Industry 5.0 or industry 4.0S? Introduction to industry 4.0 and a peek into the prospective industry 5.0 technologies

Examining customer intentions to purchase intelligent robotic products and services in Taiwan using the theory of planned behaviour

BACKGROUND

The literature for assessing online and offline shopping behaviours that are linked to intelligent robotic goods and services is inadequate. In this study, we applied the Theory of Planned Behaviour model for guidance regarding how consumer behaviour affects their purchase intentions for intelligent robotic goods and services.

METHODS

Data from 408 respondents were gathered through an online questionnaire binned into Online and Overall Shoppers, and analysed using SPSS, AMOS, and Covariance-Based Structural Equation Modelling software to evaluate the appropriateness of the measurements and to confirm data reliability, convergence, divergence, and validity. These tools were also used to track and test hypothesized relationships between the variables and model constructs used in this study.

RESULTS AND CONCLUSIONS

The overarching outcomes from the data analyses indicated the Ease of Usage, Brand Perception, and Product Pricing variables causally impacted the TPB model constructs, namely Attitude, Subjective Norms, and Perceived Behaviour Control for the two populations tested with respect to their intention to purchase intelligent robotic goods and services. The reliability measurements for Ease of Usage, Brand Perception, and Product Pricing are discussed. The results are important for companies and future investors because opportunities to study the complex relationships that ultimately drive consumer behaviour and their intention to purchase intelligent robotic goods and services are provided.

End-to-End Solution for Analog Gauge Monitoring Using Computer Vision in an IoT Platform

The emergence of Industry 4.0 and 5.0 technologies has enabled the digital transformation of various processes and the integration of sensors with the internet. Despite these strides, many industrial sectors still rely on visual inspection of physical processes, especially those employing analog gauges. This method of monitoring introduces the risk of human errors and inefficiencies. Automating these processes has the potential, not only to boost productivity for companies, but also potentially reduce risks for workers. Therefore, this paper proposes an end-to-end solution to digitize analog gauges and monitor them using computer vision through integrating them into an IoT architecture, to tackle these problems. Our prototype device has been designed to capture images of gauges and transmit them to a remote server, where computer vision algorithms analyze the images and obtain gauge readings. These algorithms achieved adequate robustness and accuracy for industrial environments, with an average relative error of 0.95%. In addition, the gauge data were seamlessly integrated into an IoT platform leveraging computer vision and cloud computing technologies. This integration empowers users to create custom dashboards for real-time gauge monitoring, while also enabling them to set thresholds, alarms, and warnings, as needed. The proposed solution was tested and validated in a real-world industrial scenario, demonstrating the solution's potential to be implemented in a large-scale setting to serve workers, reduce costs, and increase productivity.

Securing Industrial Control Systems: Components, Cyber Threats, and Machine Learning-Driven Defense Strategies

Industrial Control Systems (ICS), which include Supervisory Control and Data Acquisition (SCADA) systems, Distributed Control Systems (DCS), and Programmable Logic Controllers (PLC), play a crucial role in managing and regulating industrial processes. However, ensuring the security of these systems is of utmost importance due to the potentially severe consequences of cyber attacks. This article presents an overview of ICS security, covering its components, protocols, industrial applications, and performance aspects. It also highlights the typical threats and vulnerabilities faced by these systems. Moreover, the article identifies key factors that influence the design decisions concerning control, communication, reliability, and redundancy properties of ICS, as these are critical in determining the security needs of the system. The article outlines existing security countermeasures, including network segmentation, access control, patch management, and security monitoring. Furthermore, the article explores the integration of machine learning techniques to enhance the cybersecurity of ICS. Machine learning offers several advantages, such as anomaly detection, threat intelligence analysis, and predictive maintenance. However, combining machine learning with other security measures is essential to establish a comprehensive defense strategy for ICS. The article also addresses the challenges associated with existing measures and provides recommendations for improving ICS security. This paper becomes a valuable reference for researchers aiming to make meaningful contributions within the constantly evolving ICS domain by providing an in-depth examination of the present state, challenges, and potential future advancements.

Selective Laser Melting and Spark Plasma Sintering: A Perspective on Functional Biomaterials

Achieving lightweight, high-strength, and biocompatible composites is a crucial objective in the field of tissue engineering. Intricate porous metallic structures, such as lattices, scaffolds, or triply periodic minimal surfaces (TPMSs), created via the selective laser melting (SLM) technique, are utilized as load-bearing matrices for filled ceramics. The primary metal alloys in this category are titanium-based Ti6Al4V and iron-based 316L, which can have either a uniform cell or a gradient structure. Well-known ceramics used in biomaterial applications include titanium dioxide (TiO2), zirconium dioxide (ZrO2), aluminum oxide (Al2O3), hydroxyapatite (HA), wollastonite (W), and tricalcium phosphate (TCP). To fill the structures fabricated by SLM, an appropriate ceramic is employed through the spark plasma sintering (SPS) method, making them suitable for in vitro or in vivo applications following minor post-processing. The combined SLM-SPS approach offers advantages, such as rapid design and prototyping, as well as assured densification and consolidation, although challenges persist in terms of large-scale structure and molding design. The individual or combined application of SLM and SPS processes can be implemented based on the specific requirements for fabricated sample size, shape complexity, densification, and mass productivity. This flexibility is a notable advantage offered by the combined processes of SLM and SPS. The present article provides an overview of metal-ceramic composites produced through SLM-SPS techniques. Mg-W-HA demonstrates promise for load-bearing biomedical applications, while Cu-TiO2-Ag exhibits potential for virucidal activities. Moreover, a functionally graded lattice (FGL) structure, either in radial or longitudinal directions, offers enhanced advantages by allowing adjustability and control over porosity, roughness, strength, and material proportions within the composite.