In this paper, we study a novel approach to asynchronous hyperproperties by reconsidering the foundations of temporal team semantics. We consider three logics: , and , which are obtained by adding quantification over so-called time evaluation functions controlling the asynchronous progress of traces. We then relate synchronous to our new logics and show how it can be embedded into them. We show that the model checking problem for with Boolean disjunctions is highly undecidable by encoding recurrent computations of non-deterministic 2-counter machines. Finally, we present a translation from to Alternating Asynchronous Büchi Automata and obtain decidability results for the path checking problem as well as restricted variants of the model checking and satisfiability problems.
Electrification in the maritime sector is becoming steadily more common as part of global general decarbonisation efforts, with over 1300 vessels now utilising either hybrid or fully electric propulsion. Having already increased more than tenfold in the last decade, the number of shipborne battery energy-storage systems deployed is foreseeably set to continue growing. Presently, rechargeable lithium-ion cells predominate among installations which require appropriate management to avoid the risk of thermal runaway. Thus, it is important that applicable and useful methodologies are available to ascertain the key confluences of hazards that can threaten crew wellbeing, damage property, and impact public confidence, given the small but notable documented history of incidents. This paper develops a fundamental Petri-net approach to studying the risk assessment of such systems, using a constructed case study containing features representative of electrified seagoing vessels. The modelling yields both the system components most commonly in a failure state during an emergency and their sensitivity to operational parameters when subject to variation. Specifically, coolant circulation was most probable to have failed in a crisis event, and the greatest impact was observed with the competence of the crew with respect to their ability to identify on-board faults.
This paper studies the multi-period line planning problem with vehicle rotation, a fundamental challenge in public transportation system design that integrates line selection, service frequency determination, and inter-period vehicle transfers. The temporal coupling induced by vehicle rotation generates strong interdependencies across planning periods, resulting in large-scale, highly combinatorial optimization models that are difficult to solve with existing mixed-integer programming methods. To address this complexity, we develop two exact decomposition-based solution frameworks. The first is a constraint satisfaction algorithm that iteratively relaxes and reinstates the vehicle flow-balance constraints, progressively reconstructing feasibility through a sequence of restricted master problems. The second is a logic-based Benders decomposition framework in which the feasibility verification stage is reformulated as a max-flow and min-cut problem. This reformulation enables the efficient identification of infeasibilities and the generation of strong feasibility cuts derived from the structure of the underlying network. Extensive computational experiments on three real public transportation networks, Istanbul Metrob & uuml;s, Athens Metro, and Quito Troleb & uacute;s, demonstrate the efficiency and scalability of the proposed methods. The logic based decomposition algorithm attains optimal solutions up to 99% faster than a state-of-the-art commercial solver and successfully solves instances with more than 300 lines that remain unsolved within 24 hours by conventional algorithms. The results confirm that embedding network flow reasoning within a logic-based decomposition framework yields a computationally tractable and structurally coherent methodology for large-scale multi-period line planning with vehicle rotation.
Ensuring an adequate truck driver workforce is a key policy concern for governments seeking resilient supply chains. In recent years, workforce contraction has intensified this challenge. In the aftermath of the COVID19 pandemic, many national governments introduced recruitment-focused measures, however worker retention received comparatively less attention. Drawing on a 2023 survey designed and administered to UK truck drivers (N = 1756), Structural Equation Modelling (SEM) is used to examine how job satisfaction, health and wellbeing, flexible work arrangements and workplace discrimination are associated with intention to quit or remain working as a truck driver. Drivers reporting higher job satisfaction and wellbeing were less likely to intend to leave the occupation (inter-occupational turnover). The availability and uptake of diverse work arrangements was associated with better retention outcomes through higher satisfaction and wellbeing, whereas experiencing discrimination was associated with lower retention. Company size and worker age also had direct associations with quitting intentions and retention, while contract type and caring responsibilities operated mainly through their relationships with satisfaction, wellbeing and discrimination. The findings indicate how truck driver retention is shaped by the interaction between working conditions, health and the fit between paid work and life outside work. These findings support a shift from recruitment-led responses towards retention-oriented transport policy, including stronger health and wellbeing provision, more predictable working hours and flexible working arrangements, and effective protection against discrimination. These measures will require support through employment regulation, freight procurement standards and buyer-supplier contracting practices, rather than relying on individual firms alone.
Reconfigurable manufacturing requires robotic workcells that can adapt to changing products, processes, and equipment without extensive redesign or manual reprogramming. This paper presents the R3M framework, a ROS 2 driven integration architecture that connects model based manufacturing knowledge with execution level robotic control. Product, process, and equipment information is formalised through UML domain models and serialised in AutomationML(AML), enabling automated correspondence between assembly requirements, available skills, and executable recipes. The framework integrates automated programme generation, reinforcement learning based recipe optimisation, and a modular CAD informed six degree of freedom perception layer to support both technical and semantic interoperability across simulated and physical workcells. The approach is evaluated through Cube Kitting and Cylinder Stacking use cases implemented on distinct robotic platforms, including ABB and Universal Robots systems. Experimental results show high reliability, with environment launch performance reaching up to 99.93%, standard skill sequences achieving 100% execution success, and full use case trials exceeding 97% success in simulation and reaching 100% on physical hardware. These findings demonstrate that R3M provides a scalable foundation for adaptive robotic manufacturing, reducing programming effort while supporting modular substitution, robust perception, and simulation to real deployment.
