Search Results (5,173)

Natural lacquer, a bio-based polymer derived from Toxicodendron vernicifluum, has attracted renewed scientific interest as a sustainable coating material with exceptional mechanical durability, chemical resistance, and aesthetic qualities. This review synthesizes current knowledge on the chemical composition, enzymatic curing mechanisms, and structure–property relationships of lacquer-based polymer systems, with particular focus on recent advances in functional modification and processing technology. Key findings indicate that laccase-catalyzed oxidative polymerization, operating optimally at pH 6.0–7.5 and 20–30 °C, governs the formation of a highly cross-linked urushiol network whose properties are fundamentally determined by side-chain unsaturation and emulsion stability. Mechanistic analysis reveals that polyurethane hybridization improves weathering resistance by introducing flexible aliphatic segments and additional hydrogen-bonding cross-links, while graphene oxide incorporation enhances anticorrosion performance through a physical barrier mechanism that prolongs ionic diffusion pathways. UV-curable LPEA derivatives achieve an 83% reduction in curing time relative to ambient-cured lacquer, enabling integration with industrial spray-coating lines. Despite these advances, several critical limitations remain inadequately resolved. Allergen reduction strategies have not yet achieved sufficient quantitative efficiency for large-scale commercial deployment, and the long-term stability of nanocomposite lacquer films under sustained UV exposure and hydrothermal conditions is not well established. Furthermore, most high-performance modification systems reported in the literature are demonstrated only on laboratory scale, with scalability, substrate compatibility, and lifecycle performance remaining largely unvalidated. The review identifies the absence of standardized performance evaluation protocols and the fragmentation of structure–property data across studies as key barriers to systematic progress, and proposes that future work prioritize the development of integrated processing–modification–performance frameworks to guide the rational design of next-generation lacquer-based functional materials. Full article

In bank-dominated financial systems, the accumulation of non-performing loans is a recognised source of systemic vulnerability, as correlated corporate defaults can erode bank capital, impair liquidity, and propagate stress across interconnected portfolios. Firm-level default detection thus constitutes a microprudential foundation of macroprudential stability: the reliable early identification of risky borrowers reduces both individual credit losses and the aggregate exposures that drive system-level fragility. Yet the use of structured data-mining pipelines for this task remains underexplored in Central and Eastern Europe. This study applies the CRISP-DM methodology to predict corporate loan default using data on 302 Slovak corporate borrowers, combining financial ratios from publicly available financial statements with selected company and loan-related information from internal bank records. Seven individual classifiers were developed and compared: decision trees (CART, CHAID, C5.0), logistic regression, discriminant analysis, and neural networks (MLP, RBF), together with a stacked ensemble based on their outputs. Model performance was evaluated using sensitivity, overall classification accuracy, and area under the ROC curve (AUC), with sensitivity treated as the primary criterion because of the asymmetric costs of misclassification in credit risk assessment. The results confirm that historical firm-level information provides a reliable basis for default prediction, with tree-based models consistently outperforming statistical and neural network approaches. The stacked ensemble achieved the strongest overall performance, whereas C5.0 and CHAID showed that interpretable classifiers can also deliver competitive predictive accuracy. A champion–challenger deployment architecture is proposed, in which the ensemble serves as the performance-oriented champion and interpretable models act as challengers; this arrangement contributes to the operational resilience of the credit-risk assessment process and aligns with macroprudential expectations of model governance, auditability, and explainability. The study offers a replicable methodological framework for integrating data-driven decision support into credit evaluation in comparable banking settings. Full article

This discussion paper examines the evolution of freshwater distribution networks from smart to intelligent and ultimately meta-intelligent or wise systems, highlighting the transition from human-supervised operation to autonomous adaptive management. Smart systems integrate monitoring, automation and remote control through information technologies. Intelligent systems extend these capabilities by adding predictive analytics, demand forecasting and automated operational optimization. Wise systems further evolve through adaptive learning mechanisms that allow continuous self-improvement while minimizing dependence on operators. Evidence from Greek water utilities demonstrates practical applications and operational outcomes. The analysis discusses implementation challenges including investment costs, system complexity, data governance and resilience. Finally, the paper proposes design principles for scalable adaptive water networks applicable to utilities with different sizes, resources and levels of technological maturity. Full article

Maritime communication systems are among the most important infrastructure of global maritime safety and security. They consist of very high frequency (VHF) radio, the Global Maritime Distress and Safety System (GMDSS), contemporary satellite nets, Automatic Identification System (AIS) networks, and the emerging VHF Data Exchange System (VDES). These systems are essential for distress signaling, navigational coordination, and vessel traffic management. As maritime operations are experiencing accelerated digitalisation, the safety and security dimensions of maritime communication systems have attracted substantial and growing scientific attention. This study presents a comprehensive literature review and bibliometric analysis of the safety and security of maritime communication systems. Guided by the PRISMA 2020 guidelines and Systematic Literature Review (SLR) methodology, a structured search was conducted across three major scientific databases: Scopus, Web of Science (WoS), and IEEE Xplore. Starting from a raw pool of 6648 records retrieved between 2000 and 2026, the dataset was reduced through successive filtering to a final body of 68 high-relevance publications. Bibliometric analysis reveals a significant upward publication trend from 2015 onwards, with a marked acceleration after 2019. Thematic analysis identifies seven principal research clusters: GMDSS modernisation, AIS safety and security, VDES and VHF next-generation systems, maritime cybersecurity, satellite communications, risk assessment frameworks, and emerging technologies, including artificial intelligence and autonomous vessel communications. The review identifies significant research gaps, including the absence of integrated cross-system risk frameworks, insufficient attention to human factors in cybersecurity, limited studies addressing emerging regulatory, legal governance components and a brief analysis of the maritime communications market. This study provides a structured foundation for future research and policy development in maritime communication security. Full article

Predicting the evolution of microstructure and field quantities under varying processing and loading conditions is a central challenge in computational materials science and metal additive manufacturing (AM). While deep learning (DL) methods offer ultra-fast prediction capabilities post-training, existing models often struggle with poor spatial and temporal extrapolation, high parameter burdens, and an inability to effectively integrate diverse conditioning parameters alongside high-dimensional input fields. To address these bottlenecks, we propose a novel conditional generative adversarial network (CPGAN), which is designed to seamlessly ingest both initial fields and governing condition parameters. The CPGAN framework offers three distinct advantages: (1) it accurately maps the combined effects of initial states and process conditions onto evolved fields; (2) it demonstrates robust extrapolation capabilities across diverse spatial and temporal scales, including the unique ability to natively generate high-resolution rectangular domains; and (3) it achieves superior predictive accuracy and training stability compared to standard convolutional baselines by effectively suppressing spurious artifacts. We validate CPGAN’s performance against rigorous physics-based ground truths across three representative engineering applications: porosity evolution in selective laser sintering (SLS), spatial distribution of 2D von Mises stress fields in solid structures, and the spatiotemporal evolution of grain growth. The results confirm that CPGAN is a highly adaptable and efficient surrogate model, capable of simulating continuous structural and morphological evolutions even when driven by highly non-uniform spatial or temporal kinetics. Full article

Water scarcity in semi-arid regions threatens seed germination and early crop establishment, driving the development of biodegradable Nature-based Solutions to replace synthetic plastic mulches. Porous cellulose membranes were fabricated from rice husk (RH), banana pseudostem (BP), and sugarcane bagasse (SB) by thermo-chemical extraction and high-shear homogenization (n = 5 replicates per membrane type). Membranes were characterized by ATR-FTIR and scanning electron microscopy, confirming removal of non-cellulosic components and biogenic silica preservation in RH, and revealing biomass-dependent porous architectures linked to mechanical and transport behavior. RH produced the most compact fibrillar matrix (compressive strength: 8.16 ± 0.24 MPa; WVT: 170 ± 60 g m⁻² day⁻¹), BP an open interconnected network with superior deformability (9.83 ± 0.25% elongation) and moisture transport (WVT: 400 ± 100 g m⁻² day⁻¹), and SB the highest moisture-retention capacity (215.7 ± 15.8%). Germination assays with Brassica oleracea var. botrytis under water stress showed SB achieved the highest germination rate (90.5 ± 0.99%), confirming that sustained moisture availability governs germination more decisively than transport rate alone. Soil burial tests confirmed biodegradable behavior across all membranes (R² ≥ 0.995; k = 0.043–0.046 day⁻¹). These findings establish a hydrogen-bond-mediated structure–property–function framework for designing biomass-specific cellulose membranes as biodegradable solutions for water-limited agricultural systems. Full article

Efficient management of soil–water resources is critical for global food security under intensifying climatic and demographic pressures. This review provides a comprehensive synthesis of artificial intelligence (AI) and distributed deep learning methodologies applied to soil–water interactions in precision agriculture. The physical and hydraulic foundations of soil–water systems—including water retention, unsaturated flow governed by the Richards equation, and soil degradation processes—are examined and situated within a unified framework of AI-based modeling and decision support. Classical machine learning (ML) algorithms (Random Forests, Support Vector Machines, gradient boosting) and deep learning architectures (convolutional neural networks, long short-term memory networks, transformers) are evaluated with respect to their capacity to predict soil moisture dynamics, estimate hydraulic properties, support smart irrigation scheduling, and generate digital soil maps at field-to-regional scales. Distributed training paradigms, federated learning for privacy-preserving multi-farm analytics, and edge AI deployment on low-power IoT hardware are assessed as enabling infrastructures for scalable agricultural intelligence. This review further addresses explainability, uncertainty quantification, and ethical dimensions inherent to AI-driven agricultural systems. Key challenges—including training data scarcity in data-poor regions, model interpretability, integration with physics-based hydrological models, and real-time deployment constraints—are critically discussed. Prospective research directions encompass physics-informed neural networks, foundation models for earth observation, autonomous digital twins of soil–water systems, and federated learning architectures aligned with data sovereignty frameworks. The synthesis underscores AI’s transformative potential for sustainable agricultural water management while delineating the technical and sociotechnical barriers that must be resolved to realize this potential at a global scale. Full article

Against the backdrop of frequent global shocks and increasingly complex supply chain networks, supply chain disruption risk exposure has become a major challenge affecting firms’ operational stability and sustainable competitive advantage. Meanwhile, generative artificial intelligence is being increasingly embedded in business operations and has demonstrated strong application potential in information processing, risk identification, and decision support. Based on data from Chinese A-share listed firms from 2017 to 2024 and using text measures based on Management Discussion and Analysis (MD&A) disclosures of Generative AI application and supply chain disruption risk exposure, this study examines the relationship between Generative AI application and corporate supply chain disruption risk exposure, and further explores the channels through which this relationship may operate from the perspective of risk governance transformation. The results show that Generative AI application is significantly associated with lower corporate supply chain disruption risk exposure, and this relationship remains robust across a series of robustness checks and supplementary endogeneity analyses. Channel analyses suggest that this relationship may be related to firms’ risk governance transformation, mainly reflected in enhanced risk identification capability, improved resource allocation capability, and strengthened collaborative response capability. Heterogeneity analyses show that this association is more pronounced among firms facing higher environmental uncertainty, manufacturing firms, and firms located in cities with lower entrepreneurial vitality. This study provides text-based firm-level evidence for understanding the relationship between Generative AI application and supply chain risk governance, and offers managerial implications for firms seeking to promote scenario-based Generative AI application and enhance supply chain resilience and risk governance capability. Full article

Desert expressway corridors exposed to strong wind hazards often rely on single high-grade routes, with limited alternatives, high detour costs, and low network redundancy. These constraints make it difficult to maintain traffic operation resilience through route substitution alone. Taking the Hami–Tuyugou section of the G30 Lianhuo Expressway in Xinjiang, China, as a case study, this study investigates the formation and evaluation of traffic operation resilience in a wind-hazard-affected, low-redundancy desert expressway corridor. A hierarchical indicator system was constructed with four first-level, fourteen second-level, and thirty-one third-level indicators. Fuzzy DEMATEL(Decision Making Trial and Evaluation Laboratory)–ISM(Interpretive Structural Modeling) was used to identify causal relationships and hierarchical transmission paths; fuzzy DANP(DEMATEL-based Analytic Network Process)–AHP(Analytic Hierarchy Process) was applied to determine indicator weights; and a cloud model was employed to evaluate the overall resilience level. The results show that institutional adaptability, organizational learning, monitoring and information support, and multi-actor collaboration are the main upstream drivers. The corridor was evaluated as Grade IV, indicating a relatively high resilience level approaching Grade V. Sensitivity analyses confirm the robustness of the substantive conclusion. The findings suggest that, under low-redundancy conditions, resilience depends less on structural redundancy and more on adaptive governance, information support, and coordinated response. Full article

The health system is now more complex, uncertain, interdependent, and dynamically interconnected than ever, making traditional planning decisions based on static, reductionist models increasingly impracticable. Systems analytics approaches such as system dynamics, agent-based modeling, and network analysis are often deployed in isolation and fail to capture cross-level interactions and emergent system behavior. This study proposes an integrated multi-layer systems analytics framework that combines these analytical paradigms within a unified architecture to support adaptive health systems planning under uncertainty. The proposed framework introduces an Adaptive Policy Intelligence Layer (APIL), which enables continuous feedback-driven policy adaptation through dynamic monitoring, scenario evaluation, and real-time adjustment mechanisms. The model is evaluated under multiple simulation scenarios, including baseline conditions, demand shocks, resource constraints, and digital transformation environments. The findings provide strong quantitative and analytical evidence of improved system performance and resilience. More specifically, the digital transformation scenario achieved the lowest mean system pressure (0.128) and the highest resilience index (0.887), while the demand shock scenario produced the highest peak system pressure (0.306). The results demonstrate enhanced system resilience, more efficient resource deployment, and superior policy responsiveness compared with traditional single-method approaches. The originality of this study lies in integrating multi-level systems analytics with adaptive policy intelligence into a unified, feedback-driven decision-support framework for resilient health systems governance. The study contributes to systems analytics literature by advancing a synergistic and adaptive modeling paradigm capable of supporting policymakers in navigating complex and unstable healthcare environments. Full article

The increasing global demand for marine energy resources, coupled with the deployment of offshore oil and gas pipelines and submarine power cables, highlights the requirement for reliable subsea infrastructure. To protect these assets from environmental hazards and anthropogenic disturbances, seabed burial via trenching is widely adopted, with submarine trenchers serving as the main installation equipment. Trenching involves excavating a trench on the seabed to place pipelines, cables, or other subsea infrastructure. These operations involve complex soil–tool interactions that fundamentally govern cutting resistance, trench-wall stability, and overall equipment performance. Specifically, distinct engineering challenges arise across different trencher configurations: plough trenchers often encounter complex seabed structures, jet-type trenchers are prone to trench sidewall collapse, and mechanical trenchers face cutting difficulties in hard clay. A thorough understanding of these interactions is therefore critical for resolving operational challenges and optimizing trencher efficiency in engineering practice. To deeply understand these type-specific issues, this review summarizes the geomechanical problems associated with various trenching technologies, synthesizes recent research advances from analytical frameworks, physical experiments, and numerical simulations, and identifies existing knowledge gaps. By consolidating these findings, the paper provides a reference for addressing trencher-related engineering challenges, supporting equipment optimization, and facilitating the deployment of offshore energy transmission networks. Full article

The electrification of long-haul freight transport requires a comprehensive public charging infrastructure along motorways. This study presents a framework combining multi-agent transport simulation (MATSim) with evolutionary bi-objective optimization (NSGA-II) to determine the number and spatial distribution of high-power charging (HPC) points for battery-electric trucks (BETs) on the German motorway network. Beyond infrastructure sizing, the approach also quantifies the impact of BET charging on the duration and distance of long-haul truck trips. The optimization simultaneously addresses the perspectives of two key stakeholders: charge point operators (CPOs), who seek to maximize charger utilization, and logistics operators, who aim to minimize waiting times. The results yield a range of Pareto-optimal configurations balancing the two objectives. A multi-iteration replanning step further lets trucks adapt their routes to experienced waiting times for a more realistic performance assessment, reducing mean waiting times by up to 92%. We evaluate five electrification levels from 1% to 20% across two charging network scenarios with 347 and 779 potential locations, respectively. For the balanced solutions—the knee-point configurations that best reconcile both objectives—at a 10% electrification level, the optimized network reaches a temporal charger utilization of 23% to 32% at mean waiting times of about 1.4 to 1.9 min per charging process. Compared with an internal combustion engine truck (ICET) reference, BET trip durations increase by only 0.9% to 1.3% due to charging detours. Overall, the fast-charging network planned by the German federal government appears sufficient for the HPC demand at electrification levels up to 10% to 15%, whereas additional low-power charging (LPC) infrastructure beyond the planned locations will be needed to cover overnight charging requirements. Full article

The influence of BN particle size on lithium grease performance was systematically compared among a base grease (Li), a micro-BN (3 µm, 0.1 wt%) modified grease (Li + 0.1% mBN), and a nano-BN (50 nm, 0.1 wt%) modified grease (Li + 0.1% nBN). SEM shows that addition nano-BN leads to a more compact soap fiber networks, whereas micro-BN tends to agglomerate and provides limited reinforcement, leaving the base grease with a loose, porous network. Consequently, Li + 0.1% nBN outperforms both Li and Li + 0.1% mBN in dropping point (199.5 °C vs. 194.9 °C and 198.6 °C), oil separation (0.39% vs. 0.64% and 0.44%), and flow point (49% vs. 45% and 47%). Its plateau modulus is significantly higher, reflecting stronger network entanglement. However, Li + 0.1% nBN shows lower structural recovery (61.0%) than Li (65.8%) and Li + 0.1% mBN (67.2%) due to rigid particle–fiber junctions. Notably, Li + 0.1% mBN exhibits a unique frequency-dependent viscoelasticity: higher tanδ at low frequencies but lower tanδ at high frequencies relative to Li. Tribologically, Li + 0.1% nBN reduces friction coefficient by 35% and wear scar diameter by 12.7% compared with Li, outperforming Li + 0.1% mBN. XPS confirms a protective hybrid tribofilm (BN + organic nitrogen species + iron oxides) on the nano-BN lubricated surface. Particle size critically governs BN–fiber interactions and the resulting rheological and tribological performance. Full article

Cardiovascular disease (CVD) is the leading cause of morbidity and mortality worldwide, accounting for a substantial proportion of global deaths. Increasing evidence indicates that cardiovascular susceptibility is shaped during fetal development, where the intrauterine environment plays a critical role. Maternal–fetal crosstalk, mediated largely through placental function, coordinates the transfer of metabolic, endocrine, and immune signals that are essential for normal cardiac and vascular development. Disruptions in maternal physiology—including metabolic disorders, hypertensive conditions, inflammation, and environmental stress—can perturb this communication network and alter the intrauterine milieu. These changes induce persistent modifications in cardiomyocyte growth, endothelial function, and key regulatory pathways, thereby contributing to long-term cardiovascular risk. Emerging studies highlight that cardiovascular programming is governed by interconnected mechanisms involving epigenetic regulation, mitochondrial function, immune signaling, and intercellular communication. This review synthesizes current evidence on how maternal–fetal crosstalk shapes cardiovascular development beyond genetic determinants and provides an integrated framework linking early-life exposures to lifelong cardiovascular health. Full article

Background: The glucocorticoid receptor NR3C1 exhibits antiepileptic properties, but the mechanisms governing its stability during epileptogenesis remain elusive. This study investigated whether the E3 ubiquitin ligase TRIM8 regulates neuronal hyperexcitability and epileptic activity by modulating NR3C1. Methods: We established an in vivo epilepsy model via intrahippocampal kainic acid (KA) injection and an in vitro epileptiform model using Mg²⁺-free artificial cerebrospinal fluid in primary hippocampal neurons. The roles of TRIM8 and NR3C1 were assessed using in vivo and in vitro gain- and loss-of-function approaches, alongside co-immunoprecipitation, Western blotting, immunofluorescence and whole-cell patch-clamp recording. Results: TRIM8 is significantly upregulated in hippocampal and temporal lobe neurons in epileptic mice. TRIM8 was markedly upregulated in the hippocampal neurons of epileptic mice, inversely correlating with NR3C1 levels. Mechanistically, TRIM8 interacted with NR3C1, promoting its polyubiquitination and proteasomal degradation. This TRIM8-mediated NR3C1 reduction enhanced the phosphorylation of AMPA receptor (AMPAR) subunits GluR1 (Ser831) and GluR2 (Ser880) without affecting total receptor expression. In vitro, TRIM8 overexpression exacerbated calcium dysregulation, neuronal injury, and AMPAR phosphorylation; crucially, concurrent NR3C1 overexpression rescued these effects. In vivo, knockdown of TRIM8 significantly reduced seizure frequency, prolonged the latency to the first Stage III seizure, shortened average seizure duration, and decreased total seizure burden in KA-induced epileptic mice. Electrophysiologically, TRIM8 overexpression significantly increased the frequency of spontaneous action potentials and amplitudes of spontaneous excitatory postsynaptic currents under Mg²⁺-free conditions. Furthermore, in vivo knockdown of TRIM8 attenuated KA-induced seizure severity, restored NR3C1 protein stability, and suppressed aberrant AMPAR phosphorylation in the hippocampus. Triple immunofluorescence staining showed that KA-induced epilepsy increased TRIM8 but decreased NR3C1 immunoreactivity in NeuN⁺ hippocampal neurons, and TRIM8 knockdown reversed these changes. Conclusions: TRIM8 acts as a critical driver of epileptiform activity by targeting NR3C1 for degradation, thereby disinhibiting AMPAR phosphorylation and enhancing network hyperexcitability. The TRIM8-NR3C1-AMPAR axis emerges as a previously unrecognized molecular pathway in epileptogenesis, highlighting its potential as a promising therapeutic target for epilepsy. Full article