Background/Objectives: This study aimed to investigate the skating determinants and differences between male and female bandy players in the spatiotemporal variables during acceleration and maximum sprint skating velocity. Methods: Seventy-four female bandy players (age: 18.9 ± 4.1 years; height: 1.67 ± 0.06 m; body mass: 63.2 ± 7.4 kg; training experience: 13.4 ± 3.9 yrs.; and 26 elite and 48 junior elite) and 111 male bandy players (age: 20.7 ± 5.0 years; height: 1.80 ± 0.05 m; body mass: 76.4 ± 8.4 kg; training experience: 13.8 ± 5.0 yrs.; and 47 elite and 66 junior elite players) performed linear sprint skating over 80 m. Split times were measured every ten metres by photocells to calculate velocities for each step and spatiotemporal skating variables (glide times and length, step length, and frequency) by IMUs attached to the skates. The first six steps (acceleration phase), the six steps at the highest velocity (maximal speed phase), and the average of all steps were used for analysing glide-by-glide spatiotemporal variables. Results: These revealed that male players exhibited higher acceleration and maximal skating velocity than female players. A higher acceleration in men was accompanied by shorter gliding time, longer step length, and higher step frequency. When skating at maximal speed, male players had a longer step length and gliding time and length. The sub-group analysis revealed that step frequency did not correlate with skating velocity, acceleration, or maximal speed phases. On the other hand, glide and step lengths significantly correlated with skating velocity in both phases (r ≥ 0.60). Conclusions: In general, for faster skating in bandy, it is generally better to prioritise glide and step length than stride frequency. Hence, players should be encouraged to stay low and have more knee flexion to enable a longer extension length and, therefore, a longer path and more horizontal direction of applied force to enhance their acceleration ability.

The increasing demand for lithium-ion batteries (LIBs) and their limited lifespan emphasize the urgent need for sustainable recycling strategies. This study investigates the application of tetrabutylphosphonium-based ionic liquids (ILs) as alternative leaching agents for recovering critical metals, Li(I), Co(II), Ni(II), and Mn(II), from spent NMC cathode materials. Initial screening experiments evaluated the leaching efficiencies of nine tetrabutylphosphonium-based ILs for Co(II), Ni(II), Mn(II), and Li(I), revealing distinct metal dissolution behaviors. Three ILs containing HSO4−, EDTA2−, and DTPA3− anions exhibited the highest leaching performance and were selected for further optimization. Key leaching parameters, including IL and acid concentrations, temperature, time, and solid-to-liquid ratio, were systematically adjusted, achieving leaching efficiencies exceeding 90%. Among the tested systems, [TBP][HSO4] enabled near-complete metal dissolution (~100%) even at room temperature. Furthermore, an aqueous biphasic system (ABS) was investigated utilizing [TBP][HSO4] in combination with ammonium sulfate, enabling the complete extraction of all metals into the salt-rich phase while leaving the IL phase metal-free and potentially suitable for reuse, indicating the feasibility of integrating leaching and extraction into a continuous, interconnected process. This approach represents a promising step forward in LIB recycling, highlighting the potential for sustainable and efficient integration of leaching and extraction within established hydrometallurgical frameworks.

Future vehicles and infrastructure will rely on data from external entities such as other vehicles via V2X communication for safety-critical applications. Malicious manipulation of this data can lead to safety incidents. Earlier works proposed a trust assessment framework (TAF) to allow a vehicle or infrastructure node to assess whether it can trust the data it received. Using subjective logic, a TAF can calculate trust opinions for the trustworthiness of the data based on different types of evidence obtained from diverse trust sources. One particular challenge in trust assessment is the appropriate quantification of this evidence. In this paper, we introduce different quantification methods that transform evidence into appropriate subjective logic opinions. We suggest quantification methods for different types of evidence: security reports, misbehavior detection reports, intrusion detection system alerts, GNSS spoofing scores, and system integrity reports. Our evaluations in a smart traffic light system scenario show that the TAF detects attacks with an accuracy greater than 96% and intersection throughput increased by 42% while maintaining safety and security, when using our proposed quantification methods.

Vehicular communication via V2X networks increases road safety, but is vulnerable to data manipulation which can lead to serious incidents. Existing security systems, such as misbehavior detection systems, have limitations in detecting and mitigating such threats. To address these challenges, we have implemented a software prototype of a Trust Assessment Framework (TAF) that assesses the trustworthiness of received V2X data by integrating evidence from multiple trust sources. This interactive demonstration illustrates the quantification of trust for a smart traffic light system application. We demonstrate the impact of varying evidence coming from a misbehavior detection system and a security report generator on the trust assessment process. We also showcase internal processing steps within our TAF when receiving new evidence, up to and including the eventual decision making on the trustworthiness of the received V2X data.

This paper addresses the challenge of analyzing CVs to parse their content into structured formats suitable for further processing and analysis. The proposed solution processes CVs provided as images or PDFs, handling diverse input formats, including free-form, multi-language, non-standardized layouts, and highly structured documents. Various heuristic approaches are employed for layout analysis, complemented by lightweight language models for extracting information. While multimodal models demonstrate strong performance, their cost and deployment complexity remain significant barriers. This study explores alternative methods optimized for computational efficiency, processing accuracy, and easier deployment. A comparative analysis of approaches is conducted on a standard dataset containing CVs from diverse clients and job roles, ranging from entry-level to specialized positions in various domains. The findings highlight the potential of these tailored, efficient solutions for scalable and secure CV parsing.

The Vehicle Routing Problem (VRP) is among the most complex optimization problems. Practical solutions require addressing real-world constraints such as time windows, vehicle capacities, delivery restrictions, driver working hours, and heterogeneous vehicle fleets. Solutions are often implemented in two stages: the first involves clustering customers, while the second focuses on incremental routing of these clusters to reduce complexity and improve solution control and explainability. However, the second stage heavily depends on the quality of the first, and clustering methods vary depending on client requirements. This paper explores various clustering methods and their impact on the final routing results, with a focus on real-world examples. The study includes diverse client scenarios, ranging from small-scale distribution systems with a limited number of customers to large-scale operations managing more than thousand of deliveries daily, covering both small and large orders. From fixed clustering and geographic partitioning to dynamic clustering algorithms and hybrid approaches, the advantages and limitations of each method are analyzed. The findings aim to provide actionable insights into selecting clustering methods that align with specific use cases, ensuring enhanced efficiency and adaptability in practical applications.

Texas Instruments development kits have a wide application in practical and scientific experiments due to their small size, processing power, available booster packs, and compatibility with different environments. The most popular integrated development environments for programming these development kits are Energia and Code Composer Studio. Unfortunately, there are no existing studies that compare the benefits and drawbacks of these environments and their performances. Conversely, the performances of the FreeRTOS environment are well-explored, making it a suitable baseline for embedded systems execution. In this paper, we performed the experimental evaluation of the performance of Texas Instruments MSP-EXP432P401R when using Energia, Code Composer Studio, and FreeRTOS for program execution. Three different sorting algorithms (bubble sort, radix sort, merge sort) and three different search algorithms (binary search, random search, linear search) were used for this purpose. The results show that Energia sorting algorithms outperform other environments with a maximum of 400 elements. On the other hand, FreeRTOS search algorithms far outperform other environments with a maximum of $\mathbf{2 5 5, 0 0 0}$ elements (whereas this maximum was $\mathbf{1 0, 0 0 0}$ elements for other environments). Code Composer Studio resulted in the largest processing time, which indicates that the lowlevel registry editing performed in this environment leads to significant performance issues.

The blind and visually impaired group cannot use most of the cutting-edge technology that usually conveys information visually through different kinds of displays. Different solutions can help overcome this obstacle, such as the usage of sound output and tactile displays that use the Braille alphabet composed of mechanically raised dots. However, there is a considerable amount of visually impaired persons who cannot read Braille and an even larger amount of persons without visual impairment. This paper presents an IoT-based system that uses the Arduino Uno WiFi development board for reading Braille input from a $4 \times 4$ push button matrix, two letters at a time. The system uses the $32 \times 8$ matrix display to show the translated basic alphabet output that can be read by sighted users or to show the Braille alphabet output. It offers a quick way for the visually impaired to convey information to sighted people by typing Braille input with both hands simultaneously. The proposed system will be used to educate sighted individuals about the Braille alphabet and help reduce their learning time. It can also be used as a quick translator of Braille for sighted individuals who wish to read written Braille documents.

Warehouse Management Systems (WMS) employ advanced optimization techniques to enhance efficiency and streamline processes, from inventory positioning to order picking and packing. Among these, order picking represents the most time-consuming and resourceintensive operation. This paper presents a novel approach for monitoring worker efficiency in warehouses, focusing on estimating the complexity and time required for order picking. A variety of factors influence these estimates, including item location, quantity, dimensions and weight of items, picking sequence, and whether the location is in the stock or picking zone. Accurate estimation enables effective daily work planning, real-time monitoring of worker productivity, and overall warehouse efficiency. The proposed approach has been tested in real-world warehouse environments, demonstrating its practical applicability and potential to significantly improve worker performance, resource allocation, and operational management.

Computer games can be used not only for entertainment but also for education. Embedded systems can be used to improve the gamified learning process by making the interaction with intended users more interesting. Texas Instruments development kits with booster plug-in modules can improve the outcomes of gamified learning. However, there is a lack of studies that explore the benefits and drawbacks of different input methods for gamified learning purposes. In this paper, a snake game was developed on the Texas Instruments MSP-EXP432P401R development kit that uses the analog joystick of the BOSTXL-EDUMKII plug-in module for controlling the snake. An experimental usability study was conducted on 61 3rd year university students, comparing the analog joystick to the computer keyboard, computer mouse, and mobile touchscreen input methods. The achieved results showed that the majority of students preferred the original computer keyboard input method and that more than half of the participants preferred the 90 -degree rotation of the snake compared to the 360 degree analog joystick. However, the analog joystick improved the gaming experience by 63.6 %, and many students made positive comments about its usability in general, indicating that its application for gamified learning may be possible for other types of games.

The impressive results achieved by language recognition using a generative pre-trained transformer have led to divided opinions on whether or not the Turing test has finally been passed. After understanding the working principles of the GPT programs, it was remarked that the tokenization concept, used by GPT, resulted in the loss of the word-to-letter relationship. Through about 36 specially prepared anagrams with a description of a term in a verse in the languages of the South Slavs, it was shown that ChatGPT and similar programs are far more capable of understanding the semantic connection between words and allusions than in performing the relatively simple task of searching for an adequate word from the offered letters.

The single-diode model equation of a photovoltaic (PV) panel is a nonlinear equation that relates the current and voltage of the panel. During operation, the PV panel can be at any point along the $I-V$ characteristic defined by this equation. Catalogue data generally provide current and voltage values at three points: short-circuit, opencircuit, and maximum power under standard test conditions (STC) and nominal operating conditions (NOCT). For the purpose of PV plant modelling, the parameters $I_{\text{ph}}, I_{0}, V_{\mathrm{t}}$, $R_{\mathrm{s}}$, and $R_{\text{sh}}$ are typically estimated from catalogue data using various methods. The estimated parameters enable the determination of an $I-V$ curve that typically aligns well with measurements under real-world conditions, provided the panels meet their specified characteristics. However, due to usage or improper storage, panels may degrade, leading to changes in these parameters and deviations from those estimated from catalogue data. Existing methods for capturing $I-V$ curves often involve measuring currentvoltage pairs at numerous points, making them slow and hardware-intensive. This paper investigates the estimation of real panel parameters using current and voltage at just four points on the $I-V$ curve. The results show good agreement between the estimated parameters and the $I-V$ curves derived from them.

SUMMARY The aim of this study was to examine the effect of the lipid parameter non-high-density lipoprotein cholesterol (non-HDL-C) on the occurrence of major cardiovascular event (MACE) in patients after first-time ST-elevation myocardial infarction (STEMI) treated with primary percutaneous intervention (pPCI) and implantation of drug-eluting stent (DES). Seventy-eight patients (54 male and 24 female, median age 58.62±11.14 years) with the diagnosis of first-time STEMI who were treated with pPCI with DES implantation in the period from January 2018 until January 2020 were included in the study. Patients were followed for two years of the intervention for the occurrence of MACE and its association with baseline non-HDL-C, as well as total cholesterol, LDL-C, HDL-C and triglycerides. During 2-year follow-up, 20 (25.6%) patients had MACE. There was no significant difference in baseline parameters such as age, hypertension, presence of diabetes mellitus, and post-interventional use of statin therapy between patients with and without MACE. The levels of baseline lipid parameters were significantly higher in patients who experienced MACE, as follows: total cholesterol (p=0.009), LDL-C (p=0.028) and non-HDL-C (p=0.007). Pearson χ2-test showed that both non-HDL-C and LDL-C were significant predictors of MACE occurrence during 2-year follow-up, but non-HDL-C had a more significant correlation than LDL-C (p=0.007 vs. p=0.028). Our initial report shows that baseline non-HDL-C was a more significant predictor of the occurrence of MACE after first-time STEMI than LDL-C, which reflects the importance of the residual risk of MACE occurrence while enabling identification and close monitoring of high-risk patients.

The paper examines the impact of budgetary accounting organization on the perception of corruption in the public sector, focusing on three key independent variables: the financial reporting framework, the accounting basis, and the level of independence of state auditing. The Corruption Perceptions Index (CPI), which measures the perceived level of corruption in the public sector, is used as an indicator of the dependent variable. The study includes data from 89 countries. For statistical analysis, categorical independent variables were encoded using the one-hot encoding method. Statistical tests were applied to assess the correlation between the independent variables and the CPI. The results show variations in correlation depending on the combination of financial reporting factors, the regulatory framework, and the quality of state auditing. The obtained results of multiple linear regression indicate that the model has a statistically significant impact on the CPI (p = 0.0217) and explains 21% of its variability. Keywords: public sector accounting, budgetary accounting organization, perception of corruption, public financial management reform.