Abstract
The optimal Coalition Structure Generation (CSG) problem for a given set of agents finds a partition of the agent set that maximises social welfare. The CSG problem is an NP-hard optimisation problem, where the search space grows exponentially. The exact and approximation algorithms focus on finding an optimal solution or a solution within a known bound from the optimum. However, as the number of agents increases linearly, the search space increases exponentially and a practical option here is to use heuristic algorithms. Heuristic algorithms are suitable for solving the optimisation problems because of their less computational complexity. TACOS is a heuristic method for the CSG problem that finds high-quality solutions quickly using a neighbourhood search performed with a memory. However, some of the neighbourhood searches by TACOS can be performed simultaneously. Therefore, this paper proposes a parallel version of the TACOS algorithm (P-TACOS) for the CSG problem, intending to find a better solution than TACOS. We evaluated P-TACOS using eight (8) benchmark data distributions. Results show that P-TACOS achieves better results for all eight (8) data distributions. P-TACOS achieves the highest gain, 74.23%, for the Chisquare distribution and the lowest gain, 0.01%, for the Normal distribution. We also examine how often P-TACOS generates better results than TACOS. In the best case, it generates better results for 92.30% of the time (for the Rayleigh and Agent-based Normal distributions), and in the worst case, 38.46% of the time (for the Weibull distribution).

Abstract
Modern electrical distribution networks are prone to more severe voltage fluctuations due to the presence of variable loads such as electric vehicles and renewable energy generation units. These fluctuations decrease both the quality of power and the hosting capability of the grid. In such a condition, a Dynamic Voltage Compensator (DVC) can be used to stabilize the voltage of the LV networks. DVC is generally designed to resolve voltage fluctuations reflected from MV systems maintaining the voltage on a constant value. However, it will more effectively improve the voltage quality in the grid if the reference voltage is dynamically adjusted based on measurements inside the LV system. On the other hand, the more complex measurement and coordination strategy may lead to the inapplicability of the methods. Hence, voltage reference adjustment strategies should be developed to conform to the availability of data and measurements inside the grid. Accordingly, in this paper, novel voltage reference adjustment strategies have been developed for DVC based on the measurements at the installation point of the device. In order to examine the proposed methods, they are applied to an LV grid with real measured data and the results are discussed. Based on the provided simulation results, the developed dynamic reference voltage adjustment strategies can successfully improve the quality of voltage and improve the hosting capacity of the LV network. © 2023 IEEE.

Abstract

Abstract
The study of the evacuation of buildings in emergency fire situations has deserved the attention of researchers for decades, particularly regarding the real-time guiding of occupants in their way to exit the building. However, finding solutions to guide the occupants evacuating a building requires a thorough knowledge of that domain. Using ontological models to model the knowledge of a domain allows the understanding of that domain to be shared. This paper presents an ontological model that pretends to reinforce and deepen knowledge of the domain under study and help develop solutions and systems capable of guiding the occupants during a building evacuation. The ontology was developed following the METHONTOLOGY methodology, and for implementation, the Protege tool was used. The ontological model was successfully submitted to a thorough evaluation process and is publicly available on the Web.

Abstract
The global overview of the challenges faced in trying to minimise the risks of organisations in the face of cyber-attacks is arduous for any organisation. Defining an appropriate risk management model that proactively minimises cybersecurity incidents is a critical challenge. Many malicious attacks occur daily, and there is only sometimes an adequate response. There is a significant investment in research to identify the main factors that may cause such incidents, always trying to have the most appropriate response and, consequently, potentiating the response capacity and success. At the same time, several different methodologies evaluate risk management and the maturity level of organisations. Due to the lack of predictive models based on data (evidence), there is a significant investment in research to identify the main factors that may cause such incidents, starting to design models based on AI-Artificial Intelligence. This research will go in the direction of developing a user-friendly model supporting the assessment of the methodological aspects of an organisation. © 2023, IGI Global.

Abstract
Virtual reality (VR) has the potential to significantly boost productivity in professional settings, especially those that can benefit from immersive environments that allow a better and more thorough way of visualizing information. However, the physical demands of mid-air movements make it difficult to use VR for extended periods. DeskVR offers a solution that allows users to engage in VR while seated at a desk, minimizing physical exhaustion. However, developing appropriate motion techniques for this context is challenging due to limited mobility and space constraints. This work focuses on object manipulation techniques, exploring touch-based and mid-air-based approaches to design a suitable solution for DeskVR, hypothesizing that touch-based object manipulation techniques could be as effective as mid-air object manipulation in a DeskVR scenario while less physically demanding. Thus, we propose Scaled Indirect Touch 6-DOF (SIT6), an indirect touch-based object manipulation technique incorporating scaled input mapping to address precision and out-of-reach manipulation issues. The implementation of our solution consists of a state machine with error-handling mechanisms and visual indicators to enhance interaction. User experiments were conducted to compare the SIT6 technique with a baseline mid-air approach, revealing comparable effectiveness while demanding less physical exertion. These results validated our hypothesis and established SIT6 as a viable option for object manipulation in DeskVR scenarios. (c) 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).