Abstract
This paper studies the suitability of the novel Light Quasi-Peak (Light-QP) measurement method to assess the high-frequency disturbances generated by the vehicle-to-grid (V2G) technology, by comparing the performance of the new method with respect to the standardized CISPR 16-1-1 method. For this purpose, the quasi-peak (QP) outputs obtained by both methods are compared, a statistical study of the differences in the spectral results is performed and the computational requirements of the two methods are evaluated. This paper demonstrates that the novel Light-QP method is a lighter technique to assess the QP amplitude of the conducted disturbances, as it requires 10 times less Fourier transforms and at least less than 90 % storage to process a 3 s length measurement. Furthermore, the QP outputs provided by the Light-QP method are comparable to the outputs of a digital implementation of the CISPR 16, since the differences in results are within the uncertainty limits defined in IEC 61000-4-30 standard for power-quality instruments in the CISPR Band A. The Light-QP method could be essential for the detection of the V2G disturbances in low-voltage grid, since it can be easily implemented in inexpensive power quality measurement instruments. The Light-QP method was presented in the IEC SC77 A WG9 for its possible inclusion in the next edition of IEC 61000-4-30 standard. © 2023 IEEE.

Abstract
By 2050, and in the context of decarbonization and carbon neutrality, many companies worldwide are looking for low-carbon alternatives. Transport companies are probably the most challenging due to the continuing growth in global demand and the high dependency on fossil fuels. Some alternatives are emerging to replace conventional diesel vehicles and thus reduce greenhouse gas emissions and air pollutants. One of these alternatives is the adoption of compressed natural gas (CNG). In this paper, we provide a detailed study of the current emissions from the largest bus fleet company in the metropolitan area of Oporto. For this analysis, we used a top-down and a bottom-up methodology based on EMEP/EEA guidebook to compute the CO2 and air pollution (CO, NMVOC, PM2.5, and NOx) emissions from the fleet. Fuel consumption, energy consumption, vehicle slaughter, electric bus incorporation, and the investments made were taken into consideration in the analyses. From the case study, the overall reduction in CO2 emission was just 6.3%, and the emission factors (air pollutants) from CNG-powered buses and diesel-powered buses are closer and closer. For confirming these results and question the effectiveness of the fleet transitions from diesel to CNG vehicles, we analysed two scenarios. The obtained results reveal the potential and effectiveness of electric buses and other fuel alternatives to reduce CO2 and air pollution.

Abstract
Cloud Internet of Things (CIoT) environments, as the essential basis for computing services, have been subject to abuses and cyber threats. The adversaries constantly search for vulnerable areas in such computing environments to impose their damages and create complex challenges. Hence, using intrusion detection and prevention systems (IDPSs) is almost mandatory for securing CIoT environments. However, the existing IDPSs in this area suffer from some limitations, such as incapability of detecting unknown attacks and being vulnerable to the single point of failure. In this paper, we propose a novel distributed multi-agent IDPS (DMAIDPS) that overcomes these limitations. The learning agents in DMAIDPS perform a six-step detection process to classify the network behavior as normal or under attack. We have tested the proposed DMAIDPS with the KDD Cup 99 and NSL-KDD datasets. The experimental results have been compared with other methods in the field based on Recall, Accuracy, and F-Score metrics. The proposed system has improved the Recall, Accuracy, and F-Scores metrics by an average of 16.81%, 16.05%, and 18.12%, respectively.

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Higher Education Institutions must define and monitor strategies and policies essential for decision-making in their various areas and levels, in which Business Intelligence plays a leading role. This research addresses the problem of Business Intelligence system adoption in Higher Education Institutions, with a view, in the first instance, to identify and characterise the strategic objectives that underpin decision-making, activities, processes, indicators and information in Higher Education Institutions. After a literature review, it was found that the absence of a roadmap that can serve as a reference to implement a Business Intelligence system in Higher Education Institutions may limit the adoption of this type of solution. Therefore, this research intends to present the methodology of a proposed roadmap for the implementation of Business Intelligence systems in Higher Education Institutions, that allows for increasing its capacity for analysis and evaluation of the data and information available in the various systems and platforms. © 2023 ICSBT International Conference on Smart Business Technologies. All rights reserved.