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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.

Drinking water safety is crucial to public health safety and One-health program being advocated by the world health Organization (WHO) as a means of securing upgrade in the quality of life. A total of twelve retailed branded bottled and sachet water samples were randomly purchased for microbiological analysis from three metropolitan Universities. The physic-chemical properties of these samples were determined prior to microbiological analysis. Standard and conventional methods were followed using the membrane filter technique (diameter 0.45µm) for the isolation of microbial contaminants. Microbial isolates were cultured on nutrient agar (NA), Sabouraud dextrose agar (SDA) and Thiosulphate citrate bile salt sucrose agar (TCBS)

With increasing population and industrialization; anthropogenic activities have led to increased water pollution, creating serious problems for accessing drinking water and water for agricultural purpose. The situation is worst for rural dwellers. This study ‘’iron NPs production for treatment of polluted water for drinking and agricultural purpose: current status and future perspective’’ is a deliberate effort towards solving the menace of water pollution. The paper has reviewed the different methods of iron NPs production. It took a critical look at the chemical method, where iron salts, reducing agents, and capping agents are used for the production of iron NPs. It then moved on to green chemistry methods, where iron salts are used with plants roots and leaves, and other organic extracts as reducing agents to produce iron NPs. This method had been adjudged to be compatible with the environment, cost effective and environmentally friendly. Ultrasound-based technologies have emerged as another powerful synthesizing technique used for iron nanoparticles production; this has also been reviewed in this work. Of interest in this review is the current research break-through where DWTS and slaughter house wastewater was used to produce iron nanoparticles for polluted and wastewater treatment. The paper has clearly posited that iron NPs can be used in the treatment of wastewater. Iron NPs remove pathogens and bacteria from polluted water through a well-articulated mechanism of coagulation, adsorption and photo-catalysis. The paper has stated the WHO standard for portable drinking water that must be met while treating water with iron NPs. The emphasis of this review is on the fact that great advancements are being made in the production of iron NPs and their use for treatment of polluted water in a world that the population is slightly above 8billion people; this is very important.

Power losses in electrical networks are a significant concern for energy providers, as they lead to inefficiencies and increased costs. As Losses are inherent to the electrical power system, they cannot be eliminated. Still, they can be investigated to identify, analyze, and give recommendations on most of the major points of these losses. The private gas turbine power network in Redemption City in Nigeria is facing significant power losses due to its electrical grid with the distribution network encountering 55.2 % non-technical losses and 3.3 % technical losses in the year 2022. This study titled “Investigates the point of loading power losses on the power network of Redemption City, Mowe Ogun, State Nigeria” using field measurements, data analysis, and simulation techniques aims to identify the points of loading power losses and their contributing factors. The study evaluates the economic and social implications of these power losses and explores the potential solutions. The findings provided valuable insights into possible of mitegating losses on power network, which will be beneficial for policymakers, utility providers, energy sector and stakeholders, ultimately improving power distribution efficiency and reliability in Redemption City and similar urban areas with private power networks.

In cryptography, encryption is the process of encoding information to prevent an unauthorized party from reading it, and this process converts the original representation of the information, which is referred to as plaintext, into an alternative form called cipher text. There are many encryption algorithms; however, their performances have not been adequately evaluated. Many researchers have presented the performance analysis of public key cryptography using RSA (Rivest Shamir Adleman) and ECC (Elliptic Curve Cryptography) algorithms with different parameters of measurement and concluded that the use of smaller key sizes still provides some risk. Hence, this study evaluated the performances of RSA (Rivest Shamir Adleman), ECDH (Elliptic Curve Diffie-Hellman), ElGamal, and ECC (Elliptic Curve Cryptography) algorithms. Two hundred and thirty (230) Joint Photographic Expert Group (JPEG) medical images in the range 79.9kb to 829kb were downloaded online via www.kaggle.com and two video data (2.23mb) in Audio Video Interleave (avi), MPEG-4 (MP4) (2.23mb) were also downloaded online via www.youtube.com all were thereafter used as test bed. Individually, the image was converted to grayscale with pixel values between 0-255 characters; the video was split into frames, and converted to American Standard Code for Information Interchange (ASCII) corresponding value in order to increase the speed of encryption and decryption time. The algorithms were implemented using Matrix Laboratory (MATLAB R2016a), and the strengths of the algorithms were evaluated in terms of encryption time, decryption time and throughput