Due to the fourth industrial revolution, manufacturing companies are looking to implement digital twins in their factories to be more competitive. However, the implementation of digital twins in manufacturing systems is a complex task. Factories need a framework that can guide them in the development of digital twins. Hence, this article proposes a small-scale digital twin implementation framework for manufacturing systems. To build this framework, the authors gathered several concepts from the literature and designed a digital twin subsystem model using a model-based systems engineering (MBSE) approach and the systems engineering “Vee” model. The systems modelling defines the digital twin components, functionalities, and structure. The authors distribute most of these concepts throughout the framework configuration and some concepts next to this general configuration. This configuration presents three spaces: physical, virtual, and information. The physical space presents a physical layer and a perception layer. The information space has a single layer called middleware. Finally, the virtual space presents two layers: application and model. In addition to these layers, this framework includes other concepts such as digital thread, data, ontology, and enabling technologies. This framework could help researchers and practitioners to learn more about digital twins and apply it to different domains.

Cyberspace is a new frontier, not just for hackers, but for engineers. It is a digital ecosystem, the next generation of Internet and network applications, promising a whole new world of distributed and open systems that can interact, self-organize, evolve, and adapt. These ecosystems transcend traditional collaborative environments, such as client-server, peer-to-peer, or hybrid models (e.g., web services), to become a self-organized, evolving, interactive environment. Understanding cyberspace as a system is critical if we are to properly design systems to exist within it. Considering it to be a digital ecosystem, where systems can adapt and evolve, will enable systems engineering to become more effective in the future of networks and the Internet. While most systems engineers have only anecdotal experience with large segments of this ecosystem, in today’s world all of them must come to understand it. Engineering any system, or portion of a system, begins with an understanding of the system. This paper presents two interrelated yet distinct foundational models of the ecosystem of cyberspace: a Systemigram to narrate the cyclical nature of cyber warfare, and a modified predator–prey model, as a mathematical model. Systems engineers can utilize these models to design digital “species” that function and adapt within this ecosystem.

The proposed chapter explores how soft computing has developed to provide human-centric solutions, highlighting the difficulties it has encountered and outlining important directions for future research that will be necessary to advance the field. The need for human-centric approaches is becoming more and more apparent in today's society across a number of industries, such as healthcare, banking, education, and transportation. Because soft computing techniques are adept at handling the imprecise, uncertain, and partial information typical of human decision-making processes, they are particularly positioned to address this demand. This approach not only enhances the user experience, but also fosters a more harmonious relationship between technology and society, opening the door for breakthroughs such as tailored medical care, flexible learning environments, and compassionate service robots. Soft computing's ability to provide human-centered solutions and successfully navigate the obstacles of implementation will determine its lasting relevance and influence in social applications.

While innovation from the systems engineer is desirable at every step in all phases of systems engineering, there must be a methodology to evaluate alternatives. A formal methodology, complete with verification and validation of the results, was developed in 1946 by Soviet engineer Genrikh Saulovich Altshuller and is known as “The theory of inventor’s problem solving”, or TRIZ. This approach improves the way a systems engineer’s thinking progresses about a problem’s solution from “what is” towards “what will be” in the innovative development of a solution. The original distinguishing features of systems used in TRIZ were derived from innovations addressing physical, mechanical system, and few of them apply to digital systems. This paper presents additional characteristics that should be considered in the Reduction phase when applying TRIZ to innovation in digital systems engineering and a redefinition of the principles. With the additions of these distinguishing features for digital systems, TRIZ will become an invaluable tool for the digital systems engineer.

In recent years, there has been a growing interest in the development of digital twins. Digital twins have become a valuable tool in various fields, including healthcare, for predicting and analyzing human activity patterns. By utilizing the extension extreme gradient (XG) boosting local binary pattern (LBP) algorithm, digital twins can accurately predict human gait and provide valuable insights for healthcare professionals. In this chapter, the authors propose an innovative approach to predict human activities based on gait patterns using an extended XG boost model, enhanced with local binary patterns for feature extraction. The integration of extended XG boost, a highly efficient and interpretable machine learning algorithm, with local binary patterns, a robust technique for texture analysis, enables the extraction of discriminative features from gait data. The utilization of digital twins, specifically with the extension XG BOOST LBP algorithm, has proven to be a valuable tool in predicting and analyzing human gait.

This review aims to evaluate and compare various static analysis tools across multiple programming languages for memory management. The tools and techniques under scrutiny include pattern matching, symbolic execution, CppCheck, SharpChecker, FindBugs, CheckStyle, and Pylint. When examining the methods, pattern-matching, and symbolic execution, we identified implementations using pattern-matching and symbolic execution for each programming language. We focus on understanding the full scope of their capabilities and effectiveness in managing internal and external memory components such as RAM, SRAM, PROM, Cache, Optical Drive, etc. While static analysis tools do not directly analyze physical memory components, they are crucial in enhancing memory behavior. By detecting and addressing memory-related issues early in the development process, these tools contribute significantly to the overall quality of software systems. This review will thoroughly examine the strengths and weaknesses of each static analysis tool, aiding in selecting the most suitable tool or combination of tools for effective memory management across diverse programming environments.