Search Results (6,046)

Retrieval-augmented generation has emerged as a core technological paradigm for addressing the bottlenecks of hallucinations and knowledge lag in large language models. However, many existing reviews focus on a single technical branch or vertical application scenario, making only scattered references to hardware, evaluation methods, and cross-industry empirical evidence, and lacking a systematic, end-to-end integration. This paper conducts research based on a total of 115 papers, comprising foundational literature from 1998–2019 and core RAG literature from 2020–2026, systematically cataloging end-to-end technologies and supporting solutions, establishing a quantitative hardware comparison table and comparing 11 categories of open-source and commercial APIs, constructing a two-tier, four-level standardized evaluation framework, and compiling empirical evidence and implementation challenges across eight industries from 2024 to 2026. Based on this, the paper identifies four major structural contradictions—the retrieval–creation trade-off, the geometric–semantic misalignment as a symmetry problem between representation space and semantic structure, the autonomy–reliability paradox, and evaluation blind spots—as a unified analytical framework for the five major technological strands. Finally, this paper proposes four research directions for practical implementation—differentiable joint optimization, hybrid geometric space learning, interpretable causal reasoning, and multidimensional diagnostic evaluation—providing a systematic reference for both theoretical research on RAG and its deployment in the private sector. Full article

Phosphogypsum (PG) governance is a persistent challenge in industrial solid waste management, particularly in regions where large-scale stockpiling, uneven resource-utilization capacity, and policy implementation pressures coexist. Existing studies have paid considerable attention to regulatory instruments and recycling technologies, but less is known about how governments, waste-generating enterprises, and waste-utilizing enterprises adjust their strategies under bounded rationality. This study develops a tripartite evolutionary game model incorporating prospect theory. The technology–organization–environment (TOE) perspective is used as a parameter-identification lens to capture three external conditions: policy incentive intensity, enterprise governance input, and resource-utilization technology maturity. Based on case-informed parameter calibration from Guizhou and related policy-industrial evidence, numerical simulations are conducted to examine evolutionary paths, equilibrium conditions, and parameter sensitivity. The results show that PG governance may evolve from systemic inaction to policy-driven transformation and then to market-oriented sustainability. Technological maturity plays a threshold role, while excessive loss aversion can destabilize cooperative evolution. The interaction analysis further indicates that policy incentives are effective only when they are aligned with enterprise treatment behavior and viable market-entry conditions. These findings suggest that PG governance should move beyond short-term administrative intervention toward staged policy support, technical standardization, and market cultivation. Full article

Climate risk increasingly threatens regional industrial chain resilience, while artificial intelligence (AI) offers new tools for mitigating its effects. Using panel data for 167 Chinese prefecture-level cities from 2008 to 2023, this study examines the impact of climate risk on regional industrial chain resilience and the moderating role of AI. Climate risk significantly weakens resilience, whereas AI mitigates this adverse effect. Both effects are significant in eastern, northeastern, and coastal regions and in large and industrial cities. AI’s moderating role is more pronounced in cities with lower AI development, while heat and drought cause greater damage. The mechanism analysis shows that climate risk significantly inhibits industrial structure upgrading, total factor productivity, and industrial co-agglomeration. AI can alleviate these negative effects. Climate risk also generates negative cross-regional spillovers, whereas AI produces positive moderating spillovers. Both effects are concentrated within the first three years after a shock. These findings clarify how climate risk propagates through industrial chains and demonstrate AI’s value in risk governance, highlighting the need for timely and context-specific deployment of intelligent technologies. Full article

Recent advances in Digital Twin technology focus on visualization, operator training, and real-time machine simulation, aligning with the Industry 4.0 paradigm. However, the transition toward Industry 5.0 demands human-centric approaches that integrate workers not merely as observers but as active, monitorable parts of the system. Despite growing research interest, mature end-to-end methodologies for creating and deploying reliable Human Digital Twins (HDTs) in industrial environments are still lacking. This paper introduces Industrial Meta-Human (IMHU), an end-to-end human-centered Digital Twin methodology designed to bridge this gap. By spanning the entire lifecycle, from human modeling to production deployment, IMHU leverages Unreal Engine simulation to generate accurate human models and synthetic data, allowing safe replication of hazardous scenarios without disrupting ongoing operations. The methodology integrates Artificial Intelligence (AI) to enable real-time monitoring and support data-driven decision-making. Deployed on a fully operational production line, IMHU includes a system integration layer based on a Service-Oriented Architecture (SOA), enabling seamless interoperability with legacy Industry 4.0 infrastructures. Experimental results demonstrate the feasibility and confirm the effectiveness of real-time human-state tracking, and underscore its potential to advance scalable, human-centered Digital Twin systems for Industry 5.0. Full article

Recent advances in non-thermal and hybrid extraction technologies—such as pulsed electric field (PEF), ultrasound-assisted extraction (UAE), microwave-assisted extraction (MAE), and enzymatic treatments—have been widely reviewed in the context of general food processing. However, the current literature lacks a critical evaluation of how these modalities reshape the molecular fingerprints, authenticity markers, and cultivar-specific phenolic baselines of olive products, particularly within the Greek production landscape. Existing studies primarily emphasize extraction yield, operational parameters, or sustainability aspects, leaving important gaps regarding molecular consequences, including shifts in secoiridoids, lignans, pigments, oxidation markers, and the composition of by-products such as olive mill wastewater and pomace. This commentary addresses these gaps by integrating emerging extraction technologies with Greece’s omics-enabled analytical capacity (FoodOmicsGR_RI), highlighting how processing innovations may influence authenticity claims, phenolic integrity, and circular economy valorization routes. We discuss mechanistic pathways, molecular-level effects, and technology-specific limitations and propose a structured framework for developing national databases of processing-induced molecular markers. By linking technological mechanisms with cultivar-dependent molecular responses, this commentary aims to support coordinated Greek research efforts toward robust authenticity assurance, sustainable processing, and high-value valorization of olive by-products under evolving climatic and industrial pressures. Full article

The transition from conventional silica nanoparticle (SiNP) production based on purified alkoxysilanes and high-temperature flame hydrolysis of silicon tetrachloride to renewable and waste-derived silicon resources requires more than precursor substitution. It requires a mechanistic understanding of how feedstock mineralogy, silicon speciation, impurity chemistry, and processing history propagate through dissolution, nucleation, condensation, gelation, aging, drying, and pore evolution to determine material performance, environmental burden, and manufacturing feasibility. Although previous reviews have established the technical feasibility of producing silica from secondary resources, their predominant organization by feedstock, synthesis route, or application provides limited ability to explain why nominally similar processes generate materials with markedly different structural and functional properties. This review addresses these through a resource-pull, feedstock-to-function framework that links resource chemistry and process design to critical material attributes, application-specific specifications, sustainability, and scale-up requirements. Agricultural residues, industrial by-products, geothermal resources, waste glass, and fluorosilicate streams are critically compared according to silicon form and phase, reactivity, impurity profile, compositional variability, purification demand, and attainable product quality. Particular attention is given to waste-derived alkaline silicate systems, in which molecular, oligomeric, and colloidal silica coexist and therefore require characterization beyond bulk SiO₂ concentration. Established and emerging processing strategies, including controlled combustion and alkaline extraction, alkali fusion, ambient-pressure drying, microwave and mechanochemical activation, biogenic and biomimetic templating, and continuous processing, are evaluated according to their mechanistic effects, technological maturity, structural control, and demands for energy, reagents, water, solvents, effluent treatment, and capital. Across these routes, gelation and aging emerge as critical transfer stages through which feedstock composition is translated into network connectivity, pore architecture, shrinkage behavior, and ultimately functional performance. Evidence from secondary-source aerogels further shows that properly controlled waste-derived systems can attain BET surface areas of approximately 350–500 m² g⁻¹, within the textural range of many alkoxide-derived materials, indicating that feedstock variability, impurity management, and process control are more important constraints than an inherently lower performance ceiling. On this basis, this review proposes a minimum evidence framework comprising feedstock traceability, intermediate-speciation and colloidal characterization, silicon mass balance, gelation and aging metrics, application-specific qualification criteria, performance-normalized life cycle and techno-economic assessment, process analytical control, and staged pilot validation. Collectively, these principles provide a mechanistically grounded basis for moving sustainable silica synthesis beyond isolated proof-of-concept demonstrations toward reproducible, scalable, application-matched, and commercially credible manufacturing platforms. Full article

Inter-provincial administrative boundary areas are particularly vulnerable to ineffective regional pollution governance because they are simultaneously affected by transboundary pollution diffusion and administrative fragmentation. Existing governance practices, however, continue to rely heavily on territorially based regulation and administrative coordination, while the potential of cross-regional green-technology collaboration to address boundary pollution has received insufficient attention. Using panel data for Chinese prefecture-level cities from 2011 to 2023, this study systematically examines the impact of green innovation collaboration (GIC) on pollution control in areas along interprovincial administrative boundaries through two-way fixed-effects, instrumental-variable, Heckman two-step, and spatial Durbin models. The results show that, first, GIC significantly reduces pollution in these areas, and this finding remains robust across a range of robustness checks. GIC also generates significant spatial spillovers: while reducing local boundary pollution, it improves air quality in neighboring cities. Second, the heterogeneity analysis indicates that this effect is more pronounced in cities facing greater pollution-control pressure, lower fiscal pressure, and higher levels of openness. The regional results also reveal certain differences. Third, the mechanism analysis shows that cross-regional collaborative governance and industrial upgrading constitute two relatively independent parallel transmission pathways rather than a sequential chain, and their relative contributions vary with city conditions. These findings provide empirical evidence for improving interprovincial collaborative governance, facilitating the cross-regional diffusion of green technologies, and implementing differentiated environmental policies. Full article

Battlefield preservation is commonly understood as a matter of heritage management, historical education, or cultural tourism. This article argues that its origins and enduring significance lie elsewhere. Modern battlefield preservation emerged as a direct consequence of the transformation of warfare between the late eighteenth and twentieth centuries. Beginning with the French Revolution and the levée en masse, military service became increasingly associated with citizenship, national identity, and mass participation, while industrialization provided the logistical and technological foundations necessary for warfare on an unprecedented scale. As warfare expanded, so too did its human consequences, transforming military death from the experience of a relatively small warrior elite into a shared social reality affecting entire populations. Drawing upon military history, memory studies, sociology, religious studies, and heritage scholarship, this article examines how mass warfare generated new forms of collective bereavement, ritual commemoration, battlefield pilgrimage, and historical preservation. Through case studies ranging from the French Revolutionary and Napoleonic Wars to the Crimean War, Gettysburg, the World Wars, and the conflicts in Korea and Vietnam, it demonstrates how expanding mobilization, industrialized conflict, and rising casualty rates produced enduring landscapes of memory. The article argues that battlefield preservation developed through a cumulative historical process linking mass warfare, mass casualties, collective trauma, commemorative practices, and the creation of commemorative landscapes. Preserved battlefields are not merely remnants of past conflicts; they are cultural institutions through which societies negotiate memory, identity, and meaning. As traditional sources of collective authority weakened in modern societies, battlefields increasingly acquired functions associated with ritual, pilgrimage, and civil religion. The expansion of warfare produced an expansion of remembrance, and the preserved battlefield emerged as one of the most enduring cultural responses to the human consequences of modern conflict. Full article

The growing problem of air pollution and its direct impact on human health have created an urgent need for reliable, intelligent, and machine learning (ML)-enabled gas-sensing technologies. Among various sensing platforms, metal oxide gas sensors (MO-GSs) have emerged as promising candidates owing to their low cost, high sensitivity, and scalability. However, their practical application is limited by poor selectivity, cross-sensitivity, sensor drift, and high operating temperatures. Recent advances in ML have provided effective strategies to overcome these limitations through data-driven optimization of sensing performance. This review summarizes recent progress in ML-assisted MO-GSs, covering sensor array design, feature engineering, and classification algorithms, including support vector machines (SVMs), random forests (RFs), and deep neural networks (DNNs). In addition, key data-processing techniques such as preprocessing, dimensionality reduction, and hybrid learning approaches are critically discussed. The application of ML-enabled MO-GSs in medical diagnostics, environmental monitoring, industrial safety, and food quality assessment is also reviewed. Despite significant progress, challenges including limited dataset availability, sensor drift, and poor model generalization remain. Future research should focus on developing adaptive, energy-efficient, and IoT-enabled smart sensing systems. The integration of machine learning with metal oxide gas sensors represents a significant step toward intelligent, next-generation, high-performance gas-sensing technologies. Full article

Amid growing global economic uncertainty, strengthening tourism economic resilience is critical to the economic sustainability of tourism destinations. Using panel data for 288 Chinese prefecture-level cities from 2010 to 2024, this study uses the staggered designation of the National New-Generation Artificial Intelligence Innovation and Development Pilot Zones as a quasi-natural experiment and applies a staggered difference-in-differences (DID) model to examine whether artificial intelligence (AI) development promoted by the pilot-zone initiative enhances tourism economic resilience. Results show that the pilot-zone initiative significantly enhances city-level tourism economic resilience, and this finding remains robust across a series of endogeneity and robustness checks. Mechanism analysis identifies data factor utilization, technological innovation, and industrial structure upgrading as three parallel channels. Moderation analysis shows that the resilience-enhancing effect of the pilot-zone initiative is stronger in cities with more developed digital infrastructure, higher levels of marketization, and greater human resources. Heterogeneity analysis reveals overall regional heterogeneity and a stronger effect in resource-based cities. These findings clarify the mechanisms and boundary conditions linking AI development promoted by the pilot-zone initiative to tourism economic resilience and provide implications for technology-enabled sustainable tourism development. Full article

The increasing penetration of renewable energy has significantly intensified the demand for flexible operation of thermal power plants, making dynamic simulation an essential tool for understanding transient behaviors and developing advanced operational strategies for circulating fluidized bed (CFB) power plants. This review critically examines the existing dynamic modeling approaches for industrial-scale CFB power plants, with particular emphasis on their applicability to flexibility studies. Existing CFB flue-gas side models are systematically classified into three categories: 3D physics-based CFD models, behavioral/data-driven models, and semi-empirical mechanistic models. Their characteristics are critically compared in terms of spatial and temporal scales, empirical dependence, model generality, computational and implementation burden, and applicability to CFB flexibility studies. Dynamic modeling of the steam–water cycle is also reviewed, showing that it has reached a relatively mature stage owing to well-established thermo-hydraulic theories and standardized modeling platforms. The current research bottleneck is therefore identified as the dynamic coupling between the flue-gas side and the steam–water cycle for integrated CFB whole-plant simulation. Based on the comparative analysis, semi-empirical mechanistic models are identified as a particularly suitable framework for industrial-scale CFB flexibility studies requiring minute-to-hour transient simulation, physical interpretability, and whole-plant coupling. Finally, future research directions are discussed, highlighting how integrated dynamic models can support CFB flexibility-enhancement technologies and the development of new-generation coal-fired power plants. Full article

The rapid expansion of fiber-reinforced polymers (FRPs) in wind energy, transportation, and aerospace is generating increasing amounts of accompanied waste, making effective valorization essential to a circular economy. This review compares FRP recovery technologies in terms of recovered-fiber quality, operating conditions, post-treatment, environmental impacts, and industrial applicability. Then, it also examines direct reuse, FRP remanufacturing, and reuse in cementitious composites. Quantitative synthesis indicates that high-quality recycled carbon fibers (rCFs) generally retain more than 90% of their original strength, whereas mechanically recovered glass fibers (rGFs) typically retain approximately 70–90%. The preferred pathway depends on the intrinsic value, damage state, morphology, and residual properties. Components with sufficient residual capacity should be directly reused; high-quality fibers are better suited to polymer remanufacturing; and heterogeneous or lower-grade glass-FRP (GFRP) fractions are more compatible with cementitious applications, where mechanically recycled GFRP can provide interfacial bond strengths comparable to conventional engineering macrofibers. Future research should establish quantitative links among recovered material quality, processing, interfacial behavior, and end-use performance, while adopting consistent environmental and economic assessment boundaries. A graded utilization framework is therefore required to support both large-scale and value-added reuse of FRP waste. Full article

The core predicament of energy transition lies not in the availability of clean technologies, but in whether an economy possesses the institutional capacity and social foundation to systematically regulate its carbon-energy metabolic processes. Drawing upon co-evolutionary theory from evolutionary economics, this paper constructs a composite indicator of Low-Carbon Green Energy Eco-Co-evolution (LCEE) encompassing three functional dimensions: efficiency advancement, kinetic energy replacement, and boundary adherence. Concurrently, by integrating innovation ecosystem theory with inclusive development theory, we propose the concept of “Inclusive Potential of Regional Innovation Ecosystems” (IEP), characterizing the systemic potential for transforming innovation outcomes into social welfare across four dimensions: Knowledge Matrix Abundance (KMF), Cultural Capillary Permeation (CCP), Technological Community Succession (TCS), and Social Root Nourishment (SRN). Taking China’s 2016 intellectual property (IP) powerhouse construction pilot as the institutional prototype of Industrial Intellectual Property Upgrading Reform (IPR), we incorporate IPR, IEP, and LCEE into a unified causal analytical framework, proposing a testable transmission logic of ‘institutional supply → ecological development → co-evolutionary synergy. Using panel data from 30 Chinese provincial-level administrative regions over 2010–2022, we employ a Spatial Durbin Difference-in-Differences (SDM-DID) model to identify the direct and spatial spillover effects of IPR on LCEE, and embed a Double Machine Learning (DML) framework to test the mediating mechanism of IEP while controlling for high-dimensional nonlinear interference. The findings reveal that IPR exerts a significant and robust direct promoting effect on LCEE, generating positive spatial spillovers to neighboring regions through the public disclosure of patent information. IEP significantly promotes local LCEE, yet its spatial spillover lacks statistical support due to structural conflicts in inter-dimensional transmission attributes. IEP plays a significant partial mediating role between IPR and LCEE, with the indirect effect accounting for over one-third of the total effect, a finding robust to alternative machine learning algorithms, sample split adjustments, and exclusion of contemporaneous competing policies. Sub-path tests reveal that KMF bears the strongest mediating efficacy, serving as the primary transmission channel, while CCP exhibits full mediation—the institutional effect on LCEE in the cultural dimension depends almost entirely on the mediating transformation through the public cultural service system. Heterogeneity analysis further demonstrates full mediation in the Low-Carbon Green Energy Eco-Kinetic Replacement (KER) dimension, indicating that the institutional catalytic effect on clean energy substitution must be realized through IEP transformation. This paper provides empirical evidence for the proposed causal pathway through which institutional public goods indirectly enhance the synergistic quality of carbon-energy transition via the inclusive potential of innovation ecosystems, providing theoretical foundations and policy implications that, while grounded in China’s institutional context, may offer valuable reference points for emerging market economies facing similar dual pressures of technological constraints and green transition. Full article

With the backdrop of the coexistence of sustainable development of marine economy and ecological environment restrictions, how to enhance the efficiency of marine environmental governance (MEGE) has become a significant topic whereby coastal regions should attain green transformation. The research object in this paper is 11 coastal provincial administrative units in mainland China between 2013 and 2021, and marine environmental governance is divided into two stages of production and governance. MEGE and its stage efficiency are measured with the help of the two-stage super-efficiency Network SBM model, and the efficiency growth is characterized with the help of the Network Malmquist index. It is on this basis that, using the technology–organization–environment (TOE) framework, six antecedent conditions of digital economy, technological innovation, marine industrial structure, marine industrial agglomeration, environmental regulation, and degree of openness are chosen and fsQCA is applied to determine the multiple configuration paths of high MEGE growth. The findings indicate the following: (1) The MEGE in the coastal regions of China is improving in general, although the differences in the region remain evident. The efficiency characteristics of the production stage and the governance stage differ, and the inter-provincial differentiation of the governance stage is more pronounced. (2) No single condition is required to achieve high MEGE growth but four equifinal configurations created by the combined action of several conditions can be further reduced to three types, namely technology–environment driven, technology–organization–environment synergistic and technology–organization driven. (3) There exists apparent synergy, compensation, and substitution relationships between technology, organization, and environmental conditions. The most important aspect of high-efficiency growth is not that all positive conditions are on a high level simultaneously, but that a complex of conditions corresponding to the foundation of regional development is created. (4) High and non-high MEGE growth have a strong causal asymmetry and the effective driving routes in various regions are also different. This paper extends the study of the efficiency of marine environmental governance in the two dimensions of internal stage structure and configuration mechanism, and applies accurate, path adaptation, and regional differentiation governance to coastal regions. Full article

The valorization of agro-industrial by-products has gained increasing attention as a sustainable strategy to support circular economy principles and reduce environmental impacts. Spain, the world’s largest olive oil producer, generates substantial amounts of olive pomace (OP), which can represent up to 80% of the processed olive mass. Due to their hydrophilic nature, approximately 98% of olive phenolic compounds remain in OP after oil extraction, making this by-product a valuable source of bioactive compounds with antioxidant, anti-inflammatory, antimicrobial, and cardioprotective properties. Numerous extraction strategies have been investigated to maximize phenolic recovery while reducing processing costs and environmental impacts. Conventional solvent-based techniques, such as solid–liquid extraction (SLE) and liquid–liquid extraction (LLE), remain widely used, while greener approaches, including microwave-, ultrasound-, supercritical fluid-, pressurized liquid-, and high-pressure-assisted extraction, among others, have gained increasing attention. In addition, deep eutectic solvents (DES) have been applied either alone or in combination with green extraction technologies to enhance extraction efficiency. This review provides a comprehensive overview of extraction techniques for OP valorization and the analytical methodologies used to characterize OP extracts, including spectrophotometric and chromatographic approaches. The biological activities of OP-derived phenolics and their food applications are also discussed, highlighting their valorization potential. Full article