In the Industry 5.0 era, companies are leveraging the potential of cutting-edge technologies such as artificial intelligence for more efficient and green human-centric production. In a similar approach, project management would benefit from artificial intelligence in order to achieve project goals by improving project performance, and consequently, reaching higher sustainable success. In this context, this paper examines the role of artificial intelligence in emerging project management through a systematic literature review; the applications of AI techniques in the project management performance domains are presented. The results show that the number of influential publications on artificial intelligence-enabled project management has increased significantly over the last decade. The findings indicate that artificial intelligence, predominantly machine learning, can be considerably useful in the management of construction and IT projects; it is notably encouraging for enhancing the planning, measurement, and uncertainty performance domains by providing promising forecasting and decision-making capabilities.

This article presents the progressive evolution of NFV from the initial SDN-agnostic initiative to a fully SDN-enabled NFV solution, where SDN is not only used as infrastructure support but also influences how virtual network functions (VNFs) are designed. In the latest approach, when possible, stateless processing in the VNF shifts from the computing element to the networking element. To support these claims, the article presents the implementation of a flow-based network access control solution, with an SDN-enabled VNF built on IEEE 802.1x, which establishes services as sets of flow definitions that are authorized as the result of an end user authentication process. Enforcing the access to the network is done at the network element, while the authentication and authorization state is maintained at the compute element. The application of this proposal allows the performance to be enhanced, while traffic in the control channel is reduced to a minimum. The SDN-enabled NFV approach sets the foundation to increase the areas of application of NFV, in particular in those areas where massive stateless processing of packets is expected.

For the time being, companies and organisations are being forced to compete in utterly complex and globalised environments, facing massive natural, economic, and technological challenges on a daily basis. Addressing these challenges would be impossible without a proper approach that helps them identify, measure, understand, and control the performance of their organisations. Lean principles and techniques rise as a solution. This paper justifies and proposes the use of lean principles and techniques to identify key performance indicators (KPIs) in project-based organisations based on their organisational and operational needs. The research focuses mainly on the identification and categorisation of KPIs through a qualitative approach, based on systematic literature review (SLR) of performance indicators, project management, and project success. As a case study, an analysis of relevant information of an R&D and innovation project-based organisation, such as quality manuals, a benchmarking process, internal studies, and surveys regarding what success means for different kinds of stakeholders and for the organisation itself was conducted. As a result, this research is of a high value for project-based organisations, especially those that are not apprised of how to correctly formulate a series of KPIs, or whose path to it is still not clear.

This paper presents FlowNAC, a Flow-based Network Access Control solution that allows to grant users the rights to access the network depending on the target service requested. Each service, defined univocally as a set of flows, can be independently requested and multiple services can be authorized simultaneously. Building this proposal over SDN principles has several benefits: SDN adds the appropriate granularity (fine-or coarse-grained) depending on the target scenario and flexibility to dynamically identify the services at data plane as a set of flows to enforce the adequate policy. FlowNAC uses a modified version of IEEE 802.1X (novel EAPoL-in-EAPoL encapsulation) to authenticate the users (without the need of a captive portal) and service level access control based on proactive deployment of flows (instead of reactive). Explicit service request avoids misidentifying the target service, as it could happen by analyzing the traffic (e.g. private services). The proposal is evaluated in a challenging scenario (concurrent authentication and authorization processes) with promising results.

Recognizing trust as a pivotal element for success within Human–Robot Collaboration (HRC) environments, this article examines its nature, exploring the different dimensions of trust, analysing the factors affecting each of them, and proposing alternatives for trust measurement. To do so, we designed an experimental procedure involving 50 participants interacting with a modified ‘Inspector game’ while we monitored their brain, electrodermal, respiratory, and ocular activities. This procedure allowed us to map dispositional (static individual baseline) and learned (dynamic, based on prior interactions) dimensions of trust, considering both demographic and psychophysiological aspects. Our findings challenge traditional assumptions regarding the dispositional dimension of trust and establish clear evidence that the first interactions are critical for the trust-building process and the temporal evolution of trust. By identifying more significant psychophysiological features for trust detection and underscoring the importance of individualized trust assessment, this research contributes to understanding the nature of trust in HRC. Such insights are crucial for enabling more seamless human–robot interaction in collaborative environments.

Artificial Intelligence (AI) technology is revolutionizing the digital world and becoming the cornerstone of the modern digital systems. The capabilities of cybercriminals are expanding as they adopt new technologies like zero-day exploits or new business models such as hacker-as-a-service. While AI capabilities can improve cybersecurity measures, this same technology can also be utilized as an offensive cyber weapon to create sophisticated and intricate cyber-attacks. This paper describes an AI-powered mechanism for the automatic generation of attack techniques, ranging from initial attack vectors to impact-related actions. It presents a comprehensive analysis of simulated attacks by highlighting the attack tactics and techniques that are more likely to be generated using AI technology, specifically Large Language Model (LLM) technology. The work empirically demonstrates that LLM technology can be easily used by cybercriminals for attack execution. Moreover, the solution can complement Breach and Attack Simulation (BAS) platforms and frameworks that automate the security assessment in a controlled manner. BAS could be enhanced with AI-powered attack simulation by bringing forth new ways to automatically program multiple attack techniques, even multiple versions of the same attack technique. Therefore, AI-enhanced attack simulation can assist in ensuring digital systems are bulletproof and protected against a great variety of attack vectors and actions.