Modern vehicles are increasingly interconnected through various communication channels, which requires secure access for authorized users, the protection of driver assistance and autonomous driving system data, and the assurance of data integrity against misuse or manipulation. While these advancements offer numerous benefits, recent years have exposed many intrusion incidents, revealing vulnerabilities and weaknesses in current systems. To sustain and enhance the performance, quality, and reliability of vehicle systems, software engineers face significant challenges, including in diverse communication channels, software integration, complex testing, compatibility, core reusability, safety and reliability assurance, data privacy, and software security. Addressing cybersecurity risks presents a substantial challenge in finding practical solutions to these issues. This study aims to analyze the current state of research regarding automotive cybersecurity, with a particular focus on four main themes: frameworks and technologies, standards and regulations, monitoring and vulnerability management, and testing and validation. This paper highlights key findings, identifies existing research gaps, and proposes directions for future research that will be useful for both researchers and practitioners.

Risk mitigation has always been a special concern for organization’s strategic management. Various tools and techniques have been developed to manage risk in an effective way. Failure Mode and Effects Analysis (FMEA) is one of the tools used for effective assessment of risk. It analyzes all potential failure modes, their causes, and effects on a product or process. Moreover it recommends actions to mitigate failures in order to enhance product reliability. Organizations spend their resources and domain experts make their efforts to complete this analysis. It further helps organizations identify the expected risks and plan strategies in advance to tackle them. But unfortunately the analysis produced after spending a lot of organizational assets and experts’ struggles, is not reusable due to its natural language text based description. Information and communication technology experts proposed some solutions but they are associated with some deficiencies. Authors in [13] proposed an ontology based solution to extract and reuse FMEA knowledge from the textual documents, and this article is the first step towards its implementation. In this article we proposed our ontology for Process Failure Mode and Effects Analysis (PFMEA) for automotive domain, along with its implementation, reasoning, and data retrieval through it.

Sustainable campus management includes energy-saving measures and waste reduction and has become important to many universities, being part of the institution’s societal responsibility. Smart energy systems (SESs), as part of campus energy management, can bring many benefits, including increased efficiency, reduced energy consumption, reduced emissions, increased reliability, and real-time control, and facilitate the integration of the renewable energy systems (RES). Despite the growing interest in energy efficiency and for the initiatives and projects to implement SESs, there are no universally accepted standards for assessing the performance of SESs, with most techniques being dedicated to subsystems. A KPI (key performance indicator) framework for evaluating the SESs’ performance from university campuses is proposed, starting from the current findings and priorities from the scientific literature, energy standards, legislation, and university rankings. The framework can support the implementation, operation, and evaluation of the SESs from university campuses, based on SES requirements and the stakeholders’ goals. Unlike previously developed solutions, the framework is focused not only on the technical side of SESs but also on the role that education, research, and innovation should have in sustainable development, making universities key contributors to achieving these goals.

This study investigates the impact of remote workplace factors on employees’ social and technical self-assessed performance during the COVID-19 pandemic. The impact of the variables belonging to the employee’s profile, organizational environment, and work-life balance categories on social and technical performance were analyzed, based on a survey of 801 Romanian employees, using ordinary least squares and quantile regression techniques. While the first method provided summary point estimates that calculated the average effect of the explanatory variables for the “average employee”, the second approach allowed us to focus on the effects explanatory variables have on the entire conditional distribution of the response variables, taking into account that this effect can be different for employees with different levels of performance. Job autonomy, engagement, communication skills, trust in co-workers, occupational self-efficacy, and family-work conflict, significantly influence both social and technical performance. PhD education and trust in management significantly influence social performance, while motivation, stress, the share of time spent in remote work, organizational commitment, children in the household, and household size, influence only technical performance.

The recent COVID-19 pandemic has accelerated the interest in new software tools to monitor the computer-based activities of employees working remotely (teleworking), and the demand for better analytics functionalities to be offered, focusing on employees’ performance and work-life balance. In this paper, we aim to analyze the habits of teleworking employees based on their interaction with the computer: how the employees are involved in different types of activities (actual work, recreation, documentation), and which are the most intensive periods. A conceptual framework for workforce analytics was developed for this purpose, together with tools and applications, that can provide useful information on different categories of activities where employees are involved. Knowledge generation is performed in four phases: collecting, processing, organizing, and analyzing the data to create valuable insights for the organization. Based on this framework, we developed a case study in an IT company, where two categories of employees, developers and software consultants, were monitored for 114 days, with 3.5 million events being generated and processed. The results showed different habits for consultants and developers, in terms of working activity structure, working schedule, inactivity time and interaction with the computer. Differences were also identified when we compared our results with previous research that monitored software developers working in-house: remote workers tend to organize their program for a longer period during the workday, and spend less time on meetings but longer time for programming. On the other hand, both categories of employees (in-house and teleworkers) show highly fragmented work, switching windows after very short periods of activity, with a potential negative impact on productivity, progress on tasks, and quality of output. The research results can be used in future employee productivity studies when searching answers to a fundamental question for workforce analytics – why are some employees more productive than others?

The purpose of this article is to analyze the current state of the research and the trends in the field of Circular Economy (CE) for the automotive industry, with electric vehicles (EVs) and conventional vehicles (CVs), being analyzed separately. A systematic literature search was conducted using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method, to screen and select articles from the Clarivate Web of Science database. For the detailed analysis, the information from the selected articles was structured based on such criteria as study objective, findings, limitations, and research proposals, and was further organized into nine categories for EVs, and into four categories for CVs. There were several CE strategies identified in the review, but a major challenge remains the evaluation of these strategies, in order to determine the most relevant, effective, and efficient ones, starting from the goal of minimizing the resource functionality loss. Future studies should focus on promoting new green technologies in the automotive industry, with an emphasis on circularity, in terms of raw materials, energy, production systems, product life extension, second use application, reuse, recycling, and end of life.