Robotics and Automation in Industry 4.0

Nanobots or Nanorobots are one of the emerging applications in both nanotechnology and robotics. These bots are programmed to carry out specific applications for a specific purpose. Owing to their properties such as smaller volume, efficiency and accuracy, nanobots are being explored in different fields of study, especially nanomedicine, automation, drug delivery, chemistry, aerospace and others. These bots can be programmed and explored in such a way that they can be used to repair the specific target in the body, which is impossible using bare hands. In this chapter, we are going to explore such types of applications and their principles.

Robot path planning is a necessary requirement for today’s autonomous industry as robots are becoming a crucial part of the industry. Planning a path in a dynamic environment that changes over time is a difficult challenge for mobile robots. The robot needs to continuously avoid all the obstacles in its path and plan a suitable trajectory from the given source point to the target point within a dynamically changing environment. In this study, we will use Deep Q-Learning (Q-Learning using neural networks) to avoid the obstacles in the environment, which are being dynamically created by the user. The main aim of the robot is to plan a path without any collision with any of the obstacles. The environment is simulated in the form of a grid that initially contains information on the starting and the target location of the robot. Robots need to plan an obstacle-free path for the given points. The user introduces obstacles whenever he/she wishes during the simulation to make the environment dynamic. The accuracy of the path is judged by the path planned by the robot. Various architectures of neural networks are compared in the study that follows. Simulation results are analyzed for the evaluation of an optimized path, and the robot is able to plan a path in the dynamic environment.

Though we are improving our living standards with technology, we are neither bothered about our fitness nor our environment. The article focuses on both, this research will not only help to keep an individual fit but also help in improving air pollution. In this article using the concept of seven concerns of the innovation process, a customized Bicycle frame (Photo Voltaic (PV) frame) is selected after overcoming the flaws in many designs and final conception is analyzed for maximum deformation at different loads. In this article, five concerns out of seven are considered to achieve the final design. Flexible Solar Cell (mentioned in a magazine created by Massachusetts Institute of Technology, Energy Initiative) on the frame will help to charge the battery and that battery will operate other devices like Air Purifier, Mobile Charger, etc. that’s why this bicycle is called smart bicycle by the author. This article only focuses on innovative design analysis of customized solar frames but not the working of solar frames or air purifiers. This innovative design is going to change the concept of cycling in the future.

 Environmental issues, due to the various gases that are harmful to fossil fuels, can cause disease and sickness worldwide. Renewable energy sources (RESs) are a crucial solution for decreasing reliance on fossil fuels. This is because they offer several advantages, such as significant cost reductions in operations, minimal depreciation over time, and the ability to provide electric power for various applications. As a result, they are highly desirable for use in the power sector. This kind of trouble becomes excessively challenging by developing the extent of the electric power market step by step. The authors developed a new optimization technique by combining chaotic maps with various nature-inspired optimization algorithms, such as the Harris hawks optimizer, sine cosine algorithm, and slime mold algorithm. This approach allowed them to improve the performance of these bioinspired optimization methods. The researchers evaluated an improved technique called hCHHO-SCA and hCSMA-SCA for solving the PBUCP considering renewable energy sources. They tested the techniques on both a 10-generating-unit system and a 100-generating-unit system. The authors were able to calculate the profit generated from each system as a result of applying the improved techniques. The adequacy of the analyzer is confirmed for a few benchmark issues that have been observed. The recommended optimizer is helpful in obtaining a solution to problems related to discrete and continuous optimization, including nonlinear types of optimization.

The concept of Industry 4.0 emerged in Europe a decade ago, and later, it was investigated and adopted by academics and industries throughout the world. Due to significant technical improvements in various industries over the past few years, the world's industrial systems have transformed. The major technologies, such as IoT and Big Data, have a large impact on Industry 4.0. They affect each and every sector of the economy. The main advantages include advances in productivity, efficiency, flexibility, decision-making process, and quality of goods and services. The challenges include analyzing the data produced and integrating new technology with the staff and equipment that are already in place. Our goal is to summarize the potential and challenges associated with adopting Industry 4.0 in the manufacturing sector.

The Financial Technology (FinTech) industry has been playing a pivotal role in driving modern day’s economics, social aspects, technology, and many more areas. FinTech is majorly inspired, motivated, and empowered by Data Science and Artificial Intelligence Methodologies (DSAIM). With emerging technology, the smart FinTech industry has revolutionised economic and financial businesses, service industries, and systems. The global research communities have made significant progress in smart FinTech for Banking Tech, Trade Tech, InsurTech, Wealth Tech, Pay Tech, Risk Tech, Cryptocurrencies, Digital Payment Systems, and Blockchain using DSAIM. In this review paper, we narrow down the overview of smart financial businesses, their complex challenges, and the entire smart FinTech ecosystem. The DSAIM enables smart FinTech and poses some research problems among global academic and researcher communities.

Blockchain innovation recently led to key innovations in the advanced insurgency of medical care, but a few studies have shown that blockchain has the potential to improve the medical care environment. It is prepared to change how customary clinical frameworks and organizations have been occupied with the medical services area throughout the previous very long while. Information and communication technologies (ICTs) and blockchains are key empowering advancements for the decentralization and digitalization of medical care foundations and provide current and digitalized medical care environments to patients, similar to specialist organizations. With regard to blockchain applications for medical service information, the board provides utility for patients, specialists and medical care organizations through patient record access and control, cases and installments, clinical IoT security and exploration of information checks and trades for budgetary evaluation and straightforwardness. In these applications, constant updates to an encoded, decentralized blockchain record are never true, screen, or control clinical data. This likewise encourages medical service foundations to confine unapproved individuals to obtaining sensitive data. This paper provides insight into the blockchain methodology applied in IoT healthcare security. 

The integration of the Internet of Things (IoT) technology in the pharmaceutical industry has the potential to bring about significant advancements and improvements, particularly in areas like supply chain management, drug discovery, and patient monitoring. By leveraging IoT technology in these ways, the pharmaceutical industry can enhance efficiency, improve product quality, ensure patient safety, and mitigate the risks associated with counterfeit drugs in the market. However, it was essential to address cybersecurity concerns to safeguard sensitive data and maintain the integrity of the IoT ecosystem in healthcare. The concept of integrating IoT with blockchain in the pharmaceutical supply chain is indeed a powerful solution to address the challenges faced by the industry, particularly in supply chain management and the prevention of counterfeit drugs. Integrating IoT with blockchain technology also results in many advantages like accurate location tracking, real-time updates and visibility, counterfeit prevention, timely delivery to customers, environmental parameter control, expiry date monitoring, pharmaceutical warehouse management, automated decision making and many more. By combining the strengths of IoT and blockchain, the pharmaceutical industry can build a robust and secure supply chain ecosystem. This integrated approach not only enhances transparency and traceability but also addresses critical issues such as counterfeiting by ensuring the delivery of safe and authentic pharmaceutical products to consumers. Environmental parameters are also monitored in a warehouse to avoid spoilage of medicines. The date of expiry of medicines and related equipment are monitored and categorized into three levels such as discard the medicines if expired, near to expiry if the expiry date is nearer and store in the warehouse if the date of expiry is far, and a message is sent accordingly to the supervisor for necessary action.

Attrition is often defined as “a reduction in the number of employees as a result of retirement, resignation or death” and also as “the rate of shrinkage in size or number”. But the scenario is not so simple. We should always consider premature retirement, sudden resignation and premature death, including suicide. The real scenario is employees do leave, either because they expect extra money, dislike the working environment, get rough behavior, non-cooperation from their coworkers, need a change, or because their spouse gets a more robust chance in another region. Retention is more economical than going for brand spanking new recruitment whatsoever. Organizations should have a good retention strategy to retain their valuable employees. Employee turnover may be viewed as the outcome of unmet expectations and gaps between fundamental employee demands. Employees may simply resign under a few unfavourable conditions, but more crucially, “people depart before they leave”, according to the psychology of disengagement. It may be iterated that as they become older, their contribution gradually decreases, much like a slowly fading memory. This text presents a holistic view of attrition and retention of employees based on psychological aspects in this cut-throat competitive environment in India. Biology has a little role in management, though one cannot ignore biology in psychology. In broader terms, attrition is somehow related to psychology, and psychology and physiology are two sides of a coin. A new trend is to relate psychology with physiology to reduce attrition. 

In the modern age, we depend on technology for our daily needs, from groceries to booking tickets for rides. The technology supports us by understanding our requirements. This is done by using Machine Learning. Machine Learning deals with understanding human behavior and providing suggestions for our requirements. The changes in the ecosystem affect the living creatures who depend on the ecosystem. One of the subsets of Machine Learning that play a vital role in saving the lives of living creatures is Deep Learning. Deep Learning is a representational learning of artificial neural networks. Deep Learning keeps on improving so that it can imitate human intelligence more accurately. Artificial Neural Networks is another subset of Machine Learning and helps in the growth of Deep Learning. There are different classes of artificial neural networks, two of the important classes are the Convolutional Neural Network (CNN) and the Recurrent Neural Network (RNN). The patterns in images are recognized by CNN. So, CNN majorly deals with image recognition and processing. RNN helps recognize sequential data and uses this pattern of sequential data to predict the likely scenarios in the ecosystem. The model, which uses the algorithm of RNN and CNN, should be trained and tested with the data for better efficiency.

Non-volatile memory technologies, such as NAND flash memory, have improved storage system performance, reliability, durability, and cost. Due to their speed and density, solid-state devices (SSDs) that are based on flash memory are being used as workstations, desktops, and laptops. Despite offering superior performance, stress resistance, and energy economy as compared to mechanical hard drives, NAND flash memory has unique features and operating limits and cannot be employed as a perfect block device. The design of SSDs has developed over time to make use of the benefits offered by flash memory while, at the same time, hiding their drawbacks. SSD concurrency techniques make use of the available parallelism of flash memories. This chapter thoroughly examines SSD subjects, ranging from the physical features of a flash memory cell to the design of SSDs themselves. The subjects pertaining to the flash translation layer (FTL) are described within the context of interconnected systemlevel operations. These operations include garbage collection, wear-leveling, address mapping and bad block management. This chapter also provides a review of the most current SSD-related studies.

The availability of clean energy is crucial for both the environment and human health. Numerous harmful gasses released by conventional automobiles cause illnesses and ailments in people all over the world. Nevertheless, there is growing interest in Plug-in Electric Vehicles (PEVs) to help with the energy and climate emergency. It has been noted that the manufacturing of PEVs has dramatically increased over the past ten years. The PEVs may supply the power grid with electricity while both consuming it and storing it in batteries. By effectively managing the electric demand profile and integrating electricity from PEVs into the electric grid, operating expenses can be reduced overall. The study recommends the course of action, which, in this case, is to apply the chaotic mapped Sine Cosine Algorithm advancement method and combine chaotic maps with the Harris Hawks Optimizer. It also evaluates how well the suggested better technique is implemented while taking PEVs into account.

The object detection framework recognises real-world objects within the frame of a moving photograph or computer-generated image. The object has a location to flow to through other objects, such as people or automobiles. Item detection is widely used in sectors where it is necessary for an organization's security and growth. The vast range of applications for protest detection include image recovery, security strategy, reason for inspection, machine framework assessment, and computerised vehicle structure. In contrast to conventional object localization techniques, machine learning-based object identification makes use of the machine's greater capacity to learn and represent knowledge [1]. A difficult problem in the analysis of designs and computer frameworks is object detection. Later on, the relationship between object detection, video analysis and image processing was developed. The complicated structure that is now being constructed includes both fundamental and sophisticated features, and the evaluation is carried out depending on the classifiers used. A complex system that can accurately assess and distinguish between numerous aspects is produced as a result of this combination. Several deep-level characteristics have been developed as a result of machine learning advancements to address the problems in the old design [2]. We conducted research on one-stage and two-stage object detectors, which are further categorised into deep learning methodologies. To enhance object detection, CNN networks employ these algorithms. An evaluation of the machine learning method for object detection is presented in this paper [3]. The protest site's applications have been distilled. The various methods of object localization employ template-based, region-based, and portion-based methods.