Industry 4.0 has been provided for the last 10 years to benefit the industry and the shortcomings; finally, the time for industry 5.0 has arrived. Smart factories are increasing the business productivity; therefore, industry 4.0 has limitations. In this paper, there is a discussion of the industry 5.0 opportunities as well as limitations and the future research prospects. Industry 5.0 is changing paradigm and brings the resolution since it will decrease emphasis on the technology and assume that the potential for progress is based on collaboration among the humans and machines. The industrial revolution is improving customer satisfaction by utilizing personalized products. In modern business with the paid technological developments, industry 5.0 is required for gaining competitive advantages as well as economic growth for the factory. The paper is aimed to analyze the potential applications of industry 5.0. At first, there is a discussion of the definitions of industry 5.0 and advanced technologies required in this industry revolution. There is also discussion of the applications enabled in industry 5.0 like healthcare, supply chain, production in manufacturing, cloud manufacturing, etc. The technologies discussed in this paper are big data analytics, Internet of Things, collaborative robots, Blockchain, digital twins and future 6G systems. The study also included difficulties and issues examined in this paper head to comprehend the issues caused by organizations among the robots and people in the assembly line.
In the era of industrial digitalization, companies are increasingly investing in tools and solutions that allow their processes, machines, employees, and even the products themselves, to be integrated into a single integrated network for data collection, data analysis, the evaluation of company development, and performance improvement. To study the impact of Industry 4.0 on the company we used Porter’s (1985) value chain model, which is particularly useful when paying particular attention to corporate areas which have a primary role in customer value creation. Since the primary impact of Industry 4.0 is perceived in value-creating processes, and has so far had the greatest transformative effect in this area, the model can be considered to be appropriate. The objective of our research is to discover how companies operating in Hungary interpret the phenomenon of Industry 4.0, what Internet of Things (IoT) tools they use to support their processes, and what critical issues they face during adaptation. We applied a dual methodology in our investigation: We sent an online questionnaire to manufacturing and logistical service companies to investigate the IoT tools they use, and the problems they face, and received 43 answers we could evaluate. We also conducted four expert interviews with manufacturing firms to get deeper insights into the application, critical issues and development phases of IoT tools. During our research, we found that the spread of real-time data across companies—given the availability of appropriate analytical tools and methods—can have a significant impact on the entire company. In the case of CPS (Cyber Physical System), CPPS and Big Data Technologies, companies using them have been evaluated as having a higher level of logistic service, more efficient processes with their partners, improved cooperation between certain logistic functions, and higher market and financial performance and competitiveness. Applying more efficient production processes, and achieving better productivity and economies of scale, might also result in increased economic sustainability. Furthermore, we have found that companies have started on the path to digital evolution, and investments of this type have already begun.
Technology adoption is always a difficult task for Small and Medium-sized Enterprises (SMEs) due to lack of resources and other market issues. Many technology challenges adversely affect the sustainable business performance of SMEs. However, the incorporation of Industry 4.0 can overcome various technology issues. The goal of Industry 4.0 is to attain an advanced level of operational effectiveness and productivity, as well as a higher level of automatization. Thus, the objective of this study is to identify the role of Industry 4.0 to promote sustainable business performance in SMEs in Thailand. A survey has been prepared to collect the data from managers of SMEs and analyzed with the help of Partial Least Square. The questionnaire was used to collect the data and questionnaires were distributed by using simple random sampling. A total of 500 questionnaires were distributed amongst the managerial staff of SMEs located in Thailand. From these distributed questionnaires, 280 were returned and 270 valid responses were found. Data were analyzed by using Partial Least Square (PLS)-Structural Equation Modeling (SEM). Findings reveal that Industry 4.0 is a key to the growth of sustainable business performance among SMEs. Elements of Industry 4.0 such as big data, Internet of Things and smart factory have a positive role in promoting information technology (IT) implementation, which contributes to sustainable business performance. Moreover, organization structure and process strengthen the positive relationship between Industry 4.0 and IT implementation.
The traditional production paradigm of large batch production does not offer flexibility toward satisfying the requirements of individual customers. A new generation of smart factories is expected to support new multivariety and small-batch customized production modes. For this, artificial intelligence (AI) is enabling higher value-added manufacturing by accelerating the integration of manufacturing and information communication technologies, including computing, communication, and control. The characteristics of a customized smart factory are: self-perception, operations optimization, dynamic reconfiguration, and intelligent decision-making. The AI technologies will allow manufacturing systems to perceive the environment, adapt to the external needs, and extract the process knowledge, including business models, such as intelligent production, networked collaboration, and extended service models. This article focuses on the implementation of AI in customized manufacturing (CM). The architecture of an AI-driven customized smart factory is presented. Details of intelligent manufacturing devices, intelligent information interaction, and construction of a flexible manufacturing line are showcased. The state-of-the-art AI technologies of potential use in CM, that is, machine learning, multiagent systems, Internet of Things, big data, and cloud-edge computing, are surveyed. The AI-enabled technologies in a customized smart factory are validated with a case study of customized packaging. The experimental results have demonstrated that the AI-assisted CM offers the possibility of higher production flexibility and efficiency. Challenges and solutions related to AI in CM are also discussed.
The proliferation of big data applications across various industries has led to a paradigm shift in data architecture, with traditional approaches giving way to more agile and scalable frameworks. The evolution of big data architecture began with the emergence of the Hadoop-based data stack, leveraging technologies like Hadoop Distributed File System (HDFS) and Apache Spark for efficient data processing. However, recent years have seen a shift towards modern data stacks, offering flexibility and diverse toolsets tailored to specific use cases. Concurrently, cloud computing has revolutionized big data management, providing unparalleled scalability and integration capabilities. Despite their benefits, navigating these data stack paradigms can be challenging. While existing literature offers valuable insights into individual data stack paradigms, there remains a dearth of studies that offer practical, in-depth comparisons of these paradigms across the entire big data value chain. To address this gap in the field, this paper examines three main big data stack paradigms: the Hadoop data stack, modern data stack, and cloud-based data stack. Indeed, we conduct in this study an exhaustive architectural comparison of these stacks covering the entire big data value chain from data acquisition to exposition. Moreover, this study extends beyond architectural considerations to include end-to-end use case implementations for a comprehensive evaluation of each stack. Using a large dataset of Amazon reviews, different data stack scenarios are implemented and compared. Furthermore, the paper explores critical factors such as data integration, implementation costs, and ease of deployment to provide researchers and practitioners with a relevant and up-to-date reference for navigating the complex landscape of big data technologies and making informed decisions about data strategies.
Jingzhi Gong, Rafail Giavrimis, Paul Brookes
et al.
There is a growing interest in leveraging multiple large language models (LLMs) for automated code optimization. However, industrial platforms deploying multiple LLMs face a critical challenge: prompts optimized for one LLM often fail with others, requiring expensive model-specific prompt engineering. This cross-model prompt engineering bottleneck severely limits the practical deployment of multi-LLM systems in production environments. We introduce Meta-Prompted Code Optimization (MPCO), a framework that automatically generates high-quality, task-specific prompts across diverse LLMs while maintaining industrial efficiency requirements. MPCO leverages metaprompting to dynamically synthesize context-aware optimization prompts by integrating project metadata, task requirements, and LLM-specific contexts. It is an essential part of the ARTEMIS code optimization platform for automated validation and scaling. Our comprehensive evaluation on five real-world codebases with 366 hours of runtime benchmarking demonstrates MPCO's effectiveness: it achieves overall performance improvements up to 19.06% with the best statistical rank across all systems compared to baseline methods. Analysis shows that 96% of the top-performing optimizations stem from meaningful edits. Through systematic ablation studies and meta-prompter sensitivity analysis, we identify that comprehensive context integration is essential for effective meta-prompting and that major LLMs can serve effectively as meta-prompters, providing actionable insights for industrial practitioners.
India's growing population and economy have significantly increased the demand and consumption of natural resources. As a result, the potential benefits of transitioning to a circular economic model have been extensively discussed and debated among various Indian stakeholders, including policymakers, industry leaders, and environmental advocates. Despite the numerous initiatives, policies, and transnational strategic partnerships of the Indian government, most small and medium enterprises in India face significant challenges in implementing circular economy practices. This is due to the lack of a clear pathway to measure the current state of the circular economy in Indian industries and the absence of a framework to address these challenges. This paper examines the circularity of the 93-textile industry in India using the C-Readiness Tool. The analysis comprehensively identified 9 categories with 34 barriers to adopting circular economy principles in the textile sector through a narrative literature review. The identified barriers were further compared against the findings from a C-readiness tool assessment, which revealed prominent challenges related to supply chain coordination, consumer engagement, and regulatory compliance within the industry's circularity efforts. In response to these challenges, the article proposes a strategic roadmap that leverages digital technologies to drive the textile industry towards a more sustainable and resilient industrial model.
Companies are moving from a cottage industry to a factory approach to analytics, especially in regard to machine learning (ML) models. This change is motivating companies to adopt ML operations (MLOps) as a methodology for the timely development, deployment, and maintenance of ML models in order to positively impact business outcomes. The adoption of MLOps requires changes in processes, technology, and people, and these changes are informed by previous work on decision support systems (DSS), development operations (DevOps), and data operations (DataOps). The processes, technologies, and people needed for MLOps are discussed and illustrated using a customer purchase recommendation example. Current and future directions for MLOps practice driven by artificial intelligence (AI) are explored. Suggestions for further academic research are provided.
Context and purpose: Agricultural cooperatives are an important tool for promoting agricultural modernization, which plays an important role in organizing the production of agricultural products. Therefore, there is a need to investigate effective ways to improve the quality of cooperative management for agricultural managers. The purpose of this research is to provide a model for improving the performance and competencies of agricultural cooperative managers with the theory approach.Methodology/approach: This research is a descriptive study in terms of purpose, application, and in terms of data collection. The statistical population of the research were experts and managers of agricultural cooperatives. The data collection tool was a semi-structured interview. To investigate the validity of the qualitative part, the content validity and intra-coder and inter-coder reliability models were used. The method of data analysis was the grounded theoretical approach, which was compiled with MAXQDA software and used the coding method.Findings and conclusions: The research results showed that the competence model of agricultural cooperative managers consists of seven dimensions abilities, which include: entrepreneurship, awareness, thinking, career orientation, personality traits, knowledge and technology, and personal abilities.Originality: Based on the perspective of competence, the results of this research enrich the management occupational competence by analyzing the combined effect of different competences of agricultural managers on the performance of cooperatives and provide ideas for building general industry competence models. The results of this study help to improve the performance and competencies of managers of agricultural cooperatives as an important influencing factor in cooperative performance.
Morgan Ramsey‐Elliot, Charlie Lotterman, Cameron Wu
For years big tech was one of the major employers of, and training grounds for, industry ethnographers focused on solving complex, longer‐term strategic problems. But the wave of layoffs that hit white‐collar workers beginning in 2022 prompted many in the research community to question their historical value to companies and their offerings to them in the future. Many participants in this discourse arrived at the same conclusion: Researchers need to do more strategic research—but what does that mean? And does the core of the issue lie with research, tech, strategy, or some combination of the three? After surveying shifts in the macro context that have upended the status of researchers within businesses, specifically large technology companies, we apply Roger Martin's strategy playbook to develop a strategy for ourselves. We argue that to meet the moment, researchers should grow the strategic muscle of their companies by shifting their focus from how users interact with products to how their businesses interact with a wider world. By repositioning our offering to companies in this way, we argue that researchers can apply ethnography's unique capabilities to the most pressing strategic questions facing businesses today.
Bartłomiej Brudnowski, Kamil Zembaczyński, Aleksander Filip Żarnecki
The physics program of the Higgs factory will focus on measurements of the 125 GeV Higgs boson, with the Higgs-strahlung process being the dominant production channel at 250 GeV. However, production of extra light scalars is still not excluded by the existing experimental data, provided their coupling to the gauge bosons is sufficiently suppressed. Fermion couplings of such a scalar could also be very different from the SM predictions leading to non-standard decay paterns. Presented in this contribution are results from the ongoing studies on prospects of direct light scalar observation at future Higgs factory experiments in different decay channels.
Oscar Blanco-Novoa, Tiago M Fernandez-Carames, Paula Fraga-Lamas
et al.
The principles of the Industry 4.0 are guiding manufacturing companies towards more automated and computerized factories. Such principles are also applied in shipbuilding, which usually involves numerous complex processes whose automation will improve its efficiency and performance. Navantia, a company that has been building ships for 300 years, is modernizing its shipyards according to the Industry 4.0 principles with the help of the latest technologies. Augmented Reality (AR), which when utilized in an industrial environment is called Industrial AR (IAR), is one of such technologies, since it can be applied in numerous situations in order to provide useful and attractive interfaces that allow shipyard operators to obtain information on their tasks and to interact with certain elements that surround them. This article first reviews the state of the art on IAR applications for shipbuilding and smart manufacturing. Then, the most relevant IAR hardware and software tools are detailed, as well as the main use cases for the application of IAR in a shipyard. Next, it is described Navantia's IAR system, which is based on a fog-computing architecture. Such a system is evaluated when making use of three IAR devices (a smartphone, a tablet and a pair of smart glasses), two AR SDKs (ARToolKit and Vuforia) and multiple IAR markers, with the objective of determining their performance in a shipyard workshop and inside a ship under construction. The results obtained show remarkable performance differences among the different IAR tools and the impact of factors like lighting, pointing out the best combinations of markers, hardware and software to be used depending on the characteristics of the shipyard scenario.
Yuhao Wu, Franziska Roesner, Tadayoshi Kohno
et al.
Large language models (LLMs) extended as systems, such as ChatGPT, have begun supporting third-party applications. These LLM apps leverage the de facto natural language-based automated execution paradigm of LLMs: that is, apps and their interactions are defined in natural language, provided access to user data, and allowed to freely interact with each other and the system. These LLM app ecosystems resemble the settings of earlier computing platforms, where there was insufficient isolation between apps and the system. Because third-party apps may not be trustworthy, and exacerbated by the imprecision of natural language interfaces, the current designs pose security and privacy risks for users. In this paper, we evaluate whether these issues can be addressed through execution isolation and what that isolation might look like in the context of LLM-based systems, where there are arbitrary natural language-based interactions between system components, between LLM and apps, and between apps. To that end, we propose IsolateGPT, a design architecture that demonstrates the feasibility of execution isolation and provides a blueprint for implementing isolation, in LLM-based systems. We evaluate IsolateGPT against a number of attacks and demonstrate that it protects against many security, privacy, and safety issues that exist in non-isolated LLM-based systems, without any loss of functionality. The performance overhead incurred by IsolateGPT to improve security is under 30% for three-quarters of tested queries.
Context and purpose. One of the important issues in the agricultural production cooperatives sustainability is its role in creating and strengthening social capital. The pourpose was to measure and compare social capital between members and non-members farmers of production cooperatives.Methodology/approach. The research was applied and quasi-experimental. The statistical population consisted of 460 farmers of Araz 1 and 2 production cooperatives (as experimental group) and Qiqaj Plain (as control group) in the the margin of Aras River in Poldasht Township. According to Krejcie and Morgan table and using proportional stratified random sampling, 142 and 68 people from the experimental group and control group were selected respectively. The research tool was a researcher-made questionnaire whose validity was confirmed by university faculty members and its reliability by Cronbach's alpha coefficient (0.73 to 85).Findings and conclusions. Overall, the extent of social capital was lower than average. According to Mann-Whitney test, the rank average of social capital and components of social trust, trust in government institutions, participation and relationships network were significantly lower among production cooperative members than non-members. However, no significant difference was observed between the components of social cohesion and trust in civic institutions between these two groups. Therefore, these production cooperatives have been established by the government in a top-down manner, which has caused the destruction of rural subculture and reduce social trust.Originality. Previous studies have investigated social capital only form members viewpoint. Therefore, the use of experimental research method provides useful results for establishing and management of cooperatives.
Network resources are the data core of OSS domain of telecom operation support system. With the rapid development of telecom industry in recent decades, it has entered the era of big data due to the accumulation of a large amount of data. However, due to the imperfect resource management mechanism, network failure, cutover, complexity of resource relationship itself, human and other factors, the volume of problem data is getting larger and larger. Through big data technology, this paper builds a distributed database cluster based on greenplum, and proposes a verification tool to improve data quality from multiple perspectives such as cluster design, functional architecture, data mart, running timing, and rules, so as to realize big data governance of network resources.
With the rapid development of China’s economy, as the world’s second largest economy, China’s economy has gradually integrated into the world economic system. With the development of economy, the maritime trade has increased accordingly. Since the reform and open policy, the transportation conditions and transportation capacity have been greatly improved, but at the same time, we are faced with many problems. Among them, the most important problems are serious imbalance between port cargo throughput and container throughput, relatively backward port infrastructure conditions, obvious trend of large ships and low management level of shipping enterprises, which directly affect the development of Chinese shipping industry. This paper analyzes the above problems and puts forward corresponding solutions, which also lays a foundation for future research on ship upsizing, containerization and optimization of transportation conditions.
This paper studies the extent to which the cyclicality of occupational mobility shapes that of aggregate unemployment and its duration distribution. We document the relation between workers' occupational mobility and unemployment duration over the long run and business cycle. To interpret this evidence, we develop a multi-sector business cycle model with heterogenous agents. The model is quantitatively consistent with several important features of the US labor market: procyclical gross and countercyclical net occupational mobility, the large volatility of unemployment and the cyclical properties of the unemployment duration distribution, among many others. Our analysis shows that occupational mobility due to workers; changing career prospects, and not occupation-wide differences, interacts with aggregate conditions to drive the fluctuations of the unemployment duration distribution and the aggregate unemployment rate.