A review on digital transformation in healthcare waste management: Applications, research trends and implications.

At present, both emerging and developed economies have faced the challenge of higher healthcare waste generation. Developed countries are using these technologies to manage healthcare waste and cope with the challenge. Emerging economies are still struggling to understand and implement digital technologies in healthcare waste management, posing a danger to partners handling toxic and hazardous waste. The proper handling of healthcare waste is essential for social and environmental sustainability. Digital technologies that drive digital transformation in the healthcare sector impact the traditional way of managing healthcare waste. Digital technologies include artificial intelligence, blockchain, the Internet of Things, sensors, data analytics and radio frequency identification. These technologies can potentially address vehicle route planning and scheduling problems, resource optimisation, real-time tracking and the visibility of healthcare waste management. Apart from economic and environmental concerns, the operational workforce also takes care of societal well-being and implements waste management strategies and policies. Past research has focused on integrating blockchain technology to enhance traceability and transparency in waste collection and disposal activities. However, the application and impact of these technologies for managing different operations of healthcare management with sustainability is a gap bridged by the present study. This study adopts a systematic literature review to identify research trends, applications and implications of digital transformation. It proposes a digital technology-driven framework for healthcare waste management for further research.

Recent trends in edible algae functional proteins: Production, bio-functional properties, and sustainable food packaging applications.

In recent years, there has been a notable surge in the development and adoption of edible algae protein-based sustainable food packaging, which presents a promising alternative to traditional materials due to its biodegradability, renewability, and minimal environmental impact. Hence, this review aims to emphasize the sources, cultivation, and downstream potential of algal protein and protein complexes. Moreover, it comprehensively examines the advancements in utilizing protein complexes for smart and active packaging applications, while also addressing the challenges that must be overcome for the widespread commercial adoption of algal proteins to meet industry 4.0. The review revealed that the diversity of algae species and their sustainable cultivation methods offers a promising alternative to traditional protein sources. Being vegan source with higher photosynthetic conversion efficiency and reduced growth cycle has permitted the proposition of algae as proteins of the future. The unique combination of techno-functional combined with bio-functional properties such as antioxidant, anti-inflammatory and antimicrobial response have captured the sustainable groups to invest considerable research and promote the innovations in algal proteins. Food packaging research has increasingly benefited by the excellent gas barrier property and superior mechanical strength of algal proteins either stand alone or in synergy with other biodegradable polymers. Advanced packaging functionality such as freshness monitoring and active preservation techniques has been explored and needs considerable characterization for commercial advancement. Overall, while algal proteins show promising downstream potential in various industries aligned with Industry 4.0 principles, their broader adoption hinges on overcoming these barriers through continued innovation and strategic development.

A bibliometric analysis of IoT applications in logistics and supply chain management.

The rapid integration of the Internet of Things (IoT) into logistics and supply chain management (SCM) marks a significant transformation towards enhanced efficiencies, security, and sustainability. Through a comprehensive bibliometric analysis of 2680 publications from the Scopus database, this study charts the evolution of IoT within logistics and SCM and reveals a shift from foundational explorations to mature implementations. The research unfolds a complex thematic structure, highlighting the revolutionary impacts of IoT and related technologies such as RFID, the synergy of Industry 4.0 with SCM through Artificial Intelligence (AI) and the Industrial Internet of Things (IIoT), the strategic role of blockchain for enhanced traceability and security, and the advent of novel communication and encryption technologies for secure data exchanges. Further, the analysis categorizes the scholarly discourse into critical areas including big data and IoT optimization in SCM, IoT-driven innovation in the food supply chain, applications of blockchain and smart contracts, digital transformation through Industry 4.0, security advancements with intelligent systems, and the exploration of advanced technologies for Industry 4.0 and 5.0. This review not only delineates the intellectual landscape of IoT applications in logistics and SCM but also identifies emerging research areas such as blockchain integration, 5G potential, and AI-driven optimizations, suggesting pathways for future research to broaden the understanding in this dynamically evolving field. It serves as an essential resource for academics and practitioners, providing insights into the transformative role of IoT in logistics and SCM and proposing directions for future technological and academic endeavors.

The impact of management practices and industry 4.0 technologies on supply chain sustainability: A systematic review.

This systematic review addresses a significant gap in the existing literature by examining the intricate relationship between management practices and Industry 4.0 technologies in shaping supply chain sustainability. While prior studies have explored their individual impacts, this review synthesizes and categorizes findings to identify nuanced trends that contribute to supply chain efficiency, waste reduction, and environmental footprints. To achieve the goal of the study, a rigorous search strategy was employed to select peer-reviewed journal articles focusing on total quality management, just-in-time, vendor-managed inventory, lean, manufacturer-led decentralized systems, blockchain, the internet of things, and big data in the context of supply chain sustainability. The selected studies underwent a thorough evaluation to ensure quality and relevance. The findings highlight key insights: the adoption of management practices, particularly total quality management and just-in-time, significantly contributes to reducing waste, enhancing efficiency, and minimizing environmental footprints across supply chains. Simultaneously, the integration of industry 4.0 technologies like blockchain, the Internet of things, and big data empowers data-driven decision-making, transparency, and traceability, amplifying sustainability efforts. In conclusion, this review contributes a novel perspective by synthesizing, categorizing, and analyzing the impact of management practices and Industry 4.0 technologies on supply chain sustainability. Its findings offer valuable insights for addressing contemporary challenges and advancing sustainable practices amid dynamic global scenarios.

Genetic Algorithm-Based Data-Driven Process Selection System for Additive Manufacturing in Industry 4.0.

Additive manufacturing (AM) has impacted the manufacturing of complex three-dimensional objects in multiple materials for a wide array of applications. However, additive manufacturing, as an upcoming field, lacks automated and specific design rules for different AM processes. Moreover, the selection of specific AM processes for different geometries requires expert knowledge, which is difficult to replicate. An automated and data-driven system is needed that can capture the AM expert knowledge base and apply it to 3D-printed parts to avoid manufacturability issues. This research aims to develop a data-driven system for AM process selection within the design for additive manufacturing (DFAM) framework for Industry 4.0. A Genetic and Evolutionary Feature Weighting technique was optimized using 3D CAD data as an input to identify the optimal AM technique based on several requirements and constraints. A two-stage model was developed wherein the stage 1 model displayed average accuracies of 70% and the stage 2 model showed higher average accuracies of up to 97.33% based on quantitative feature labeling and augmentation of the datasets. The steady-state genetic algorithm (SSGA) was determined to be the most effective algorithm after benchmarking against estimation of distribution algorithm (EDA) and particle swarm optimization (PSO) algorithms, respectively. The output of this system leads to the identification of optimal AM processes for manufacturing 3D objects. This paper presents an automated design for an additive manufacturing system that is accurate and can be extended to other 3D-printing processes.

Sustainable Digital Transformation: Its Impact on Perceived Value and Adoption Intention of Industry 4.0 in Moderating Effects of Uncertainty Avoidance.

Background: Industry 4.0 is a significant technical revolution that combines big data analytics, the Internet of Things (IoT), and cyber-physical systems to improve manufacturing productivity. This study investigates the impact of digital trust and sustainable attitude on perceived value and the intention to adopt Industry 4.0 technologies. It also examines the moderating role of uncertainty avoidance in these relationships. Methods: Data were collected from 189 employees of leading manufacturing companies in Indonesia that are recognized for their Industry 4.0 practices. The data were analyzed using Partial Least Squares (PLS) methodology with SmartPLS software to test the proposed hypotheses and explore the moderating effects. Results: The findings reveal that both digital trust and sustainable attitude significantly influence perceived value. However, these factors do not directly affect the intention to adopt Industry 4.0 technologies. Uncertainty avoidance moderates the relationship between digital trust and adoption intention. Specifically, in environments with high uncertainty avoidance, digital trust becomes a critical factor influencing the decision to adopt Industry 4.0 technologies. Conclusions: The study provides valuable insights for organizations aiming to implement Industry 4.0 initiatives. It highlights the importance of fostering digital trust and considering cultural dimensions, such as uncertainty avoidance, in their technology adoption strategies.

Pharma 4.0: A deep dive top management commitment to successful Lean 4.0 implementation in Ghanaian pharma manufacturing sector.

The primary aim of this study is to assess the significance of top management commitment in the context of Lean 4.0 implementation within the pharmaceutical manufacturing industry in Ghana. The study seeks to understand and evaluate the overall effectiveness and achievements associated with adopting Lean 4.0. Employing a positivist mindset, the research utilizes an explanatory quantitative research design and a survey technique. Data collected from 181 employees of pharmaceutical companies in Ghana undergo analysis using SmartPLS (version 4) and IBM SPSS version 26. The study employs a combination of descriptive statistics to summarise data characteristics and inferential statistics to test various hypotheses related to Lean 4.0 adoption. The analysis reveals that the successful integration of lean methods and Industry 4.0 technologies requires meticulous management. Simultaneously, individual implementations of lean principles and Industry 4.0 technologies positively impact business performance. Surprisingly, the study does not observe a substantial positive influence of Lean 4.0 on corporate performance, suggesting that immediate improvements in efficiency or profitability may not result from the adoption of this framework. This research contributes to the field by highlighting the need for careful management in integrating lean methods and Industry 4.0 technologies. It also emphasizes the positive impacts of lean principles and Industry 4.0 technology on business performance. The unexpected finding regarding the lack of immediate improvements in corporate efficiency or profitability with Lean 4.0 adoption prompts considerations of initial implementation challenges or the organization's need for time to adapt to this integrated approach.

Digital competence of lecturers and its impact on student learning value in higher education.

This paper explores the relationship between lecturers' digital competence and the learning value of students in higher education. By conducting an empirical study with a sample of 626 lecturers, we validated the positive impact of the six dimensions of digital competence outlined in the DigCompEdu framework, including: (i) Professional engagement, (ii) Digital resources, (iii) Teaching and learning, (iv) Assessment, (v) Empowering learners, and (vi) Facilitating learners' digital competence on the student learning value. Our findings underscore the profound significance of these dimensions in shaping students' learning experiences and outcomes, particularly within the dynamic context of Industry 4.0. Based on these findings, we propose recommendations to enhance lecturers' digital competence, targeting not only the lecturers themselves but also university administrations and governmental agencies responsible for educational oversight.

Industry 4.0 Technologies in Maternal Health Care: Bibliometric Analysis and Research Agenda.

BACKGROUND: Industry 4.0 (I4.0) technologies have improved operations in health care facilities by optimizing processes, leading to efficient systems and tools to assist health care personnel and patients. OBJECTIVE: This study investigates the current implementation and impact of I4.0 technologies within maternal health care, explicitly focusing on transforming care processes, treatment methods, and automated pregnancy monitoring. Additionally, it conducts a thematic landscape mapping, offering a nuanced understanding of this emerging field. Building on this analysis, a future research agenda is proposed, highlighting critical areas for future investigations. METHODS: A bibliometric analysis of publications retrieved from the Scopus database was conducted to examine how the research into I4.0 technologies in maternal health care evolved from 1985 to 2022. A search strategy was used to screen the eligible publications using the abstract and full-text reading. The most productive and influential journals; authors', institutions', and countries' influence on maternal health care; and current trends and thematic evolution were computed using the Bibliometrix R package (R Core Team). RESULTS: A total of 1003 unique papers in English were retrieved using the search string, and 136 papers were retained after the inclusion and exclusion criteria were implemented, covering 37 years from 1985 to 2022. The annual growth rate of publications was 9.53%, with 88.9% (n=121) of the publications observed in 2016-2022. In the thematic analysis, 4 clusters were identified-artificial neural networks, data mining, machine learning, and the Internet of Things. Artificial intelligence, deep learning, risk prediction, digital health, telemedicine, wearable devices, mobile health care, and cloud computing remained the dominant research themes in 2016-2022. CONCLUSIONS: This bibliometric analysis reviews the state of the art in the evolution and structure of I4.0 technologies in maternal health care and how they may be used to optimize the operational processes. A conceptual framework with 4 performance factors-risk prediction, hospital care, health record management, and self-care-is suggested for process improvement. a research agenda is also proposed for governance, adoption, infrastructure, privacy, and security.

The Artificial Intelligence-Powered New Era in Pharmaceutical Research and Development: A Review.

Currently, artificial intelligence (AI), machine learning (ML), and deep learning (DL) are gaining increased interest in many fields, particularly in pharmaceutical research and development, where they assist in decision-making in complex situations. Numerous research studies and advancements have demonstrated how these computational technologies are used in various pharmaceutical research and development aspects, including drug discovery, personalized medicine, drug formulation, optimization, predictions, drug interactions, pharmacokinetics/ pharmacodynamics, quality control/quality assurance, and manufacturing processes. Using advanced modeling techniques, these computational technologies can enhance efficiency and accuracy, handle complex data, and facilitate novel discoveries within minutes. Furthermore, these technologies offer several advantages over conventional statistics. They allow for pattern recognition from complex datasets, and the models, typically developed from data-driven algorithms, can predict a given outcome (model output) from a set of features (model inputs). Additionally, this review discusses emerging trends and provides perspectives on the application of AI with quality by design (QbD) and the future role of AI in this field. Ethical and regulatory considerations associated with integrating AI into pharmaceutical technology were also examined. This review aims to offer insights to researchers, professionals, and others on the current state of AI applications in pharmaceutical research and development and their potential role in the future of research and the era of pharmaceutical Industry 4.0 and 5.0.

Revolutionizing SIEM Security: An Innovative Correlation Engine Design for Multi-Layered Attack Detection.

Advances in connectivity, communication, computation, and algorithms are driving a revolution that will bring economic and social benefits through smart technologies of the Industry 4.0 era. At the same time, attackers are targeting this expanded cyberspace to exploit it. Therefore, many cyberattacks are reported each year at an increasing rate. Traditional security devices such as firewalls, intrusion detection systems (IDSs), intrusion prevention systems (IPSs), anti-viruses, and the like, often cannot detect sophisticated cyberattacks. The security information and event management (SIEM) system has proven to be a very effective security tool for detecting and mitigating such cyberattacks. A SIEM system provides a holistic view of the security status of a corporate network by analyzing log data from various network devices. The correlation engine is the most important module of the SIEM system. In this study, we propose the optimized correlator (OC), a novel correlation engine that replaces the traditional regex matching sub-module with a novel high-performance multiple regex matching library called "Hyperscan" for parallel log data scanning to improve the performance of the SIEM system. Log files of 102 MB, 256 MB, 512 MB, and 1024 MB, generated from log data received from various devices in the network, are input into the OC and simple event correlator (SEC) for applying correlation rules. The results indicate that OC is 21 times faster than SEC in real-time response and 2.5 times more efficient in execution time. Furthermore, OC can detect multi-layered attacks successfully.

A Review of Artificial Intelligence and Machine Learning in Product Life Cycle Management.

The pursuit of harnessing data for knowledge creation has been an enduring quest, with the advent of machine learning and artificial intelligence (AI) marking significant milestones in this journey. Machine Learning (ML), a subset of AI, emerged as the practice of employing mathematical models to enable computers to learn and improve autonomously based on their experiences. In the pharmaceutical and biopharmaceutical sectors, a significant portion of manufacturing data remains untapped or insufficient for practical use. Recognizing the potential advantages of leveraging available data for process design and optimization, manufacturers face the daunting challenge of data utilization. Diverse proprietary data formats and parallel data generation systems compound the complexity. The transition to Pharma 4.0 necessitates a paradigm shift in data capture for manufacturing and process operations. This paper highlights the pivotal role of artificial intelligence in converting process data into actionable knowledge to support critical functions throughout the whole process life cycle. Furthermore, it underscores the importance of maintaining compliance with data integrity guidelines, as mandated by regulatory bodies globally. Embracing AI-driven transformations is a crucial step toward shaping the future of the pharmaceutical industry, ensuring its competitiveness and resilience in an evolving landscape.

Fully Scalable Fuzzy Neural Network for Data Processing.

The primary objective of the research presented in this article is to introduce an artificial neural network that demands less computational power than a conventional deep neural network. The development of this ANN was achieved through the application of Ordered Fuzzy Numbers (OFNs). In the context of Industry 4.0, there are numerous applications where this solution could be utilized for data processing. It allows the deployment of Artificial Intelligence at the network edge on small devices, eliminating the need to transfer large amounts of data to a cloud server for analysis. Such networks will be easier to implement in small-scale solutions, like those for the Internet of Things, in the future. This paper presents test results where a real system was monitored, and anomalies were detected and predicted.

Smart Spare Parts (SSP) in the Context of Industry 4.0: A Systematic Review.

The implementation of Industry 4.0 has integrated manufacturing, electronics, and engineering materials, leading to the creation of smart parts (SPs) that provide information on production system conditions. However, SP development faces challenges due to limitations in manufacturing processes and integrating electronic components. This systematic review synthesizes scientific articles on SP fabrication using additive manufacturing (AM), identifying the advantages and disadvantages of AM techniques in SP production and distinguishing between SPs and smart spare parts (SSPs). The methodology involves establishing a reference framework, formulating SP-related questions, and applying inclusion criteria and keywords, initially resulting in 1603 articles. After applying exclusion criteria, 70 articles remained. The results show that while SP development is advancing, widespread application of AM-manufactured SP is recent. SPs can anticipate production system failures, minimize design artifacts, and reduce manufacturing costs. Furthermore, the review highlights that SSPs, a subcategory of SPs, primarily differs by replacing conventional critical parts in the industry, offering enhanced functionality and reliability in industrial applications. The study concludes that continued research and development in this field is essential for further advancements and broader adoption of these technologies.

Threat or prospect? Exploring the impact of digital entrepreneurs' artificial intelligence perception and intention to adopt blockchain technology on the achievement of SDGs.

This paper explores how digital entrepreneurs' intention toward blockchain technology adoption, perception of reduced costs, and knowledge of Artificial Intelligence impact achieving UN's Sustainable Development Goals (SDGs), drawing attention from various sectors. Present study applies explanatory sequential mixed method for data collection. Moreover, to work with the dual face patterned data, PLS-SEM is used to perform quantitative analysis of the data collected from 389 digital entrepreneurs who are chosen through purposive sampling and then content analysis is performed for the qualitative data according to the explanatory sequential mixed method's rule of thumb. The study's quantitative phase shows that factors such as perceived ease of use and usefulness of Industry 4.0 technologies, knowledge of artificial intelligence (KAI), and perception of reduced cost positively influence digital entrepreneurs' intention to adopt blockchain technology (BCT). Notably, KAI has the strongest impact. In the qualitative phase, it's found that digital entrepreneurs' KAI and willingness to adopt BCT strongly align with achieving several UN Sustainable Development Goals (SDGs), suggesting BCT adoption's potential for sustainable outcomes. The outcomes of this study set a new benchmark in the domain of SDGs achievement with careful integration to Industry 4.0, AI and BCT. This study results undoubtedly instigate the digital entrepreneurs to adopt BCT in doing their start-up and convince the policymakers to set regulatory landscape with convenient environment for the utilization of BCT which then ultimately accelerates the achievement of SDGs.

Advanced Monitoring of Manufacturing Process through Video Analytics.

The digitization of production systems has revolutionized industrial monitoring. Analyzing real-time bottom-up data enables the dynamic monitoring of industrial processes. Data are collected in various types, like video frames and time signals. This article focuses on leveraging images from a vision system to monitor the manufacturing process on a computer numerical control (CNC) lathe machine. We propose a method for designing and integrating these video modules on the edge of a production line. This approach detects the presence of raw parts, measures process parameters, assesses tool status, and checks roughness in real time using image processing techniques. The efficiency is evaluated by checking the deployment, the accuracy, the responsiveness, and the limitations. Finally, a perspective is offered to use the metadata off the edge in a more complex artificial-intelligence (AI) method for predictive maintenance.

How do income inequality, poverty and industry 4.0 affect environmental pollution in South Asia: New insights from quantile regression.

While many factors have been studied as potential causes of environmental degradation, the impact of poverty and inequality has been largely overlooked in the research. The Sustainable Development Goals are aligned with the intersection of poverty, inequality, and the environment. In addition, most previous research has used carbon dioxide (CO2) emissions as a surrogate for pollution. These gaps are filled by this study, which uses ecological footprint (a comprehensive measure of pollution) and CO2 emissions to examine the effects of income disparity and poverty on environmental pollution in 13 nations. Dynamic panel Quantile regression methods are used in this study because of their resilience to various econometric problems that can crop up during the estimate process. The empirical results reveal that the whole panel's carbon emissions and ecological footprint rise when income disparity and poverty exist. When the panel is subdivided, however, we see that income inequality reduces carbon emissions and environmental footprint for the wealthy but has the opposite effect on the middle class. While high-income households see no impact from poverty on their carbon emissions, middle-income households see an increase in both. Overall, the results of this study suggest that income disparity and poverty are major factors in ecological degradation. Therefore, initiatives to reduce environmental degradation should pay sufficient attention to poverty and inequality to achieve ecological sustainability.

Reading between the Lines: Process Mining on OPC UA Network Data.

The introduction of the Industrial Internet of Things (IIoT) has led to major changes in the industry. Thanks to machine data, business process management methods and techniques could also be applied to them. However, one data source has so far remained untouched: The network data of the machines. In the business environment, process mining, for example, has already been carried out based on network data, but the IIoT, with its particular protocols such as OPC UA, has yet to be investigated. With the help of design science research and on the shoulders of CRISP-DM, we first develop a framework for process mining in the IIoT in this paper. We then apply the framework to real-world IIoT network traffic data and evaluate the outcome and performance of our approach in detail. We find tremendous potential in network traffic data but also limitations. Among other things, due to the dependence on process experts and the existence of case IDs.

The implications of industry 4.0 for the tourism sector: A systematic literature review.

The tourism sector is presently facing new challenges resulting from the increasing digitalization of society. Boosted by industry 4.0, new tourism dynamics are emerging. Nonetheless, the real significance of this revolutionary trend and its implications still lack further development. Aiming to assess the state-of-the-art about the digital transformation on the tourism sector, triggered by the 4.0 paradigm, the present study followed a systematic literature review approach, adopting the PRISMA protocol guidelines. A total of 44 manuscripts were considered relevant for analysis. The findings illustrate that the 4.0 paradigm is being embraced from three main perspectives: the visitor-technology interaction and its influence on decision-making, the digital competencies in tourism students, and the technology penetration in different sub-sectors of the supply chain. However, studies conceptualizing the 4.0 paradigm in the tourism sector are lacking, beyond empirical research on areas such as digital skills, pros and cons of industry 4.0 technologies, and spatial consequences.

Predictive maintenance in Industry 4.0: a survey of planning models and machine learning techniques.

Equipment downtime resulting from maintenance in various sectors around the globe has become a major concern. The effectiveness of conventional reactive maintenance methods in addressing interruptions and enhancing operational efficiency has become inadequate. Therefore, acknowledging the constraints associated with reactive maintenance and the growing need for proactive approaches to proactively detect possible breakdowns is necessary. The need for optimisation of asset management and reduction of costly downtime emerges from the demand for industries. The work highlights the use of Internet of Things (IoT)-enabled Predictive Maintenance (PdM) as a revolutionary strategy across many sectors. This article presents a picture of a future in which the use of IoT technology and sophisticated analytics will enable the prediction and proactive mitigation of probable equipment failures. This literature study has great importance as it thoroughly explores the complex steps and techniques necessary for the development and implementation of efficient PdM solutions. The study offers useful insights into the optimisation of maintenance methods and the enhancement of operational efficiency by analysing current information and approaches. The article outlines essential stages in the application of PdM, encompassing underlying design factors, data preparation, feature selection, and decision modelling. Additionally, the study discusses a range of ML models and methodologies for monitoring conditions. In order to enhance maintenance plans, it is necessary to prioritise ongoing study and improvement in the field of PdM. The potential for boosting PdM skills and guaranteeing the competitiveness of companies in the global economy is significant through the incorporation of IoT, Artificial Intelligence (AI), and advanced analytics.