Search Results (24,229)

Driven by the global energy transition and carbon neutrality goals, hybrid power systems have become a core technical path for energy conservation and carbon reduction in the transportation and power sectors, and the performance of energy management strategies directly determines the system’s overall energy efficiency. Traditional energy management methods have inherent bottlenecks of high model dependence and poor adaptability, making it difficult to satisfy real-time decision-making requirements under complex operating conditions. Deep Reinforcement Learning (DRL) provides an innovative solution to this technical bottleneck, and has become a cutting-edge research direction in this field. However, existing reviews have not yet constructed a full-chain analysis framework covering its algorithms, applications, verification, challenges and prospects. Focusing on the engineering application of DRL in the real-time energy management of hybrid power systems, this paper systematically sorts out domestic and international research results up to the first quarter of 2026. The core quantitative findings of this review are as follows: (1) DRL-based strategies can achieve 93–99.5% of the Dynamic Programming (DP) theoretical global optimum in fuel economy, which is 5–25% higher than rule-based methods; (2) DRL strategies only have 3.1–4.8% performance degradation under unseen operating conditions, which is significantly better than the 10.3–14.7% degradation of the Equivalent Consumption Minimization Strategy (ECMS); (3) Actor–Critic (AC) algorithms (Twin Delayed Deep Deterministic Policy Gradient (TD3)/Soft Actor–Critic (SAC)) have become the mainstream in this field, with a 3–5 times higher sample efficiency than value function-based algorithms; and (4) offline DRL and transfer learning can reduce the training time of DRL strategies by more than 80% while maintaining equivalent optimization performance. This paper first analyzes the essential attributes and core technical challenges of hybrid power system energy management; second, classifies DRL algorithms from the perspective of control engineering and analyzes their technical characteristics; third, disassembles the application design logic of DRL around four major scenarios: land vehicles, water vessels, aerial vehicles and fixed microgrids; fourth, summarizes the mainstream verification platforms and evaluation systems; fifth, analyzes core bottlenecks and cutting-edge solutions; and finally, prospects the development trends of next-generation intelligent energy management systems combined with cross-fusion technologies. This paper aims to build a complete technical system map for this field and promote the engineering deployment and practical application of intelligent energy management technologies integrating data and knowledge. Full article

Global water pollution driven by industrial and agricultural expansion has resulted in the widespread occurrence of persistent contaminants, particularly pharmaceuticals and heavy metals, in groundwater systems. Conventional treatment methods often prove inefficient, costly, and environmentally unsustainable, highlighting the need for innovative in situ remediation technologies. Permeable Reactive Barriers (PRBs) have emerged as a promising and energy-efficient solution for the long-term purification of contaminated aquifers. Their efficiency arises from passive operation, relying on natural groundwater flow to promote pollutant removal through adsorption, ion exchange, precipitation, and redox-driven transformations. This review emphasizes the superior performance of materials such as Activated Carbon, Biochar, Zeolites, and Zero-Valent Iron (ZVI) in the immobilization and reduction in pharmaceuticals and metal ions. Key challenges to PRB longevity include permeability loss and reactive media depletion due to mineral precipitation and biofouling. Advances in hybrid PRB configurations, coupled with electrokinetic (EK) and bioreactor systems, and predictive modeling, particularly Artificial Neural Networks (ANNs), offer pathways to enhance performance, optimize design, and ensure sustainable operation. Overall, PRBs represent a scalable and environmentally sound approach to groundwater remediation, with future progress relying on the development of multifunctional, regenerable materials and integrated design strategies. Full article

Under China’s dual-carbon goals, Green Finance Policy (GFP) and the Environmental Protection Tax Policy (ETP) are key tools for firm-level green transformation, yet their joint micro-effects remain underexplored. Using Shanghai and Shenzhen A-share listed firms from 2011–2022, this study treats the overlapping rollout of the Green Finance Reform and Innovation Pilot Zones and the Environmental Protection Tax reform as a staggered quasi-natural experiment and applies a multi-period DID to identify their synergistic effect on Corporate Green Technology Innovation. Results show that each policy alone promotes green innovation and that their coordination further strengthens the effect. The synergy operates mainly by easing financing constraints and increasing R&D investment. The effect is stronger among firms with better resources, governance, and digitalization, and in regions with stronger institutional environments; it is also more evident in non-heavy-polluting and non-manufacturing sectors. While the policy mix raises both innovation quantity and quality, it does not significantly improve total factor productivity, indicating a “weak Porter effect.” These findings provide micro-level evidence on GFP–ETP synergy and inform the refinement of green finance, environmental tax design, and firm-level green transition policies. Full article

Background: Open banking (OB) is rapidly transforming financial ecosystems by enabling controlled data sharing among multiple actors through application programming interfaces (APIs). While this transformation promises innovation and competition, it also introduces complex security challenges that extend beyond purely technical considerations. Despite growing attention in academic and professional domains, existing reviews provide limited integration of security concerns with global adoption patterns and cross regional variation. Methods: This systematic review analyses empirical and conceptual research on security in OB published between 1999 and 2025, capturing early digital banking studies that later informed the development of OB. The literature is structured into three distinct phases: foundational digital banking developments, regulatory formalisation of OB frameworks, and post-implementation expansion of OB ecosystems. A comprehensive search was conducted across major academic databases and scholarly portals, complemented by relevant regulatory and policy sources. Following duplicate removal, title and abstract screening, full-text eligibility assessment, and methodological quality appraisal, 117 studies were retained for qualitative synthesis. Results: The findings reveal recurring security challenges arising from the interaction between technological infrastructures, regulatory frameworks, and user behaviour within OB ecosystems. Technical safeguards such as APIs, strong customer authentication, and encryption are necessary but insufficient when they are misaligned with regulatory implementation and user behaviour. Behavioural factors, including trust, consent understanding, and security-related decision making, play a central role in shaping ecosystem resilience. Based on this synthesis, the study develops a tri-dimensional security framework integrating technological, regulatory, and behavioural dimensions. The bibliometric analysis of 117 studies reveals that technological security dominates the literature (58%), followed by regulatory governance (44%) and behavioural dimensions (42%). However, only 17.9% of studies integrate all three dimensions simultaneously. APIs and authentication mechanisms represent the most frequent technological terms, while PSD2 and GDPR dominate regulatory discourse. Trust and decision-making are the most recurrent behavioural constructs. The relatively low proportion of fully integrated studies confirms a structural fragmentation within OB security research, thereby empirically justifying the proposed tri-dimensional framework. Chronologically, early studies (1999–2015) predominantly focused on technical security mechanisms and regulatory compliance, whereas more recent research (2020–2025) increasingly highlights the interplay between regulatory frameworks and user behaviour, suggesting a shift towards a more holistic understanding of security within OB adoption. Conclusions: This systematic review concludes that integrating technological, regulatory, and behavioural perspectives advances a more comprehensive understanding of security in OB ecosystems. The proposed tri-dimensional security framework provides a structured foundation for future research and supports policy-relevant and practice-oriented security design. Full article

Accident reports provide a detailed account of environmental causes, unsafe human behaviors, and subsequent chain reactions. These records serve as essential resources for analyzing accident mechanisms and exploring potential risk patterns within production safety processes. Currently, Graph based Retrieval-Augmented Generation (RAG), which integrates Large Language Models (LLMs) with Knowledge Graphs (KGs), has emerged as a leading approach for complex causal question answering over extensive unstructured accident documentation. However, the application of this technology in the production safety domain still encounters two primary challenges. First, knowledge graph construction using a single granularity fails to capture fine-grained case details and macro-level standard systems. Second, traditional one-step retrieval paradigms lack the capacity to track deep causal chains or interpret the complex logic of multi-factor coupling. To address these limitations, we propose CausalAgent, a hierarchical graph-enhanced multi-agent framework for causal question answering in production safety accident reports. This framework innovatively combines a Hierarchical Causal Graph (HC-Graph) and a Multi-Agent Collaborative Reasoning (MACR) mechanism. Specifically, the HC-Graph employs a two-layer architecture that links a fine-grained instance layer with a national standard causation layer to resolve conflicts in semantic granularity. The MACR mechanism converts complex natural language queries into executable structured queries and logic verification steps through the sequential cooperation of four specialized agents, namely the Graph Parsing Agent, the Problem Analysis Agent, the Query Generation Agent, and the Reasoning Insight Agent. CausalAgent enables in-depth mining of accident causation mechanisms and provides scientific, robust and interpretable intelligent support for data-driven risk assessment and emergency decision-making. Experiments on real-world accident datasets demonstrate that CausalAgent achieves a $100.0\%$ query execution rate and an $87.3\%$ reasoning accuracy, outperforming the SOTA baseline by $45.2\%$ in terms of absolute accuracy. Full article

The agrifood sector faces well-documented barriers to climate-smart agriculture (CSA) adoption, reflecting systematic difficulty identifying where digital tools address specific stakeholder needs rather than a technology shortage. This paper presents a prescriptive framework for mapping digital innovation opportunities in complex, multi-stakeholder agrifood systems. Grounded in Jobs-to-be-Done (JTBD) theory and structured as a two-dimensional matrix of meta-jobs and value chain segments, the framework was developed through a design science research (DSR) paradigm evaluated on utility, coherence, and actionability. Construction involved a purposive synthesis of three literature streams and iterative refinement through 136 stakeholder engagements within a six-month university-affiliated startup studio cycle. Applied to climate-smart agriculture, the framework produces 54 strategic opportunity areas across nine meta-jobs and six value chain segments. A cross-cutting pattern analysis identifies three structural constraints: agricultural data fragmentation and absence of interoperability standards; inadequate measurement, reporting, and verification infrastructure; and misalignment between financing mechanisms and climate-smart time horizons. The framework equips entrepreneurs and investors with a segment-differentiated opportunity map, supports agribusiness portfolio analysis, and directs policymakers toward three priority areas where coordinated systemic action generates value across the opportunity landscape. Full article

The scientific data of the Earth surface system is characterized by multi-source heterogeneity and dynamic correlation, so constructing an efficient data association network and enabling intelligent knowledge services is a hot topic. Nevertheless, confronted with the existing challenges of onerous data acquisition, inadequate precision of data recommendation, excessive time and labor consumption, as well as insufficient semantic reasoning in intelligent question-and-answer (Q&A) systems, we propose an intelligent framework that integrates dynamic optimization and retrieval-augmented generation (RAG) technology to address the problems of strong subjectivity in the setting of edge weight thresholds in association networks and insufficient semantic inference in intelligent Q&A. First, a multidimensional association network is constructed based on metadata features, redundant edge pruning is achieved through dynamic threshold analysis, and key nodes are identified by combining complex network centrality theory to optimize network structure and storage efficiency. Secondly, the RAG-based intelligent Q&A model is designed to transform the association triples into a paragraph-based knowledge base, generate a domain Q&A dataset using a large language model GPT-4o, and fine-tune the word embedding model to improve the semantic representation accuracy. Experiments show that the number of network edges is reduced by about 70% after optimization, and the node importance analysis accurately identifies key data nodes; the fine-tuned model improves each index by 6% on average in the retrieval task, and the Q&A system significantly outperforms the traditional method in terms of indexes such as relevance and completeness. This study provides innovative solutions for the intelligent service of scientific data in Earth surface systems and promotes the deep integration of association networks and generative AI. Full article

Across modern Asia and many other regions, artificial intelligence is transforming religious life in diverse and profound ways. Robot priests chant sutras at Japanese Buddhist temples, AI-powered apps offer personalised coaching in Quranic recitation to millions of Muslims, and bereaved families consult algorithm-generated avatars of the deceased in China. They are neither merely tools for instrumental use nor channels for transmitting pre-existing religious authority. Instead, they create new forms of religious content, new types of spiritual encounters for religious users, and new structures of authority. This paper argues that understanding these phenomena requires theoretical innovation beyond simply applying existing concepts to new domains. Drawing on Actor–Network Theory, algorithmic culture studies, and scholarship on Asian religious traditions, the paper proposes the theoretical framework of generative charisma, theorising how AI systems gain religious authority through three interconnected mechanisms: captivation by generation, intimacy trust through personalisation, and oscillating enchantment. It also highlights accountability as a structural issue that needs critical discussion regarding governance. The paper demonstrates the framework’s usefulness by examining AI recitation coaching in Islamic practice and AI grief avatars in Chinese Buddhist mourning, showing its relevance across different religious traditions and technological forms. Full article

The recirculating aquaculture system (RAS) marks a significant shift in global aquaculture, transitioning to controlled, land-based production. This review highlights technological advancements that enable the treatment and reuse of over 90% of water, thereby enhancing water quality and production efficiency. These features position RAS as a cornerstone of sustainable seafood production. This review introduces the RAS Readiness Level (RRL) framework which is a novel, structured approach to assess the commercial maturity of emerging RAS technologies. Applying the RRL to six key technological domains (from digital AI systems to biological PHB recovery) reveals a pervasive pilot-scale purgatory where most innovations stagnate at RRL 4–6. It further addresses advanced processes such as membrane bioreactors, denitrification reactors, and the conversion of waste into valuable products. Furthermore, this review addresses persistent challenges, including high energy demand, economic viability, and the accumulation of pathogens. Finally, it focuses on the emergent integration of the Internet of Things (IoT) and artificial intelligence (AI), which are revolutionizing RAS management through data-driven optimization. By synthesizing current innovations, this review envisions a future of intelligent, closed-loop RAS where advanced IoT- and AI-driven technologies optimize water quality and feeding strategies to minimize ecological impact while enhancing sustainability and productivity. Full article

Improving productivity under resource and environmental constraints is a key challenge for sustainability-oriented transformation in manufacturing. Using panel data from 30 Chinese provinces during the period 2010–2024, this study examines how regional digital innovation capability is associated with manufacturing total factor productivity at the provincial level. A multidimensional digital innovation index is constructed using the entropy-weighting method, while manufacturing total factor productivity (TFP) is measured using the DEA–Malmquist index. In this study, conventional manufacturing TFP is treated as a productivity-oriented proxy within a sustainability-oriented transformation context, rather than as a direct measure of environmental performance. The empirical framework applies a two-way fixed-effects model and is complemented by supplementary instrumental-variable estimation, mediation analysis, and threshold regression to examine transmission channels and nonlinear effects. The results indicate that digital innovation capability is positively associated with manufacturing TFP, with stronger associations observed in regions that have more developed digital and innovation foundations. Decomposition results show that the gains are mainly related to technological progress rather than short-term efficiency improvements, suggesting that digitalization is reflected primarily through innovation-led upgrading. Mechanism tests further show that improvements in R&D efficiency, data element allocation, and human capital structure play important mediating roles. A significant threshold effect is also observed: When the share of digital-skilled labor exceeds a critical level, the productivity return from digital innovation increases markedly. These findings underscore the role of digital innovation and digital maturity in supporting manufacturing productivity upgrading within a sustainability-oriented transformation context and imply that policy should prioritize coordinated investment in digital infrastructure, data governance, and digital skills development. Full article

Recent years have witnessed the rapid proliferation of Industry 4.0 technologies in smart grids, leading to a revolution in energy generation and management, which provides improved operational efficiency and intelligent automation for smart grids. Nevertheless, this highly integrated infrastructure, while making energy more secure and reliable, simultaneously creates greater vulnerability to sophisticated cyber threats such as Distributed Denial of Service (DDoS) attacks, data manipulation and unauthorized access. The task of addressing these challenges requires innovative approaches that maintain the resilience as well as security of critical energy infrastructures. A novel Blockchain-Enhanced Cybersecurity Framework (BCF) specific to Industry 4.0-enabled smart grid systems is presented in this paper. The proposed framework integrates advanced security protocols with real-time threat detection capabilities through the decentralized, transparent and tamper-resistant nature of blockchain technology. Authentication, data validation and secure communication are accomplished through smart contracts to automate it, eliminating human intervention and single points of failures. The framework is able to allow for high transaction volumes, typical of modern smart grid networks, whilst maintaining integrity via a hybrid consensus mechanism that ensures scalability. In addition, the framework is further augmented with a Machine Learning-Based Intrusion Detection System (ML-IDS) to detect and mitigate cyber-attacks in real time. The proposed system achieves excellent performance in identifying malicious activities with high accuracy, precision and recall on the UNSW-NB15 dataset. Analysis with traditional methods indicates that the Blockchain Enhanced Cybersecurity Framework significantly lowers false positive rates and increases detection reliability. The framework is justified in terms of its strength to secure the systems in Industry 4.0-enabled smart grids against emerging cyber threats through extensive simulations and case studies. The value of this work is that it shows that blockchain and machine learning can be used to improve cybersecurity in renewable energy systems, and concrete insights and recommendations on implementing secure and cost-effective systems of energy infrastructure are provided. The proposed framework creates an enabling environment on which the creation of resilient and future-ready smart grids to facilitate the global goal of sustainable and secure energy can be developed. Full article

The agriculture and food processing sectors are essential, meeting the fundamental needs of global populations. However, it is crucial to adopt sustainable practices that fulfill these needs while minimizing environmental impact. Climate change, once a theoretical concern, is now an urgent and tangible challenge, requiring immediate action to mitigate its effects. As such, all human activities, particularly those in resource-intensive sectors like agriculture, must be reevaluated. This study explores and reviews the potential of applying waterjet systems and their evolution in agricultural and food processes to improve efficiency and minimize resource consumption; while the use of pure waterjet technology for soft foods has emerged as an established practice, its extension to agricultural applications and the use of abrasive waterjet in this field are still in the research and experimentation phase. This work presents preliminary results, discussing the key waterjet components, their economical modeling, and food safety. Three main categories of applications—cutting of soft, plant-based products, cutting of animal products, and in-field agricultural applications—are reviewed, with detailed use cases on strawberry de-calyxing, meat–bone cutting and sugarcane harvesting, respectively. These applications are analyzed by highlighting waterjet main advantages in terms of cutting performance, as well as food quality and preservation.. At the end, future directions are delineated, suggesting potential advancements that could allow us to replace traditional methods with more innovative and sustainable alternatives. A specific focus is given to abrasive ice waterjets. Full article

Promoting low-carbon and sustainable development has become an important policy objective in the context of digital transformation. Using China’s National Big Data Comprehensive Pilot Zone (NBDCPZ) policy as a quasi-natural experiment, this study employs a staggered difference-in-differences approach to examine the policy’s effects on carbon emission quantity and carbon emission quality. The results show that the NBDCPZ reduces carbon emission quantity by 12.3% and improves carbon emission quality by 2.6%. These effects are more pronounced in the central and eastern regions and in areas with stronger government support. The analysis suggests that the NBDCPZ may affect carbon outcomes through green technological innovation and energy structure adjustment. Full article

The growing interest in clean-label and naturally preserved foods has pushed the scientific community to research essential oils (EOs) as sustainable, multifunctional alternatives to chemical preservatives. These plant volatile compounds exhibit strong antimicrobial and antioxidant activities, making them promising ingredients for natural preservation. Fermented meat products, though highly nutritional, are particularly at risk of microbial spoilage and contamination by foodborne pathogens due to their complex microbiota and processing conditions. This review examines the role of EOs as natural antimicrobials in fermented meat systems, summarizing their mechanisms of action, efficiency against key pathogens, and impact on safety, shelf life, and sensory attributes. Additionally, it discusses technological challenges related to volatility, stability, and sensory alterations, and outlines mitigation strategies such as encapsulation, nanoemulsions, and controlled-release delivery systems. By critically presenting current progress and identifying research gaps such as standardization and matrix interactions, this review contributes to the development of effective, natural, and clean-label preservation strategies. These insights support innovation and sustainability in the meat processing industry by bridging the gap between antimicrobial efficacy and sensory acceptability. Full article

With the deepening construction of ecological civilization, the realization of ecological product value, referring to the value derived from ecosystems’ material goods, regulation, support, and cultural services, has become a strategic key point for national sustainable development. Data factors, distinguished from digital technologies as the actual resources used in production, exchange, and consumption, are becoming increasingly important as a new catalyst for empowering the realization of ecological product value. Drawing on panel data spanning 2011 to 2023 across China’s 31 provinces, this research employs the entropy weight method to construct evaluation indices for both the development of data factors and the realization of ecological product value, deriving weights from the data’s intrinsic variability. The effect of data factors on the realization of ecological product value is examined using a two-way fixed effects framework. Our outcomes are presented below. First, data factors can significantly promote the realization of ecological product value, and this conclusion is supported by a series of robustness checks and endogeneity treatments. Second, the mechanism analysis reveals that data factors empower the realization of ecological product value through new quality productive forces, energy consumption intensity, and innovation and entrepreneurship. Third, results from the threshold model suggest that the promoting effect of data factors on the realization of ecological product value is subject to a threshold constraint, characterized by diminishing marginal returns beyond this point. Fourth, regarding regional disparities, the results indicate that data factors primarily drive ecological product value realization in the central region, as it is at a critical stage of digital transformation, with a secondary effect in the east, while their influence in the western region remains insignificant. These findings provide important guidance for integrating data factors and ecological resources to achieve sustainable development. Full article