Abstract
The availability of public services through online platforms has improved the coverage and efficiency of essential services provided to citizens worldwide. These services also promote transparency and foster citizen participation in government processes. However, the increased online presence also exposes sensitive data exchanged between citizens and service providers to a wider range of security threats. Therefore, ensuring the security and trustworthiness of online services is crucial to Electronic Government (EGOV) initiatives' success. Hence, this work assesses the security posture of online platforms hosted in 3068 governmental domain names, across all UN Member States, in three dimensions: support for secure communication protocols; the trustworthiness of their digital certificate chains; and services' exposure to known vulnerabilities. The results indicate that despite its rapid development, the public sector still falls short in adopting international standards and best security practices in services and infrastructure management. This reality poses significant risks to citizens and services across all regions and income levels.

Abstract
Studying the orbital motion of stars around Sagittarius A* in the Galactic center provides a unique opportunity to probe the gravitational potential near the supermassive black hole at the heart of our Galaxy. Interferometric data obtained with the GRAVITY instrument at the Very Large Telescope Interferometer (VLTI) since 2016 has allowed us to achieve unprecedented precision in tracking the orbits of these stars. GRAVITY data have been key to detecting the in-plane, prograde Schwarzschild precession of the orbit of the star S2 that is predicted by general relativity. By combining astrometric and spectroscopic data from multiple stars, including S2, S29, S38, and S55 - for which we have data around their time of pericenter passage with GRAVITY - we can now strengthen the significance of this detection to an approximately 10 sigma confidence level. The prograde precession of S2's orbit provides valuable insights into the potential presence of an extended mass distribution surrounding Sagittarius A*, which could consist of a dynamically relaxed stellar cusp comprising old stars and stellar remnants, along with a possible dark matter spike. Our analysis, based on two plausible density profiles - a power-law and a Plummer profile - constrains the enclosed mass within the orbit of S2 to be consistent with zero, establishing an upper limit of approximately 1200 M-circle dot with a 1 sigma confidence level. This significantly improves our constraints on the mass distribution in the Galactic center. Our upper limit is very close to the expected value from numerical simulations for a stellar cusp in the Galactic center, leaving little room for a significant enhancement of dark matter density near Sagittarius A*.

Abstract
Lean and green (L&G) manufacturing in Industry 4.0 (I4.0) has brought many advantages in manufacturing industries by minimizing waste and maximizing efficiency with integration of renewable energy sources and sustainable materials. Multi-layer Stream Mapping (MSM) is a new framework for the performance assessment of complex manufacturing processes. MSM is used for multi-domain analysis of manufacturing processes to assess resources, and processes, that are used to identify Non-ValueAdded (NVA) procedures or steps that consume unnecessary time and resources, and/or release emissions and waste that can no longer be reused or recycled to be eliminated or replaced to create a Value Added (VA) process flow that avoids waste in a clean, green and environmental friendly manner. This paper presents the implementation of the L&G strategy through MSM in metal working production systems. In metalworking production systems, the variables of operational performance and resources consumption considered are process time, number of operators, consumables, raw material, and energy. These can be suitably used for reduction in water emissions, gas emissions, solid waste and scrap generated in metalworking production systems.

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.