This study presents a log-based learning analytics pipeline for quantifying user engagement in Moodle, demonstrating how event log data can be transformed into analyzable interaction patterns through extraction, anonymization, categorization, and statistical modeling. The approach applies distributional testing using chi-square statistics and Cramér’s V to identify structural differences in user activity. As a case study, the method was implemented in an English for Specific Purposes (ESP) course, comparing a control group following a traditional LMS configuration (N = 40) and an experimental group using a gamified Moodle environment (N = 40). Results indicated that the gamified configuration generated significantly higher frequencies of system-level and assessment-related interactions, as well as more sustained activity across the instructional period, while the control group relied primarily on static content access and exhibited declining participation over time. Engagement was operationalized through event frequencies, capturing observable behavioral differences rather than cognitive or learning outcomes. Beyond the educational setting, the study illustrates how reproducible event-log analytics can be used to detect behavioral shifts in technology-supported environments, offering a methodological template that is potentially transferable to similar contexts in applied computer science.
Data normalisation is a critical preprocessing step for machine learning, especially for high-dimensional, small-sample datasets such as those encountered in microarray analysis. This study comprehensively investigates the impact of eight distinct normalisation methods, including Vector Normalisation (L2 Normalisation), Quantile Normalisation (Gaussian and Uniform), Maximum Absolute Scaling, Z-score, Min-Max, Power Transformation, and Robust Scaling, on the classification performance of microarray data. Using an Extreme Learning Machine (ELM) as the classifier, the research evaluates performance across three leukaemia datasets with varying numbers of classes, namely 2, 3 and 4 classes. The results demonstrate that Vector Normalisation consistently outperforms all other methods. In the 2-class scenario, it achieved the highest accuracy (87.50%) and F1-score (87.08%). Although unnormalised data showed a similar average accuracy, Vector Normalisation proved empirically superior due to its significantly lower standard deviation, which is10.89, indicating a more stable and reproducible model. This stability became even more critical in the 3-class scenario, where overall performance declined, but Vector Normalisation still led with 61.67% accuracy and 52.18% F1-score, while other methods, particularly simple scaling techniques like Min-Max, showed a sharp drop and extreme instability. In the 4-class scenario, a performance rebound occurred, and Vector Normalisation maintained its top position, achieving 72.92% accuracy and an F1-score of 66.17%. The findings confirm that Vector Normalisation is the most effective normalisation method for microarray data, delivering both high performance and superior stability across varying levels of class complexity.
Additive manufacturing is widely used for prototyping and producing functional parts. With the growing capabilities of industrial robots, robotic additive manufacturing is becoming an attractive alternative to conventional 3D printing. Robots enable the fabrication of large-scale, structurally complex, and non-planar components that exceed the limitations of traditional printing. However, the flexibility of robotic systems comes with increased complexity in process control – particularly in the selection of appropriate printing parameters, which is critical for ensuring the quality and stability of the printed parts. This paper addresses the need for a systematic approach to parameter selection in robotic 3D printing to ensure optimal process performance and part quality. First, control software to manage and execute the printing process was developed. Secondly, the impact of changes in the industrial robot TCP's velocity and orientation on the quality of manufactured parts was investigated. Furthermore, to optimize the selection of process parameters, the TOPSIS multi-criteria decision-making method was employed. The presented approach provides a methodology for parameter selection and optimization in robotic 3D printing.
The paper presents a new approach to optimising cluster structure by selecting servers to meet specified performance requirements while minimising costs. Modern applications are placing increasing demands on performance and are critical elements of business operations. As a result, the operation of such applications increasingly relies on server clusters. Selecting the type and number of servers is not a trivial task. The problem is further complicated by the widespread use of layered application architectures, which means that different hardware solutions may be optimal for handling different layers. The article proposes a technique that uses the Tabu Search heuristic in conjunction with a BCMP-based application model. An optimisation algorithm for the cluster structure is presented in two versions: minimising the solution cost while meeting performance requirements, and maximising performance while meeting budget constraints.
Water Vortex Hydro Turbines are a promising renewable energy solution for ultra-low-head applications, with performance strongly influenced by blade geometry in converting vortex-induced momentum into mechanical energy. This study numerically investigates the hydrodynamic characteristics including velocity distribution, air-core formation, and pressure gradient to determine the optimal blade angle using transient Computational Fluid Dynamics (CFD) with the Realizable k-ε turbulence model. A six-bladed turbine was analysed at blade angles of 75°, 90°, and 105° under a constant inlet velocity of 1 m/s. The results demonstrate that the 90° configuration delivers the best overall performance, with the maximum velocity increasing from 3.096 m/s (75°) to 4.376 m/s (90°), representing an improvement of approximately 41.4% and indicating a significantly stronger and more stable vortex structure. Although the 105° configuration reaches the highest peak velocity of 4.657 m/s, severe flow separation reduces flow stability and limits effective energy transfer. In terms of pressure, the 90° configuration produces the most favourable and symmetric gradient, with a maximum of 5777.397 Pa and a minimum of 3.154 Pa, compared to the more distorted distributions observed in the 75° and 105° cases (5741.087 Pa and 5892.066 Pa, respectively). Additionally, the 90° configuration generates a more compact and stable air-core, reducing hydraulic losses and enhancing fluid–blade interaction. Overall, the 90° blade angle provides the optimal balance between velocity magnitude, vortex stability, and pressure distribution, making it the most effective configuration for maximizing energy conversion in ultra-low-head water vortex hydro turbines.
