Intelligent Production Research Articles

The architecture design of emergency logistics management system based on the Internet of Things

The Internet of Things (IoT) aims to connect intelligent products with network functions through both wireless and wired technologies, enabling the safe and efficient distribution of services across global networks, including computers, mobile phones, and other electronic devices. The emergence of network devices with communication, sensing, and actuation capabilities since 2005 has led to a wide range of applications, such as inventory management, environmental monitoring, and tracking moving objects. However, the progress of IoT technology is hindered by the lack of adequate infrastructure capable of supporting trillions of devices. This paper explores the role of radio frequency identification (RFID) sensor networks within the IoT architecture for logistics management, positioning these networks as key “network-enabled” devices. The study investigates the components of the system, which include the domain sensor name server (DSNS), sensor service publisher (SSP), historical database, and RFID sensors. The strategic and operational implications of IoT architecture in logistics management are analyzed, with a particular focus on emergency response scenarios. The findings highlight the potential of IoT to improve resource allocation, enhance situational awareness, and support real-time decision-making during critical operations. This research offers valuable insights into the scalability challenges and the strategic benefits of IoT in logistics management, particularly in emergency contexts where timely data can significantly influence outcomes.

The Maaloop Framework: Enabling a Systems‐Level Common Operating Picture in Multiagency Disaster Management

ABSTRACTEmergency management operations are a complex system involving multiagency hierarchical command structures requiring coordination of significant volumes of information, resourcing and effort across expansive disaster zones within restricted timeframes and with potentially catastrophic consequences. Within these environments establishing a timely and shared understanding among different levels of command and across agencies is crucial yet remains a significant challenge. The problem is subsequently framed as ʻhow can the establishment of a systems‐level common operating picture across hierarchical commands and different agencies be improved in the context of complex emergency management and disaster response?ʼ To address this, we present the Maaloop Framework which builds upon the insights gained from previous research within the context of threat assessment, sensemaking, and critical decision‐making in police, military, ambulance, and firefighting contexts. To the observer trained in its application, it aims to provide an instant, point‐in‐time, global and detailed visual intelligent product of (i) the amount of information identified by decision makers at each location; (ii) how confident those decision makers are in the information and its application to resolve an incident; (iii) how the decision maker in adjacent command levels are using the information being fed to them. In turn, it will assist in improving individual and collective understanding regardless of position within a command hierarchy or agency jurisdiction. We submit that the Maaloop Framework does no more than describe the common constructs and concepts identified in the literature describing decision‐making within these industries and the broader study of decision‐making in the field of psychology. Therefore, this framework is intended to provide a solution to provide systems‐level common operating picture and an initial basis for increased communication and understanding across complex crisis events.

Model of self-regulated learning of students – future teachers based on self-assessment of readiness for professional teaching activities

Introduction. The main requirement for a professional is to constantly learn which is put forward by the modern social situation and constant changes. New learning models based on self-regulation of learning are needed in order to create an educational environment where everyone can achieve success. For effective self-regulated learning of a student, it is important to conduct self-assessment of readiness for professional pedagogical activity. The use of self-assessment of readiness for professional activity of students – future teachers will avoid the destructive use of artificial intelligence products as author’s (non-original qualification, control works, abstracts, essays) in diagnostic procedures, increase the level of responsibility of the student himself for the individual educational result. The study was aimed at developing and testing the implementation efficiency of the self-regulated learning model of students – future teachers based on self-assessment of readiness for professional pedagogical activity. Materials and methods. In order to structure the self-assessment, a survey of 428 students of pedagogical training programs (bachelor’s, master’s, postgraduate) was conducted at the Tambov State University named after G.R. Derzhavin and the State University of Education (Russian Federation). The efficiency of the implementation of the model components was assessed by 106 students who took part in the experimental work. The U-Mann- Whitney criterion and the simplified J. Phillips ROI method were used to analyze the results. Results. A model of self-regulated student learning (components: self-assessment → goal; content; means; methods; forms of organization; stages of the cyclic process) and a toolkit for self-assessment of readiness for professional pedagogical activity have been developed, which can also be useful for individual support, tutoring, and mentoring of future and existing teachers. It has been revealed that the model of self-regulated student learning is more effective in comparison with traditional teaching models. When calculating the Mann-Whitney U criterion, a significant difference was determined between the control and experimental groups (according to the cognitive-technological criterion: U EMP = 217.5 (p≤0.05); according to the motivational-value criterion: U EMP = 204 (p≤0.01); according to the reflexive-technological criterion: U EMP = 190.5 (p≤0.01)). Conclusion. The novelty of the study consists in developing a model of self-regulated learning based on self- assessment of students’ readiness for professional pedagogical activity, which contributes to improving the quality of training of future teachers and the efficiency of the educational process. The results obtained can be used in developing models of self-regulated learning for students in other areas of training.

Research on Empowering the Development of Traditional ‎Chinese Medicine with New Quality Productivity

The Traditional Chinese Medicine (TCM) industry, deeply rooted in cultural heritage and complex production processes, has been advancing through technological innovations such as intelligent production systems, digital management, blockchain traceability, and personalized health services. Despite its historical and cultural significance, the integration of these modern technologies offers significant potential to enhance the industry’s modernization and internationalization. Empirical research and case studies indicate that adopting intelligent production systems, blockchain-enabled traceability, and data-driven resource management strategies can address industry challenges while promoting sustainable development. This paper examines the current state of new quality productivity in TCM, evaluates the challenges and opportunities of its application, and proposes strategic recommendations for the industry’s sustainable growth. The findings suggest that new quality productivity can improve production efficiency, ensure consistent product quality, and boost consumer confidence, particularly in global markets. These insights offer a theoretical foundation and practical strategies for applying advanced productivity models in TCM, with potential implications for other traditional healthcare industries.

Intelligent Control Systems for Mechanical Engineering Technology Tasks

The article is devoted to solving the main tasks set in the work with the aim of analyzing and substantiating the implementation of intelligent control systems in technological processes of mechanical engineering, with an emphasis on increasing efficiency, accuracy, and reliability of production. The use of multi-agent systems and decentralized control systems, which significantly enhance the flexibility and adaptability of production, is analyzed. Special attention is paid to the role of physics-informed neural networks in fault diagnosis, which ensures increased reliability and reduced maintenance costs for equipment. The effectiveness of applying machine learning algorithms to optimize production processes, particularly in material processing and equipment maintenance, is evaluated. The impact of integrating intelligent control systems on production performance and quality, especially in the processes of milling and bonding large parts, is considered. Practical recommendations have been developed for the implementation of an adaptive intelligent production management system (AIPMS), which combines multi-agent systems, neural networks, digital twins, and innovative materials. The implementation of the artificial intelligence concept in production processes will contribute to the further development of mechanical engineering from a technological perspective, enabling enterprises to adopt innovations more rapidly, increase automation, and enhance the adaptability of technological processes, which in turn will lead to significant improvements in product quality and competitiveness. The use of such systems allows optimizing technological processes, reducing the number of defects, lowering energy consumption, and improving environmental efficiency

Challenges, Solutions, and Policy Suggestions for China's New Energy Vehicle Industry

This article deeply analyzes the main problems facing China's new energy vehicle industry, briefly analyzes the solution, and discusses how the government conducts top-level design and the government's specific measures to promote the development of the new energy vehicle industry. First of all, this article pointed out issues such as insufficient innovation capabilities, limited intelligent application investment, and lagging development of the waste recycling business. In response to these issues, this article proposes solutions such as strengthening technological innovation, connecting the connection between patent cooperation networks, promoting intelligent production, and establishing a standardized waste recycling system. Secondly, this article discusses the policy synergy that the government should consider in the top-level design, including the three dimensions of content, structure, and process, and emphasizes the importance of policy target integration, incentive policy optimization, and policy coherence. Finally, this article proposes that the government can introduce suggestions such as subsidy policies, financial support, talent training, and industrial support, and emphasize the need to improve the need for the new energy vehicle industry chain.

Development and research status of intelligent ophthalmology in China.

This paper analyzes the current status, technological developments, academic exchange platforms, and future challenges and solutions in the field of intelligent ophthalmology (IO) in China. In terms of technology, significant progress has been made in various areas, including diabetic retinopathy, fundus image analysis, quality assessment of medical artificial intelligence products, clinical research methods, technical evaluation, and industry standards. Researchers continually enhance the safety and standardization of IO technology by formulating a series of clinical application guidelines and standards. The establishment of domestic and international academic exchange platforms provides extensive collaboration opportunities for professionals in various fields, and various academic journals serve as publication platforms for IO research. However, challenges such as technological innovation, data privacy and security, lagging regulations, and talent shortages still pose obstacles to future development. To address these issues, future efforts should focus on strengthening technological research and development, regulatory framework construction, talent cultivation, and increasing patient awareness and acceptance of new technologies. By comprehensively addressing these challenges, IO in China is poised to further lead the industry's development on a global scale, bringing more innovation and convenience to the field of ophthalmic healthcare.

Revolutionizing End-of-Life Product Recovery with Product 4.0: An Examination of Intelligent Products in Industry 4.0

In the context of growing environmental concerns and the increasing impact of the manufacturing sector on sustainability, this paper introduces the concept of “Product 4.0” (P4.0) as a novel approach to harnessing the potential of Artificial Intelligence (AI) within Industry 4.0 (I4.0) technologies. P4.0 focuses on optimizing the performance of the product throughout its lifecycle and improving recovery strategies at End of Use (EoU) and End of Life (EoL) stages. Through a comprehensive review of the literature, this study identifies critical gaps in the current application of AI within I4.0 for sustainable manufacturing, particularly in regard to smart product systems and their interactions with external environments. To address these gaps, the paper proposes a holistic approach for the P4.0 that leverages AI-driven data analysis and decision making to facilitate efficient product recovery and resource utilization. Additionally, a Causal Loop Diagram (CLD) model is developed to illustrate the relationships between sustainability dimensions—environmental, economic, and social—and product demand influenced by P4.0, while also discussing the challenges and limitations associated with its implementation. By bridging theoretical insights with practical recovery solutions, this research contributes to the sustainable manufacturing discourse and offers actionable directions for future investigations into AI-enhanced P4.0 applications within the manufacturing industry.

A Novel Sensor Deployment Strategy Based on Probabilistic Perception for Industrial Wireless Sensor Network

The rapid development of Industrial Internet of Things (IIoT) technology has highlighted the critical role of wireless sensor networks in enabling intelligent production and equipment monitoring. Effective sensor deployment is essential for ensuring communication quality and transmission speed in IIoT environments. This paper presents a novel sensor deployment strategy that integrates four key metrics: deployment cost, energy consumption, network connectivity, and sensing probability. To address the challenges of multi-dimensional optimization, the proposed method normalizes these metrics and assigns appropriate weights based on their relative importance. A major innovation of this approach is the inclusion of larger-scale environmental obstacles, which enhances its adaptability to diverse industrial settings and specific deployment scenarios. Through a comprehensive set of simulation experiments across different scenarios, the proposed particle swarm/genetic hybrid algorithm demonstrates superior performance compared to existing methods, even surpassing 10% in performance. Specifically, it excels in optimizing the newly introduced network performance metric and significantly improves search convergence time, making it a highly efficient and effective solution for sensor network optimization in IIoT applications.

Cardiac function monitoring during marathon training based on smart medical wearable sensor device

In recent years, intelligent wearable sensor devices have developed rapidly and can be seen everywhere in daily life. With the rapid development of electronic components and the continuous improvement of their performance, intelligent wearable intelligent products have gradually become possible and have shown explosive growth. In addition, intelligent wearable electronic devices have many advantages that traditional devices do not have. With the popularity of fitness wearable devices, intelligent wearable devices can also be used for real-time heart rate and dynamic electrocardiogram (ECG) monitoring during marathon sports. It can effectively prevent sudden death. During marathon training and other health services, it is very important to use intelligent wearable sensor devices to monitor heart function. This paper puts forward a heart function monitoring system for marathon training based on intelligent wearable sensor, expounds the origin of marathon sports and the importance of heart function monitoring for marathon athletes during training. This paper discusses the technology and construction method of heart rate monitoring system based on intelligent wearable sensor device. At the same time, relevant experiments are carried out to verify the relevant performance of the intelligent wearable sensor device in the algorithm. The results show that the R wave detection accuracy of wearable devices based on traditional algorithms is usually between 92% and 93%. The R wave detection accuracy of the intelligent wearable sensor device improved by the algorithm in this paper has been improved to more than 97%, and the R wave detection accuracy of the algorithm in this paper is much higher than that of the traditional algorithm. This also reflects the effectiveness of the intelligent wearable sensor device of the algorithm during the training of marathon athletes.

Research on the Construction of Electrical and Electronic Experimental Teaching Platforms in Higher Vocational Colleges

With the advancement of vocational education reform, education informationization, and digitalization have become the important direction of the reform of electrical and electronic teaching in high vocational colleges. In this context, intelligent product development of professional electrical and electronic teaching should also do a good job in practice and innovation, especially in actively building a digital experimental teaching platform and promoting the reform of experimental teaching mode, so that students can learn more useful knowledge and skills in the new platform, and cultivate more applied and skilled talents for society. While analyzing the problems existing in the traditional electrical and electronic experimental teaching mode, this paper analyzes the significance and practical path of the construction of electrical and electronic experimental teaching platforms in higher vocational colleges which can be of reference in future research.

Large language models can accurately populate Vascular Quality Initiative procedural databases using narrative operative reports.

Participation in the Vascular Quality Initiative (VQI) provides important resources to surgeons, but the ability to do so is often limited by time and data entry personnel. Large language models (LLMs) such as ChatGPT (OpenAI) are examples of generative artificial intelligence (AI) products that may help bridge this gap. Trained on large volumes of data, the models are used for natural language processing (NLP) and text generation. We evaluated the ability of LLMs to accurately populate VQI procedural databases using operative reports. A single-center retrospective study was performed using institutional VQI data from 2021-2023. The most recent procedures for carotid endarterectomy (CEA), endovascular aneurysm repair (EVAR) and infrainguinal lower extremity bypass (LEB) were analyzed using Versa, a HIPAA-compliant institutional version of ChatGPT. We created an automated function to analyze operative reports and generate a shareable VQI file using two models: gpt-35-turbo and gpt-4. Application of the LLMs was accomplished with a cloud-based programming interface. The outputs of this model were compared to VQI data for accuracy. We defined a metric as "unavailable" to the LLM if it was discussed by surgeons in <20% of operative reports. unavailable. 150 operative notes were analyzed, including 50 CEA, 50 EVAR, and 50 LEB. These procedural VQI databases included 25, 179, and 51 metrics, respectively. For all fields, gpt-35-turbo had a median accuracy of 84.0% for CEA (IQR 80.0-88.0%), 92.2% for EVAR (IQR 87.2-94.0%), and 84.3% for LEB (IQR 80.2-88.1%). There were 3 of 25, 6 of 179, and 7 of 51 VQI variables were unavailable in the operative reports, respectively. Excluding metric information routinely unavailable in operative reports, the median accuracy rate was 95.5% for each CEA procedure (IQR 90.9-100.0%), 94.8% (IQR 92.2-98.5) for EVAR, and 93.2% for LEB (IQR 90.2-96.4%). Across procedures, gpt-4 did not meaningfully improve performance compared to gpt-35 (p=0.97, 0.85, 0.95 for CEA, EVAR, LEB overall performance). The cost for 150 operative reports analyzed with gpt-35-turbo and gpt-4 were $0.12 and $3.39, respectively. LLMs can accurately populate VQI procedural databases with both structured and unstructured data, while incurring only minor processing costs. Increased workflow efficiency may improve center ability to successfully participate in the VQI. Further work examining other VQI databases and methods to increase accuracy are needed.

An empirical study of factors influencing usage intention for generative artificial intelligence products: A case study of China

The rapid advancement of generative artificial intelligence technologies has sparked widespread interest, yet understanding of user adoption patterns for these tools remains limited. While the unified theory of acceptance and use of technology model has been widely applied to various technologies, its applicability to generative artificial intelligence products, which present unique challenges and opportunities, has not been thoroughly explored. This gap in knowledge hinders the development of effective strategies for promoting user acceptance and optimising the design of generative artificial intelligence tools. This study extends the unified theory of acceptance and use of technology model to investigate the factors influencing usage intention for generative artificial intelligence products. The study incorporates additional variables such as digital literacy, perceived risk and perceived trust to provide a more comprehensive framework. Using structural equation modelling to analyse survey data, the study found that effort expectancy, performance expectancy, social influence, and perceived trust significantly and positively impact usage intention. In addition, digital literacy indirectly enhances usage intention through effort expectancy, while perceived risk negatively influences usage intention through reduced trust. Notably, facilitating conditions did not exhibit a significant effect on usage intention. These findings offer valuable insights for developers and researchers in the field of generative artificial intelligence, highlighting the importance of user-friendly design, performance optimisation, and trust-building measures. By identifying key factors that drive user adoption, this study contributes to a more nuanced understanding of technology acceptance in the context of advanced artificial intelligence systems, paving the way for more effective development and implementation strategies in this rapidly evolving field.

Precision manufacturing based on thermal environment sensors, artificial intelligence production, computer-aided product design

Precision manufacturing based on thermal environment sensors, artificial intelligence production, computer-aided product design

ChatGPT in Science Education: A Visualization Analysis of Trends and Future Directions

ChatGPT, as one of the products of artificial intelligence (AI)-based technology, has shown significant potential in science education. This study aims to analyze the development trends and focal points of ChatGPT research, especially in science education from the Scopus database in 2022-2024. This study used Bibliometric analysis which is a quantitative and qualitative evaluation technique of documents in a database. The search method was carried out with the Dimensions and Publish or Perish (PoP) databases using Scopus and data visualization using VOSviewer. Searches were conducted on article titles, abstracts, and keywords at once (TITLE-ABS-KEY) with the keywords "ChatGPT" and "science education". The results of the bibliometric analysis showed a significant increase in the number of ChatGPT-related publications in the field of science education, with several key topics taking center stage, such as pedagogical adaptation, AI-based learning, and evaluation of technology effectiveness in the teaching and learning process. Visual analysis using VOSviewer identified a clustering of research covering the integration of ChatGPT in the science education curriculum, the role of AI in facilitating collaborative learning, and the impact of using ChatGPT on student motivation and learning outcomes. This suggests that the use of AI, particularly ChatGPT, in science education is a growing area of research with significant potential impact. This research provides a comprehensive overview of recent developments in the use of ChatGPT in the field of science education and provides insights for future research.

Baking Duration Prediction and Management System Based on Image Features

Research on the remaining time of tobacco baking is an important necessity and significance for optimizing tobacco production and processing process, improving tobacco quality and yield, saving resources and costs, and realizing intelligent production. This study devised an innovative Tobacco Leaf Baking Duration Prediction and Management System, capitalizing on the profound capabilities of image processing and machine learning algorithms. The focal point of this system is the precise prediction of the baking duration vital for tobacco leaves, which subsequently affects the color development and aromatic properties of the final producta determinant of quality. Utilizing the ResNet model, renowned for its proficiency in image processing, this study meticulously analyzed the coloration changes during the baking cycle. This rigorous analysis, deployed through the pre-trained model, facilitated the accurate prediction of baking times, tailored to optimize the tobacco leaves' quality. To further enhance the practical application of the system, this study incorporated a dataset embellished with varied tobacco leaf images collected at strategic intervals within the baking timeline. This dataset was pivotal in training and validating the model, ensuring its adaptability and accuracy in real-world scenarios. To complement the front-end strengths of the system, this study integrated a user-friendly web-based graphical interface.

Application of Machine Learning to Predict the Capacity of Fractured Horizontal Wells in Shale Reservoirs

Shale oil wells typically have numerous volume fracturing segments in their horizontal sections, resulting in significant variability in productivity across these segments. Conventional productivity prediction and fracturing effect evaluation methods are challenging to apply effectively. Establishing a stable and efficient intelligent productivity prediction method using machine learning is a promising approach for the effective development of shale oil reservoirs. This study is based on geological data, fracturing records, and a production database of 91 production wells in a shale oil reservoir in a specific area. Fourteen key parameters affecting productivity were selected from geological and engineering perspectives, and the recursive feature elimination method based on support vector machines identified five optimal main controlling factors. Three machine learning methods—decision tree, random forest, and gradient boosting decision tree (GBDT)—were used to model productivity prediction, with root mean square error (RMSE) employed to evaluate model performance. The study results indicate that formation coefficient, cluster spacing, treatment volume, sand volume, and fracturing segment length are the main controlling factors influencing productivity in fractured horizontal wells. Among the models, the random forest algorithm with bootstrap sampling produced the most stable prediction results, achieving a prediction accuracy of 94% and an RMSE of 0.934 on the test set, outperforming the decision tree and GBDT models in terms of minimum RMSE on the test set.

Integrating large language models in mental health practice: a qualitative descriptive study based on expert interviews.

Progress in developing artificial intelligence (AI) products represented by large language models (LLMs) such as OpenAI's ChatGPT has sparked enthusiasm for their potential use in mental health practice. However, the perspectives on the integration of LLMs within mental health practice remain an underreported topic. Therefore, this study aimed to explore how mental health and AI experts conceptualize LLMs and perceive the use of integrating LLMs into mental health practice. In February-April 2024, online semi-structured interviews were conducted with 21 experts (12 psychiatrists, 7 mental health nurses, 2 researchers in medical artificial intelligence) from four provinces in China, using snowballing and purposive selection sampling. Respondents' discussions about their perspectives and expectations of integrating LLMs in mental health were analyzed with conventional content analysis. Four themes and eleven sub-themes emerged from this study. Firstly, participants discussed the (1) practice and application reform brought by LLMs into mental health (fair access to mental health services, enhancement of patient participation, improvement in work efficiency and quality), and then analyzed the (2) technological-mental health gap (misleading information, lack of professional nuance and depth, user risk). Based on these points, they provided a range of (3) prerequisites for the integration of LLMs in mental health (training and competence, guidelines for use and management, patient engagement and transparency) and expressed their (4) expectations for future developments (reasonable allocation of workload, upgrades and revamps of LLMs). These findings provide valuable insights into integrating LLMs within mental health practice, offering critical guidance for institutions to effectively implement, manage, and optimize these tools, thereby enhancing the quality and accessibility of mental health services.

Factors influencing the digital intelligence transformation of offshore wind power enterprise

China's offshore wind power industry is advancing toward a digital transformation, and its development is considered a crucial strategic pillar of China's energy structure transformation. Facing challenges such as high costs, technical difficulties, lack of industry standards, insufficient industrial economies of scale, and even ecological threats, China's offshore wind power industry in the new period urgently requires innovative paths for further development. Driven by policy guidance and market demand, many enterprises have embarked on digital upgrading by introducing digital technology into their business processes, thus achieving a more efficient and intelligent production management mode to enhance industry competitiveness. However, due to the inherent peculiarities of the offshore wind power industry, its implementation of digital technology faces certain technical difficulties and systemic risks. This study presents a case study of Mingyang Smart Energy and related organizations conducted using in-depth interviews, and focuses on the practical circumstances of China's offshore wind power industry undergoing digital transformation. This study also applies the grounded theory to analyze the driving factors and mechanisms of digital transformation and provides an in-depth discussion of the characteristics of different stages of the transformation, including digital technology function leap, management and platform construction, as well as digital operation and value creation. The study results can enrich the theoretical study of digital transformation and provide practical guidance for the offshore wind power industry to achieve intelligent, efficient, and sustainable development.

Deconstruct artificial intelligence's productivity impact: A new technological insight

Some viewpoints suggest that IT investment seems to fail to significantly stimulate enterprise productivity in some cases. Therefore, revealing the impact of AI on firm productivity is an important topic to analyze whether Solow's paradox can be valid in the digital age. Based on panel data of 3235 listed companies in China from 2007 to 2021, we comprehensively discuss the impact and mechanism of AI on firm productivity using fixed-effects model, systematic GMM model, and mediated-effects model. Key findings include: AI significantly improves firm productivity, especially in state-controlled, internationally minded, and innovative firms. Mitigating information asymmetry is a key channel, while specialized division of labor and independent green innovation are potential ones. Supply chain digital transformation policies enhance the productivity effect of AI, and AI shows green development benefits. Additionally, the dynamic decomposition effect shows that the productivity-enhancing effect of AI is slowing down in the long run. This research provides important insights into understanding AI's role in the digital age and holds significance for firms and policymakers.