Search Results (49,579)

Arsenic (As) contamination of soils is a critical environmental and geochemical concern, with its mobility and bioavailability largely controlled by molecular-scale interactions with soil minerals. This study investigates the adsorption behavior of arsenate [As(V)] and arsenious acid [As(III)] on major clay minerals to elucidate fundamental controls on As retention in soil and sediment systems. Molecular modeling approaches were employed to investigate these interactions. Density functional theory (DFT) calculations were performed on cluster models of illite, chlorite, montmorillonite, and kaolinite to evaluate adsorption configurations and binding energies of arsenate and arsenious acid. In addition, semiempirical (PM6) and classical force-field (UFF) methods were used to examine the influence of vermicompost-derived organic matter on arsenate-mineral interactions. Multiple adsorption configurations, including atop atom, bridge, three-fold filled, and three-fold hollow sites, were evaluated, and binding energies were calculated with correction for basis set superposition error. The results indicate that three-fold hollow sites are the most favorable, with As(V) binding energies of 60–65 kcal mol⁻¹ on illite, chlorite, and montmorillonite, reaching 75 kcal mol⁻¹ on kaolinite at a surface distance of 2.7 Å. In contrast, As(III) shows weaker and energetically flatter adsorption, with binding energies of 28–54 kcal mol⁻¹ and larger equilibrium distances of 3.2–4.0 Å. Modeling of vermicompost addition suggests a substantial reduction in arsenate binding on most clay minerals, except illite, indicating competitive or disruptive interactions at mineral surfaces. These findings provide quantitative, atomistic insight into mineral- and amendment-specific controls on As stabilization and mobility in soil and sediment systems. Full article

The design of sustainable power systems requires planning tools that jointly account for economic, environmental, and social dimensions. However, multi-objective energy system models typically prioritize economic–environmental trade-offs, while the social dimension is still rarely included as an explicit optimization objective. Furthermore, many formulations adopt a low temporal resolution (e.g., annual time steps) and assume fully flexible power plants, potentially overlooking temporal variability and operational constraints. This paper presents a three-objective optimization model for sustainable power system design that minimizes (i) costs, (ii) greenhouse gas (GHG) emissions, and (iii) social opposition (i.e., the public resistance to certain energy technologies). Temporal variability and operational detail are preserved using weighted representative periods with intra-period time steps and a clustered unit commitment (CUC) formulation. The Pareto frontier is generated using the normalized normal constraint (NNC) method, highlighting the space of efficient economic, environmental, and social solutions. A case study focused on the Italian electricity system exemplifies the model application by providing the cost-optimal, emissions-optimal, and social-optimal solutions, together with trade-off solutions. Among the trade-off solutions, the selected best balance solution achieves a significant reduction in emissions (−20%) compared to the cost-optimal solution, with a limited cost increase (+5%) and a marginal increase in social opposition (+0.7%). Overall, the proposed model enables transparent quantification of multi-dimensional trade-offs to support decision-making in sustainable power system design. Full article

Fuzzy multi-criteria decision-making (MCDM) methods remain popular for addressing decision-making problems involving uncertainty and explainability. However, decisions are usually made using data with different dimensions or even modalities. Therefore, existing MCDM methods incorporate various normalization techniques in order to transform attribute values into dimensionless quantities, ensuring the robustness and reliability of the decision-making results. Nevertheless, these normalization techniques may affect the ranking of alternatives. This study therefore proposes a novel method to investigate the effect of various normalization techniques on fuzzy MCDM methods. The study introduces a novel method for creating a fuzzy decision-making matrix using Tukey’s fences method, enabling the evaluation of alternatives using attributes under uncertain conditions. This method was evaluated in the context of web service quality assessments involving multi-dimensional and random variable attributes. The study demonstrated that Vector and Linear normalization techniques yield similar alternative rankings when using fuzzy MCDM methods, whereas rankings differ when Non-linear normalization techniques are applied. We believe that the current study will allow researchers and practitioners to address various practical uncertain decision-making problems with multi-dimensional attributes, thus promoting the digital transformation of complex, real-world decision-making issues. Full article

Background: Severe fever with thrombocytopenia syndrome (SFTS) has become an increasing public health threat in China, with Yantai City representing a major endemic focus. A fine-scale, long-term epidemiological analysis integrating human case data with vector surveillance is essential for understanding local transmission dynamics. Methods: We conducted a retrospective analysis using 12-year (2013–2024) county-level SFTS surveillance data from Yantai City. Temporal trends were analyzed by Joinpoint regression. Concurrent field surveillance of Haemaphysalis longicornis (2019–2024) was used to quantify local SFTSV infection rates in ticks. Associations between SFTS incidence and environmental/livestock factors were evaluated using Spearman’s correlation and multivariable negative binomial regression. Results: A total of 1964 SFTS cases were reported. The annual incidence rate increased from 0.65 to 5.12 per 100,000 population, with an average annual percentage change (AAPC) of 13.56% 2013–2024, showing the most substantial rise among the elderly. Marked spatial heterogeneity was observed, with county-level mean incidence ranging from 0.30 to 5.23 per 100,000. The SFTSV infection rate in ticks surged from 0.54% in 2019 to 3.24% in 2024, and showed a strong positive correlation with human incidence both seasonally (ρ = 0.998) and across counties (ρ = 0.79), a pattern likely driven by shared environmental factors. Multivariable analysis identified grassland coverage (adjusted IRR [aIRR] = 1.21), woodland coverage (aIRR = 2.31), goat density (aIRR = 1.49), and tick infection rate (aIRR = 1.65) as independent risk factors, while urban land was protective (aIRR = 0.83). The overall case fatality rate was 8.86%, showing a declining trend, but was significantly higher in males (10.90%) than in females (7.04%), particularly among the elderly. Conclusions: SFTS incidence in Yantai increased significantly over the past decade, characterized by a heightened burden on the elderly and strong spatiotemporal clustering. Risk is independently mediated by ecological interfaces, notably woodland/grassland habitats and goat rearing. These findings delineate high-risk areas and populations, offering crucial insights for developing targeted public health strategies. Full article

The evaluation of the seismic behavior of masonry aggregates, which characterize Italian historic centres, is a challenging and widely debated topic in the field of structural engineering. These constructions, composed of several adjacent structural units, tend to exhibit both global and local damage when subjected to horizontal seismic actions—loads that were not considered at the time of their original construction. Developed over centuries of unplanned urban growth, they are based on empirical construction rules and locally sourced materials. Due to their poor thermal properties, these buildings are also affected by significant heat losses, resulting in reduced indoor comfort. In this context, the present study aims to evaluate the seismic performance of a masonry aggregate and two of its constituent structural units located in Visso, in the province of Macerata, an area severely affected by the 2016 Central Italy seismic sequence, both before and after the application of an innovative integrated retrofitting solution. The proposed strengthening system combines aluminium alloy exoskeleton with insulating sandwich panels, simultaneously addressing seismic vulnerability and energy inefficiency. The assessment is carried out through numerical analyses, including nonlinear static and dynamic approaches, to achieve a comprehensive understanding of the structural response. Moreover, a comparative analysis between the masonry aggregate and the two individual structural units, modelled as isolated buildings, is performed to investigate the influence of structural interaction among adjacent units. The results demonstrate the effectiveness of the proposed retrofitting strategy, highlighting a significant improvement in global stability. Furthermore, the comparison confirms the critical role of inter-unit interaction and underscores the necessity of modelling historic masonry aggregates rather than isolated buildings to obtain a more realistic seismic performance evaluation. Full article

Sound plays a fundamental role in human perception, conveying information about events, objects, and spatial dynamics that may not be visually accessible. However, current technologies such as Acoustic Event Detection typically reduce complex soundscapes to textual labels, often failing to preserve their semantic richness. This limitation motivates the exploration of sound-to-image (S2I) translation as an alternative connection between audio and visual modalities. Unlike multimodal approaches guided by intermediary constraints during the learning process, we investigate S2I translation without class supervision, cluster-based alignment, or textual mediation, a paradigm we refer to as direct S2I translation. To the best of our knowledge, apart from our previous work, no prior study addresses S2I translation under this fully direct setting. We propose a convolutional–attention generative framework composed of an audio encoder and a densely connected GAN integrating self-attention and cross-attention mechanisms. The attention-based model is systematically compared with a purely convolutional baseline. Results show that introducing attention at early stages of the generator significantly improves translation performance, increasing the likelihood of producing interpretable and semantically coherent visual representations of sound. These findings indicate that attention strengthens semantic correspondence between audio and vision while preserving the fully direct nature of the translation process. Full article

Digital transformation has become important for SMEs that want better control, transparency, and coordinated operations. Yet, many studies treat digital tools in isolation and do not explain how AI and big data capabilities, together with process integration, drive governance outcomes. This gap limits a clear understanding of how digital transformation supports governance performance in SMEs. This study examines how digital transformation (DT) influences digital governance performance (DGP) in SMEs, with AI and big data analytical capability (AIBDAC) and process integration capability (PIC) as mediators. The research is grounded in the Resource-Based View, Dynamic Capabilities Theory, and the Technology Organization Environment framework. Data were collected from SMEs across five regions of Saudi Arabia using cluster and purposive sampling to target employees and managers involved in digital, analytical, and process integration work. A total of 396 valid responses were included in the analysis. Partial Least Squares Structural Equation Modelling (PLS SEM) was used to assess the measurement model, test the hypothesized paths, and evaluate mediation and moderation effects. The findings show that DT, AIBDAC, PIC, and top management support (TMS) have significant direct effects on DGP. AIBDAC and PIC act as key mediators, fully transmitting the effects of digital innovation capability and strategic readiness and partially mediating the effects of DT and TMS. Multi-group analysis shows that small and medium-large firms rely on different capability combinations. The study contributes by explaining how SMEs strengthen governance through capability development and offers practical guidance for improving governance through digital transformation. Full article

Traumatic brain injury (TBI) initiates a cascade of molecular and cellular reactions leading to long-term disturbances of neuronal and glial homeostasis. One of the key mechanisms of secondary injury is a pathological increase in intracellular Ca²⁺ concentration. Parvalbumin (PV) plays an important role in the regulation of Ca²⁺ homeostasis in neurons. In turn, connexin 43 (Cx43) is the principal protein of astrocytic gap junctions (GJs), which ensure neuroglial communication. The spatiotemporal changes in these proteins and the mechanisms of their interaction after TBI remain insufficiently studied. In the present study, a comprehensive analysis of the expression, localization, and spatial organization of PV and Cx43 in the cerebral cortex following TBI was performed. In intact tissue, PV was localized predominantly in neurons, whereas Cx43 formed typical punctate structures of astrocytic GJs. Twenty-four hours after TBI, a sharp activation of PV with pronounced nuclear translocation was observed against the background of a catastrophic decrease in Cx43 expression, accompanied by a reduction in the number of NeuN⁺ neurons and signs of apoptosis. However, after 7 days, a mirror-opposite effect was detected, characterized by decreased PV expression and increased Cx43 levels with its aggregation into cluster-like structures, as well as partial restoration of NeuN immunoreactivity. In addition, molecular dynamics simulations demonstrated that the stability of the PV–Cx43 complex is determined by the presence of Ca²⁺ and physiological pH, whereas acidosis and Ca²⁺ overload destabilize their interaction. Taken together, these results reveal a phase-dependent mirror-opposite pattern of PV and Cx43 expression and localization and emphasize the key role of Ca²⁺- and pH-dependent neuroglial interactions in TBI. Full article

China is one of the world’s major lychee producers, and the fruit’s soft texture, small size, and thin peel make non-destructive robotic harvesting particularly challenging. Accurate fruit detection, branch segmentation, and precise picking-point localization are critical for enabling automated harvesting in complex natural orchard environments. This study proposes an integrated perception framework for lychee harvesting that combines object detection, density-based clustering, and semantic segmentation. An improved YOLO11s-based detection network incorporating SimAM attention, CMUNeXt feature enhancement, and MPDIoU loss is developed to enhance robustness under illumination variation, occlusion, and scale changes. The proposed detector achieves a precision of 84.3%, recall of 73.2%, and mAP of 81.6%, outperforming baseline models. Density-based clustering is employed to group individual detections into fruit clusters. Comparative experiments demonstrate that MeanShift achieves the highest clustering consistency, with an average Adjusted Rand Index (ARI) of 0.768, outperforming k-means and other baselines. An improved DeepLab v3+ semantic segmentation network with a ResDenseFocal backbone and Focal Loss is designed for accurate branch extraction under complex backgrounds. Finally, a rule-based geometric picking-point localization algorithm is formulated in the image coordinate system by integrating detection, clustering, and branch segmentation results. Experimental validation demonstrates that the proposed framework can reliably localize picking points in two-dimensional images under natural orchard conditions. The proposed method provides a practical perception solution for intelligent lychee harvesting and establishes a foundation for future 3D robotic manipulation and field deployment. Full article

Ground deformation is a major geohazard in many urban areas, requiring reliable monitoring and forecasting for hazard mitigation. Although multi-temporal InSAR enables high-resolution deformation monitoring, most prediction approaches rely on single-point modeling and fail to exploit spatial dependencies within deformation fields. This study proposes a spatiotemporally synchronous prediction framework for large-scale InSAR deformation fields, integrating sequence preprocessing, spatiotemporal modeling, and deformation pattern analysis. First-order differencing reduces sequence non-stationarity, while a patch-based encoder-decoder structure preserves spatial topology during dimensionality reduction. The core prediction model, built on PredRNNv2, captures the long-term spatiotemporal evolution of InSAR deformation sequences. In addition, independent component analysis (ICA) combined with K-means clustering identifies dominant deformation patterns and their geological associations. The framework is evaluated using synthetic datasets simulating multiple deformation mechanisms and Sentinel-1 InSAR time-series data over the Beijing Plain from 2015 to 2025. Results show that the model accurately captures deformation evolution and identifies transitions associated with groundwater regulation. These findings demonstrate the potential of deep spatiotemporal learning for large-scale InSAR deformation prediction and geohazard mechanism interpretation. Full article

Ovarian cancer (OC) is frequently diagnosed at an advanced stage and characterized by high rates of recurrence despite aggressive cytoreductive surgery and chemotherapy. Relapse is driven by microscopic residual tumors that are disseminated most often throughout the peritoneal cavity, posing significant challenges with conventional systemic therapy. Targeted alpha-particle therapy (TAT) combines molecular targeting with alpha-emitting radionuclides to deliver highly potent and localized cellular damage, uniquely suited for the eradication of small OC tumor clusters within the peritoneal cavity. We conducted an extensive literature search for clinical trials (clinicaltrials.gov) and pre-clinical studies (PubMed, Scopus, Google Scholar) between September 2025 and November 2025. Peer-reviewed articles published in English over the past 20 years that used OC mouse models with reported treatment data were included. Review articles without original data and clinical trials that have been terminated or withdrawn were excluded. In this review, we (1) summarize the biological and physical rationale supporting the use of TAT in OC, (2) discuss the relevant molecular and immunological anti-tumor mechanisms, and (3) critically evaluate early treatment outcomes of 19 pre-clinical and four clinical studies with respect to efficacy, safety, and feasibility. Despite the progress and promising survival outcomes, several challenges remain, including heterogeneous antigen expression, delivery and retention within the peritoneal cavity, off-target toxicity, radiation resistance, radionuclide availability, dosimetry uncertainties, and limitations in clinical trial design. We highlight future directions to overcome these barriers and the continued multidisciplinary efforts essential to translate TAT into effective clinical strategies to treat advanced stages of OC and other solid tumors resistant to conventional treatment. This work was supported with funding available to Kurt R. Zinn as the Hickman Family Endowed Chair in Oncology at Michigan State University. Full article

Background: Prognostic heterogeneity in heart failure (HF) is substantial and not fully captured by conventional left-sided echocardiographic parameters. Growing evidence highlights the importance of right ventricular–pulmonary arterial (RV–PA) interaction in HF pathophysiology and outcomes. The echocardiographic tricuspid annular plane systolic excursion-to-systolic pulmonary artery pressure (TAPSE/sPAP) ratio has been proposed as a simple noninvasive surrogate of RV–PA coupling, yet its prognostic value across the HF spectrum remains incompletely defined. Methods: This systematic review followed PRISMA guidelines and was registered in INPLASY. PubMed, Scopus, and EMBASE were searched from inception through January 2026 for observational studies evaluating the prognostic value of TAPSE/sPAP in adult patients with HF. Study selection, data extraction, and risk-of-bias assessment were performed independently by two reviewers. Owing to substantial heterogeneity, a qualitative synthesis with weighted pooled descriptive statistics was performed. Results: Fifteen observational studies including 5389 patients were analyzed, with a median follow-up of approximately 1.9 years, ranging from in-hospital outcomes to long-term follow-up of up to 15 years. Study populations encompassed a wide range of HF phenotypes and clinical settings, including acute and chronic HF, preserved and reduced ejection fraction, valvular heart disease, infiltrative cardiomyopathies, and advanced HF. Across studies, reduced TAPSE/sPAP was generally associated with adverse outcomes, including all-cause mortality and HF-related events, with reported hazard ratios ranging from approximately two- to five-fold. Prognostically relevant TAPSE/sPAP cut-off values tended to cluster within a relatively narrow range, with most thresholds between 0.36 and 0.40 and a weighted median of approximately 0.36. When reported, TAPSE/sPAP showed favorable discriminative performance for adverse outcomes. Overall methodological quality was predominantly fair. Conclusions: Across heterogeneous HF populations, impaired TAPSE/sPAP appears to be a consistent marker of adverse prognosis. These findings support TAPSE/sPAP as a practical, noninvasive indicator of RV–PA uncoupling that may contribute to risk stratification and phenotyping in heart failure. Prospective studies focusing on specific HF phenotypes are needed to clarify its role in longitudinal monitoring and therapeutic decision-making. Full article

Unmanned Aerial Vehicle (UAV) computing clusters face severe operational constraints due to limited computing capabilities and battery capacities, which complicate the simultaneous optimization of low offloading latency, long task endurance, and high cluster efficiency. To address these challenges, this paper proposes a Multi-Objective Reinforcement Learning framework based on Latency and Power Balance (MORL-LAPB). Instead of broad situational awareness descriptions, our framework directly combines a reward-shaping reinforcement learning algorithm with an evolutionary mechanism to construct a closed-loop optimization paradigm. Crucially, in this context, ’balancing’ extends beyond traditional computational workload distribution; it represents a joint optimization that balances task allocation to ensure short service delays while simultaneously equating the energy depletion rates across UAV nodes to maximize overall cluster efficiency and operational duration. By efficiently identifying Pareto optimal trade-offs, MORL-LAPB dynamically regulates UAV energy allocation and computational resource scheduling. Experimental results demonstrate that, compared to RSO, NSO, and DRLSO baselines, the proposed MORL-LAPB significantly reduces offloading latency, extends effective task execution duration, and improves cluster energy efficiency. The framework offers flexible adaptability and long-term sustainability for diverse operational scenarios under strict multi-objective constraints. Full article

The rapid adoption of artificial intelligence (AI) across public services and critical infrastructure is reshaping digital governance. While AI promises efficiency and innovation, its reliance on large, high-dimensional datasets introduces privacy, bias, transparency and accountability risks that existing frameworks struggle to address. This study evaluates the maturity of current AI governance frameworks and develops an integrated risk-tiering model that connects ethical principles to auditable technical controls, aligning with Sustainable Development Goal 9 on industry, innovation and infrastructure. A systematic literature review of 450 records from major databases was conducted using PRISMA 2020 guidelines; 95 high-quality studies were analyzed using principal component analysis and k-means clustering. The analysis produced a heat map of governance frameworks, a co-occurrence network of themes, a cluster analysis of framework coverage and an integrated governance risk framework supported by a risk-tiering matrix. Findings reveal a fragmented landscape dominated by ethics/privacy-centric and compliance/risk-focused approaches, with few integrated frameworks and evident tension between privacy and security. This synthesis bridges the gap between values and practice, offering a policy-ready model for secure and sustainable AI governance. Full article

The environmental and social problems caused by overfishing and unsustainable aquaculture practices make it necessary to implement the principles of the circular economy to steer the sector towards sustainability and responsible use of resources. The objective of this study was to assess the sustainability of the aquaculture sector in the context of current environmental and social concerns in the fisheries sector and to understand the state of research in terms of implementing circular practices, providing a comprehensive mapping of scientific articles focusing on circular practices adopted in the aquaculture sector over the last ten years, describing them and identifying their potential advantages and disadvantages. A bibliometric analysis was conducted using the Scopus database to obtain a clear picture of the sustainable innovations carried out in the aquaculture sector over the last ten years. The analysis focused on the terms ‘aquaculture’ and ‘circular economy’. The results indicate a rising trend in the number of studies on the circular economy in aquaculture from 2020 onwards, which can be attributed to an escalating awareness of environmental concerns. Subsequently, the analysis carried out by the VOSviewer software allowed the articles to be classified in four clusters, according to the relevance of the different adopted circularity practices. A particular focus was placed on the significance of practices minimising environmental impact, optimising resources and pursuing innovative strategies to ensure sustainability. Full article