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This study explores the transformative potential of Quantum Neural Networks (QNNs) in revolutionizing teaching and learning through the lens of non-anthropomorphic intelligence. Unlike traditional AI systems that mimic human cognition, QNNs leverage quantum mechanical principles such as superposition and entanglement to create novel computational paradigms. These architectures offer exponential improvements in processing efficiency, scalability, and learning adaptability compared to classical neural networks. The paper examines key QNN architectures and their application in personalized education, adaptive learning systems, and complex problem-solving environments, highlighting their ability to support emergent learning behaviors that are not constrained by human cognitive limitations. Empirical findings suggest that QNNs exhibit superior training efficiency, generalization performance, and the capacity to dynamically adapt to complex educational data patterns. These qualities position QNNs as powerful tools for designing highly responsive and intelligent educational environments. However, challenges remain, including hardware limitations, algorithmic interpretability, and the lack of scalable quantum infrastructure. The paper addresses these issues while proposing a roadmap for integrating QNNs into future educational paradigms. Ultimately, this research underscores the potential of quantum AI to move beyond human-centric learning models and to establish intelligent, scalable, and innovative educational ecosystems that redefine the boundaries of teaching and learning in the post-digital era

The growing complexity of urban mobility, coupled with escalating environmental concerns, has prompted the need for innovative solutions to optimize traffic management. Intelligent Transportation Systems (ITS) have emerged as a transformative framework designed to enhance urban transportation efficiency, safety, and sustainability. This review investigates the integration of route optimization algorithms and environmental data within ITS, focusing on how these technologies can address the dual challenges of congestion and pollution in urban areas. The synergy between advanced algorithms and real-time environmental data, such as air quality, weather conditions, and noise pollution, can significantly improve traffic flow, reduce fuel consumption, and lower carbon emissions. The integration of machine learning (ML) and artificial intelligence (AI) further enhances the adaptive capabilities of ITS by predicting traffic patterns, optimizing routes, and mitigating environmental impacts. Additionally, the rise of smart cities powered by 5G and Internet of Things (IoT) technologies offers vast potential for more responsive and sustainable urban mobility systems. Despite its promising capabilities, the widespread implementation of ITS encounters challenges. These includes data accuracy, real-time processing, and privacy concerns. To overcome these obstacles, it is recommended that there should be an improve sensor technology and integrate data across various sources such as IoT, satellite imagery, and social media. Additionally, leveraging AI and machine learning for dynamic route optimization can lead to more efficient transportation networks. Investing in smart city infrastructure and promoting public-private partnerships for data sharing will help address regulatory and privacy issues. These steps will ensure the scalability and sustainability of ITS, driving the development of smarter and greener cities

This study investigates the impact of hyperparameter optimization on the performance of Random Forest classifiers for Human Activity Recognition (HAR), a critical component in wearable computing applications such as health monitoring, rehabilitation, and fitness tracking. A baseline Random Forest model, trained using default hyperparameters without optimization, was first established for reference. Subsequently, three optimization techniques, Grid Search Cross Validation, Randomized Search Cross Validation, and Bayesian Optimization using the Tree-Structured Parzen Estimator (TPE), were applied to fine-tune model parameters for improved predictive accuracy. The publicly available UCI-HAR dataset, which was collected from 30 individuals using a waist mounted smartphone to record human activities, served as the experimental platform. Hyperparameter optimization resulted in a 0.15 percent increase in classification accuracy, from 96.23 percent to 96.37 percent, and a 0.08 percent improvement in the Receiver Operating Characteristic Area Under the Curve (ROC-AUC), from 0.9976 to 0.9984, while precision, recall, and F1 score remained stable across models. However, Grid Search Cross-Validation and Randomized Search Cross-Validation significantly increased training times to 1197 seconds and 210 seconds, respectively, compared to 1 second for the baseline model. Bayesian Optimization maintained a model training time similar to the baseline but required 172 seconds for parameter selection, representing a moderate computational cost. These findings highlight the trade off between marginal performance improvements and computational efficiency, identifying Bayesian Optimization as the most practical and computationally efficient strategy for real time HAR applications.

The research examined the influence of cyber threats on the administration of students’ medical records at public secondary schools in Kaduna State, Nigeria. The research aimed to assess the influence of cyber risks on the administration of students’ medical data in public secondary schools in Kaduna State. This purpose was articulated as a research question and a hypothesis, respectively. The study used a survey research design. The study’s target population consists of 360 people from the Sabon-Gari Local Government Area in Kaduna State. Within the Sabon-Gari Local Government Area of Kaduna State, there are a total of 341 educators, 5 representatives from the ministry of education, and 14 principals. A total of 196 respondents were selected for the research, including 3 officials from the Ministry of Education, 7 principals, and 186 teachers from Sabon-Gari Local Government Area, Kaduna State. The instrument named “Questionnaire on Cyber Threats and Educational Records in Public Secondary Schools (QUCYTERPUBS)” was used for data collection in the research. The validated instrument underwent pilot testing, and the reliability coefficient was assessed using the Cronbach Alpha statistic, yielding a reliability value of 0.86. The data gathered in the research was digitized into a database using Statistical Package for Social Sciences (SPSS) version 23.0. Descriptive statistics, including frequency counts, mean, and standard deviation, were used to address the study topic, while the Kruskal-Wallis test was utilized to evaluate the hypothesis at a significance level of 0.05. Research indicated that cyber risks adversely affected the administration of students’ medical data in public secondary schools in Kaduna State. The report advised, among other measures, that school administration constantly update antivirus software on all computers to mitigate vulnerabilities that may be exploited by cyber threats, therefore preventing the manipulation of kids’ medical information

This paper presents an automated scheduling approach for constructing efficient and conflict-free timetables in educational institutions by leveraging the Non-dominated Sorting Genetic Algorithm II (NSGA-II). Timetable scheduling is a complex, NP-hard problem due to numerous conflicting constraints, such as instructor availability, room capacity, and course requirements, which render manual solutions both time-consuming and suboptimal. NSGA-II, a well-known evolutionary algorithm, is employed to solve the multi-objective optimization problem by balancing conflicting goals like minimizing overlaps and optimizing resource allocation. The algorithm utilizes population-based search methods and non-dominated sorting to derive a Pareto front that allows stakeholders to select the best-fit timetable from a range of nearoptimal solutions. In this study, multiple objective functions have been defined to capture the various aspects of timetable quality, including minimized gaps in faculty schedules and improved classroom utilization. The experimental results indicate that NSGA-II produces significantly better timetables in terms of reduced conflicts and efficient resource use compared to traditional heuristic-based methods. The proposed system also offers flexibility for institutions to prioritize objectives dynamically, depending on real-time requirements, which enhances the adaptability of the timetable generation process. Furthermore, the study provides a detailed analysis of the tradeoffs involved in optimizing multiple conflicting objectives, which is critical for understanding the operational implications of different scheduling choices. The effectiveness of NSGA-II in managing timetable complexities suggests its potential as a viable and practical tool for educational scheduling applications. This approach not only saves administrative time but also ensures a fairer and more efficient use of institutional resources, ultimately contributing to an improved educational experience. Future work will focus on incorporating real-time adaptability into the scheduler by integrating IoT data feeds and further enhancing the algorithm’s computational efficiency for larger datasets

Optimizing the fuel injection system of marine diesel engines is critical for improving overall efficiency and minimizing troubleshooting issues. This paper explores the key parameters of the fuel injection system, such as fuel injection timing, pressure, and nozzle efficiency, to enhance fuel combustion and engine performance. By optimizing these factors, the system can achieve better fuel economy, lower emissions, and extended engine life. The study also incorporates preventive maintenance strategies to detect and resolve potential issues before they escalate, ensuring smoother operation and reducing downtime. The overall goal is to improve the efficiency and reliability of the marine diesel engine through a comprehensive evaluation and optimization of its fuel injection system. The baseline emissions increase steadily from 155 grams/hour in 2014 to 200 grams/hour in 2024, reflecting a continuous rise in CO2 emissions over time. The optimized emissions are consistently lower than the baseline but follow a similar upward trend, starting at 155 grams/hour in 2014 and reaching 200 grams/hour by 2024. For NOx baseline emissions decrease gradually from 50 grams/hour in 2015 to 45.5 grams/hour in 2024, a modest 9% reduction. In contrast, optimized emissions drop significantly from 50 grams/hour to 32 grams/hour in the same period, achieving a 36% reduction. This highlights the effectiveness of optimization strategies in reducing NOx emissions, suggesting they are crucial for achieving better environmental outcomes and regulatory compliance. Particulate emissions (g/h) from 2015 to 2024 under baseline and optimized conditions shows that baseline emissions decrease from 10 g/h in 2015 to 8.2 g/h in 2024, while optimized emissions drop more significantly, from 10 g/h to 5.5 g/h over the same period

Detecting fraud and anomalies in financial transactions is crucial in safeguarding institutional assets, maintaining regulatory compliance and ensuring customers trust in financial system. This study investigated methods of detecting frauds or anomalies in transactions within financial institutions, a vital task to prevent financial losses, reduce investigative costs, and comply with regulatory standards. The efficiency of Logistic Regression, Linear Discriminant analysis (LDA) and Quadratic Discriminant (QDA) statistical models were compared with a view of identifying fraudulent activity. Secondary data of over 280,000 financial transactions from an online website (kaggle) was used to evaluate each model based on accuracy, precision, and error rates, for both fraudulent and non-fraudulent classifications. The results indicated that Logistic Regression outperformed LDA, and QDA, achieving the highest accuracy and lowest error rate, making it the most effective model among the models considered for fraud detection. 

Personality traits are characteristic patterns of thought, feelings and behaviors that define an individual’s personality. In spite of a number of works done on personality traits, there is limitation on the use of models that is fully representative of diverse cultural contexts, such as Nigeria. This study explored the underlying personality traits prevalent among Nigerians using factor analysis, a statistical approach to identify latent variables influencing observed behavior. The study examined how Nigerian personality traits align with widely recognized models, such as the big five personality traits, and investigated variations across demographic factors like age, gender, occupation, educational level and primary area of studies. Data were collected from 108 respondents using an online questionnaire and the study also utilized multivariate regression analysis to examine how some traits influenced these psychological factors. Factor analysis was employed to identify the underlying structures in the data. Five factors were extracted from personality traits and they jointly accounted for 63.07% of the total variability. Three factors were extracted from personality needs which accounted for 70.26% of the total variability and the factors are: social support empathy, achievement & motivation goals and emotional regulation & resilience. The findings from multivariate regression model revealed that conscientiousness, agreeableness, and openness significantly influenced various psychological factors, particularly self-motivation, goal setting, emotional regulation, and resilience. While extroversion and neuroticism had less influence, they still played a minor role in some of the analyzed models. The study highlights the importance of personality traits in shaping psychological outcomes and underscores the potential for developing interventions to enhance these traits in individuals to improve their psychological resilience and motivation

Health and socio-economic status are the most critical elements when researching a community or state. One way to look at social aspects that influence a country’s development is to look at its exports, health, imports, income, inflation, life expectancy, total fertility, and GDP per capita. Cluster analysis and other machine learning (ML) forms are crucial for extracting useful information from the Socio-economic dataset, as is the evaluation of dimensional reductions and rakings. The dataset was collected via Rohan Kukkula’s Kaggle uploads, which included (16710) entries for many nations and their socio-economic variables. We use cluster and statistical analysis to find the nations needing the most aid to improve their economic and social situations. The characteristics in the dataset are reduced in dimensionality via principal component analysis. Two clustering methods, hierarchical and K-Means, were used. The results are the same when using both methods; however, K-means plots are easier to see than those using Hierarchical clustering. Using the clustering process, we categorise the nations according to their socio-economic status and health indicators. Policymakers, NGOs, and international development agencies might utilise the results of this study to help severely underdeveloped nations

The design and fabrication of a double-sided wellhead adapter were undertaken, to improve the efficiency and safety of natural gas well operations. This innovative adapter facilitates simultaneous connections to multiple production lines, allowing for more streamlined management of gas extraction. By optimizing the flow of natural gas, the adapter enhances operational performance and reduces downtime during maintenance and repairs. The design process involved assessments of the structural integrity and compatibility with existing wellhead equipment, material properties, and load conditions, allowing for the precise calculation of stress distribution and potential failure points, ensuring that the final product meets rigorous industry standards. A stress analysis to assess the mechanical integrity of components under various loading conditions, and a thorough thermal validation process to examine the behavior of materials when subjected to extreme temperatures. The results indicate that the adapter is capable of withstanding pressures up to 5000 psi, demonstrating its durability in high-stress environments. Furthermore, it boasts a safety factor exceeding 2.0, which confirms its reliability under extreme conditions and ensures its longevity during prolonged use. This proactive approach improves the overall performance and reliability of wellhead systems, and streamlines the manufacturing process, ensuring that the equipment can be produced efficiently and sustainably. Ultimately, these improvements contribute to increased operational efficiency and safety in energy extraction processes.