simultaneously scaling its tuning coefficient (α) in response to real-time packet loss indicators. To rigorously validate the algorithmic resilience, performance evaluations were conducted within a highly volatile network environment governed by the Gilbert–Elliott dynamic loss model across multi-hop linear (1 × 6) and grid (3 × 6, 5 × 6) topologies. Experimental results demonstrate that the proposed Dynamic RTO consistently optimizes the throughput–latency trade-off, achieving a total communication time of 25.92 s in complex grids—outperforming CoCoA+ and FASOR by 14.28% and 8.89%, respectively. Furthermore, the proposed mechanism significantly curtails transmission overhead, restricting the cumulative retransmission footprint to just 59 counts under severe localized impairments, thereby establishing a scalable, resource-efficient, and empirically robust transport-layer solution for next-generation edge-computing infrastructures. Full article

Background: Immune mechanisms are increasingly implicated in the heterogeneity of schizophrenia spectrum disorders. Cytomegalovirus (CMV), a latent immunomodulatory herpesvirus, is linked to cognitive and immunological alterations, but its electrophysiological correlates remain largely unexplored. This study investigates the relationships among CMV serostatus, EEG features, inflammatory markers, and clinical–cognitive variables. Methods: In this prospective cross-sectional study, 123 patients with schizophrenia spectrum disorders underwent integrated clinical, cognitive, laboratory, and qualitative visual EEG assessments. CMV exposure was determined via IgG serology. Results: Global electroencephalographic EEG organization did not differ by CMV serostatus. However, a descriptive increase in resting-state sharp-wave discharges was observed in CMV-seronegative patients, independent of baseline cortical rhythms. Immunologically, CMV-seropositive individuals exhibited significantly higher total leukocyte counts, consistent with latent viral immune remodeling rather than overt systemic inflammation. Clinically, CMV-seropositive patients demonstrated descriptively higher scores on the disorganization dimension derived from the PANSS (Positive and Negative Syndrome Scale) five-factor consensus model. While these variations did not retain statistical significance after multiple testing correction, separate dimensional analyses revealed that patients exhibiting sharp waves demonstrated better overall cognitive functioning and superior performance within a memory-related item grouping. Notably, the presence of sharp-wave activity was independent of both peripheral inflammatory profiles and treatment-resistant status, underscoring a distinct electrophysiological phenotype. Conclusions: CMV exposure represents a modulating biological background associated with corrected leukocyte elevations and subtle electrophysiological variability, rather than a direct determinant of global clinical severity. The nominal EEG variations and their independent link to better-preserved memory performance highlight non-linear neuroimmune interactions. Given the cross-sectional design, these exploratory patterns warrant a non-causal interpretation but outline a foundation for future longitudinal investigations. Full article

This study investigates how AI-assisted plagiarism changes dishonest academic behavior in a socially interactive learning environment under different educational conditions. To this end, this study develops a scenario-based simulation to examine how AI-assisted plagiarism influences dishonest academic behavior in socially interactive learning environments. The model represents students as autonomous agents embedded in local peer networks who adapt their weekly behavior under academic pressure, institutional intervention, and available cheating options. Two behavioral scenarios are considered: a conventional plagiarism environment, in which agents choose between honest submission and direct copying, and an AI-augmented environment, in which AI-assisted plagiarism is introduced as an additional dishonest strategy. Intervention is modeled through environmental and institutional conditions, specifically detection probability and sanction severity, rather than through direct internal reward manipulation. Q-learning is used as a simplified adaptive mechanism for repeated agent choice. Experimental results show that the possibility of producing and assessing a simulation to see the availability of AI-assisted plagiarism substantially changes the behavioral composition of misconduct by increasing total dishonest behavior and shifting a large share of it toward the AI-assisted category. In the simulation, active intervention reduces dishonest behavior overall but does not eliminate AI-assisted plagiarism as the dominant dishonest strategy in the AI-augmented environment. These observations in the simulation suggest that academic misconduct in the AI era should be understood not only as a problem of deterrence but also as a problem of behavioral adaptation under changing technological and institutional conditions. To support the realism assessment of the simulation design, the study also conducts a structured qualitative plausibility review using multiple large language models under a shared prompt. Across these reviews, the model is judged to be acceptable as a first-stage stylized baseline, while important limitations are identified in agent heterogeneity, social influence depth, and the use of Q-learning as a simplified adaptive heuristic to reproduce the behaviors of actors in there. Full article

This article analyses trading-performance patterns in a stock market simulation conducted with 134 second-year students at the University of Mons (Belgium) on 11 December 2025. Participants had a virtual capital of 100,000 euros and were free to trade CAC 40 securities without any restrictions on the number or volume of transactions. An academic incentive scheme, combining a participation bonus and bonuses for the three best portfolios, created a tournament-style environment with continuous ranking feedback. This feature is considered as part of the experimental context rather than as a separately identified causal mechanism. We estimate a quadratic model linking performance to activity, measured by the number of mean-centered transactions to reduce the collinearity between the first-degree term and its square, and control exposure via the average percentage of cash in the portfolio, portfolio variability (measured as the standard deviation of portfolio value) and the average trade size. Breusch–Pagan and White tests indicate heteroscedasticity, justifying a robust inference. The results highlight a convex relationship between activity and performance: the marginal association is initially negative but becomes positive above a model-implied upper-tail level corresponding to approximately 46 transactions. This value should not be interpreted as a behavioral level or as a trading rule. The percentage of cash in the portfolio and the average trade size are negatively associated with performance, while the portfolio variability does not show a statistically significant association with performance. Overall, the results indicate heterogeneous trading patterns rather than a single activity–performance profile. Full article

The environmental effects of green finance policies involve complex systemic interactions across multiple levels, yet existing studies often adopt fragmented analytical approaches. Drawing on the multi-level perspective (MLP), this study conceptualizes the environmental impacts of Green Finance Reform and Innovation Pilot Zones (GFRIPZs) as a process of systemic green transformation involving interactions among landscape, regime, and niche levels. Using panel data of 287 prefecture-level and above cities in China from 2012 to 2022, this study applies a staggered difference-in-differences (DID) model to evaluate the environmental impacts of GFRIPZs. The results show that GFRIPZs significantly reduce both PM_2.5 concentrations and CO₂ emissions. Mechanism analyses based on multiple mediation models and GSEM reveal pollutant-specific differences in underlying channels. Green technological innovation (GTI) constitutes one observable pathway for PM_2.5, whereas the policy effect is more closely associated with energy structure adjustment for CO₂. Heterogeneity analysis further shows that PM_2.5 mitigation is stronger in colder cities, while CO₂ reduction is more pronounced in developed cities. These findings reveal pollutant-specific mechanisms of green finance and offer policy implications for developing countries seeking to promote systemic green transformation. Full article

Background: Adipose tissue is increasingly recognized as a highly dynamic endocrine and immunometabolic organ with marked functional heterogeneity. It serves as a reservoir for the active form of vitamin D₃, 1α,25-dihydroxyvitamin D₃ or calcitriol (1α,25-D₃), since it expresses enzymes responsible for its activation and inactivation and contains the vitamin D receptor (VDR). Through both classical and non-classical mechanisms, calcitriol modulates adipocyte proliferation and differentiation, protein expression and energy metabolism. This review aims to explore the signal transduction mechanisms of calcitriol in adipocytes, detailing the classical pathways mediated by the nuclear VDR (VDRn), as well as non-classical pathways involving membrane-associated VDR (VDRm), microRNAs, AMP-activated protein kinase (AMPK), and sirtuin 1 (SIRT1). Methods: A literature search was conducted using PubMed, ScienceDirect, and MDPI-indexed journals, prioritizing studies published within the last 10 years to ensure the inclusion of up-to-date evidence. Results: This review summarizes current knowledge on both classical and non-classical signaling pathways that are activated by calcitriol and highlights key molecular targets with potential relevance for drug development and therapeutic intervention. Through VDRn, calcitriol regulates the expression of proteins involved in inflammation and energy metabolism. Additionally, it modulates cellular processes such as energy production and secretion via the AMPK/SIRT1 axis and microRNA-mediated pathways, contributing to mitochondrial function and metabolic homeostasis. Conclusions: Calcitriol plays a central role in adipocyte biology by integrating multiple signaling pathways that regulate metabolic and inflammatory responses. These mechanisms highlight its potential as a therapeutic target and biomarker in metabolic diseases. Moreover, microRNAs emerge as critical posttranscriptional regulators in these processes, reinforcing their relevance as both biomarkers and targets for future interventions. Full article

To address the insufficient accuracy of short-term load forecasting caused by the strong randomness of distributed energy output, variable electricity consumption patterns, and complex meteorological factors, this study proposes a load forecasting method that integrates K-means scene clustering and a Transformer–BiGRU–Attention (CTBA) hybrid deep learning architecture. Different from conventional Transformer–BiGRU hybrid forecasters that train a single global predictor across all operating conditions, the proposed CTBA framework first partitions daily load curves into representative scenes and then routes each sample to a scene-specific Transformer–BiGRU–Attention predictor, thereby reducing distributional heterogeneity before temporal modeling. First, the K-means algorithm is used to perform scene clustering on historical daily load curves, and the optimal number of clusters is selected according to the silhouette coefficient and downstream prediction performance. Subsequently, the CTBA model is trained separately for each clustering subset. The Transformer encoder captures the long-range global dependencies of load sequences through the self-attention mechanism, the BiGRU module extracts local bidirectional temporal fluctuation features, and the Attention mechanism further focuses on key time nodes such as morning and evening peaks while fusing multi-source data including historical load, day-ahead electricity price, and multi-dimensional meteorological factors. Experimental results based on the German ENTSO-E power dataset show that the coefficient of determination R² of the proposed model reaches 0.9893, with MAE, RMSE, and MAPE as low as 0.0141, 0.0187, and 3.92%, respectively, which are significantly improved compared to benchmark models such as SVR, LSTM, CNN, and TCN-BiGRU. Ablation experiments further demonstrate that removing the clustering, Transformer, BiGRU, or attention layer will degrade performance, thus verifying the effectiveness and superiority of the method in short-term load forecasting and providing an accurate solution for the short-term load forecasting of power systems. Full article

This study examines the relationship between insurance market development and economic performance measured by GDP per capita levels in mature OECD economies, focusing on whether insurance depth, market size, and life insurance structure have distinct long-run implications. Although the insurance–income nexus is documented in developed and emerging markets, the literature rarely separates the qualitative depth of insurance use from the mechanical size of the sector relative to GDP, and seldom examines life insurance structural features such as retention and foreign participation within a non-stationary panel framework; this study addresses that gap. Using a balanced panel of 33 OECD countries from 2011 to 2021, the analysis applies panel time-series methods that account for non-stationarity, cointegration, cross-sectional dependence, and heterogeneous country dynamics. The results show that total insurance density is positively associated with GDP per capita, and life insurance density remains positive and significant across the long-run estimators, suggesting that more intensive insurance use remains economically relevant even in advanced financial systems. By contrast, life insurance penetration is negatively associated with GDP per capita, even after accounting for its mechanical link to GDP. Life insurance retention also enters negatively, while foreign insurer participation shows no statistically significant association in the panel. The findings support a depth-not-size interpretation of the long-run association between insurance market structure and income levels in mature OECD markets, and suggest that policy attention should shift from expanding insurance-sector scale toward improving efficiency, risk allocation, and market sophistication. These results reflect long-run associations within the OECD panel and should not be interpreted as evidence of direct causal effects. Full article