The dynamic development of the entertainment market entails the need to develop new methods enabling the application of current scientific achievements.
Motion capture is one of the cutting-edge technologies that plays a key role in movement and trajectory computer mapping. The use of optical systems allows one to obtain highly precise motion data that is often applied in computer animations. This study aimed to define the research methodology proposed to analyze the movement of remotely controlled cars utilizing developed gap filling algorithm, a part of post-processing, for creating realistic vehicle animation.
On a specially prepared model, six various types of movements were recorded, such as: driving straight line forward, driving straight line backwards, driving on a curve to the left, driving on a curve to the right and driving around a roundabout on both sides. These movements were recorded using a VICON passive motion capture system. As a result, three-dimensional models of vehicles were created that were further post-processed, mainly by filling in the gaps in the trajectories. The case study highlighted problems such as missing points at the beginning and end of the recordings. Therefore, algorithm was developed to solve the above-mentioned problem and allowed for obtaining an accurate movement trajectory throughout the entire route. Realistic animations were created from the prepared data.
The preliminary studies allowed one for the verification of the research method and implemented algorithm for obtaining animations reflecting accurate movements.

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Satellite imagery plays an important role in detecting algal blooms because of its ability to cover larger geographical regions. Excess growth of Sea surface algae, characterized by the presence of Chlorophyll-a (Chl-a), is considered to be harmful. The detection of algal growth at an earlier stage may prevent hazardous effects on the aquatic environment. Semantic segmentation of algal blooms is helpful in the quantization of algal blooms. A rule-based semantic segmentation approach for the segregation of sea surface algal blooms is proposed. Bloom concentrations are classified into three different concentrations, namely, low, medium, and high. The chl_nn band in the Sentinel-3 satellite images is used for experimentation. The chl_nn band has exclusive details of the presence of chlorophyll concentrations. A dataset is proposed for the semantic segmentation of algal blooms. The devised rule-based semantic segmentation approach has produced an average accuracy of 98%. A set of 100 images is randomly selected for testing. The tests are repeated on 5 different image sets. The results are validated by the pixel comparison method. The proposed work is compared with other relevant works. The Arabian Sea near the coastal districts of Udupi and Mangaluru has been considered as the area of study. The methodology can be adapted to monitor the life cycle of blooms and their hazardous effects on aquatic life.

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This article addresses the challenges of fraud in card-based financial systems and proposes effective detection and prevention strategies. By leveraging recent data analytics and real-time monitoring, the study aims to enhance transaction security and integrity. The authors review existing fraud detection methodologies, emerging trends, and the evolving tactics of fraudsters, emphasizing the importance of collaboration among financial institutions, regulatory agencies, and technology providers. Our proposed solution is an ensemble model combining Bidirectional Gated Recurrent Unit (BiGRU) and Bidirectional Long Short-Term Memory (BiLSTM) networks, designed to capture complex transactional patterns more effectively. Comparative analysis of six machine learning classifiers—AdaBoost, Naïve Bayes, Decision Tree, Logistic Regression, Random Forest, and Voting—demonstrates that our BiLSTM-BiGRU ensemble model outperforms traditional methods, achieving a fraud detection performance score of 89.22%. This highlights the advanced deep learning model's superior ability to enhance the robustness and reliability of fraud detection systems.

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The purpose of this study is to examine the suitability of machine learning (ML) techniques for predicting students’ performance. By analyzing various ML algorithms, the authors assess the accuracy and reliability of these approaches, considering factors such as data quality, feature selection, and model complexity. The findings indicate that certain ML methods are more effective for student performance forecasting, emphasizing the need for a deliberate evaluation of these factors. This study provides significant contributions to the field of education and reinforces the growing use of ML in decision-making and student performance prediction.

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In the present work, an automatic R410A refrigerant charger for residential air conditioners is fabricated and tested. The charger operates on the throttling principle and uses the suction pressure of the compressor to estimate the refrigerant charge level. This helps to reduce the risk of compressor damage and ensures the correct composition ratio of R410A refrigerant when charged into the machine. The charging process is controlled by the LabVIEW platform, which provides adequate control and visualization of the charging process. The developed charger meets expectations in solving the technical problems encountered when charging R410A refrigerant for residential air conditioners. It is compact, portable and can be directly controlled through the LabVIEW interface, allowing real-time visualization of the charging process. The present work is expected to make a significant practical contribution, serving as a useful reference for the future manufacturing of compact portable equipment in the residential air conditioning field.

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This systematic literature review examines the role of ChatGPT in communication. ChatGPT's ability to imitate human-like interactions has broad implications in various sectors, such as education, healthcare, and customer service in the digital-based economy. The authors used a systematic and structured manuscript selection method in this research to collect and analyze literature on the use of ChatGPT in a communication context. A systematic literature review (SLR) method was used, involving an extensive search through the Scopus and Google Scholar databases with the keywords "ChatGPT" and "communication." Manuscript selection required strict inclusion and exclusion criteria. Of the 623 articles found, 30 were selected for further review. The research results show that using ChatGPT in communication has had both positive and negative impacts. Positive impacts involve increasing the efficiency and effectiveness of communications, especially in education, marketing, ethics, and health. However, challenges such as ethical considerations, the risk of plagiarism, and a limited understanding of context and emotional interactions were also identified. The use of ChatGPT in education, health, and various other fields has demonstrated great potential to improve communication processes, decision-making, and work efficiency. However, to ensure responsible and sustainable use, we must address specific ethical challenges and risks. This study provides a comprehensive overview of recent developments in using ChatGPT in communications, while also highlighting the practical and ethical implications that must be considered. With careful consideration of the advantages and limitations, ChatGPT in communications can significantly contribute to various fields.

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The main objective of the work was to develop a comprehensive model of energy consumption simulation of heavy duty vehicles using the VECTO simulation tool. The research issue was the impact of aerodynamic drag and rolling resistance on fuel consumption and emissions under various driving conditions described in four driving cycles: Urban Delivery, Regional Delivery, Urban, and Suburban. Each cycle differed in driving time, distance and average speed to represent different operational scenarios. The methodology involved defining vehicle parameters such as weight, aerodynamic coefficients and tyre rolling resistance. The main findings show that the impact of both aerodynamic drag and rolling resistance on fuel consumption can be efficiently modelled. It has been proven that the proposed modifications to aerodynamic drag and rolling resistance can reduce fuel consumption by more than 8%. The lowest fuel consumption was achieved in the Regional Delivery cycle, while the Urban cycle had the highest fuel consumption due to frequent vehicle stops. The results show that optimization of vehicle design and its performance can significantly improve energy efficiency and reduce emissions. A computational modelling tool such as VECTO can contribute to sustainable transport solutions and improve the efficiency of heavy duty vehicle.

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Cardiovascular disease (CVD) has become a significant contributor to premature deaths for many years in Fiji. CVD's late detection also significantly impacts annual deaths and casualties. Currently, Fiji lacks diagnosis tools to enable people to know their risk levels. In this paper, a machine learning mobile application was developed that can be easily accessible to the local population for early prediction of CVD risk. The design science approach was used to guide the development of the application. The design process involved identifying the problem and motivation, setting objectives, creating a machine-learning mobile application for medical record analysis, demonstrating the application to selected participants, evaluating its usability and the machine-learning model's performance, and communicating the findings. The results revealed that the proposed machine learning application achieved a high usability score of 87 on the System Usability Scale, indicating strong user-friendliness and adaptability. The machine learning model by random forest algorithm demonstrated the accuracy of 89% and was selected for implementation for CVD prediction in Fiji, as it outperformed other algorithms in the study: k-nearest neighbour, support vector machine, decision tree, and Naïve Bayes. The results highlight the effectiveness and user acceptance of the developed system in Fiji’s medical facilities for CVD prediction.

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The growth of Artificial Intelligence (AI) technologies is revolutionizing Human Resource (HR) practices, offering new opportunities for organizations to optimize their operations and better support for their workforce in an era defined by technological advancement. In this context, the emergence of industry 5.0 highlights human-centricity, resilience, and sustainability, promoting collaboration between humans and technology. This article conducts a bibliometric analysis to explore the intersection of AI and Human Resources Management (HRM), highlighting trends, research directions, and the evolving landscape of this thematic. Through performance analysis, social structure assessment, and thematic evolution examination, this study identifies key themes, emerging topics, and research trends. The findings underscore the transformative potential of AI in reshaping HRM and organizational dynamics, calling for more research and strategic applications of AI technologies to foster adaptive strategies and informed decision-making in the era of industry 5.0.

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Parkinson's Disease (PD) is a neurodegenerative disorder that impacts movement, speech, dexterity, and cognition. Clinical assessments primarily diagnose PD, but symptoms' variability often leads to misdiagnosis. This study examines ML algorithms to distinguish Healthy People (HP) from People with Parkinson's Disease (PPD). Data from 106 HP and 106 PPD participants, who underwent the Parkinson’s Disease Sleep Test (PDST), Hopkin’s Verbal Learning Test (HVLT), and Clock Drawing Test (CDT) from the Parkinson's Progression Markers Initiative (PPMI) were used. A custom HYBRID dataset was also created by integrating these 3 datasets. Various Machine Learning (ML) Classification Algorithms (CA) were also studied: Random Forest (RF), Naïve Bayes (NB), Support Vector Machine (SVM), and Logistic Regression (LR). Multiple feature sets: the first quartile (Q1: 25 % most important features), second quartile (Q2: 50 % most important features), third quartile (Q3: 75 % most important features), and fourth quartile (Q4: All 100 % features) were generated using various Feature Selection (FS) algorithms and ensemble mechanisms. Results showed that all the ML CA achieved over 73±8.4 % accuracy with individual datasets, while the proposed HYBRID dataset achieved a remarkable accuracy of 98±0.6 %. This study identified the optimal quantity of non-motor features, dataset, the best FS and CA in hierarchical approach for early PD diagnosis and also proved that PD may be diagnosed with great accuracy by analyzing non-motor PD parameters using ML algorithms. This suggests that extended data collection could serve as a digital biomarker for PD diagnosis in the future.

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This article presents the results of simulation research of a diesel engine for a light helicopter. The simulations were performed using the 1D software AVL Boost RT. The engine model includes elements such as cylinders, turbine, compressor, inlet and outlet valves, ambient environment definition, and fuel injection control strategy. The simulations aimed to evaluate the engine's response to step changes in the main rotor load, both increasing and decreasing power demands. Parameters analyzed included power deviation, torque, engine rotational speed, and stabilization time of the main rotor rotational speed. All tests were conducted using a single set of PI controller settings. The results demonstrate that these parameters are dependent on the magnitude of the step change in the main rotor load demand. The study compares the maximum engine rotational speed deviation from the nominal value for both increasing and decreasing main rotor load demands. The findings indicate that using PI regulator to control rotational speed in the diesel engine in a light helicopter significantly depends on the change in the load torque on the rotor.

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