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The Model-Based Requirements Engineering (MBRE) approach provides an answer to the increasing complexity of contemporary engineered systems by combining the approaches of formal and semi-formal modeling with classic requirements engineering methods. This chapter considers the usage of such modeling languages as SysML, AADL, and KAOS to achieve accurate representation, verification, and traceability of functional, structural, and behavioral requirements. Specifically, the chapter is dedicated to tracing back and forth requirements, architecture, implementation, and verification artifacts, and consistency management and validation of models. MBRE in combination with agile and DevSecOps practices will be considered in relation to examples from aerospace, automotive, and healthcare industries.
The convergence of high-throughput sequencing, systems biology and distributed computing has made microbiology a data-intensive field, generating vast genomic, proteomic and metabolomic datasets. Traditional infrastructures—single servers, relational databases and sequential pipelines—are no longer sufficient to manage the scale and complexity of modern data. This chapter analyzes high-performance big data architectures designed for advanced microbiological research. It begins with the data challenges in contemporary microbiology and explores distributed frameworks such as Apache Hadoop, Spark, Kafka and Flink, alongside cloud-native platforms, containerized pipelines, real-time surveillance systems and machine learning integration. It also addresses governance and security for ethical and reliable data sharing. Supported by architectural figures and comparative tables, the chapter bridges theory and practice. It concludes by highlighting emerging technologies like quantum-classical computing, neuromorphic biosensing and federated learning shaping future microbiological data systems.
This chapter will look at the architecture of organized cybercrime, specifically exploring the substantive change in criminal operations from being confined territorially and hierarchical structures to becoming technology-based and global syndicates operating digitally. From a theoretical and practical approach of criminology combined with intelligence regarding current cybercrimes, the chapter outlines four main pillars for its analysis: definition and differentiation of terms; periodization of the history of cybercrime; comparison of traditional versus digital models of crime; and criticism of the model of CaaS (Cybercrime as a Service). The chapter contextualizes the study of organized cybercrime within the wider literature on transnational crimes while also emphasizing the unique features of digital syndicates vis-a-vis terrestrial syndicates.
Quality problems of software development often arise during requirements engineering, leading to early defects being some of the most expensive problems within software development. In this chapter, we discuss how the application of Model-Based Requirements Engineering (MBRE) and automated traceability of requirements can help with improving software quality and efficiency. Instead of textual requirements, MBRE provides more precise requirements represented by models such as use cases, SysML models, goal models, feature models, and language-specific models. Automated traceability makes use of technologies like information retrieval, machine learning, deep learning, and large language models in order to keep track of the relationships between requirements, design, source code, and test cases. These techniques help achieve software quality properties defined in ISO/IEC 25010 standards, such as reliability, maintainability, security, performance, and compatibility.
Despite growing interest in salespeople’s social media use, the role of customers in motivating and shaping such use has received limited scholarly attention. Moreover, little is known about whether—and how—social media use enhances salespeople’s effectiveness in managing service recovery situations. Using the conservation of resources theory and analyzing matched survey responses from B2B salespeople and their managers, we find that while customer interest in digital technology has a curvilinear (inverted U-shape) relationship with salesperson social media use, this relationship decreases at higher levels of job engagement, indicating that salespeople with high job engagement have greater resources to manage the stress caused by customer interest in digital technology. Moreover, social media use improves manager-rated salesperson service recovery performance. The study offers significant implications for both theory and practice.
