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South African pulp and paper mills have encountered frequent breakdowns of centrifugal pumps, resulting in considerable production losses. A case study was conducted to determine the factors contributing to these failures and devise ways to enhance pump reliability. Data were collected through questionnaires and operational records. The study revealed that inadequate maintenance, misalignment of pump components, lack of lubrication, improper baseplate installation, subpar sealing arrangements, inadequate design of the pumping system, and foreign materials entering the system were the primary causes of pump failure. To improve reliability, this study suggests employing machine learning methods to select the optimal maintenance strategy and provide internal maintenance personnel with training in condition monitoring systems. This will enable both pulp and paper mills and pump manufacturers to identify blind spots and improve the dependability of centrifugal pumps. [ABSTRACT FROM AUTHOR]

South African pulp and paper mills have encountered frequent breakdowns of centrifugal pumps, resulting in considerable production losses. A case study was conducted to determine the factors contributing to these failures and devise ways to enhance pump reliability. Data were collected through questionnaires and operational records. The study revealed that inadequate maintenance, misalignment of pump components, lack of lubrication, improper baseplate installation, subpar sealing arrangements, inadequate design of the pumping system, and foreign materials entering the system were the primary causes of pump failure. To improve reliability, this study suggests employing machine learning methods to select the optimal maintenance strategy and provide internal maintenance personnel with training in condition monitoring systems. This will enable both pulp and paper mills and pump manufacturers to identify blind spots and improve the dependability of centrifugal pumps. [ABSTRACT FROM AUTHOR]

South African pulp and paper mills have encountered frequent breakdowns of centrifugal pumps, resulting in considerable production losses. A case study was conducted to determine the factors contributing to these failures and devise ways to enhance pump reliability. Data were collected through questionnaires and operational records. The study revealed that inadequate maintenance, misalignment of pump components, lack of lubrication, improper baseplate installation, subpar sealing arrangements, inadequate design of the pumping system, and foreign materials entering the system were the primary causes of pump failure. To improve reliability, this study suggests employing machine learning methods to select the optimal maintenance strategy and provide internal maintenance personnel with training in condition monitoring systems. This will enable both pulp and paper mills and pump manufacturers to identify blind spots and improve the dependability of centrifugal pumps. [ABSTRACT FROM AUTHOR]

Assuring product quality has been always a main concern of a production process. PT IGP Internasional Sleman is a company that produces Printed Tissue Paper. This company's defect tolerance limit is 2%. Based on early observations, the defect rate reaches 5.2%. The Eight Disciplines (8D) method was used to solve the problem. First, the team was built to engage all related divisions in the project. Then, Failure Mode and Effects Analysis (FMEA) was carried out to sort the problems. Root Cause Analysis using Fishbone diagram was also used to find the causes. Proposed improvements to reduce defects have been implemented. There are adding Rotogravure machine operators, conducting supervision/inspection by production leader, and providing socialization related to the importance of product quality and production. The implementation has successfully reduced the defects to fulfill the company's standard. [ABSTRACT FROM AUTHOR]

This study investigated the trends in quality 4.0 and what they mean for various industries. Scopus was the main database used in the systematic literature review. Only papers written in English that directly addressed the study's research objectives and were published between 2017 and 2023 were included in the analysis. The number of papers on quality 4.0 has increased exponentially over the years. The majority of papers came from the TQM Journal, followed by the International Journal of Quality Research and Sustainability Switzerland. In our investigation into what quality 4.0 means for industries, we used a sample of 14 papers. We used qualitative data analysis known as thematic coding, and through inductive thematic coding, we were able to identify 99 codes that were associated with quality 4.0. The study found that quality 4.0 is a broad term encompassing nearly all aspects of quality management, including big data, digital quality management, connectivity, cybernetics, managing disruptions, simulation, and more. According to the study, quality 4.0 is unlikely to change the traditional quality management strategy; rather, it will make things smarter, faster, and more intelligent. The study's findings were consistent with previous research and had both reliability and validity. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak has brought about major health crises globally and the persistent nature of long COVID has put a significant strain on hospitals as a result of scarce healthcare resources. Heart problems are discovered to be the most prevalent among persons who have recovered from COVID-19 among several long-lasting effects (Gupta et al., 2021). There are so many cases of what is generally occurring with post-COVID patients. This work tries to find out the proper machine learning methods to predict the post-COVID complexities which can provide the highest accuracy. The true objective of this endeavor is to conduct additional machine learning-based research in this area of medical science and to develop an accurate machine-learning model. In the case of COVID patients, there are a lot of outcomes in a post-COVID condition. Proper problem definition, data acquisition, Model selection, evaluation of training and testing models, etc., are also significant obstacles to constructing a decent prediction model. There were proposed machine learning models according to previous research which model is combined with multiple models. This research might contribute to making further approaches to predicting the complexities of other diseases like COVID-19. [ABSTRACT FROM AUTHOR]

In the current era, one of the key challenges in utilizing the Holt-Winters Method of forecasting is the accurate selection of smoothing coefficients. To address this issue, researchers have explored an optimization approach that aims to minimize forecasting errors, such as Mean Squared Errors (MSE) or Mean Absolute Deviation (MAD). This paper presents a novel methodology that employs a Genetic Algorithm (GA) to optimize the forecasting error by determining the optimal smoothing coefficients for the Holt-Winters Method. The objective value of the optimization problem is the Mean Square Error (MSE), which serves as a measure of the accuracy of the forecast. To evaluate the effectiveness of the proposed approach, actual test cases based on rice stock commercial commodity in the Philippines during the COVID-19 pandemic were utilized. The paper examines different variants of the Holt-Winters Method and assesses their suitability for capturing the characteristics of the rice stock data. The findings indicate that an additive seasonal effect is more appropriate for modeling the seasonal patterns observed in the rice stock data. Furthermore, the performance of the proposed GA-based approach is compared to other forecasting models to ascertain its efficacy. The results demonstrate promising outcomes, suggesting that the GA-based optimization approach for determining the smoothing coefficients in the Holt-Winters Method improves the accuracy of rice stock forecasting during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to cover the many different pieces of research that have come out in the last six years. By classifying the topics covered in these papers, and then analyzing the contents and suggestions made by them, this paper offers a conglomeration of the information stored to help others plan future research in the field. Analysis of how certain proposed solutions work, and how their results are achieved is also done with some real-world statistics. Using all this information, examples of future research into smart mobility are given based on the information parsed in the making of this paper. [ABSTRACT FROM AUTHOR]

This paper aims to review several related papers on Quality Management and Industry 4.0 in the pharmaceutical industry environment to achieve the benefits required future and give a better vision of the extant regulatory and technical barriers for realizing it. Quality Management and industry 4.0 require efforts to better understand a GMP (Good Manufacturing Practices) pharmaceutical industry situation. Product quality in the pharmaceutical industry is a fundamental thing that must be achieved as a fulfillment of regulatory requirements. GMP for the Pharmaceutical Industry is a minimum standard of compliance for the pharmaceutical quality system requirement that must be designed, applied, managed, and maintained properly so that the main objective is achieved where the production process so that medicinal products always have robust quality in fulfilling efficacy and patient safety. Industry 4.0 in the pharmaceutical industry (which is popular with the term Pharma 4.0) is challenging to implement within the GMP 'high-regulated industry' environment. It will have consequences to moving transition into it step by step in a delicate careful slide. The internet of things (IoT), artificial intelligence (AI), robotics, machine learning, cyber-physical system, and advanced computing as a character of industry 4.0 will dramatically change the landscape of manufacturing including the quality system inside. The research results presented in this paper show an increasing trend towards research that focuses on the slices of 'quality management', 'pharmaceutical quality system' and 'industry 4.0'. Further research needs to be done to create a pharma 4.0 quality system implementation model. [ABSTRACT FROM AUTHOR]

This review paper delves into the significant advancements and explores future directions in the application of digital twin technology within machining processes, offering a comprehensive synthesis of current research, technological breakthroughs, and real-world applications. By systematically examining the integration of digital twins in enhancing operational efficiency, predictive maintenance, quality control, and customization in machining, the paper identifies key benefits such as improved productivity, reduced downtime, and enhanced product quality. Furthermore, it highlights existing challenges, including the complexity of implementation, data management issues, and the need for substantial initial investment. Drawing on a wide range of academic and industry sources, the review identifies gaps in current research and proposes potential interdisciplinary applications and technological innovations that could overcome these hurdles. The paper concludes with a forward-looking perspective, suggesting areas for further research and the development of standards to fully realize the transformative potential of digital twins in machining, thereby setting a new paradigm for future manufacturing practices. [ABSTRACT FROM AUTHOR]

The traditional software space (1.0) has seen more than fifty years of creation, testing, and delivery of deterministic software, but this tradition is being disrupted by machine learning (2.0). However, ML and traditional software are considerably different, and the nascent ML industry uses unique workflows and toolsets for both life cycles. This "one foot in each raft" scenario forces companies to support duplicate resources which are essentially doing the same thing. This paper proposes an answer to the research question: Can software 2.0 quality assurance be performed effectively using the same process as 1.0? A systematic literature review was performed and process documents from a machine learning company reviewed. 132 papers were selected and refined to 24. ML process documents supported what is an industry standard ML life cycle. While the literature review shows a gap relative to holistic QA solutions for ML products, process documents showed the answer to the research question is...perhaps, since it was determined that the typical ML life cycle can be mapped to the standard software 1.0 life cycle. This paper proposes using that mapping to extend an existing 1.0 QA process architecture, the Quality Assurance Machine (QAM), to effectively manage both 1.0 and 2.0 QA. [ABSTRACT FROM AUTHOR]

The global population is estimated to increase by 2.6 billion by 2050, in which 70% of the world's population will be staying in the cities. Countries will have to introduce solutions to traffic congestion, pollution, and security. South Africa is not immune to the global challenges and the rise in population. The purpose of this study is to evaluate the effectiveness of Smart Transport Systems in developing countries. The study used a qualitative approach, and scoping research methodology technology to review the papers published between 2015-2021, which were specifically concentrated on South African smart cities and transport. Articles relating to smart cities and transport in South Africa were intensively reviewed and 250 papers were selected. It was found that despite the challenges which South Africa as a country is facing, relating to the socio-economic, its initiatives are gradually developing especially in different metropolitan municipalities which are leading to very successful projects in both the public and the private sector. South Africa does have smart transport infrastructure in place which gives a good kick start and development of smart transport. However, good monitoring, evaluation, and planning of the current infrastructure are of great importance. [ABSTRACT FROM AUTHOR]

The notion of risk exists in every aspect of the business, as it cannot be eliminated but rather reduced to an attainable level through the utilization of effective risk management techniques. For the insurance industry in particular, risk is traded and transferred to the insurance providers as the company offers a shield from the exposure to risk consequences and the likelihood of loss, therefore, escalating the risk from the insured entity to the insurer for a given premium. This research is a development on a previously published paper by the author which had focused on the same issue but with the utilization of binary regression. The paper now proposes a modern model to risk classification which will be used for property lines insurance. The significance of the research lies in the fact that the process of risk prediction can be extremely complex since there are many parameters to keep in mind. In addition, accurate premium pricing can be difficult to estimate given the unpredictability of the risks occurring to the covered property. The data will be categorized into "A", "B", "C", and "D" using multinomial regression followed by a pricing model. The proposed model will be validated via data collected from surveyed properties of a UAE based insurance company. The model is expected to serve as a tool that helps provide better estimates of risk, premiums, and precise pricing. [ABSTRACT FROM AUTHOR]

Existing evidence indicates that Free/Libre Open-Source Software (FLOSS) ecosystems offer extensive learning opportunities. Community members actively participate in various activities, both during their interactions with peers and while utilizing these environments. Given that FLOSS repositories contain valuable data on participant interactions and activities, our study focuses on analyzing knowledge exchange and interactions within emails to track learning activities across different phases of the learning process, with a focus on the first phase (Initiation). In this paper, we leverage Natural Language Processing (NLP) and Machine Learning (ML) techniques within a process mining framework. Specifically, we employ NLP techniques to analyze the contents of emails and messages exchanged in these FLOSS repositories to generate event logs for the purpose of modeling learning patterns. Subsequently, we construct corresponding event logs, which serve as input to Disco, the process mining tool, for learning process discovery in these environments. The output comprises visual workflow nets that we interpret as representations of learning activity traces within FLOSS, capturing their sequential occurrences. To enhance the understanding of these models, we incorporate additional statistical details for contextualization and description. This approach enables a nuanced exploration of learning dynamics within FLOSS environments, emphasizing the role of NLP and ML in uncovering valuable insights on how FLOSS participants acquire and exchange knowledge. [ABSTRACT FROM AUTHOR]

The traditional software space (1.0) has seen more than fifty years of creation, testing, and delivery of deterministic software, but this tradition is being disrupted by machine learning (2.0). However, ML and traditional software are considerably different, and the nascent ML industry uses unique workflows and toolsets for both life cycles. This "one foot in each raft" scenario forces companies to support duplicate resources which are essentially doing the same thing. This paper proposes an answer to the research question: Can software 2.0 quality assurance be performed effectively using the same process as 1.0? A systematic literature review was performed and process documents from a machine learning company reviewed. 132 papers were selected and refined to 24. ML process documents supported what is an industry standard ML life cycle. While the literature review shows a gap relative to holistic QA solutions for ML products, process documents showed the answer to the research question is...perhaps, since it was determined that the typical ML life cycle can be mapped to the standard software 1.0 life cycle. This paper proposes using that mapping to extend an existing 1.0 QA process architecture, the Quality Assurance Machine (QAM), to effectively manage both 1.0 and 2.0 QA. [ABSTRACT FROM AUTHOR]

The paper discusses various failure modes associated to the links of bucket chains used in gold and tin mining in marine and alluvial environments. Failure control and the simulation of static and dynamic stresses developed in the operation of bucket ladder dredges using the Finite Element Method are also examined in detail. [ABSTRACT FROM AUTHOR]

Color sorting, control by touch or sound and ability to perform auto-sorting are characteristics of modern robotic arms and make them desirable in automatic lines. The paper discusses the implementation of sensor control in a robotic arm suitable for flexible manufacturing and emphasizes the benefits of such addition in terms of scalability. [ABSTRACT FROM AUTHOR]

Lightweight and stable robotic arms are essential devices in flexible manufacturing. The paper introduces a scalable robotic arm with 5 degrees of freedom controlled either by mouse or joystick and powered by serial bus servos and discusses its stability, kinematic characteristics and hardware and software implementation. The authors emphasize the benefit of 3D computer design in the mechanical development of scalable robotic arms. Such approach allows a quick and precise evaluation of distances between the joints with direct consequences in kinematic evaluations, interchangeability of bus servos required in scalability and overall an optimized design. [ABSTRACT FROM AUTHOR]

The paper emphasizes the benefits of employing magnetic fluids as lubricants in the development of hydrodynamic bearings in terms of carrying abilities. A brief discussion regarding the preparation of lubricants with unique magnetic properties is followed by analytical and numerical approaches for establishing the carrying ability of hydrodynamic bearings. The flow simulations, performed in identical geometrical and dynamic conditions, shows the increase in carrying ability and reduction of cavity in hydrodynamic bearing lubricated with magnetic fluids. In addition, the retention of the magnetic lubricant between the journal and bearing in the presence of a constant magnetic field permanently provides fluid friction for all flow regimes. [ABSTRACT FROM AUTHOR]

The notion of risk exists in every aspect of the business, as it cannot be eliminated but rather reduced to an attainable level through the utilization of effective risk management techniques. For the insurance industry in particular, risk is traded and transferred to the insurance providers as the company offers a shield from the exposure to risk consequences and the likelihood of loss, therefore, escalating the risk from the insured entity to the insurer for a given premium. This research is a development on a previously published paper by the author which had focused on the same issue but with the utilization of binary regression. The paper now proposes a modern model to risk classification which will be used for property lines insurance. The significance of the research lies in the fact that the process of risk prediction can be extremely complex since there are many parameters to keep in mind. In addition, accurate premium pricing can be difficult to estimate given the unpredictability of the risks occurring to the covered property. The data will be categorized into “A”, “B”, “C”, and “D” using multinomial regression followed by a pricing model. The proposed model will be validated via data collected from surveyed properties of a UAE based insurance company. The model is expected to serve as a tool that helps provide better estimates of risk, premiums, and precise pricing. [ABSTRACT FROM AUTHOR]

Existing evidence indicates that Free/Libre Open-Source Software (FLOSS) ecosystems offer extensive learning opportunities. Community members actively participate in various activities, both during their interactions with peers and while utilizing these environments. Given that FLOSS repositories contain valuable data on participant interactions and activities, our study focuses on analyzing knowledge exchange and interactions within emails to track learning activities across different phases of the learning process, with a focus on the first phase (Initiation). In this paper, we leverage Natural Language Processing (NLP) and Machine Learning (ML) techniques within a process mining framework. Specifically, we employ NLP techniques to analyze the contents of emails and messages exchanged in these FLOSS repositories to generate event logs for the purpose of modeling learning patterns. Subsequently, we construct corresponding event logs, which serve as input to Disco, the process mining tool, for learning process discovery in these environments. The output comprises visual workflow nets that we interpret as representations of learning activity traces within FLOSS, capturing their sequential occurrences. To enhance the understanding of these models, we incorporate additional statistical details for contextualization and description. This approach enables a nuanced exploration of learning dynamics within FLOSS environments, emphasizing the role of NLP and ML in uncovering valuable insights on how FLOSS participants acquire and exchange knowledge. [ABSTRACT FROM AUTHOR]

The notion of risk exists in every aspect of the business, as it cannot be eliminated but rather reduced to an attainable level through the utilization of effective risk management techniques. For the insurance industry in particular, risk is traded and transferred to the insurance providers as the company offers a shield from the exposure to risk consequences and the likelihood of loss, therefore, escalating the risk from the insured entity to the insurer for a given premium. This research is a development on a previously published paper by the author which had focused on the same issue but with the utilization of binary regression. The paper now proposes a modern model to risk classification which will be used for property lines insurance. The significance of the research lies in the fact that the process of risk prediction can be extremely complex since there are many parameters to keep in mind. In addition, accurate premium pricing can be difficult to estimate given the unpredictability of the risks occurring to the covered property. The data will be categorized into "A", "B", "C", and "D" using multinomial regression followed by a pricing model. The proposed model will be validated via data collected from surveyed properties of a UAE based insurance company. The model is expected to serve as a tool that helps provide better estimates of risk, premiums, and precise pricing. [ABSTRACT FROM AUTHOR]

Existing evidence indicates that Free/Libre Open-Source Software (FLOSS) ecosystems offer extensive learning opportunities. Community members actively participate in various activities, both during their interactions with peers and while utilizing these environments. Given that FLOSS repositories contain valuable data on participant interactions and activities, our study focuses on analyzing knowledge exchange and interactions within emails to track learning activities across different phases of the learning process, with a focus on the first phase (Initiation). In this paper, we leverage Natural Language Processing (NLP) and Machine Learning (ML) techniques within a process mining framework. Specifically, we employ NLP techniques to analyze the contents of emails and messages exchanged in these FLOSS repositories to generate event logs for the purpose of modeling learning patterns. Subsequently, we construct corresponding event logs, which serve as input to Disco, the process mining tool, for learning process discovery in these environments. The output comprises visual workflow nets that we interpret as representations of learning activity traces within FLOSS, capturing their sequential occurrences. To enhance the understanding of these models, we incorporate additional statistical details for contextualization and description. This approach enables a nuanced exploration of learning dynamics within FLOSS environments, emphasizing the role of NLP and ML in uncovering valuable insights on how FLOSS participants acquire and exchange knowledge. [ABSTRACT FROM AUTHOR]

Renewable energy has gained immense attention due to its potential to reduce the world's dependency on fossil fuels and mitigate climate change. As the energy harnessing rate from renewable sources depends on some external parameters like solar radiation, wind speed, direction, and turbulence, the generation rate in such sources comes with a high level of fluctuations. Fluctuations in their output can increase operating costs for the electricity system and be quite challenging for utility companies to always maintain a proper balance between the generation and usage of electricity. To reduce the operation cost and increase the reliability of the system, robust forecasting models are used to predict the generation rate and energy demand. This review paper offers a thorough examination of cutting-edge data-driven forecasting models utilized in forecasting renewable energy generation and demand. The paper organizes previous studies into five distinct groups based on prediction time frame: immediate, very short-term, short-term, medium-term, and long-term. It subsequently assesses the performance of various forecasting models, including three primary categories: time series, machine learning, and ensemble models, concerning predicting energy demand and generation rates across different time frames, using standard performance evaluation metrics. The findings indicate that ensemble models employing neural networks and support vector machines demonstrate notably higher accuracy rates in predicting energy demand and generation rates compared to the other models. [ABSTRACT FROM AUTHOR]

Lean thinking is an essential concept that has emerged in most organizations and industries. This is due to its proven outcomes in improving the process by removing waste and delivering value to the customer. Applying lean has also benefited the process by making it more sustainable by reducing the waste that negatively affects the three pillars of sustainability (Environment, Economy, and Society). Although lean has been established as a concept to improve the manufacturing process, it has extended to cover the maintenance process with reliance on established maintenance principles like Total Productive Maintenance (TPM) and existing lean methods. This paper aims to study and analyze a preventive maintenance (PM) process of a construction company in Qatar and implement lean maintenance methods with TPM to improve the process by minimizing lean waste with a focus on improving process sustainability. The methodology has been assessed through a real case study from a construction project of the company selected. The project equipment preventive maintenance process was analyzed, sustainability indicators were identified, and lean maintenance tools were implemented. The results have revealed that the process has been improved, and sustainability wastes have been minimized. [ABSTRACT FROM AUTHOR]

There is a growing interest in the concept of Industry 5.0 (I5.0), which supports a transition towards a more sustainable, resilient and human-oriented industrial paradigm. This paper focuses on the I5.0 sustainability pillar, which is crucial to optimize and support I5.0’s data-driven and circular supply chains. In order to pursue a Sustainable Supply Chain (SSC) and improve data quality management across it, it is first imperative to understand to what extent the existing SSC quality indicators are aligned with the opportunities of the digitalization era, and if they support the path to I5.0 sustainable practices. This paper aims to do a comprehensive study and analysis of relevant groups of SSC metrics and indicators. Through a broad literature review, SSC metrics and indicators are identified. Then, they are assessed to determine if they are suitable and relevant to assist decision makers in the challenges towards SSC 5.0. Results reveal that despite growing interest in the progress of data-driven SSC in recent years, there are still research gaps, mainly concerning the standardization of indicators, the simplification of models, and the use of consistent models for the treatment and conversion of proper data into relevant information. [ABSTRACT FROM AUTHOR]

The internal combustion engine (IC engine) is a complex mechanical system that plays a vital role in many industries. In order to design and optimize IC engines, accurate modeling and simulation of the engine's geometry and fluid flow are essential. This review paper provides an overview of the latest research in the field of geometric modeling and fluid flow simulation of IC engines. Firstly, the different techniques used for geometric modeling of IC engines are discussed, including the advantages and disadvantages of each approach. The impact of geometric modeling on the accuracy of fluid flow simulations is also examined. Next, the various approaches to simulating fluid flow in IC engines are reviewed, including the use of different turbulence models and boundary conditions. The challenges involved in accurately modeling and simulating IC engines are highlighted, such as the complex geometry and the presence of multiple phases. Furthermore, this review paper discusses the potential future research directions in this field, including the use of artificial intelligence and machine learning algorithms to improve the accuracy of simulations, and the incorporation of real-world driving conditions in simulations. In conclusion, this review paper provides a comprehensive overview of the latest research in the field of geometric modeling and fluid flow simulation of IC engines, highlighting the challenges and potential future directions in this important area of research. [ABSTRACT FROM AUTHOR]

This study conducts a comprehensive bibliometric examination to analyze Model-Based Customer Churn Research. Through Scopus, PRISMA, and VOSviewer. last two decades, academic publications and interest have increased. Empirical research, growth, and trends are examined in this paper. Evidence suggests churn prediction models using advanced data analytics, machine learning, and industry-specific methods. This shows scientific collaboration to predict industry churn. The study highlights "Expert Systems with Applications" and "ACM International Conference Proceedings Series," which spurred field dialogue. These publications provide cutting-edge research and expert opinions. Geographically, we study China, India, and the US. Global churn research matters. Bibliometric research shows model-based customer churns evolve. The resource can help academic and business researchers develop innovative client retention methods in different markets. By analyzing Model-Based Customer Churn the research studies, growth, and trends are examined. Evidence suggests churn prediction models use advanced data analytics, machine learning, and industry-specific methods. This shows scientific collaboration to predict industry churn. The study highlights "Expert Systems with Applications" and "ACM International Conference Proceedings Series," which spurred field dialogue. These publications provide cutting-edge research and expert opinions. Geographically, we study China, India, and the US. Global churn research matters. Bibliometric research shows that model-based customer churns evolve. The resource significantly assists academic and business researchers in developing innovative client retention methods in different markets. [ABSTRACT FROM AUTHOR]

This study is about sign language translation into text; this technology is for those experiencing difficulty in communicating, i.e., those who have speech disorders, hearing impairment, and are deaf. Sign language translation into text is done through a process when one uses sign language that could be translated into text. Deep learning is a machine learning and artificial intelligence (AI) technique that mimics how humans acquire knowledge; the complexity and abstraction of deep learning algorithms are stacked in a hierarchy. The researchers utilized Convolutional Neural Networks (CNN), Connectionist Temporal Classification (CTC), and Deep Belief Network (DBN) to collect and gather the results of their system through different methods of translating sign language into text. Through the systematic literature review of various published papers related to sign language translation using deep learning techniques, this paper discusses and systematically analyzed various studies and come up with a proposed model in translating sign language into text. [ABSTRACT FROM AUTHOR]

This paper discusses the various works and researches in Supply Chain Risk Assessment SCRA. The methodology chosen is the systematic literature review and the network analysis in order to collect and analysis methods and tendences of supply chain risk assessment in the period between 2019 and 2022. To this end, the study detected four methods extracted from 72 article, which are: Analytic Hierarchy Process (AHP), Failure Mode and Effect Analysis (FMEA), Decision Making Trial and Evaluation Laboratory (DEMATEL) and House of risks method HOR. This study analyses also the bibliometric data and the visualization of similarities (VOS) viewer method to graphically map that data collected from the Web of Science database. The graphical analysis uses citation, co-citation, and cooccurrence analysis. All these results provide a strong starting point for operations researches in this domain for future works. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) has emerged as a transformative force, reshaping traditional manufacturing methodologies and fostering agility and efficiency to respond effectively to disruptions and fluctuating demands. The research landscape has witnessed a surge in interest and exploration of AM, encompassing diverse applications such as automotive spare parts, electronics, medical accessories, and fashionable consumer goods. The fundamental objective of AM is to establish seamless collaboration within manufacturing functions, particularly in the face of disruptive events, by facilitating the rapid availability of required products or components through the concept of digitally holding inventory. The integration of AM has engendered substantial improvements in the resilience of supply chains, although it has also introduced unique challenges and implications. This review paper aims to provide a comprehensive overview of studies conducted in the past two decades, elucidating the transformative impact of AM on supply chain resilience within manufacturing organizations. This study employs a bibliometric analysis to identify prominent trends and thematic patterns within this research domain. Furthermore, a narrative review analysis is presented, examining the framework of AM processes across specific industries and delineating how AM has revolutionized traditional manufacturing procedures. The study highlights the tangible benefits of AM implementation and sheds light on the challenges encountered during its adoption. By offering a comprehensive review, this paper seeks to contribute to the existing knowledge base and serve as a foundation for future research endeavours in this evolving field. [ABSTRACT FROM AUTHOR]

Predictive maintenance shows significant impact in managing and controlling the utility systems of commercial buildings. Here in this paper, the predictive maintenance that is in line with Industry 4.0/ Quality 4.0 is applied. The chilled water system is highlighted in this research and the main goal is to reach out the most occurred faults in one of the chilled water system, which is cooling tower. An engineering management framework was used through three parts, set up, machine learning, and quality control. Having said machine learning, decision tree algorithm is used in this research and prediction model showed high prediction accuracy. During an empirical period, the most occurred faults were malfunctioning blowdown system, over current, and low water basin level. [ABSTRACT FROM AUTHOR]

Digital transformation is a concept that is currently being discussed and emphasized in many policies, industrial and corporate strategies. In the scientific literature, digital transformation is often discussed as one of the pillars of a sustainable economy. On the other hand, the question is how to ensure that the digital transformation process itself is sustainable in the long term. That is, not only the transformation process itself but also the application of digital technologies in companies afterwards. This paper aims to explore the sustainability of digital transformation by analyzing its environmental, economic, and social impact. Through a literature review, this paper identifies several key factors that contribute to the sustainability of digital transformation, including the term energy transformation and education and digital skills, security, and business continuity. Based on the conducted research a model of sustainable digital transformation was proposed. The model presents the key elements for the sustainability of digital transformation. The paper concludes by discussing the implications of these findings for organizations, and individuals. [ABSTRACT FROM AUTHOR]

This study conducts a comprehensive bibliometric examination to analyze Model-Based Customer Churn Research. Through Scopus, PRISMA, and VOSviewer. last two decades, academic publications and interest have increased. Empirical research, growth, and trends are examined in this paper. Evidence suggests churn prediction models using advanced data analytics, machine learning, and industry-specific methods. This shows scientific collaboration to predict industry churn. The study highlights "Expert Systems with Applications" and "ACM International Conference Proceedings Series," which spurred field dialogue. These publications provide cutting-edge research and expert opinions. Geographically, we study China, India, and the US. Global churn research matters. Bibliometric research shows model-based customer churns evolve. The resource can help academic and business researchers develop innovative client retention methods in different markets. By analyzing Model-Based Customer Churn the research studies, growth, and trends are examined. Evidence suggests churn prediction models use advanced data analytics, machine learning, and industry-specific methods. This shows scientific collaboration to predict industry churn. The study highlights "Expert Systems with Applications" and "ACM International Conference Proceedings Series," which spurred field dialogue. These publications provide cutting-edge research and expert opinions. Geographically, we study China, India, and the US. Global churn research matters. Bibliometric research shows that model-based customer churns evolve. The resource significantly assists academic and business researchers in developing innovative client retention methods in different markets. [ABSTRACT FROM AUTHOR]

Fabric, a versatile material composed of interwoven fibers, serves diverse purposes spanning from clothing and furnishings to industrial applications. It exists in both natural forms like cotton and wool, and synthetic forms such as polyester and nylon, each impacting characteristics like texture, breathability, and strength. Fabric structure refers to the construction of textiles, particularly evident in woven fabrics where yarns interlace perpendicular to each other, forming geometric patterns like plain, herringbone, and diamond. The textile industry continuously innovates fabric production techniques, including automated processes and software-driven quality control. This paper explores the development of a novel software system for automatic fabric structure detection, leveraging advanced algorithms to analyze images and categorize fabric patterns. This innovation holds promise for streamlining quality control and aiding design processes in industries like textiles and fashion. The research demonstrates the efficacy and accuracy of the software, marking a significant advancement in automated fabric analysis. Furthermore, it highlights the potential applications in textile manufacturing and quality assurance, contributing to improved efficiency and laying the groundwork for enhanced quality control processes. Ultimately, this paper aims to innovate fabric formula detection, empowering industries and consumers, fostering efficiency, and advancing knowledge in the field. [ABSTRACT FROM AUTHOR]

Driver impairment is yet another human factor that is likely to cause dangerous driving behavior (aka distracted driving), leading to safety hazards on the roads. Assuring acceptable drivers' cognitive state can improve safe driving performance. Behavioral and self-report measures are used to assess drivers' cognitive states, but psychophysiological measures can add more considerable value in the real-world driving context. Hence, monitoring, detecting, and responding to drivers' psychophysiological state compose a safety countermeasure that can reduce the risk of distracted driving and thus eliminate or mitigate safety hazards. The research investigates the recent used measures to detect drivers' psychophysiological state, in addition to the drivers' psychophysiological state classification using machine learning. Eye movement, respiration rate, electrocardiogram, electroencephalography, and electromyography are all examples of measures used to deduce drivers' overall psychophysiological state. Tracking and assessing these measures rely on several different metrics, methods, and technologies. For example, drowsiness, as a well-known psychophysiological state, can be assessed and tracked by the eye aspect ratio metric. This paper reviews the recent state-of-the-art measures used for detecting the drivers' overall psychophysiological state. The review considers different psychophysiological states such as fatigue, drowsiness, distraction, and stress. The paper also discusses the strengths and limitations of each reviewed measure, its usage, and its real-time applications. The result showed that the most common used measures are eyes and mouth state measures. Also, several machine learning methods have proven their reliability in classifying drivers' psychophysiological states, especially SVM and CNN as they have wider applications. [ABSTRACT FROM AUTHOR]

Supply Chain Management (SCM) cannot distance itself from seeking both sustainability and resilience to deal with consecutive change. To achieve this objective, SCM has seen the adoption of digital technologies as a decisive tool to face challenges, such as the COVID-19 pandemic and the war in Ukraine. These disruptions guide SC managers to search for new opportunities and strategies that impact them in the short and long term. The terms “supply chain sustainability” and “supply chain resilience” have been well explored in literature as independent terms, but there is a lack of studies on their combination, especially in the digital field. This paper aims to explore the extant literature focused on the adoption of digital technologies that contribute to the development of sustainable and resilient SCs, recurring to a systematic literature review. The objective is to identify the trade-offs and the synergies that characterize the relationship between technological-enabled practices for sustainability and resilience. The database used was Scopus and the formulated query resulted from de author’s discussion including words related to supply chain, sustainability, resilience, and digital technologies. Only articles published in journals and in English were considered, and there was no restriction regarding the date of publication. The first search presented a total of 136 articles (March 2023). The selection was performed in two stages: (1) reading the abstracts and keywords of all papers; (2) full reading of the papers selected in the first stage. After the first analysis (step 1), we selected 63 articles. After step 2, 42 papers that address the relationship between digital technologies and sustainability and resilience were kept. The results showed an increase in the production of papers approaching the themes, possibly (at least in part) due to the COVID-19 pandemic and the pressure that competition provokes on the stakeholders. The technologies majorly identified are related to Blockchain, Additive Manufacturing, Big Data, and the Internet of Things as means to achieve SCs with higher levels of resilience and sustainability, being also identified papers that approach the terms “industry 4.0” and “industry 5.0”. The main fields where papers have been conducted are food supply chain, agriculture, and automotive industries because of the traceability capabilities of most technologies. These findings lead to the conclusion that these technologies are the ones with the potential to change organizations and research is focusing on the initial links of SC. According to the quantitative analysis, 2022 was the year when more articles were published in the journals Operations Management Research, Annals of Operations Research, and Production Planning and Control as the ones that published more. Most papers focused on the utilization of one technology and do not identify a specific theory. We conclude that there is crescent interest in this topic and a lack of theoretical support. This study contributes to increasing the body of knowledge related to digital technologies in SCs that simultaneously contributes to achieving resilience and sustainability in those SCs. The findings resulting from this systematic literature review help consolidate the theme for future research. [ABSTRACT FROM AUTHOR]

Reinforcement learning has emerged as a transformative paradigm in artificial intelligence, showcasing remarkable applications across diverse domains. Simultaneously, simulation has proven to be a crucial tool for constructing environments conducive to interactions with agents trained through reinforcement learning algorithms. This synergy opens avenues for refining policies within simulation models. While existing research has predominantly harnessed reinforcement learning in agent-based and discrete event simulation models, there is a noticeable gap in its application within the realm of system dynamics simulation. This simulation paradigm, widely employed in Industrial Engineering and Operations Management for decision-making processes, presents a unique set of challenges. This paper explores the integration of reinforcement learning techniques into system dynamics models, aiming to address the scarcity of applications in this context. By doing so, it seeks to extend the benefits of reinforcement learning to enhance decision-making processes in industrial and operational settings. The research delves into the nuances of policy exploration within the framework of system dynamics, providing insights into novel alternatives for optimizing and improving policies. Through this exploration, the paper contributes to the evolving landscape of reinforcement learning applications, particularly in the crucial domain of system dynamics simulation. [ABSTRACT FROM AUTHOR]

When it comes to brick-and-mortar retailing and E-Commerce, anticipating demand accurately is one of the most important aspects in minimizing loss (through unavailability of items) and maximizing profit for any time period. This literature review aims to pursue a deeper understanding in founding a prediction model for forecasting retail sales by using deep learning techniques. The researchers gathered various papers from reputable publishers and institutions and sorted those that dealt with the use of deep learning techniques and machine learning techniques. The researchers found that the most frequent techniques used are Long-Short Term Memory and Random Forest for deep and machine learning respectively. Thus, the researchers proposed a model using LSTM with learning features inherited from common features observed through the related studies gathered and Random Forest as the proposed comparator or baseline model. [ABSTRACT FROM AUTHOR]

This paper proposes a novel data-driven optimization framework that integrates building design decisions with simulation results to optimize sustainability across environmental, social, and economic dimensions. The building industry faces increasing pressure to reduce emissions and environmental impacts, but existing design tools do not fully account for how buildings will be operated by occupants. The proposed optimization framework utilizes statistical learning via metamodels and decision optimization to explore the complex tradeoffs involved in building decisions. The focus of this paper is on developing an optimization approach performed on common machine learning models such as classification and regression trees (CART), gradient boosted trees and random forest to investigate the causal relationship between building decisions and performance metrics. We integrate both descriptive and prescriptive analytics to inform an optimization model that provides decisions for both the design and operations of a given building. A case study is presented demonstrating the potential benefits of the optimization model in the design phase of a building. The integrative, data-driven optimization approach incorporates both categorical and continuous variables to create higher-performing, more sustainable building designs that better balance Planet, People, and Prosperity goals. [ABSTRACT FROM AUTHOR]

The digital twin concept is pivotal in Industrial 4.0, integrates physical and cyber spaces to address product design challenges. In the early design phase, digital twins offer valuable insights, while topology optimization, often integrated with digital twins, is prevalent during the optimization process. However, traditional topology optimization methods may fall short for soft robots due to their complex physical constraints and the high costs associated with physical simulations. Evolutionary algorithms, with their inherent adaptability, hold promise for autonomous optimization of soft robots based on available data. These algorithms are well-suited for generative design, potentially enabling data-driven generative approaches. This study introduces an intuitional evolutionary algorithm (iEA) to explore a generative optimization method for product design within the context of the digital twin. This paper presents a case study involving the design of soft robots for a speed competition in a simulated 3D environment, demonstrating the viability of combining the iEA with a data-driven generative design (dGD) model to develop a fast- walking soft robot. The results include learning curves and convergence diagrams, illustrating the efficacy of the design solution. The paper will also discuss information hierarchy, requirement analysis and functional modeling of the dGD model. [ABSTRACT FROM AUTHOR]

Palletizing is an important job in logistic operations. It is considered an extremely labor-intensive process when performed manually, as it includes awkward and repetitive working postures, which result in serious back injuries. This paper introduces a new assistive device that helps reduce the exposure to physical loads among palletizing workers. The risk factors associated with manual palletizing in the food industry were analyzed using the Rapid Upper Limb Assessment (RULA) method. Different postures were observed during manual palletizing in a food factory that had a RULA score of 7. An assistive device was proposed to improve the palletizing operation. As a result, after implementing the proposed assistive device, the RULA final score was dropped to 4 based on lifting at the elbow height of the pallet, 3 when lifting at low and medium heights of the loaded pallet during wrapping, and 4 when lifting at the highest point of the full pallet. In conclusion, the proposed assistive device helped improve the palletizing job and reduce its negative consequences for workers. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive analysis of Qatar Petroleum's maintenance system, focusing on its core objectives and the six integrated elements of Maintenance Best Practices spanning across upstream, midstream, and downstream production facilities. With an emphasis on the Centralized Maintenance Organization (CMO) concept, the study underscores its crucial role in achieving predefined outcomes. Our analysis centers on the primary goal of attaining best-in-class performance in Safety, Reliability, Integrity, and Cost. To achieve this, the maintenance system employs a cohesive set of processes and procedures, including workflow implementation, work selection via RBWS (Right Base Work Selection), meticulous planning and scheduling, and execution emphasizing quality and efficiency. Continuous improvement strategies, supported by Key Performance Indicators (KPIs), are instrumental in further enhancing system performance. Additionally, the maintenance system encompasses setting objectives, task prioritization, system stewardship, and overall performance enhancement. Through the integration of supporting elements such as equipment design, tools and procedures, information systems, and organizational structures, the system strives to achieve best-in-class safety, reliability, integrity, and cost performance (Bao et al.2023, Fatem et al.2022, Hamed et al. 2018) [ABSTRACT FROM AUTHOR]

The Industry 4.0 has significantly influenced the industry, compelling businesses to adopt to become more efficient, safe, reliable and environmentally responsible. Maintenance is recognized as a critical component in achieving these objectives, thus highlighting the importance of implementing effective maintenance policies. However, rapidly evolving nature of maintenance strategies introduces both challenges and opportunities in the selection and adaptation of fit for purpose maintenance policies. Increased availability and accessibility of Internet of Things (IoT) has accelerated the adoption of predictive maintenance technologies for shipping companies. Nonetheless, as the industry progresses towards prescriptive maintenance, launching such initiatives without a comprehensive strategy might present significant obstacles for shipping companies. This paper develops a strategic framework to investigate the potential of prescriptive maintenance paradigm in shipping industry. The study has strengthened with a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis. The study provides insights about challenges and opportunities to integrate prescriptive maintenance into shipping companies to increase its utility. [ABSTRACT FROM AUTHOR]

The study explores the integration of machine learning techniques (ML) to improve the composting process of organic waste, amid the growing global challenge of waste management. A decade-long bibliometric analysis (2013-2022) using the Scopus database examined the evolution and impact of ML applications on composting. Their analysis identified important contributions to research and emerging trends, highlighting the fundamental role of ML in optimizing compost production, a critical solution for managing the growing volumes of organic waste generated worldwide. The study revealed how ML models, including artificial neural networks and genetic algorithms, revolutionized composting processes by predicting, improving, and monitoring various composting parameters. In addition, the paper delved into the geographical distribution of research efforts, highlighting the dominance of countries such as China, India and the United States in this area of research. Through the analysis of 180 articles, the study not only mapped out the current picture of ML in composting, but also identified gaps and opportunities for future research. The results defended the potential of the ML to significantly improve the efficiency and effectiveness of composting operations, thus contributing to more sustainable waste management practices. This work is a fundamental resource for both researchers and professionals, with the aim of harnessing the power of the ML in favour of the environment. [ABSTRACT FROM AUTHOR]

With the recent inflationary experience in the United States, and the rise in interest rates in the economy, the role of money demand, and the stability of money demand, have both come into sharp focus. This paper studies the factors that affect aggregate demand for money, using quarterly time series data for the United States. We pay special attention to income, interest rate and inflation effects on money demand. We also examine the impacts of government debt, demographic factors, and financial innovation on the money demand function. For robustness purposes, the study utilizes different money measures, and also controls for the pandemic time period. The modelling techniques address issues of unit roots and cointegration, and error-correction methods. Our paper further derives long-term elasticity and semi-elasticity estimates. We conclude with policy recommendations, and the implications for business management and entrepreneurship. [ABSTRACT FROM AUTHOR]

Predictive maintenance shows significant impact in managing and controlling the utility systems of commercial buildings. Here in this paper, the predictive maintenance that is in line with Industry 4.0/Quality 4.0 is applied. The chilled water system is highlighted in this research and the main goal is to reach out the most occurred faults in one of the chilled water system, which is cooling tower. An engineering management framework was used through three parts, set up, machine learning, and quality control. Having said machine learning, decision tree algorithm is used in this research and prediction model showed high prediction accuracy. During an empirical period, the most occurred faults were malfunctioning blowdown system, over current, and low water basin level. [ABSTRACT FROM AUTHOR]

Driver impairment is yet another human factor that is likely to cause dangerous driving behavior (aka distracted driving), leading to safety hazards on the roads. Assuring acceptable drivers' cognitive state can improve safe driving performance. Behavioral and self-report measures are used to assess drivers' cognitive states, but psychophysiological measures can add more considerable value in the real-world driving context. Hence, monitoring, detecting, and responding to drivers' psychophysiological state compose a safety countermeasure that can reduce the risk of distracted driving and thus eliminate or mitigate safety hazards. The research investigates the recent used measures to detect drivers' psychophysiological state, in addition to the drivers' psychophysiological state classification using machine learning. Eye movement, respiration rate, electrocardiogram, electroencephalography, and electromyography are all examples of measures used to deduce drivers' overall psychophysiological state. Tracking and assessing these measures rely on several different metrics, methods, and technologies. For example, drowsiness, as a well-known psychophysiological state, can be assessed and tracked by the eye aspect ratio metric. This paper reviews the recent state-of-the-art measures used for detecting the drivers' overall psychophysiological state. The review considers different psychophysiological states such as fatigue, drowsiness, distraction, and stress. The paper also discusses the strengths and limitations of each reviewed measure, its usage, and its real-time applications. The result showed that the most common used measures are eyes and mouth state measures. Also, several machine learning methods have proven their reliability in classifying drivers' psychophysiological states, especially SVM and CNN as they have wider applications. [ABSTRACT FROM AUTHOR]

When it comes to brick-and-mortar retailing and E-Commerce, anticipating demand accurately is one of the most important aspects in minimizing loss (through unavailability of items) and maximizing profit for any time period. This literature review aims to pursue a deeper understanding in founding a prediction model for forecasting retail sales by using deep learning techniques. The researchers gathered various papers from reputable publishers and institutions and sorted those that dealt with the use of deep learning techniques and machine learning techniques. The researchers found that the most frequent techniques used are Long-Short Term Memory and Random Forest for deep and machine learning respectively. Thus, the researchers proposed a model using LSTM with learning features inherited from common features observed through the related studies gathered and Random Forest as the proposed comparator or baseline model. [ABSTRACT FROM AUTHOR]

The advancement of digital technology in the industry is making it possible for products and processes to connect people, materials, energy, plant, and equipment efficiently. More productive business processes will have an impact throughout the economy and the environment. Connected products generate data that is being seen as a key source of competitive advantage, and the management and processing of that data is generating new challenges in the industrial environment. The work to be presented looks into the framework of the adoption of Artificial Intelligence and Machine Learning in the Industrial sector under industry 4.0 or smart manufacturing framework. This work is focused on the discussion around Artificial Intelligence as a driver for Industrial Process optimization and looks into its link with process operators and people in the shop floor, and IIoT. The paper includes a bibliometric analysis of the key topics around Artificial Intelligence under Industry 4.0 or Smart Manufacturing paradigm. The main findings are related to the importance that the subject has adquired since 2017 in terms of published articles, and the complexity of the approach of the issue proposed by this work at the industrial environment. [ABSTRACT FROM AUTHOR]