Tuijin Jishu/Journal of Propulsion Technology
Tuijin Jishu, which translates to "Propulsion Technology" in English, is a prestigious and cutting-edge academic journal dedicated to advancing the field of propulsion engineering and technology. Our mission is to serve as a platform for researchers, engineers, and enthusiasts alike to share their knowledge, discoveries, and innovations in the propulsion domain. At Tuijin Jishu, we believe that propulsion technology is at the forefront of modern scientific and engineering achievements, driving humanity forward in space exploration, aviation, transportation, and beyond. As such, we are committed to promoting the dissemination of high-quality, peer-reviewed research that pushes the boundaries of propulsion systems, fuels, and related technologies. Tuijin Jishu is a peer-reviewed open-access journal, ensuring that all published articles undergo rigorous scrutiny by expert reviewers in the field. We strive to maintain the highest standards of integrity and scientific accuracy, upholding the trust and credibility of our readers and contributors. Our journal covers a broad spectrum of propulsion-related topics, including but not limited to: Rocket Propulsion: Advancements in chemical propulsion, hybrid systems, electric propulsion, and novel propulsion concepts. Aerospace Propulsion: Cutting-edge research on aircraft engines, propulsion systems, and aero-thermodynamics. Space Exploration: Innovations in space propulsion, interplanetary travel, and propulsion challenges in long-duration missions. Green Propulsion: Sustainable and eco-friendly propulsion technologies, biofuels, and reducing environmental impact. Advanced Materials: Research on materials for propulsion systems, thermal management, and structural integrity. Computational Fluid Dynamics (CFD): Modeling and simulation techniques to optimize propulsion performance. Propulsion Control Systems: Developments in autonomous and intelligent control of propulsion systems. Why Tuijin Jishu? Expertise: Our editorial board comprises esteemed professionals and researchers from diverse propulsion-related disciplines, ensuring a balanced and multidisciplinary approach to propulsion technology. Global Community: Tuijin Jishu fosters collaboration among propulsion enthusiasts worldwide, breaking down geographical barriers and promoting international cooperation. Innovation: We celebrate innovation and originality, encouraging authors to present groundbreaking research that contributes to the advancement of propulsion technology. Open Access: Tuijin Jishu follows the open-access model, making all published articles freely available to the global scientific community, accelerating the exchange of knowledge. Ethics and Integrity: We uphold the highest ethical standards in academic publishing, ensuring transparency, fairness, and confidentiality throughout the review process. Become a Contributor: Tuijin Jishu welcomes contributions from researchers, academics, and practitioners involved in propulsion technology. If you have exciting research to share or wish to stay updated with the latest developments in the field, subscribe to our journal or consider submitting your work to us.
Corporate Economic Sustainability and Competitiveness of Selected Banks in Nigeria: The Role of Female Leadership
Corporate sustainability has become a central driver of long-term competitiveness, yet the role of female leadership in shaping sustainable outcomes within developing economies remains insufficiently explored. Prior research has emphasized the environmental, social, and economic dimensions of sustainability but has often overlooked how gender diversity in leadership may strengthen these sustainability and competitiveness linkages. This study addresses this gap by examining the mediating role of corporate sustainability, measured through economic, social, and environmental dimensions, in the relationship between female leadership and competitiveness among selected Nigerian banks. Using a cross-sectional survey design, data were collected from 247 respondents across top, middle, and lower management levels in five leading banks. Employing PROCESS Macro (Model 4, parallel mediation), both direct and indirect relationships were analyzed. The findings reveal that female leadership positively influences all dimensions of corporate sustainability, each of which significantly enhances bank competitiveness. Mediation results indicate that female leadership partially transmits the effect of corporate sustainability on competitiveness, with social sustainability exerting the strongest mediating influence. The study concludes that while female leadership directly contributes to competitiveness, its full potential is realized when integrated with sustainability-driven strategies. Practically, the study underscores the need for gender-inclusive governance and sustainability-oriented leadership development in Nigeria’s banking sector.
"Integration of Solar-Wind Energy for EV Battery Charging with Active Battery Equalization"
Electric vehicles (EVs) are a cornerstone of sustainable transportation, but their real‑world performance suffers from two major issues: (i) voltage and state‑of‑charge (SOC) imbalances among series‑connected battery cells, which reduce usable capacity, and (ii) dependence on grid electricity that may come from fossil fuels. On‑board renewable energy (solar and wind) offers a solution for the second issue, but its benefits are only fully realized if the battery pack is balanced. This paper presents a detailed MATLAB/Simulink simulation study of a solar‑wind hybrid EV that incorporates a sequential active battery equalization system designed for parked charging (e.g., during daytime parking at work or home). The system consists of constant DC sources representing 535 W photovoltaic (PV) and 135 W wind turbine output, a five‑cell series battery pack initially mismatched from 10.5 V to 13.0 V, and a simple but effective minimum‑voltage selection algorithm. During equalization, the vehicle load is disconnected (parked condition), so the entire renewable power (670 W) can be routed to the weakest battery using relay‑based switching logic. Simulation over 10,000 seconds shows that all five voltages converge to 13.0 V (final spread <0.1 V), and SOC equalizes from 70–86.7% to ≈87%. Consequently, the usable pack energy increases by 10.2%, which translates into a theoretical driving range extension of 10–15%. A comparison with a case where equalization is disabled confirms that without active balancing the imbalance persists, wasting renewable energy. The model is a proof‑of‑concept with explicitly stated simplifications (constant renewables, linear battery model), yet it clearly demonstrates the feasibility and benefit of combining hybrid renewable charging with low‑cost sequential equalization. The work is particularly relevant for sunny and windy regions like India, where EVs can harvest significant energy while parked.
Hardware-Aware Quantized Generative Radiance Fields for Edge-Based Architectural Heritage Reconstruction
Architectural heritage preservation increasingly relies on neural rendering techniques such as Neural Radiance Fields (NeRF) for high-fidelity 3D reconstruction. However, the computational demands of these models limit their deployment on resource-constrained edge devices commonly used in field documentation. This study investigates the feasibility of hardware-aware quantized generative radiance fields for edge-based architectural heritage reconstruction. A co-designed framework integrating quantization-aware training (8-, 6-, 4-, and 2-bit), multi-scale perceptual loss, and hardware-specific inference optimization was evaluated on six architecturally diverse heritage datasets. Reconstruction quality was assessed using PSNR, SSIM, LPIPS, edge preservation, texture analysis, and expert evaluation, while computational efficiency was measured through latency, memory usage, and energy consumption on representative edge platforms. Results show that 8-bit quantization preserves reconstruction quality comparable to full precision while reducing memory requirements by four times and significantly improving energy efficiency. The 6-bit and 4-bit models maintain most diagnostically important architectural details, with the 4-bit configuration representing the practical lower limit for heritage applications. Quantization-aware training consistently outperformed post-training quantization, and perceptual loss improved the preservation of fine architectural features under moderate compression. The proposed framework achieved near workstation-level reconstruction quality on dedicated edge accelerators while dramatically reducing computational cost, demonstrating the practicality of edge-based neural rendering for architectural heritage documentation and supporting broader adoption of efficient digital preservation technologies.
Performance Comparison of Deep Learning Methods with Traditional Machine Learning Methods in Early Childhood Autism Spectrum Disorder Detection
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by social communication deficits and restricted, repetitive behaviors. In order to improve long term developmental outcomes by prompt therapeutic intervention, early detection means ideally before the age of three is essential. Conventional diagnostic approaches rely on clinical observation and standardized behavioral assessments, which are time intensive and often result in delayed identification, particularly in limited resource settings. This study presents a rigorous, systematic benchmarking of six machine learning models and six deep learning models for early ASD screening in children aged 18 to 60 months. This study evaluate both traditional machine learning (TML) classifiers including Random Forest, Support Vector Machines, XGBoost, Naive Bayes, k-Nearest Neighbors, and Logistic Regression and contemporary deep learning (DL) architectures including CNNs, LSTM, Bi-LSTM with Attention, ResNet-50, EfficientNet-B3, and BERT-based Transformer models. Experiments are conducted on five publicly available and clinically validated datasets ABIDE I & II, SFARI Gene, ADOS-2, Q-CHAT, and NDAR (NIMH). Multimodal data including fMRI imaging, behavioral observation scores, eye tracking sequences, and genomic markers are preprocessed using standardized pipelines. Models are evaluated under 10-fold stratified cross validation using accuracy, precision, recall, F1-score, and AUC-ROC as performance metrics. ROC curves are generated to visualize sensitivity-specificity trade-offs across all classifiers. Deep learning models consistently outperformed traditional counterparts across all metrics. The Bi-LSTM with Attention and BERT-based Transformer achieved the highest AUC-ROC of 0.97. ROC curve analysis confirmed visually and statistically that DL model curves lie uniformly closer to the upper left ideal corner, with DeLong's test confirming significant pairwise AUC differences (p < 0.001) between DL and TML groups. DL models demonstrate superior diagnostic discrimination for ASD screening and hold significant promise for clinical decision support. Future work should address explainability and federated learning for privacy preserving deployment.
Design of an Intelligent Model Selection Framework for Unified Review Authenticity Detection with Real-Time E-Commerce Integration
Objectives: Develop a machine learning system with online classification ability for detecting fake reviews of hotels using a web-based application. Methods: The analysis was done on 11,912 samples of hotel reviews. Preprocessing of the data set and extraction of the features were done using the TF-IDF method, after which seven machine learning algorithms including Decision Tree, K-Nearest Neighbor (KNN), Gaussian Naive Bayes, Multinomial Naive Bayes, Logistic Regression, Stochastic Gradient Descent, and Support Vector Machine (SVM) were trained using various kernels. The most efficient model was implemented using API on a demo hotel website to provide real-time prediction. Findings: An SVM model using Radial Basis Function (RBF) kernel yielded the highest prediction accuracy of 88% among the evaluated classifiers such as Decision Tree (63%), KNN (73%), Gaussian Naive Bayes (74%), Multinomial Naive Bayes (79%), Logistic Regression (81%) and SGD (83%). It can be inferred from the results that the RBF kernel is very effective in handling non-linearity present in the data set. Contrary to offline models, the proposed system provides real-time detection of fake hotel reviews using API. Novelty: This research has presented a unified framework that involves multi-model comparison, SVM kernel optimization, and real-time API-based deployment, enabling the practical implementation of machine learning algorithms for fake reviews detection applicable in online applications.
IntelliGas: An Intelligent Hazardous Gas Monitoring System
The rising rate of urbanization and industrialization has significantly increased the probability of unintended gas emissions. Those emissions can be highly dangerous to affect humans, work zone safety and even the environment. Traditional approaches of gas monitoring applications are limited to alarming users about the presence of hazardous gases in real-time with negligible scope for predicting the events beforehand. In this paper, we present an Edge AI-based hazardous gas detection and prediction monitoring framework that allows real-time monitoring along with predicting the chances of any hazardous event using Machine Learning algorithms. MH-Z19E and MQ-series gas sensors connected to Raspberry Pi edge-device are used to sense the real-time levels of carbon dioxide (CO₂), liquefied petroleum gas (LPG) and smoke. The data fetched from sensors will be locally analyzed by implementing edge-computing and MQTT-integrated pipeline stores the sensor data to MongoDB database for long-term historical data analysis and model training. Built Machine Learning algorithms based on Random Forest, XGBoost, and Long Short-Term Memory (LSTM) are used for predicting future levels of sensed gas as well as identifying abnormal environmental signatures. The integrated approach of predicting gas levels based on historical data along with providing real-time monitoring alerts for gas sensing provides a fool-proof solution for early warning systems, abnormality detection, and safety management use-cases. Designed framework can be utilized for various industries for commercial as well as environmental monitoring purposes
Optimizing Wear Resistance in Carbidic Austempered Ductile Iron through Austempering Parameter Variation
Austempered ductile iron (ADI), as implied by its name, is a variant of ductile iron known for its enhanced strength and toughness compared to cast iron. Distinguishing itself from cast iron, ductile iron exhibits higher strength and toughness, particularly in tensile tests. The nomenclature "Austempered" in ADI stems from its unique heat treatment process called Austempering. While numerous research journals delve into the wear resistance of Compacted Graphite Iron (CGI) materials, there is a notable gap in understanding the correlation between Austempering temperature and abrasion properties. Material wear is influenced by various factors within the application environment, broadly categorized as time, load, and speed. This journal provides a comprehensive exploration of how these variables collectively impact the wear characteristics of Austempered ductile iron.
Techno-Economic Viability Assessment of Offshore Wind Power Generation in the Bight of Benin - Nigeria Coast
The offshore wind energy data of the Bight of Benin, southwest Nigeria coast in the Gulf of Guinea Atlantic Ocean, were collected at 10m and 50m Above Sea Level (ASL) from various ground and high-resolution satellite recording stations for comparison, conformity, and confirmation of results for accurate analysis and modelling with various technical and economic feasibility measurement tools for offshore wind power generation based on Direct Drive Horizontal Axis Wind Turbine (HAWT) with Synchronous Permanent Magnet Generator (SPMG) configuration in Nigeria’s Bight of Benin littoral zone. The results were analysed to determine the technical and commercial viability of an offshore wind farm for sustainable power generation via interoperable mini-grids as well as in hybridization with other energy sources using proven techno-economic tools, which include: Techno-Economic Renewable Energy Viability Index (TEREVI), Techno-Economic Levelized Cost of Energy (TELCOE), conventional Levelized Cost of Electricity (LCOE), and HOMER software simulated Cost of Electricity. Wind speeds were found to be significantly higher on average at altitudes of 10m and above on the offshore compared to onshore and the average windspeed in all the data gathering stations were found to meet or exceed the 3.5m/s minimum windspeed threshold required for power generation with values varying strongly with seasons resulting in very good above average commercial prospect yielding TEREVI of 0.7625, TELCOE of 0.0265, LCOE of 0. 0.0249, and a HOMER generated COE of 0.019.
Techno-Economic Viability of Solar Thermal Power Generation in Southwest Nigeria
Being the capital of global energy poverty with over 0.65billion people representing more than 50% of its population lacking access to electricity, Sub-Saharan Africa, led by Nigeria with over 90million people without modern energy, of which over 70% depend on biomass, the need for a sustainable energy solution is an urgent imperative for the whole region. Despite having the highest solar energy potential globally, solar mini-grids are yet to achieve documented commercial success, which has hampered investment over the years, culminating in a high unemployment rate, insecurity, and socioeconomic underdevelopment. The viability of solar thermal energy (STP) systems for sustainable commercial Solar Thermal Power (STP) generation in Southwest Nigeria was investigated and analysed by acquiring a 21–year mean generic solar insolation and ancillary data for 23 evenly distributed cities across the geopolitical zones, with at least 3 selected from each of the 6 states. The technical and commercial feasibility was evaluated and analysed using Techno–Economic Renewable Energy Viability Index (TEREVI), Techno–Economic Levelized Cost of Electricity (TELCOE), conventional Levelized Cost of Electricity (LCOE), MATLAB Simulink, and HOMER Software simulated Cost of Electricity (COE) based on a 100kW plant mini-grid model in hybrid configuration interoperable with other grids, which yielded values of 0.836, $0.023/kWh, $0.022/kWh, $0.0.0309010/kWh and $0.018/kWh, respectively. Southwest Nigeria has a high solar heating intensity with a year-round average above the minimum threshold for STE power generation, which, coupled with the results of its techno–economic metrics, shows high market competitiveness for viable commercial operation, whether in short, medium, or long-term applications for mini-grid generation or steam integration with conventional power plants to optimize energy supply, cost, and greenhouse gas emission.
Techno–Economic Viability of Commercial-Scale Photovoltaic Solar-Powered Mini–Grid Systems in Sub-Saharan Africa
Sub-Saharan Africa is currently the darkest part of the world socioeconomically despite having the most solar energy potential globally with half a billion people lacking access to electricity and Nigeria contributing the biggest share resulting in huge unemployment rates, famine, infant mortality, insecurity, low human development index, and political instability among other hydra headed problems. This research aims to probe the commercial viability of solar-powered mini-grid systems for sustainable electricity generation in Sub-Saharan Africa, using Southwest Nigeria as a case study, given its median geographical and geopolitical environment in the region. This is against the background that over 97% of solar mini-grid projects in Sub-Saharan Africa have failed within 5years or are being propped up by external funding instead of being commercially self-sustaining. The 2000 to 2021 solar energy data for 23 locations across Southwest, Nigeria, were obtained from the Langley Research Center, the National Renewable Energy Laboratory, and the Nigerian Meteorological Agency, in addition to scant and isolated data as comparators. the mean mainland and offshore (Bight of Benin) All Sky Solar Insolation was 4.7KWH/m2/day and 4.9KWH/m2/day. A hypothetical 100kW solar mini-grid system was designed and optimally computed with HOMER simulation, MATLAB Simulink, and TELCOE evaluation, which yielded competitive values of $0.0137/kWh, $0.018928/kWh, and $0.019633/kWh, respectively. The system was finally tested by computing the Techno-Economic Renewable Energy Viability Index (TEREVI), which gave a very high value of 0.850911, well above the 0.60 threshold for the region. This conclusively shows that solar photovoltaic energy mini-grid in standalone or hybrid configuration or catenated clusters is commercially viable with the use of a proper comprehensive modeling technique, such as TEREVI, for planning and decision making, as well as keeping the grid capacity within optimal limits
Techno-Economic Viability of Bioenergy Resources for Sustainable Mini–Grid Power Generation in Southwest Nigeria
Given the lingering inadequate power problems, low clean energy access, and increasing energy cost, Biomass energy presents an opportunity for local energy solutions, especially for rural communities, farms, and agro-allied industries. Comprehensive data on biomass were obtained from various primary and secondary sources, and conservative estimates of the potential of the various bioenergy technologies and power generation technologies in Southwest Nigeria were made. The commercial and technical viability of bioenergy resources was investigated using proven tools such as Techno–Economic Renewable Energy Viability Index (TEREVI), Techno–Economic Levelized Cost of Energy (TELCOE), Levelized Cost of Electricity (LCOE), and HOMER Software simulation Cost of Electricity (COE). Southwest Nigeria is found to have a bioenergy very conservative potential of 3.07 × 10^9 MWh, for non–food Dedicate Energy Crops (DEC); 1.21 X 109m3 of biomethane or 6.64 X 106MWH, for landfill gas (LFG) Waste – to – Energy (WtE) scheme; 2.1 X 109m3 or 21.97 X 106MWH of biogas methane. The viability assessment test result using various tools and evaluation models shows very highly competitive techno-economic feasibility of bioenergy technologies for sustainable commercial-scale off-grid distributed power generation in Southwest Nigeria with competitive indices of $0.014651/KWH TELCOE, 0.791839506 TEREVI, $0.014077/KWH LCOE, MATLAB Simulink value of $0.01862/kWh, and a HOMER COE value of $0.0127/KWH. The overall funding reveals that biomass is a great energy prospect for power generation in southwest Nigeria with a high score of 0.791 given a regional potential of 3.07X109MWH, for non – food Dedicate Energy Crops (DEC); 1.21 X 109m3 of biomethane or 6.64 X 106MWH, for landfill gas WtE scheme; 2.1 X 109m3 or 21.97 X 106MWH of biogas methane, for animal wastes and so on.
Techno-Economic Viability Assessment of Onshore Wind Power Generation in Southwest Nigeria
There is currently no commercial or mini-grid wind power generation in Southwest Nigeria, and previous research efforts generally consider the region non–feasible for a wind farm. A 20-year atmospheric meteorological data including wind speed was acquired for 23 evenly distributed stations strategically located in the mainland involving at least 3 stations in each of the 6 states that make up the zone based on historical and real time records from high resolution satellite, ground atmospheric data measurements from research institutions, weather records of private organizations, and individual sources. The wind energy data from satellite was compared and found to corroborate with other sources, given negligible variance. The viability of wind power generation was evaluated by applying empirical wind energy principles, proven technical and commercial feasibility measurement tools, such as the Techno-Economic Renewable Energy Viability Index (TEREVI), as well as HOMER and MATLAB Simulink simulations based on a 100KW direct drive wind turbine configurable with other energy generation technologies for mini-grid power generation. About 4.3% of southwest Nigeria has strong potential for commercially viable wind power generation at 10m AMSL and 87% at 50m AMSL altitudes, respectively. At 10m AMSL, the mean wind speeds range from 0.99m/s at Okitipupa to 6.29m/s at Obafemi Owode, with an average of 2.45m/s peaking between June and September, by which Obafemi Owode is the only site that passed the 3.5m/s threshold for commercial wind farm electricity generation. The wind energy potential generally shows strong localization with overall year – round 50mAMSL average of 3.76m/s while 75% of the year have values above 3.5m/s threshold ranging from 3.54m/s to 4.46m/s with Okitipupa, Ondo Town, Shagamu, and Osogbo being the only sites having below commercial threshold value while Obafemi at 7.2m/s has the highest.
Techno-Economic Viability of Tidal Power Generation in the Bight of Benin, Nigerian Coast
Despite its prodigious endowment with ocean renewable energy resources, Nigeria, especially the Southwest industrial hub, suffers massive power problem, leading to numerous socioeconomic and security problems such as high poverty rate, unemployment, collapse of the manufacturing sector, difficulty in doing business, falling human development index, and so on. Over 65% of the population of Southwest Nigeria are residents of the three. Given that about 70% of Southwest Nigeria’s population and the nation’s industrial hub is concentrated in the coastal states of Lagos, Ogun, and Ondo, with a total of 273Km shoreline, it is imperative to explore and harness ocean renewable energy sources such as tidal energy conversion for mini-grid electrification. A 21-year tidal energy data of the Bight of Benin between 2000 and 2021 was acquired and analysed with a hypothetical 100kW tidal-powered mini-grid in hybrid configuration. The systems were simulated and evaluated with HOMER, Matlab Simulink, Techno-Economic Levelized Cost of Energy (TELCOE), and generic Cost of Energy (COE) computation. Finally, the Techno-Economic Renewable Energy Viability Index (TEREVI) was applied as a confirmatory determinant energy model. Tidal power generation technology has a TEREVI score of 0.65, which is well below the proven threshold due to the relatively low tidal range in the 2 to 4 sea storm class; thus, the recorded mean tidal range is 1.1m with periodic and seasonal spikes which follow quite a similar pattern as the windspeed and wave energy variations. Tables 4.3 and 4.4 show information on the tidal heights and power potentials in the Bay of Benin. The cost of generating electricity is also very high at $0.096153237/kWh from HOMER optimization results, $0.091201/kWh for Matlab simulation results, $0.062248695/kWh for LOCE, and $0.07019/kWh for TELCOE, which makes it uncompetitive at current market rates except for long-term research and development purposes. The low tidal range at the Bight of Benin, coupled with the technological infancy of tidal energy, makes it non – commercially feasible and below the TEREVI threshold.
TEREVI, TELCOE, Tidal Power, Tidal Stream Generator, Bight of Benin, Ocean Renewable Energy Technology.
Techno–Economic Viability of Ocean Wave Power Generation in the Bight of Benin, Southwest Nigerian Coast
With about 40% of Nigeria’s population representing over 92million and about 45% of Sub-Saharan Africa representing over 600million lacking access to electricity or clean energy, being the energy poverty capital of the world, developing autonomous renewable energy sources based on isolated or interconnected mini-grid systems is exigent. Ocean renewable energies like wave energy present a great option given the fact that coastal and cities tend to have higher populations, while islets, remote settlements, and swampy areas in Nigeria’s littoral zones tend to be primarily underserved due to technical difficulty, logistic challenges, and cost of grid extension, among other things. The wave energy electric power generation potential for Southwest Nigeria was assessed by obtaining and averaging the significant wave heights (SWH), periods, and ancillary datasets at various times of the day and for three geolocations evenly spread from west to east across the region’s coastline in the Bight of Benin. The data for wave energy variables were evaluated using various models and methods, including Techno-Economic Renewable Viability Index (TEREVI), conventional Levelized Cost of Electricity (LCOE), Techno-Economic Levelized Cost of Energy (TELCOE), and HOMER simulation to measure the techno-economic viability and general competitiveness among other proven and probable renewable energy resources in Southwest Nigeria based on a 100kW pilot wave power plant. From the results, HOMER optimization energy cost is $0.094003211/kWh, the Matlab energy generation cost simulation is $0.09t11/kWh, the LCOE value is $0.082307404/kWh, and the TELCOE value is $0.092809/kWh. Wave power potential in the Bight of Benin shows a conservative value of low to marginal commercial viability based on existing conversion technologies owing to low learning rate, thus presenting a medium to long-term electrification promise as a fair future sustainable energy strategy especially given Nigeria’s drive towards blue economy, energy transition, and the trend towards low wave height conversion technologies that is likely going to increase with the advance in research and the development of high-efficiency wave turbines and site preparation techniques serving not just power generation but also integral to windbreaks for ports and harbours.
wave power, significant wave height, wave period, renewable energy, Bight of Benin, marine energy resources, wave energy potential.
Techno-Economic Viability of Small Hydro Power Plants for Mini-Grid Electrification in Southwest Nigeria
The commercial viability of small, mini, and micro hydroelectric power generation collectively termed Small Hydro Power (SHP) potential in Southwest Nigeria was investigated by collecting proven historical and real-time data of extant SHP-convertible water bodies as well as general hydrology and population settlement pattern in the region based on reservoir hydroelectric power technology in distributed mini-grid application using a 100kW pilot model. Small hydropower with scores high on the Techno-Economic Renewable Energy Viability Index (TEREVI) scale with a value of 0.795 compared to the minimum viability threshold of 0.70 which is a reflection of good hydro potential given widespread water resources and high year – round precipitations with mean runoff flowrate being 180.65m3/s ranging from 23.12m3/s in January to 430.67m3/s in June representing corresponding rainfalls of 0.59mm and 10.99mm respectively. The cost of small hydro energy generation is relatively low compared to other sources, given values of $0.022069123/kWh from HOMER simulation, $0.02316/kWh from Matlab simulation, $0.027878615/kWh from LCOE, and $0.0296/kWh from Techno-Economic Levelized Cost of Energy (TELCOE), with a mean COE of $0.025676935/kWh. Southwest West Nigeria’s surface runoff hydro power potential is 448.16kW/m2, the overall finding reveals that reservoir SHP harnessed in distributed generation power mini–grids is a very technically and commercially viable sustainable clean energy opportunity for Southwest Nigeria with proven potentials between 70MW to 350MW and probable potential up to 3900MW, which could be much higher with flood and stormwater optimization.
Hydroelectric power, reservoir hydro, Small Hydroelectric Power (SHP), TEREVI, TELCOE, HOMER, LCOE, stormwater hydropower.
Development and Evaluation of Techno-Economic Renewable Energy Viability Index (TEREVI) for Sustainable Distributed Hybrid Mini-Grid Power Generation in Sub-Saharan Africa
Empirically, all notable past and existing renewable energy mini-grid projects in Sub-Saharan Africa have not been commercially sustainable, thus necessitating financial propping by government grants and external funding from donor agencies, else outright run-to-comatose after a few years of operation. A techno-economic evaluation of the proven and probable renewable energy resources cum technologies based on potentials in South-West Nigeria was carried out as a case study using a Multi-Criteria Decision Method (MCDM) based on an Analytical Hierarchical Program (AHP) scheme from which a comprehensive Techno-Economic Renewable Energy Viability Index (TEREVI) was developed. A hypothetical 100 kW mini-grid power plant powered by 1 or more of the 8 2R renewable energy sources was designed. A technical feasibility analysis, followed by HOMER and MATLAB simulation, was carried out. The TEREVI was calculated and used for the Mutative Genetic Algorithm (MGA) scheme for each hybrid configuration. The TEREVI shows a mean of 0.756914 and a standard deviation of 0.075644 with Solar Photovoltaic, Solar–Thermal, Small Hydro, Bioenergy, and Offshore Wind technologies having above mean values and overall best ranking respectively for off-grid power generation and hybridization. The overall results proved that the TEREVI energy model is a comprehensive technique for local and global application in techno-economically assessing, selecting, sizing, planning, designing, and optimizing off-grid power systems based on existing renewable energy resources and local variables for self-sustainable commercial mini-grids in Sub-Saharan Africa.
TEREVI, techno-economic renewable energy, sustainable mini-grid, Sub-Saharan Africa, hybrid energy system, southwest Nigeria.
Women Entrepreneurial Financing and Business Sustainability
This study examines women entrepreneurs’ financing and business sustainability in Southwest Nigeria, with particular emphasis on the complementary roles of formal and informal financing mechanisms. A survey research design was employed, drawing on primary data from 874 women entrepreneurs selected through purposive and stratified random sampling techniques. Data were analysed using descriptive statistics (percentages, means, and averages) and multiple regression for inferential analysis. The results reveal that both formal financing (β = 0.528, p < 0.001) and informal financing (β = 0.691, p = 0.003) significantly enhance business sustainability, with informal financing exerting a stronger influence. This underscores the continued importance of indigenous, trust-based systems such as ajo, esusu, and cooperatives in bridging gaps created by limited access to formal credit, particularly for micro- and small-scale women-owned enterprises. The findings corroborate Schumpeter’s Theory of Economic Development by confirming that access to finance drives entrepreneurial innovation and growth, while also aligning with Gender and Development (GAD) Theory, which highlights the transformative potential of women’s financial inclusion. The study demonstrates strong linkages with the Sustainable Development Goals (SDGs), notably SDG 1 (No Poverty), SDG 5 (Gender Equality), SDG 8 (Decent Work and Economic Growth), and SDG 10 (Reduced Inequalities). Policy implications suggest the integration of informal financing structures into national financial inclusion frameworks, the development of gender-sensitive financial products by formal institutions, and the promotion of hybrid financing ecosystems that leverage both institutional credit and community-based financial systems. The study recommends capacity building, digital financial inclusion, and gender-lens investing as critical strategies to enhance women’s access to finance and sustain their enterprises in Southwest, Nigeria.
Comprehensive Approach to Intelligent Intrusion Detection in Cyber Security Using Machine Learning
Cybersecurity attacks are growing at an unprecedented rate, making conventional Security Operations Centers (SOCs) increasingly inefficient due to overwhelming alert volumes, elevated false-positive rates, and isolated security data sources. This paper introduces a user-focused machine learning framework for intelligent intrusion detection in cybersecurity systems. The proposed approach combines multiple sources of security information, including network traffic data, endpoint activity logs, and threat intelligence feeds, to enable comprehensive threat assessment. Machine learning techniques such as Decision Trees, Random Forests, and anomaly detection algorithms are utilized to detect malicious activities and classify potential threats. Natural Language Processing (NLP) methods are applied to extract meaningful information from unstructured sources, including phishing emails, threat reports, and security documents. The framework also incorporates interactive visualization dashboards that support analysts in alert prioritization and enhance decision-making capabilities. Continuous feedback and adaptive learning mechanisms facilitate model refinement and retraining, leading to improved detection performance over time. Furthermore, explainable artificial intelligence and role-based access control mechanisms are integrated to promote transparency, accountability, and trust in automated security decisions. The proposed framework effectively decreases false-positive alerts, accelerates threat response, and enhances the overall resilience of cybersecurity infrastructures.
Deep Learning Framework for Automated Worker Helmet Detection And Safety Compliance Monitoring Using The Yolo Object Detection Model
Construction automation tools that implement computer vision technologies like “YOLO and Re-CNNs actively contribute to enhancing safety at construction sites with their automated monitoring and detection systems. The system presented in this paper harnesses the YOLO detection model, and utilizes the Flask web application to detect in real time if construction workers are wearing safety helmets, and to check if construction safety compliance is being upheld. This framework offers continuous automated monitoring of construction safety compliance in lieu of the traditional manual check systems, and therefore reduces both systematic and human errors. The system incorporates an automated notification system to alert construction safety compliance officers to the occurrence of a safety compliance violation in order to facilitate on the spot corrective actions. The Flask web application offers an easy-to-use Upload interface for video files and real time web cam control with no technical skills required of the user. The system is designed to record safety compliance violations for future reviews and to facilitate safety compliance violation reporting. The built-in YOLO detection model reportedly offers powerful detection capabilities in a variety of different environments, in addition to being able to handle changes in visibility and abrupt occlusions. The combination of deep learning, computer vision, and web technologies offers a highly effective system to ensure compliance with construction safety regulations.
Hydraulic Analysis, Calibration, and Validation of Developed QGIS–EPANET Model
Hydraulic model calibration and validation are essential components of water distribution system analysis, providing a quantitative basis for evaluating model reliability and its ability to reproduce real-world hydraulic behaviour. This study presents the calibration and validation of a QGIS–EPANET model developed for Enugu Metropolis, integrating spatial data with hydraulic simulation to improve prediction accuracy. Field measurements from ENSWC operational records were utilized to adjust pipe roughness, nodal demand, and minor loss coefficients until the simulated pressures and flows aligned with observed data. Calibration yielded an average pressure deviation of 0.88 m, while computed statistical indices—RMSE (1.12 m), MAPE (3.87%), and R² (0.93)—indicated excellent agreement between observed and simulated values. Validation using independent datasets further confirmed model robustness, with RMSE = 0.95 m, MAPE = 2.64%, and R² = 0.92. The results demonstrate that the QGIS–EPANET framework offers high predictive reliability and provides a dependable decision-support tool for optimizing water distribution in Enugu Metropolis.
Negotiation Strategies and Textual Structure in Global War Diplomacy
This article examined the relationship between negotiation strategies and textual structure in global war diplomacy. It argued that diplomatic texts used during war are not neutral containers of political intention; rather, they are strategic instruments through which threats, concessions, legitimacy claims, procedural designs, and peace options are organized. Drawing on scholarship in international negotiation, bargaining theory, coercive diplomacy, mediation studies, and diplomatic discourse analysis, the article developed an integrative conceptual framework for understanding how the structure of diplomatic texts supports, constrains, or transforms negotiation strategy. The analysis showed that global war diplomacy operates through a dual logic: it seeks to alter material incentives while simultaneously managing meaning, audience expectations, legal legitimacy, and the adversary’s capacity to comply without total humiliation. Strategies such as coercive bargaining, issue-linkage, sequencing, mediation, multilateralization, face-saving, strategic ambiguity, and ceasefire-to-settlement transition are realized in recurring textual features, including preambles, operative clauses, modalities, conditional formulations, politeness markers, intertextual references, bracketed drafts, and multilingual concordance. The article concluded that the effectiveness of war diplomacy depends not only on power and interests but also on the disciplined production of texts that make negotiated movement possible. It contributes to international relations and discourse studies by proposing a strategy-text matrix that links negotiation goals to the formal organization of diplomatic communication.
Patient-Focused Communication Practices of Nigerian Medical Officers When Conveying Adverse Diagnoses: An Integrative Narrative Review and Context-Sensitive Practice Framework
Conveying an adverse diagnosis is among the most ethically sensitive and emotionally demanding duties of medical practice. In Nigeria, adverse diagnosis communication occurs within a pluralistic health system marked by family involvement, religious meaning-making, language diversity, variable health literacy, resource constraints, and hierarchical doctor-patient relations. Although structured breaking-bad-news models such as SPIKES are widely cited, the extent to which these models are patient-focused and contextually adapted in Nigerian practice remains an important scholarly and clinical question. This article synthesised current evidence on patient-focused communication practices among Nigerian medical officers and related physician groups when conveying adverse diagnoses and proposed a context-sensitive framework for improving disclosure practice, training, and institutional support. An integrative narrative review was conducted using accessible peer-reviewed and policy-relevant literature on breaking bad news, patient-centered communication, Nigerian doctor-patient interaction, and communication barriers in sub-Saharan African clinical settings. Evidence was synthesized thematically around protocol use, patient autonomy, family and cultural mediation, emotional support, religious/spiritual considerations, health-system constraints, and communication training. The reviewed evidence suggested that Nigerian doctors increasingly recognized the importance of structured, patient-focused disclosure. A recent cross-sectional study among 245 Nigerian doctors reported 72.7% full adherence to SPIKES, with very high reported adherence to knowledge sharing, setting, empathy, and strategy, but weaker adherence to invitation, which concerns how much information the patient wishes to receive (Ipinnimo et al., 2025). A qualitative study of Nigerian clinical oncologists found that breaking cancer news was overwhelming, required individualized communication, should keep the patient as the primary focus, and was constrained by workload and insufficient training (Okoye et al., 2024). Broader Nigerian communication research indicated that clinical encounters may remain doctor-centered because of power distance, limited disclosure depth, insufficient empathy, and institutional weaknesses in patient-centered care (Adam, 2014; Adebayo, 2021; Akinkurolere, 2022; Lateef & Mhlongo, 2022). The synthesis led to the development of RESPECT-NG, a six-domain framework: Readiness and setting; Explore perception and disclosure preference; Share knowledge plainly; Pause for emotion, family, and spiritual meaning; Enable decisions and next steps; and Continuity, team support, and navigation. Patient-focused adverse diagnosis communication in Nigeria requires more than technical delivery of diagnostic information. It requires ethically grounded, culturally responsive, emotionally supportive, and system-enabled communication. The proposed RESPECT-NG framework may inform medical education, continuing professional development, hospital policy, and future empirical research on adverse diagnosis disclosure in Nigeria.
Evaluating Broadcast Station Licensing in Ekiti State, Nigeria: Regulatory Standards, Public-Interest Performance, and Democratic Accountability
Broadcast station licensing is a central instrument through which Nigeria regulates access to the scarce radio-frequency spectrum, structures ownership of broadcast media, and imposes public-interest obligations on radio and television operators. This article evaluated broadcast station licensing in Ekiti State by examining the legal framework established under the Constitution of the Federal Republic of Nigeria and the National Broadcasting Commission Act, the institutional mandate of the National Broadcasting Commission (NBC), the distribution of selected licensed broadcast stations in Ekiti State, and the post-licensing compliance record illustrated by the 2018 sanction and closure of the Broadcasting Service of Ekiti State. The study adopted a qualitative desk-review method based on statutory provisions, official regulatory information, government institutional records, broadcast-station listings, scholarly literature, and credible news and press-freedom reports. The analysis found that licensing in Ekiti State has contributed to broadcast pluralisation, especially through the presence of federal, state, university, and private radio stations. Nevertheless, the licensing landscape remains spatially concentrated around Ado-Ekiti and a small number of urban centres, while the 2018 BSES case demonstrated the continuing vulnerability of state-owned broadcasting to political influence during elections. The article argued that an effective licensing evaluation should not stop at issuance of licences; it should also assess geographic equity, ownership diversity, technical compliance, editorial independence, electoral responsibility, sanction proportionality, and public-service performance. It concluded that Ekiti State’s broadcast licensing regime has created an operational foundation for plural broadcasting, but its democratic value would be strengthened through transparent licensing data, clearer local coverage audits, stronger safeguards for state-owned broadcasters, periodic public-interest reviews, and regulator accountability.
A Data Science Approach to Analysing and Understanding Crime Patterns for Improved Law Enforcement Strategies
This project focuses on analyzing crime patterns across different states and union territories in India using historical crime data. The dataset contains information on various crime types recorded annually, enabling temporal and spatial analysis. The study applies machine learning techniques, including K-Means clustering, to group states with similar crime trends. Classification algorithms such as Random Forest, Logistic Regression, and Support Vector Machines (SVM) are used to predict crime categories based on historical patterns. K-Means clustering helps in identifying regions with high, medium, and low crime incidence. Random Forest provides insights into the most significant factors influencing crime rates. Logistic Regression is employed for probabilistic prediction of crime occurrence. SVM is used to classify crime types with optimized decision boundaries. The integration of clustering and classification offers both descriptive and predictive analysis of crime patterns. Overall, the system supports policymakers and law enforcement agencies in understanding trends, allocating resources effectively, and implementing targeted crime prevention strategies.
Intelligent Cyber Attack Prediction Using Data-Driven Machine Learning Models for Enhanced Network Security
The rapid growth of internet technologies and digital communication platforms has resulted in a substantial increase in cybersecurity risks and cyberattack incidents. Conventional security systems that depend on static, rule-based mechanisms are often inadequate for identifying and preventing modern, dynamic, and sophisticated threats. To overcome these challenges, this paper introduces an intelligent cyber-attack prediction framework based on data-driven machine learning techniques aimed at strengthening network security. The proposed system processes user-submitted URLs along with associated communication protocols to assess potential security vulnerabilities. A Flask-based web application is developed to support real-time interaction between users and the predictive model. Machine learning algorithms are applied to analyze URL behavior and classify them as either safe or malicious. The system further categorizes threats into low, medium, and high risk levels while generating automated mitigation recommendations. All prediction results are stored in a database to enable visualization and trend analysis. This approach enhances detection accuracy, minimizes manual intervention, and supports proactive cybersecurity defense mechanisms.
A Comprehensive Deep Learning Framework for Railway Track Fault Identification Using Region Convolutional Neural Networks.
Railway track inspection plays a vital role in maintaining transportation safety, operational efficiency, and infrastructure reliability. Conventional manual inspection techniques are labor-intensive, expensive, and susceptible to human errors. This paper proposes an advanced deep learning-based framework for automated railway track defect detection using Region Convolutional Neural Networks (RCNN). The proposed approach utilizes images acquired from a rolling camera installed beneath a moving inspection vehicle. The captured images are subjected to preprocessing and feature extraction procedures to improve defect detection performance. A hybrid combination of Convolutional Neural Networks (CNN) and RCNN is employed to identify and classify railway track defects accurately. The framework effectively differentiates between defective and non-defective track conditions. Experimental evaluation demonstrates that the proposed approach achieves an accuracy exceeding 90%, making it a dependable and efficient solution for real-time railway track inspection and maintenance management.
Data Driven AI Framework for Improving Healthcare Outcomes using Natural Language Processing and Machine Learning Techniques
The adoption of Artificial Intelligence (AI) in healthcare has created new opportunities for improving medical diagnosis, patient support, and treatment planning. This paper proposes a data-centric AI framework that integrates Natural Language Processing (NLP), Machine Learning, and Generative AI to deliver intelligent healthcare assistance. The framework analyzes patient-reported symptoms using TF-IDF feature extraction and a Logistic Regression classifier to identify potential diseases. Furthermore, a Generative AI component generates comprehensive medical information, including disease descriptions, risk assessments, and preventive recommendations. The proposed solution is implemented as a Flask-based web application featuring secure user authentication and conversation history management. Experimental evaluation demonstrates that the framework can accurately classify diseases from textual symptom descriptions while providing informative healthcare guidance. The system improves access to preliminary medical support, minimizes dependence on manual assessment, and enhances healthcare decision-making. Overall, the proposed framework contributes toward the development of intelligent, scalable, and user-friendly healthcare technologies.
An Advanced CNN-Driven Image Analysis System for Proactive Prediction and Automated Detection Drivers Disease Conditions in Rice Crop Cultivation
This work proposes a CNN-driven image analysis framework for the automated identification and classification of rice crop diseases using deep learning within a web-enabled environment. The system utilizes a rice leaf image dataset consisting of both healthy samples and diseased leaves affected by bacterial leaf blight, brown spot, and leaf smut. To improve data quality and model reliability, preprocessing operations such as image resizing, normalization, and augmentation are performed before training. A Convolutional Neural Network (CNN) is employed to automatically learn discriminative features from leaf images and perform disease classification without requiring manual feature extraction. The trained model is deployed through a Flask-based web application that supports secure user authentication, image uploading, prediction generation, and real-time disease diagnosis. In addition, the framework provides appropriate treatment recommendations based on the detected disease category. Performance evaluation is carried out using visualization tools, including accuracy curves and training-performance graphs, to analyze learning behavior and classification effectiveness. The proposed solution offers a scalable, accurate, and efficient approach for automated rice disease detection, thereby supporting precision farming practices and enhancing agricultural productivity
Comprehensive Analysis of Music Streaming Behavior Using Spotify Data and Insights
The rapid expansion of digital music streaming platforms has produced vast amounts of user interaction data that can be leveraged to understand listener behavior and forecast music popularity trends. This study introduces a comprehensive analytical framework for examining music streaming behavior using Spotify datasets combined with machine learning approaches. The proposed system employs regression-based predictive models, including Linear Regression, Decision Tree Regressor, and Random Forest Regressor, to estimate track popularity using audio features such as danceability, energy, loudness, speechiness, acousticness, valence, and tempo. Data preprocessing techniques, including normalization, feature scaling, and dataset cleaning, are applied to improve model performance and ensure reliable predictions. A Flask-based web application is implemented to provide user authentication, model training, interactive visualization dashboards, and real-time popularity prediction capabilities. Visualization techniques such as histograms, scatter plots, correlation heatmaps, and trend analysis graphs are used to interpret streaming patterns and listener engagement behavior. Experimental results indicate that ensemble learning models effectively capture complex relationships among features and outperform traditional regression approaches in prediction accuracy. The proposed framework supports data-driven decision-making for music streaming platforms, artists, and marketers by identifying key musical attributes and emerging popularity trends. The study demonstrates the effectiveness of machine learning in enhancing recommendation systems, improving user experience, and optimizing strategic planning within modern music streaming ecosystems.
AI-Driven Techniques for Detecting Malicious and Fake Profiles Across Social Media Networks
With billions of users worldwide, social media platforms have become essential channels for communication and information sharing. However, the rapid expansion of these platforms has also led to a significant increase in fake accounts used for malicious activities such as phishing, spam campaigns, fraudulent advertising, and the spread of misinformation. Traditional detection approaches based on rule-based filtering and manual moderation are insufficient to handle large-scale data and are ineffective against sophisticated fraudulent behaviors. To address these limitations, this study proposes an artificial intelligence-driven fake account detection system that integrates machine learning techniques with behavioral feature analysis. The system evaluates user profile attributes such as follower count, account age, posting frequency, and username characteristics to classify accounts as genuine or fake. Multiple machine learning models, including Random Forest, Support Vector Machine, and Neural Networks, are trained and evaluated for performance comparison. A Flask-based web interface enables real-time prediction and visualization of results. Feature scaling, encoding, and automated model selection further enhance detection accuracy and system efficiency. Experimental results demonstrate improved classification performance compared to conventional approaches, supporting scalable deployment in real-world environments. The proposed framework provides an intelligent, automated, and transparent solution for strengthening security in online social networks.