Search Results (182)

This systematic review investigates the transformative impact of Healthcare 4.0 (HC4.0) technologies on healthcare service quality (HCSQ), focusing on their potential to enhance healthcare delivery while addressing critical challenges. This study reviewed 168 peer-reviewed articles from the Scopus database, published between 2005 and 2023. The selection process used clearly defined inclusion and exclusion criteria to identify studies focusing on advanced technologies such as artificial intelligence (AI), the Internet of Things (IoT), and big data analytics. Rayyan software facilitated systematic organization and duplicate removal, while manual evaluation ensured relevance and quality. The findings highlight HC4.0’s potential to improve service delivery, patient outcomes, and operational efficiencies but also reveal challenges, including interoperability, ethical concerns, and access disparities for underserved populations. The results were synthesized descriptively, uncovering key patterns and thematic insights while acknowledging heterogeneity across studies. Limitations include the absence of a formal risk-of-bias assessment and the diversity of methodologies, which precluded quantitative synthesis. This review emphasizes the need for future research on integration frameworks, ethical guidelines, and equitable access policies to realize HC4.0’s transformative potential. No external funding was received, and no formal protocol was registered. Full article

This study examines how digital technologies can improve a company’s overall sustainability. It also explores whether lean manufacturing practices can mediate the connection between digital technologies and sustainability. Data were collected from 319 senior managers, production managers, and general managers at Egyptian manufacturing companies and examined using the software Smart-PLS 4. The results show that digital technologies (i.e., blockchain, the Internet of Things, big data analytics, cloud computing, and the digital twins) have a positive impact on all three aspects of sustainability: environmental, social, and economic. Additionally, lean manufacturing was found to play a key role in this relationship. The model explained 34.3% of lean manufacturing practices, 59.7% of sustainable economic performance, 40.3% of sustainable social performance, and 40.6% of sustainable environmental performance. The findings of this study have some implications for companies, investors, and policymakers regarding how to improve economic, social, and environmental performance through fostering LMP and proper implementation of Digital Technologies (DTs). The current investigation extends the discourse on the role of digital technologies and new manufacturing techniques like lean manufacturing and how they can lead to sustainable performance. Positioned as one of the initial studies to examine the mediating role of lean manufacturing in the association between digital technologies and sustainable performance, this study provides insights within an emerging market context. Full article

The industrial shift from Industry 4.0 to Industry 5.0 has transformed organizational thinking, moving the focus from purely technological implementation to a more human-centered approach. The current study has focused on the Industry 5.0 technological capabilities to bring into circular economy practices aligned with sustainable development goals, aiming to enhance sustainable performance. Moreover, the resource-based theory has grounded the development of the comprehensive framework on Industry 5.0 technological capabilities (artificial intelligence capabilities, big data analytical capabilities, Internet of Things capabilities, machine learning capabilities, and blockchain technology capabilities) and circular economy practices (eco-design, management system, and investment recovery) to achieve sustainable performance (environmental performance, social performance, and economic performance). Data have been collected from 179 respondents from the Chinese manufacturing industry. Additionally, data have been analyzed using the structural equation modeling technique. The results showed that Industry 5.0 technological capabilities directly affect sustainable performance. Moreover, circular economy practices played a dual, moderating, and mediating role between Industry 5.0 technological capabilities and sustainable performance. The current study has contributed to filling a gap in the literature on Industry 5.0 capabilities, especially in the circular economy and sustainable performance perspective. The practical contribution recommended is that if organizations focused on their Industry 5.0 technological capabilities, it would boost circular economy practices and sustainable performance to achieve sustainable development goals. Full article

This article explores the transformative potential of artificial intelligence (AI) tools across the agricultural value chain, highlighting their applications, benefits, challenges, and future prospects. With global food demand projected to increase by 70% by 2050, AI technologies—including machine learning, big data analytics, and the Internet of things (IoT)—offer critical solutions for enhancing agricultural productivity, sustainability, and resource efficiency. The study provides a comprehensive review of AI applications at multiple stages of the agricultural value chain, including land use planning, crop selection, resource management, disease detection, yield prediction, and market integration. It also discusses the significant challenges to AI adoption, such as data accessibility, technological infrastructure, and the need for specialized skills. By examining case studies and empirical evidence, the article demonstrates how AI-driven solutions can optimize decision-making and operational efficiency in agriculture. The findings underscore AI’s pivotal role in addressing global agricultural challenges, with implications for farmers, agribusinesses, policymakers, and researchers. This article aims to advance the evolving research and discussions on sustainable agriculture, contributing insights that promote the adoption of AI technologies and influence the future of farming. Full article

The fourth industrial revolution (Industry 4.0) brought significant changes in manufacturing, driven by technologies like artificial intelligence (AI), Internet of Things (IoT), 5G, robotics, and big data analytics. For industries to remain competitive, the primary goals must be the improvement of the efficiency and safety of machinery, the reduction of production costs, and the enhancement of product quality. Predictive maintenance (PdM) utilizes historical data and AI models to diagnose equipment’s health and predict the remaining useful life (RUL), providing critical insights for machinery effectiveness and product manufacturing. This prediction is a critical strategy to maximize the useful life of equipment, especially in large-scale and important infostructures. This study focuses on developing an unsupervised machine state-classification solution utilizing real-world industrial measurements collected from a pneumatic pressing machine. Unsupervised machine learning (ML) models were tested to diagnose and output the working state of the pressing machine at each given point (offline, idle, pressing, defective). Our research contributes to extracting valuable insights regarding real-world industrial settings for PdM and production efficiency using unsupervised ML, promoting operation safety, cost reduction, and productivity enhancement in modern industries. Full article

In recent years, the rapid advancement of digital technologies has driven a profound transformation in both individual lives and business operations. The integration of Industry 4.0 with advanced mechatronic systems is at the forefront of this digital transformation, reshaping the landscape of smart manufacturing. This article explores the convergence of digital technologies and physical systems, with a focus on the critical role of mechatronics in enabling this transformation. Using technologies such as advanced robotics, the Internet of Things (IoT), artificial intelligence (AI), and big data analytics, industries are developing intelligent and interconnected systems capable of real-time data exchange, distributed decision making, and automation. The paper further explores two case studies: one on a smart plastic injection moulding machine and another on soft robots. These examples illustrate the synergies, benefits, challenges, and future potential of integrating mechatronics with Industry 4.0 technologies. Ultimately, this convergence fosters the development of smart factories and products, enhancing manufacturing efficiency, adaptability, and productivity, while also contributing to sustainability by reducing waste, optimising resource usage, and lowering the environmental impact of industrial production. This marks a significant shift in industrial production towards more sustainable practices. Full article

The manufacturing industry continues to suffer from inefficiency, excessively high prices, and uncertainty over product quality. This statement remains accurate despite the increasing use of automation and the significant influence of Industry 4.0 and AI on industrial operations. This review details an extensive analysis of a substantial body of literature on artificial intelligence (AI) and Industry 4.0 to improve the efficiency of material processing in manufacturing. This document includes a summary of key information (i.e., various input tools, contributions, and application domains) on the current production system, as well as an in-depth study of relevant achievements made thus far. The major areas of attention were adaptive manufacturing, predictive maintenance, AI-driven process optimization, and quality control. This paper summarizes how Industry 4.0 technologies like Cyber-Physical Systems (CPS), the Internet of Things (IoT), and big data analytics have been utilized to enhance, supervise, and monitor industrial activities in real-time. These techniques help to increase the efficiency of material processing in the manufacturing process, based on empirical research conducted across different industrial sectors. The results indicate that Industry 4.0 and AI both significantly help to raise manufacturing sector efficiency and productivity. The fourth industrial revolution was formed by AI, technology, industry, and convergence across different engineering domains. Based on the systematic study, this article critically explores the primary limitations and identifies potential prospects that are promising for greatly expanding the efficiency of smart factories of the future by merging Industry 4.0 and AI technology. Full article

A significant advancement in wireless communication has occurred over the past couple of decades. Nowadays, people rely more on services offered by the Internet of Things, cloud computing, and big data analytics-based applications. Higher data rates, faster transmission/reception times, more coverage, and higher throughputs are all necessary for these emerging applications. 5G technology supports all these features. Antennas, one of the most crucial components of modern wireless gadgets, must be manufactured specifically to meet the market’s growing demand for fast and intelligent goods. This study reviews various 5G antenna types in detail, categorizing them into two categories: conventional design approaches and machine learning-assisted optimization approaches, followed by a comparative study on various 5G antennas reported in publications. Machine learning (ML) is receiving a lot of emphasis because of its ability to identify optimal outcomes in several areas, and it is expected to be a key component of our future technology. ML is demonstrating an evident future in antenna design optimization by predicting antenna behavior and expediting optimization with accuracy and efficiency. The analysis of performance metrics used to evaluate 5G antenna performance is another focus of the assessment. Open research problems are also investigated, allowing researchers to fill up current research gaps. Full article

Background: The IoT and big data are newer technologies that can provide substantial support for healthcare systems, helping them overcome their shortcomings. The aim of this paper was to analyze the relevant literature descriptively, thematically, and chronologically from an interdisciplinary perspective in a holistic way to identify the most prolific research entities and themes. Methods: Synthetic knowledge synthesis qualitatively and quantitatively analyzes the production of literature through a combination of descriptive bibliometrics, bibliometric mapping, and content analysis. For this analysis, the Scopus bibliometric database was used. Results: In the Scopus database, 2272 publications were found; these were published between 1985 and 10 June 2024. The first article in this field was published in 1985. Until 2012, the production of such literature was steadily increasing; after that, exponential growth began, peaking in 2023. The most productive countries were the United States, India, China, the United Kingdom, South Korea, Germany, and Italy. The content analysis resulted in eight themes (four from the perspective of computer science and four from the perspective of medicine) and 21 thematic concepts (8 from the perspective of computer science and 13 from the perspective of medicine). Conclusions: The results show that the IoT and big data have become key technologies employed in preventive healthcare. The study outcomes might represent a starting point for the further development of research that combines the multidisciplinary aspects of healthcare. Full article

This paper investigates the integration of Structural Health Monitoring (SHM) within the frame of Industry 4.0 (I4.0) technologies, highlighting the potential for intelligent infrastructure management through the utilization of big data analytics, machine learning (ML), and the Internet of Things (IoT). This study presents a success case focused on a novel SHM methodology for detecting and locating damages in metallic aircraft structures, employing dimensional reduction techniques such as Principal Component Analysis (PCA). By analyzing strain data collected from a network of sensors and comparing it to a baseline pristine condition, the methodology aims to identify subtle changes in local strain distribution indicative of damage. Through extensive Finite Element Analysis (FEA) simulations and a PCA contribution analysis, the research explores the influence of various factors on damage detection, including sensor placement, noise levels, and damage size and type. The findings demonstrate the effectiveness of the proposed methodology in detecting cracks and holes as small as 2 mm in length, showcasing the potential for early damage identification and targeted interventions in diverse sectors such as aerospace, civil engineering, and manufacturing. Ultimately, this paper underscores the synergistic relationship between SHM and I4.0, paving the way for a future of intelligent, resilient, and sustainable infrastructure. Full article

The integration of the Internet of Things (IoT), big data, and machine learning (ML) has pioneered a transformation across several fields. Equipping electrical engineering students to remain abreast of the dynamic technological landscape is vital. This underscores the necessity for an educational tool that can be integrated into electrical engineering curricula to offer a practical way of learning the concepts and the integration of IoT, big data, and ML. Thus, this paper offers the IoT-Edu-ML-Stream open-source platform, a graphical user interface (GUI)-based emulation software tool to help electrical engineering students design and emulate IoT-based use cases with big data analytics. The tool supports the emulation or the actual connectivity of a large number of IoT devices. The emulated devices can generate realistic correlated IoT data and stream it via the message queuing telemetry transport (MQTT) protocol to a big data platform. The tool allows students to design ML models with different algorithms for their chosen use cases and train them for decision-making based on the streamed data. Moreover, the paper proposes learning outcomes to be targeted when integrating the tool into an electrical engineering curriculum. The tool is evaluated using a comprehensive survey. The survey results show that the students gained significant knowledge about IoT concepts after using the tool, even though many of them already had prior knowledge of IoT. The results also indicate that the tool noticeably improved the students’ practical skills in designing real-world use cases and helped them understand fundamental machine learning analytics with an intuitive user interface. Full article

The rapid progression of emerging technologies like the Internet of Things (IoT) and Big Data analytics for manufacturing has driven innovation across various industries worldwide. Production data are utilized to construct a model for quality evaluation and analysis applicable to components processed by machine tools, ensuring process quality for critical components and final product quality for the machine tools. Machine tool parts often encompass several quality characteristics concurrently, categorized into three types: smaller-the-better, larger-the-better, and nominal-the-better. In this paper, an evaluation index for the nominal-the-better quality characteristic was segmented into two single-sided Six Sigma quality indexes. Furthermore, the process quality of the entire component product was assessed by n single-sided Six Sigma quality indexes. According to numerous studies, machine tool manufacturers conventionally base their decisions on small sample sizes (n), considering timeliness and costs. However, this often leads to inconsistent evaluation results due to significant sampling errors. Therefore, this paper established fuzzy testing rules using the confidence intervals of the q single-sided Six Sigma quality indices, serving as the fuzzy quality evaluation model for components of machine tools. Full article

This paper explores the potential of smart crop management based on the incorporation of tools like digital agriculture, which considers current technological tools applied in agriculture, such as the Internet of Things (IoT), remote sensing, and artificial intelligence (AI), to improve crop production efficiency and sustainability. This is essential in the context of varying climatic conditions that affect the availability of resources for agriculture. The integration of tools such as IoT and sensor networks can allow farmers to obtain real-time data on their crops, assessing key health factors, such as soil conditions, plant water status, presence of pests, and environmental factors, among others, which can finally result in data-based decision-making to optimize irrigation, fertilization, and pest control. Also, this can be enhanced by incorporating tools such as drones and unmanned aerial vehicles (UAVs), which can increase monitoring capabilities through comprehensive field surveys and high-precision crop growth tracking. On the other hand, big data analytics and AI are crucial in analyzing extensive datasets to uncover patterns and trends and provide valuable insights for improving agricultural practices. This paper highlights the key technological advancements and applications in smart crop management, addressing challenges and barriers to the global adoption of these current and new types of technologies and emphasizing the need for ongoing research and collaboration to achieve sustainable and efficient crop production. Full article

The transition to smart manufacturing introduces heightened complexity in regard to the machinery and equipment used within modern collaborative manufacturing landscapes, presenting significant risks associated with equipment failures. The core ambition of smart manufacturing is to elevate automation through the integration of state-of-the-art technologies, including artificial intelligence (AI), the Internet of Things (IoT), machine-to-machine (M2M) communication, cloud technology, and expansive big data analytics. This technological evolution underscores the necessity for advanced predictive maintenance strategies that proactively detect equipment anomalies before they escalate into costly downtime. Addressing this need, our research presents an end-to-end platform that merges the organizational capabilities of data warehousing with the computational efficiency of Apache Spark. This system adeptly manages voluminous time-series sensor data, leverages big data analytics for the seamless creation of machine learning models, and utilizes an Apache Spark-powered engine for the instantaneous processing of streaming data for fault detection. This comprehensive platform exemplifies a significant leap forward in smart manufacturing, offering a proactive maintenance model that enhances operational reliability and sustainability in the digital manufacturing era. Full article

Water constitutes an indispensable resource crucial for the sustenance of humanity, as it plays an integral role in various sectors such as agriculture, industrial processes, and domestic consumption. Even though water covers 71% of the global land surface, governments have been grappling with the challenge of ensuring the provision of safe water for domestic use. A contributing factor to this situation is the persistent contamination of available water sources rendering them unfit for human consumption. A common contaminant, pesticides are not frequently tested for despite their serious effects on biodiversity. Pesticide determination in water quality assessment is a challenging task because the procedures involved in the extraction and detection are complex. This reduces their popularity in many monitoring campaigns despite their harmful effects. If the existing methods of pesticide analysis are adapted by leveraging new technologies, then information concerning their presence in water ecosystems can be exposed. Furthermore, beyond the advantages conferred by the integration of wireless sensor networks (WSNs), the Internet of Things (IoT), Machine Learning (ML), and big data analytics, a notable outcome is the attainment of a heightened degree of granularity in the information of water ecosystems. This paper discusses methods of pesticide detection in water, emphasizing the possible use of electrochemical sensors, biosensors, and paper-based sensors in wireless sensing. It also explores the application of WSNs in water, the IoT, computing models, ML, and big data analytics, and their potential for integration as technologies useful for pesticide monitoring in water. Full article