Topic modeling is a critical tool for understanding the thematic structures of unstructured text data, particularly in specialized domains like the dark web. This study compares the effectiveness of large language models (LLMs) and traditional topic modeling techniques in analyzing dark web Q&A forums, which are characterized by short, informal, and context-specific posts. We evaluate two LLMs—GPT and Gemini—against traditional methods, TF-IDF (scikit-learn) and Latent Dirichlet Allocation (LDA, Gensim), in their ability to generate meaningful and coherent topics. Additionally, we explore the impact of topic granularity by comparing GPT single-word topics with two-word topics. Using semantic similarity and Levenshtein distance metrics, we quantify the alignment and divergence between the topic representations produced by these methods. Our findings demonstrate that LLMs consistently outperform traditional methods in capturing contextually relevant themes, such as "scam," "bitcoin," and "hacking," while traditional techniques often produce generic or non-topical terms like "questions" and "know." Semantic similarity and Levenshtein distance metrics further highlight the strong alignment between LLMs and the divergence between LLMs and traditional methods. The comparison between single-word and two-word topics reveals that while two-word labels offer additional nuance, their benefits are limited in the context of dark web forums, where concise posts often make single-word topics sufficient. These results underscore the importance of selecting appropriate topic modeling techniques based on the characteristics of the text and the requirements of the analysis. This study highlights their potential as a valuable tool for analyzing complex and unstructured text data, particularly in specialized domains like the dark web.

High-performance computing (HPC) in data centers increases energy use and operational costs. Therefore, it is necessary to efficiently manage resources for the sustainability of and reduction in the carbon footprint. This research analyzes and optimizes ENEA HPC data centers, particularly the CRESCO6 cluster. The study starts by gathering and cleaning extensive datasets consisting of job schedules, environmental conditions, cooling systems, and sensors. Descriptive statistics accompanied with visualizations provide deep insight into collated data. Inferential statistics are then used to investigate relationships between various operational variables. Finally, machine learning models predict the average hot-aisle temperature based on cooling parameters, which can be used to determine optimal cooling settings. Furthermore, idle periods for computing nodes are analyzed to estimate wasted energy, as well as for evaluating the effect that idle node shutdown will have on the thermal characteristics of the data center under consideration. It closes with a discussion on how statistical and machine learning techniques can improve operations in a data center by focusing on important variables that determine consumption patterns.

Mobile Instant Messaging applications noted a growing number of active users recently. Although having millions of users, the previous research results have shown usability flaws in this type of mobile applications. Difficulties when performing tasks, poor design of user interfaces, or lack of information about privacy and security methods were some of the identified flaws. Consequently, a collection of usability recommendations was proposed. The goal of these recommendations is to improve the experience in the use of these mobile applications and, thus, enhancing the usability of this kind of apps. These recommendations were taken into account during the development process of a new instant messaging application. Thus, in this study, the authors present the resulting application, its characteristics, how usability recommendations were covered through design and development phases, and a preliminary usability evaluation performed by conducting two research methods: KLM and Mobile Heuristic Evaluation. Both methods ranked the prototype at top positions (i.e. good usability results) when compared with the results obtained in previous studies about other existing instant messaging applications.