Healthcare and Medicine are under constant pressure to provide patient-driven medical expertise to ensure a fast and accurate treatment of the patient. In such scenarios, the diagnosis contains, the family history, long term medical data and a detailed consultation with the patient. In time-critical emergencies, such conversation and time-consuming elaboration are not possible. Rescue services need to provide fast, reliable treatments for the patient in need. With the help of modern technologies, like treatment recommendations, real-time vitals-monitoring, and situation detection through artificial intelligence (AI) a situation can be analyzed and supported in providing fast, accurate patient-data-driven medical treatments. In KIRETT, a wearable device is developed to support in such scenarios and presents a way to provide treatment recommendation in rescue services. The objective of this paper is to present the quantitative results of a two-day KIRETT evaluation (14 participants) to analyze the needs of rescue operators in healthcare.

Our aim in this paper is to outline how the design space for the ontologization process is richer than current practice would suggest. We point out that engineering processes as well as products need to be designed - and identify some components of the design. We investigate the possibility of designing a range of radically new practices, providing examples of the new practices from our work over the last three decades with an outlier methodology, bCLEARer. We also suggest that setting an evolutionary context for ontologization helps one to better understand the nature of these new practices and provides the conceptual scaffolding that shapes fertile processes. Where this evolutionary perspective positions digitalization (the evolutionary emergence of computing technologies) as the latest step in a long evolutionary trail of information transitions. This reframes ontologization as a strategic tool for leveraging the emerging opportunities offered by digitalization.

Facial Recognition is a technique, based on machine learning technology that can recognize a human being analyzing his facial profile, and is applied in solving various types of realworld problems nowadays. In this paper, a common real-world problem, finding a missing person has been solved in a secure and effective way with the help of facial recognition technology. Although there exist a few works on solving the problem, the proposed work is unique with respect to its security, design, and feasibility. Impeding intruders in participating in the processes and giving importance to both finders and family members of a missing person are two of the major features of this work. The proofs of the works of our system in finding a missing person have been described in the result section of the paper. The advantages that our system provides over the other existing systems can be realized from the comparisons, described in the result summary section of the paper. The work is capable of providing a worthy solution to find a missing person on the digital platform.

To stay competitive, the Low- or Middle-Income Countries (LMICs) need to embrace Industry 4.0 and Logistics 4.0. This requires government-level interventions and policy-making to incentivize quality product solutions and drive innovation in traditionally resistant economic sectors. In this position paper, we support the establishment of Smart Industrial Parks (SIPs) with a focus on enhancing operational efficiencies and bringing together MSMEs and startups targeting niche clientele with innovative Industry 4.0 solutions. SIPs along with the phased deployment of well-planned robotic automation technologies shall enable bringing down India's untenable logistics costs. Toward the successful execution of SIPs, we are required to implement the efficient allocation of manufacturing resources and capabilities within SIPs. Thus, we emphasize the importance of efficient resource utilization, collaboration, and technology adoption in industrial parks to promote industrial development and economic growth. We advocate the use of a cloud-based cyber-physical system for real-time data access and analysis in SIPs. Such centralized cloud-based monitoring of factory floors, warehouses, and industrial units using IoT infrastructure shall improve decision-making, efficiency, and safety. Digital Twins (DTs), which are cyber-replicas of physical systems, could play a significant role in enabling simulation, optimization, and real-time monitoring of smart manufacturing and distributed manufacturing systems. However, there are several challenges involved in implementing DTs in distributed manufacturing systems, such as defining data schemas and collaboration protocols, ensuring interoperability, the need for effective authentication technology, distributed machine learning models, and scalability to manage multiple DTs.

Businesses of all sizes are bracing for a recession. Still, while it may sound counterintuitive, this is actually the right time to accelerate digital transformation. Historically, an economic downturn is a boon for innovation. According to Morgan Stanley, roughly half of Fortune 500 companies were founded in times of recession or economic crisis. Investing in digital transformation will help businesses overcome a slowdown and address talent shortages.

In my last article, I explained how businesses need to differentiate between digital strategy, digitization and digitalization. The piece focused on how everyone uses the terms differently and, ultimately, how digitalization was really about thinking through how companies can best automate processes and practices in their organization. Case in point: Digitalization is converting an entirely or partially manual process to be entirely digital. This could involve automating workflows or processes. Digitization, on the other hand, is converting analog content to digital format.

Digital engineering transformation is a crucial process for the engineering paradigm shifts in the fourth industrial revolution (4IR), and artificial intelligence (AI) is a critical enabling technology in digital engineering transformation. This article discusses the following research questions: What are the fundamental changes in the 4IR? More specifically, what are the fundamental changes in engineering? What is digital engineering? What are the main uncertainties there? What is trustworthy AI? Why is it important today? What are emerging engineering paradigm shifts in the 4IR? What is the relationship between the data-intensive paradigm and digital engineering transformation? What should we do for digitalization? From investigating the pattern of industrial revolutions, this article argues that ubiquitous machine intelligence (uMI) is the defining power brought by the 4IR. Digitalization is a condition to leverage ubiquitous machine intelligence. Digital engineering transformation towards Industry 4.0 has three essential building blocks: digitalization of engineering, leveraging ubiquitous machine intelligence, and building digital trust and security. The engineering design community at large is facing an excellent opportunity to bring the new capabilities of ubiquitous machine intelligence and trustworthy AI principles, as well as digital trust, together in various engineering systems design to ensure the trustworthiness of systems in Industry 4.0.

Digital transformation is the megatrend driving billions in investment across the corporate world to reinvent the way they do business. In the enterprise digital transformation guide, we will address the following topics to help you master the art and science of digitalization. "When digital transformation is done right, it's like a caterpillar turning into a butterfly, but when done wrong, all you have is a really fast caterpillar." Digital transformation is a customer-centric reimagination of the future of an enterprise and subsequently rethinking the business model. Reshaping the product/service portfolio, restructuring the processes, re-platforming technologies, reskilling the workforce, and instilling a new culture to get to the end goal. That definition of what is digital transformation packs a lot of punch.

Fintech startups are flourishing since our lives shifted to an online format. Statista reveals that as of November 2021, there were 10,755 startups in the Americas alone. This number is still growing in 2022, and that's why traditional financial companies encounter difficulties trying to withstand competition and remain effective. By adopting machine learning solutions, such companies can optimize main business processes and foster customer loyalty. So, in this article, we're going to lift the curtain on machine learning (ML) approaches that enable process automation.

One of the most widely used buzzwords in the logistics sector in 2022 is "digitalization." The word is a useful umbrella term for the evolution to computer-based processes from manual procedures that relied on pencils and clipboards in the warehouse or printed manifests at the loading dock. But references to the trend nearly always ignore the tactical steps needed to make digitalization happen. Your DC probably doesn't have a magic wand that transforms basic paper checklists into cloud-based software platforms. So how are practitioners driving toward the goal of pulling logistics processes into the 21st century?