This paper thoroughly assesses the ARM and RISC-V architectures in the context of high-performance computing (HPC). It includes an analysis of Docker and Kubernetes integration. Our study aims to evaluate and compare these systems’ performance, scalability, and practicality in a general context and then assess the impact they might have on special use cases, like HPC. ARM-based systems exhibited better performance and seamless integration with Docker and Kubernetes, underscoring their advanced development and effectiveness in managing high-performance computing workloads. On the other hand, despite their open-source architecture, RISC-V platforms presented considerable intricacy and difficulties in working with Kubernetes, which hurt their overall effectiveness and ease of management. The results of our study offer valuable insights into the practical consequences of implementing these architectures for HPC, highlighting ARM’s preparedness and the potential of RISC-V while acknowledging the increased complexity and significant trade-offs involved at this point.

In this paper, we propose a new algorithm, called FOCUSED (FOrecast Correction Using Successive prEDictions), for forecast correction of short-term wind speed predictions. We developed FOCUSED with the aim of improving the forecast of bora gusts, which frequently result in high-speed wind situations dangerous for traffic. The motivation arises from occasionally ambiguous results of the currently deployed decision support system, which aids traffic management in strong and gusty wind conditions at the coast of Croatia. The proposed correction algorithm uses characteristics of numerical weather prediction models to iteratively forecast the wind speed multiple times for the same future window. We use these iterative predictions as input features of the FOCUSED algorithm and get the corrected predictions as the output. We compared the proposed algorithm with artificial neural networks, random forests, support vector machines, and linear regression to demonstrate the superiority of the algorithm’s performance on a data set comprising five years of real data measurements at the Croatian bridge “Krk” and complementary historical forecasts by ALADIN (Aire Limitée Adaptation dynamique Développement InterNational) numerical weather prediction model.

Honeypots are vital security tools that enable cybersecurity specialists to scrutinize the tactics employed by attackers. By doing so, they can get valuable insights into existing and upcoming internet dangers. Cyber-attacks are in a perpetual evolution, increasingly sophisticated and tailored to exploit specific vulnerabilities. Hence, it is imperative for both organizations and individuals not only to consider but also implement methods to safeguard themselves against cyberattacks and malware. This paper assesses a specific group of honeypot systems by considering detection range, emulation accuracy, data quality, dependability, scalability, performance, extensibility, installation options, setup difficulty, and maintenance requirements. Furthermore, it provides conclusions regarding the relative effectiveness of diverse honeypot systems in different situations. As a part of the research, it analyzes the latest progress and innovations in honeypot development, including enhancements in machine learning, heightened automation, integration with other security tools, cloud-based honeypots, and deception technology by using simulated assaults to showcase the logging and recording capabilities of these honeypots in capturing attack information. After carefully analyzing each honeypot, each has unique advantages, disadvantages, and special characteristics.

This paper thoroughly assesses the ARM and RISC-V architectures in the context of High-Performance Computing (HPC). It includes an analysis of Docker, Kubernetes, and KVM virtualization integration. Our study aims to evaluate and compare these systems' performance, scalability, and practicality in a general context and then assess the impact that might have on special use cases, like HPC. ARM-based systems exhibited better performance and seamless integration with Docker and Kubernetes, underscoring their advanced development and effectiveness in managing high-performance computing workloads. On the other hand, despite their open-source architecture, RISC-V platforms presented considerable intricacy and difficulties in coordinating with Kubernetes, which hurt their overall effectiveness and ease of management. The results of our study offer valuable insights into the practical consequences of implementing these architectures for HPC, highlighting ARM's preparedness and the potential of RISC-V while acknowledging the increased complexity involved and significant trade-offs at this point.

Forecasting spare parts demand is challenging due to its intermittent, sparse, and highly irregular nature. Traditional inventory strategies, based on stable demand patterns, often lead to inefficiencies, including excess inventory and poor service performance. This study examines the impact of feature engineering combined with ABC–XYZ inventory segmentation on forecasting accuracy in a real-world industrial context. A biweekly forecasting framework was developed using six years (2019–2024) of transactional data from ERP and Field Service Management (FSM) systems of a forklift service company. Fifteen derived features capturing demand dynamics, intermittency, service behavior, and statistical structure were constructed and evaluated using Random Forest, XGBoost, and Support Vector Regression (SVR) models. The results show that restricting modeling to AY/BY inventory categories substantially improves predictive accuracy, reducing RMSE from >22 to <3 compared to full-SKU modeling. A reduced seven-feature set further lowers XGBoost’s RMSE to 2.51 (MAE = 2.14), achieving the best performance across all tested configurations on the 2024 hold-out period. The best-performing configuration achieves a Predicted-Demand Turnover Index (PDTI) of 44.13, compared with a baseline actual stock turnover of 2.78 (€65,944 actual demand/€23,721 historical average stock). PDTI is a theoretical scenario index; operationalizing it requires inventory-policy simulation under realistic constraints. These findings highlight that forecasting performance in intermittent-demand environments depends more on data representation and segmentation than on model selection alone. The study provides a reproducible, interpretable framework for integrating feature engineering and inventory segmentation into data-driven inventory management.

This paper addresses the challenges of improving the accuracy of low-cost particulate matter sensors (LCPMS) under high humidity conditions. Such weather conditions often lead to the overestimation of particle counts due to hygroscopic particle growth, resulting in a potential public concern, although most of the detected particles consist of just water. The paper presents an innovative design for an indicative air-quality measuring station that integrates the particulate matter sensor with a preconditioning subsystem designed to mitigate the impact of humidity. The preconditioning subsystem works by heating the incoming air, effectively reducing the relative humidity and preventing the hygroscopic growth of particles before they reach the sensor. To validate the effectiveness of this approach, parallel measurements were conducted using both preconditioned and non-preconditioned sensors over a period of 19 weeks. The data was analyzed to compare the performance of the sensors in terms of accuracy for PM1, PM2.5, and PM10 particles. The results demonstrated a significant improvement in measurement accuracy for the preconditioned sensor, especially in environments with high relative humidity. When the conditions were too severe and both sensors started measuring incorrect values, the preconditioned sensor measured values were closer to the actual values. Also, the period of measuring incorrect values was shorter with the preconditioned sensor. The results suggest that the implementation of air preconditioning subsystems in LCPMS can provide a cost-effective solution to overcome humidity-related inaccuracies, thereby improving the overall quality of measured air pollution data.