Caritas Journal of Engineering Technology
CJET is a peer-reviewed, Open Access multidisciplinary engineering journal that publishes original research articles as well as review articles in all areas of engineering technology. It publishes both theoretical and experimental high-quality papers of permanent interest, not previously published in journals, in the field of engineering technology. In addition to peer-reviewed original research papers, the Editorial Board welcomes original research reports, state-of-the-art reviews, and communications in the broadly defined field of engineering science and technology.
Adaptation of Mathematical Models for the Determination of Crude Oil Blends Stability
Crude oil blend formulation has been topical in recent times with research focus on yield maximization by appropriate feedstock preparation. The present work developed a simple mathematical algorithm to predict with precision, asphaltene precipitation as measure of crude oil blend stability and compatibility. Crude oil Blend stability was measured in terms of Colloidal Insolubility Index (CII), Asphaltene to Resin (A/R), ratio and ration of the Solubility Blending Number ( ) and Insolubility Index (R). Results of the present work are in good agreement with plant data and simulation results of previous work for all parameters of blend stability determination. In each case, both studies gave stable blends as shown above. Main contribution of this work to the subject matter is in its unambiguous and precise nature, and its simplicity, both of formulation and application.
Copyright (c) 2025 Caritas Journal of Engineering Technology
A Comprehensive Pressure Distribution Model for Horizontal Well in Bottom-Water Reservoir
The growing application of horizontal well technology provided far-reaching solutions to more realistic phenomena in well test analyses. However, existing models restrict the types of flow regimes by assuming that the dynamics of fluid are controlled by the geometry of the reservoir only. Such an assumption limits the number of flow regimes and hence the accuracy of well test analyses. In this article, all potential flow regimes of a horizontal well in a reservoir supported by bottom water were considered. The conceptual model assumed that the dynamics of the reservoir fluid are a function of the petrophysical properties of the reservoir, the properties of the fluid, and the geometry of the reservoir system. The physical model and the mathematical model were developed based on the conceptual model. The mathematical model is the pressure distribution of the horizontal well in the reservoir. The pressure distribution is in the form of dimensionless pressure and dimensionless pressure derivative functions of reservoir system properties, fluid properties, reservoir system geometry, and dimensionless time. Results of the study show a series of flow patterns along the principal axes or along combinations of the principal axes. Each flow regime could be distinguished by one of the flow patterns and the axes of orientation of the pattern. Each flow regime could be recognized by its characteristic signature in the log-log graph plots of the pressure distribution.
Experimental Assessment of Local Demulsifiers for Crude Oil Emulsion Breaking: A Study on Jatropha curcas, Indigenous Materials, and their Blends
Crude oil emulsion is one of the most challenging problems plaguing the phases of oil and gas production. Crude oil emulsion stability causes high viscosity of crude oil which leads to challenges in pipeline transportation and processing. This paper investigated the effectiveness of locally sourced material as a demulsifier, where the stability crude oil emulsion of varying viscosity by using three different formulated local demulsifiers such as Jatropha curcas extract, local materials, and their blends were investigated. The laboratory experiment was carried out using Crude oil I, II and III, blend of Jatrophas curcas and local materials into Sample A (100% Jatropha curcas extract), B (6:4), C (1:1), D (4:6), E (3:7), F (2:8), and G (100% local materials), at 60, 90, 120, 180, 240. 300, 360, 420 and 480 mins. The result generally showed that the lower the viscosity of crude oil the higher the water recovery (WR). Also, the demusifier blends had better WR when compared to without blends. Irrespective of the type of crude oil, demulsifier blends of equal ratio 1:1, 4:6, and 3:7 % led to maximum WR. For crude oil I, without delmusifier achieved 5.4 % WR, while Sample A had 86 % 77 % WR for Sample G at constant time of 480 mins, and 100 % WR for Sample D but at 36mins. Crude oil II, had WR of 4.0% at 480mins without demulsifier, Sample A, B, and D had water recovery of 79%, 72%, and 100% at 480 mins, respectively. Lower recoveries were observed for crude III majorly due to its higher viscous nature, as all samples had WR between 48.2 and 75%. This study has demonstrated that blend of locally formulated demulsifiers had better water separation capability.
Performance Characteristics of Palm Kernel Shell-based Biomass Fuel
The transition toward sustainable and renewable energy sources has become imperative in addressing climate change and reducing greenhouse gas emissions. Biomass, particularly in the form of agricultural waste, presents a viable alternative to traditional fossil fuels. This study investigates the potential of palm kernel shells (PKS), an abundant yet underutilized by-product of palm oil processing in Nigeria as a clean and efficient biofuel through the process of briquetting. Two PKS samples, sourced from local and improved palm varieties, were subjected to pyrolysis to enhance their fuel characteristics. The charred PKS was then combined with varying proportions of cassava starch (25%, 30%, and 35%) as a binder and compressed into cylindrical briquettes. Proximate and ultimate analyses, along with calorific value determination and combustion performance tests, were conducted to assess the energy efficiency and environmental suitability of the briquettes. Results indicate that a 25% starch binder ratio produced the most efficient briquettes, characterized by high fixed carbon content, lower ash and moisture levels, and superior calorific value. Additionally, combustion tests revealed favorable ignition times, burning rates, and fuel consumption efficiency. The findings demonstrate the viability of PKS briquettes as a sustainable alternative to fuelwood and charcoal, offering both environmental and economic benefits. This study contributes to the development of renewable energy solutions in developing regions and underscores the value of waste-to-energy innovations in enhancing energy security and reducing ecological degradation.
Maintenance And Characterisation Of Rock Well Hardness Testing Machine
Rockwell hardness testing machines are crucial in determining material hardness. However, their accuracy and reliability can be compromised if not properly maintained. This project focuses on the rehabilitation and maintenance of the Rockwell hardness testing machine in the Mechanical Engineering Department Caritas University, Amorji Nike, Enugu. The machine’s accuracy and reliability were restored through diagnostic tests, replacement of worn-out parts, calibration, and development of a maintenance schedule.The reliability and functionality of the Rockwell Hardness Testing Machine are critical for material testing and mechanical engineering applications. However, frequent power fluctuations, component wear, and inadequate maintenance practices often compromise the performance of this essential equipment, particularly in educational institutions like Caritas University Amorji Nike Enugu. This study investigates the application of a Fuzzy Logic-Based Static Var Compensator (SVC) to improve the rehabilitation and maintenance of the Rockwell Hardness Testing Machine. The fuzzy-based SVC system is designed to enhance voltage stability, mitigate harmonic distortions, and ensure a consistent power supply to the machine. By integrating intelligent control algorithms, the system can predict and adapt to varying operational conditions, enabling proactive maintenance and reducing the likelihood of system failures. This approach not only safeguards sensitive components from power-related damages but also extends the machine’s operational lifespan and minimizes downtime. The study employs a combination of experimental analysis and simulation to evaluate the performance of the fuzzy-based SVC system. Results demonstrate significant improvements in power quality, reduced maintenance costs, and enhanced operational reliability of the Rockwell Hardness Testing Machine. The findings underscore the potential of intelligent power systems in modernizing equipment maintenance practices and ensuring sustainable engineering education.
Design And Construction Of a Departmental Mini-Radio Station At The Faculty Of Engineering Caritas University Enugu
The "Design and Construction of a Departmental Mini-Radio Station in the Faculty of Engineering" focuses on creating a functional radio broadcasting system for educational purposes. These radio waves are a type of electromagnetic wave with frequencies ranging from about 3 KHz to 300 GHz. The mini-radio station encompasses the design and assembly of essential components, including a low-power FM transmitter, an antenna system, and audio processing equipment. The project follows a systematic approach, beginning with a comprehensive analysis of the requirements, followed by the selection of appropriate hardware, circuit design, and implementation. The goal is to ensure reliable signal transmission within a limited range while adhering to regulatory standards. The test result of this research work revealed the successful achievement of the primary objective; the design and construction of Mini-Radio station in faculty of Engineering Caritas University Enugu (Transmitter Part) with Audio Console Using Frequency Modulation (FM) With Power Rating of 20 Watt of appreciable range operating on 9v power supply. This system has been implemented with the locally source material in our electronic markets and used to implement the system using the necessary rudiment usually employed in electronic system implementation from the above stated results. The constructed radio station will serve as a valuable educational tool; provide students with practical experience and understanding of radio frequency principles, signal propagation, and broadcast engineering.
Design and Construction of a Quadcopter System with Surveillance Facilities
The design and construction of a drone delivery (quadcopter) system with a surveillance facility encompass the development and implementation of an unmanned aerial vehicle (UAV) capable of performing precise delivery tasks while also capturing high-quality aerial images and videos. The primary objective is to create an efficient, reliable, and autonomous drone capable of performing dual functions: delivering packages and capturing high-quality aerial images. The photography subsystem incorporates a gimbal-stabilized camera to achieve superior image quality, minimizing motion blur and vibration. Despite advancements in battery technology, the limited battery life of drones remains a significant constraint, restricting flight duration and operational range. Extended flights for both delivery and continuous photography require efficient power management, which is challenging to achieve with current battery capacities. Combining delivery and photography functions into a single drone system presents significant design and operational challenges. Optimizing the drone for both purposes without compromising performance in either area is complex and may result in trade-offs. In the construction phase, meticulous attention is given to assembling and calibrating the drone to achieve optimal performance. Rigorous testing is conducted to evaluate the system's reliability under various environmental conditions and payload scenarios. The final prototype demonstrates the potential for a wide range of applications, showcasing the drone's ability to deliver packages promptly and capture stunning aerial imagery. Additionally, the integration of real-time data transmission allows for instant monitoring (data collection) and adjustments during flight. i.e. allowing live streaming of footage to the ground station, enabling immediate monitoring and adjustments. Through the internet (Real-time Transmission). Testing phases focus on assessing flight performance, delivery accuracy, and image resolution under various environmental conditions. Results indicate that the drone system successfully meets the design specifications, demonstrating reliable delivery capabilities and high-quality photographic output.
Voltage Stability Compensation in Electronics –Saturated Network Using Unified Power Flow Controller
Voltage stability is a critical aspect of power system operation, especially in networks with a high penetration of power electronic loads such as variable speed drives, converters, and renewable energy systems. These electronics-saturated networks often experience voltage fluctuations, harmonic distortions, and reactive power imbalances, which can lead to instability and reduced power quality. The Unified Power Flow Controller (UPFC), a key Flexible AC Transmission System (FACTS) device, offers a comprehensive solution to these challenges by providing simultaneous control of voltage, impedance, and phase angle in the transmission line. This paper presents an approach for voltage stability compensation in electronics-saturated networks using the UPFC. The shunt converter of the UPFC provides reactive power support, maintaining bus voltage levels and mitigating voltage sags caused by nonlinear electronic loads. Simultaneously, the series converter regulates line impedance and power flow, reducing line losses and improving load distribution. Additionally, the coordinated control of the shunt and series converters helps damp power oscillations and improve system resilience against sudden load variations. Advanced control strategies such as Proportional-Integral (PI) controllers, Fuzzy Logic Controllers (FLC), and Model Predictive Control (MPC) are discussed for optimizing the UPFC’s performance under varying load conditions. Simulation results demonstrate that the UPFC significantly enhances voltage stability, reduces harmonic distortion, and improves overall power quality in an electronics-saturated network. The proposed solution is suitable for applications in modern power grids, including industrial plants, electric vehicle charging stations, and smart grid environments.
Affordable Micro-Controller Arduino-Based Board Experimental Training Kit Implementation in Nigerian Universities
Engineering and technology education in Nigeria faces challenges, including limited access to practical learning tools and high-cost laboratory equipment. To bridge this gap, this paper proposes the development of an affordable, Arduino-based experimental training kit tailored for Nigerian universities. The kit aims to provide hands-on experience in embedded systems, electronics, and programming, fostering practical skills and innovation among students. The training kit is designed with cost-effective components, modularity, and scalability, ensuring it can support a wide range of experiments, from basic circuit design to advanced microcontroller-based projects. The Arduino platform was selected for its affordability, open-source ecosystem, and ease of integration with various sensors and actuators. To validate its effectiveness, the training kit was piloted in selected Nigerian universities, where it demonstrated improved student engagement, better understanding of theoretical concepts, and enhanced problem-solving abilities. Additionally, the kit's affordability makes it accessible to institutions with limited budgets, promoting inclusive education. This study highlights the potential of low-cost, locally adaptable solutions in addressing the practical learning challenges in Nigerian higher education, emphasizing the importance of integrating such tools into engineering curricula to produce industry-ready graduates.
Improving Rehabilitation And Maintenance Of Workshop Equipments Using Artificial Neural Network (Ann) Based Technique. a Case Study Of Foundry Crucible Furnace In Caritas University Amorji Nike Emene Enugu
The effective rehabilitation and maintenance of workshop equipment are critical for ensuring optimal performance, reducing operational downtime, and extending the lifespan of machinery. This study focuses on improving the rehabilitation and maintenance process of a foundry crucible furnace in Caritas University Amorji Nike Emene, Enugu, using an Artificial Neural Network (ANN)-based technique. The research explores the application of ANN in predictive maintenance by analyzing historical operational data and identifying patterns that indicate potential faults or performance degradation.A structured methodology was adopted, involving data collection from the crucible furnace, preprocessing, and training of the ANN model. The ANN-based system was designed to predict faults and recommend maintenance actions before critical failures occur. Results from the study demonstrated that the ANN model accurately detected anomalies and provided timely alerts, significantly reducing downtime and improving operational efficiency. The findings highlight the potential of ANN-based techniques in transforming traditional maintenance practices into proactive and intelligent systems. The study recommends the integration of ANN systems into workshop maintenance frameworks, the training of technical personnel, and the adoption of sustainable practices to enhance reliability and productivity. This approach not only optimizes equipment performance but also contributes to the advancement of intelligent maintenance technologies in educational institutions.
Improving Rehabilitation And Maintenance Of Characterization Of a Rock Well Hardness Testing Machine In Caritas University Amorji-Nike Enugu Using Fuzzy Based Static Var Compensator(SVC)
The reliability and functionality of the Rockwell Hardness Testing Machine are critical for material testing and mechanical engineering applications. However, frequent power fluctuations, component wear, and inadequate maintenance practices often compromise the performance of this essential equipment, particularly in educational institutions like Caritas University Amorji Nike Enugu. This study investigates the application of a Fuzzy Logic-Based Static Var Compensator (SVC) to improve the rehabilitation and maintenance of the Rockwell Hardness Testing Machine. The fuzzy-based SVC system is designed to enhance voltage stability, mitigate harmonic distortions, and ensure a consistent power supply to the machine. By integrating intelligent control algorithms, the system can predict and adapt to varying operational conditions, enabling proactive maintenance and reducing the likelihood of system failures. This approach not only safeguards sensitive components from power-related damages but also extends the machine’s operational lifespan and minimizes downtime. The study employs a combination of experimental analysis and simulation to evaluate the performance of the fuzzy-based SVC system. Results demonstrate significant improvements in power quality, reduced maintenance costs, and enhanced operational reliability of the Rockwell Hardness Testing Machine. The findings underscore the potential of intelligent power systems in modernizing equipment maintenance practices and ensuring sustainable engineering education. This research contributes to the advancement of intelligent maintenance systems and highlights the role of fuzzy logic in addressing power-related challenges in engineering equipment. It provides a practical framework for optimizing the functionality and longevity of critical workshop tools, fostering technological development within academic environments
Improving Rehabilitation And Maintenance Of a Rock Weld Hardness Testing Machine In Caritas University Amorji Nike Enugu Using Fuzzy Based Stattic Var Compensator (SVC)
Rehabilitation And Upgrade For The Configuration Techniques Of a Spark Ignition Engine Test Bed. a Case Study Of Caritas University Amorji Nike Emene Enugu
This paper presents the configuration of a spark ignition engine test bed, a device which allow measurement on engines by making them run in a static way, where there is no need to use the vehicle to which the motor was made, used specifically for research and development department of motor manufacture, in order to ensure the design and the operation of prototypes, and to evaluate the performance of spark ignition engines. The testing bed incorporates a dynamometer, fuel measurement system, emissions measurement system, and data acquisition system to measure engine performance parameters, including power output, torque, fuel consumption, and emissions. The configuration of the testing bed is discussed in detail, including the selection of components which thousands of different component where manufactured in different factories with high degree of accuracy and interchangeability, integrated to the systems. A case study is also presented at the Caritas University, a configured internal combustion engine testing bed to demonstrate the effectiveness of the testing bed in evaluating the performance of a spark ignition engine. The results show that the testing bed is capable of accurately measuring engine performance parameters, making it a valuable tool for engine development, testing and research.
Assessment of Oil and Gas Waste Water Treatment Technologies. A Review
The rapid growth of the oil and gas industry has led to an increase in the production of wastewater containing contaminants such as hydrocarbons, heavy metals, and salts. These contaminants pose significant environmental and health risks if not properly treated. Therefore, various technologies have been developed to treat oil and gas wastewater. This review article aims to assess the various treatment technologies available and their efficiency in treating these wastewater. The technologies reviewed include physical, chemical, and biological treatments. Physical methods including flocculation, sedimentation, and filtration techniques are effective in removing suspended solids. Chemical methods, such as coagulation and advanced oxidation processes, are effective in removing hydrocarbons and heavy metals. Biological treatments, including microbial fuel cells and constructed wetlands, have also shown promising results in treating oil and gas wastewater. The review found that a combination of two or more of these technologies can provide efficient and cost-effective treatment of oil and gas wastewater. However, further research is needed to determine the best treatment approach for various types of oil and gas wastewater.
Assessment of the Integration of Renewable Energy in Industrial Processes
There are several chances for both technological and financial breakthroughs when renewable energy is included in industrial processes. When used as process heat, biomass exhibits a feasible economic potential of 15–19 EJ annually for ambitious deployment scenarios and 13–14 EJ annually under accelerated deployment scenarios. With an estimated 81 EJ annually for aggressive deployment and 41 EJ annually for rapid deployment, its technically achievable potential is noticeably greater. When used as feedstock, biomass provides an extra economic potential of 1 to 2 EJ annually in ambitious scenarios and 0.5 to 1 EJ annually in accelerated scenarios. With a technological potential of 14.9 EJ per year, solar thermal energy has an economic potential of 0.9 to 3.8 EJ annually. In line with its technical capability of 1.9 EJ annually, geothermal energy has an economic potential of 1.7 to 1.9 EJ annually. Heat pumps have a technical potential of 2.3 EJ annually and an economic potential of 1.2 to 1.9 EJ annually. As a renewable energy source, electricity continues to have 1.1 EJ of annual economic and technical potential. These numbers highlight the significant contribution renewable energy sources can make to improving industrial sustainability and energy efficiency.
Development Of Pumps And Source Of Combustion In Automated Tilting Furnace
The previously used tilting crucible furnace in our foundry workshops are associated with many challenges such as lack of manpower involved in carrying the crucible pot with the tongs or the ladle alongside the with the molten metal at such a high temperature from the crucible furnace. The development and fabrication of automated tilting crucible furnace has a five main components which includes; the furnace shell, the refractory wall(lining) the burner, and its pipe, fuel tank and crucible pot, the furnace is a crucible type of furnace which melt only aluminium scraps. The furnace body is enclosed in a cylindrical metal sheet measuring 355mm diameter and 445mm high and it is lined internally with superior refractory bricks of 50mm thickness. The furnace by calculation has these specifications; circumference of 395mm and 0.395m^3, weight of 30kg. the melting process is done in a crucible rammed externally with fired clay and made from heat resistance sheet at temperature of 700*C to 800*C. An air blast from electrically powered centrifugal blower is atomized with spent engine oil of which flow down by gravity and is controlled by throttle valve in the fuel line and from the heat source when they mixed inside the combustion chamber.
Implementation of Fatigue Strength Analysis of Washing Machine Drum Using Von Mises Approach
The fatigue strength analysis of washing machine drum was analyzed using von mises approach. The expected life time of the washing machine drum with respect to fatigue strain at the welded parts was successfully estimated using Miner’s rule. From the results obtained it was observed that increasing the speed of the washing machine from 400 to 2200 rpm resultedto decrease in its life time from 120 to 20 months and this were attributed to increase in stress – strain concentration and then fatigue failure of the welded parts. Autodesk simulation of the washing drum activities at speeds and temperatures ranging from 400 to 2200 rpm and 30℃ to 120℃ respectively increased the von mises stress of the machine from 62 to 549Mpa. The simulation shows that increase in speed and temperature of the machine drum, decreased the fatigue limits of the welded thin sheets. Node shell element result of the strains at front, rear, and middle of the welded parts of the drum produced varying results at speeds in the range of 400 to 2200 rpm. Higher strains values ranging from 11 to 278 Mpa. Were obtained in the middle of the drum were stress concentration was more, which could be as a result of location of greater washing activity than in the front and rear side of the cylinder with less strain values (-1104 to -58Mpa), increase in speed (400- 2200 rpm) and the loads (10 to 100kg) in the washing machine drum were observed to increase the strain effects at the rear, front and middle of the cylinder.
Assessment of Diethylene Glycol (Deg) Dehydration System in a Natural Gas Processing System
The natural gas found in Nigeria's Obagi field, specifically in OML 58 where the Obite gas processing plant is located, typically contains water vapour. It is generally assumed that about 98% of gas discoveries in this area are saturated with water. The presence of water in either liquid or vapour form creates technical challenges. This problem is prevented through dehydration to prevent hydrate formation. To achieve optimal production, cost-effectiveness, and improved marginal gains in gas processing, dehydration systems need to be carefully evaluated. This paper offers the use of a simulation tool to tackle the natural gas dehydration system at the Obite Gas Plant. Process conditions of 84bar and 35oC and gas flow rate of 8MMSCFD, as the input data. Results indicate a DEG flow rate of 12.735m3/h, the water volume was reduced to 2.3915lb/MMSCF from an early value of 9.92lb/MMSCF. This is below the pipeline specification. The volume of methane recovered with a flow rate of 74%, for a DEG flow rate of 2.4m3/h, 4.6lb/MMSCF of water recovered in the gas stream. DEG dehydration can be used for dehydration systems due to its ability to meet low temperature and dew point specifications, and low initial and operating costs. After investigating various dehydration units at Obite gas processing facility, it is apparent that DEG can be used alongside TEG and many of the problems encountered and costs incurred could have been prevented with a better engineering understanding of the dehydration system.
Sensing Mechanism of Cognitive Radio Interference Reduction using Convolution Neural Network (CNN) Based Internet of Things (IOT)
The persistent failure in communication network failure were anchored by Received signal to noise ratio, Interference to noise ratio, Interference to signal ratio, Receiver noise figure, and Quantized effect bits that could not attain their respective thresholds. This failure in communication network was overcome by introducing sensing mechanism of cognitive radio interference reduction using convolution neural network. To achieve this, it was done in this manner, the present metric causes of poor sensing mechanism of cognitive radio as a result of interference was characterized and established, conventional SIMULINK model for sensing mechanism of cognitive radio was designed, CNN was trained in the present metric causes of poor sensing mechanism of cognitive radio as a result of interference for quick minimization, SIMULINK model for designed and an algorithm that would implement the process was developed. Then, the SIMULINK model for sensing mechanism of cognitive radio interference reduction using convolution neural network (CNN) based intern ate of things (IOT) was designed and the results obtained were justified and validated. the results obtained were the conventional Received signal to noise ratio that causes poor sensing mechanism of cognitive radio was 0.8dB. Om the other hand, when CNN was integrated into it , it instantly increased to 1.04dB. and the conventional Interference to noise ratio that causes poor sensing mechanism of cognitive radio was 1.4dB. Meanwhile, when CNN was integrated into the system, It automatically reduced to 1db thereby meeting the threshold of 1 dB. Finally, with these results obtained, the percentage improvement in sensing mechanism of cognitive radio interference reduction when convolution neural network (CNN) based intern ate of things (IOT) was28.6%, the CNN-based interference reduction mechanism provides a more intelligent, efficient, and adaptive solution for spectrum sensing in IoT-enabled cognitive radio networks. Future work can focus on hybrid deep learning models and edge computing integration to further enhance real-time performance and system scalability.
Copyright (c) 2026 Caritas Journal of Engineering Technology
Interference Reduction In Sensing Mechanism Of Cognitive Radio Using Fuzzy Based Smartcity Technique
Cognitive Radio (CR) technology has emerged as a key solution to spectrum scarcity by enabling dynamic spectrum access through intelligent sensing and adaptive transmission. However, one of the major limitations of CR systems is the presence of interference in the sensing mechanism, which degrades detection accuracy, increases false alarms, and reduces overall spectrum utilization efficiency. This challenge becomes more critical in the context of smart city environments, where dense wireless communication networks coexist and demand reliable spectrum management. This study proposes an interference reduction framework for cognitive radio systems using a Fuzzy-Based Smart City Technique. The fuzzy logic controller is employed to intelligently evaluate uncertain sensing parameters such as signal-to-noise ratio (SNR), channel occupancy probability, and interference levels, thereby improving decision-making in dynamic spectrum allocation. By leveraging the adaptive capabilities of fuzzy logic, the proposed technique minimizes interference, enhances spectrum sensing accuracy, and ensures reliable connectivity across heterogeneous smart city networks. Simulation results indicate that the fuzzy-based smart city approach outperforms conventional sensing methods by reducing false detection rates, improving spectrum efficiency, and supporting seamless communication in complex urban environments. This research provides a robust pathway toward interference-resilient cognitive radio deployment in future smart city applications. The conventional Signal to noise ratio(SNR) that cause interference in the cognitive radio sensing mechanism was 8dB, On the other hand, when Fuzzy based smart city technique was incorporated into the system, it automatically increased to10.32dB and the conventional interference to noise ratio INR that cause interference in the cognitive radio sensing mechanism was6dB. Meanwhile, when Fuzzy based smart city technique was integrated into the system, it simultaneously reduced to5.3dB. Finally, with these results obtained, it definitely meant that the percentage interference reduction in sensing mechanism of cognitive radio when Fuzzy based smart city technique was integrated into the system was13.2%.
Enhancing Data Transmission In a Wireless Communication Network Using Fuzzy Based Smartcity Technique
The rapid evolution of wireless communication networks within the framework of smart cities has necessitated the development of intelligent solutions to overcome data transmission challenges, such as latency, interference, signal degradation, and unreliable bandwidth allocation. This study presents a fuzzy-based Smart City technique aimed at enhancing data transmission efficiency and reliability in wireless communication networks. The proposed approach integrates fuzzy logic controllers to dynamically manage communication parameters, including signal strength, network congestion, and channel availability, enabling adaptive transmission strategies in real-time. By incorporating context-aware data prioritization and intelligent routing decisions, the fuzzy-based system ensures optimal data flow, particularly in densely populated urban environments with high device connectivity. Simulation results demonstrate significant improvements in throughput, reduced packet loss, and lower transmission delays when compared to conventional techniques. This research highlights the potential of fuzzy logic in addressing complex, nonlinear issues in smart city wireless infrastructure, paving the way for more resilient and responsive communication networks. The results obtained were the conventional bandwidth that caused poor data transmission in a wireless communication network was 0.7 Mbps. On the other hand, when FUZZY based smart city technique was integrated into the system, it instantly increased to 1 Mbps and the conventional multipath fading that caused poor data transmission in a wireless communication network was 1.3 µs. Meanwhile, when FUZZY based smart city technique was imbibed into the system, it simultaneously reduced to1 µs.. Finally, with these results obtained, it definitely meant that the percentage enhancement in the data transmission in a wireless communication network when FUZZY based smart city technique was incorporated in the system was 30%.
Improving Data Transmission in a Wireless Communication Network Using Convolutional Neural Network (CNN)
In recent years, the increasing demand for high-speed and reliable data transmission in wireless communication networks has presented significant challenges due to dynamic channel conditions, interference, and bandwidth limitations. This study proposes the application of Convolutional Neural Networks (CNNs) to improve data transmission efficiency and reliability in wireless networks. The CNN-based model is designed to intelligently learn and adapt to the spatiotemporal characteristics of wireless signals, enabling enhanced feature extraction, error correction, and adaptive modulation strategies. Through simulation and comparative analysis, the CNN-based system demonstrated significant improvements in key performance metrics such as throughput, signal-to-noise ratio (SNR), bit error rate (BER), and latency when compared with traditional methods. The findings highlight the potential of CNNs to optimize real-time decision-making processes, reduce data loss, and support robust communication in complex and high-interference environments. This approach provides a promising pathway toward the development of intelligent and autonomous wireless networks, essential for the advancement of next-generation communication systems including 5G and Smart Cities. The results obtained were the conventional High Noise Level that caused poor data transmission in a wireless communication network was13 dB. On the other hand, when CNN was integrated into the system, it automatically increased to15.08 dB thereby boosting the performance of the wireless communication network and the conventional improper antenna configuration that caused poor data transmission in a wireless communication network was 2.2dB. Meanwhile, when CNN was integrated into the system, it simultaneously increased it to 3.19 dB. Finally, with these results obtained, it definitely meant that percentage improvement in data transmission in a wireless communication network was 45%.
A Convex Optimization-Based Beamforming Framework for Spectral Efficiency Enhancement in 5G Heterogeneous Massive MIMO Systems
This paper presents a convex optimization-based beamforming framework for enhancing spectral efficiency (SE) in heterogeneous massive multiple-input multiple-output (mMIMO) 5G networks. Although universal frequency reuse improves spectral utilization, it introduces significant multi-cell interference that degrades system performance. To address this challenge, coordinated uplink (UL) and downlink (DL) beamforming schemes are developed to maximize the weighted sum SE under practical constraints. The resulting optimization problem is inherently non-convex and NP-hard; however, by reformulating the signal-to-interference-plus-noise ratio (SINR) and co-channel interference constraints, a tractable convex formulation is obtained. The proposed framework is efficiently solved using CVX, ensuring global optimality. Simulation results demonstrate that the proposed method achieves a minimum SE of 30 bit/s/Hz in the downlink and significantly outperforms benchmark techniques under practical conditions (Kr ≥ 4, N ≥ 12, SNR = 30 dB). Furthermore, increasing the number of users and base station antennas yields substantial additional SE gains, highlighting the scalability of the proposed approach for dense 5G deployments. Simulation results demonstrate that the proposed method achieves a minimum spectral efficiency of 30 bit/s/Hz in the downlink, outperforming existing benchmark schemes under practical conditions (Kr ≥ 4, N ≥ 12, SNR = 30 dB). Furthermore, increasing the number of users and base station antennas leads to significant additional SE gains, highlighting the scalability of the proposed approach for dense 5G deployments.
Technology-Driven OHSM Frameworks for Sustainable Engineering
This research explores the integration of emerging technologies—Artificial Intelligence, Internet of Things, Building Information Modeling, wearables, and blockchain—into Occupational Health and Safety Management (OHSM) to foster sustainable engineering practices in construction and manufacturing. Through a mixed-methods synthesis of recent studies, the research reveals that these technologies enable proactive safety management, reducing workplace incidents by 10–20% via real-time monitoring and predictive analytics, while enhancing sustainability by cutting construction waste by 10%. Despite these advances, high costs, technical expertise gaps, data privacy concerns, and regional disparities hinder adoption, particularly for small enterprises and developing nations. The study’s novelty lies in its interdisciplinary approach, bridging OHSM, technology, and sustainability to propose scalable frameworks that address these barriers. It fills critical research gaps, including the lack of theoretical models and longitudinal data, by offering context-specific strategies and stakeholder collaboration models. Results underscore the transformative potential of technology-driven OHSM, providing actionable insights for practitioners and policymakers to institutionalize safer, sustainable workplaces globally.