The study explores the importance of game progression patterns in sports, particularly in terms of their role in increasing player excitement, maintaining observer interest, and optimizing game design. Using quantitative methods, the research explores independent variables, such as game progress patterns—balanced, one-sided, and seesaw—and their effects on dependent variables like engagement, satisfaction, and entertainment levels. Data from sports events between 2010 and 2023 were analyzed to validate five hypotheses: the importance of balanced games in maintaining player excitement, the role of one-sided games in diminishing spectator engagement, the ability of seesaw games to sustain long-term interest, the universality of game progression patterns across sports, and the optimization of game design using these patterns. It shows a strong correlation of the dynamics between balanced and seesaw game situations with high levels of excitement and engagement. It draws the general patterns in all sports and shows that the current patterns do have some gaps in terms of cultural and demographic representation, and the long-term effects of these patterns. Further research should be focused on new technologies and bigger datasets to further refine our understanding of game progression patterns and their global applicability.
This paper discusses the evolution and application of intelligent systems in extracting ocean internal wave parameters, with special emphasis on the advancements enabled by remote sensing technologies. It investigates historical development, their role in improving data accuracy, efficiency in measurement, challenges in current methodologies, and potential improvements for future systems. The study, using qualitative research methods such as literature reviews and case studies, highlights advancements in intelligent systems, especially in realtime data processing and algorithmic enhancements. Findings show that there is significant improvement in the accuracy of data and efficiency in measurement, which is driven by modern remote sensing technologies and adaptive algorithms. However, challenges persist in integrating diverse data sources and adapting to dynamic oceanographic conditions. This paper concludes with the proposal for the incorporation of machine learning techniques and the development of adaptive algorithms to solve the problems presented. This work contributes further to the knowledge of intelligent systems' transformative potential, especially in oceanographic studies, for parameter extraction of ocean internal waves.
Increased concern about data security is now experienced with cloud computing, mainly on the aspect of data confidentiality of the CSP. This research examines the use of cryptographic algorithms such as DES and AES in cloud computing to alleviate the security risk. Simulations in Matlab R2009a are conducted based on a quantitative approach for evaluating the encryption speed, data security, and the resource usage of both algorithms. Findings show that although DES improves data security, the short key length and susceptibility to cryptographic attacks make it less efficient for large-scale cloud applications. On the other hand, AES has better security and scalability but is hindered by its computational intensity. The comparative analysis suggests that AES is a better choice for cloud security if optimization strategies overcome its resource-intensive nature. It, therefore, contributes to understanding solutions in cryptographic secure cloud computing and emphasizes the need for tailored encryption standards that respond to specific cloud security needs. Future research should further address emerging cryptographic technologies and real-world implications for diverse cloud environments.
This research works on the optimization of pairwise testing techniques for cloud application development by focusing on their effectiveness in dynamic cloud environments. The sub-research questions addressed by the study are five, namely: the effectiveness of existing combinatorial testing techniques, challenges in the generation of minimal pairwise test sets, the scalability of Testing-as-a-Service (TaaS), the comparison of existing pairwise techniques, such as IPOG, AETG, MIPOG, and ACO, and the feasibility of optimized pairwise test cases. This will quantitatively evaluate the factors using data from 2015-2023, making use of statistical analyses in hypothesis testing to check the hypotheses set forth for testing efficiency, fault detection, and scalability. The results validate the need for optimization of present combinatorial methods, reveal advancements in algorithms used for test set generation, and emphasize the contribution of TaaS towards the scalability. The study also sheds comparative light on existing techniques and displays benefits in optimized test cases to the detection of faults and testing efficiency. Findings fill in literature gaps by suggesting a tailored approach toward cloud testing and paving way for future innovations in the realm of efficient methodologies related to testing cloud applications.
This paper develops and evaluates a microphone-free GUI-based speech recognition system capable of high accurate speech-to-text conversion without the use of any device. It explores in further detail the interplay amongst user interface design, techniques for noise removal, and database management in terms of enhancing the usability and performance. Five hypotheses are tested: the influence of GUI design on user interaction, the efficiency of noise removal by cross-correlation, the accuracy of microphone-free recognition, the role of database management in system scalability, and the comparative efficiency of the proposed system against existing technologies. A quantitative methodology is used with controlled experiments to analyze data from diverse users and environments. The results show that the GUI needs to be intuitive; cross-correlation is the efficient method for noise cancellation; and it is quite feasible to achieve recognition accuracies comparable to that using a microphone. Good management of the database ensures greater scalability and real-time processing. Finally, the system has an accuracy and efficiency compared with the existing technology in the field, thus providing great advancements in speech recognition. The study also considers limitations in environmental diversity and data availability, proposing further research into improving noise removal techniques and database strategies for broader applicability.