ABUAD Journal of Engineering Research and Development (AJERD)

Techno-economic Evaluation of Grid-connected Hybrid Energy System Based on Run-of-River and Solar Energy Plants for Sustainable Electrification of a Rural Community

Abdulkadir Adamu, Usman Alhaji Dodo — Tue, 06 May 2025 00:00:00 +0000

The connection between energy access and greenhouse gas emissions is an issue that continues to garner attention. Presently, hundreds of millions of people globally do not have access to sufficient electricity, and those who do, rely on expensive fossil resources characterized by greenhouse gases. A viable solution is to explore renewable energy (RE) sources to satisfy the electricity demand and curtail the effect of greenhouse gases. This study performed a techno-economic analysis of a grid-connected hybrid RE system that included micro-hydro and solar photovoltaic power plants for a Nigerian rural community. The optimal system, according to the analysis done with HOMER software tool, has an overall NPC, operating cost, and LCOE of $3,202,139.00, $37,515.81, and $0.06053/kWh, respectively. A 98.1 kW micro-hydro turbine, a 150 kW converter, 100 kW solar panels, and 704 battery strings constitute the system components. An annual emission of 4,483 kg of CO₂, 0.356 kg of CO, 22.5 kg of SO₂, 4.86 kg of NO, and 1.66 kg of particulate matter will be released into the atmosphere. The implementation of this hybrid power system will not only increase access to energy but also help lessen greenhouse gas emissions.

Principal Component Analysis-Multilinear Perceptron-based model for Distributed Denial of Service Attack Mitigation

Tue, 06 May 2025 00:00:00 +0000

The increasing occurrence of Distributed Denial of Service (DDoS) attacks has caused significant disruptions in global network services, overwhelming targets by flooding them with requests from various sources. This ease of execution and gaining entry to distributed systems for rent has led to increasing financial losses. This paper addresses the growing challenge of IoT devices-targeted Distributed Denial of Service (DDoS) attacks within 4G networks. In this study, a PCA-MLP (Principal Component Analysis-Multi-Layer Perceptron) intrusion detection model combined with a packet-filtering firewall for enhanced prevention is presented. The firewall, utilizing IPtables, selectively permits traffic from trusted sources, successfully blocking nearly 70% of DDoS threats. The PCA-MLP model proposed in this study demonstrated high performance, accurately identifying different types of DDoS attacks with an overall accuracy of 95.35%.

Development and Evaluation of Mechanical Properties of Rubber Matrix Composite for Automobile Transmission Belt Application

Sat, 10 May 2025 00:00:00 +0000

The importance of automobile transmission belts (ATB) in mechanical systems cannot be overemphasized. In developing countries, conventional ATB are mostly imported. Most of the imported ones lack sufficient strength, which makes them prone to frequent fracture, they are weak and break easily. This could lead to accidents and damage to engines. Also, frequent replacement of these belts increases the cost of maintenance. In this study, Rubber Matrix Composite has been developed using natural rubber reinforced with polyester fiber and carbon black particulates to modify and overcome these challenges. The produced samples were subjected to physical and mechanical tests. It was observed that the composite hardness increased gradually as polyester fiber reinforcement increased. The sample with fiber reinforcement of 8% exhibited a hardness value of 25.6 HV. Also, the sample without carbon black showed higher levels of water absorption of 20.5%, other samples showed lower levels of water absorption. The result of tensile strength revealed that the sample reinforced with only carbon black exhibited a low tensile strength of 30.30MPa, while the sample reinforced with both materials exhibited the highest tensile strength of 52.61MPa. Generally, the composites exhibited an increase in the mechanical properties as the weight percentage (wt.%) of the reinforcement increased. This study established that high-quality ATB can be produced locally using natural rubber and reinforcements.

Dynamic Hospital Resource Scheduling During Pandemics with Stochastic Optimization

Tue, 13 May 2025 00:00:00 +0000

The COVID-19 pandemic has highlighted the need to effectively manage hospital resources: ICU beds and ventilators. These resources are significant for sustaining life, especially in severe cases. Traditional deterministic models often fall short in addressing the uncertainties associated with patient inflows and resource availability. This paper develops a novel two-stage stochastic programming model which aims to dynamically allocate resources to deal with the variability of inpatient admissions. To this end, the scenarios are developed using Monte Carlo simulation based on the probabilities estimated from the historical data. The model is created in Python language and solved using the Gurobi optimizer in 0.05s, a large-scale scenario optimization analysis problem with 42 variables and 35 constraints. The KPIs show the highest utilization of ventilators at 66. 67% and the average reduction of 53.5 in the number of offers an ICU practical shortfall leading to better patient care and shorter wait times. This research presents a data-driven tool to enhance the decision-making process and the healthcare system's overall readiness to maintain its strategic reserves by implementing flexible staffing models to improve preparation for disasters such as the pandemic. Its stochastic optimization framework makes hospital resource allocation more efficient, offering a scalable, resilient solution for tackling future pandemic challenges.

Experimental Study on the Impact of Soil Type Variations on Compressive Strength and Settlement Characteristics of Spread Footing Foundations

Ubong Nkamare Tobby, Ben Uchechukwu Ngene — Tue, 13 May 2025 00:00:00 +0000

This research investigates the influence of soil type variations on the compressive strength and settlement behavior of spread footing foundations. Soil properties such as moisture content, dry density, void ratio, cohesion, and internal friction angle play a crucial role in determining how foundations respond to applied loads. Variations in these properties can lead to uneven settlements and structural instability, posing significant challenges in construction. The study aims to provide a comprehensive understanding of these interactions to enhance foundation design and prevent structural failures. We applied machine learning techniques for data analysis and visualized patterns using Power BI, enabling a detailed exploration of the relationships between soil characteristics, compressive strength, and settlement behavior. The results showed that soil cohesion and internal friction angle had the most significant impact on compressive strength, while moisture content and void ratio were key contributors to settlement behavior. The optimized model achieved high accuracy of 82% in classifying settlement levels, reinforcing the dataset's reliability. This research highlights the importance of thorough soil testing and data-driven modeling in foundation design. We recommend integrating predictive models into geotechnical practice to support safer, more resilient structures, especially in areas with diverse soil profiles. The findings provide a valuable tool for engineers to make informed decisions, reducing the risk of foundation failure and enhancing the long-term stability of infrastructure.

Development of Adaptive Resource Allocation and Interference Mitigation for Spectrum Sharing in D2D-Enabled 5G Heterogeneous Networks: A Case Study of Urban Microcell Environments

Fri, 16 May 2025 00:00:00 +0000

Device-to-device (D2D) communication in heterogeneous networks (HetNets) poses significant challenges in resource allocation and interference management, especially within 5G networks where spectrum sharing between cellular users (CUEs) and D2D user equipment (DUEs) is critical. This study developed an adaptive resource allocation framework using Long Short-Term Reinforcement Learning (LSRL), which integrated Long Short-Term Memory (LSTM) networks with Deep Reinforcement Learning (DRL) technique. The proposed approach addressed the dynamic nature of interference in urban microcell environments by leveraging a Hierarchical Data Format (HDF5) dataset generated from network simulations. These simulations incorporate diverse scenarios, including varying user densities, transmission power levels, and interference conditions. The LSRL-based scheme was evaluated against conventional DRL methods, demonstrating notable improvements in network performance. Specifically, the proposed framework achieved up to a 6.67% increase in sum throughput and an 8.2% enhancement in power efficiency, even under dense user conditions. Additionally, the LSRL model proved resilient to variations in D2D pair distances, maintaining robust spectral efficiency and quality of service (QoS). These findings underscore the potential of the LSRL-based adaptive approach for improving resource management in 5G HetNets, particularly in dense urban deployments, and provide valuable insights for optimizing next-generation wireless communication systems.

Development of an Automated Aquarium Monitoring System with an IoT Interface using Google Sheets

Sun, 18 May 2025 00:00:00 +0000

This paper presents the development of an automated aquarium monitoring system with an IOT interface using google sheets; the system autonomously monitors key water quality parameters temperature, pH, and turbidity while automating fish feeding and water replacement functions. An ESP32 microcontroller serves as the system’s core, control unit which is programmed using C++ to transmit environmental data to a cloud-based Google Sheet. A servo motor dispenses feed precisely every 12 hours, while two DC pumps are triggered automatically when turbidity exceeds 50 NTU, ensuring proactive water quality management. The designed system is powered by a 30W solar panel and a charge controller coupled with a 12V lead-acid battery, allowing continuous operation in off-grid locations. The system performance test was conducted over a period of five days and was validated by comparing the sensor outputs with results of the manual measurements obtained by using laboratory-grade instruments. The results demonstrated high accuracy, with average deviations of only 1.95% for temperature, 2.09% for pH, and 1.96% for turbidity when compared with the result obtained from the manual measurement. Also the automated feeding and water replacement mechanisms operated with 100% reliability by being able dispense the feed from the hoper at every 12 hours interval and changing the water once the turbidity is equals or above 50 NTU. Hence the proposed system successfully enhanced automation, real-time cloud integration, and renewable power supply for improved fish aquarium management, thereby offering a compelling alternative to labour-intensive and manually operated systems while laying the groundwork for intelligent, data-driven fish farming practices.