FIGURE 2 - available via license: Creative Commons Attribution 4.0 International
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Simplified entity relationship diagram (barker notation) of the data needed in a typical database for the inspection process.
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Safety-Critical Systems (SCSs) often manage sensible data that must be trustworthy, especially in many cases in which different actors participate whose interests may not coincide. Blockchain is a disruptive technology that has emerged to ensure the trustfulness of data. The nuclear industry incorporates many SCSs where blockchain can be applied. T...
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... these locations inside their network sending the data, receiving the reports and frequently operating in parallel with other management systems or protocols. Once the data management systems complete processing the information and all the requirements have been fulfilled, the inspection is officially finished and the equipment can be removed [2]. Fig. 2 shows a simplified Entity Relationship diagram of the data involved in an inspection. The inspection plan or WORK is divided into a set of calibrations. CALIBRATION groups a set of tubes that are actually acquired until the acquisition equipment is calibrated again. The entity TUBE refers to the actual tubes that belong to a SG of the ...
Citations
... Use cases are the primary source of communication among all stakeholders and provide the functionality of the system explicitly [1]. The applicability of use cases is found across several areas including big data [2,3], software effort estimation [4], formal specification [5], augmented reality [6], e-commerce [7], health care [8], safety critical systems and blockchain [9]. The wider range of its applicability is the source of motivation for us. ...
Software engineering artifact extraction from natural language requirements without human intervention is a challenging task. Out of these artifacts, the use case plays a prominent role in software design and development. In the literature, most of the approaches are either semi-automated or necessitate formalism or make use of restricted natural language for the extraction of use cases from textual requirements. In this paper, we resolve the challenge of automated artifact extraction from natural language requirements. We propose an automated approach to generate use cases, actors, and their relationships from natural language requirements. Our proposed approach involves no human intervention or formalism. To automate the proposed approach, we have used Natural Language Processing and Network Science. Our proposed approach provides promising results for the extraction of use case elements from natural language requirements. We validate the proposed approach using several literature-based case studies. The proposed approach significantly improves the results in comparison to an existing approach. On average, the proposed approach achieves around 71.5% accuracy (F-Measure), whereas the baseline method achieves around 16% accuracy (F-Measure) on average. The evaluation of the proposed approach on the literature-based case studies shows its significance for the extraction of use case elements from natural language requirements. The approach reduces human effort in software design and development.
... Vertical integration is the second category and it is defined as a connection between two participating entities in the value chain that if it becomes automated, the information can be gathered and dispatched automatically to any relevant deployed system in the value chain [17]. Several research works have adopted the blockchain technology for assistance in different applications of vertical integration, i.e., monitoring the status update of Industrial IoT software versions [20], implementing automatic access control for employees of civil nuclear industry [21], creating a historian to ensure transparency while issuing updates on vehicles' delivery, repair, and ownership in China [22], and to provide traceability and management in additive manufacturing [23]. ...
Blockchain technology (BCT), or more specifically its ability to maintain an immutable distributed ledger, is uniquely suited to a broad range of applications within patient safety. Potential use cases include, but are not limited to, medication safety, patient identification, event tracking, critical inventory management, internet-of-things (IoT), with many other potential uses to be explored. In this chapter, we explore key applications and considerations related to blockchain technology use in the area of patient safety. In addition to outlining potential existing and future use cases, we will also dedicate a significant portion of the chapter to known and proposed implementations of blockchain technology across healthcare environments, both clinical and non-clinical.KeywordsBlockchain technologyInnovationInternet-of-ThingsPatient safety
Over the past few months, we faced a serious pandemic throughout the world that shuts down the entire country for several weeks at a time. Employees belonging to different industries, businesses, educational, and coaching institutes have started working from home, and student classes have been shifted online. Hence, a lot of data is being generated through online Web sites and communication applications like WebEx, Zoom, MS Team, Google Meet, Google Classroom, etc. Using this data, related industries and companies can forecast or predict the revenues and needs of customers (users) from time to time. During the process of cleaning or analyzing this large amount of data, many businesses and industries are facing several problems regarding security, privacy, network connectivity, etc. On another side, artificial intelligence has taken the lead for identifying COVID-19 patients based upon their body temperatures and vital signs. Several other implementations like automated gates, homes, cars, industries, etc., are in trend for avoiding touching any devices. Blockchain will be used for preserving the privacy of users and secure transactions of many industries like logistics, blockchain-enabled IoT-based cloud systems, etc. People will start working from home and will use many smart devices together for making communications for a long time with another party. This chapter discusses popular issues like “How the near future will be with futuristic technology like Artificial Intelligence (AI), blockchain technology, cloud/edge technology”, “How AI can help Society for Tracking/Tracing Covid 19 affected patients”, etc. This chapter discusses several issues raised after COVID-19 pandemic (in this smart era) in detail.
The emergence of the Internet of Things (IoT) in healthcare has created a global upsurge of medical data as healthcare providers deploy numerous sensor devices providing medical services like patient monitoring, drug management, and online appointments and treatment in real time. However, the availability and security of massive data generated by these sensors devices from cyber-criminals become a concern for healthcare providers and patients. The chapter provides protection against distributed denial of service (DDoS), which is the most famous threat to cloud medical data availability. The work introduces a fog layer to provide additional services to the cloud network, where the medical resources like data centers reside. The fog layer serves as a packet filter for all received packets by computing the network delay, server response time, and comparing it with the cloud servers’ threshold value. If the network delay and the server response time are higher than the initial threshold value, the packet is considered suspicious. Captcha is sent to the packet source to avoid Bonet; otherwise, the packet is normal.
Network trustworthiness is considered a very crucial element in network security and is developed through positive experiences, guarantees, clarity, and responsibility. Trustworthiness becomes even more compelling with the ever-expanding set of Internet of Things (IoT) smart city services and applications. Most of today’s network trustworthy solutions are considered inadequate, notably for critical applications where IoT devices may be exposed and easily compromised. In this article, we propose an adaptive framework that integrates both federated learning and blockchain to achieve both network trustworthiness and security. The solution is capable of dealing with individuals’ trust as a probability and estimates the end devices’ trust values belonging to different networks subject to achieving security criteria. We evaluate and verify the proposed model through simulation to showcase the effectiveness of the framework in terms of network lifetime, energy consumption, and trust using multiple factors. Results show that the proposed model maintains high accuracy and detection rates with values of
$\approx 0.93$
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This paper presents a ranking method of operating sequences based on the actual condition of complex systems. This objective is achieved using the health checkup concept and the multiattribute utility theory. Our contribution is the proposal of sequences ranking process using data and experts’ judgments. The ranking results in a decision-making element; it allows experts to have an objective and concise overall ranking to be used for decision making. A case study is presented based on an experimental platform; it allows us to compare two aggregation operators: the weighted mean and the Choquet integral.
