Search Results (1,471)

The performance of a sequencing batch biofilter granular reactor (SBBGR), followed by a dual media granular activated carbon (GAC) column, was evaluated in terms of its ability to remove selected per- and polyfluoroalkyl substances (PFAS) from landfill leachate. The results show that the SBBGR achieved an overall reduction of 51%, with the preferential removal of long-chain PFAS, while short-chain PFAS were only partially removed. Subsequent GAC treatment exhibited compound-specific breakthrough behavior, which was governed by chain length. Short-chain PFAS (e.g., perfluorobutanoic acid) exhibited rapid bed volumes at 50% breakthrough (BV₅₀ ≈ 88), whereas long-chain PFAS (e.g., perfluorooctanoic acid and perfluorooctanesulfonic acid) were substantially more retained (BV₅₀ ≈ 446 and 361, respectively), with perfluorohexanesulfonic acid and perfluorodecanoic acid failing to reach BV₅₀ within the monitored period. Mass balance analysis showed that the hybrid GAC column captured ~73% of the influent PFAS mass. This resulted in >80–95% retention of long-chain PFAS and <40% retention of short-chain PFAS. Although long-chain PFAS were preferentially adsorbed, mobile short-chain species dominated residual effluent loads. These findings highlight the need for optimized contact times or dual-media strategies to control the breakthrough of short-chain PFAS. Full article

Battery Energy Storage Systems (BESSs) are increasingly deployed in grid-scale applications, electric mobility, and renewable integration, where safety, reliability, and longevity are critical. Thermal runaway remains one of the most severe failure modes in lithium-ion batteries, often triggered by complex interactions between electrochemical, thermal, and mechanical phenomena. This paper presents an extended hybrid Physics-Informed Neural Network (PINN) framework for thermal anomaly prediction and early detection of runaway precursors in BESS. The proposed architecture integrates governing physical laws, specifically the Bernardi heat generation equation and Fick’s diffusion law, within a deep learning pipeline composed of a physics module, a temporal Bi-LSTM, and an attention mechanism for explainability, which may represent an obstacle in the application of deep learning algorithms. Beyond the initial formulation, the extended version presented here provides a deeper theoretical background, an expanded methodological justification, a more comprehensive comparison with state-of-the-art approaches, and a detailed discussion on scalability, uncertainty, and deployment challenges. The results for synthetic yet physically consistent datasets represent a proof of concept of the PINN approach, which can achieve superior generalization, robustness to noise, and interpretability compared to purely data-driven baselines, achieving an accuracy above 90% and an AUC of 0.95. The framework contributes to proactive safety management in cyber-physical energy systems and establishes a foundation for real-time, physics-aware anomaly detection in safety-critical BESS applications, e.g., marine transportation contexts and port environments. Full article

Sybil attacks remain a primary challenge for Proof-of-Stake (PoS) blockchain systems, as low-cost identity creation can distort validator participation and limit consensus reliability. This study proposes a hybrid participation–governance framework that integrates Attribute-Based Access Control (ABAC) and Reputation-Based Access Control (RpBAC) with a trust-based PoS workflow to reduce the influence of suspicious identities during validator selection and block validation. The proposed framework also incorporates graylisting and dynamic reward–penalty updates to support adaptive participation control. The strategy was evaluated in a simulation environment informed by Ethereum-derived block metadata, using network sizes ranging from 100 to 1000 nodes and Sybil attack ratios of 30%, 40%, and 50%. Its performance was compared with PoS-only and PoS + ABAC baselines using both security and performance indicators. The results show that the full ABAC + RpBAC configuration achieved the strongest and most stable security performance across the evaluated settings while introducing additional overhead at larger network sizes. These findings suggest that combining policy-based eligibility control with behavior-based reputation control strengthens the resilience against Sybil in PoS-like blockchain environments. However, this improvement requires a measurable trade-off between security and performance. Full article

The process of drug discovery is one of the most expensive, time-consuming, and high-risk endeavors in modern science. Translating initial scientific insights into safe and effective therapies, supported by genomics, structural biology, and computational chemistry, typically requires more than a decade and substantial financial investment. Machine learning (ML) has emerged as a powerful tool for improving efficiency across the drug discovery pipeline. By enabling the analysis of large and complex datasets, ML supports target identification, lead discovery, optimization, and prediction of preclinical and clinical outcomes. Its integration with experimental validation and automation is illustrated by recent advances such as protein structure prediction, AI-driven antifibrotic compound discovery, and antibiotic identification. Despite these advances, significant challenges remain. Model generalizability is limited by data scarcity, heterogeneity, and hidden biases. In addition, the translation of in silico predictions into clinically validated outcomes remains a major bottleneck, and regulatory acceptance is constrained by limited model interpretability. Ethical considerations, including data privacy, equitable representation, and the potential misuse of generative models, further complicate adoption. This review examines the applications of ML across the drug discovery pipeline, with a focus on translational and regulatory considerations. It also discusses emerging directions, including hybrid physics–AI approaches, multimodal foundation models, federated learning, and explainable AI. The effective integration of ML will depend on rigorous validation, interdisciplinary collaboration, responsible data governance, and alignment with regulatory frameworks. Full article

Conventional truck emission accounting methods based on average activity levels and static emission factors are increasingly inadequate for dynamic regulation and policy comparison at high spatiotemporal resolution. This review synthesizes recent progress in dynamic truck emission estimation from four perspectives: multi-source data support, key feature extraction, physics-constrained emission modeling, and governance-oriented applications. The literature was collected from Web of Science Core Collection and ScienceDirect for the period 2014–2026, supplemented by backward reference checking, and was analyzed through a progressive framework linking data, features, models, and governance tasks. Unlike previous reviews that usually discuss emission inventories, conventional emission models, or data-driven prediction methods separately, this review highlights an integrated governance-oriented chain that connects multi-source data fusion, mechanism-related feature construction, physics-constrained modeling, and environmental management applications. Existing studies suggest that multi-source data, including GPS trajectories, on-board diagnostics (OBDs), on-board monitoring (OBM), portable emissions measurement system (PEMS) measurements, traffic flow monitoring, and road network attributes, provide an important basis for representing real-world operating processes. Meanwhile, key features have expanded from surface-level variables such as vehicle velocity to mechanism-related factors, including payload, road grade, engine operating conditions, vehicle-specific power, and roadway context. Truck emission modeling has also evolved from unconstrained or weakly constrained approaches toward frameworks that place greater emphasis on physical consistency, interpretability, and result credibility. In parallel, application scenarios have extended from emission quantification to high-emission vehicle identification, dynamic inventory development, hotspot detection, policy comparison, and transport optimization. These developments can support policymakers, transportation planners, and environmental agencies in moving from aggregate emission accounting toward targeted and process-based truck emission governance. Current research, however, still faces challenges related to data consistency, model generalizability, uncertainty propagation, and real-time application. Future work should focus on standardized datasets, hybrid AI–physics modeling frameworks, uncertainty-aware validation, real-time deployment in intelligent transportation systems, and improved links between dynamic estimation and practical environmental management. Full article

Artificial intelligence (AI) systems increasingly depend on complex, multi-stage supply chains that incorporate pre-trained models, third-party datasets, open-source libraries, and automated training pipelines. This dependency creates a rapidly expanding attack surface in which model poisoning, dependency compromise, and provenance manipulation can undermine system integrity long before deployment. Existing AI governance frameworks—including the NIST AI Risk Management Framework and NIST’s Secure Software Development Framework—acknowledge supply chain risks but do not define a verifiable model provenance structure or cryptographically durable integrity guarantees. Simultaneously, the transition to post-quantum cryptography (PQC) introduces new requirements for long-lived AI artifacts: classical digital signatures used to verify model lineage, dataset integrity, and pipeline attestation will become vulnerable to quantum-enabled forgery within the expected operational lifetime of many AI systems. This paper synthesizes evidence from policy, standards, and benchmark sources to characterize the emerging AI supply chain threat landscape and identify cryptographic dependencies that the PQC transition disrupts. We propose a formal Model Bill of Materials with PQC-safe extensions (MBOM-PQC), a unified signing and attestation pipeline integrating ML-DSA and hybrid signature modes, and a five-level Supply Chain Assurance Maturity Model (SCAMM) supporting repeatable organizational evaluation. Together, these contributions aim to provide a structured foundation for AI supply chain integrity, supporting verifiable model lineage, authenticity, and trustworthiness through the PQC transition and beyond. The framework is presented as a design-science contribution comprising three integrated artifacts and is extended with operational guidance for continuous-learning pipelines (§6.5), a formal scoring methodology for organizational assessment (§7.3.5), and a hardware-root-of-trust migration cost matrix (§8.3.6). Full article

In tennis, where performance is governed by complex kinetic chain interactions, objective skill classification is vital for coaching and talent identification. This study presents a hierarchical deep learning framework leveraging synchronized bilateral Inertial Measurement Unit (IMU) data from 39 participants (11 elite, 28 amateur). The proposed system successfully distinguishes expertise levels across a total of 4594 strokes, including augmented samples.. A hybrid Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) architecture was developed to autonomously extract spatiotemporal features from the raw kinematic signals of forehand, backhand, service, and volley strokes. The proposed model achieved an accuracy of 95.54%, significantly outperforming both traditional machine learning and state-of-the-art deep learning benchmarks. Qualitative t-distributed Stochastic Neighbor Embedding (t-SNE) analyses revealed that elite athletes form highly homogeneous clusters in the feature space. Furthermore, quantitative Asymmetry Index assessments confirmed that professionals exhibit superior bilateral coordination stability. These findings demonstrate that the proposed end-to-end system offers a robust, field-applicable solution for identifying technical excellence. It provides coaches with reliable digital biomarkers, thereby overcoming the limitations of subjective visual observation. Full article

Ultra-high-performance concrete incorporating coarse aggregates (UHPC-CA) exhibits pronounced multi-scale heterogeneity and staged damage evolution. However, existing single-scale reinforcement strategies often fail to address the complete micro-to-macro fracture process, leaving a critical research gap in achieving full-stage crack control. To address this, this study introduces a novel cross-scale toughening strategy using hybrid steel fibers (SF) and calcium carbonate whiskers (CCW), and decouples the coupled influences of water-to-binder (W/B) ratio, coarse aggregate (CA), and multi-scale fibers via an orthogonal design. Mechanical properties, fiber dispersion, and pore structure are jointly characterized to establish structure–property relationships. An optimal composition (W/B = 0.32, CA = 18%, SF = 2%, CCW = 1%) is identified, achieving a balanced enhancement of strength and ductility. Results indicate that matrix densification is primarily controlled by W/B via pore refinement, while mechanical performance is governed by the interplay between fiber spatial uniformity and interfacial integrity; the roles of CA and CCW are clearly stress-state dependent. Furthermore, a novel cross-scale synergistic mechanism is revealed, in which micro-scale CCW regulates microcrack initiation and stabilizes the pre-peak response, whereas macro-scale SF dominates post-peak behavior through crack bridging and pull-out energy dissipation. This sequential activation enables a full-stage enhancement of tensile performance, shifting failure from brittle localization to pseudo-ductile multiple cracking. The findings provide a correlative framework for tailoring UHPC-CA through multi-scale hybrid reinforcement. Full article

As the core pillar of international trade, the global shipping industry has seen its carbon and pollutant emissions become a key challenge in global environmental governance. Statistics indicate that ship carbon emissions account for 3% of the world’s total anthropogenic CO₂ emissions, while contributing 20% of global NO_x and 12% of SO₂ emissions, posing a serious threat to coastal ecosystems and public health. In response to the International Maritime Organization (IMO) “Net Zero Framework” and national green shipping policies, the transformation of ship power systems toward low-carbon and zero-carbon operation has become an inevitable trend. This paper systematically reviews the research progress and application status of green energy materials for ships, focusing on the working principles, technical characteristics, and engineering application cases of solar photovoltaic (PV) materials, wind energy utilization technologies, fuel cell materials, and alternative clean energy fuels (e.g., liquefied natural gas (LNG), methanol, and hydrogen energy). It also discusses the integration mode and optimization strategy of multi-energy hybrid power systems. The research findings show that solar photovoltaic technology has achieved large-scale application in coastal ships; hydrogen fuel cells are suitable for long-range ocean navigation scenarios due to their high energy density; LNG and methanol have become the current mainstream alternative fuels, relying on mature infrastructure; and hybrid energy systems can significantly improve power supply reliability and emission reduction efficiency through multi-energy complementarity. Finally, aiming at the existing bottlenecks (e.g., cost, energy storage, and safety) of various technologies, future development directions are proposed. This study provides a reference for the technological breakthrough and engineering practice of green energy power systems for ships and contributes to the realization of the “carbon neutrality” goal in the global shipping industry. Full article

Introduction: This article examines the roles of chiefs and traditional leaders in fostering environmental sustainability, collective responsibility, and accountability in Ghana. It argues that chieftaincy functioned as a key institution for regulating human relationships with land, natural resources, and social order in precolonial governance systems. By grounding environmental stewardship in customary authority, moral obligation, and spiritual legitimacy, chiefs helped sustain communal balance and cohesion. Methods: The article uses a conceptual and historical-interpretive approach to analyze the chieftaincy institution’s normative, political, and spiritual functions in environmental governance. It draws on interpretations of precolonial governance structures, customary practices, and indigenous cosmologies to examine how chiefs exercised authority and shaped collective conduct. Results: The analysis shows that chiefs, with their councils, established and enforced rules, norms, and sanctions that promoted sustainable community life. Their authority included custodianship of land, social order, and sacred obligations. As representatives of ancestors and intermediaries between the human and spiritual realms, chiefs reinforced a moral framework in which environmental harm was seen as both a social offence and a disruption of divine and ancestral balance. The nonpartisan nature of chieftaincy provided a unifying platform for guiding communities toward shared responsibilities, regardless of political differences. Discussion: The article concludes that chieftaincy historically served as an important mechanism for environmental stewardship and ethical governance in Ghana. Chiefs were positioned as custodians of a balanced relationship between people, land, and spiritual order. Revisiting these indigenous governance principles offers insight into how traditional authority can contribute to contemporary discussions on sustainability, accountability, and community-based environmental governance. Full article

This study investigates how the adhesive curing state before riveting influences material flow during riveting, joint formation, and the mechanical performance of CFRP/aluminum hybrid joints. Hybrid joints were fabricated in a single-lap configuration using electromagnetic self-piercing riveting (E-SPR) at curing times of 0, 20, 40, 60, and 80 min, and the adhesive distribution, joint geometry, load–displacement behavior, energy absorption, and failure mode were examined. As curing time increased, adhesive squeeze-out decreased and adhesive displacement during riveting was progressively restricted, leaving more adhesive near the contact point. Consequently, the head height increased from 0.12 to 0.21 mm, whereas the interlock distance decreased from 0.67 to 0.54 mm. In the bonded region, the peak load increased with curing time, and a peak load of 11.15 kN was observed at 40 min, indicating an increased contribution of the adhesive layer. In contrast, the load in the riveted region decreased at 60 and 80 min because the increased resistance of the adhesive interlayer limited the rivet deformation and mechanical interlocking. A maximum energy absorption of 32.13 J was observed at 40 min, where the joint exhibited relative contributions of the adhesive and the rivet. Failure analysis showed bearing failure at 40 min, whereas rivet pull-out was observed at 60 min, consistent with the curing-dependent changes in joint formation. These results indicate that curing-induced changes in adhesive behavior govern the interaction between adhesive flow and rivet deformation, thereby influencing joint formation and mechanical performance. Full article

Quantitative risk assessment of flash floods is crucial for developing disaster prevention and mitigation strategies. This study developed a refined framework that innovatively integrates field-validated data from Chongqing’s flash flood disaster investigation project with AHP, factor analysis, and cluster analysis to quantify hazard, vulnerability, resistance, and risk indicators at a 30 m grid. Unlike existing coarse-scale assessments that rely on generic indicators, this hybrid model, calibrated by observed disaster evidence, significantly enhanced the local relevance and reliability of risk zoning. The validity of this framework was confirmed through validation against objective weighting methods and historical flash flood locations. The results indicated that the risk value of flash floods in Chongqing was between 0.24 and 0.69, with extremely high-risk and high-risk zones covering 42,388 km² (51.47%) of the study area. This accurately identifies areas at high risk of flash floods and provides a basis for government decision-making regarding priority areas for disaster risk reduction investments. Verification showed that 83.44% of historical disaster points fall within medium-risk or above zones, confirming the framework’s accuracy in identifying flood-prone hotspots and providing actionable support for targeted early warning and resource allocation. Full article

The NAC transcription factor superfamily is one of the most prominent plant-specific regulatory gene families, extensively participating in multiple metabolic processes that govern plant growth, tissue development and stress adaptation. Masson pine (Pinus massoniana Lamb.) is a native dominant conifer widely cultivated across South China, whose timber resources possess great exploitation potential in pulp manufacturing and the paper industry. In this study, a total of 98 non-redundant NAC family members were mined at the genome-wide level. Functional validation revealed that PmNAC82, a member belonging to the VND evolutionary subgroup, acts as a core regulatory factor controlling wood formation. Subcellular localization tests confirmed PmNAC82 exclusively resides in the cell nucleus. Heterologous genetic transformation in poplar demonstrated that this gene positively regulates the accumulation of lignin and cellulose. Furthermore, through RT-qPCR, yeast one-hybrid assays, and EMSA, we confirmed that PmNAC82 can bind to the promoters of PtrMYB3, PtrMYB21 and PmCesA7. These findings provide a solid foundation for further investigation into the molecular functions of NAC genes in Masson pine as well as their potential application towards molecular breeding strategies aimed at improving wood quality. Full article

The recycling and reuse of discarded express delivery cartons can yield environmental, economic, and social benefits. A key factor influencing the volume of express packaging collected is the uncertainty in the total amount of such packaging within the service range of each collection point. Additional uncertainties include the costs associated with the construction of recycling stations, operational expenses, transportation costs, additional recycling fees, and government subsidies. To address the issue of express packaging recycling, a fuzzy integer programming model for the reverse logistics network of express packaging is constructed. The model aims to minimise the total network cost and maximise the total recycling rate while enabling decisions regarding the location of recycling facilities and the flow between facilities. Then, a memetic algorithm based on dynamic local search is designed. Several alternative solution approaches were considered to evaluate the proposed algorithm, including the precision optimization method (CPLEX) and a hybrid priority-based genetic algorithm. The results confirm the feasibility of the memetic algorithm. Finally, the applicability of this fuzzy programming model is analysed and validated by changing the confidence level. The case study results reveal quantifiable trade-offs: as the confidence level (α) increases from 0.75 to 0.90 under a fixed recycling rate threshold (ε = 80%), the total network cost rises approximately linearly, while the required number of recycling stations increases, with their average facility level upgrading accordingly. Variations in confidence levels and the degree of total recycling rate achievement can significantly influence the increase in target values. Moreover, the magnitude of this influence exhibits irregularity, indicating that changes in confidence levels entail a certain degree of risk. Full article

Makerspaces can serve as distributed urban infrastructures for repair, reuse, tool sharing, and peer learning, yet their contributions to circular economy (CE) goals often occur without being explicitly recognized or framed as CE practices. Inspired by practice theory and the literature on quiet sustainability, this study introduces implicit circularity as circular practices enacted without an explicit sustainability/CE framing by participants, and examines how such practices shape bottom-up circular transitions. Using reflexive thematic analysis informed by constructivist grounded theory procedures, we examined three linked questions: which circular practices occur in makerspaces and how they cluster into domains, how these practices vary across makerspace types, and which barriers and governance arrangements shape makerspaces’ consolidation as circular urban infrastructure. A qualitative multi-method design was employed in Czechia, combining field mapping with in-depth qualitative inquiry. Data included 40 semi-structured interviews with makerspace founders and operators, documentary analysis based on websites, social media, event listings, rules, and other documents, and 21 observations. Using reflexive thematic analysis informed by constructivist grounded theory procedures, we analyzed how circular practices cluster into domains, how implicit versus explicit circularity varies across makerspace types, which barriers constrain makerspaces’ consolidation as circular urban infrastructure, and what governance arrangements could mitigate them. Circularity was dominated by implicit, routine practices rather than formal, CE-branded programs. Three practice domains were identified: repair and maintenance, material flows, and learning/education. Explicit programming was comparatively less common and context-dependent. Barriers formed a reinforcing system spanning institutional fragmentation and coordination deficits, capability gaps, infrastructural constraints, and tensions around autonomy and legitimacy, which together kept many circular contributions low-visibility. Makerspaces constitute an under-recognized form of circular micro-infrastructure that couples technical capacity with social learning and can translate CE ambitions into everyday practice. To mobilize these latent capacities, cities need hybrid governance, especially light-touch coordination platforms, long-horizon operational support, and integration of makerspaces into municipal material-flow systems and repair/reuse strategies. The study offers a practice-based framework and a cross-case typology to support comparative research and grounded urban CE policy design. Full article