Search Results (112,723)

Arid inland river basins are constrained by severe water scarcity and fragile ecosystems. Although large-scale water transfer projects are critical interventions, studies of their comprehensive impacts on eco-socioeconomic systems remain limited. To address this gap, this study proposes an integrated assessment framework. A global Remote Sensing Ecological Index (gRSEI) was developed by incorporating a salinity indicator, employing optimal indicator selection, and utilizing a full-period global normalization strategy. A Gridded Socioeconomic Index (GSEI) was constructed by integrating nighttime light (NTL), population (POP), and gross domestic product (GDP) data. The coupling coordination degree (CCD) model, spatial autocorrelation analysis, and the optimal parameters-based geographical detector (OPGD) were applied to analyze spatial patterns across subregions. Focusing on the Shiyang River Basin (SYRB), this study analyzed the spatiotemporal responses and coupling coordination of the eco-socioeconomic system to the 2001 Jingdian Phase II Water Transfer Project. Results indicate that ecological quality improved significantly after the water transfer, with gRSEI increasing from 0.225 to 0.334. Socioeconomic development also improved overall. The eco-socioeconomic system exhibited high coupling but moderate coordination. The coupling degree (C) and coordination degree (D) increased from 0.824 and 0.370 to 0.852 and 0.442, respectively, with clear regional heterogeneity. The water transfer project shifted the dominant driver of coordinated development from water-related factors to land cover. This study provides a practical framework for assessing ecological and socioeconomic dynamics and their interactions in arid basins under major water transfer project interventions. Full article

Identifying the nectar sources of stingless bees is essential for understanding plant–pollinator interactions and for promoting sustainable meliponiculture. Traditionally, this has been achieved through melissopalynological analysis of honey; however, this approach has certain limitations. Here, we aimed to evaluate whether pollen analysis of bee crop nectar can reliably identify nectar sources for stingless bees. We conducted palynological analyses of honey samples (n = 12) and crop nectar samples (n = 757, considering individual foragers), and conducted field surveys to determine nectar sources for the stingless bee Tetragonula pagdeni in West Bengal, India. From the honey analysis, 42 pollen types were identified, with Eucalyptus tereticornis as the predominant pollen type. In contrast, 67 pollen types were recorded from the crop nectar samples. The most frequently occurring pollen types were Acacia auriculiformis, Borassus flabellifer, Eucalyptus tereticornis, Peltophorum pterocarpum, Tridax procumbens, and Ziziphus mauritiana. Through field surveys, 73 plant species were identified as nectar sources. By integrating these methods, 85 plant species were recognised as nectar sources for stingless bees. The findings indicate that palynological analysis of bees’ crop nectar is an effective method for identifying the nectar sources of a bee species. Furthermore, combining palynological analysis of crop nectar with melissopalynological analysis of honey provides a more comprehensive and potentially more accurate assessment of nectar sources than reliance on honey analysis alone. Full article

Background: Hashimoto’s thyroiditis (HT) is a chronic autoimmune disorder characterized not only by thyroid dysfunction but also by metabolic disturbances, micronutrient inadequacies, and low-grade inflammation. Composite indices derived from routine laboratory parameters may therefore help capture the broader systemic profile of the disease. This study explored within-cohort associations of immunonutritional indices including the Prognostic Nutritional Index (PNI), Nutritional Risk Index (NRI), and Controlling Nutritional Status (CONUT), and hemogram-derived inflammatory markers including the Neutrophil-to-Lymphocyte Ratio (NLR), Monocyte-to-Lymphocyte Ratio (MLR), Platelet-to-Lymphocyte Ratio (PLR), and Systemic Immune-Inflammation Index (SII), with thyroid function, thyroid autoimmunity, metabolic characteristics, disease duration, and vitamin D status in adults with Hashimoto’s thyroiditis. Methods: This cross-sectional study included 229 adults diagnosed with HT. PNI, NRI, CONUT, and complete blood count-derived inflammatory markers were evaluated in relation to thyroid function, thyroid autoimmunity, disease duration, metabolic characteristics, and vitamin D status. Because most variables were not normally distributed, the main analyses were conducted using non-parametric tests. Correlations were evaluated using Spearman’s rank correlation coefficients. Exploratory regression models were estimated using HC3 heteroscedasticity-consistent robust standard errors, and CRP-based sensitivity analyses were performed by excluding participants with CRP > 10 mg/L. Results: Vitamin D deficiency was highly prevalent and affected 70.3% of the participants. Among the immunonutritional indices, NRI differed significantly according to BMI category and HOMA-defined insulin resistance (both p < 0.001), indicating a closer relationship with metabolic burden. PNI was associated with disease duration (p = 0.009), whereas the inflammatory indices were largely similar across the clinical groupings examined. In exploratory robust regression models, the explanatory power remained modest (R² = 0.066–0.171). PLR showed the most consistent index-related association with TSH, whereas the CONUT–FT3 association observed in the full-sample robust model was not retained after CRP-based sensitivity analysis. Conclusions: Adults with HT in this study showed frequent vitamin D deficiency together with a substantial burden of excess weight and insulin resistance. Routine immunonutritional and inflammatory indices may provide supportive information on within-cohort biochemical and metabolic heterogeneity, but they should not be interpreted as stand-alone diagnostic or prognostic markers. In particular, NRI appeared to reflect metabolic and adiposity-related burden more than nutritional risk alone, while PLR showed the most internally consistent index-related association with TSH. Full article

Municipal solid waste landfills may remain sources of environmental concern long after closure because heavy metals can persist in soils and affect ecosystem recovery. This study presents an integrated assessment of a closed municipal solid waste landfill in Kokshetau, Northern Kazakhstan, by combining field-based soil geochemical analysis with remote sensing monitoring of vegetation dynamics. A radial-gradient sampling design was used to characterize spatial patterns of contamination and to distinguish zones with different levels of anthropogenic impact. The results showed a clear concentration of heavy metals, particularly Zn and Pb, in the central part of the landfill, where integrated pollution and ecological risk indices indicated the highest levels of technogenic pressure. Time-series analysis of Landsat-derived vegetation indices for 2017–2025 revealed poorer vegetation condition in the most contaminated areas, with NDVI and EVI values increasing toward the landfill periphery. The observed negative association between vegetation indices and ecological risk suggests that remote sensing indicators can provide useful information on the ecological condition of closed landfill sites, although they should be interpreted together with field measurements. The novelty of this study lies in the combined use of geochemical contamination indices and long-term vegetation-index monitoring to assess post-closure landfill conditions in an arid continental region of Central Asia, where such integrated studies remain limited. The findings highlight the persistence of environmental risks after landfill closure and support the use of vegetation indices as non-invasive tools for monitoring rehabilitation and prioritizing further field investigations. Full article

Background: Major depressive disorder (MDD) is increasingly recognized as a highly heterogeneous disorder. Although the person-based similarity index (PBSI) provides a useful framework for characterizing individualized brain structural similarity, existing studies in MDD remain limited by either small samples or a lack of integration across different morphological features. Methods: We used structural MRI data from 1442 patients with MDD and 1277 healthy controls to calculate PBSI scores of cortical morphology measures based on cortical thickness (CT), cortical volume (CV), cortical surface area (SA), and sulcal depth (SD). Group comparisons of whole-brain PBSI and regional contributions to PBSI scores were then performed, and a subgroup analysis in 243 first-episode, drug-naive (FEDN) patients with MDD was further conducted. Results: Patients with MDD showed significant alterations in PBSI. Specifically, PBSI scores were significantly reduced for CT, CV, and SD, whereas no significant group difference was observed for SA in the main analysis. Analyses of regional contributions to PBSI further revealed significant between-group differences across multiple cortical regions. These alterations were mainly distributed in the default mode, ventral attention, and visual networks for CT; in the default mode, ventral attention, sensorimotor, and visual networks for CV; and in the default mode, dorsal attention, frontoparietal, and sensorimotor networks for SD. Similar patterns were also observed in the FEDN MDD subgroup. Conclusions: These findings provide neurobiological evidence for the marked structural heterogeneity of MDD and highlight the potential of PBSI as an individualized neuroimaging marker for more precise diagnosis and personalized intervention. Full article

Useful quantum neural networks should not merely explore large Hilbert spaces but should organise their expressive capacity according to the symmetries of the learning problem. We introduce symmetry-organised complexity as an ansatz-level, representation-theoretic trajectory diagnostic for quantum neural networks. The diagnostic combines symmetry-sector organisation, cross-irreducible representation organised complexity, and symmetry metastability into a composite index, which is then multiplied by a compliance factor that penalises apparent complexity arising from symmetry violation. This compliance factor is defined at the level of the implemented trainable generators rather than as a representation-independent channel metric. The representation-theoretic basis of the construction is that, for an exactly equivariant network, the effective trainable operators lie in the commutant of the group action and are controlled by multiplicity dimensions rather than by the full Hilbert-space dimension. We show that joint sector collapse and state freezing force the index to vanish under an explicit multiplicity–purity condition and that networks with identical qubit and parameter counts can have different values of the index. Two analytically tractable four-qubit examples with excitation number and total spin symmetry illustrate how the diagnostic separates sector-collapsed, symmetry-organised, and symmetry-breaking behaviour. A controlled $U\left(1\right)$-compatible teacher–student classification task further shows that, in this validation setting, the ordering of the composite index across equivariant, hybrid, and non-equivariant ansatze agrees with the ordering of generalisation accuracy. The framework is most informative when the relevant symmetry of the learning problem is known. Full article

Poly(trimethylene ether) glycol (PO3G), a bio-based polyether polyol with excellent flexibility and superior hydrolytic stability, has emerged as a critical raw material for the preparation of high-performance polymer materials. This work optimized the sulfuric acid-catalyzed polymerization process and assessed the feasibility of using p-toluenesulfonic acid (PTSA) as an alternative catalyst. A parametric study was conducted to establish a reliable operating window for the sulfuric acid system. DFT calculations demonstrated that the driving force for chain growth decreases with increasing chain length, that recombination between chains of significantly different lengths is more favorable than between chains of equal length, and that the formation of disulfate esters is thermodynamically more favorable. Although PTSA required a higher catalyst loading, the resulting polymer had a markedly lower yellowness index. Prolonged reaction times lead to a molecular weight plateau, especially at high PTSA concentrations, while the yellowness index continues to increase after reaching the plateau. ¹H NMR analysis indicated the formation of benzenesulfonate monoester intermediates during PTSA catalysis, suggesting a potentially milder pathway and possibly fewer side reactions compared to the sulfuric acid system. This paper provides theoretical and experimental foundations for the green, efficient synthesis of PO3G and the catalyst optimization for analogous bio-based polyether polyols. Full article

Artificial intelligence (AI) has become a central driver of transformation in innovation management, reshaping how organizations design strategies, develop offerings, and generate knowledge. This study examines how innovation management has evolved from the pre-ChatGPT era—characterized by analytics, automation, and decision support—to the post-ChatGPT period, marked by the widespread adoption of generative AI (GenAI) and human–AI collaboration. Using a structured literature review of Scopus-indexed studies published between 2020 and 2025, the paper identifies the following six dominant thematic dimensions of AI-enabled innovation management: strategic and business model innovation, product and service innovation, sustainability-oriented innovation, organizational agility and capabilities, human-centric innovation, and knowledge, learning, and research. The findings reveal a conceptual shift from efficiency-driven applications toward more creative, strategic, and collaborative uses of AI, with generative models acting as co-creators rather than mere analytical tools. The study contributes by synthesizing the fragmented literature into an integrative framework that captures this transition and by highlighting emerging research gaps, particularly in sustainability and human-centered innovation. Practical implications for managers and policymakers are discussed. Full article

Background: Major depressive disorder (MDD) affects not only patients but also their relatives, who often carry substantial emotional and practical responsibilities. Family psychoeducation has shown benefits in several psychiatric conditions, yet its effects on relatives of adults with MDD remain insufficiently documented. Aim: We aimed to examine whether a brief group-based family psychoeducation program improves relatives’ well-being and perceived family functioning compared with an active social-support control condition and to explore whether intervention response varies across caregiver subgroups. Methods: Relatives of patients with MDD were enrolled in a two-center randomized controlled trial and allocated to either a four-week psychoeducation program or a structurally matched social-support group. Outcomes were assessed at baseline, post-intervention, and 9-month follow-up using the WHO-5 Well-Being Index (WHO-5), the Family Attitude Scale (FAS), and the Family Assessment Device (FAD). Repeated-measures ANCOVA models tested time × group interactions, with and without adjustment for age and gender. Results: Eighty-nine relatives were included (n = 43 intervention; n = 46 control). No significant intervention effects were observed on well-being (WHO-5) or family attitudes (FAS). A significant time × group interaction was found only for the FAD affective involvement subscale, with short-term improvement in the intervention group compared with deterioration in the control group. Subgroup analyses suggested a heterogeneous pattern of response, with more consistent patterns of improvement among older relatives (≥50 years), non-partner relatives, and those with a history of psychiatric treatment, while effects appeared more limited among partners and younger participants. Women showed worsening communication in the intervention group, whereas men demonstrated improvements in selected well-being and general functioning outcomes. Conclusions: The intervention showed limited effects at the whole-sample level, but exploratory subgroup analyses suggested that responsiveness to brief family psychoeducation may vary according to caregiver characteristics. These findings support further investigation of more targeted psychoeducational approaches for relatives of adults with MDD. Full article

Due to rapid urbanization, climate and socio-economic change, vernacular architecture in the Kara region of Togo is now facing mutations that threaten its existence. In the Kara region, new forms of housing, inspired by ancestral building practices and green technologies, are emerging as neo-vernacular architecture. This study aims to evaluate the overall performance of the CIDAP social hall, which is considered a model of neo-vernacular architecture. Through a series of both qualitative and quantitative tools, including the VerSus tool, the PTVA method and the calculation of the temperature difference ratio (TDR), the CIDAP social hall was analyzed regarding the criteria of durability, vulnerability to climatic hazards and thermal comfort. This work indicates that this building achieves a sustainability score of 88.33%. In terms of vulnerability to climatic hazards, the vulnerability index is around 0.392 for heavy rainfall, 0.389 for high heat and 0.309 for strong wind hazard. For thermal behavior, the TDR is of the order of 0.634. All these results reveal a satisfactory performance of the CIDAP social hall in terms of durability, vulnerability and thermal comfort. Full article

Optical chromatography (OC) has emerged as a powerful, label-free technique for the precise manipulation and separation of micro- and nanoparticles based on their intrinsic biophysical properties, including size, refractive index, and morphology. By balancing optical radiation pressure with fluid drag forces, OC enables high-resolution sorting of diverse analytes—from synthetic colloids to biological cells and pathogens—without the need for fluorescent labels or chemical modifications. Recent advancements in integrated optofluidic platforms, such as plasmonic microlens arrays, fiber-based systems, and hybrid optical–electrical detection approaches, have significantly enhanced OC capabilities, addressing long-standing challenges in scalability, throughput, and sensitivity, and facilitated its transition toward compact, application-oriented analytical platforms. These innovations have expanded OC applications in critical biomedical fields, including exosome isolation, pathogen detection, and viral infection monitoring. Furthermore, the integration of OC with tunable resistive pulse sensing (TRPS) presents a promising avenue for simultaneous particle fractionation and characterization, overcoming key limitations of conventional resistive pulse techniques. In this review, we provide a comprehensive overview of the fundamental principles of OC, followed by recent progress in particle separation strategies and integrated optofluidic system design. We further highlight emerging applications in bioanalysis and discuss future directions toward high-throughput, multimodal, and clinically relevant OC platforms. Full article

Against the backdrop of the expansion of sustainable finance and the growing relevance of ESG-related information, disclosure and regulation, this paper examines the dynamic relationship between sustainability-related uncertainty and ESG equity market volatility in a global framework. Sustainability-related uncertainty is proxied by the Global GDP-Weighted ESG-Based Sustainability Uncertainty Index (ESGUI), while ESG market volatility is measured through a monthly proxy constructed from estimated daily conditional variances obtained from GJR-GARCH(1,1) models with Student-t innovations. The paper explicitly distinguishes sustainability-related uncertainty, understood as ambiguity in the ESG information environment, from ESG market volatility, understood as market-pricing instability in ESG equity benchmarks. Empirically, the study combines bootstrap full-sample Granger-causality tests, parameter-stability diagnostics, and rolling-window bootstrap analysis. Robustness and extended analyses use an EGARCH-based volatility proxy, alternative rolling-window lengths, macro-financial controls, an emerging-market ESG benchmark, impulse-response analysis, forecast-error variance decomposition, and out-of-sample forecasting tests. The full-sample results indicate an asymmetric predictive pattern: ESG market volatility contains Granger-causal predictive information for changes in sustainability-related uncertainty, whereas the reverse direction is not supported on average. However, parameter-stability tests reject constancy, and rolling-window evidence shows that predictive effects arise episodically in both directions, with changes in sign, magnitude and significance. The uncertainty-to-volatility channel becomes statistically relevant and locally stronger during stress episodes, especially around 2019–2021, while macro-control results show that broader market stress absorbs part of the volatility-to-uncertainty linkage. The findings indicate a regime-dependent uncertainty–volatility nexus and support dynamic approaches to ESG risk monitoring, portfolio management and regulatory communication. All results are interpreted as predictive evidence, not structural causality. Full article

This study evaluates the impact of Dynamic Soil–Structure Interaction (DSSI) on the efficiency of an algorithm based on the existing literature on Vibration-Based Structural Health Monitoring (VBSHM). The algorithm is designed for Level 3 detection, that is, to accurately estimate the presence, location in height, and extent of structural damage simultaneously. Using computer simulations of a hypothetical two-dimensional six-story symmetrical reinforced concrete building, the study analyzes the algorithm’s performance under increasing soil flexibility. Efficiency is measured through four key metrics: the number of false positives and negatives, a weighted stress index, the iterations required for damage intensity estimation, and the accuracy of the identified versus simulated stiffness reduction. Results indicate that the algorithm remains effective even when input motions correspond to actual soft-soil ambient vibration recordings modified by kinematic DSSI effects, despite frequency contents differing from white-noise conditions. Conversely, inertial DSSI negatively impacts performance, leading the VBSHM algorithm to underestimate damage as soil deposits become softer. Full article

Aerosol scattering strongly influences the Earth’s atmosphere energy balance and actinic flux, yet its efficiency remains uncertain due to limited understanding of chemical effects. Scattering efficiency primarily depends on aerosol size, scattering refractive index, and hygroscopicity, which are determined by emissions and chemical processes; however, their covariation characteristics are rarely explored. Here, we use long-term measurements of submicron aerosol size distributions, chemical composition, scattering properties, and hygroscopicity in Guangzhou to investigate their covariations and links to secondary aerosol formation. The results indicate that dry-state volume scattering efficiency (VSE) was mainly driven by variations in aerosol size (R² = 0.74), despite substantial refractive index variability (1.4–1.6), which showed overall independent variations with size. Source apportionment and case analyses suggest distinct size ranges for secondary organic (SOA) and inorganic aerosols (SIA). Accordingly, a new lognormal fitting methodology is proposed to retrieve particle volume size distribution (PVSD)-associated aerosol components by combining PVSD and composition data. Retrieved geometric mean diameters of SOA (Dg,_SOA, 175–400 nm; 246 ± 44 nm) and SIA (Dg,_SIA, 200–600 nm; 382 ± 68 nm) are significantly correlated (R² = 0.43), indicating coupled formation of SOA and SIA and their interdependent roles in aerosol scattering. In addition, pronounced joint increases in dry-state VSE and aerosol hygroscopicity driven by the co-enhancement of aerosol size and hygroscopicity are further revealed. These results demonstrate the interconnected roles of secondary aerosol formation in controlling scattering efficiency and underscore the need to better represent SOA–SIA interactions in simulating aerosol radiative effects and address the covariations of aerosol hygroscopicity and dry-state scattering efficiency in aerosol remote sensing. Full article

Soil salinization is projected to intensify under global warming, posing significant constraints on crop growth and agricultural productivity. Although numerous quantitative studies have investigated soil salinization, comprehensive assessments specifically targeting global croplands remain limited. We hypothesize that, within a machine learning framework, combining soil properties, climate variables and anthropogenic management factors can yield global maps of soil salinity in croplands. For this purpose, we use a random forest (RF) model, with irrigation involved, to predict global cropland soil salinity (ECe) at 0.1° resolution for 1981–2010, capturing its spatiotemporal dynamics. The results indicate that the model performs well (R² = 0.63), with soil depth, the aridity index and pH being particularly significant factors. High values of ECe were found across central South America, southwestern Africa, central India, and south-central and northeastern China. The proportion of salinized croplands exhibits a long-term upward trend, averaging 4.88%. Ultimately, this study delivers long-term global cropland salinity maps, offering critical insights for safeguarding food security under climate change. Full article