Search Results (18,898)

Arsenic contamination in groundwater is a severe and widespread environmental and public health challenge. Recent years have witnessed rapid advances in catalytic remediation technologies, particularly those integrating advanced oxidation processes (AOPs), bifunctional materials, and field-scale applications. This comprehensive review synthesizes recent developments, emphasizing the synergy between catalytic oxidation and adsorption, the design of innovative and recyclable materials, and the practical translation of laboratory findings to real-world remediation scenarios. Key breakthroughs include dual-function catalysts for combined contaminant removal, scalable systems compatible with renewable energy, and hybrid strategies integrating conventional and catalytic routes. Case studies from arsenic hotspots worldwide demonstrate not only technological feasibility but also highlight knowledge gaps and sustainability challenges. By evaluating catalytic mechanisms, operational performance, and environmental impact, this review identifies promising directions for the next generation of arsenic remediation and offers a critical roadmap to guide future research and practice. Full article

Sulfur-mediated autotrophic denitrification (SAD) is an innovative and sustainable water treatment technology, which operates without an external carbon source and achieves lower sludge production. Firstly, this review provides a detailed examination of sulfur-based fillers, encompassing their respective types, preparation methods, advantages and drawbacks. Subsequently, it reviews the mainstream functional microbial communities across various process stages, such as Thiobacillus, Sulfurimonas, and Ignavibacterium. Moreover, the process characteristics of mainstream SAD reactor types, such as fluidized bed, fixed bed, and moving bed biofilm reactors, are reviewed, and the effects of key process parameters like pH, temperature, and dissolved oxygen on treatment efficiencies are further analyzed. Additionally, the applications cases of SAD in advanced wastewater treatment, river remediation, wetland restoration, and groundwater purification are summarized, demonstrating its broad and diverse application potential in environmental engineering. Finally, key challenges of SAD are identified, including the complexity of microbial metabolic interactions, the accumulation of intermediate products, and the need for improved fillers and reactor configurations. Future research priorities are discussed in three areas: microbial community regulation, control and utilization of intermediate products, and development of advanced fillers and reactor configurations. Overall, this review integrates key technical parameters and operational experience of SAD, providing a consolidated reference for researchers and practitioners interested in the development and application of this technology. Full article

This study reveals the internal mechanism of the synergetic evolution of financial support, technological innovation, and industrial upgrading from the perspective of system synergy. It aims to provide a theoretical basis and reference for promoting benign interactions among these elements, thereby driving high-quality economic development. During the research process, an evaluation indicator system was constructed based on China’s industrial development data, utilizing the entropy method to determine indicator weights and the Haken model to analyze synergy effects. In a methodological innovation, this study identifies the system’s order parameters to derive the potential function. Through this approach, it systematically analyzes the dynamic evolution characteristics and synergetic mechanisms of the composite system. The research results indicate that the three systems have formed a mutually promoting and closely coupled compound synergetic mechanism, rather than following a single linear transmission path. The overall synergy level presents a medium-to-low development trend, following an asymmetric U-shaped evolution trajectory that first decreases and then slowly recovers. Furthermore, the degree of synergy exhibits an inverse relationship with the volatility of the subsystems, suggesting that the stability of synergy is highly susceptible to external forces and remains in a state of dynamic flux. Full article

Count time series often exhibit extremal dependence that may not be adequately captured by Gaussian copula models. We develop a likelihood-based framework for count-valued time series using Student-t copulas with latent ARMA dependence. The latent process is constructed through a scale-mixture representation of a Gaussian ARMA process, preserving the second-order dependence structure while introducing tail dependence and greater persistence of extreme events. Likelihood inference requires evaluating high-dimensional truncated multivariate t probabilities, which is computationally demanding under heavy tails and strong serial dependence. To address this challenge, we develop scalable likelihood approximations tailored to the time series structure. In particular, we formulate a time series version of minimax exponential tilting for multivariate t probabilities, termed Time Series Minimax Exponential Tilting (TMET), which exploits the exact conditional representation of the latent ARMA process. The resulting algorithm reduces computational complexity from cubic to near-linear in the series length while retaining the high accuracy of minimax exponential tilting. For comparison, we also extend two widely used Gaussian copula approximations—the continuous extension (CE) method and the Geweke–Hajivassiliou–Keane (GHK) simulator—to the Student-t copula setting. Simulation studies show that TMET outperforms CE and GHK, particularly under strong dependence, heavy tails, and low-count regimes. The framework also supports predictive inference and residual diagnostics. An application to weekly rotavirus counts illustrates how the Student-t copula provides a flexible extension of the Gaussian copula while retaining stable inference even when tail dependence is weak or absent. Full article

Carbon monoxide (CO) is a poisonous gas because it disrupts functional oxygen transport of red blood cell (RBC) by binding heme of hemoglobin with high affinity. Contrarily, endogenous CO, which is constantly generated in the process of heme degradation by heme oxygenase, functions as a gaseous mediator necessary for maintaining physiological homeostasis. This toxicological (Yin) and physiological (Yang) duality presents a distinctive problem in medical and pharmaceutical applications, prompting the central question of this review: How can strict control over CO’s exposure dynamics, magnitude, kinetics, and tissue context be achieved to enable its safe therapeutic use? Here, we integrate the Yin and Yang of CO through an innovative exposure-engineering framework, leveraging the inherent RBC characteristics to offer a novel conceptualization for therapeutic development. We highlight the role of native RBCs as a biologically grounded platform that can convert hemoglobin binding—classically viewed as the basis of CO toxicity—into a measurable and controllable buffering mechanism. Then, reconciling the Yin and Yang of CO based on RBCs enables medical and pharmaceutical modulation that is attractive for clinical situations, therapeutics and diagnostics. Finally, we discuss key translational challenges—local concentration control, patient-specific risk stratification, manufacturability and critical quality attributes, and regulatory positioning—and outline how quantifiable exposure control can enable the safe clinical development of RBC-based CO therapy. Full article

Addressing the lack of high-fidelity test cycles for fuel cell light-duty trucks, this paper proposes an automated driving cycle construction method that integrates unsupervised clustering and reinforcement learning. Firstly, based on large-sample real-world driving data, four libraries of typical driving pattern segments are extracted through dimensionality reduction via Principal Component Analysis (PCA) and K-means clustering. Subsequently, the cycle construction process is formulated as a sequential decision-making problem, and a framework based on the Proximal Policy Optimization (PPO) algorithm, incorporating an action masking mechanism, is designed. This framework innovatively injects macro-level time budget allocation as a hard constraint into the agent’s policy space via action masking, while utilizing micro-level Markov transition probabilities as a soft guide. This dual approach drives the agent to learn an optimal segment concatenation strategy, thereby simultaneously ensuring both the macro-level statistical representativeness and the micro-level driving logic coherence of the synthesized cycle. Validation results demonstrate that the cycle constructed by the proposed method achieves an average relative error of only 7.53% in key characteristic parameters, and its joint speed-acceleration distribution exhibits a similarity as high as 0.9886 with the original data, significantly outperforming traditional methods such as the clustering method, the Markov chain method, and standard driving cycles. This study provides an effective tool for generating high-fidelity driving cycles and testing energy management strategies for fuel cell commercial vehicles. Full article

Blue space, as an important natural and social composite feature system in cities, not only provides supporting, regulating, and provisioning services, but also plays a key role in human well-being, recreational experience, and urban sustainable development. The blue space cultural ecosystem service (CES) has gradually attracted the attention of academia in recent years, but there is a lack of systematic integration research in related fields. Therefore, it is necessary to conduct a comprehensive analysis of current studies to clarify how, and to what extent, blue spaces influence CESs. This study adopts a PRISMA-based systematic search combined with qualitative synthesis, aiming to review the research status of CES and its developmental trajectory within blue space studies, and to identify future research trends and critical gaps. A total of 52 studies meeting the inclusion criteria were finally selected through database screening. The research innovatively divides the evolution of blue space CES into three stages (2012–2017/2018–2022/2023–2025), revealing a shift in research focus from single value identification to complex policy support. Secondly, through the mapping of six typical blue space types (such as rivers and wetlands) and 10 CES indicators, combined with a Pearson correlation heatmap, it provides quantitative insights into the coupling mechanisms between indicators, such as the significant synergy between spiritual and educational values. Methodologically, it systematically discriminates between the application boundaries of monetary valuation based on the contingent valuation method and non-monetary valuation represented by social media big data and PPGIS, pointing out that technological progress is driving the evaluation toward high dynamics and refinement. Finally, the study points out current bottlenecks such as uneven geographical distribution and insufficient planning transformation, emphasizing that future research should use artificial intelligence to improve data processing accuracy and transform blue space CESs from “invisible welfare” into “explicit policy assets” to guide sustainable urban renewal and healthy space design. Full article

Background/Objectives: Intranasal carboxymethyl-β-glucan (CMBG)-resveratrol represents an innovative therapeutic approach for upper airway diseases, combining antimicrobial, anti-inflammatory, antioxidant, immunomodulatory, and antiallergic properties. Despite growing preclinical and clinical evidence, consensus on its clinical applications remains poorly defined. To establish evidence-based recommendations for the clinical use of intranasal CMBG-resveratrol through a multidisciplinary Delphi Consensus process. Methods: A two-round Delphi Consensus was conducted. In the first round, an expert board prepared, reviewed, and validated 22 statements based on current scientific evidence from preclinical and clinical studies. In the second round, 38 multidisciplinary experts evaluated each statement using a 5-point Likert scale (from 5 = strongly agree to 1 = strongly disagree). Consensus was defined as ≥80% agreement (scores 4 + 5). Results: All 22 statements achieved consensus (range: 83–100%). Strong agreement (≥90%) was reached for statements regarding the pathophysiological rationale (infection-inflammation-oxidative stress cycle), resveratrol’s pleiotropic mechanisms of action, the role of CMBG in enhancing stability and bioavailability, and clinical efficacy in respiratory infections and allergic rhinitis. The mean scores ranged from 4.2 to 4.9, indicating high expert agreement across all domains. Conclusions: This multidisciplinary Delphi Consensus provides evidence-based recommendations for the use of intranasal CMBG-resveratrol to manage upper airway diseases, particularly respiratory infections and allergic rhinitis. The formulation’s multitarget approach addresses the complex pathophysiology of these conditions through simultaneous antimicrobial (mainly antiviral), anti-inflammatory, and immunomodulatory effects. Full article

Long-term stability of dam infrastructure is crucial for flood protection, water resource management, and drinking water supply. In many regions, the increasing impact of climate change and structural aging necessitates advanced monitoring approaches for embankment and gravity dams. PSI has emerged as a valuable technique for detecting surface deformation rates with millimeter precision. This study presents a comprehensive monitoring concept that combines satellite-based PSI analyses with the first operational use of ECRs at dam sites in North Rhine-Westphalia (NRW), Germany. Over a period of more than two years, ECRs were observed under real-world conditions using Sentinel-1 data. Compared to traditional passive reflectors, ECRs offer improved signal stability and a compact design, making them particularly suitable for confined or sensitive dam environments. The analysis of displacement time series confirms the suitability of ECRs for long-term deformation monitoring in complex dam settings. Intercomparison of two PSI time series demonstrated high internal consistency (correlation > 0.9, RMSE < 1 mm), while validation against in situ measurements confirmed millimeter-level agreement with RMSE values between 2 and 5 mm and correlations up to 0.7. In addition, a dedicated web-based platform was developed to provide processed ECR-based PSI results to dam operators, offering interactive visualizations, time-series access, and standardized downloads. This integration of advanced interferometric synthetic aperture radar (InSAR) methods, innovative hardware, and user-oriented service delivery marks a significant step toward operational dam monitoring using satellite remote sensing. Full article

In low-latency edge-intelligence scenarios such as autonomous driving and industrial edge analytics, the processing of large-scale sensor data imposes extremely stringent requirements on communication latency. However, the high overhead of the traditional TCP protocol makes it difficult to satisfy such demands, while the semantic gap between the high-performance RoCE protocol and the standard Socket API prevents existing applications from directly exploiting its advantages. To address this problem, this paper proposes TransBridge, a lightweight user-space communication middleware that transparently bridges TCP and RoCE. Its design is realized through three key innovations: a transparent user-space compatibility architecture that enables unmodified Socket-based applications to benefit from RoCE performance; a microsecond-level low-latency transmission engine that bypasses kernel and protocol stack overhead; and a lightweight lock-free resource management mechanism based on a decentralized peer-to-peer architecture and deferred buffer updates. Experiments on a real RoCE network show that TransBridge significantly outperforms mainstream schemes: it achieves an average round-trip latency of 5.926 $\mathsf{\mu }$s for 16 B messages and a throughput of 20.254 Gbps for 16 KB messages; in the Fast DDS application-level evaluation, it achieves a throughput of 188 Mbps and an average round-trip latency of about 150 $\mathsf{\mu }$s. The results indicate that TransBridge can provide transparent and effective RoCE acceleration for existing Socket-based applications in resource-constrained edge environments. Full article

Integrating artificial intelligence (AI) into design education is no longer optional; it has become an essential tool for enhancing innovative design and preparing students for data-driven practice and rapid technological acceleration. However, ignoring AI risks professional irrelevance; it introduces a range of concerns about students’ cognitive skills and comes with many drawbacks in the education process, as it threatens the attainment of learning outcomes, renders a fair assessment process unachievable, and places academic integrity in a vulnerable position. Using a qualitative case study approach, this research employs semi-structured interviews with 27 senior-year students in the interior design department to gain in-depth academic insights into how AI influenced their design process in their term project and its impact on their cognitive development and decision -making. Instructors’ observations on students’ skills, their pace in the project, and their end-products were documented. This study demonstrates that integrating AI into design education cannot be avoided, making a new paradigm for addressing design education inevitable. Based on the analysis, the paper proposes a conceptual framework outlining key dimensions in teaching and assessing strategies in design education adopting AI, focusing on analysis, critical thinking, reasoning, and process rather than on the end-product and its presentation. Full article

Modular integrated construction (MiC) is an innovative construction method that shifts on-site activities to a controlled factory environment, thereby offering sustainability benefits. However, current carbon management relies on labor-intensive manual data collection, causing delayed and inaccurate carbon accounting that increases greenwashing risks. Existing approaches lack real-time, automated, and trustworthy carbon tracking capabilities across fragmented supply chains. This study develops and validates the Blockchain-enabled IoT-BIM Platform (BIBP), which combines Internet of Things (IoT), Building Information Modeling (BIM), and blockchain for real-time carbon monitoring. IoT sensors automate data capture from construction equipment and BIM provides spatial visualization of carbon at the module and building levels. A Hyperledger Fabric blockchain ensures the authenticity, immutability, and traceability of carbon records. Validated on a 15-story MiC project in Hong Kong, BIBP established a cradle-to-end-of-construction baseline of 949.84 kgCO_2e/m², identifying steel and concrete as the primary hotspots (80% of material emissions). Real-time analytics demonstrated that combining high-volume ground granulated blast furnace slag (GGBS) concrete substitution, new energy sea–land multimodal transport, and 10% steel waste reduction achieves over 20% carbon savings. Furthermore, the BIBP automated data acquisition and calculation, improving assessment efficiency by 92.4%. The platform demonstrates the potential to transform carbon management from a static, retrospective evaluation into a proactive, data-driven monitoring process, equipping stakeholders with a tool to dynamically track emissions and make timely interventions toward carbon reduction targets. Full article

In recent years, Visual Education has emerged as an innovative and interdisciplinary teaching approach aimed at promoting meaningful learning through the conscious use of visual tools and languages. This educational paradigm helps to facilitate the understanding of complex concepts, translating them into clear and intuitive visual representations, while enhancing memorisation skills, critical information processing and the practical application of acquired knowledge. This systematic review, conducted according to the PRISMA (2020) protocol, analyses the most recent empirical evidence on the effectiveness of Visual Education in educational contexts. The main objective is to assess how the intentional use of visual tools—images, concept maps, educational videos, interactive digital materials, and virtual manipulatives—contributes to enhancing learning processes and developing transversal skills. Through a comparative analysis of fourteen international contributions published between 2020 and 2025, selected from the Scopus, Web of Science and EBSCO databases, the research highlights how Visual Education significantly influences the improvement of academic performance, motivation and cognitive and emotional engagement of students. The results also confirm the inclusive function of visual teaching, which can encourage participation, self-esteem and cooperation even in individuals with special educational needs. The discussion emphasises the need for the systematic integration of Visual Education into school curricula as a strategy to enhance soft skills and promote more equitable, effective learning geared towards the integral development of the individual. Full article

This study falls within the field of lifelong education and investigates the role of aesthetic-sensory experiences—particularly those mediated by digital visual arts—in the development of critical thinking, a key competence identified in the European LifeComp Framework. In light of contemporary social transformations characterised by complexity, uncertainty, and change, critical thinking emerges as a transversal skill of fundamental importance for the education of conscious and autonomous citizens. The objective of this research is to analyse how digital sensory experiences, integrated with innovative teaching methodologies—such as laboratory-based teaching and collaborative learning—can foster the activation and enhancement of critical thinking in educational contexts. The work is based on a narrative analysis of the literature, conducted through the consultation of academic databases (e.g., Scopus, ERIC, and Google Scholar), and is developed within a theoretical framework encompassing digital education, laboratory didactics, and collaborative learning strategies. Studies published in recent years were selected according to their relevance to digital aesthetic experiences and critical thinking in educational contexts and were analysed through a thematic synthesis of the main conceptual contributions. The knowledge activities include the selection, categorisation, and discussion of recent studies that relate aesthetic experiences to the development of soft skills, in line with the principles of visual education understood as aesthetic and critical literacy in visual languages. The results of the review indicate that the intentional use of aesthetic digital environments, in combination with active and reflective teaching approaches, can stimulate complex cognitive processes and significantly contribute to the formation of critical thinking. The contribution implements an interdisciplinary approach among visual education, digital education, and experiential aesthetics, emphasising the need for further empirical research to consolidate the evidence and guide the implementation of innovative educational practices based on this approach. Full article

Coastal cities are located at the critical interface of land–sea interaction, and scientifically assessing their ecological importance is essential for identifying conservation priority areas. Existing assessments focus primarily on static function while neglecting dynamic system processes and resilience characteristics. To address this limitation, this study developed an innovative “State-Interaction-Resilience” (SIR) assessment framework. It integrates ecosystem services (state), ecological connectivity and network supply-demand relationships (interaction), and social-ecological system adaptive capacity (resilience) and incorporates differentiated weighting based on the unique “sea–land gradient” pattern of coastal zones. Using Dongying City in the Yellow River Delta as a case study, the results show the following: (1) The SIR framework evaluation results demonstrate balanced and significant positive correlations with all dimensional indicators (r = 0.3~0.8), showing greater comprehensiveness and scientific validity than traditional evaluation methods, with 81% spatial agreement between identified extremely important areas and existing protected areas. (2) From 2000 to 2020, the overall ecological importance of Dongying City showed an upward trend, with the proportion of extremely important areas significantly increasing from 6.03% to 10.24%, while maintaining a stable spatial gradient pattern of “high along the coast, low inland”. (3) The improvement in ecological importance in coastal core areas mainly resulted from state improvement and resilience enhancement driven by restoration projects such as “aquaculture retreat and wetland restoration”, while inland areas were constrained by both habitat fragmentation and ecological supply-demand mismatch. This study confirms that the SIR framework can accurately capture the spatial heterogeneity of coastal zones. The proposed “core protection-corridor restoration-function enhancement” hierarchical and zonal spatial governance strategy provides scientific evidence and actionable spatial guidance for coastal territorial spatial planning, ecological protection redline optimization, and targeted ecological restoration. Full article