Intelligent Production Research Articles

AI-driven cognition for advanced injection molding and industrial implementation

Abstract Plastic injection molding has been an essential part of mass production in numerous industries for many years. However, this traditional production technique cannot provide sufficient efficiency and quality in today’s competitive environment. With the growing emphasis on sustainability, the increasing use of recycled raw materials, rising turnover rates, and labor costs, an advanced and intelligent production process has become essential. This article proposes an AI-driven cognition, capable of operating independently of part geometry, raw material, and production equipment in the plastic injection molding. In pursuit of this objective, cavity pressure sensors are placed in the critical areas of the plastic injection mold. Using the data collected for each cycle, a reliable zone is identified to ensure the manufacture of high-quality parts. One of the key innovations of this study is establishing the relationship between fluctuations in the cavity pressure curve for both quality of the part and machine parameters. Based on this relationship, a CNN-based baseline knowledge learner has been developed to provide operators with actionable suggestions when the production process deviates from the reliable zone. The proposed method has been implemented with an accuracy of 98%. Following the development of the baseline knowledge, the proposed method was applied to two industrial applications. The task-oriented knowledge adaptation method was applied to these parts, which exhibit distinct characteristics regarding part shape, raw material, and quality criteria. The integration to the production site was achieved with an average accuracy of 95%.

Intelligent Production Decision-Making System for Injection Molding Factories

This paper addresses the intelligent transformation needs of the traditional injection molding industry by proposing a comprehensive intelligent production decision-making system inte-grating smart scheduling, auxiliary decision-making, and digital twin simulation. By combining the Weighted Shortest Processing Time (WSPT) algorithm, dynamic programming, and equip-ment health assessment, the system achieves dynamic multi-machine scheduling and resource optimization. An injection molding industry-specific large model is constructed, leveraging re-al-time data analysis and a knowledge base to optimize process parameters and enable anomaly early warning. A digital twin platform is developed using Unity and OPC UA protocols, employing multi-physics simulation and Markov decision processes to predict production issues. The re-search provides a practical technical solution for the intelligent upgrading of injection molding factories, offering both theoretical innovation and engineering application value.

How can working conditions for online crowdworkers be improved? Institutional experiments for cross-jurisdictional polycentric work

This article articulates the theory and design of the Crowdsourcing Wage Pledge, an action research initiative exploring how academic users of paid crowdsourcing can contribute to improving the working conditions of online crowdworkers. ‘Crowdwork’ describes remote information work comprising short tasks – for example, surveys, transcription, translation, data cleaning – organized through online platforms (for example, Amazon Mechanical Turk, Clickworker, Scale API) and typically remunerated under a self-employed piecework model. The digital labour platforms mediating this work are important in a growing range of sectors, having become, for example, key nodes in the global supply chains for artificial intelligence products, including self-driving cars and chatbots (for example, ChatGPT). Academics also use crowdsourcing – social scientists, for example, to recruit study participants, and computer scientists to outsource data-cleaning work. While crowdwork creates new income opportunities with, sometimes, greater time flexibility and lower barriers to entry than traditional employment, crowdworkers typically earn less than traditional employees doing similar work, often below minimum wage, and face other decent work deficits, including arbitrary nonpayment and termination and misclassification. While some jurisdictions are developing laws to address these challenges, these typically focus on in-person platform work (for example, delivery). Crowdwork, however, is often cross-jurisdictional and therefore presents challenges for national regulation. The Crowdsourcing Wage Pledge is a voluntary regulatory initiative aiming to recruit academic users of crowdsourcing into adopting best practices identified by previous research. It is informed both by past practical efforts to improve crowdwork working conditions and by theory from political science, human-computer interaction, and industrial relations.

Probabilistic Measurement of CTI Quality for Large Numbers of Unstructured CTI Products

This paper addresses the critical challenge of evaluating the quality of Cyber Threat Intelligence (CTI) products, particularly focusing on their relevance and actionability. As organizations increasingly rely on CTI to make cybersecurity decisions, the absence of CTI quality metrics challenges the assessment of intelligence quality. To address this gap, the article introduces two innovative metrics. Relevance (Re) and Actionability (Ac) are designed to evaluate CTI products in relation to organizational information needs and defense mechanisms. Using probabilistic algorithms and data structures, these metrics provide a scalable approach for handling large numbers of unstructured CTI products. Experimental findings demonstrate the effectiveness of metrics in filtering and prioritizing CTI products, offering organizations a tool to prioritize their cybersecurity resources. Furthermore, experimental results demonstrate that, using the metrics, organizations can reduce candidate CTI products by several orders of magnitude, understand weaknesses in defining information needs, guide the application of CTI products, assess CTI products’ contribution to defense, and select CTI products from information sharing communities. In addition, the study has identified certain limitations, which open avenues for future research, including the real-time integration of CTI into organizational defense mechanisms. This work significantly contributes to standardizing the quality evaluation of CTI products and enhancing organizations’ cybersecurity posture.

Nature-Inspired Intelligent Cotton Fabrics with Excellent Shape Memory and Superhydrophobic Properties.

Driven by technological advancements and rising living standards, the demand for high-performance cotton textiles continues to grow. Drawing inspiration from the stimuli-responsive behavior of Mimosa pudica and the inherent superhydrophobicity of lotus leaf surfaces, this study presents the development of a new class of smart cotton fabrics integrating superhydrophobicity, shape memory functionality, and wear resistance. The engineered smart cotton fabrics incorporate Eucommia ulmoides gum (EUG) and surface-tailored sepiolite particles as core functional elements. Central to this work is an innovative surface modulation strategy leveraging shape memory effects to dynamically control material hydrophobicity through thermoresponsive structural reconfiguration. Fabricated via a scalable dip-coating technique, these composites achieve tunable wettability without fluorine-based chemicals, marking a departure from conventional approaches. The innovation of this manuscript also lies in the cotton fabric's fluorine-free composition and its eco-friendly preparation process. These characteristics enable cotton fabrics to adjust their surface wettability based on different usage environments and needs, offering vast possibilities for creating and designing intelligent products.

Is Anthropomorphism Better for Older People? Exploring the Effects of Anthropomorphic Companion Robots on Satisfaction

As a new artificial intelligence product, Companion robots have gradually appeared in the elderly market. However, in practice, older people have low satisfaction and low intention to use companion robots, which makes it challenging to realize the intrinsic value of companion robots effectively. Therefore, this study examined the effect of companion robot anthropomorphism on the satisfaction of older adults through a laboratory experimental study (N = 87) and a questionnaire study (N = 305). The results show that older people have higher satisfaction with companion robots with higher levels of anthropomorphism, and anthropomorphism has a significant positive effect on satisfaction. Psychological distance partially mediated the relationship between anthropomorphism and satisfaction. Perceived control positively moderates the relationship between anthropomorphism and psychological distance and satisfaction. This study aims to expand the existing literature on anthropomorphism and provide practical guidance for designers of companion robots.

FDA-approved artificial intelligence products in abdominal imaging: A comprehensive review.

FDA-approved artificial intelligence products in abdominal imaging: A comprehensive review.

AI-Powered Personalization in Retail: Technical Implementation and Business Impact

This comprehensive article examines the transformative impact of artificial intelligence on retail personalization strategies. The article explores the technical architecture underpinning AI-powered retail systems, including data collection infrastructure, processing pipelines, and specialized machine learning models that enable personalized customer experiences. It addresses implementation challenges like real-time processing requirements and cold start problems while detailing key business applications such as intelligent product recommendations, dynamic pricing optimization, personalized marketing automation, and conversational commerce. It evaluates business impact across revenue metrics (conversion rates, order values, customer lifetime value), operational efficiencies (marketing costs, inventory management, return rates), and customer experience indicators. Ethical considerations including data privacy compliance, algorithmic fairness, and transparency practices are thoroughly examined. Finally, the article identifies emerging technologies shaping the future of retail AI, including computer vision applications, voice commerce integration, and augmented reality experiences. This synthesis of technical implementation and business outcomes provides stakeholders with evidence-based insights into the strategic value of AI personalization in contemporary retail environments.

The Influencing Mechanism of Intelligent Production on Carbon Emission Efficiency of Equipment Manufacturing Industry in China

Intelligent production has changed the traditional mode of production. However, how to achieve an improvement in carbon emission efficiency via intelligent production needs to be further explored. This study focuses on the mechanism of how production intelligence affects the equipment manufacturing sector’s carbon emission efficiency. Using data from 247 businesses in the equipment manufacturing sector between 2015 and 2021, this study applies fixed-effects models and statistical analysis methods to explore the correlation between production intelligence and the equipment manufacturing sector’s carbon emission efficiency. The results indicate that intelligent production can effectively improve the carbon emission efficiency of equipment manufacturing businesses and improve the carbon emission efficiency by enhancing the energy utilization rate of enterprises. Environmental regulation has a regulating function in the connection between the production intelligence and carbon emission efficiency of equipment manufacturing enterprises. There are also regional and industrial differences in the effect of intelligent production on carbon emission efficiency. Based on these findings, our study proposes three policy suggestions for policymakers.

One Voice or Many? Stakeholder Interactions in Building a Public Intelligence Culture

Hybrid threats weaponize the right to freedom of expression and make democratic societies vulnerable by further subverting trust in publicly available information. In this context, citizens become a target of ill-intended information operations and, subsequently, broadcasters of hybrid aggressors’ narratives, unwarily favoring foreign actors’ interests, with negative impact on the security of their own countries, against liberal democracy and, eventually, against their own interests. Intelligence Communities (ICs) have perfected methods of collecting, processing, analyzing, and evaluating information in order to provide threat assessments to policymakers. The public dimension of intelligence has rarely been approached and seldom as more than a risk or vulnerability to be mitigated or accepted only in extreme situations. Therefore, intelligence products are not generally open to citizens, as ICs have self-imposed limitations concerning what and to whom they communicate, as they need to protect sources and processes and to remain outside of the political debate. We argue there is a need for a public intelligence culture, which would translate into a sharing of intelligence-related skills and cocreation of knowledge involving a wider pool of societal actors. The present research aims to define the concept of public intelligence culture, with a focus on its dimensions and on the opportunities it offers to empower citizens in liberal democratic societies, making them resilient in the face of hybrid threats.

Intelligent Production in the Silicone Rubber Processing Industry: Applications and Challenges

This paper explores the current applications, challenges, and future development trends of intelligent production technology in the silicone rubber processing industry. By analyzing the practical applications of intelligent production technology in silicone rubber processing, such as the application status of automated production lines and intelligent inspection systems, this paper discusses the technical difficulties encountered in implementing intelligent production, such as equipment compatibility and data integration issues. It also analyzes how intelligent production can improve production efficiency, product quality, and market competitiveness in the silicone rubber industry and proposes strategies and suggestions for promoting intelligent production in this field.

Keeping AI on Track: Regular monitoring of algorithmic updates in mammography.

To demonstrate a method of benchmarking the performance of two consecutive software releases of the same commercial artificial intelligence (AI) product to trained human readers using the Personal Performance in Mammographic Screening scheme (PERFORMS) external quality assurance scheme. In this retrospective study, ten PERFORMS test sets, each consisting of 60 challenging cases, were evaluated by human readers between 2012 and 2023 and were evaluated by Version 1 (V1) and Version 2 (V2) of the same AI model in 2022 and 2023 respectively. Both AI and humans considered each breast independently. Both AI and humans considered the highest suspicion of malignancy score per breast for non-malignant cases and per lesion for breasts with malignancy. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated for comparison, with the study powered to detect a medium-sized effect (odds ratio, 3.5 or 0.29) for sensitivity. The study included 1,254 human readers, with a total of 328 malignant lesions, 823 normal, and 55 benign breasts analysed. No significant difference was found between the AUCs for AI V1 (0.93) and V2 (0.94) (p=0.13). In terms of sensitivity, no difference was observed between human readers and AI V1 (83.2% vs 87.5% respectively, p=0.12), however V2 outperformed humans (88.7%, p=0.04). Specificity was higher for AI V1 (87.4%) and V2 (88.2%) compared to human readers (79.0%, p<0.01 respectively). The upgraded AI model showed no significant difference in diagnostic performance compared to its predecessor when evaluating mammograms from PERFORMS test sets.

Development of a runtime-condition model for proactive intelligent products using knowledge graphs and embedding

Development of a runtime-condition model for proactive intelligent products using knowledge graphs and embedding

The intelligent production line configuration strategy

The intelligent production line configuration strategy

Research on the closed-loop supply chain of intelligent products considering government subsidies in the context of the internet of things

The rapid development of Internet of Things technology and the continuous improvement of consumer demand have spawned the emergence of intelligent products based on Internet of Things technology. The existing research on the closed-loop supply chain of intelligent products under the background of the Internet of Things still lacks consideration of different subsidy objects and recyclers. Therefore, for different subsidy objects in the three-stage closed-loop supply chain of intelligent products, a pricing decision model involving manufacturers, recyclers, retailers and consumers under the minimum standard supervision of recycling technology is constructed, and different recyclers are distinguished according to the characteristics of intelligent products. By comparing the profits of each subject in different models, the optimal model of government subsidy is obtained. It is found that under the four modes, subsidizing recyclers without self-recovery technology makes the total profit of the supply chain reach the optimal solution; capital recyclers can obtain the largest number of old intelligent product recycling by using self-recovery technology; when subsidizing manufacturers, the total profit and product recovery rate of the supply chain are at the bottom. In addition, recycling subsidies, subsidy impact coefficients and technical standards will affect the best profits of enterprises. Through the analysis of the closed-loop supply chain of intelligent products, the research provides theoretical support and decision-making basis for the supply chain members to formulate relevant strategies and the government to take relevant measures to promote resource utilization and environmental protection.

MANAGERIAL MECHANISM FOR ACCELERATING INNOVATIONS OF TOURISM AND HOTEL BUSINESS ENTITIES

The article proposes the solutions for scientific task of determining the essence and components of the mechanism for accelerating the innovative activity of tourism and hotel business entities. The evolution of the scientific understanding of innovations and the specifics of modern innovation processes in Ukraine, which are carried out in conditions of a stochastic socio-economic environment and extraordinary military-political threats, are discussed in the paper. The paper proposes and grounds the theoretical approach to understanding the essence of the innovative activity of tourism and hotel business entities, which takes into account the systematicity, continuity and interconnectedness of innovation processes in travel business as a reflection for the high elasticity of demand for tourist services in relation to socio-economic and military-political changes. The paper argues the necessity for tourism and hotel business entities in proactive development strategies based on a constant search for innovative ideas, implementation of innovative tourism service technologies, flexible changes in organizational structures and marketing procedures. The paper reveals and discusses the inherent tendency in development of the tourism and hotel business in Ukraine to expand personalized and intelligent products and services based on artificial intelligence and other digital technologies. The changes that occur in the organizational and economic structure of the tourism and hotel business as a result of the intensification of innovation processes are characterized, and a model of continuous innovative activity of tourism and hotel business entities is proposed. The paper determines factors of the internal and external environment that may restrain the introduction of innovations in the tourism and hotel business and discusses sectors of the organizational and economic mechanism responsible for responding to the proper changes. The paper proposes the organizational and economic components of the managerial mechanism for accelerating innovations of tourism and hotel business entities at the micro and macro levels of management

355 Validation of an artificial intelligence Algorithm for predicting diagnosis-related groups in a community health system

Objectives/Goals: This study aims to evaluate the performance of a third-party artificial intelligence (AI) product in predicting diagnosis-related groups (DRGs) in a community healthcare system. We highlight a use case illustrating how clinical practice leverages AI-predicted information in unexpected yet advantageous ways and assess the AI predictions accuracy and practical application. Methods/Study Population: DRGs are crucial for hospital reimbursement under the prospective payment model. The Mayo Clinic Health System (MCHS), a network of clinics and hospitals serving a substantial rural population in Minnesota and Wisconsin, has recently adopted an AI algorithm developed by Xsolis (an AI-focused healthcare solution provider). This algorithm, a 1D convolutional neural network, predicts DRGs based on clinical documentation. To assess the accuracy of AI-generated DRG predictions for inpatient discharges, we analyzed data from 930 patients hospitalized at MCHS Mankato between March 2 and May 13, 2024. The Xsolis platform provided the top three DRG predictions for the first 48 hours of each inpatient stay. The accuracy of these predictions was then compared against the final billed DRG codes from the hospital’s records. Results/Anticipated Results: In our validation set, Xsolis achieved a top-3 DRG prediction accuracy of 71% at 24 hours and 81% at 48 hours, which is lower than the originally reported accuracy of 81.1% and 83.3%, respectively. Interestingly, discussions with clinical practice leaders revealed that the most valuable information derived from the AI predictions was the expected geometric mean length of stay (GMLOS), which Xsolis was perceived to predict accurately. In the Medicare system, each DRG is associated with an expected GMLOS, a critical factor for efficient hospital flow planning. A subsequent analysis comparing predicted GMLOS with the actual length of stay showed variances of -0.10 days on day 1 and 0.14 days on day 2, indicating a high degree of accuracy and aligning with clinical practice perceptions. Discussion/Significance of Impact: Our research underscores that clinical practice can leverage AI predictions in unexpected yet beneficial ways. While initially focused on DRG prediction, the associated GMLOS emerged as more significant. This suggests that AI algorithm validation should be tailored to specific clinical needs rather than relying solely on generalized benchmarks.

Research on Intelligent Supply Chain for Intelligent Manufacturing

Leveraging computer systems and information technologies, intelligent manufacturing demonstrates enhanced collaborative capabilities and elevated decision-making capacity. From a digital cognitive framework and information system-oriented view, this paper focuses on the process of supply chain digital transformation, interpreting the methodology and approaches to eliminate obstacles such as the lack of data standards and inaccurate data analysis, lack of supply chain resilience, contradiction between customized and operational efficiency in production systems, uncooperative market conditions and other internal or external factors. To address these challenges, the systematic integration of big data analytics, deep learning-based intelligent optimization, and blockchain-enabled collaborative mechanisms establishes practical technical pathways. This convergence augments supply chain cognitive capabilities, advances intelligent production scheduling competencies, amplifies market adaptive intelligence, and facilitates sustainable evolution of intelligent supply chain ecosystems.

The value of simulation testing for the evaluation of ambient digital scribes: a case report.

The objective of this work is to demonstrate the value of simulation testing for rapidly evaluating artificial intelligence (AI) products. Researcher-physician teams simulated the use of 2 Ambient Digital Scribe (ADS) products by reading scripts of outpatient encounters while using both products, yielding a total of 44 draft notes. Time to edit, perceived amount of effort and editing, and errors in the AI-generated draft notes were analyzed. Ambient Digital Scribe Product A draft notes took significantly longer to edit, had fewer omissions, and more additions and irrelevant or misplaced text errors than ADS Product B. Ambient Digital Scribe Product A was rated as performing better for most encounters. Artificial intelligence-enabled products are being rapidly developed and implemented into practice, outpacing safety concerns. Simulation testing can efficiently identify safety issues. Simulation testing is a crucial first step to take when evaluating AI-enabled technologies.

AML-Based Multi-Dimensional Co-Evolution Approach Supported by Blockchain: Architecture Design and Case Study on Intelligent Production Lines for Industry 4.0

Based on Automation ML (AML), Intelligent Production Lines (IPLs) for Industry 4.0 can effectively organize multi-dimensional data and models. However, this process requires interdisciplinary and multi-team contributions, which often involve the dual pressures of private data encryption and public data sharing. As a transparent decentralized network, blockchain’s compatibility with the challenges of AML collaboration processes, data security, and privacy is not ideal. This paper proposes a new method to enhance the collaborative evolution of IPLs. Its innovations are, firstly, developing a comprehensive two-layer management model, combining blockchain with the Interplanetary File System (IPFS) to build an integrated solution for private and public hybrid containers based on a collaborative model; secondly, designing a version co-evolution management method by combining smart contract workflows and AML multi-dimensional modeling processes; meanwhile, introducing a specially designed conflict resolution mechanism based on the graph model to maintain consistency in version multi-batch management and; finally, using the test cases established in the lab’s I5Blocks for verification.