Search Results (1,417)

This study draws on socio-technical transition theory to examine how multi-actor dynamics among producers, consumers, and the media within an experimental niche—Korea’s regulatory sandbox—shape policy responsiveness and the regulatory speed of governmental responses to emerging technologies, thereby influencing socio-technical transitions. We construct a longitudinal dataset of 2136 sandbox approvals between 2019 and 2025 and 1374 cases in which related legal or administrative adjustments have been completed. Changes in actor couplings before and after sandbox approval are first assessed using Pearson correlation analysis, while temporal lead–lag relationships are identified via vector autoregression (VAR) and Granger causality tests. Building on these dynamic analyses, the study subsequently investigates the determinants of regulatory response speed using ordered logistic regression, incorporating government policy orientation (progressive vs. conservative) as a moderating variable. The results show, first, that the strong producer–consumer coupling observed prior to sandbox approval weakens afterwards, whereas the consumer–media linkage becomes substantially stronger. Second, the time-series analysis of technologies within the regulatory sandbox reveals a typical technology-push pattern and a self-reinforcing feedback loop. Specifically, producer activity initiates the signal sequence, preceding consumer reactions; subsequently, media coverage significantly drives consumer engagement, and the resulting increase in consumer attention, in turn, stimulates further media coverage. Third, in the ordered logit model, media activity accelerates legal and regulatory reform, whereas consumer activity acts as a delaying factor, with producer activity showing no significant direct effect. Finally, government policy orientation systematically moderates the magnitude and direction of these effects. Overall, the study proposes an actor-centered mechanism in which learning generated in the sandbox is externalized through consumer–media channels and translated into regulatory pacing. Based on these findings, we derive practical implications for firms and regulators regarding proactive media engagement, transparent use of evidence, institutionalized channels for consumer input, and robust feedback standards that support sustainable commercialization of emerging technologies. Full article

Visible-Near-Infrared (Vis-NIR) spectroscopy, spanning approximately 400 to 2500 nm, is an innovative technology with growing relevance for diagnostics performed at the point of care (POC). This review explores the potential of Vis-NIR in veterinary medicine, highlighting its advantages over complex techniques like Raman and Fourier transform infrared spectroscopy (FTIR) by being rapid, non-invasive, reagent-free, and compatible with miniaturized, portable devices. The methodology involves directing a broadband light source, often using LEDs, toward the sample (e.g., blood, urine, faeces), collecting spectral information related to molecular vibrations, which is then analyzed using chemometric methods. Successful veterinary applications include hemogram analysis in dogs, cats, and Atlantic salmon, and quantifying blood in ovine faeces for parasite detection. Key limitations include spectral interference from strong absorbers like water and hemoglobin, and the limited penetration depth of light. However, combining Vis-NIR with Self-Learning Artificial Intelligence (SLAI) is shown to isolate and mitigate these multi-scale interferences. Vis-NIR spectroscopy serves as an important complement to centralized laboratory testing, holding significant potential to accelerate clinical decisions, minimize stress on animals during assessment, and improve diagnostic capabilities for both human and animal health, aligning with the One Health concept. Full article

Chatter is a self-excited vibration that limits productivity, accelerates roll wear and compromises strip surface quality in high-speed tandem cold-rolling. This work presents a predictive hybrid model that couples the strip-deformation physics to the structural dynamics of a five-stand, 4-high mill, providing a fast decision tool for process optimization and real-time control. The model represents each stand as a four-degree-of-freedom mass–spring–damper system whose parameters are extracted from manufacturing automation datasheets and roll-gap sensing. Linearization about the nominal point yields analytical sensitivity matrices that close the electromechanical loop; the delay between stands is also included in the model. Implemented in MATLAB/Simulink, the computational model, based on data provided by Danieli & C. Officine Meccaniche S.p.A., reproduces the onset of chatter for two types of steel. The framework therefore supports automation-ready scheduling, active vibration mitigation and design-space exploration for next-generation mechatronic cold-rolling systems. Full article

Insulin resistance (IR) is a central driver of cardiometabolic disease and an increasingly recognized modifier of inflammatory and vascular pathology. Beyond impaired glucose homeostasis, IR emerges from chronic, metabolically induced inflammation (“meta-inflammation”) and convergent cellular stress programs that propagate across tissues and organ systems, ultimately shaping endothelial dysfunction, atherogenesis, and cardiometabolic complications. Here, we synthesize multilevel links between insulin receptor signaling, intracellular stress modules (oxidative, endoplasmic reticulum, inflammatory, and fibrotic pathways), tissue-level dysfunction, and systemic inflammatory amplification. This work is a conceptual narrative review informed by targeted database searches and citation tracking, with explicit separation of mechanistic/experimental evidence from human observational and interventional data; causal inferences are framed primarily on mechanistic and interventional findings, whereas associative statements are reserved for observational evidence. We propose an integrative framework in which stress-response pathways are context-dependent and become maladaptive when chronically activated under nutrient excess and persistent inflammatory cues, generating self-reinforcing loops between IR and inflammation that accelerate vascular injury. This framework highlights points of convergence that can guide mechanistic prioritization and translational hypothesis testing. Full article

Machine learning (ML)-powered vision for robotic inspection has accelerated with smart manufacturing, enabling automated defect detection and classification and real-time process optimization. This review provides insight into the current landscape and state-of-the-art practices in smart manufacturing quality control (QC). More than 50 studies spanning across automotive, aerospace, assembly, and general manufacturing sectors demonstrate that ML-powered vision is technically viable for robotic inspection in manufacturing. The accuracy of defect detection and classification frequently exceeds 95%, with some vision systems achieving 98–100% accuracy in controlled environments. The vision systems use predominantly self-designed convolutional neural network (CNN) architectures, YOLO variants, or traditional ML vision models. However, 77% of implementations remain at the prototype or pilot scale, revealing systematic deployment barriers. A discussion is provided to address the specifics of the vision systems and the challenges that these technologies continue to face. Finally, recommendations for future directions in ML-powered vision for robotic inspection in manufacturing are provided. Full article

Atomic Layer Deposition (ALD) is a key thin-film fabrication technique that enables the growth of ultra-thin, conformal, and compositionally controlled layers for applications in nanoelectronics, optoelectronics, and energy devices. However, the high cost and operational complexity of commercial ALD systems limit their accessibility in academic and emerging research environments. In this work, a low-cost, automated thermal ALD system is designed, assembled, and experimentally validated for the deposition of zinc oxide (ZnO) thin films. The developed system enables precise control of precursor dosing, purge sequences, and substrate temperature via an integrated LabVIEW–Arduino control architecture, allowing reproducible and stable thin-film growth. The design allows the use of various precursors through high-precision three-way diaphragm valves. In addition, the system allows continuous purge gas flow in the reaction chamber, which enhances the drag velocity of the precursor gas, reducing dosage requirement, accelerating chamber saturation time and lowering the total consumption of precursors per deposition cycle. ZnO thin films were successfully grown on silicon and glass substrates at 200 °C using diethylzinc (DEZ) as the metal precursor and hydrogen peroxide (H₂O₂) as the oxidant. The process exhibited self-limiting growth characteristics typical of ALD, yielding a growth per cycle of approximately 0.8 Å. The deposited ZnO films exhibited optical transparency of 70–80% in the visible region, a refractive index of approximately 1.9, and an optical bandgap close to 3.4 eV, which are consistent with values reported for high-quality ZnO films grown in commercial ALD systems. These results demonstrate that the proposed low-cost platform is capable of producing functional ZnO thin films with properties comparable to those obtained with conventional commercial reactors. Overall, this work presents an accessible and scalable thermal ALD system that significantly reduces equipment costs while maintaining reliable process control and film quality, offering a practical framework for expanding thin-film research capabilities across microelectronics, optoelectronics, and nanotechnology laboratories. Full article

This study investigates the demographic transformation of Spain’s settlement system from 2000 to the present, driven by intersecting forces of rural depopulation, metropolitan concentration, immigration, and welfare-state dynamics. Building on an integrated theoretical framework that combines Maslow’s hierarchy of needs, demographic accounting, territorial carrying capacity, and spatial centrality, the research aims to (1) identify the mechanisms governing population redistribution across Spanish municipalities, and (2) simulate future demographic trajectories under current policy regimes. Key findings reveal that all net population growth since 2000 stems exclusively from immigration and its demographic sequelae, while the native Spanish cohort has experienced a net decline of 5.5 million due to negative natural change. The analysis further uncovers a self-reinforcing “demographic trap,” wherein welfare eligibility tied to household size incentivizes higher fertility among economically vulnerable immigrant groups, even as native families delay childbearing due to economic precarity. These dynamics are accelerating a process of “territorial transmutation,” projected to culminate in a shift in de facto governance by 2045. The study concludes that immigration alone cannot reverse rural depopulation or ensure fiscal sustainability without structural reforms to welfare design, territorial incentives, and demographic foresight. Full article

Diabetic wounds are often accompanied by severe inflammation, which is unfavorable for vascular growth and wound repair. Therefore, promoting the healing of diabetic wounds is of great significance. In this study, carboxymethyl chitosan (CMCS) was grafted with 4-formylphenylboronic acid (FPBA) and then crosslinked with oxidized sodium alginate (OAlg) to form a dual-dynamic covalent hydrogel (CPOA) based on borate ester bond and Schiff base bonds. Mesenchymal stem cells’ exosomes (Exos) were incorporated into the CPOA to construct CPOA@Exos for diabetic wound healing. Owing to the dual-dynamic covalent crosslinking network, the CPOA hydrogel showed good injectability and self-healing ability. In addition, the hydrogel displayed reactive oxygen species (ROS) responsive properties, enabling both scavenging of multiple free radicals and on-demand release of Exos in the ROS-rich wound microenvironment. A diabetic wound model was established on C57 mice, and treatment with CPOA@Exos demonstrated that it could promote the polarization of macrophages toward the M2 phenotype, enhance cellular proliferation in the wounded area, and thereby accelerate the healing of diabetic wounds. In conclusion, this study provides a new hydrogel wound dressing that can inhibit inflammation for the management of diabetic wounds. Full article

Bipolar plates are critical components in high-efficiency energy conversion devices such as electrolyzers, fuel cells, and flow batteries, and their durability directly affects the overall performance and lifespan of the system. Although graphite bipolar plates exhibit excellent electrical conductivity and corrosion resistance, their inherent brittleness and porous structure render them prone to thermal-stress-induced damage under dynamic temperature conditions. In this study, a self-designed thermal shock testing system was utilized to perform 16,000 cycles of accelerated aging tests on graphite bipolar plates, alternating between high-temperature (90 °C) and low-temperature (30 °C) water bath environments. Systematic analysis was conducted on the performance degradation behaviors under such thermal cycling conditions using multi-scale characterization techniques, including scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), electrical conductivity, contact angle, surface roughness, and corrosion current density analysis. The results demonstrate that the degradation in electrical conductivity, loss of hydrophobicity, and increased surface roughness were primarily attributed to thermal-stress-induced microcrack initiation and propagation, surface oxidation, and physical structural deterioration. Notably, the corrosion current density did not increase significantly after 16,000 thermal cycles, but slightly decreased in the later stage, indicating that the aging of graphite bipolar plates is dominated by physical fatigue damage, and the graphite matrix has excellent chemical stability. The novelty of this study lies in the construction of a thermal shock testing system under long-cycle conditions, revealing the synergistic mechanism of thermal cycle-induced performance degradation of graphite bipolar plates, which provides experimental evidence and theoretical guidance for the material selection, structural design, and protection strategies of highly durable bipolar plates. Full article

High-voltage equipment imposes increasingly stringent demands on polymeric insulating materials, particularly in terms of dielectric strength, space charge suppression, thermo-electrical stability, and interfacial reliability. Conventional polymers are prone to critical failure modes under high electric fields, including electrical treeing, partial discharge, interfacial degradation, and thermo-oxidative aging. This review systematically summarizes recent advances in polymer modification strategies specifically designed for high-voltage applications, covering nanofiller reinforcement, plasma surface engineering, and the development of self-healing insulating polymers. Multi-scale structural control and interface engineering, aligned with the specific requirements of high-voltage environments, have emerged as pivotal approaches to enhance insulation performance. Moreover, the integration of artificial intelligence-driven materials design, digital characterization, and application-oriented modeling holds significant promise for accelerating the development of next-generation high-voltage polymeric systems, thereby offering robust materials solutions for the reliable long-term operation of high-voltage equipment. Full article

The aim of this article is to present the social–ecological system (SES) as a unit of analysis for sustainable water management under conditions of anthropogenic pressure in Poland. In the face of accelerating climate change and growing human impacts, Polish water systems are exposed to increasing ecological stress and to material and immaterial losses affecting local communities. The SES approach provides an integrative analytical framework that links ecological and social components, enabling a holistic view of adaptive and governance processes at multiple spatial scales, from municipalities to areas that transcend administrative boundaries. Methodologically, this study triangulates three complementary approaches to strengthen explanatory inference. This conceptual SES review defines the analytical categories used in the paper, the bibliometric mapping (Scopus database with VOSviewer) identifies dominant research streams and underexplored themes, and the qualitative Polish case studies operationalize these categories to diagnose mechanisms, feedbacks, and governance vulnerabilities under anthropogenic pressure. The bibliometric analysis identifies the main research streams at the intersection of SES, water management and sustainable development, revealing thematic clusters related to climate change adaptation, environmental governance, ecosystem services and hydrological extremes. The case studies - the 2024 flood, the 2022 ecological disaster in the Odra River, and water deficits associated with lignite opencast mining in Eastern Wielkopolska - illustrate how anthropogenic pressure and climate-related hazards interact within local SES and expose governance gaps. Particular attention is paid to attitudes and social participation, understood as configurations of behaviors, knowledge and emotions that shape decision-making in local self-government, especially at the municipal level. This study argues that an SES-based perspective can contribute to building the resilience of water systems, improving the integration of ecological and social dimensions and supporting more sustainable water management in Poland. Full article

This study critically examines the sectoral dynamics of renewable energy (RE) adoption across the EU-27 from 1990 to 2023, addressing the persistent gap between electricity generation and end-use sectors. Utilizing Eurostat energy balance data, the research employs a robust multi-methodological framework. We apply the Logarithmic Mean Divisia Index (LMDI) decomposition to isolate driving factors, and the Self-Organizing Maps (SOM) of Kohonen to cluster countries with similar transition structures. Furthermore, the Method of Moments Quantile Regression (MMQR) is used to estimate heterogeneous drivers across the distribution of RE shares. The empirical findings reveal a sharp dichotomy: while the share of renewables in the electricity generation mix (RES-E-Renewable Energy Share in Electricity) reached approximately 53.8% in leading member states, the aggregated share in the transport sector (RES-T) remains significantly lower at 9.1%. This distinction highlights that while power generation is decarbonizing rapidly, end-use electrification lags behind. The MMQR analysis indicates that economic growth drives renewable adoption more effectively in countries with already high renewable shares (upper quantiles) due to established market mechanisms and grid flexibility. Conversely, in lower-quantile countries, regulatory stability and direct infrastructure investment prove more critical than market-based incentives, highlighting the need for differentiated policy instruments. While EU policy milestones (RED I–III-) align with progress in power generation, they have failed to accelerate transitions in lagging sectors. This study concludes that achieving climate neutrality requires moving beyond aggregate targets to implement distinct, sector-specific interventions that address the unique structural barriers in transport and thermal applications. Full article

Under the dual pressures of global climate change and accelerating urbanization, landscape design has been tasked with the critical mission of enhancing urban environmental resilience and ecological livability. However, conventional design practices often struggle to efficiently integrate complex sustainability norms with aesthetic creativity, leading to a disconnect between form and function. To address this issue, this study proposes and validates an AI-enabled sustainability decision-support framework. The framework is based on a “Generative-Critical” multi-agent workflow that enables “Self-Correcting” iterative optimization of design schemes through a built-in expert knowledge base and a quantitative scorecard. The framework’s effectiveness was validated through a cultural park case study and a blind evaluation by 10 experts. It guided a design from an initial concept with only aesthetic forms and lacking effective stormwater management, to an ecologically integrated scheme that strategically incorporated bioretention ponds at key nodes and converted hard plazas into permeable pavements. This transformation significantly elevated the scheme’s sustainability score from 59.3 to 88.0 (p < 0.001), while the framework itself achieved a high system usability scale (SUS) score of 85.5. These results confirm that the proposed “Generative-Critical” mechanism can effectively guide AIGC to adhere to ecological-technical norms and constraints while pursuing aesthetic innovation, thereby achieving a scientific integration of aesthetic form and ecological function at the early conceptual design stage. This study offers a scalable methodology for AI-assisted sustainable design and provides a novel intelligent tool for creating resilient urban landscapes that possess both environmental performance and aesthetic value. Full article

Peptide-based biomaterials have emerged as versatile tools for pharmaceutical drug delivery due to their biocompatibility and tunable sequences, yet a comprehensive overview of their categories, mechanisms, and optimization strategies remains lacking to guide clinical translation. This review systematically collates advances in peptide-based biomaterials, covering peptide excipients (cell penetrating peptides, tight junction modulating peptides, and peptide surfactants/stabilizers), self-assembling peptides (peptide-based nanospheres, cyclic peptide nanotubes, nanovesicles and micelles, peptide-based hydrogels and depots), and peptide linkers (for antibody drug-conjugates, peptide drug-conjugates, and prodrugs). We also dissect sequence-based optimization strategies, including rational design and biophysical optimization (cyclization, stapling, D-amino acid incorporation), functional motif integration, and combinatorial discovery with AI assistance, with examples spanning marketed drugs and research-stage candidates. The review reveals that cell-penetrating peptides enable efficient intracellular payload delivery via direct penetration or endocytosis; self-assembling peptides form diverse nanostructures for controlled release; and peptide linkers achieve site-specific drug release by responding to tumor-associated enzymes or pH cues, while sequence optimization enhances stability and targeting. Peptide-based biomaterials offer precise, biocompatible and tunable solutions for drug delivery, future advancements relying on AI-driven design and multi-functional modification will accelerate their transition from basic research to clinical application. Full article

With the acceleration of digital transformation, open-source software has become a fundamental component of modern smart grids and other critical infrastructures. However, the complex dependency structures of open-source ecosystems and the continuous emergence of vulnerabilities pose substantial challenges to software supply chain security. In power information networks and cyber–physical control systems, vulnerabilities in open-source components integrated into Supervisory Control and Data Acquisition (SCADA), Energy Management System (EMS), and Distribution Management System (DMS) platforms and distributed energy controllers may propagate along the supply chain, threatening system security and operational stability. In such application scenarios, large language models (LLMs) often suffer from limited semantic accuracy when handling domain-specific security terminology, as well as deployment inefficiencies that hinder their practical adoption in critical infrastructure environments. To address these issues, this paper proposes KD-SecBERT, a domain-specific semantic bidirectional encoder optimized through multi-level knowledge distillation for open-source software supply chain security in smart grid applications. The proposed framework constructs a hierarchical multi-teacher ensemble that integrates general language understanding, cybersecurity-domain knowledge, and code semantic analysis, together with a lightweight student architecture based on depthwise separable convolutions and multi-head self-attention. In addition, a dynamic, multi-dimensional distillation strategy is introduced to jointly perform layer-wise representation alignment, ensemble knowledge fusion, and task-oriented optimization under a progressive curriculum learning scheme. Extensive experiments conducted on a multi-source dataset comprising National Vulnerability Database (NVD) and Common Vulnerabilities and Exposures (CVE) entries, security-related GitHub code, and Open Web Application Security Project (OWASP) test cases show that KD-SecBERT achieves an accuracy of 91.3%, a recall of 90.6%, and an F1-score of 89.2% on vulnerability classification tasks, indicating strong robustness in recognizing both common and low-frequency security semantics. These results demonstrate that KD-SecBERT provides an effective and practical solution for semantic analysis and software supply chain risk assessment in smart grids and other critical-infrastructure environments. Full article