Search Results (402)

Based on a comprehensive review of the current research status of ecological compensation both domestically and internationally, combined with field survey data, this study delves into the issue of multi-stakeholder participation in the ecological compensation mechanisms of the Xin’an River Basin. This research reveals that the joint participation of multiple stakeholders is crucial to achieving the goals of ecological compensation in river basins. The government plays a significant role in macro-guidance, financial support, policy guarantees, supervision, and management. It promotes the comprehensive implementation of ecological environmental protection by formulating relevant laws and regulations, guiding the public to participate in ecological conservation, and supervising and punishing pollution behaviors. The public, serving as the main force, forms strong awareness and behavioral habits of ecological protection through active participation in environmental protection, monitoring, and feedback. As participants, enterprises contribute to industrial transformation and green development by improving resource utilization efficiency, reducing pollution emissions, promoting green industries, and participating in ecological restoration projects. Scientific research institutions, as technology enablers, have effectively enhanced governance efficiency through technological research and innovation, ecosystem value accounting to provide decision-making support, and public education. Social organizations, as facilitators, have injected vitality and innovation into watershed governance by extensively mobilizing social forces and building multi-party collaboration platforms. Communities, as supporters, have transformed ecological value into economic benefits by developing characteristic industries such as eco-agriculture and eco-tourism. Based on the above findings, further recommendations are proposed to mobilize the enthusiasm of upstream communities and encourage their participation in ecological compensation, promote the market-oriented operation of ecological compensation mechanisms, strengthen cross-regional cooperation to establish joint mechanisms, enhance supervision and evaluation, and establish a sound benefit-sharing mechanism. These recommendations provide theoretical support and practical references for ecological compensation worldwide. Full article

This systematic literature review explores the socio-ethical implications of Artificial Intelligence (AI) in contemporary media content generation. Drawing from 44 peer-reviewed sources, policy documents, and industry reports, the study synthesizes findings across three core domains: bias detection, storytelling transformation, and ethical governance frameworks. Through thematic coding and structured analysis, the review identifies recurring tensions between automation and authenticity, efficiency and editorial integrity, and innovation and institutional oversight. It introduces the Human–AI Co-Creation Continuum as a conceptual model for understanding hybrid narrative production and proposes practical recommendations for ethical AI adoption in journalism. The review concludes with a future research agenda emphasizing empirical studies, cross-cultural governance models, and audience perceptions of AI-generated content. This aligns with prior studies on algorithmic journalism. Full article

To evaluate the efficiency of innovation development in China’s high-tech manufacturing industry, this paper constructs a two-stage dynamic network cross-efficiency model. This model divides innovation activities into two stages: technology research and development and achievement transformation and introduces a 2-year lag period in the technology research and development stage and a 1-year lag period in the achievement transformation stage. It proposes the overall efficiency and efficiency models for each stage. The model was applied to 30 provinces in China, and the results showed that most provinces have achieved relatively ideal results in the overall efficiency and achievement transformation stage of high-tech manufacturing, while the efficiency in the technology research and development stage is generally lower than that in the achievement transformation stage. It is recommended that enterprises increase their R&D investments, break through technological barriers, and optimize the innovation chain. 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

As a key enabling technology for energy transition, the smart grid is propelling the global power system to evolve toward greater efficiency, reliability, and sustainability. Based on the three-dimensional analysis framework of “technology–policy–application”, this study systematically sorts out the technical architecture, regional development mode, and typical application scenarios of the smart grid, revealing the multi-dimensional challenges that it faces. By using the methods of literature review, cross-national case comparison, and technology–policy collaborative analysis, the differentiated paths of China, the United States, and Europe in the development of smart grids are compared, aiming to promote the integration and development of smart grid technologies. From a technical perspective, this paper proposes a collaborative framework comprising the perception layer, network layer, and decision-making layer. Additionally, it analyzes the integration pathways of critical technologies, including sensors, communication protocols, and artificial intelligence. At the policy level, by comparing the differentiated characteristics in policy orientation and market mechanisms among China, the United States, and Europe, the complementarity between government-led and market-driven approaches is pointed out. At the application level, this study validates the practical value of smart grids in optimizing energy management, enhancing power supply reliability, and promoting renewable energy consumption through case analyses in urban smart energy systems, rural electrification, and industrial sectors. Further research indicates that insufficient technical standardization, data security risks, and the lack of policy coordination are the core bottlenecks restricting the large-scale development of smart grids. This paper proposes that a new type of intelligent and resilient power system needs to be constructed through technological innovation, policy coordination, and international cooperation, providing theoretical references and practical paths for energy transition. Full article

Enhancing market integration levels is crucial for advancing sustainable regional collaborative development and achieving ecological protection and high-quality development goals within the Yellow River Basin, fostering a balance between economic efficiency, social equity, and environmental resilience. This study analyzed the retail price data of goods from prefecture-level cities in the Yellow River Basin from 2010 to 2022, employing the relative price method to measure the market integration index. Additionally, it examined the temporal and spatial evolution patterns and driving factors using the Dagum Gini coefficient and panel regression models. The results indicate the following. (1) The market integration index of the Yellow River Basin shows a fluctuating upward trend, with an average annual growth rate of 9.8%. The spatial pattern generally reflects a situation where the east is relatively high and the west is relatively low, as well as the south being higher than the north. (2) Regional disparities are gradually diminishing, with the overall Gini coefficient decreasing from 0.153 to 0.104. However, internal differences within the downstream and midstream areas have become prominent, and contribution rate analysis reveals that super-variable density has replaced between-group disparities as the primary source. (3) Upgrading the industrial structure and enhancing the level of economic development are the core driving forces, while financial support and digital infrastructure significantly accelerate the integration process. Conversely, the level of openness exhibits a phase-specific negative impact. We propose policy emphasizing the need to strengthen development in the upper reach of the Yellow River Basin, further improve interregional collaborative innovation mechanisms, and enhance cross-regional coordination among multicenter network nodes. Full article

This paper applies a multi-level social network analysis to examine Aragón’s innovation ecosystem, focusing on a decade of competitive public projects (2014–2023) aligned with the region’s Smart Specialisation Strategy (S3) 2021–2027. By mapping and weighting the participation of regional entities across regional, national, and European calls, the study uncovers how all types of local actors organise themselves around key specialisation areas. Moreover, a comparative benchmark is introduced by analysing more than 33,000 Horizon 2020 and Horizon Europe initiatives without Aragonese partners, revealing how to fill structural gaps and enrich the regional ecosystem through international collaboration. Results show strong funding concentration in four fields—Energy, Health, Agri-Food, and Advanced Technologies—while other historically strategic areas like Hydrogen and Water remain underrepresented. Although leading institutions (UNIZAR, CIRCE, ITA, AITIIP) play central roles in connecting academia and industry, direct collaboration among them is limited, pointing to missed synergies. Expanding previous SNA-based assessments, this study introduces a diagnostic tool to guide policy, proposing targeted actions such as challenge-driven calls, dedicated support programs, and cross-border consortia with top EU partners. Applied to two contrasting specialisation areas, the method offers sector-specific recommendations, helping policymakers align Aragón’s innovation capabilities with EU priorities and strengthen its position in both established and emerging domains. Full article

This study investigates how new R&D institutions mediate policy–market disjunctures to foster integrated innovation and entrepreneurship ecosystems. Employing a longitudinal case analysis (2013–2023) of the Jiangsu Industrial Technology Research Institute (JITRI), we delineate a three-phase evolutionary process: (1) an initial government-dominated phase, stimulating foundational capability development through contract R&D; (2) a subsequent marketization phase, enabling systemic resource integration via co-creation centers and global networks; and (3) a culminating synergy phase, where policy–market alignment facilitates ecosystem optimization through crowdsourced R&D and cross-domain collaboration. Three core mechanisms underpin this adaptation: policy–market coupling (providing external momentum), endogenous capability development (absorption to innovation), and dynamic resource orchestration (acquisition to optimization). JITRI’s hybrid governance model demonstrates that stage-contingent interventions—specifically, policy anchoring in early stages followed by market-responsive resource allocation—effectively transmute inherent tensions into productive synergies. These findings yield implementable frameworks for structuring innovative ecosystems and underscore the necessity for comparative studies to establish broader theoretical generalizability. Full article

Despite navigation advancements in enhanced sensor utilization and increased focus on maritime training and education, most marine accidents still involve collisions with high human involvement. Furthermore, navigators’ knowledge and application of the most often misunderstood Rule 10 Traffic Separation Schemes (TSS) according to the Convention on the International Regulations for Preventing Collisions at Sea (COLREG) represents the first focus in this study. To provide insight into the level of understanding and knowledge regarding COLREG Rule 10, a customized, worldwide survey has been created and disseminated among marine industry professionals. The survey results reveal a notable knowledge gap in Rule 10, where we initially assumed that more than half of the respondents know COLREG regulations well. According to the probability calculation and chi-square test results, all three categories (OOW, Master, and others) have significant rule misunderstanding. In response to the COLREG misunderstanding, together with the increasing density of maritime traffic, the implementation of Decision Support Systems (DSS) in navigation has become crucial for ensuring compliance with regulatory frameworks and enhancing navigational safety in general. This study presents a structural approach to vessel prioritization and decision-making within a DSS framework, focusing on the classification and response of the own vessel (OV) to bow-crossing scenarios within the TSS. Through the real-time integration of AIS navigational status data, the proposed DSS Architecture offers a structured, rule-compliant architecture to enhance navigational safety and the decision-making process within the TSS. Furthermore, implementing a Fall-Back Strategy (FBS) represents the key innovation factor, which ensures system resilience by directing operator response if opposing vessels disobey COLREG rules. Based on the vessel’s dynamic context and COLREG hierarchy, the proposed DSS Architecture identifies and informs the navigator regarding stand-on or give-way obligations among vessels. Full article

As semiconductor manufacturing advances into the angstrom-scale era characterized by three-dimensional integration, conventional metrology technologies face fundamental limitations regarding accuracy, speed, and non-destructiveness. Although optical spectroscopy has emerged as a prominent research focus, its application in complex manufacturing scenarios continues to confront significant technical barriers. This review establishes three concrete objectives: To categorize AI–optical spectroscopy integration paradigms spanning forward surrogate modeling, inverse prediction, physics-informed neural networks (PINNs), and multi-level architectures; to benchmark their efficacy against critical industrial metrology challenges including tool-to-tool (T2T) matching and high-aspect-ratio (HAR) structure characterization; and to identify unresolved bottlenecks for guiding next-generation intelligent semiconductor metrology. By categorically elaborating on the innovative applications of AI algorithms—such as forward surrogate models, inverse modeling techniques, physics-informed neural networks (PINNs), and multi-level network architectures—in optical spectroscopy, this work methodically assesses the implementation efficacy and limitations of each technical pathway. Through actual application case studies involving J-profiler software 5.0 and associated algorithms, this review validates the significant efficacy of AI technologies in addressing critical industrial challenges, including tool-to-tool (T2T) matching. The research demonstrates that the fusion of AI and optical spectroscopy delivers technological breakthroughs for semiconductor metrology; however, persistent challenges remain concerning data veracity, insufficient datasets, and cross-scale compatibility. Future research should prioritize enhancing model generalization capability, optimizing data acquisition and utilization strategies, and balancing algorithm real-time performance with accuracy, thereby catalyzing the transformation of semiconductor manufacturing towards an intelligence-driven advanced metrology paradigm. Full article

Calorific value and moisture content are the key indices to evaluate Caragana pellet fuel’s quality and combustion characteristics. Calorific value is the key index to measure the energy released by energy plants during combustion, which determines energy utilization efficiency. But at present, the determination of solid fuel is still carried out in the laboratory by oxygen bomb calorimetry. This has seriously hindered the ability of large-scale, rapid detection of fuel particles in industrial production lines. In response to this technical challenge, this study proposes using hyperspectral imaging technology combined with various chemometric methods to establish quantitative models for determining moisture content and calorific value in Caragana korshinskii fuel. A hyperspectral imaging system was used to capture the spectral data in the 935–1720 nm range of 152 samples from multiple regions in Inner Mongolia Autonomous Region. For water content and calorific value, three quantitative detection models, partial least squares regression (PLSR), random forest regression (RFR), and extreme learning machine (ELM), respectively, were established, and Monte Carlo cross-validation (MCCV) was chosen to remove outliers from the raw spectral data to improve the model accuracy. Four preprocessing methods were used to preprocess the spectral data, with standard normal variate (SNV) preprocessing performing best on the quantitative moisture content detection model and Savitzky–Golay (SG) preprocessing performing best on the calorific value detection method. Meanwhile, to improve the prediction accuracy of the model to reduce the redundant wavelength data, we chose four feature extraction methods, competitive adaptive reweighted sampling (CARS), successive pojections algorithm (SPA), genetic algorithm (GA), iteratively retains informative variables (IRIV), and combined the three models to build a quantitative detection model for the characteristic wavelengths of moisture content and calorific value of Caragana korshinskii fuel. Finally, a comprehensive comparison of the modeling effectiveness of all methods was carried out, and the SNV-IRIV-PLSR modeling combination was the best for water content prediction, with its prediction set determination coefficient $\left(R_P^2\right)$, root mean square error of prediction (RMSEP), and relative percentage deviation (RPD) of 0.9693, 0.2358, and 5.6792, respectively. At the same time, the moisture content distribution map of Caragana fuel particles is established by using this model. The SG-CARS-RFR modeling combination was the best for calorific value prediction, with its $R_P^2$, RMSEP, and RPD of 0.8037, 0.3219, and 2.2864, respectively. This study provides an innovative technical solution for Caragana fuel particles’ value and quality assessment. Full article

Lignin, the most abundant renewable aromatic biopolymer on Earth, is rapidly emerging as a powerful enabler of next-generation sustainable technologies. This review shifts the focus to the latest industrial breakthroughs that exploit lignin’s multifunctional properties across energy, agriculture, healthcare, and environmental sectors. Lignin-derived carbon materials are offering scalable, low-cost alternatives to critical raw materials in batteries and supercapacitors. In agriculture, lignin-based biostimulants and controlled-release fertilizers support resilient, low-impact food systems. Cosmetic and pharmaceutical industries are leveraging lignin’s antioxidant, UV-protective, and antimicrobial properties to create bio-based, clean-label products. In water purification, lignin-based adsorbents are enabling efficient and biodegradable solutions for persistent pollutants. These technological leaps are not merely incremental, they represent a paradigm shift toward a materials economy powered by renewable carbon. Backed by global sustainability roadmaps like the European Green Deal and China’s 14th Five-Year Plan, lignin is moving from industrial residue to strategic asset, driven by unprecedented investment and cross-sector collaboration. Breakthroughs in lignin upgrading, smart formulation, and application-driven design are dismantling long-standing barriers to scale, performance, and standardization. As showcased in this review, lignin is no longer just a promising biopolymer, it is a catalytic force accelerating the global transition toward circularity, climate resilience, and green industrial transformation. The future of sustainable innovation is lignin-enabled. Full article

Oenological residues may cause environmental pollution when processing does not significantly reduce volume and/or harmful conditions. The lack of proper valorisation alternatives entails high disposal costs and resource inefficiency that jeopardise the sustainability and competitiveness of the industry. Interestingly, wine by-products are underappreciated sources of multipurpose bioactive compounds, such as anthocyanins, associated with health benefits. Alternatively, transforming oenological by-products into valuable co-products will promote sustainability and thus, create new business opportunities. In this context, the present study has assessed the applicability of winery by-products (grape pomace and wine lees) as ingredients to develop new functional kombucha-analogous beverages “3S” (safe, salubrious, and sustainable) by the Symbiotic Culture of Bacteria and Yeast (SCOBY). Concerning the main results, during the kombucha’s development, the fermentation reactions modified the physicochemical parameters of the beverages, namely pH, total soluble solids, acetic acid, ethanol, and sugars, which remained stable throughout the monitored shelf-life period considered (21 days). The fermented beverages obtained exhibited high anthocyanin concentration, especially when using wine lees as an ingredient (up to 5.60 mg/L at the end of the aerobic fermentation period (10 days)) compared with the alternative beverages produced using grape pomace (1.69 mg/L). These findings demonstrated that using winery by-products for the development of new “3S” fermented beverages would provide a dietary source of bioactive compounds (mainly anthocyanins), further supporting new valorisation chances and thus contributing to the competitiveness and sustainability of the winery industries. This study opens a new avenue for cross-industry innovation, merging fermentation traditions with a new eco-friendly production of functional beverages that contribute to transforming oenological residues into valuable co-products. Full article

The global trend toward Industry 4.0 has intensified the demand for intelligent, adaptive, and energy-efficient manufacturing systems. Machine learning (ML) has emerged as a crucial enabler of this transformation, particularly in high-mix, high-precision environments. This review examines the integration of machine learning techniques, such as convolutional neural networks (CNNs), reinforcement learning (RL), and federated learning (FL), within Taiwan’s advanced manufacturing sectors, including semiconductor fabrication, smart assembly, and industrial energy optimization. The present study draws on patent data and industrial case studies from leading firms, such as TSMC, Foxconn, and Delta Electronics, to trace the evolution from classical optimization to hybrid, data-driven frameworks. A critical analysis of key challenges is provided, including data heterogeneity, limited model interpretability, and integration with legacy systems. A comprehensive framework is proposed to address these issues, incorporating data-centric learning, explainable artificial intelligence (XAI), and cyber–physical architectures. These components align with industrial standards, including the Reference Architecture Model Industrie 4.0 (RAMI 4.0) and the Industrial Internet Reference Architecture (IIRA). The paper concludes by outlining prospective research directions, with a focus on cross-factory learning, causal inference, and scalable industrial AI deployment. This work provides an in-depth examination of the potential of machine learning to transform manufacturing into a more transparent, resilient, and responsive ecosystem. Additionally, this review highlights Taiwan’s distinctive position in the global high-tech manufacturing landscape and provides an in-depth analysis of patent trends from 2015 to 2025. Notably, this study adopts a patent-centered perspective to capture practical innovation trends and technological maturity specific to Taiwan’s globally competitive high-tech sector. Full article

This paper presents platforms as a solution to rethink how we build, addressing the pressing paradox between meeting growing housing demands. The construction sector has not fully grasped the advantages of platforms beyond standardisation and efficiency. In contrast, other sectors have begun acknowledging that platforms can capture increased value through interactions among firms within a networked ecosystem. Learning from other sectors, this paper investigates platforms in the construction context, aiming to define, classify, and assess their impact on the construction value chain. The research approach was abductive, involving a cross-sectoral review of 190 platforms across 16 Australian and New Zealand Standard Industrial Classification (ANZSIC) industries and semi-structured interviews with stakeholder groups of the construction value chain in Australia. The findings categorise platforms as physical, digital, or hybrid, highlighting their potential to move value-added activities upstream, facilitate collaboration, and foster innovation through data-driven insights. The paper’s novelty lies in the exhaustive cross-sectoral review, the classification of platforms in the construction context, and the proposition of a platform approach as a versatile framework tailored to diverse needs and circumstances that offers a fresh perspective on sustainable building practices. The practical contribution of this study lies in offering guidelines for industry practitioners aiming to develop or refine a platform-based approach tailored to the construction context. Full article