Search Results (1,773)

To systematically reveal the influence mechanisms and spatial effects of industrial intellectual property (IP) reform strategies and manufacturing craftsmanship spirit on regional energy intensity, this study aims to provide theoretical support and practical pathways for emerging market economies pursuing dual goals of energy efficiency governance and manufacturing transformation. Based on a “technology–culture synergistic innovation ecology” theoretical framework, the study deepens the understanding of energy intensity governance and introduces two spatial weight matrices—the economic distance matrix and the nested economic–geographic matrix—to uncover the spatial heterogeneity of policy and cultural effects. Using panel data from 30 Chinese provinces from 2010 to 2022 (excluding Tibet, Hong Kong, Macao, and Taiwan), we construct an index of manufacturing craftsmanship spirit (CSM) and its four dimensions—excellence in detail, persistent dedication, breakthrough orientation, and innovation inheritance—via the entropy method. Empirical analysis is conducted through Spatial Difference-in-Differences (SDID) and Double Machine Learning (DML) models. The results show that: (1) Industrial IP reform strategies significantly reduce local energy intensity through improved property rights definition and technology transaction mechanisms, but may increase energy intensity in economically proximate regions due to intensified technological competition. (2) All four dimensions of craftsmanship spirit indirectly mitigate regional energy intensity via distinct pathways, with particularly strong mediating effects from persistent dedication and innovation inheritance. In contrast, breakthrough orientation shows no significant impact, possibly due to limitations from the current stage of the technology lifecycle. (3) Spatial spillover effects are heterogeneous: under the nested economic–geographic matrix, IP reform strategies reduce neighboring regions’ energy intensity through synergistic effects, while under the economic distance matrix, competitive spillovers lead to an increase in adjacent energy intensity. Based on these findings, we propose the following: deepening IP reform strategies to build a technology–culture synergistic ecosystem; enhancing regional policy coordination to avoid technology lock-in; systematically cultivating the core of craftsmanship spirit; and establishing a dynamic incentive mechanism for breakthrough orientation. These measures can jointly drive systemic improvements in regional energy efficiency. Full article

Recent advancements in the healthcare sector have reached a pivotal juncture, catalysed by the emergence of Digital Twin (DT) technologies. These innovations facilitate the development of virtual replicas that accurately simulate real-world conditions, thereby transforming traditional approaches to medical analysis, diagnostics, and treatment planning. Although widely successful in manufacturing, the adoption of Digital Twins in healthcare is relatively limited, particularly regarding their impact on clinical efficiency and patient outcomes. This study addresses three primary research questions: (1) How does Digital Twin technology improve individualised patient treatments and care quality? (2) What is the role of Digital Twin technology in accurately predicting patient responses to medical interventions? (3) What are the significant challenges of integrating Digital Twin technology into healthcare? Synthesising findings from 70 peer-reviewed articles, this review identifies critical knowledge gaps and provides practical recommendations for healthcare stakeholders to effectively navigate these challenges. This research proposes a conceptual framework illustrating the lifecycle of Digital Twin implementation in healthcare and outlines essential strategies for successful adoption. It emphasises the importance of robust infrastructure, clear regulatory guidance, and ethical practices to fully leverage the advantages of DT technologies. Nevertheless, this review acknowledges its limitations, including reliance on secondary data and the absence of empirical validation. Future research should focus on practical applications, diverse healthcare contexts, and broader stakeholder perspectives to comprehensively assess real-world impacts. Full article

Manufacturing enterprises, as significant contributors to high carbon emissions, play a crucial role in effectively reducing carbon emission intensity, which is essential for China to successfully achieve its “dual carbon” goals. This study examines the period from 2010 to 2022, focusing on manufacturing enterprises listed on the Shanghai and Shenzhen A-shares to investigate the effects of green financial reform on carbon and pollutant emissions. Our findings reveal that the results from the parallel trend test and the regression analysis of the Difference-in-Differences (DID) model indicate that the implementation of green financial reform has a negative impact on the carbon and pollutant emissions of manufacturing enterprises, which is supported by a series of robustness tests. Heterogeneity analysis shows that the emission reduction effect of green financial reform on pollutants is significant only in manufacturing enterprises with low industry competitiveness, while the inhibitory effect on carbon emissions is significant only in those with high industry competitiveness. Furthermore, the emission reduction effects are significant in highly polluting industries, non-state-owned enterprises, and small-scale firms. Green technological innovation and financing constraints serve as the channels connecting green financial reform with emission reduction and carbon mitigation. The tax burden negatively moderates this process, while environmental, social, and governance (ESG) performance positively moderates it. Full article

In the manufacturing supply chain, management reports often begin with concise messages that summarize key inventory insights. Traditionally, human analysts manually crafted these summary messages by sifting through complex data—a process that is both time-consuming and prone to inconsistency. In this research study, we present an AI-based system that automatically generates high-quality inventory insight summaries, referred to as “headline messages,” using real-world inventory data. The proposed system leverages lightweight natural language processing (NLP) and machine learning models to achieve accurate and efficient performance. Historical messages are first clustered using a sentence-translation MiniLM model that provides fast semantic embedding. This is used to derive key message categories and define structured input features for this purpose. Then, an explainable and low-complexity classifier trained to predict appropriate headline messages based on current inventory metrics using minimal computational resources. Through empirical experiments with real enterprise data, we demonstrate that this approach can reproduce expert-written headline messages with high accuracy while reducing report generation time from hours to minutes. This study makes three contributions. First, it introduces a lightweight approach that transforms inventory data into concise messages. Second, the proposed approach mitigates confusion by maintaining interpretability and fact-based control, and aligns wording with domain-specific terminology. Furthermore, it reports an industrial validation and deployment case study, demonstrating that the system can be integrated with enterprise data pipelines to generate large-scale weekly reports. These results demonstrate the application and technological innovation of combining small-scale language models with interpretable machine learning to provide insights. Full article

Digital transformation enhances the processes and efficiency of enterprise green innovation through technological empowerment, while the ESG framework guides the direction and value of such innovation via institutional norms. However, existing studies often examine digital transformation and ESG in isolation, resulting in insufficient exploration of their synergistic effects. Based on data from manufacturing high-tech enterprises, this study employs necessary condition analysis (NCA) and fuzzy-set qualitative comparative analysis (FsQCA) to systematically examine the synergistic effects of digital transformation and ESG on enterprise green innovation. The key findings are as follows: (1) While no single factor constitutes a necessary condition for high green innovation, the elements of social governance and digital management demonstrate universal applicability in enabling enterprises to achieve high levels of green innovation. (2) The dual-core-driven configuration achieves green innovation through the synergy between social governance and digital management, with its specific pathways varying according to the coordinated combinations of auxiliary factors. This delineates three distinct types, including compliance-oriented, environmentally empowered, and comprehensively balanced pathways. (3) The digitally driven configuration establishes an endogenous linkage between technological innovation and green development through the deep coupling of digital technology R&D and application. (4) The low green innovation configuration exhibits insufficient efficacy due to either isolated single elements or the absence of digital management, resulting in suboptimal green innovation performance. This study empirically demonstrates that the effective advancement of green innovation fundamentally relies on the endogenous dynamics of social governance, the technological underpinnings of digital management, and the systemic synergy among key elements, offering significant strategic implications for enterprises to develop differentiated green innovation approaches. Full article

As sustainable development gains importance, corporate ESG performance has become a key factor in investment decisions and long-term business growth. Drawing on upper echelon theory and stakeholder theory, this study examines the impact of executives with overseas backgrounds on ESG performance using data from A-share listed companies in Shanghai and Shenzhen from 2010 to 2022. The findings show that: (1) Executives with overseas backgrounds significantly enhance ESG performance; (2) this effect operates through three main channels—promoting corporate green technology innovation, improving the quality of corporate internal control, and enhancing the level of corporate risk-taking—while digital transformation positively moderates the relationship; (3) the effect is more pronounced in non-polluting, manufacturing, capital-intensive, and technology-intensive firms. This study clarifies the internal mechanisms by which executive backgrounds influence ESG outcomes and offers insights into enhancing ESG practices to support China’s “dual carbon” goals. Full article

Machine vision technologies play a critical role in the advancement of modern human-centric manufacturing systems. This study investigates their practical applications in improving both safety and productivity within industrial environments. Particular attention is given to areas such as quality assurance, worker protection, and process optimization, illustrating how intelligent visual inspection systems and real-time data analysis contribute to increased operational efficiency and higher safety standards. The research methodology combines an in-depth analysis of industrial case studies, including one from the frozen dough industry, with a systematic review of the current literature on machine vision technologies in manufacturing. The findings highlight the potential of such systems to reduce human error, maintain consistent product quality, minimize material waste, and promote safer and more adaptable work environments. This study offers valuable insights into the integration of advanced visual technologies within human-centered production environments, while also addressing key challenges and future opportunities for innovation and technological evolution. Full article

Pesticides are essential for modern agriculture but leave harmful residues that threaten human health and ecosystems. This paper reviews key pesticide detection technologies, including chromatography and mass spectrometry, spectroscopic methods, biosensing (aptamer/enzyme sensors), and emerging technologies (nanomaterials, AI). Chromatography-mass spectrometry remains the gold standard for lab-based precision, while spectroscopic techniques enable non-destructive, multi-component analysis. Biosensors offer portable, real-time field detection with high specificity. Emerging innovations, such as nano-enhanced sensors and AI-driven data analysis, are improving sensitivity and efficiency. Despite progress, challenges persist in sensitivity, cost, and operational complexity. Future research should focus on biomimetic materials for specificity, femtogram-level nano-enhanced detection, microfluidic “sample-to-result” systems, and cost-effective smart manufacturing. Addressing these gaps will strengthen food safety from farm to table while protecting ecological balance. This overview aids researchers in method selection, supports regulatory optimization, and evaluates sustainable pest control strategies. Full article

This review analyzes the Electronic Digital Twin (EDT) tools characterizing the industrial transformation phase from Industry 4.0 to Industry 5.0. The goal is to provide innovative research EDT solutions to integrate in manufacturing production processes. Specifically, this research is focused on the possibility of combining the advanced technologies and electronics and mechatronics of industrial machines with Artificial Intelligence (AI) algorithms. Furthermore, this review provides important elements about possible future implementations of AI-EDTs and some circuital examples to support the understanding of the concept of circuit simulation in EDT models. EDTs are useful to comprehend the modeling concepts functional to the AI application using the output of the circuit simulations. The output of the circuit is used to train the AI model, thus strengthening the capability to classify and predict the real behavior of production machines with a good accuracy. This review discusses perspectives, limits, and advantages of EDTs and is useful to define new research patterns integrating structured EDTs in advanced industrial environments. The focus of this paper is the definition of possible perspectives of EDT implementations, including AI, in data-driven processes in specific strategic areas of industrial research by classifying the scientific topics in six main pillars. This paper is also suitable for the researcher to develop innovative topics for projects scaled into different work packages based on EDT facilities. Full article

Against the background of the unprecedented advancements related to Industry 4.0 and beyond, transitioning from classical mathematical models to fully embodied digital twins represents a critical change in the planning, monitoring, and optimization of complex industrial systems. This work outlines the subject within the broader field of applied mathematics and computational simulation while highlighting the critical role of sound mathematical foundations, numerical methodologies, and advanced computational tools in creating data-informed virtual models of physical infrastructures and processes in real time. The discussion includes examples related to smart manufacturing, additive manufacturing technologies, and cyber–physical systems with a focus on the potential for collaboration between physics-informed simulations, data unification, and hybrid machine learning approaches. Central issues including a lack of scalability, measuring uncertainties, interoperability challenges, and ethical concerns are discussed along with rising opportunities for multi/macrodisciplinary research and innovation. This work argues in favor of the continued integration of advanced mathematical approaches with state-of-the-art technologies including artificial intelligence, edge computing, and fifth-generation communication networks with a focus on deploying self-regulating autonomous digital twins. Finally, defeating these challenges via effective collaboration between academia and industry will provide unprecedented society- and economy-wide benefits leading to resilient, optimized, and intelligent systems that mark the future of critical industries and services. Full article

Accurate stress evaluation of structural components during manufacturing and operation is essential for ensuring the safety and reliability of advanced equipment in aerospace, defense, and other high-performance fields. However, existing electromagnetic ultrasonic stress detection methods are often limited by low signal amplitude and limited adaptability to complex environments, hindering their practical deployment for in situ testing. This study proposes a novel surface wave transducer structure for stress detection based on acoustoelastic theory combined with electromagnetic ultrasonic technology. It innovatively designs a surface wave transducer composed of multiple proportionally scaled dislocation meandering coils. This innovative configuration significantly enhances the Lorentz force distribution and coupling efficiency, which accurately measure the stress of components through acoustic time delays and present an experimental method for applying electromagnetic ultrasonic technology to in situ stress detection. Finite element simulations confirmed the optimized acoustic field characteristics, and experimental validation on 6061 aluminum alloy specimens demonstrated a 111.1% improvement in signal amplitude compared to conventional designs. Through multiple experiments and curve fitting, the average relative error of the measurement results is less than 4.53%, verifying the accuracy of the detection method. Further testing under random stress conditions validated the transducer’s feasibility for in situ testing in production and service environments. Owing to its enhanced signal strength, compact structure, and suitability for integration with automated inspection systems, the proposed transducer shows strong potential for in situ stress monitoring in demanding industrial environments. Full article

Enhancing capacity utilization (CU, hereinafter referred to as CU) is crucial for effectively solving the overcapacity problem, optimizing industrial structure, and promoting premium economic development. While extensive academic research has been conducted on CU, supply chain finance (SCF, hereinafter referred to as SCF) and its influence on corporate capacity constraints remain largely unexplored. This study carefully examines how SCF affects corporate CU and the transmission mechanism, with a focus on China’s A-share listed businesses (2010–2023). The result shows that SCF improves businesses’ CU. After applying various robustness and endogeneity tests, the findings still hold that SCF largely affects the growth in CU throughby alleviating financing constraints, reducing internal agency costs, enhancing technological innovation, and improving inefficient investment. Further analysis indicates that close supply chain relationships, lower supply chain efficiency and non-state ownership, higher industry competition, a high marketization level, and a high level of financial development all enhance the “de-capacity” effect of SCF. Besides enriching the theoretical framework of SCF’s economic impacts, this research develops an operational solution to mitigate production overcapacity, a long-standing structural issue in China’s manufacturing industries, and provides a solid theoretical support for SCF to strengthen the foundation of the real economy and spearhead the sustainable, productivity-driven development of China’s economic landscape. Full article

Municipal sewage treatment plants generate significant amounts of polluting sludge, which demands innovative valorization approaches to support its sustainable recycling. This work aimed to evaluate the valorization potential of sludge from a municipal sewage treatment plant (STP) as an alternative raw material to traditional limestone in red wall tile formulations. For this purpose, four red wall tile formulations were performed with 0%, 5%, 10%, and 15% weight of STP sludge replacing traditional limestone. The tile formulations prepared by the dry process were characterized to determine their chemical and mineral compositions, thermal analysis, and sintering behavior. The red wall tile pieces were manufactured by pressing and firing at temperatures ranging from 1150 °C to 1180 °C. The effects of STP sludge incorporation and firing temperature on the densification behavior and technological properties were investigated. The results indicated that the STP sludge exhibited good chemical compatibility for use in red wall tile formulations. Water absorption values varied between 16.52% and 19.70%, indicating compliance with the red wall tile production (BIII group). These findings demonstrate the valorization potential of STP sludge in red wall tiles, which offers a relevant recycling option for the sanitation sector and the circular economy. Full article

This study explores the innovation barriers and implementation strategies within small and medium-sized manufacturing enterprises (SMEs) in the Mazowieckie Voivodeship of Poland. Despite their crucial role in the regional economy, these enterprises face significant hurdles that impede their growth potential and innovation capabilities. Using a mixed-methods approach, the research analyzes both quantitative and qualitative data from 426 manufacturing enterprises. The findings reveal that the primary barriers include limited access to capital, outdated technologies, and a shortage of skilled labor. Furthermore, the study identifies that while company size and age do not significantly influence the type of innovations introduced, external factors such as market reach and capital availability play critical roles. The study underscores the need for tailored policy interventions to support SMEs in overcoming these barriers and fostering an environment conducive to innovation. Full article

The manufacturing segment is undergoing a rapid transformation as manufacturers integrate artificial intelligence (AI) and machine learning (ML). These technologies increasingly rely on data-driven architectures, which enable manufacturers to manage large volumes of data from machines, sensors, and other sources. As a result, they optimize operations, increase productivity, and reduce costs. This paper examines the role of AI in manufacturing through the lens of data-driven architecture. It focuses on the key components, challenges, and opportunities involved in implementing these systems. The paper explores various data types and architecture models that support AI-driven manufacturing, with an emphasis on real-time analytics. It highlights key use cases in manufacturing, including predictive maintenance, quality control, and supply chain optimization, and identifies the essential components required to implement AI successfully in smart manufacturing. The paper emphasizes the critical importance of data governance, security, and scalability in developing resilient and future-proof AI systems. Finally, it reviews a data-centric framework with essential components for manufacturers aiming to leverage these technologies to drive sustained growth and innovation. Full article