Search Results (24,286)

This paper focuses on the Pinghu Formation in the K region of the Xihu Depression, conducting a systematic study on the channel types, migration patterns, and the coupling mechanisms of tectonics, paleogeomorphology, and tidal dynamics in the tidal-controlled and river-controlled composite delta system of the region. By integrating core, well logging, and 3D seismic data, and addressing the challenges of channel identification under the influence of coal seams, methods such as PCA, K-means clustering, and fuzzy c-means clustering were employed for multi-attribute fusion analysis. An indicator system for channel identification and type classification was established, revealing the sedimentary characteristics of tidal-modified delta channels and their planar distribution and migration evolution process. The results of the study indicate that: (1) The early stage of the Pinghu Formation developed a tidal-controlled delta, with channels in network, linear, and dendritic shapes, where individual channels were small and fragmented; in the later stage, it transformed into a river-controlled delta, with sandbodies more concentrated; (2) In areas with weak tectonic constraints, the control of geomorphic boundaries became more prominent, and the barrier islands’ shielding effect on tides led to river-controlled migration of the channels, with limited tidal channels and tidal-modified sandbodies developed only in local areas; (3) The planar distribution and evolution of channels in the study area showed significant differences at different times due to the influences of geomorphology and tectonics. The findings of this paper provide new insights into the sedimentary evolution of tidal-modified delta channels. Full article

The rapid spread of antibiotic resistance through plasmid-mediated conjugation remains a primary global health concern. Despite its critical role in horizontal gene transfer, no approved drugs currently target this process, leaving a critical therapeutic gap. Integration Host Factor (IHF), a DNA-binding protein essential for plasmid replication and mobilization, emerges as a promising yet underexplored target for anti-conjugation strategies. This work aimed to develop a predictive computational model and identify small molecules that disrupt IHF function, thereby reducing plasmid transfer and limiting resistance gene dissemination. A curated dataset of 65 compounds with reported anti-plasmid activity was analyzed using a 3D-QSAR model based on algebraic descriptors computed with QuBiLS-MIDAS. The model was validated through leave-one-out cross-validation (Q² = 0.82), Tropsha’s criteria, and Y-scrambling. Representative compounds were selected via pharmacophore clustering and evaluated through molecular docking at both the DNA-binding site and a predicted allosteric pocket of IHF. The most promising complexes underwent 200 ns molecular dynamics simulations to assess stability and interaction patterns. The QSAR model demonstrated strong predictive performance (R² = 0.90). Docking simulations revealed more favorable binding energies at the allosteric site (up to −12.15 kcal/mol) compared to the DNA-binding site. Molecular dynamics confirmed the stability of these interactions, with allosteric complexes showing lower RMSD fluctuations and consistent binding energy profiles. Dynamic cross-correlation analysis revealed that allosteric ligand binding induces conformational changes in key catalytic residues, including Pro65, Pro61, and Leu66. These alterations may compromise DNA recognition and disrupt the initiation of replication. To our knowledge, this is the first computational study proposing allosteric inhibition of IHF as an anti-conjugation strategy. These findings provide a foundation for experimental validation and the development of novel agents to prevent horizontal gene transfer, offering a promising approach to restoring antibiotic efficacy against multidrug-resistant pathogens. Full article

The elemental composition of the vitreous humor may reflect physiological and pathological processes occurring in the eye. The objective of this study was to provide a complex multielemental analysis of human vitreous humor. Vitreous humor samples (n = 57) were collected post-mortem during autopsies. Inductively coupled plasma mass spectrometry (ICP-MS) was employed to quantify micro-, trace-, ultra-trace, and toxic elements. The study showed the occurrence of elements at the ppm (Na, K, P, Ca, Mg), ppb (Al, Rb, Zn, Fe, Sr, Cu), and ppt (Ce, La, Nd, Tb) levels. Hierarchical clustering using Ward’s method and k-means analysis revealed four distinct clusters, including two major clusters representing the baseline macro- and microelement profile characteristic for the studied population. Correlations between elements revealed statistically significant (p < 0.05) positive and negative correlations between elements with (I) chemical similarity Ce-La, Cs-Rb, Rb-K, Ca-P, Zn-Cu, and Cs-K; (II) a possible common environmental origin, Cd-P, and Rb-P; (III) involvement in similar biological processes as K-P; and (iv) a common geochemical origin and similar biological functions, i.e., Se-Zn. The study identified several quantitative trends in the demographic and medical characteristics of the participants. Alcohol users had significantly higher Zn concentrations than non-alcohol users; women had significantly higher Ca concentrations than men; higher BMI correlated positively with Cs and negatively with Be and Cr levels; and Cu, Sb, Cd, Se, and Ca concentrations increased with age. The presence of several toxic and potentially toxic elements was identified in the vitreous body: Al (>10 ppb); Cd, Cr, Pb, Ni, Mn; and Ba (<10 ppb); As, Hg, Sb, Tl, Bi, Be (<1 ppb). The study showed that, within a given geographic region, the accumulation profiles of toxic metals are quite homogeneous, indicating common sources of exposure. Full article

This paper focuses on the coordinated development and barrier factors of green logistics (GL) and regional economy (RE) in the Yellow River Basin (YRB). Based on data from 2014 to 2023, it constructs an index system covering the development foundation, benefits, potential and sustainability of GL, as well as regional economic structure, scale and potential. Using methods such as the entropy method, coupling coordination degree (CCD) model, kernel density estimation, Moran’s index and Obstacle degree model, it reveals that the average comprehensive CCD improved from 0.38 to 0.65 over the decade, but with significant regional differences. Eastern provinces like Shandong and Henan are ahead, while central and western provinces lag. The coupling coordination degree shows an overall upward trend, moving toward coordinated development with an expanding spatial pattern from east to west and narrowing regional gaps. Global Moran’s index (ranging from 0.356 to 0.524) indicates a spatial positive correlation, and local spatial autocorrelation analysis shows coexistence of high–high and low–low clusters. For Obstacle factors, GL is primarily constrained by low labor productivity (indicator B3, accounting for 23.1% to 44.7% of the total obstacle degree) and shortcomings in logistics industry benefits and scale, while RE is hindered by lagging economic structure optimization, weak foreign trade, and insufficient economic scale and vitality. This study provides a theoretical basis and decision-making reference for the high-quality coordinated development of GL and RE in the YRB, promoting regional coordination and sustainable development. Full article

Global climate warming has increased the risk of glacial lake outburst debris flows (GLODFs) in high mountain regions. It is characterized by frequent and clustered occurrences, particularly in the Himalayan region, and represents an inescapable challenge for high mountain areas in the future. GLODF susceptibility assessment is critical to risk mitigation but remains a challenge owing to its complex triggering mechanisms and watershed structure. GLODF is a complex system failure process, including the failure probabilities of multiple glacial lakes in a watershed, the complex flow path of flood, the transition probability from flood to debris flow, and the overlapping of debris flows formed in different branches in the watershed. Therefore, multiple trigger factors, hazard sources and flow paths should be considered in the assessment of susceptibility to GLODFs. In this study, a systematic approach and mapping for GLODF susceptibility assessment are proposed based on the theory of system failure analysis. The main steps include: (1) identification and classification of the potential hazard sources in the target watershed; (2) arrangement of the flow path and abstraction of the key-node diagram; (3) establishment of the system failure structure of a GLODF; and (4) predisposing factor analysis and susceptibility assessment. Moreover, the predisposing indexes of GLODF susceptibility assessment are proposed, combining the main factors affecting both glacial lake outbursts and subsequent debris flows. The proposed model was applied in the Congduipu River basin, Nyalam, Tibet, China, which has more than 6 glacial lakes and 11 gullies, with an area of 366 km², and encountered more than four GLODFs in recent years. The results show that there are one very high-susceptibility glacial lake, two high-susceptibility glacial lakes, and gullies that are in series with high-susceptibility glacial lakes that are mostly medium–highly susceptible to glacial outbursts. The results were verified by historical records and field investigations in the Congduipu River basin. This method is applicable to quickly evaluate the susceptibility to GLODFs at the watershed and regional scales with multiple glacial lakes and gullies. Full article

The digital divide between large enterprises and SMEs (Small and Medium-sized Enterprises) within industrial clusters poses a significant challenge to achieving collective digital transformation, exacerbated by the quasi-public goods, attributes of digital inclusion ecosystems, and the prevalence of free-riding behavior. This paper investigates whether platform enterprises, as core actors occupying structural holes in cluster networks, can foster the co-construction of a digitally inclusive ecosystem. We developed a complex network public goods game model, incorporating performance feedback into a modified Fermi learning to capture firms’ adaptive decision-making based on historical and social aspirations. The model simulates strategic interactions on both small-world and scale-free networks, characteristic of industrial clusters. Numerical simulations reveal that: (1) The core driver of co-construction is the investment return coefficient; (2) Performance feedback amplifies individual rationality, accelerating the formation or collapse of cooperation depending on the investment return coefficient; (3) Platform empowerment—specifically, selectively connecting and incentivizing cooperative firms—effectively promotes ecosystem co-construction, with this strategy proving most impactful when investment returns are moderate. Furthermore, while this selective empowerment strategy benefits the cluster overall, its effect on the platform’s own revenue is network-dependent, showing a more pronounced decline in small-world structures. This study provides a novel analytical framework for understanding strategic interactions in digital inclusion and offers practical insights for policymakers and platform leaders in orchestrating collaborative digital transformation. Full article

Background/Objectives: Autism research has often interpreted autistic sociality through neurotypical norms, limiting ecological accounts of autistic meaning-making and context-sensitive support needs. Social virtual environments (SVEs), such as VRChat, allow modulation of sensory exposure, social distance, and participation pace, potentially enabling autistic-led interaction with greater autonomy and predictability. This study examined how autistic young adults co-construct meanings around social interaction, identity, and self-regulation in peer-led discussions within an SVE; identified context-sensitive processes relevant to well-being; and evaluated the feasibility and acceptability of SVEs as a participatory research setting. Methods: Sixteen autistic young adults (18–38 years; DSM-5-TR, Level 1) participated in nine remote sessions conducted in VRChat, coordinated via a co-designed Discord server. The peer-led discussions were audio-video recorded, transcribed, and anonymized. Data were analyzed using reflexive thematic analysis, combining inductive session-level coding, cross-session thematic clustering, and participatory refinement with community partners. Results: Autistic experience was framed as a context-dependent negotiation of interpretive risk, interactional workload, masking-related energy costs, and epistemic injustice, alongside future-oriented accounts emphasizing access, dignity, and systemic redesign. Observational memos documented multimodal participation, distributed peer facilitation, and accessibility-relevant sensitivities to environmental stability. Community partners reported positive experiences and supported the acceptability of private-world VRChat sessions. Conclusions: Peer-led discussions in an SVE can support ecologically grounded, participant-centered qualitative research, offering methodological opportunities to study autistic meaning-making under conditions that reduce demands and risks. Full article

The tourism sector is widely recognized as a pivotal catalyst for global development and economic growth. However, it faces significant challenges, which have intensified the search for alternative and more sustainable tourism models. Digital technologies have become essential tools for transformation, with the metaverse emerging as a disruptive and promising innovation strategy for the tourism industry. Thus, this study aims to provide a comprehensive review of all previous scientific literature related to the adoption of the metaverse in the context of sustainable tourism, developing a bibliometric analysis (through techniques such as co-citation analysis of references and author keyword co-occurrence) of all articles indexed in Web of Science database from January 2021 to September 2025. Specifically, the references co-citation analysis has concluded three main thematic clusters related to conceptual foundations, technological advances, and metaverse adoption possibilities, respectively. The results obtained indicate that research on the metaverse in sustainable tourism is still at an early stage of development and is primarily conceptual in nature. This study contributes to the emerging research field of metaverse and sustainable tourism by offering a comprehensive review to establish its theoretical foundations and identify opportunities for future research. Full article

Coastal provinces in eastern China are experiencing rapid urbanization that challenges ecosystem services and low-carbon development. In this study, Zhejiang, Fujian, and Guangdong Provinces were selected, and the influence of land use/land cover change (LUCC) on carbon storage and its spatial heterogeneity was quantified. LUCC datasets for 2000, 2005, 2010, 2015, and 2020 were compiled to describe land-use dynamics over 2000–2020. Carbon storage was estimated with the InVEST model. Land-use patterns for 2035 were simulated using the PLUS model under three scenarios: natural development, ecological protection, and development priority. Spatial autocorrelation analysis and multiscale geographically weighted regression (MGWR) were then used to determine the key drivers of spatial variability in carbon storage. Between 2000 and 2020, farmland, forest, grassland, and unused land showed an overall decline, while water bodies and tt-up land expanded; together, these changes corresponded to a carbon-storage loss of 121.19 Tg. Carbon density exhibited pronounced spatial clustering, with higher values concentrated in mountainous and less urbanized areas; built-up expansion and forest degradation were the primary contributors to carbon loss. By 2035, total carbon storage is projected to decrease by 74.67 Tg under natural development and by 108.54 Tg under development priority, whereas ecological protection is projected to yield the smallest decline (35.71 Tg). These results underscore the importance of sustainable coastal land-use planning and integrated coastal zone management, which balance development and ecosystem services by prioritizing ecological protection, curbing built-up expansion, and promoting forest restoration. Such nature-based solutions can enhance carbon sequestration, strengthen climate resilience, and support China’s low-carbon transition toward its dual-carbon targets. Full article

Clustering is a fundamental unsupervised learning technique used to uncover hidden patterns in unlabeled data. Although metaheuristic algorithms have demonstrated effectiveness in clustering, many suffer from premature convergence and limited population diversity. This study employs the Black-Winged Kite Algorithm (BKA) and its enhanced variants, Chaotic BKA (CBKA), Lévy Flight-based BKA (LBKA), and Chaotic Levy BKA (CLBKA), to address these limitations in centroid-based clustering formulated as a Sum of Squared Errors (SSE) minimization problem. Chaotic logistic mapping improves search diversity and adaptability, while Levy flight introduces long-range exploration. In addition, Cauchy based perturbations are incorporated to enhance convergence stability. The algorithms are evaluated on sixteen UCI benchmark datasets, with 30 independent runs conducted under different population and iteration settings. Experimental results show that CLBKA consistently achieves superior clustering performance in terms of accuracy and stability. Statistical validation using the Friedman and Wilcoxon tests confirms significant performance differences, with CLBKA obtaining the lowest mean rank across configurations. The findings indicate that integrating chaotic dynamics and Levy flight mechanisms enhances clustering robustness and optimization efficiency. Full article

Periodontitis is a chronic infectious disease characterized by the destruction of the tooth-supporting tissues, including the gingiva, periodontal ligament, and alveolar bone, which may ultimately lead to tooth loss. However, blood-based biomarkers reflecting systemic inflammation in periodontitis remain poorly defined. We analyzed plasma proteomic data from the UK Biobank using Olink Explore proteomics to identify systemic protein signatures distinguishing chronic periodontitis patients (n = 90) from healthy controls (n = 2234). Among 2151 proteins passing quality control, 29 proteins showed significant differential expression (FDR < 1.0 × 10⁻⁵). Growth differentiation factor 15 (GDF15) exhibited the strongest upregulation (mean NPX: −0.183 to 0.157, effect size = 0.337, FDR = 2.82 × 10⁻¹²), followed by N-terminal pro-B-type natriuretic peptide (NT-proBNP) (effect size = 0.594), Interleukin-6 (IL-6) (effect size = 0.450), and Insulin-like growth factor binding protein-(4IGFBP4) (effect size = 0.269). Multiple TNF receptor superfamily members (TNFRSF1A/1B, TNFRSF10A/10B) and proteins involved in extracellular matrix remodeling (COL6A3, ADAM12) and vascular stress (ADM) were significantly elevated. In contrast, EGFR and DNER showed decreased expression. Protein–protein interaction network analysis revealed IL-6 as a central hub protein forming a tightly interconnected cluster with TNF receptor family members. These findings indicate systemic plasma protein profiles associated with chronic periodontitis within this population-based cohort. The identified proteins may provide a basis for future evaluation of blood-based biomarkers for chronic periodontitis, pending further validation. Full article

The digital nomad economy—the ecosystem in which professional skills are traded through online platforms independent of geographic co-location—dynamically recombines skills into project-based portfolios with absent firm-level hierarchy. Yet it remains shaped by platform taxonomies, interfaces, and ranking/recommendation incentives. This study examines the emergent structure within this setting using the Semantic-Structural Systems Analysis (S²SA) framework, which integrates LLM-assisted skill extraction, transformer-based semantic embeddings, and multi-layer network analysis. We analyze a dual-source dataset comprising approximately 50,000 public Upwork profiles from a top-rated/high-earning segment (January–March 2023) and 2.0 million Reddit posts and comments (2018–2023) from remote-work and digital-nomad communities. The resulting skill network exhibits a pronounced core–periphery organization and modular “skill ecotopes” corresponding to coherent functional specializations. In predictive models of skill-level effective hourly rates, semantic brokerage and semantic diversity function as robust predictors of higher rates, significantly outperforming popularity-only baselines. Longitudinal discourse analyses surrounding the COVID-19 pandemic and the generative AI shock reveal rapid attentional shifts followed by the emergence and recombination of new skill clusters. We interpret these results as evidence consistent with constrained self-organization in platform-mediated labor markets. To support replication, prompts, parameters, and robustness checks are fully reported. Full article

What are the nature and characteristics of freelance journalism in the UAE media system? To answer the main research paper question, this study investigates the usage and influence of freelancers in the Emirati media ecosystem through a comparison among the national media organizations. Self-determination theory (SDT) is used to analyze four dimensions of global freelance journalism. The study uses semi-structured interviews with 15 subjects, including three accountable for dealing with freelancers and twelve freelancers who freelance in the U.A.E., and applies a thematic analysis to this data. Additionally, SDT interprets the clustered themes. This paper discovered that freelance journalism is still taking shape in the UAE; the national journalism and media industry are using freelancers who undergo strict standards to add a valuable output within a harmonious, cooperative, and participatory form that influences the Emirati media ecosystem positively. The paradox is that all use their professional autonomy voluntarily to serve the national media’s interests through their contributions in accordance with national trends. However, their usage nationally has decreased to control the budgets with a tendency to depend on Emirates freelancers as new players, which increases the competition with residents and foreign freelancers in particular, who are preferred over domestic freelancers. Full article

Background: Conventional omics analysis often treats outliers as noise, yet they may harbor critical biological insights. Objetive: This study proposes a paradigm shift: actively investigating outliers to discover biologically relevant subtypes within metabolic–inflammatory syndromes. Methods: We applied a comprehensive analytical framework for outlier detection based on a multi-algorithm consensus (IQR, MAD, Isolation Forest) to a clinical cohort of diabetic neuropathy (n = 93) and an in vitro 3T3-L1 adipocyte model (n = 39). The identified outliers were characterized using robust PCA, co-expression networks, unsupervised clustering, and Random Forest predictive modeling. Results: In the clinical cohort, an outlier subgroup (47.3%) exhibited an extreme immune–metabolic phenotype characterized by hyperactivation of Th1/Th17 pathways (elevated T-bet and IL-17; p < 0.001), hypertriglyceridemia, and network reconfiguration (TGFβ and STAT4 hubs). In the cellular model, outlier samples (12.8%) showed autonomous pro-inflammatory behavior characterized by IL-6 overproduction (p = 0.002) and IL-10 suppression. Conclusions: Multivariate analysis confirmed spatial segregation of these profiles. Systematic outlier investigation revealed discrete pathophysiological subtypes invisible to mean-focused analyses, demonstrating that extreme values encapsulate potent biological signals. This framework offers a generalizable approach for uncovering clinical heterogeneity and identifying therapeutic targets in complex diseases. Full article

Modeling nonstationary time series in financial and energy markets remains challenging due to nonlinear dynamics, volatility clustering, and frequent regime shifts that distort the underlying probabilistic structure of the data. This study introduces a novel probabilistic–statistical decomposition framework, termed Robust Adaptive Decomposition (RAD), designed to preserve probabilistic symmetry between deterministic and stochastic components. In this context, symmetry refers to maintaining statistical balance—particularly in the means, variances, and distributional structures—between the extracted modes and the residual series, thereby preventing artificial bias or variance distortion during decomposition. The RAD framework adaptively determines the optimal number of modes needed to effectively separate short-term fluctuations from long-term structural movements. Unlike conventional techniques, such as Empirical Mode Decomposition (EMD), Ensemble EMD (EEMD), and CEEMDAN, the proposed method incorporates a robustness mechanism that mitigates mode mixing and reduces distortions induced by extreme shocks and regime transitions. The empirical evaluation is conducted on six oil-related energy commodities—Brent crude oil, kerosene, propane, sulfur diesel, heating oil, and gasoline—whose price dynamics exhibit pronounced nonlinearity and structural volatility. When integrated with ARIMA forecasting models, the RAD-based framework consistently outperforms benchmark decomposition approaches. Across all datasets, RAD–ARIMA achieves reductions of approximately 65–90% in MAE, 60–85% in RMSE, and up to 95% in MAPE relative to CEEMDAN-based models. These results demonstrate that RAD provides a mathematically rigorous and computationally efficient preprocessing mechanism that preserves statistical equilibrium while effectively disentangling deterministic structures from stochastic noise. Beyond oil markets, the framework offers broad applicability in econometric modeling, financial forecasting, and risk management, contributing to probability- and statistics-driven symmetry analysis in complex dynamic systems. Full article