Search Results (2,203)

Against the dual backdrop of iterative AI advancement and deepening green development imperatives, AI-driven industrial intelligence (INT) has emerged as a pivotal force in driving sustainable economic growth. While the existing literature has explored the correlation between INT and green total factor productivity (GTFP), significant gaps remain in the design of multidimensional variables, analysis of environmental regulation (ER), and capture of dynamic effects. From the perspective of ER, this study utilizes provincial panel data from China (2012–2023) to construct an 11-indicator evaluation system for INT development and employs the EBM super-efficiency model to measure GTFP. Furthermore, a two-way fixed effects model combined with a moderated mediation model is established to systematically elucidate the intrinsic linkage mechanism between INT and GTFP. The key findings are as follows: First, INT has a significant positive impact on GTFP. Second, green innovation and spatio-economic synergy are crucial pathways through which INT empowers GTFP. Third, ER exhibits a substitutive effect within both the direct and indirect impacts of INT on GTFP, where intensified ER significantly attenuates INT’s positive impacts. Fourth, the enhancement effect of INT on GTFP remains statistically significant with a one-year lag, and the substitution effect of ER persists. This study provides an in-depth analysis of the mechanisms of INT-driven green economic transformation, offering valuable insights for governments to implement differentiated environmental governance strategies tailored to local conditions. Full article

As the demand for lightweight and high-performance conductive materials grows in power transmission systems, aluminum alloy rods have emerged as a cost-effective and scalable alternative to copper conductors. This review systematically examines the development status and technological progress in the purification and casting–rolling processes used in the production of Electrical Round Aluminum Rods (ERARs). It explores current challenges in improving electrical conductivity and mechanical strength while addressing issues such as hydrogen and oxide inclusion removal, grain refinement, and impurity segregation. Key purification techniques—including flux refining, gas treatment, filtration, and rotary injection—are compared in terms of performance, cost, and environmental impact. The paper also analyzes different casting–rolling methods, including continuous casting and rolling, twin-roll casting, and extrusion processes, with attention to process optimization and equipment design. Furthermore, emerging applications of artificial intelligence (AI) in predictive modeling, defect detection, and process parameter optimization are highlighted, offering a novel perspective on intelligent and sustainable ERAR production. This paper aims to provide insights for facilitating the industrial-scale production and performance enhancement of ERAR materials. Full article

Accurate forecasting of language service demand is essential for language industry planning and resource allocation, yet it remains challenging due to small sample sizes, noisy data, and nonlinear dynamics in industry-level time series. To enhance forecasting accuracy, this study proposes a novel hybrid forecasting framework, called the Sine Cosine Algorithm-optimized wavelet analysis-based new information priority nonhomogeneous discrete grey model (SCA–WA–NIPNDGM). By integrating wavelet-based denoising with the NIPNDGM, the model effectively extracts intrinsic signals and prioritizes recent observations to capture short-term trends while addressing nonlinear parameter estimation via heuristic optimization. Empirical studies are conducted across three high-demand sectors in China from 2000 to 2024, including manufacturing; water conservancy, environmental, and public facilities management; and wholesale and retail. The findings show that the proposed model displays superior performance to 11 benchmark grey models and five optimization algorithms across six evaluation metrics, achieving test Mean Absolute Percentage Error (MAPE) values as low as 1.2%, with strong generalization, stable iterations, and fast convergence. These results underscore its effectiveness in forecasting complex time series and offer valuable insights for language service market planning under emerging AI-driven disruptions. Full article

The digital revolution has profoundly influenced the automotive industry, shifting the paradigm from conventional vehicles to smart cars (SCs). The SCs rely on in-vehicle communication among electronic control units (ECUs) enabled by assorted protocols. The Controller Area Network (CAN) serves as the de facto standard for interconnecting these units, enabling critical functionalities. However, inherited non-delineation in SCs— transmits messages without explicit destination addressing—poses significant security risks, necessitating the evolution of an astute and resilient self-defense mechanism (SDM) to neutralize cyber threats. To this end, this study introduces a lightweight intrusion mitigation mechanism based on an adaptive momentum-based deep denoising autoencoder (AM-DDAE). Employing real-time CAN bus data from renowned smart vehicles, the proposed framework effectively reconstructs original data compromised by adversarial activities. Simulation results illustrate the efficacy of the AM-DDAE-based SDM, achieving a reconstruction error (RE) of less than 1% and an average execution time of 0.145532 s for data recovery. When validated on a new unseen attack, and on an Adversarial Machine Learning attack, the proposed model demonstrated equally strong performance with RE < 1%. Furthermore, the model’s decision-making capabilities were analysed using Explainable AI techinques such as SHAP and LIME. Additionally, the scheme offers applicable deployment flexibility: it can either be (a) embedded directly into individual ECU firmware or (b) implemented as a centralized hardware component interfacing between the CAN bus and ECUs, preloaded with the proposed mitigation algorithm. Full article

While the corporate adoption of artificial intelligence (AI) is accelerating, its environmental consequences remain insufficiently understood, particularly in absolute firm-level energy consumption. The main objective of this study is to empirically determine the causal impact of AI adoption on absolute firm-level energy consumption in Chinese publicly listed companies, with a particular focus on the mediating role of green innovation and the moderating role of digital capabilities. This study provides the first large-scale micro-level evidence on how AI adoption shapes corporate energy use, drawing on panel data from Chinese non-financial listed firms during 2011–2022. We construct a novel AI adoption index via Word2Vec-based textual analysis of annual reports and estimate its impact using firm fixed effects, instrumental variables, mediation models, and multiple robustness checks. Results show that AI adoption significantly reduces total energy consumption, with a 1% increase in AI intensity associated with an estimated 0.48% decrease in energy use. Green innovation emerges as a key mediating channel, while the energy-saving benefits are amplified in firms with advanced digital transformation and IT-oriented executive teams. Heterogeneity analyses indicate more substantial effects among large firms, private enterprises, non-energy-intensive sectors, and firms in digitally lagging regions, suggesting capability-driven and context-dependent dynamics. This study advances the literature on digital transformation and corporate sustainability by uncovering the mechanisms and boundary conditions of AI’s environmental impact and offers actionable insights for aligning AI investments with carbon reduction targets and industrial upgrading in emerging economies. Full article

In recent years, artificial intelligence (AI) technology has advanced rapidly and gradually permeated fields such as healthcare, the Internet of Things, and industrial production, and the dance field is no exception. Currently, various aspects of dance, including choreography, teaching, and performance, have initiated exploration into integration with AI technology. This paper focuses on the research and application of AI technology in the dance field, expounds on the core technical system and application scenarios of AI, analyzes existing issues restricting the prosperity and development of the dance field, summarizes and introduces specific research and application cases of AI technology in this domain, and presents the practical achievements of technology–art integration. Finally, it proposes the problems to be addressed in the future application of AI technology in the dance field. Full article

Fermentation has been a crucial process in the preparation of foods and beverages for consumption, especially for the purpose of adding value to nutrients and bioactive compounds; however, conventional approaches have certain drawbacks such as not being able to fulfill the requirements of the ever-increasing global population as well as the sustainability goals. This review aims to evaluate how the application of advanced fermentation techniques can transform the food production system to be more effective, nutritious, and environmentally friendly. The techniques discussed include metabolic engineering, synthetic biology, AI-driven fermentation, quorum sensing regulation, and high-pressure processing, with an emphasis on their ability to enhance microbial activity with a view to enhancing product output. Authentic, wide-coverage scientific research search engines were used such as Google Scholar, Research Gate, Science Direct, PubMed, and Frontiers. The literature search was carried out for reports, articles, as well as papers in peer-reviewed journals from 2010 to 2024. A statistical analysis with a graphical representation of publication trends on the main topics was conducted using PubMed data from 2010 to 2024. In this present review, 112 references were used to investigate novel fermentation technologies that fortify the end food products with nutritional and functional value. Images that illustrate the processes involved in novel fermentation technologies were designed using Adobe Photoshop. The findings indicate that, although there are issues regarding costs, the scalability of the process, and the acceptability of the products by the consumers, the technologies provide a way of developing healthy foods and products produced using sustainable systems. This paper thus calls for more research and development as well as for the establishment of a legal frameworks to allow for the integration of these technologies into the food production system and make the food industry future-proof. Full article

Food safety management has evolved with the Hazard Analysis and Critical Control Point (HACCP) system serving as a global benchmark. However, conventional HACCP does not explicitly address environmental sustainability, leading to challenges such as excessive water use, chemical discharge, and energy inefficiency. Green HACCP extends traditional HACCP by integrating Environmental Respect Practices (ERPs) to fill this critical gap between food safety and sustainability. This study is presented as a conceptual paper based on a structured literature review combined with illustrative industry applications. It analyzes the principles, implementation challenges, and economic viability of Green HACCP, contrasting it with conventional systems. Evidence from recent reports and industry examples shows measurable benefits: water consumption reductions of 20–25%, energy savings of 10–15%, and improved compliance readiness through digital monitoring technologies. It explores how digital technologies—IoT for real-time monitoring, AI for predictive optimization, and blockchain for traceability—enhance efficiency and sustainability. By aligning HACCP with sustainability goals and the United Nations Sustainable Development Goals (SDGs), this paper provides academic contributions including a clarified conceptual framework, quantified advantages, and policy recommendations to support the integration of Green HACCP into global food safety systems. Industry applications from dairy, seafood, and bakery sectors illustrate practical benefits, including waste reduction and improved compliance. This study concludes with policy recommendations to integrate Green HACCP into global food safety frameworks, supporting broader sustainability goals. Overall, Green HACCP demonstrates a cost-effective, scalable, and environmentally responsible model for future food production. Full article

Nowadays, integrating Artificial Intelligence (AI) in military communication systems is reshaping current defense strategies by enhancing secure data exchange, situational awareness, and autonomous decision-making. This survey examines advancements of AI in tactical communication networks, including UAV networks, radar-based transmission, and electronic warfare resilience, thereby addressing a key gap in the existing literature. This is the first comprehensive review of AI applied exclusively to current tactical communication systems, synthesizing fragmented literature into a unified defense-oriented framework. A key contribution of this survey is its cross-sectoral perspective, exploring how civilian AI techniques are applied in military contexts to enhance resilient and secure communication networks. We analyze state-of-the-art research, industry initiatives, and real-world implementations. Additionally, we introduce a three-criteria evaluation methodology to systematically assess AI applications based on system objectives, military communication constraints, and tactical environmental factors, enabling a study of AI strategies for multidomain interoperability. Finally, we draft future research directions, emphasizing the need for AI standardization, enhanced adversarial resilience, and AI-powered self-healing networks. This survey provides key insights into the evolving role of AI in modern military communications for researchers, policymakers, and defense professionals. Full article

Under the background of uncertainty brought by the rapid development of AI, participation in AI standardisation is becoming the key for strategic emerging enterprises (SEEs) to break through and achieve sustainable development. This paper selects listed SEEs from the China Strategic Emerging Industries Composite Index jointly issued by China Securities Index Co., Ltd. and the Shanghai Stock Exchange in 2017 as the initial sample. We collect 3430 observations from 380 companies spanning 2010 to 2023. This paper employs a two-way fixed effects model incorporating enterprise clustering. It thoroughly investigates and empirically tests how participation in AI standardisation affects the sustainable development of SEEs under uncertainty. It is found that participation in AI standardisation in the context of uncertainty has a significant positive effect on the sustainable development of SEEs, and this conclusion still holds after employing instrumental variables, difference-in-difference, and a series of robustness tests. Mechanism tests indicate that two transmission paths exist between participation in AI standardisation and the sustainable development of SEEs under uncertainty: digital technology innovation and the dynamic capabilities in the dimensions of learning and absorption as well as change and reconfiguration. However, the dynamic capabilities in the coordination and integration dimensions do not play a significant mediating role. Heterogeneity analyses indicate that participation in AI standardisation contributes more significantly to the sustainable development of SEEs that are not state-owned, face lower environmental and information uncertainty, and are under higher economic policy uncertainty. The findings enrich the research related to AI standardisation and firm sustainability and provide policy recommendations for the sustainable development of SEEs in the context of uncertainty. Full article

As the core driving force of the new generation of industrial revolution, artificial intelligence technology has brought new opportunities for empowering enterprise innovation and advancing sustainability. Focusing on Chinese A-share listed enterprises and based on the unbalanced panel data from 2011 to 2023, this study systematically examines the relationship mechanism between artificial intelligence (AI) application and enterprise breakthrough innovation, and further explores the mediating effect of knowledge recombination and the moderating role of market competition. The empirical results show that AI application has a significant promoting effect on the enterprise breakthrough innovation. Knowledge recombination creation and knowledge recombination reuse play mediating roles in the relationship between the AI application and enterprise breakthrough innovation, forming the key transmission path for empowering breakthrough innovation with AI. In addition, market competition positively moderates the relationship between knowledge recombination and enterprise breakthrough innovation and strengthens the driving effect of knowledge recombination on innovation output, thus fostering more sustainable competitive advantages. Full article

This systematic review examines the methodologies and applications of integrated risk indicators in positron emission tomography (PET) radiopharmaceutical production, focusing on occupational, technological, and environmental risks. Conducted in accordance with PRISMA 2020 guidelines and utilizing the Ryyan software 2023 for article screening, the review synthesizes findings from 70 studies published between 2020 and 2025 in English and Spanish, including articles, conference papers, and reviews. The review was registered on PROSPERO (CRD420251078221). Key disciplines contributing to risk assessment frameworks include environmental science, occupational health and safety, civil engineering, mining engineering, maritime safety, financial/economic risk, and systems engineering. Predominant risk assessment methods identified are probabilistic modeling (e.g., Monte Carlo simulations), machine learning (e.g., neural networks), multi-criteria decision-making (e.g., AHP and TOPSIS), and failure mode and effects analysis (FMEA), each offering strengths, such as uncertainty quantification and systematic hazard identification, alongside limitations like data dependency and subjectivity. The review explores how frameworks from other industries can be adapted to address PET-specific risks, such as radiation exposure to workers, equipment failure, and waste management, and how studies integrate these factors into unified risk indicators using weighted scoring, probabilistic methods, and fuzzy logic. Gaps in the literature include limited stakeholder engagement, lack of standardized frameworks, insufficient real-time monitoring, and under-represented environmental risks. Future research directions propose developing PET-specific tools, integrating AI and IoT for real-time data, establishing standardized frameworks, and expanding environmental assessments to enhance risk management in PET radiopharmaceutical production. This review highlights the interdisciplinary nature of risk assessment and the critical need for comprehensive, tailored approaches to ensure safety and sustainability in this field. Full article

With the rapid advancement of artificial intelligence (AI), its deep integration into corporate operations has become the key driver for firms to reconfigure factor resources, boost green total factor productivity, and achieve green transformation. This analysis empirically investigates the influence of AI on corporate green transformation using panel data of China’s listed companies from 2015 to 2022. This research employs a multidimensional fixed effects linear model to analyze the relationship, finding that AI significantly enhances corporate green transformation. Mechanism analysis reveals that AI promotes green transformation by enhancing firm research and development (R&D) and firm green innovation capabilities. Heterogeneity analysis shows that the positive impact of AI on corporate green transformation is more significant in the eastern region, post-COVID−19, and in low-pollution industries. The impact is also significantly and positively moderated by the development of the non-state-owned economy and the development degree of product markets. These findings suggest that AI is a critical tool for promoting sustainable economic growth and green transformation in businesses. Full article

Demand for OIS (Optical Image Stabilization) actuator modules, developed for shake correction technologies in industries such as smartphones, drones, IoT, and AR/VR, is increasing. To enable real-time and precise inspection of these modules, an AI algorithm that maximizes defect detection accuracy is required. This study proposes an unsupervised learning-based algorithm that is robust to noise and capable of real-time processing for accurate defect classification of OIS actuators in a smart factory environment. The proposed algorithm performs noise-reduction preprocessing, considering the sensitivity of small components and lighting imbalances, and defines three dynamic Regions of Interest (ROIs) to address positional deviations. A customized AutoEncoder (AE) is trained for each ROI, and defect classification is conducted based on reconstruction errors, followed by a final comprehensive decision. Experimental results show that the algorithm achieves an accuracy of 0.9944 and an F1 score of 0.9971 using only a camera without the need for expensive sensors. Furthermore, it demonstrates an average processing time of 2.79 ms per module, ensuring real-time capability. This study contributes to precise quality inspection in smart factories by proposing a robust and scalable unsupervised inspection algorithm. Full article

The proliferation of fake profiles on social media presents critical cybersecurity and misinformation challenges, necessitating robust and scalable detection mechanisms. Such profiles weaken consumer trust, reduce user engagement, and ultimately harm brand reputation and platform credibility. As adversarial tactics and synthetic identity generation evolve, traditional rule-based and machine learning approaches struggle to detect evolving and deceptive behavioral patterns embedded in dynamic user-generated content. This study aims to develop an AI-driven, multi-modal deep learning-based detection system for identifying fake profiles that fuses textual, visual, and social network features to enhance detection accuracy. It also seeks to ensure scalability, adversarial robustness, and real-time threat detection capabilities suitable for practical deployment in industrial cybersecurity environments. To achieve these objectives, the current study proposes an integrated AI system that combines the Robustly Optimized BERT Pretraining Approach (RoBERTa) for deep semantic textual analysis, ConvNeXt for high-resolution profile image verification, and Heterogeneous Graph Attention Networks (Hetero-GAT) for modeling complex social interactions. The extracted features from all three modalities are fused through an attention-based late fusion strategy, enhancing interpretability, robustness, and cross-modal learning. Experimental evaluations on large-scale social media datasets demonstrate that the proposed RoBERTa-ConvNeXt-HeteroGAT model significantly outperforms baseline models, including Support Vector Machine (SVM), Random Forest, and Long Short-Term Memory (LSTM). Performance achieves 98.9% accuracy, 98.4% precision, and a 98.6% F1-score, with a per-profile speed of 15.7 milliseconds, enabling real-time applicability. Moreover, the model proves to be resilient against various types of attacks on text, images, and network activity. This study advances the application of AI in cybersecurity by introducing a highly interpretable, multi-modal detection system that strengthens digital trust, supports identity verification, and enhances the security of social media platforms. This alignment of technical robustness with brand trust highlights the system’s value not only in cybersecurity but also in sustaining platform credibility and consumer confidence. This system provides practical value to a wide range of stakeholders, including platform providers, AI researchers, cybersecurity professionals, and public sector regulators, by enabling real-time detection, improving operational efficiency, and safeguarding online ecosystems. Full article