Abstract
As software applications continue to become more complex and attractive to cyber-attackers, enhancing resilience against cyber threats becomes essential. Aiming to provide more robust solutions, different approaches were proposed for vulnerability detection in different stages of the application life-cycle. This article explores three main approaches to application security: Static Application Security Testing (SAST), Dynamic Application Security Testing (DAST), and Software Composition Analysis (SCA). The analysis conducted in this work is focused on open-source solutions while considering commercial solutions to show contrast in the approaches taken and to better illustrate the different options available. It proposes a baseline comparison model to help evaluate and select the best solutions, using comparison criteria that are based on community standards. This work also identifies future opportunities for application security, highlighting some of the key challenges that still need to be addressed in order to fully protect against emerging threats, and proposes a workflow that combines the identified tools to be used for vulnerability assessments.

Abstract
This study explores asset management performance of Portuguese water supply companies operating in the bulk market. The focus of the analysis are the managerial practices and the condition of infrastructures. This assessment is based on the information conveyed by the indicators collected by the Portuguese water and waste services' regulator authority (ERSAR) between 2016 and 2020. The main contribution of this research is to propose innovative methods to enhance the knowledge on asset management practices in the water sector. Two Benefit-of-the-Doubt (BoD) Composite Indicators are developed to highlight different aspects of asset management approaches. The first reflects organisations' performance in maintaining their infrastructures at acceptable operational levels, and the other reveals their maturity in asset management practices. Robust and conditional approaches for estimating the BoD indicators are applied, allowing to obtain results that account for the effect of contextual variables on companies' performance. Additionally, the performance of the companies is analysed over a 5-year period. The results show that there is significant room for improvement given the indicators' values estimated in the benchmarking analysis. The type of management systems and areas of intervention (urban, semi-urban or rural) are factors that present significant impact in asset management performance. The analysis of trends in the evolution of performance over time revealed improvements both in the companies' managerial practices and operational results.

Abstract
The COVID-19 pandemic highlighted the inadequate readiness of numerous nations to address diseases that could potentially evolve into epidemics or pandemics, posing risks to health systems and supply chains. Statistical analysis and predictive models were developed to manage COVID-19 and other diseases that harm public health. However, few public-policy decision-support tools are documented in the literature, although several governments have created them. In line with the previous developments, this tool uses socioeconomic features to model the COVID-19 province's mortality rates. This paper presents a tool to predict the mortality rate of a province using supervised learning techniques, named CuraZone. This tool was validated using 196 provinces in Peru for training and considering 31 characteristics. The tool displays the dataset's most essential characteristics, shows the country's mean square error (MSE), and predicts a province's mortality rate. In addition, the tool contributes to the field of Explainable AI (XAI), as it shows the importance of each feature. Highlighted contributions of this work include the support for the decision-making of governments or stakeholders in epidemics, providing the source code in an open and reproducible way, and the estimated mortality rate for specific populations of a neighborhood, city, or country.

Abstract

Abstract
This study explores data mining techniques for predicting student dropout in higher education. The research compares different methodological approaches, including alternative algorithms and variations in model specifications. Additionally, we examine the impact of employing either a single model for all university programs or separate models per program. The performance of models with students grouped according to their position on the program study plan was also tested. The training datasets were explored with varying time series lengths (2, 4, 6, and 8 years) and the experiments use academic data from the University of Porto, spanning the academic years from 2012 to 2022. The algorithm that yielded the best results was XGBoost. The best predictions were obtained with models trained with two years of data, both with separate models for each program and with a single model. The findings highlight the potential of data mining approaches in predicting student dropout, offering valuable insights for higher education institutions aiming to improve student retention and success. © 2023 Associacao Portuguesa de Sistemas de Informacao. All rights reserved.

Abstract
Water scarcity is becoming an issue of more significant concern with a major impact on global sustainability. For it, new measures and approaches are urgently needed. Digital technologies and tools can play an essential role in improving the effectiveness and efficiency of current water management approaches. Therefore, a solution is proposed and validated, given the limited presence of models or technological architectures in the literature to support intelligent water management systems for domestic use. It is based on a layered architecture, fully designed to meet the needs of households and to do so through the adoption of technologies such as the Internet of Things and cloud computing. By developing a prototype and using it as a use case for testing purposes, we have concluded the positive impact of using such a solution. Considering this is a first contribution to overcome the problem, some issues will be addressed in a future work, namely, data and device security and energy and traffic optimisation issues, among several others.