Abstract
Memorising passwords poses a significant challenge for individuals, leading to the increasing adoption of password managers, particularly browser password managers. Despite their benefits to users’ daily routines, the use of these tools introduces new vulnerabilities to web and network security. This paper aims to investigate these vulnerabilities and analyse the security mechanisms of browser-based password managers integrated into Google Chrome, Microsoft Edge, Opera GX, Mozilla Firefox, and Brave. Through malware development and deployment, Dvorak is capable of extracting essential files from the browser’s password manager for subsequent decryption. To assess Dvorak functionalities we conducted a controlled security analysis across all aforementioned browsers. Our findings reveal that the designed malware successfully retrieves all stored passwords from the tested browsers when no master password is used. However, the results differ depending on whether a master password is used. A comparison between browsers is made, based on the results of the malware. The paper ends with recommendations for potential strategies to mitigate these security concerns. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

Abstract
Dropout from Higher Education (HE), that is, the number of students that totally leave a given HE institution is concerningly high, especially in times of crisis. Institutions struggle to minimize dropout, but limited data is available likely because gathering data from learners who dropped out is sensitive, likely involving private information. This paper presents a case study research on student dropout from a very large Portuguese engineering faculty. The main objectives of this research include to gain a better understanding about the reasons for dropout, from the former student’s point of view, and to build a profile for the dropout-at-risk student. The collected data was retrieved from institutional records and from 134 telephonic interviews with former students. The resulting data is analysed in both quantitative and qualitative ways. Results of all gathered dropout data are clustered into three profiles of students who dropout: those that “pull out”, those who were “pushed out” and those who “fall out”. Findings include that students do not decide to dropout by a simple single reason but rather a set of reasons. This research article includes 5 concrete improvement suggestions that are likely to reduce dropout. The two main suggestions are to better prepare the transition to HE and to make policies more flexible in times of crisis, example more flexible schedule. © 2024, Author. All rights reserved.

Abstract
This paper presents the Unmanned Aerial Vehicle (UAV) MANTIS, developed for indoor inventory management in large-scale warehouses. MANTIS integrates a visual odometry (VIO) system for precise localization, thus allowing indoor navigation in complex environments. The mechanical design was optimized for stability and maneuverability in confined spaces, incorporating a lightweight frame and efficient propulsion system. The UAV is equipped with an array of sensors, including a 2D LiDAR, six cameras, and two IMUs, which ensures accurate data collection. The VIO system integrates visual data with inertial measurements to maintain robust, drift-free localization. A behavior tree (BT) framework is responsible for the UAV mission planner assigned to the vehicle, which can be flexible and adaptive in response to dynamic warehouse conditions. To validate the accuracy and reliability of the VIO system, we conducted a series of tests using an OptiTrack motion capture system as a ground truth reference. Comparative analysis between the VIO and OptiTrack data demonstrates the efficacy of the VIO system in maintaining accurate localization. The results prove MANTIS, with the required payload sensors, is a viable solution for efficient and autonomous inventory management.

Abstract
The automotive industry deals with complex processes. Becoming aware of the importance of agile management they are contributing to a creative fusion known as leagile. However, this concept needs further study and investigation. This work presents a systematic literature review on Lean Agile implementation in the automotive industry. Thirty-three publications were reviewed and characterized according to the year of publication, country of origin, industrial sector, used tools and their contributions to the automotive sector. The results show that 50% of the articles were published after 2018. The countries with the most publications are India, Portugal, and United Kingdom. The most cited tools are Value Stream Mapping (VSM), Just in Time (JIT) and 5S (23%). This study confirms the growing use of leagile in the automotive industry and the growing potential for research development in the area.

Abstract
The rising adoption rates and integration of Renewable Energy Sources (RES) and Electric Vehicles (EVs) into the energy grid introduces complex challenges, including the need to balance supply and demand and smooth peak consumptions. Addressing these challenges requires innovative solutions such as Demand Response (DR), Renewable Energy Communities (RECs), and more specifically for EVs, Vehicle-to-Grid (V2G). However, existing V2G approaches often fall short in real-world applicability, adaptability, and user engagement. To bridge this gap, this paper proposes EnergAIze, a Multi-Agent Reinforcement Learning (MARL) energy management algorithm leveraging the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. EnergAIze enables user-centric multi-objective energy management by allowing each prosumer to select from a range of personal management objectives. Additionally, it architects' data protection and ownership through decentralized deployment, where each prosumer can situate an energy management node directly at their own dwelling. The local node not only manages local EVs and other energy assets but also fosters REC wide optimization. EnergAIze is evaluated through a case study using the CityLearn framework. The results show reduction in peak loads, ramping, carbon emissions, and electricity costs at the REC level while optimizing for individual prosumers objectives.

Abstract