Wireless networks are increasingly targeted by sophisticated cyber threats, demanding anomaly detection systems that are both accurate and interpretable. In this paper, we present a modular and explainable anomaly detection pipeline tailored for WiFi environments. Our approach is based on the Isolation Forest algorithm, enhanced with two novel extensions: DIFFI, a depth-based feature importance metric for instance-level interpretability, and BS-iForest, a statistically-informed sampling strategy that improves model stability. To enable rigorous and repeatable experimentation, we develop a synthetic dataset generator that simulates realistic WiFi traffic with controllable anomalies, avoiding the legal and ethical challenges associated with real-world data. We benchmark our system against widely used unsupervised methods, including One-Class SVM, Local Outlier Factor, and Autoencoders. Experimental results show that our enhanced framework achieves state-of-the-art detection performance.

The state of health of workers is becoming increasingly important both for the performance of the company and for the lives of the workers. This paper focuses on the experiences of Antur SRL. This company implemented a fiveyear protocol (2020-2024) with the aim of improving the wellbeing of employees and the performance of the company. This was achieved by integrating a series of tailored nutritional strategies, postural exercises, music therapy and stress reduction techniques into the workplace. The project involved over 12, 000 employees from 40 companies across a range of industries and roles. Data was collected through bio-impedance analysis (BIA) and detailed medical and nutritional histories. A Self-Organising Map (SOM) network was then used to cluster employees and analyse health trends over time. The results showed significant improvements, including reduced absenteeism, increased productivity and improved environmental, social and governance (ESG) sustainability, therefore underlining the value of comprehensive workplace health initiatives.

Transitive closure computation is a fundamental operation in graph theory with applications in various domains. However, the increasing size and complexity of real-world graphs make traditional algorithms inefficient, especially when dealing with large datasets. This paper investigates the optimisation of transitive closure algorithms for high performance computing (HPC) applications. We implement and compare three different methods for computing the adjacency matrix of the transitive closure, based on three different Python libraries (NetworkX, PyTorch and NumPy). Our approach is benchmarked on seven real-world datasets of varying size and density to evaluate performance and scalability. The results show that NumPy achieves the best performance for large and dense graphs. The paper concludes with a discussion of the potential benefits of algorithmic optimization in HPC and security.

In Data Science, data modeling (including relational databases and big data) has traditionally used Entity-Relationship (ER) diagrams to represent structural characteristics of data. However, ER diagrams lack the capability to capture the flow and transformation of data through analytic pipelines, which are essential to manage modern data science workflows. On the other hand, flowcharts have been used for decades to describe the processing. Based on this motivation, this paper provides a historical perspective identifying the limitations of employing ER diagrams and Data Flow Diagrams (DFDs), separately, emphasizing the need to integrate both solutions. We examine established diagram notations and design models, including traditional models such as UML, ER, DFD, BPMN, and FLOWER. Our literature analysis suggests that integrative diagram approaches can provide a more intuitive and comprehensive understanding of data collection, data integration, and data transformation for big data analytics in the future.

In this research paper, we explore the essential role of High Performance Computing (HPC) in the current techno-logical era, highlighting its extensive use in various sectors, while also considering growing alarm over its environmental footprint. High performance computing systems are essential for managing large data sets and solving complex challenges. However, their significant contribution to escalating energy consumption and carbon emissions in the information and communication technology (ICT) sector cannot be ignored. Our study identifies the increasing energy demands and environmental challenges associated with HPC activities, including resource use, electrical waste and greenhouse gas emissions. It highlights the importance of understanding these environmental impacts in detail. It also contributes to the ongoing dialogue on sustainable computing, promoting a harmonious future where technological progress and environmental sustainability can coexist in unison.

The COVID-19 pandemic has dramatically transformed healthcare systems globally, therefore improving health information technology sector. From the moment that pandemic has broken out, the use of information and communication technologies (ICT) has become absolutely necessary for the continuation of healthcare services. Furthermore, data digitization has enabled the extraction of meaningful insights through big data analytics. E-Health, encompassing a wide range of ICTs used in healthcare, has become a critical component in addressing the challenges posed by the pandemic. The main goal of this paper is to examine the impact of COVID-19 on health information technology and explores the rapid growth of e-Health and big data-driven innovation in healthcare processes. Through the analysis of the major tools, techniques, and innovative processes that have emerged in response to the pandemic, this paper has the aim of highlight their potential to improve system efficiency and enhance citizen health. We explore the current state of e-Health and big data in healthcare and discuss the future implications of these technologies for the sector. Our analysis underscores the need for continued investments in health information technology and highlights the role of policymakers, healthcare providers, and researchers in fostering innovation and driving positive change in the sector.

The coronavirus disease pandemic which broke out in 2019 heavily affected world wide health systems which found themselves unprepared for managing this wide-ranging event and were therefore forced to reorganize and review the entire system management. In this scenario, the use of information technology in health has played a fundamental role; in fact, e-Health, which indicates all the information and communication technologies (ICT) supporting the health system, has allowed not only the continuation of activities otherwise not executable, but has also left a strong legacy both to the health system in terms of improvements in efficiency and effectiveness and towards the health of the citizen. Covid-19 has pushed towards a rapid growth of e-Health by modifying the operation of the health system, deeply changing the vision of management processes, and favoring data digitization from which meaningful information and insights can be extracted. The aim of this article is to analyze the impact of Covid on the acceleration of the digital transformation of the healthcare system, the innovative processes, the tools used and the best adapted Big Data techniques.