Abstract
Memory safety issues in C are the origin of various vulnerabilities that can compromise a program's correctness or safety from attacks. We propose a different approach to tackle memory safety, the replication of Rust's Mid-level Intermediate Representation (MIR) Borrow Checker, through the usage of static analysis and successive source-to-source code transformations, to be composed upstream of the compiler, thus ensuring maximal compatibility with most build systems. This allows us to approximate a subset of C to Rust's core concepts, applying the memory safety guarantees of the rustc compiler to C. In this work, we present a survey of Rust's efforts towards ensuring memory safety, and describe the theoretical basis for a C borrow checker, alongside a proof-of-concept that was developed to demonstrate its potential. This prototype correctly identified violations of the ownership and aliasing rules, and accurately reported each error with a level of detail comparable to that of the rustc compiler.

Abstract
This work applies Natural Language Processing (NLP) techniques, specifically transformer models, for the emotional evaluation of open-ended responses. Today's powerful advances in transformer architecture, such as ChatGPT, make it possible to capture complex emotional patterns in language. The proposed transformer-based system identifies the emotional features of various texts. The research employs an innovative approach, using prompt engineering and existing context, to enhance the emotional expressiveness of the model. It also investigates spaCy's capabilities for linguistic analysis and the synergy between transformer models and this technology. The results show a significant improvement in emotional detection compared to traditional methods and tools, highlighting the potential of transformer models in this domain. The method can be implemented in various areas, such as emotional research or mental health monitoring, creating a much richer and complete user profile.

Abstract
In this paper, the performance analysis of Machine Learning (ML) algorithms for fault analysis in photovoltaic (PV) plants, is given for different algorithms. To make the comparison more relevant, this study is made based on a real dataset. The goal was to use electric and environmental data from a PV system to provide a framework for analysing, comparing, and discussing five ML algorithms, such as: Multilayer Perceptron (MLP), Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Light Gradient Boosting Machine (LightGBM). The research findings suggest that an algorithm from the Gradient Boosting family called LightGBM can offer comparable or better performance in fault diagnosis for PV system.

Abstract
Implementation of information and communication technologies (ICTs) in education is defined as the incorporation of ICTs into teaching and learning activities, both inside and outside the classroom. Despite widely studied, there is still no consensus on how it affects student performance. However, before evaluating this, it is crucial to identify which factors impact students' use of ICT for educational purposes. This understanding can help educational institutions to effectively implement ICT, potentially improving student results. Thus, adapting the conceptual framework proposed by Biagi and Loi (2013) and using the 2018 database of the Program for International Student Assessment (PISA) and a decision tree classification model developed based on CRISP-DM framework, we aim to determine which socio-demographic factors influence students' use of ICT for educational purposes. First, we categorized students according to their use of ICT for educational purposes in two situations: during lessons and outside lessons. Then, we developed a decision tree model to distinguish these categories and find patterns in each group. The model was able to accurately distinguish different levels of ICT adoption and demonstrate that ICT use for entertainment and ICT access at school and at home are among the most influential variables to predict ICT use for educational purposes. Moreover, the model showed that variables related to teaching best practices of Internet utilization at school are not significant predictors of such use. Some results were found to be country-specific, leading to the recommendation that each country adapts the measures to improve ICT use according to its context.

Abstract
We resolve the multiple images of the binary-lens microlensing event ASASSN-22av using the GRAVITY instrument of the Very Large Telescope Interferometer (VLTI). The light curves show weak binary-lens perturbations, complicating the analysis, but the joint modeling with the VLTI data breaks several degeneracies, arriving at a strongly favored solution. Thanks to precise measurements of the angular Einstein radius theta E = 0.724 +/- 0.002 mas and microlens parallax, we determine that the lens system consists of two M dwarfs with masses of M 1 = 0.258 +/- 0.008 M circle dot and M 2 = 0.130 +/- 0.007 M circle dot, a projected separation of r perpendicular to = 6.83 +/- 0.31 au, and a distance of D L = 2.29 +/- 0.08 kpc. The successful VLTI observations of ASASSN-22av open up a new path for studying intermediate-separation (i.e., a few astronomical units) stellar-mass binaries, including those containing dark compact objects such as neutron stars and stellar-mass black holes.

Abstract
Histopathological analysis is an essential exam for detecting various types of cancer. The process is traditionally time-consuming and laborious. Taking advantage of deep learning models, assisting the pathologist in the diagnosis process is possible. In this work, a study was carried out based on the DenseNet neural network. It consisted of changing its architecture through combinations of Transformer and MBConv blocks to investigate its impact on classifying histopathological images of penile cancer. Due to the limited number of samples in this dataset, pre-training is performed on another larger lung and colon cancer histopathological image dataset. Various combinations of these architectural components were systematically evaluated to compare their performance. The results indicate significant improvements in feature representation, demonstrating the effectiveness of these combined elements resulting in an F1-Score of up to 95.78%. Its diagnostic performance confirms the importance of deep learning techniques in men's health.