This paper presents the design methodology used to specify a Human-in-the-Loop Cyber-Physical System (HITL-CPS) for online monitoring and active mitigation of the indoor radon gas concentration in non-residential granitic buildings with regular human occupation restricted to a work schedule, such as public buildings like administration offices, schools, kindergartens, etc. The paper follows the methodology that is being carried out in the RnMonitor R&D project in order to design the overall CPS architecture specification using the following approach: i) Conceptual Approach and Application Requirements; ii) Design Specifications and iii) Architecture Definition. At the end, main conclusions are presented alongside with a set of promising avenues for future development.

Selecting the best desirable Enterprise Resources Planning (ERP) system has been a critical problem for organizations for a long time, as the failure on the selection process may have a highly negative impact in terms of costs and market share of a company. It is one of the most important decision making issues covering both qualitative and quantitative factors for organization. Multiple-criteria decision-making has been proved to be a useful approach to analyze these conflicting qualitative and quantitative factors. Literature offers proposals and approaches to handle this kind of problem; Analytic Hierarchy Process (AHP) has been applied successfully in most cases of software packages selection problems. This paper proposes an AHP model for the selection of an ERP system. The model's set of criteria was extracted from the literature review and validated by Portuguese organizations. This model can be applied in the ERP system selection using a software application that is under development. This software application eases the application of the AHP process to the selection of ERP packages and will provide input from real-world cases that will allow updating and refining the model.

Radon (²²²Rn) is a well-known source of indoor air contamination since in its gaseous form it is a reported source of ionizing radiation that belongs to the group of rare gases. Radon occurs naturally in soils and rocks and results from the radioactive decay of its longer-lived progenitors, i.e., radium, uranium, and thorium. Radon releases itself from the soil and rocks, which mainly occurs in outdoor environments, not causing any kind of impact due to its fast dilution into the atmosphere. However, when this release occurs in confined and poorly ventilated indoor environments, this release can result in the accumulation of high concentrations of radon gas, being recognized by the World Health Organization (WHO) as the second cause of lung cancer, after smoking. Assessing the indoor radon concentration demands specific know-how involving the implementation of several time-consuming tasks that may include the following stages: (1) radon potential assessment; (2) short-term/long-term radon measurement; (3) laboratory data analysis and processing; and (4) technical reporting. Thus, during stage 1, the use of indirect methods to assess the radon occurrence potential, such as taking advantage of existent natural radiation maps (which have been made available by the uranium mineral prospecting campaigns performed since the early 1950s), is crucial to put forward an ICT (Information and Communication Technology) platform that opens up a straightforward approach for assessing indoor radon potential at an early stage, operating as a pre-diagnosis evaluation tool that is of great value for supporting decision making towards the transition to stage 2, which typically has increased costs due to the need for certified professionals to handle certified instruments for short-term/long-term radon measurement. As a pre-diagnosis tool, the methodology proposed in this article allows the assessment of the radon potential of a specific building through a WebGIS-based platform that adopts ICT and Internet technologies to display and analyze spatially related data, employing a multicriteria approach, including (a) gamma radiation maps, (b) built environment characteristics, and (c) occupancy profile, and thus helping to determine when the radon assessment process should proceed to stage 2, or, alternatively, by eliminating the need to perform additional actions.

This study evaluated machine learning and deep learning models for Intrusion Detection Systems (IDS) using the UNSW-NB15, NSL-KDD, and CIC-IDS 2017 datasets. Among the tested models, XGBoost consistently achieved the highest accu- racy, demonstrating its robustness in handling complex network traffic. Feature selection methods, including Lasso Regularization and SelectKBest (ANOV A), were employed to simplify the models, with Lasso maintaining near-optimal performance and SelectKBest showing dataset-specific sensitivity. These findings highlight the efficacy of XGBoost and the importance of feature selection in improving IDS performance while balancing model complexity.