Electric vehicle (EV) charging infrastructures are increasingly exposed to sophisticated cyber threats, including replay, spoofing, privilege escalation, and geolocation-based attacks. While standards such as ISO 15118 and OCPP 2.0.1 provide interoperability and cryptographic guarantees, they rely on static policies or isolated detection mechanisms, leaving gaps against adaptive adversaries. This paper presents an AI-driven Zero Trust Blockchain (AI-ZTB) framework whose novelty lies in the system-level integration of identity and access management, AI-based risk assessment, and blockchain-backed decentralized auditability with IPFS-based evidence storage, while operational governance remains centrally managed by the service provider. Unlike prior AI-only or blockchain-only frameworks, AI-ZTB introduces a fully integrated and enforceable Zero Trust control loop in which AI-generated risk scores are operationally bound to access enforcement decisions through smart contracts, enabling adaptive, auditable, and context-aware security governance in real time. The framework was implemented in Python with Solidity smart contracts and evaluated through a large-scale network simulation involving batches of 10,000 EV-charging sessions, trained on a dataset of 50,000 legitimate and adversarial behaviours using Random Forest, Autoencoder, and Isolation Forest models. Results demonstrate that AI-ZTB achieves access-decision accuracy above 95%, reducing false acceptance and rejection rates to approximately 3%. A comparative analysis evaluates AI-ZTB against industry standards (ISO 15,118 and OCPP 2.0.1) as secure communication baselines, and against prior integrated frameworks from the literature, highlighting differences in architectural scope, policy enforceability, and auditability rather than protocol-level performance. Despite modest inference and logging overheads, performance remained within real-time operational tolerances. The framework establishes a robust foundation for securing EV infrastructures, with extensibility to smart grids and other cyber-physical environments

Abstract Today’s factories are considered as smart ecosystems with humans, machines and devices interacting with each other for efficient manufacturing of products. Industry 4.0 is a suite of enabler technologies for such smart ecosystems that allow transformation of industrial processes. When implemented, Industry 4.0 technologies have a huge impact on efficiency, productivity and profitability of businesses. The adoption and implementation of Industry 4.0, however, require to overcome a number of practical challenges, in most cases, due to the lack of modernisation and automation in place with traditional manufacturers. This paper presents a first of its kind case study for moving a traditional food manufacturer, still using the machinery more than one hundred years old, a common occurrence for small- and medium-sized businesses, to adopt the Industry 4.0 technologies. The paper reports the challenges we have encountered during the transformation process and in the development stage. The paper also presents a smart production control system that we have developed by utilising AI, machine learning, Internet of things, big data analytics, cyber-physical systems and cloud computing technologies. The system provides novel data collection, information extraction and intelligent monitoring services, enabling improved efficiency and consistency as well as reduced operational cost. The platform has been developed in real-world settings offered by an Innovate UK-funded project and has been integrated into the company’s existing production facilities. In this way, the company has not been required to replace old machinery outright, but rather adapted the existing machinery to an entirely new way of operating. The proposed approach and the lessons outlined can benefit similar food manufacturing industries and other SME industries.

Kernel P systems have been introduced as a unifying formalism allowing to specify, simulate and analyse various problems. Several applications of this model have been considered and a powerful tool built in order to support their development and analysis. Testing represents an important aspect of any system analysis and correctness. In this paper we introduce for the first time a bounded test generation approach for kernel P systems by considering bounded input sequences. A learning algorithm for kernel P systems is based on learning X-machine models that are equivalent to these systems for sequences of steps up to a certain limit, ℓ. The Lℓ learning algorithm is used. The testing approach is then devised from the inferred X-machines. The method is applied to a case study illustrating the key parts of the approach.

Motivation: Formal verification is a computational approach that checks system correctness (in relation to a desired functionality). It has been widely used in engineering applications to verify that systems work correctly. Model checking, an algorithmic approach to verification, looks at whether a system model satisfies its requirements specification. This approach has been applied to a large number of models in systems and synthetic biology as well as in systems medicine. Model checking is, however, computationally very expensive, and is not scalable to large models and systems. Consequently, statistical model checking (SMC), which relaxes some of the constraints of model checking, has been introduced to address this drawback. Several SMC tools have been developed; however, the performance of each tool significantly varies according to the system model in question and the type of requirements being verified. This makes it hard to know, a priori, which one to use for a given model and requirement, as choosing the most efficient tool for any biological application requires a significant degree of computational expertise, not usually available in biology labs. The objective of this article is to introduce a method and provide a tool leading to the automatic selection of the most appropriate model checker for the system of interest. Results: We provide a system that can automatically predict the fastest model checking tool for a given biological model. Our results show that one can make predictions of high confidence, with over 90% accuracy. This implies significant performance gain in verification time and substantially reduces the 'usability barrier' enabling biologists to have access to this powerful computational technology. Availability and implementation: SMC Predictor tool is available at http://www.smcpredictor.com. Supplementary information: Supplementary data are available at Bioinformatics online.

This paper proposes a formal methodology to analyse bio-systems, in particular synthetic biology systems. An integrative analysis perspective combining different model checking approaches based on different property categories is provided. The methodology is applied to the synthetic pulse generator system and several verification experiments are carried out to demonstrate the use of our approach to formally analyse various aspects of synthetic biology systems.

Abstract As ubiquitous computing becomes a reality, its applications are increasingly being used in business-critical, mission-critical and even in safety-critical, areas. Such systems must demonstrate an assured level of correctness. One approach to the exhaustive analysis of the behaviour of systems isformal verification, whereby each important requirement is logically assessed against all possible system behaviours. While formal verification is often used in safety analysis, it has rarely been used in the analysis of deployed pervasive applications. Without such formality it is difficult to establish that the system will exhibit the correct behaviours in response to its inputs and environment. In this paper, we show how model-checking techniques can be applied to analyse the probabilistic behaviour of pervasive systems. As a case study we apply this technique to an existing pervasive message-forwarding system,Scatterbox. Scatterbox incorporates many typical characteristics of pervasive systems, such as dependence on sensor reliability and dependence on context. We assess the dynamic temporal behaviour of the system, including the analysis of probabilistic elements, allowing us to verify formal requirements even in the presence of uncertainty in sensors. We also draw some tentative conclusions concerning the use of formal verification for pervasive computing in general.

As unconventional computation matures and non-standard programming frameworks are demonstrated, the need for formal verification will become more prevalent. This is so because “programming” in unconventional substrates is difficult. In this paper we

Model checking is a well-established technique for the formal verification of concurrent and distributed systems. In recent years, model checking has been extended and adapted for multi-agent systems, primarily to enable the formal analysis of belief–desire–intention systems. While this has been successful, there is a need for more complex logical frameworks in order to verify realistic multi-agent systems. In particular, probabilistic and real-time aspects, as well as knowledge, belief, goals, etc., are required. However, the development of new model checking tools for complex combinations of logics is both difficult and time consuming. In this article, we show how model checkers for the constituent temporal, probabilistic, and real-time logics can be re-used in a modular way when we consider combined logics involving different dimensions. This avoids the re-implementation of model checking procedures. We define a modular approach, prove its correctness, establish its complexity, and show how it can be used to describe existing combined approaches and define yet-unimplemented combinations. We also demonstrate the feasibility of our approach on a case study.

Vehicular ad hoc networks (VANETs), which are a class of Mobile ad hoc networks, have recently been developed as a standard means of communication among moving vehicles. Since VANETs are vital to the safety of the vehicles, the infrastructure, and the humans involved, a deep analysis of their potential behaviours is clearly required. In this paper we provide this analysis through the use of formal verification. Specifically, we formally analyse a specific congestion control protocol for VANETs using a probabilistic model checking technique, and investigate its correctness and effectiveness.

This paper introduces a new interval temporal logic, TPL*. Existing interval temporal logics, we claim, are inadequate to represent the meanings of certain natural language constructions, despite exhibiting high computational complexity. TPL* overcomes these problems, presents the semantics of some natural language constructions, and captures important real-time problems like behaviour of complex systems