The exponential rise of healthcare problems like human aging and road traffic accidents have developed an intrinsic challenge to biomedical sectors concerning the arrangement of patient-specific biomedical products. The additively manufactured implants and scaffolds have captured global attention over the last two decades concerning their printing quality and ease of manufacturing. However, the inherent challenges associated with additive manufacturing (AM) technologies, namely process selection, level of complexity, printing speed, resolution, biomaterial choice, and consumed energy, still pose several limitations on their use. Recently, the whole world has faced severe supply chain disruptions of personal protective equipment and basic medical facilities due to a respiratory disease known as the coronavirus (COVID-19). In this regard, local and global AM manufacturers have printed biomedical products to level the supply–demand equation. The potential of AM technologies for biomedical applications before, during, and post-COVID-19 pandemic alongwith its relation to the industry 4.0 (I4.0) concept is discussed herein. Moreover, additive manufacturing technologies are studied in this work concerning their working principle, classification, materials, processing variables, output responses, merits, challenges, and biomedical applications. Different factors affecting the sustainable performance in AM for biomedical applications are discussed with more focus on the comparative examination of consumed energy to determine which process is more sustainable. The recent advancements in the field like 4D printing and 5D printing are useful for the successful implementation of I4.0 to combat any future pandemic scenario. The potential of hybrid printing, multi-materials printing, and printing with smart materials, has been identified as hot research areas to produce scaffolds and implants in regenerative medicine, tissue engineering, and orthopedic implants.

In recent years, ICSs have become increasingly commonplace in virtually every industry. The abbreviation “ICSs” refers to industrial control systems. These are specially designed computers used for monitoring, managing, and controlling procedures and tasks across a wide range of industries and vital infrastructure sectors. Production, power, disinfection of water, transport, and other sectors all greatly benefit from ICS use. The authors of this paper aim to detect ICS cyber hazards in industry. This article is the result of the writers’ extensive research on ICS programs and the impact of cyberattacks on them as well. The study narrowed its attention to just three ICS applications because there are simply too many to count: power plants, water reservoirs, and gas pipelines. The present paper focuses on the development and evaluation of neural networks for use in cyberattacks. An early form of neural network, the residual system, came first in the field. When a breach is detected in the ICS, the neural network sorts it into one of several categories. The produced datasets must not compromise users’ privacy or cause harm to the relevant industry if they fall into the wrong hands. An encoding device, decoder, pseudo-encoder, and critical model neural networks work together to generate random data. Finally, a set of trials is conducted in which a residual neural network is utilized to classify cyberattacks based on both the created and original datasets. Results from a series of studies indicate that using the created dataset is an effective technique to train high-quality neural networks for use in cybersecurity on a large amount of data without sacrificing the accuracy of the models. The Kullback-Leibler and Jensen-Shannon divergences also serve as the theoretical foundation and technique, respectively. In particular, the paper recommends operational and maintenance cybersecurity standards for ICS. This entails such things as secure password practices, patch management, and anti-malware defense. Physical safeguards for ICS is another topic that is covered.

The publication of this article unfortunately contained mistakes. The funding note was not correct. The corrected funding note is given below.

The idea for the Internet of Robotic Things (IoRT) comes from activities. IoRT allows smart devices to combine sensor information from multiple sources, use broader nearby insights to determine the best reason for the activity and display events on-screen. The Internet of Things can connect web applications to other gadgets to make them smarter. Automated engineers combine the two and arm themselves with sensors that can analyze the current situation. Mechanical improvements such as ordinary voice links (PC) vision sensor innovations and artificial intelligence (AI) have resulted in a turn of events and programmed placement frameworks under advanced machinery. This article examines frameworks for arranging programmed objects to assemble robots and innovative patterns in ordinary language programmed programming, and three-dimensional (3D) visual insight. The relevant statement "regulation scene" should describe the combination of all three modules. This computation enables people to relate through a three-tiered information scene and guides managers to use discourse to provide reasonable criteria whenever an order is legitimate. After receiving precise criteria, the robot can use this diary's planned execution calculations and programmed programming to direct the placement of programmed items. The play area provides a context for the natural product, while the landscape design provides a valuable part of the framework.