Bells University of Technology

Spoilage of apples continues to be a significant issue in the fruit industry. This study aimed to isolate and identify fungal species on deteriorated apples collected from three different locations in Ota market, Ota, Ogun State, Nigeria. A total of eighteen (18) samples of red delicious and Granny Smith apples with obvious spoilage were collected, and their surfaces were sterilized using 85% ethanol. After that, the samples were cultivated on potato dextrose agar (PDA) supplemented with 30 mg/l of chloramphenicol, and incubated at 30 °C for five to seven days. From the subcultures of the primary plates, pure fungal cultures were obtained and were identified by morphological characterization and internal transcribed spacer (ITS1/ITS4) gene method. Ten fungi that cause spoilage in apples have been identified and grouped into six distinct classes. Among the 40 isolates, the most common one was Trametes polyzona strain MT9, accounting for 27.5% of the total isolates. The second most prevalent isolate was Geotrichum candidum strain MT10, with six isolates, representing 15% of the total. The least frequent was Fusarium sp. strain MT3, with only one isolate, amounting to 2.5%. It was in this connection, that a sequence analysis of the ITS regions of the nuclear-encoded rDNA was conducted, revealing significant alignments with Aspergillus sp., Lasiodiplodia theobromae, Curvularia aeria, and Trametes polyzona. This research investigation sought to elucidate the relationships between specified species, yielding a biocontrol strategy for mitigating fruit deterioration and conserving quality.

This study explored eggshells as an eco-friendly and cost-effective material for synthesizing hydroxyapatite. The phase compositions and morphological structure of polylactic acid composite with and without co-doped hydroxyapatite addition via a melt blending approach were evaluated. Furthermore, the biodegradation profile of the polylactic acid composite in phosphate buffer solution was studied. The concentrations of PLA/HAp, PLA/7.5MgO-7.5ZnO, and PLA/12.5MgO-2.5ZnO samples, respectively, were examined in this study. The results of morphological evaluation showed a well-distributed irregular spherical phase of hydroxyapatite. Meanwhile, the co-doped hydroxyapatite phases have variations in sizes and shapes. The polylactic acid composites showed fractured, rough, and honeycomb surfaces with interconnected pores suitable for cell propagation and enhancement, and the elemental composition proved precipitation of apatite formation. Characteristics of absorption bands of the hydroxyapatite, magnesium, zinc, and polylactic acid were present, respectively. The XRD spectra confirmed the presence of crystalline and semi-crystalline structures with percent crystallinity of 48.57%, 56.64%, and 60.08%, respectively.

Conventional approaches to analyzing power losses in electrical transmission networks have largely emphasized generic power loss minimization through the integration of loss-reducing devices such as shunt capacitors. However, achieving optimal power loss minimization requires a more data-driven and intelligent approach that transcends traditional methods. This study presents a novel classification-based methodology for detecting and analyzing transmission line losses using real-world data from the Ikorodu–Sagamu 132 kV double-circuit line in Nigeria, selected for its dense concentration of high-voltage consumers. Twelve (12) transmission lines were examined, and the collected data were subjected to comprehensive preprocessing, feature engineering, and modeling. The classification capabilities of advanced deep learning models—Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), and Gated Recurrent Unit (GRU)—were explored through six experimental scenarios: LSTM, LSTM with Attention Mechanism (LSTM-AM), BiLSTM, GRU, LSTM-BiLSTM, and LSTM-GRU. These models were implemented using the Python programming environment and evaluated using standard performance metrics, including accuracy, precision, recall, F1-score, support, and confusion matrices. Statistical analysis revealed significant variability in transmission losses, particularly in lines such as I1, Ps, Ogy, and ED, which exhibited high standard deviations. The LSTM-AM model achieved the highest classification accuracy of 83.84%, outperforming both standalone and hybrid models. In contrast, BiLSTM yielded the lowest performance. The findings demonstrate that while standalone models like GRU and LSTM are effective, the incorporation of attention mechanisms into LSTM architecture enhances classification accuracy. This study provides a compelling case for employing deep learning-based classification techniques in intelligent power loss classification across transmission networks. It also supports the realization of SDG 7 by aiming to provide access to reliable, affordable, and sustainable energy for all.

This research assesses the anticorrosive activity of Musa Paradisiaca (plantain peel extract) on AA6063 aluminum alloy when immersed in a 1M HCl solution. The plantain extract was tested in concentrations of 0.2g, 0.4g, 0.6g, and 0.8g and is considered a natural green corrosion inhibitor. The corrosion studies on AA6063 were carried out by the techniques of potentiodynamic polarization, optical microscopy, SEM/EDS, and XRD analysis. Also, tests were done in the range of 30°C to investigate the inhibition efficiency. It was observed from results that the plantain extract acts as a mixed-type inhibitor and it helps in reducing the corrosion rate by depositing a protective coating on the surface of the alloy. It was found from the Tafel plots that as the concentration of extract was increased, there was a decrease in corrosion current density (jcorr) and an increase in Polarisation Resistance (Rp). The adsorption mechanism agreed with the Langmuir isotherm suggesting that only monolayer adsorption took place. Analysis of the protective film by SEM/EDS and XRD revealed the presence of Fe2O3, CaO, Mg, SiO2, and Zn components. Maximum inhibition efficiency of 82% was recorded at the extract concentration of 0.8g. The study highlights the potential of plantain extract to serve as a viable green inhibitor for aluminium in acidic medium.

Aluminium Metal Matrix Composites (AMMCs) play a significant role in diverse industries such as automotive, aerospace, and structural sectors due to their unique characteristics, including low density, high hardness, wear-resistance, and corrosion resistance. Typically, these composite materials employ synthetic reinforcements like SiC and Al2O3, which contribute to higher production costs. However, agricultural waste materials, which are abundantly available worldwide and pose environmental and health risks, have shown potential as suitable reinforcement materials for AMMCs. This study focuses on the development of a novel aluminium metal matrix composite by incorporating Palm Kernel Shell (PKS) particles into AA 7075 in varying percentages (5wt%, 10wt%, 15wt%, 20wt%). Stir casting was employed to produce the composite samples. Mechanical and anticorrosive experiments were conducted to evaluate the resulting materials. The research findings indicate a significant enhancement in the tensile strength and hardness of the composites, along with a reduction in corrosion rates. The most favorable samples exhibited an 8.25% increase in tensile strength, a 23.9% improvement in hardness, and a remarkable 61.6% decrease in corrosion rate.

The need to obtain a uniformly distributed reinforced particulate on AA6063 aluminium alloy for improved mechanical, corrosion and structural properties has necessitated this study. A well synthesised biocompactable particulate of rice hulls/periwinkle shells under varying matrix of 85Al-9.0RHA-6.0PSA, 85Al-7.5RHA-7.5PSA, and 85Al-6.0RHA-9.0PSA was developed and compared with the control for manufacturing application. The microstructural evolution was observed using SEM/EDS quantification. The intermetallic assessment was done using X-ray Diffractometer (XRD). The diameter of indentation was used to measured the microhardness respnses. The corrosion rate and polarization resistance was examined using Liner polarization resistance technique and open circult potential route under simulated 3.65% NaCl. From the results, 85Al-9RHA-6PSA composite sample exhibited slightly lower Cr, lower jcorr, and higher Pr of 0.3562 mm/year, 3.066E-05 A/cm² and 139.33 Ω, respectively against the control sample. An indication of a significant passive characteristics. The 85Al-9RHA-6PSA composite sample also exhibited few dimples, shrinkage cavities and micropores. With composite alloys, good crystalline were observed inform of Al16Co7Zr6Al15Co4 and Al0.52Co0.48Al16Co7Zr6. The hardness properties improvement from 54.8 to 63.8% provides a significant effect of solid strengthening performance of the hulls/shells as a biocompactability infringement of structural alloy.

This study investigates the ability of sun-dried cashew-nut powder (CNP) to serve as a renewable partial replacement for cement in concrete manufacturing. The objective of this research is to examine the mechanical properties and longevity of cement-based composites enhanced with different amounts of CNP. Specific objectives are: to define the chemical, mineralogical and physical attributes of CNP, to measure mechanical performance at replacement levels of 0%, 10%, 20%, and 30%, to establish durability indicators and to establish optimum level of CNP replacement. X-ray fluorescence (XRF), particle size distribution, and sieve analysis help define CNP. Cubic specimens with CNP at age twenty-eight and seven days are examined for, flexural resistance, resistance to compression, Stiffness (Elastic Modulus) and resistance to tensile cracking. Accelerated chloride permeability tests and water absorption tests are used to evaluate durability. The mix ratio is optimized and the relationship of CNP content, water-binder ratio, and curing time is studied with the help of response surface methodology. The study aims to ascertain a CNP replacement level that achieves the highest mechanical properties and long-term life with the lowest environmental impact of cement manufacturing. Anticipated outcomes envision CNP as a viable, sustainable cement replacement, thereby enhancing the performance and sustainability of concrete. Especially in regions where cashews are cultivated, this research invites the use of agricultural waste and offers practical guidance for green building practice in Nigeria. It also serves as the foundation for subsequent studies on long-term performance and extended application of CNP-modified concrete for infrastructure development next year.

Microgrids are modern small-scale versions of centralized electricity systems, and due to their complexity and the significant impact of financial loss or damage in the event of a fault, the need for an effective method of fault detection is crucial. This study addressed the critical need for effective fault detection and classification to ensure timely system restoration in the vent of fault. The investigation was based on design and simulation of a microgrid model, strategically engineered to manifest fault scenarios such as varying transient faults to different types of short circuit faults. The microgrid served the dual purpose of simulating real-world challenges and generating a robust dataset for the artificial intelligence-based fault detection models. The dataset was used for training and validating the long-short term memory (LSTM) and recurrent neural network (RNN) fault detection and classification models. The microgrid simulation served as a controlled yet representative environment for fault detection model assessment. A comparative analysis of the fault detection models was carried out by evaluating their performance using metrics like as precision, recall, F1-score, and accuracy across multiple fault classes. Notably, the LSTM model demonstrated a high accuracy of 93% while the RNN model excelled in achieving perfect precision and recall scores which resulted in the model’s 100% accuracy. This study has the potential to revolve the field of microgrid fault detection and classification thereby enhancing microgrid resilience. This study finds application in sustainable microgrids design and operation consequently, promoting the realization of SDG 7 and 11.

Rift Valley fever (RVF) is one of the neglected tropical diseases in Africa, likely to spread to other countries outside the continent, and capable of wreaking havoc on livestock and human populations. This study presents a novel mathematical model for RVF, taking into account time-dependent treatment, vaccination, and environmental sanitation controls. The existence of both RVF-free (disease-free) and RVF-present (endemic) equilibrium points are established analytically. Using the center manifold theory, the co-existence of both equilibrium points is characterized via bifurcation analysis. Castillo-Chavez’s M-matrix approach and Lyapunov function are used to carry out the global stability analysis of the model around the disease-free and endemic equilibrium points, respectively. Furthermore, existence of triple optimal control is rigorously proved and characterized using Pontryagin’s maximum principle. Consequently, the most efficient and cost-effective of each of the controls and several combinations of the controls are investigated through efficiency and cost-effectiveness analyses. The findings of the study provide insights into long term behavior of the RVF dynamics in the population, suggesting efficient prevention and optimal control measures at minimal cost of intervention.

This study evaluates the Fuzzy Analytical Hierarchy Process (FAHP) as a multi-criteria decision (MCD) support tool for selecting appropriate additive manufacturing (AM) techniques that align with cleaner production and environmental sustainability. The FAHP model was validated using an example of the production of aircraft components (specifically fuselage) employing AM technologies such as Wire Arc Additive Manufacturing (WAAM), laser powder bed fusion (L-PBF), Binder Jetting (BJ), Selective Laser Sintering (SLS), and Laser Metal Deposition (LMD). The selection criteria prioritized eco-friendly manufacturing considerations, including the quality and properties of the final product (e.g., surface finish, high strength, and corrosion resistance), service and functional requirements, weight reduction for improved energy efficiency (lightweight structures), and environmental responsibility. Sustainability metrics, such as cost-effectiveness, material efficiency, waste minimization, and environmental impact, are central to the evaluation process. A computer-aided modeling approach was also used to simulate the performance of aluminum (AA7075 T6), steel (304), and titanium alloy (Ti6Al4V) for fuselage development. The results demonstrate that MCD approaches such as FAHP can effectively guide the selection of AM technologies that meet functional and technical requirements while minimizing environmental degradation footprints. Furthermore, the aluminum alloy outperformed the other materials investigated in the simulation with the lowest stress concentration and least deformation. This study contributes to advancing cleaner production practices by providing a decision-making framework for sustainable and eco-friendly manufacturing, enabling manufacturers to adopt AM technologies that promote environmental responsibility and sustainable development, while maintaining product quality and performance.

The current study develops an understanding of the corrosion inhibition ability of Pennisetum glaucum (millet) extract in inhibiting the corrosion of AA6063 aluminium alloy in 1 M hydrochloric acid. Electrochemical measurements such as Tafel polarization, open circuit potential and linear sweep voltammetry were used to measure inhibition efficiency of the extract with varying concentrations (0.2–0.8 g) of the extract. The supplementation of millet extract was found to reduce corrosion as evidenced by decrease in corrosion current density (Icorr) from 1.11E-04 A/cm² (blank) to 6.18E-05 A/cm² for the highest concentration of the extract in the study (0.8 g). Within the same time, the corrosion rate diminished due to the higher concentration of extract, namely from 10.47 to 7.85 mm/yr. The corrosion inhibition concentration dependence revealed that the inhibitor conformed to Temkin adsorption isotherm with high Kads value of 14.1705 mol⁻¹ implying strong adhesion of millet extract onto AA6063. The surface morphology analysis carried out using SEM/EDS supported the hypothesis with evidence of protective layer formation which reduced the corrosion attack. On top of that, when doing the mathematical optimization, it was also determined that the best inhibition condition occurred at the temperature of 32.4 °C and the inhibitor concentration of 0.224 g with the resulting corrosion rate of 2.399 mm/yr.

This study considers the development of composite from biodegradable bioplastic obtained from waste starch reinforced with chitosan obtained from snail shells. About 30 g of the starch, 8 mL of glycerol, 2 mL of olive oil, and 8 mL of vinegar were added without chitosan and made up to 150 mL with distilled water. For other samples, 0.5, 1, 2, and 4 g of chitosan were added as reinforcements. The solution was thoroughly mixed, then heated to a temperature of 70°C and stirred continuously till it started to gel, after which it was dried for 3 days. The developed composite was evaluated via physical, mechanical, and structural analyses. The results indicated that the sample with 0.5 g of chitosan reinforcement outperformed others with or without chitosan reinforcement, showing evidence of low water content, solubility, absorption, high tensile strength, and Young's modulus. The Fourier transform infrared (FTIR) spectroscopy results revealed that the chitosan amino group chemically reacted with the starch hydroxyl group, and a bio‐blend was formed. From the scanning electron microscopy (SEM) test, the morphology of the composite surface showed homogeneity with no visible agglomerates, while the x‐ray diffraction (XRD) results showed a sharp peak at 2 θ of 29°. In addition, the thermogravimetric analysis (TGA) shows that the thermoplastic starch with 0.5 g of chitosan has the highest thermal stability at 750°C, leaving 19.63% residue. This study is significant as it enhances the application of bioplastics, encourages waste‐to‐wealth conversion, reduces waste generation, and promotes environmental sustainability.

This study assesses the connection between out-of-pocket expenditure and human welfare in Nigeria using a Fully Modified Ordinary Least Squares regression. The assessment is based on data from the World Development Indicators (2023), from the periods of 2000 to 2023. The results attest that the status of human welfare in the areas of health, education and living standard is just slightly above the average global benchmark of 0.5. However, on an average basis, households in Nigeria pay 71% of their total health expenses directly from their pockets to health care providers. Also, OOP spending and human welfare had a significant negative relationship. Therefore, to improve the health component of human welfare in Nigeria, the policymakers should heavily subsidize the health care services. Both government health expenditure and GDP per capita had a direct but insufficient impact on human welfare in Nigeria. Therefore, for Nigeria to experience a significant improvement in human welfare via public health input and GDP per capita, the policymakers in the country should be allocating at least 15% of its total annual budget towards healthcare as recommended during the Abuja Declaration in 2001 with a view to lessening OOP spending of the households.

This work presents a multi-stage stochastic optimization model intended to improve investment decision-making for energy projects by incorporating uncertainty in contexts and changes in market pricing. In contrast to conventional deterministic models, which generally concentrate on a singular stage while neglecting the intricacies associated with policy and market uncertainties, our methodology incorporates Conditional Value at Risk as a pivotal risk metric. Across a span of five years, the model predicts how investments will be distributed among three types of electricity projects: Solar Farm, Wind Farm, and Hydro Plant. The stochastic model strategically allocates an investment of USD 16.5 million to achieve an expansion in the capacity of 925 megawatts and an expected portfolio return of USD 1,822,500. Notably, the model maintains a Conditional Value at Risk of USD 100,000 and an impressive Sharpe Ratio of 18.2250, demonstrating its ability to offer improved risk-adjusted returns. This study illustrates the effectiveness of stage stochastic optimization in enhancing diverse and robust renewable energy portfolios.

Aluminium alloy finds increasing industrial applications due to its desirable properties. However, its low thermal conductivity often limits its application at elevated temperatures, thus the need to investigate the temperature variation during milling operation for aerospace applications. The response surface methodology (RSM) carried out in the Design-Expert 2022 software environment was used to investigate the temperature variation of Al 6065 T6 during milling operation and the designed experiment (DoE) comprises of three process parameters with a feasible range of values which produced 20 experimental trials. These include feed per tooth (0.1–0.25 mm/tooth), cutting speed (5–35 m/min) and axial depth of cut (0.10–0.35 mm). The milling operation was carried out on a 5-axis computer numeric control (CNC) milling machine (DMU80 monoBLOCK), and the response of the designed experiment (cutting temperature) was measured using the infrared video thermometer. The support vector regression (SVR) machine learning algorithm was also used to approximate the relationship between the process parameters (input variables) and a continuous target variable (cutting temperature) while reducing the prediction error. The statistical analysis of the physical experimentation results obtained produced a predictive model for estimating the magnitude of the cutting temperature. The range of process parameters that produced the least temperature (73.1 °C) are feed per tooth (0.25 mm/tooth), cutting speed (5 m/min) and depth of cut (0.10 mm). This study adds to the literature empirically and contributes to the understanding of temperature variation during the milling operation of Al 6065 T6. The results may promote the utilisation of Al 6065 T6 for component development for aerospace applications.

The inclusion of renewable energy in the global energy mix has emerged as one of the viable solutions for addressing energy demand and ensuring decarbonization of the energy sector. However, their proliferation faces various challenges related to decision making, optimization and design complexity that cuts across various disciplines. Hence, transitioning into renewable energy is a transdisciplinary subject integrating expertise from diverse fields, including engineering, environmental science, economics, political and social sciences. This study presents a review of transdisciplinary approach to accelerate the adoption of hybrid renewable energy systems through sustainable design. The review starts with a discussion on the sustainable design principles with emphasis on lifecycle assessments, modularity, and resilience to enhance hybrid renewable energy systems (HRES) efficiency and adaptability. Next, the study surveyed various optimization techniques that have been used in the sizing of HRES, including linear programming and metaheuristics approaches. Furthermore, the study reviewed multi-criteria methods that can be used in the evaluation and prioritization of optimal HRES obtained from the optimization techniques based on multiple attributes. Also, the study examined how spatial optimization can be used to improve the adoption of HRES. Finally, the study proposed a transdisciplinary framework which synthesizes various disciplines that can help in accelerating the adoption of Hybrid Renewable Energy Systems. It is expected that this approach would provide a robust approach to the widespread adoption of HRES technologies.

Polylactic acid (PLA) and polyvinyl alcohol (PVA) are promising biocompatible and biodegradable materials for biomedical uses, yet they have limitations. Similarly, lignin is a precursor for carbon fiber but requires plasticizers to be spun into fibers. This hampers their use in areas like carbon fiber production and tissue engineering, thus the reason for this study. Lignin was extracted from the plantain stem, and a lignin blend with PLA and PVA was made and electrospun into fibers. Thereafter, the physiochemical properties of the composite fibers were analyzed. The XRD spectra revealed increased crystallinity in PLA/Lignin fiber. When 0.75 wt.% of lignin was added to PVA, a new peak and peak shift were formed in the composite fiber, indicating strong interaction. The crystallinity of PVA/lignin decreased from 71.5 to 60.1% when 0.25 wt. % of lignin was added. DSC showed miscibility of polymers and improved melting temperatures from 155 to 228 °C, for PLA/lignin (0.5wt.%) fiber, but a reduction in melting temperatures of PVA, with higher lignin content (149–143 °C). FTIR showed notable functional groups, typical of PLA, PVA, and lignin, such as the OH group between 3800 and 3459 cm ⁻¹ . The minor peak shift in PLA/lignin showed that the level of molecular interaction is less than that of PVA/lignin. PLA/lignin displayed better fiber morphology compared to PVA/lignin, where fibers became sheet-like with higher lignin content. The addition of lignin improved the tensile strength of PVA (0.7 to 2.7 MPa). Conversely, PLA/lignin’s tensile strength decreased, due to reduced load transfer efficiency. Overall, PVA/lignin and PLA/lignin composites exhibit potential as reinforcement materials for biopolymers and carbon fiber precursors, with PVA showing more promise for carbon fiber production due to robust polymer-lignin interaction.