Search Results (4,203)

Soil environmental quality in arid oases is crucial for regional ecological security but faces multi-source heavy metal (HM) contamination risks. This study aimed to (1) characterize the spatial distribution of soil HMs (As, Cd, Cr, Cu, Hg, and Zn) in the Ka Shi gar oasis, Xinjiang, (2) quantify the driving effect of irrigation water, and (3) elucidate interactions between HMs, soil properties, and land use types. Using 591 soil and 12 irrigation water samples, spatial patterns were mapped via inverse distance weighting interpolation, with drivers and interactions analyzed through correlation and land use comparisons. Results revealed significant spatial heterogeneity in HMs with no consistent regional trend: As peaked in arable land (5.27–40.20 μg/g) influenced by parent material and agriculture, Cd posed high ecological risk in gardens (max 0.29 μg/g), and Zn reached exceptional levels (412.00 μg/g) in gardens linked to industry/fertilizers. Irrigation water impacts were HM-specific: water contributed to soil As enrichment, whereas high water Cr did not elevate soil Cr (indicating industrial dominance), and Cd/Cu showed no significant link. Interactions with soil properties were regulated by land use: in arable land, As correlated positively with EC/TN and negatively with pH; in gardens, HMs generally decreased with pH, enhancing mobility risk; in forests, SOM adsorption immobilized HMs; in construction land, Hg correlated with SOM/TP, suggesting industrial-organic synergy. This study advances understanding by demonstrating that HM enrichment arises from natural and anthropogenic factors, with the spatial heterogeneity of irrigation water’s driving effect critically regulated by land use type, providing a spatially explicit basis for targeted pollution control and sustainable oasis management. Full article

Accurate full-body motion reconstruction from sparse sensors is crucial for VR/AR applications but remains challenging due to the under-constrained nature of limited observations and the computational constraints of mobile platforms. This paper presents LGSIK-Poser, a unified and lightweight framework that supports real-time motion reconstruction from heterogeneous sensor configurations, including head-mounted displays, handheld controllers, and up to three optional inertial measurement units, without requiring reconfiguration across scenarios. The model integrates temporally grouped LSTM modeling, anatomically structured graph-based reasoning, and region-specific inverse kinematics refinement to enhance end-effector accuracy and structural consistency. Personalized body shape is estimated using user-specific anthropometric priors within the SMPL model, a widely adopted parametric representation of human shape and pose. Experiments on the AMASS benchmark demonstrate that LGSIK-Poser achieves state-of-the-art accuracy with up to 48% improvement in hand localization, while reducing model size by 60% and latency by 22% compared to HMD-Poser. The system runs at 63.65 FPS with only 3.74 M parameters, highlighting its suitability for real-time immersive applications. Full article

The increasing complexity of real-world engineering problems, ranging from manufacturing scheduling to resource optimization in smart grids, has driven demand for adaptive and high-performing heuristic methods. Automatic Heuristic Design (AHD) and neural-enhanced metaheuristics have shown promise in automating strategy development, but often suffer from limited flexibility and scalability due to static operator libraries or high retraining costs. Recently, Large Language Models (LLMs) have emerged as a powerful alternative for exploring and evolving heuristics through natural language and program synthesis. This paper proposes a novel LLM-based memetic framework that synergizes LLM-driven exploration with domain-specific local refinement and memory-aware reflection, enabling a dynamic balance between heuristic creativity and effectiveness. In the experiments, the developed framework outperforms other LLM-based state-of-the-art approaches across the designed AGV-drone scheduling scenario and two benchmark combinatorial problems. The findings suggest that LLMs can serve not only as general-purpose optimizers but also as interpretable heuristic generators that adapt efficiently to complex and heterogeneous domains. Full article

Biomass and other solid wastes create potential environmental and health hazards in our modern society. Conversion of the wastes into energy presents a promising avenue for sustainable energy generation. However, the feasibility of the approach is limited by the challenges in material handling because of the special properties of the materials. Despite their critical importance, the complexities of material handling often evade scrutiny until operational implementation. This paper highlights the challenges inherent in standard solid material-handling processes, preceded by a concise review of common solid waste typologies and their physical properties, particularly those related to biomass and biowastes. It delves into the complexities of material flow, storage, compaction, agglomeration, separation, transport, and hazard management. Specialised characterisation techniques essential for informed process design are also discussed to mitigate operational risks. In conclusion, this paper emphasises the necessity of a tailored framework before the establishment of any further conversion processes. Given the heterogeneous nature of biomaterials, material-handling equipment must demonstrate adaptability to accommodate the substantial variability in material properties in large-scale production. This approach aims to enhance feasibility and efficacy of any energy conversion initiatives by using biomass or other solid wastes, thereby advancing sustainable resource utilisation and environmental stewardship. Full article

We investigate whether China’s Value-Added Tax (VAT) Credit Refund policy influences firms’ sustainable development capability (SDC), which reflects innovation-driven growth and green development. Exploiting the 2018 implementation of the VAT Credit Refund policy as a quasi-natural experiment, we employ a difference-in-differences (DID) approach and find causal evidence that the policy significantly enhances firms’ SDC. This suggests that fiscal instruments like VAT refunds are valued by firms as drivers of long-term sustainable and high-quality development. Our mediating analyses further reveal that the policy promotes firms’ SDC by strengthening artificial intelligence (AI) capabilities and facilitating intelligent transformation. This mechanism “AI Capability Building—Intelligent Transformation” aligns with the socio-technical systems theory (STST), highlighting the interactive evolution of technological and social subsystems in shaping firm capabilities. The heterogeneity analyses indicate that the positive effect of VAT Credit Refund policy on SDC is more pronounced among small-scale and non-high-tech firms, firms with lower perceived economic policy uncertainty, higher operational diversification, lower reputational capital, and those located in regions with a higher level of marketization. We also find that the policy has persistent long-term effects, with improved SDC associated with enhanced ESG performance and green innovation outcomes. Our findings have important implications for understanding the SDC through the lens of STST and offer policy insights for deepening VAT reform and promoting intelligent and green transformation in China’s enterprises. Full article

Calcific aortic stenosis (CAS) is a newly emerging pandemic in elderly individuals due to the aging of the population in the world. Surgical Aortic Valve Replacement (SAVR) and Transcatheter Aortic Valve Replacement (TAVR) are the cornerstone of the management of severe aortic stenosis accompanied by one or more symptoms. Moreover, an appropriate interventional treatment of CAS, in elderly patients, is a very complex decision for heart teams, to avoid bad outcomes such as operative mortality, cardiovascular and all-cause death, hospitalization for heart failure, worsening of quality of life. In fact, CAS in the elderly is not only a focal valve disease, but a very complex clinical picture with different risk factors and etiologies, differing underlying pathophysiology, large phenotypic heterogeneity in a context of subjective biological, phenotypic and functional aging until frailty and disability. In this review, we analyzed separately and in a more integrated manner, the natural and prognostic histories of the progression of aortic stenosis, the phenotypes of myocardial damage and heart failure, within the metrics and aging trajectory. The aim is to suggest, during the clinical timing of valve disease, the best interval time for an appropriate and effective interventional treatment in each older patient, beyond subjective symptoms by integration of clinical, geriatric, chemical, and advanced imaging biomarkers. Full article

The IoE forms the foundation of the modern digital ecosystem by enabling seamless connectivity and data exchange among smart devices, sensors, and systems. However, the inherent nature of this structure, characterized by high heterogeneity, distribution, and resource constraints, renders traditional security approaches insufficient in areas such as data privacy, authentication, access control, and scalable protection. Moreover, centralized security systems face increasing fragility due to single points of failure, various AI-based attacks, including adversarial learning, model poisoning, and deepfakes, and the rising threat of quantum computers to encryption protocols. This study systematically examines the individual and integrated solution potentials of technologies such as Blockchain, Edge Computing, Artificial Intelligence, and Quantum-Resilient Cryptography within the scope of IoE security. Comparative analyses are provided based on metrics such as energy consumption, latency, computational load, and security level, while centralized and decentralized models are evaluated through a multi-layered security lens. In addition to the proposed multi-layered architecture, the study also structures solution methods and technology integrations specific to IoE environments. Classifications, architectural proposals, and the balance between performance and security are addressed from both theoretical and practical perspectives. Furthermore, a future vision is presented regarding federated learning-based privacy-preserving AI solutions, post-quantum digital signatures, and lightweight consensus algorithms. In this context, the study reveals existing vulnerabilities through an interdisciplinary approach and proposes a holistic framework for sustainable, scalable, and quantum-compatible IoE security. Full article

This study assesses the quality of financial reporting in ten Central and Eastern European countries using a methodology based on natural language processing (NLP) techniques. 570 annual reports of companies listed on the main index on the stock exchanges of 10 Central and Eastern European (CEE) countries, over the period 2019–2023, were evaluated to determine the degree of convergence of the following four measurable qualitative characteristics: relevance, exact representation, comparability and understandability. The main objective is to identify consistency in the quality of accounting information based on the application of an international financial reporting framework. The applied methodology eliminates subjective variability by implementing a standardized scoring system, aligned with the criteria developed by NiCE, using libraries such as spaCy and NLTK for term extraction, respective sentiment analysis and word frequency evaluation. The results reveal significant heterogeneity in all characteristics examined, with statistical tests confirming substantial differences between countries. The investigation of relevance revealed partial convergence, with three dimensions achieving complete uniformity, while the exact representation showed the highest variability. The assessment of comparability showed a significant difference between countries’ extreme values, and in terms of comprehensibility a formalistic approach was evident, with technical dimensions outweighing user-oriented aspects. The overall quality index varied significantly across countries, with a notable average deterioration in 2023, indicating structural vulnerabilities in financial reporting systems. These findings support initial hypotheses on the lack of homogeneity in the quality of financial reporting in the selected region, despite the implementation of international standards. Full article

Background: Peritoneal metastases (PM) represent a common and challenging manifestation of several gastrointestinal and gynecologic malignancies. Bidirectional treatment—combining Pressurized Intraperitoneal Aerosol Chemotherapy (PIPAC) with systemic chemotherapy—has emerged as a strategy to enhance locoregional control while maintaining systemic coverage. Objective: This systematic review aimed to analyze the study design, characteristics, and timing of the treatments administered—including the type of systemic chemotherapy, intraperitoneal agents used in PIPAC, and interval between administrations—as well as the clinical outcomes, safety profile, and overall methodological quality of the available literature on bidirectional treatment for peritoneal metastases. Methods: A systematic literature search was conducted across the PubMed, Embase, and Cochrane Library databases up to April 2025. Studies were included if they reported clinical outcomes of patients undergoing bidirectional treatment. Data extraction focused on survival, response assessment (PRGS, PCI), adverse events, systemic and intraperitoneal regimens, treatment interval, and study methodology. Results: A total of 22 studies involving 1015 patients (742 treated with bidirectional therapy) were included. Median overall survival ranged from 2.8 to 19.6 months, with the most favorable outcomes observed in gastric and colorectal cancer cohorts. PRGS improvement after multiple PIPAC cycles was reported in >80% of evaluable cases. High-grade adverse events (CTCAE ≥ 3) occurred in up to 17% of patients in most studies, with only one study reporting treatment-related mortality. However, methodological quality was generally moderate, with considerable heterogeneity in treatment protocols, response criteria, systemic regimens, and toxicity attribution. Conclusions: Bidirectional therapy with PIPAC and systemic chemotherapy appears to be a feasible and potentially effective strategy for selected patients with peritoneal metastases. Despite encouraging outcomes, definitive conclusions are limited by the retrospective nature and heterogeneity of available studies. Prospective standardized trials are needed to confirm efficacy, clarify patient selection, and optimize treatment protocols. Full article

With the advent of the big data era, multi-view clustering (MVC) methods have attracted considerable acclaim due to their capability in handling the multifaceted nature of data, which achieves impressive results across various fields. However, two significant challenges persist in MVC methods: (1) They resort to learning view-invariant information of samples to bridge the heterogeneity gap between views, which may result in the loss of view-specific information that contributes to pattern mining. (2) They utilize fusion strategies that are susceptible to the discriminability of views, i.e., the concatenation and the weighing fusion of cross-view representations, to aggregate complementary and consistent information, which is difficult to guarantee semantic robustness of fusion representations. To this end, a simple yet effective cluster complementarity and consistency learning framework (CommonMVC) is proposed for mining patterns of multiview data. Specifically, a cluster complementarity learning is devised to endow fusion representations with discriminate information via nonlinearly aggregating view-specific information. Meanwhile, a cluster consistency learning is introduced via modeling instance-level and cluster-level partition invariance to coordinate the clustering partition of various views, which ensures the robustness of multi-view data pattern mining. Seamless collaboration between two components effectively enhances multi-view clustering performance. Finally, comprehensive experiments on four real-world datasets demonstrate CommonMVC establishes a new state-of-the-art baseline for the MVC task. Full article

To investigate the spatiotemporal evolution characteristics and primary driving factors of Gross Primary Productivity (GPP) on the Qinghai-Tibet Plateau, we employed an enhanced MODIS-PSN model. Utilizing the fifth-generation global climate reanalysis dataset (ECMWF ERA5), we generated GPP remote sensing products by integrating six natural factors. Through correlation analysis and geographical detector modeling, we quantitatively analyzed the spatiotemporal dynamics and key drivers of vegetation GPP across the Qinghai-Tibet Plateau from 2001 to 2022. The results demonstrate that GPP changes across the Qinghai-Tibet Plateau display pronounced spatial heterogeneity. The humid northeastern and southeastern regions exhibit significantly positive change rates, primarily distributed across wetland and forest ecosystems, with a maximum mean annual change rate of 12.40 gC/m²/year. In contrast, the central and southern regions display a decreasing trend, with the minimum change rate reaching −1.61 gC/m²/year, predominantly concentrated in alpine grasslands and desert areas. Vegetation GPP on the Qinghai-Tibet Plateau shows significant correlations with temperature, vapor pressure deficit (VPD), evapotranspiration (ET), leaf area index (LAI), precipitation, and radiation. Among the factors analyzed, LAI demonstrates the strongest explanatory power for spatial variations in vegetation GPP across the Qinghai-Tibet Plateau. The dominant factors influencing vegetation GPP on the Qinghai-Tibet Plateau are LAI, ET, and precipitation. The pairwise interactions between these factors exhibit linear enhancement effects, demonstrating synergistic multifactor interactions. This study systematically analyzed the response mechanisms and variations of vegetation GPP to multiple driving factors across the Qinghai-Tibet Plateau from a spatial heterogeneity perspective. The findings provide both a critical theoretical framework and practical insights for better understanding ecosystem response dynamics and drought conditions on the plateau. Full article

Background/Objectives: Metabolic syndrome (MetS) is a multifactorial condition characterized by abdominal obesity, dyslipidemia, insulin resistance, hypertension, and chronic inflammation. As its global prevalence rises, there is increasing interest in natural, multi-targeted approaches to manage MetS. Curcuma longa Linn. (turmeric), especially its active compound curcumin, has shown therapeutic promise in preclinical studies. This systematic review and meta-analysis evaluated the effects of Curcuma longa and its derivatives on MetS-related outcomes in rodent models. Methods: A comprehensive search was conducted across six databases (PubMed, Scopus, AMED, LILACS, MDPI, and Google Scholar), yielding 47 eligible in vivo studies. Data were extracted on key metabolic, inflammatory, and oxidative stress markers and analyzed using random-effects models. Results were presented as mean differences (MD) with 95% confidence intervals (CI). Results: Meta-analysis showed that curcumin significantly reduced body weight (rats: MD = −42.10; mice: MD = −2.91), blood glucose (rats: MD = −55.59; mice: MD = −28.69), triglycerides (rats: MD = −70.17; mice: MD = −24.57), total cholesterol (rats: MD = −35.77; mice: MD = −52.61), and LDL cholesterol (rats: MD = −69.34; mice: MD = −42.93). HDL cholesterol increased significantly in rats but not in mice. Inflammatory cytokines were markedly reduced, while oxidative stress improved via decreased malondialdehyde (MDA) and elevated superoxide dismutase (SOD) and catalase (CAT) levels. Heterogeneity was moderate to high, primarily due to variations in curcumin dosage (ranging from 10 to 500 mg/kg) and treatment duration (2 to 16 weeks) across studies. Conclusions: This preclinical evidence supports Curcuma longa as a promising functional food component for preventing and managing MetS. Its multi-faceted effects warrant further clinical studies to validate its translational potential. Full article

South Dongting Lake is an essential aquatic ecosystem that receives substantial water inflows from the Xiangjiang and Zishui Rivers. However, it is significantly impacted by human activities, including mining, smelting, and farming. These activities have led to serious contamination of the lake’s sediments with heavy metals (HMs). This study investigated the distribution, mobility, and influencing factors of HMs at the sediment–water interface. To this end, sediment samples were analyzed from three key regions (Xiangjiang River estuary, Zishui River estuary, and northeastern South Dongting Lake) using traditional sampling methods and Diffusive Gradients in Thin Films (DGT) technology. Analysis of fifteen HMs (Pb, Bi, Ni, As, Se, Cd, Sb, Mn, Zn, V, Cr, Cu, Tl, Co, and Fe) revealed significant spatial heterogeneity. The results showed that Cr, Cu, Pb, Bi, Ni, As, Se, Cd, Sb, Mn, Zn, and Fe exhibited high variability (CV > 0.20), whereas V, Tl, and Co demonstrated stable concentrations (CV < 0.20). Concentrations were found to exceed background values of the upper continental crust of eastern China (UCC), Yangtze River sediments (YZ), and Dongting Lake sediments (DT), particularly at the Xiangjiang estuary (XE) and in the northeastern regions. Speciation analysis revealed that V, Cr, Cu, Ni, and As were predominantly found in the residual fraction (F4), while Pb and Co were concentrated in the oxidizable fraction (F3), Mn and Zn appeared primarily in the exchangeable fractions (F1 and F2), and Cd was notably dominant in the exchangeable fraction (F1), suggesting a high potential for mobility. Additionally, DGT results confirmed a significant potential for the release of Pb, Zn, and Cd. Contamination assessment using the Pollution Load Index (PLI) and Geoaccumulation Index (Igeo) identified Pb, Bi, Ni, As, Se, Cd, and Sb as major pollutants. Among these, Bi and Cd were found to pose the highest risks. Furthermore, the Risk Assessment Code (RAC) and the Potential Ecological Risk Index (PERI) highlighted Cd as the primary ecological risk contributor, especially in the XE. The study identified sediment grain size, pH, electrical conductivity, and nutrient levels as the primary influencing factors. The PMF modeling revealed HM sources as mixed smelting/natural inputs, agricultural activities, natural weathering, and mining/smelting operations, suggesting that remediation should prioritize Cd control in the XE with emphasis on external inputs. Full article

Conventional immunotherapy, including immune checkpoint blockade and chimeric antigen receptor (CAR)-T cells, has revolutionized cancer therapy over the past decade. Yet, the efficacy of these therapies is limited by tumor resistance, antigen escape mechanisms, poor persistence, and T-cell exhaustion, particularly in the treatment of solid tumors. The emergence of unconventional immunotherapies offers novel opportunities by leveraging diverse immune cell subsets and synthetic biologics. This review explores various immunotherapy platforms, including gamma delta T cells, invariant natural killer T cells, mucosal-associated invariant T cells, engineered regulatory T cells, and universal CAR platforms. Additionally, it expands on biologics, including bispecific and multispecific antibodies, cytokine fusions, agonists, and oncolytic viruses, showcasing their potential for modular engineering and off-the-shelf applicability. Distinct features of unconventional platforms include independence from the major histocompatibility complex (MHC), tissue-homing capabilities, stress ligand sensing, and the ability to bridge adaptive and innate immunity. Their compatibility with engineering approaches highlights their potential as scalable, efficient, and cost-effective therapies. To overcome translational challenges such as functional heterogeneity, immune exhaustion, tumor microenvironment-mediated suppression, and limited persistence, novel strategies will be discussed, including metabolic and epigenetic reprogramming, immune cloaking, gene editing, and the utilization of artificial intelligence for patient stratification. Ultimately, unconventional immunotherapies extend the therapeutic horizon of cancer immunotherapy by breaking barriers in solid tumor treatment and increasing accessibility. Continued investments in research for mechanistic insights and scalable manufacturing are key to unlocking their full clinical potential. Full article

In the context of worsening climate change and biodiversity loss, forest carbon offset projects are viewed as important nature-based solutions to mitigate these trends. However, there is limited evidence on whether these projects provide net benefits for biodiversity conservation. This study uses a staggered difference-in-differences model with balanced panel data from 128 counties in Sichuan Province, China, spanning from 2000 to 2020, to examine whether these projects bring biodiversity conservation co-benefits. The results show that the implementation of forest carbon offset projects leads to a 55.1% decrease in the ecosystem service value of forest biodiversity, with the negative impact particularly pronounced in areas facing agricultural land use and livestock pressures. The dynamic effect tests indicate that the benefits of biodiversity conservation generally begin to decline significantly 5 years after project implementation. Additional analyses show that although projects certified under biodiversity conservation standards also exhibit negative effects, the magnitude of decline is substantially smaller compared to uncertified projects, and certified projects achieve greater carbon stock gains. Heterogeneity analysis demonstrates that projects employing native tree species show significant positive effects. Moreover, spatial econometric results demonstrate significant negative spillover effects within an 80 km radius surrounding the project sites, with the effect attenuating over distance. To maximize the potential of forest carbon offset projects in addressing both climate change and biodiversity loss, it is important to mitigate the negative impacts on biodiversity within and beyond project boundaries and to enhance the continuous monitoring of projects that have been certified for biodiversity conservation. Full article