Search Results (3,447)

How do regulatory policies, funding structures, and cross-sector coordination shape knowledge flows and institutional transformation? Focusing on the smart medical device sector in Taiwan, this study explores how governance dynamics accelerate system transformation and foster demand for adaptive and integrative innovation systems. Building on the National Biotechnology Innovation System framework and qualitative system dynamics modeling, the study analyzes institutional interactions through 28 semi-structured interviews and 18 policy documents. Findings identify systemic bottlenecks, including translational gaps, coordination challenges, and barriers for traditional manufacturers. These gaps have enabled tech firms to emerge as system leaders by bridging these institutional gaps. This study extends innovation systems theory by conceptualizing an emergent governance function that addresses institutional gaps. At the policy level, the study highlights the importance of enabling institutional change in governance to address structural fragmentation and support system-wide transformation. Full article

The integration of artificial intelligence (AI), big data analytics, and blockchain technologies within the digital economy presents transformative opportunities for promoting low-carbon urban development. However, a systematic understanding of how these digital innovations influence urban carbon mitigation remains limited. This study addresses this gap by proposing two research questions (RQs): (1) What are the key success factors for artificial intelligence, big data, and blockchain in urban carbon emission reduction? (2) How do these technologies interact and support the transition to low-carbon cities? To answer these questions, the study employs a hybrid methodological framework combining the decision-making trial and evaluation laboratory (DEMATEL) and interpretive structural modeling (ISM) techniques. The data were collected through structured expert questionnaires, enabling the identification and hierarchical analysis of twelve critical success factors (CSFs). Grounded in sustainability transitions theory and institutional theory, the CSFs are categorized into three dimensions: (1) digital infrastructure and technological applications; (2) digital transformation of industry and economy; (3) sustainable urban governance. The results reveal that e-commerce and sustainable logistics, the adoption of the circular economy, and cross-sector collaboration are the most influential drivers of digital-enabled decarbonization, while foundational elements such as smart energy systems and digital infrastructure act as key enablers. The DEMATEL-ISM approach facilitates a system-level understanding of the causal relationships and strategic priorities among the CSFs, offering actionable insights for urban planners, policymakers, and stakeholders committed to sustainable digital transformation and carbon neutrality. Full article

In the digital era, understanding the intention to use generative AI is critical, as it enhances productivity, transforms workflows, and enables humans to focus on higher-value tasks. Drawing upon the unified theory of acceptance and use of technology (UTAUT) and the technology threat avoidance theory (TTAT), this research integrates perceived AI literacy into the AI acceptance–avoidance framework as a central variable. This study gathered 583 valid survey responses from China and validated its model using a dual-phase, combined method that integrates structural equation modeling and artificial neural networks. Research findings indicate that the model explains 51.6% of the variance in generative AI usage intention. Except for social influence, all variables within the extended framework significantly impact the usage intention, with perceived AI literacy being the strongest predictor (β = 0.33, p < 0.001). Additionally, perceived AI literacy mitigates the adverse effect of perceived threats on the intention to use AI. Practical implications suggest that enterprises adopt a tiered strategy, as follows: maximize perceived benefits by integrating AI skills into reward systems and providing task-automation training; minimize perceived costs through dedicated technical support and transparent risk mitigation plans; and cultivate AI literacy via progressive learning paths, advancing from data analysis to innovation. Full article

The intersection of green finance and artificial intelligence (AI) represents a rapidly emerging and high-impact research domain with the potential to reshape sustainable economic systems. This study presents a comprehensive bibliometric and network analysis aimed at mapping the scientific landscape, identifying research hotspots, and highlighting methodological trends at this nexus. A dataset of 268 peer-reviewed publications (2014–June 2025) was retrieved from the Web of Science Core Collection, filtered by the Business Economics category. Analytical techniques employed include Bibliometrix in R, VOSviewer, and science mapping tools such as thematic mapping, trend topic analysis, co-citation networks, and co-occurrence clustering. Results indicate an annual growth rate of 53.31%, with China leading in both productivity and impact, followed by Vietnam and the United Kingdom. The most prolific affiliations and authors, primarily based in China, underscore a concentrated regional research output. The most relevant journals include Energy Economics and Finance Research Letters. Network visualizations identified 17 clusters, with focused analysis on the top three: (1) Emission, Health, and Environmental Risk, (2) Institutional and Technological Infrastructure, and (3) Green Innovation and Sustainable Urban Development. The methodological landscape is equally diverse, with top techniques including blockchain technology, large language models, convolutional neural networks, sentiment analysis, and structural equation modeling, demonstrating a blend of traditional econometrics and advanced AI. This study not only uncovers intellectual structures and thematic evolution but also identifies underdeveloped areas and proposes future research directions. These include dynamic topic modeling, regional case studies, and ethical frameworks for AI in sustainable finance. The findings provide a strategic foundation for advancing interdisciplinary collaboration and policy innovation in green AI–finance ecosystems. Full article

This study endeavors to elucidate the mechanisms and pathways through which the digital economy shapes agricultural green development, providing theoretical underpinnings and practical guidance for the green transformation of regional agriculture. (1) Using panel data from 18 prefecture-level cities in Sichuan Province (2013–2022), a comprehensive evaluation index system for agricultural green development was formulated. Fixed-effects, mediating-effects, and threshold-effects models were employed to systematically analyze the direct effects, transmission pathways, and nonlinear characteristics of the digital economy on agricultural green development. (2) The fixed-effects model shows that the digital economy markedly propels agricultural green development in Sichuan Province. The mediating-effects model verifies two transmission pathways: “digital economy → technological progression → agricultural green development” and “digital economy → industrial structure upgrading → agricultural green development”. The threshold-effects model suggests that when the digital economy is in the low-threshold interval, it exerts a suppressive impact on agricultural green development; however, once the threshold is surpassed, its promoting effect strengthens significantly. (3) The results demonstrate the following findings: First, the digital economy exerts a significant positive effect on agricultural green development. Second, this promoting effect exhibits significant nonlinear characteristics that vary with the level of digital economy development. Third, the impact manifests remarkable regional heterogeneity, necessitating context-specific development strategies. (4) Five optimization recommendations are proposed: promote the categorized development of agricultural digital technologies and industrial upgrading; advance digital infrastructure and technology adaptation in phases; design differentiated regional policies; establish a hierarchical and classified long-term guarantee mechanism; and strengthen the “industry-university-research-application” collaborative innovation and dynamic monitoring system. Full article

The current work is established with the object of modifying the source of Fenton system and substituting iron source as a catalyst with magnetite/potato peels composite material (POT400-M) to be an innovative solar photocatalyst. The structural and morphological characteristics of the material are assessed through X-ray diffraction (XRD) and scanning electron microscopy (SEM). The technique is applied to treat oil spills that pollute seawater. The effectiveness of the operating parameters is studied, and numerical optimization is applied to optimize the most influential parameters on the system, including POT400-M catalyst (47 mg/L) and hydrogen peroxide reagent (372 mg/L) at pH 5.0, to maximize oil removal, reaching 93%. Also, the aqueous solution and wastewater temperature on the oxidation reaction is evaluated and the reaction exhibited an exothermic nature. Kinetic modeling is evaluated, and the reaction is found to follow the second-order kinetic model. Thermodynamic examination of the data exhibits negative enthalpy (∆H′) values, confirming that the reaction is exothermic, and the system is verified to be able to perform at the minimal activation energy barrier (−51.34 kJ/mol). Full article

This research addresses the challenges of proactive scheduling and routing in manufacturing systems governed by the Material Requirement Planning (MRP) method. Such systems often face capacity constraints, difficulties in resource balancing, and limited traceability of component requirements. The lack of seamless integration between customer orders and production tasks, combined with the manual and time-consuming nature of schedule adjustments, highlights the need for an automated and optimized scheduling method. We propose a novel optimization-based approach that leverages mixed-integer linear programming (MILP) combined with a proprietary procedure for reducing the size of the modeled problem to generate feasible and/or optimal production schedules. The model incorporates dynamic routing, partial resource utilization, limited additional resources (e.g., tools, workers), technological breaks, and time quantization. Key results include determining order feasibility, identifying unfulfilled order components, minimizing costs, shortening deadlines, and assessing feasibility in the absence of available resources. By automating the generation of data from MRP/ERP systems, constructing an optimization model, and exporting the results back to the MRP/ERP structure, this method improves decision-making and competes with expensive Advanced Planning and Scheduling (APS) systems. The proposed innovation solution—the integration of MILP-based optimization with the proprietary PT (data transformation) and PR (model-size reduction) procedures—not only increases operational efficiency but also enables demand source tracking and offers a scalable and economical alternative for modern production environments. Experimental results demonstrate significant reductions in production costs (up to 25%) and lead times (more than 50%). Full article

The regio- and stereoselective adjacent bifunctionalization of olefins with amine and halogen groups can be effectively accomplished through catalytic haloamination methods. Stereoselective haloamination has emerged as a pivotal methodology for the introduction of halogen functional groups into chiral amines, demonstrating substantial applications in medicinal chemistry and organic synthesis. Since 1999, significant advancements have been achieved in this field, driven by innovations in catalytic systems and methodologies. The stereoselective haloamination of both functionalized and nonfunctionalized alkenes employing chiral catalysts has emerged as a prominent area of research. This review provides a comprehensive overview of the research progress in stereoselective haloamination reactions from 1999 to 2023. It examines the innovations in catalyst design that have facilitated more efficient and selective transformations. The review also analyzes the optimization of reaction conditions, which has been crucial in improving the overall performance and applicability of these reactions. Furthermore, it explores the diverse range of haloamination reactions that have been developed, emphasizing their potential for the synthesis of complex and valuable chemical structures. Additionally, this review offers insightful perspectives on future research directions in stereoselective haloamination reactions. Full article

This study demonstrates the successful fabrication of high-performance reaction-bonded silicon carbide (RBSC) ceramics through an optimized liquid silicon infiltration (LSI) process employing multi-modal SiC particle gradation and nano-carbon black (0.6 µm) additives. By engineering porous preforms with hierarchical SiC distributions and tailored carbon sources, the resulting ceramics achieved a compressive strength of 2393 MPa and a flexural strength of 380 MPa, surpassing conventional RBSC systems. Microstructural analyses revealed homogeneous β-SiC formation and crack deflection mechanisms as key contributors to mechanical enhancement. Ultrafine SiC particles (0.5–2 µm) refined pore architectures and mediated capillary dynamics during infiltration, enabling nanoscale dispersion of residual silicon phases and minimizing interfacial defects. Compared to coarse-grained counterparts, the ultrafine SiC system exhibited a 23% increase in compressive strength, attributed to reduced sintering defects and enhanced load transfer efficiency. This work establishes a scalable strategy for designing RBSC ceramics for extreme mechanical environments, bridging material innovation with applications in high-stress structural components. Full article

The exponential increase in textual data generated across sectors such as healthcare, finance, and smart manufacturing has intensified the need for effective Big Data analytics. Large language models (LLMs) have become critical tools because of their advanced language processing capabilities. However, their static nature limits their ability to incorporate real-time and domain-specific knowledge. Retrieval-augmented generation (RAG) addresses these limitations by enriching LLM outputs through external content retrieval. Nevertheless, traditional RAG systems remain inefficient, often exhibiting high retrieval latency, redundancy, and diminished response quality when scaled to large datasets. This paper proposes an innovative structured RAG framework specifically designed for large-scale Big Data analytics. The framework transforms unstructured partial prompts into structured semantically coherent partial prompts, leveraging element-specific embedding models and dimensionality reduction techniques, such as principal component analysis. To further improve the retrieval accuracy and computational efficiency, we introduce a multi-level filtering approach integrating semantic constraints and redundancy elimination. In the experiments, the proposed method was compared with structured-format RAG. After generating prompts utilizing two methods, silhouette scores were computed to assess the quality of embedding clusters. The proposed method outperformed the baseline by improving the clustering quality by 32.3%. These results demonstrate the effectiveness of the framework in enhancing LLMs for accurate, diverse, and efficient decision-making in complex Big Data environments. Full article

Tocopherols, major lipid-soluble components of vitamin E, are essential natural products with significant nutritional and pharmacological value. Their structural diversity and uneven distribution across vegetable oils require accurate analytical strategies for compositional profiling, quality control, and authenticity verification, amid concerns over food fraud and regulatory demands. Analytical challenges, such as matrix effects in complex oils and the cost trade-offs of green extraction methods, complicate these processes. This review examines recent advances in tocopherol analysis, focusing on extraction and detection techniques. Green methods like supercritical fluid extraction and deep eutectic solvents offer selectivity and sustainability, though they are costlier than traditional approaches. On the analytical side, hyphenated techniques such as supercritical fluid chromatography-mass spectrometry (SFC-MS) achieve detection limits as low as 0.05 ng/mL, improving sensitivity in complex matrices. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) provides robust analysis, while spectroscopic and electrochemical sensors offer rapid, cost-effective alternatives for high-throughput screening. The integration of chemometric tools and miniaturized systems supports scalable workflows. Looking ahead, the incorporation of Artificial Intelligence (AI) in oil authentication has the potential to enhance the accuracy and efficiency of future analyses. These innovations could improve our understanding of tocopherol compositions in vegetable oils, supporting more reliable assessments of nutritional value and product authenticity. Full article

This study evaluated and compared two encapsulation techniques—co-crystallization and ionic gelation—for stabilizing bioactive components derived from lavender distillation residues. Utilizing aqueous ethanol extraction (solid residues) and concentration (liquid residues), phenolic-rich extracts were incorporated into encapsulation matrices and processed under controlled conditions. Comprehensive characterization included encapsulation efficiency (Ef), antioxidant activity (AA), moisture content, hygroscopicity, dissolution time, bulk density, and color parameters (L*, a*, b*). Co-crystallization outperformed ionic gelation across most criteria, achieving significantly higher Ef (>150%) and superior functional properties such as lower moisture content (<0.5%), negative hygroscopicity (−6%), and faster dissolution (<60 s). These features suggested enhanced physicochemical stability and suitability for applications requiring long shelf life and rapid solubility. In contrast, extruded beads exhibited high moisture levels (94.0–95.4%) but allowed better control over morphological features. The work introduced a mild-processing approach applied innovatively to the valorization of lavender distillation waste through structurally stable phenolic delivery systems. By systematically benchmarking two distinct encapsulation strategies under equivalent formulation conditions, this study advanced current understanding in bioactive microencapsulation and offers new tools for developing functional ingredients from aromatic plant by-products. Full article

The European Union Emissions Trading System (EU ETS) and its underlying market structure play a central role in the EU’s climate policy. This study analyses how the network of trading relationships within the EU ETS has evolved from a hub-dominated architecture to one marked by structural change and the emergence of new trading dynamics. Using transaction data from Phases I–IV, we apply complex network analysis to assess changes in connectivity, centrality, and community structure. We then construct a Digital Twin of the EU ETS, integrating graph neural networks and logistic regression models to simulate the entry of new participants and predict future trading links. The results indicate shifts in network composition and connectivity, especially in Phase IV, where regulatory innovations and institutional mechanisms appear to play a key role. While our analysis focuses on structural dynamics, these patterns may have broader implications for market performance and policy effectiveness. These findings underscore the importance of monitoring the evolving trading network alongside price signals to support a resilient, efficient, and environmentally credible carbon market. Full article

Electrospun nanofiber membranes (ESNFMs) are exceptional biomaterials for tissue engineering, closely mimicking the native extracellular matrix. However, their inherent fragility poses significant handling, processing, and integration challenges, limiting their widespread application in advanced 3D tissue models and biofabricated devices. This study introduces a novel and on-mat framing technique utilizing extrusion-based printing of a UV-curable biocompatible resin (Biotough D90 MF) to create rigid, integrated support structures directly on chitosan–polyethylene oxide (PEO) ESNFMs. We demonstrate fabrication of these circular frames via precise 3D printing and a simpler manual stamping method, achieving robust mechanical stabilization that enables routine laboratory manipulation without membrane damage. The resulting framed ESNFMs maintain structural integrity during subsequent processing and exhibit excellent biocompatibility in standardized extract assays (116.5 ± 12.2% normalized cellular response with optimized processing) and acceptable performance in direct contact evaluations (up to 78.2 ± 32.4% viability in the optimal configuration). Temporal assessment revealed characteristic cellular adaptation dynamics on nanofiber substrates, emphasizing the importance of extended evaluation periods for accurate biocompatibility determination of three-dimensional scaffolds. This innovative biofabrication approach overcomes critical limitations of previous handling methods, transforming delicate ESNFMs into robust, easy-to-use components for reliable integration into complex cell culture applications, barrier tissue models, and engineered systems. Full article

As a novel carbon material, multi-walled carbon nanotubes (MWCNTs) have attracted significant research interest in energetic applications due to their high aspect ratio and exceptional physicochemical properties. However, their inherent structural characteristics and poor dispersion severely limit their practical utilization in solid propellant formulations. To address these challenges, this study developed an innovative reverse-engineering strategy that precisely confines MWCNTs within a three-dimensional Fe₂O₃ gel framework through a controllable sol-gel process followed by low-temperature calcination. This advanced material architecture not only overcomes the traditional limitations of MWCNTs but also creates abundant Fe-C interfacial sites that synergistically catalyze the thermal decomposition of glycidyl azide polymer-based energetic thermoplastic elastomer (GAP-ETPE). Systematic characterization reveals that the MWCNTs@Fe₂O₃ nanocomposite delivers exceptional catalytic performance for azido group decomposition, achieving a >200% enhancement in decomposition rate compared to physical mixtures while simultaneously improving the mechanical strength of GAP-ETPE-based propellants by 15–20%. More importantly, this work provides fundamental insights into the rational design of advanced carbon-based nanocomposites for next-generation energetic materials, opening new avenues for the application of nanocarbons in propulsion systems. Full article