Nigerian Journal of Technology

Evaluation of flexural and distortional elastic stability of mono-symmetric box girders with simply supported conditions using Vlasov theory and power series approach

C. O. Ugwoke — 2025-02-01

Mono-symmetric box girders are widely used in bridge construction for their strength, durability, and design flexibility. Traditional single-variable analysis methods, such as trigonometric series under simply supported (SS) conditions, often oversimplify and converge slowly. This study addresses these limitations using Vlasov theory combined with a multi-variable power series approach. Varbanov's modified generalized displacement functions were applied to derive the governing Vlasov differential equations, simplifying strain fields through the unit displacement method at the pole and shear center. Enhanced product integrals computed critical section properties, and power, trigonometric, and Taylor-Maclaurin series shape functions solved the reduced equations, enabling detailed analysis of flexural and distortional behaviors. The results revealed significant deformation patterns and rapid convergence. In the power series, maximum deflections occurred at 5 m and 45 m, attributed to localized bending moments caused by eccentric loading. Minimum distortion points were observed away from load concentrations, with reduced cross-sectional warping. Taylor-Maclaurin series deflections peaked at mid-span, consistent with beam theory predictions, while distortional curves showed linear trends with deformation neutralization at mid-span due to opposing end constraints. Trigonometric series displayed cyclic deformation patterns, reflecting the effects of fluctuating loads, and distortional curves stabilized at mid-span. These findings emphasize the ability of mono-symmetric box girders to mitigate torsional moments and improve structural efficiency. The proposed multi-variable power series approach provides precise deformation analysis and insights into localized bending, shear forces, and distortional effects. This study validates the methodology and demonstrates its potential to enhance bridge design practices through accurate and efficient structural analysis, addressing critical performance factors and advancing the understanding of mono-symmetric girder behavior under various loading conditions

Mechanical properties of laundry wastewater concrete incorporating polyethylene 2 terephthalate (PET) as partial replacement for sand

A. Oke — 2025-02-01

Today’s scarcity of fresh water and a rise in polyethylene terephthalate (PET) waste in the environment results from the increase in population. This study examines the impact of utilizing laundry wastewater and PET in concrete production. Laundry wastewater (LWW) and varying percentages of PET (0% to 30%) used as partial replacement for fine aggregate were used to produce 84 concrete cylinders and 84 concrete cubes. The results showed that a 30% PET replacement significantly reduced the workability of concrete by 50% compared to the control mix, and the compaction factor was reduced by 5%. The PET-modified concrete with 5%, 10% and 15 % achieved a target compressive strength of 13.5 N/mm² at 7 days but did not meet the target at 14 and 28 days, unlike the control mix. Prolonged curing time resulted in increased split tensile strength, except for the 5% PET replacement, which showed a decrease at 28 days. Scanning electron microscope (SEM) analysis revealed that the cement and 10% PET aggregates possessed the strongest bond, while the 15% to 30% PET replacement exhibited weak interfacial transition zone (ITZ). Certain properties of the LWW such as increased suspended solids and organic compounds present in detergents and bleach could have reduced the bond strength between cement and aggregates. Regression analysis indicated that the percentage of pulverized PET is a reliable predictor of slump and compressive strength, but less so for tensile strength.

Quality evaluation of sandcrete blocks produced in selected local government areas of Kaduna State, Nigeria

U. N. Wilson — 2025-02-01

Nigeria has witnessed problems in the construction industry such as the collapse of buildings which is a consequence of the use of substandard materials in building construction, poor supervision, negligence to standard code of practice, poor maintenance culture, and faulty design amongst others. Quality assurance and quality control are highly imperative to take cognisance of while examining building companies. This research examined the quality of nine-inch hollow concrete blocks produced using machine moulding in Kaduna Metropolis, which includes Kaduna North, Kaduna South, parts of Igabi, and parts of Chikun Local Government Areas in Kaduna State. The study employed a mixed research design method comprising experimental design and interviews. The result of the laboratory test carried out on the blocks showed the mean compressive strength of the blocks ranges from 0.21N/mm² to 0.62N/mm² which does not meet the minimum of 1.6N/mm² compressive strength provided by the Nigerian Industrial Standard (NIS) 87:2000. The bulk densities of the samples tested range from 1286kg/m³ to 1537kg/m³ and are below the minimum of 1,800kg/m3 provided by NIS 87:2000. The result of the interview conducted across 32 block industries showed a complete ignorance of standards as block industries determine their standard based on market forces. Only 3% of the block industries complied with the mix ratio of 20-25 blocks per one bag of cement, and less than 10% carried out quality assurance checks such as weekly inspection of mould for wear and tear and taking measurements of the dimensions to ensure that it meets the standard. In all, only one of the block industries provided evidence of certification from the Standard Organisation of Nigeria (SON). The study further underscored the urgent need for enforcing industry standards in the production of hollow sandcrete blocks in the Kaduna metropolis.

Workability, compressive strength, and optimal temperature scrutiny of geopolymer concrete containing bespoke activator and superplasticizer using different prediction models

L. O. Agashua — 2025-02-01

Geopolymers (GP) are novel substances made of aluminosilicate and alkaline activator that are sustainable and environmental-friendly. Geopolymers possess exceptional mechanical properties, and other admirable properties including fire and corrosion resistance. Thus, the efficiency of ash-based geopolymer was assessed. The binders include a combination of rice-husk ash (RHA), kaolin clay powder (KCP) and flyash (FA). RHA and KCP were introduced to mitigate respectively the problems of poor workability and efflorescence associated with flyash (FA) based Geopolymer concrete (GPC). A bespoke sodium silicate produced using RHA was found a more suitable alkaline activator than factory ready-made brand and was therefore utilized together with sodium hydroxide for binder activation. The activator was added at 0, 2.5, 5, 7.5, and 10% of the combined weight of the binder content. A bespoke superplasticizer produced from rice husk in the laboratory was introduced to improve workability. The GPC were cured at various temperatures. The result showed an increase in slump with the addition of the bespoke plasticizer while the compressive strength decreases at sodium hydroxide content above 2.5% of total binder weight. Fourier Transform Infrared Spectroscopy (FTIR) results show key absorbance band at the area between 949 and 3251 cm⁻¹ indicating that addition of the bespoke superplasticizer to the geopolymer concrete reduced the viscosity and improved the flow characteristics. Brunauer-Emmett-Teller (BET) shows RHA, FA and KCP each has higher surface area than cement, thus they can serve as appropriate pozzolanic material and cement proxy. At temperatures above 700C, both compressive strength and weight decrease, for both the bespoke and ready-made sodium silicate. The optimal geopolymer product showed substantial strength and durability enhancements at 70℃, while strength and durability values decline above 70℃, indicating material deterioration. Among models used for prediction, Feret model performed best with R2 of 0.967, indicating its excellent predictive performance.

Free vibration analysis of thin rectangular plates using piecewise shape functions in Ritz procedure

A. Adamou — 2025-02-01

In the present work, piecewise functions have been successfully built in the form of polynomials to be utilised in the Ritz procedure to carry out the free vibration analysis of thin rectangular plates. They were consistently constructed by considering the plate as consisting of equal strips in its two perpendicular directions, and could be generated for all the combinations of plate’s classical edge supports. The procedure was performed for different combinations of simple and/or clamped plate’s boundary supports, taking into account four aspect ratios (1, 1.5, 2 and 2.5), and the first six frequency parameters were retained. These frequency parameters were found to be in good concordance with the available exact and approximate solutions. For example, for a square plate with simple supports, the percentage differences, comparatively to the exact Navier solutions, ranged from - 0.007% (for the fundamental mode) to - 1.534% (for the sixth mode). Similar trends were obtained for the other aspect ratios and sets of boundary conditions considered. For all the boundary conditions studied, it was observed an increase in value of the frequency parameters with that of the plates’ side ratios. In addition, for each of the modes considered, it was found out that the computed frequency parameters increased consistently when the number of clamped edges increased in the set of the plate’s boundary conditions. The practical consequence is that thin rectangular plates with clamped edges may witness resonance when the forcing frequencies are high, while they can resist the low and medium ones.

Preparation and characterization of different eco-friendly demulsifiers from calabash seed for emulsion management

I. Okafor — 2025-02-01

The process of crude oil demulsification is still confronted with numerous challenges within the petroleum industry. Consequently, it is pertinent to develop innovative means or materials to accomplish the efficient separation of oil–water emulsions. In this work, three different Eco-friendly demulsifier: oil-based, ethanol-based and Nano-based demulsifiers were prepared via a simple one-step hydrothermal route using Lagenaria siceraria (calabash) seed as raw materials. The eco-friendly demulsifiers were evaluated by Fourier transform infrared spectroscopy (FT-IR) and Gas chromatographic-mass spectroscopic (GC-MS) and their chemical content and Physico-chemical properties compared with a commercial demulsifier (Phase treat). The results obtained showed that the seed have an oil content of 31%. The phytochemical screening of the extracted oil reveals the presence of most compounds found in chemical demulsifiers such as phenols, flavonoids, tannins, saponins, steroids, terpenoids etc. The FT-IR spectra of the chemical demulsifier was found to be similar to that of the oil and most of the functional groups present in the ethanol and oil-bases demulsifiers whereas that of Nano-based was observed to differ. The GC-MS analysis reveals the presence of both lipophilic and hydrophilic compound needed for demulsifiers preparation. In the bottle test analysis carried out to determine the efficacy of the eco-friendly demulsifiers, it was observed that the nano-based demulsifier performed better than the commercial demulsifier in the following trend: Nano-based > commercial > ethanol-based > oil-based. This current study not only encourage the effectual application of agricultural waste (Calabash seed), but also creates an understanding into the search of new demulsifying materials that would offer excellent performance. Finally, future investigations should focus on assessing the efficacy, stability, and potential industrial applications of these bio-demulsifiers and Nano-based demulsifiers.

Comprehensive study of extraction and applicability of nano silicate particles from natural waste for biopolymer reinforcement

H. O. Onovo — 2025-02-01

Biopolymers have emerged as a promising alternative to traditional petroleum-based plastics owing to their inherent biodegradability and renewability. However, augmenting their mechanical and barrier properties remains pivotal for diverse applications, particularly in packaging applications. This study explores corncob nano-silica (ccnSi) extraction, evaluating its performance in biopolymer films. A synergistic chemical-mechanical process yielded uniformly sized ccnSi particles (69.23-97.70 nm). To optimize ccnSi incorporation, surface treatments with varying NaOH concentrations (3.0M, 3.5M, 4.0M) were applied. The extracted ccnSi's performance metrics rivaled commercial nano-silica (cnSi). X-ray fluorescence (XRF) analysis unveiled a heightened silicon content in ccnSi (94 %) relative to cnSi (71.61 %). Scanning electron microscopy (SEM) and Energy dispersive X-ray (EDX) analyses substantiated the commendable dispersion and amorphous characteristics of the ccnSi/biopolymer composites. Using Fourier transform infrared spectroscopy (FT-IR) provided empirical evidence for the existence of groups of silanol as well as the silane, signifying surface modifications. Substantial surface areas (324.9-833.6 m²/g) were ascertained through Brunauer-Emmett-Teller (BET) analysis, a finding further affirmed through the dynamic light scattering (DLS) and particle size distribution (PSD) measurements. The incorporation of ccnSi markedly elevated the tensile strength of biopolymer films (0.572 MPa) in contrast to cnSi (0.49 MPa). Thermogravimetric and Differential thermal analysis (TGA-DTA) indicated commendable thermal stability, with polymer degradation initiating at 400 °C. The glass transition temperature (Tg) of 32 °C, coupled with the amorphous nature confirmed by Differential scanning calorimetry (DSC), underscores the promising potential of ccnSi as a biopolymer. This research underscores the successful extraction and application of ccnSi in biopolymer-based films, presenting a paradigm shift towards enhanced performance and heightened environmental sustainability in next-generation packaging materials.

Recycling pet bottles for additive manufacturing: a method for 3D printer filament production

A. Ogunsakin — 2025-02-01

Relevant technologies require reliable filaments from virgin materials and for exploration of waste PET bottles which simultaneously reduces environmental pollution associated with them. Desirable size, mechanical, chemical, thermal and physical properties of filaments are the significant determinants of their (filaments) respective applications and quality of products when in use on 3D printers. In this current work, an existing single screw extruder is modified to produce contaminant-free filament at various diameter from recycled polyethylene terephthalate (PET) for 3D printers. Flow rate test and tensile test were conducted to determine the mechanical properties of the filaments The V-PET, R-PET and R-PETG samples were fabricated with a gauge length of 50mm, and a tensile test was performed on each specimen. A weight proportion method was used to add an additive to the recyclable waste which yielded a good result in the by-product mechanical properties with a tensile strength up to 42MPa. A weight ratio of 1:4 (EG:V-PET) resulted in the least melting temperature with the least extrusion time in the case of V-PET. Increasing the reaction temperature in R-PET leads to an increase in melting time that results in an increase in total extrusion time. An experiment conducted in this study found that the extrusion time required for R-PET was reduced from 120 seconds to 80 seconds at 75% conversion rate.

Characterization of eggshell reinforced aluminium composites

P. E. Emumejaye — 2025-02-01

The study evaluated the mechanical properties of eggshell-reinforced aluminium. Six different specimens of aluminium composites were prepared using varying percentages of eggshell powder as reinforcement as follows: 0%, 2%, 4%, 6%, 8% and 10% identified as samples A, B, C, D, E and F respectively. Eggshell was used as reinforcement material in order to support the ever-increasing scientific research efforts to convert waste to wealth. One of the study’s objectives was to develop an eco-friendly composite and manage food wastes safely in our environment. The base aluminium material used for the study was designated as 0% eggshell reinforced samples. Test specimens were subjected to tensile, impact, compressive and hardness tests. Results showed that adding 4%, 6%, 8% and 10% eggshell powder to aluminium improved hardness by 5.61%, 19.31%, 15.32% and 19.03%, respectively. Samples were ranked in the descending order of performance as: D, B, F, A, C, and E using combination of properties criteria, which implies that sample D had the best combined properties. In contrast, sample E had the most minor performance. The study concluded that reinforcing aluminium with 6% eggshell powder produced a composite with best combination of mechanical properties. Manufacturing of Boat hulls and other marine structures are among the possible uses of the developed composites since they have good compressive strength. Eggshell-reinforced aluminium can also improve the performance and durability of various athletic goods, such as helmets, racquets, and bats. The study has demonstrated the potential use of eggshell powder as a reinforcement material for aluminium to produce light weight composites with good mechanical properties. The outcome of this study could benefit poultry farmers, food sellers, manufacturers and egg consumers across the globe.

Dual combat technique-based cyber systems protection against password attacks

A. I. Erike — 2025-02-01

The rise in machine-enabled password attacks and the cost per record lost in an average case of a data breach necessitate the need for a more robust technique for combating password attacks. Organizations of different sizes and global reputation have been victims of cyber-attacks. The problem of cyber-attacks has attracted several research responses from researchers with some attending results. This article presents the Dual Combat Technique-based Cyber-Systems protection against password attack. The proposed system utilizes a-three-tier model for detection, notification, and combat. The dual combat technique involves the System Protection Model (SPM) and the User Protection Model (UPM). While the SPM implemented a time delay algorithm powered by a geometric progression model, the UPM uses a dual handshake method for data communication between the user and the server. In the first instance, the UPM sends data to the Cyber-system server through an HTTP Request over an SMS gateway to virtualize a user’s account upon a trigger by the attack detection model. In the second instance, the deactivation of the virtualization operation uses the authentication of the user’s email and phone number. The result of the work presents a system that introduces a time-delay after a number of login attempts defined by a certain threshold value, and a user response action for account virtualization. The application testing presented a success rate of 90.16% on the number of times the request response was induced over an eight-day period of testing and 9.84% failed attempts.

Predicting wheat yield in agricultural industry using deep learning techniques: a review

P. Bari — 2025-02-01

In the post-pandemic future, technology in the agriculture industry can improve food sustainability while moderating the use of resources of nature in a variety of conditions. Robotic tools for agriculture have been developed for crop planting, nursing, clearing weeds, pest management, and harvesting. The key aspects of crop growth and innovative agricultural engineering help farmers maximize crop yield. In the present investigation, it has been found that deep learning (DL) algorithms are used to enhance the predictability of wheat crop yield. The assessment and forecasting of wheat crop yields can be done with precision and dependability using satellite imagery. The scientific investigations in this study to predict wheat crop yield considered distinct factors, including various vegetation indices with remotely sensed imaging, climate-related conditions, nutrients in the soil, wheat plant diseases, and water scheduling. This study expounds a variety of DL strategies for predicting wheat production and found that many publications make use of long short-term memory (LSTM), along with residual network (ResNet) and deep neural networks (DNN). The performance measures, commonly harnessed in publications, are highlighted in this study, including coefficient of determination (R²), root mean square error (RMSE) and accuracy. This systematic evaluation of the literature on the wheat crop will open possibilities for future research for scholars.

Vehicle detection, tracking, counting and classification using deep learning

O. T. Ajayi — 2025-02-01

This paper explores vehicle counting system, which is a crucial component of building a robust transportation management system (TMS) that proffers solution to various challenges facing transportation systems in modern cities around the world. Although there are existing approaches such as the manual vehicular counting and hardware-based systems but they are plagued with various limitations such as being intrusive on roads, difficult to scale and also very expensive to maintain. Hence, there are not enough viable solutions to the current complex and diverse traffic challenges. This paper focused on the development of a vehicle counting system designed to capture and read video in real-time from a camera placed strategically to capture traffic scenes and thereafter counts and classify the vehicles as they cross a detection line. A visualization of the results is displayed onscreen in real-time and the count data for all vehicle classes are saved in a database for future analysis. The counting and classification obtained accuracy is greater than 80%. This research achieved a software-based video counting system that runs on computer vision algorithms and presents an accurate, inexpensive, flexible, scalable and non-intrusive approach to obtaining vehicle count on highways.

Medium term load forecasting of 33 kV line loading: a case study of Ota 132/33 kV sub-station Ogun State, Nigeria

O. O. Olusanya — 2025-02-01

Peak load forecasting plays a pivotal role in the efficient operation and planning of power systems, influencing decision-making processes for resource allocation and infrastructure development. The Ota 132/33 kV substation in Nigeria is facing increasing demand due to rapid industrialization and urbanization. This has strained the substation's infrastructure, leading to issues like transformer overloading, voltage fluctuations, and power outages. The area's trade center status and numerous industries further stress transformers, increasing wear and tear and potentially jeopardizing their reliability. This study delves into the realm of 33 kV feeders at the OTA Transmission Substation, aiming to unravel the intricacies of peak load patterns and provide a forecast for the next five years. Leveraging historical data spanning from 2018 to 2022, sourced from OTA Transmission Substation was used to forecast from 2023 to 2027. The research employs the Auto-Regressive Integrated Moving Average (ARIMA) model to discern trends and project future peak loads. Performance metrics, including Mean Absolute Error, Mean Absolute Percentage Error, Root Mean Square Error, and R-squared, are meticulously evaluated to assess the robustness of the forecasting model. The findings shed light on the unique characteristics of each feeder, with Sumo, Amje, and Idiroko having a better predictive accuracy performance with minimal errors, while Sango, FSM, and Estate show a moderate level of predictive accuracy probably due to the presence of little nuance in their data set. Where the Sango 33 kV feeder displayed an upward trend of 18.91MW in 2023 and 19.34 MW in 2027. Sumo 33 kV feeder exhibits a decline trend from 3.21 MW (2023) to 2.23 MW in 2027. FSM 33 kV feeder shows a fluctuation pattern while Amje 33 kV feeder indicates a highly stable trend of 22.29 MW all over. Idiroko 33 kV feeder shows a steadily increasing trend of 16.09 MW (2024) to 16.25 MW in 2027. The Estate 33 kV feeder on the other hand depicts a relatively stable pattern. This study not only contributes to the localized understanding of peak load dynamics but also serves as a template for similar investigations in other power distribution networks and unveils other alternative data science-based models for future researchers.

Accurate path loss prediction is vital for efficient resource allocation, interference reduction, and overall network reliability in 5G networks, particularly in the widely deployed mid-band frequency spectrum (such as 3.5 GHz). This study evaluates the e

F. E. Shaibu — 2025-02-01

Accurate path loss prediction is vital for efficient resource allocation, interference reduction, and overall network reliability in 5G networks, particularly in the widely deployed mid-band frequency spectrum (such as 3.5 GHz). This study evaluates the effectiveness of machine learning models for path loss prediction at 3.5 GHz with a focus on feature prioritization. A feature selection method, recursive feature elimination, was used to identify significant features from datasets obtained through measurement campaigns, weather stations, 3-D ray tracing, geographical data, and simulations. Out of eighteen features, eleven, including new environmental features, were identified as significant features contributing to path loss. These selected variables were then utilized to optimize and train four common machine learning models (ANN, XGBoost, RF, and k-NN) to evaluate their performance in predicting path loss in a specific urban area called an irregular urban environment. The performance of these models was assessed by comparing their predictions with the measured path loss. The Random Forest model closely matched the measured path loss over the entire path length in both LoS and NLoS scenarios, achieving the lowest MAE of 0.15 dB and RMSE of 0.57 dB in the LoS scenario and 0.62 dB and 1.42 dB in the NLoS scenario, with R2 scores of 0.999995437 and 0.999996828, respectively. This indicates its superior performance in predicting path loss in the urban environment.

Evaluating the effectiveness of machine learning models for path loss prediction at 3.5 GHz with focus on feature prioritization

F. E. Shaibu — 2025-02-01

Towards an automatic pain intensity levels evaluation from multimodal physiological signal using machine learning approaches

M. S. Patil — 2025-02-01

pain assessment is necessary in order to identify and manage pain. Self-report has been the prime method of measuring intensity of pain. To address this, an impartial methodology to recognizing pain that is both scalable and inexpensive must be developed. In this study, a Bio-Vid Heat Pain Database (Part A) dataset containing 86 individuals in good health condition who experience extreme pain was utilized to develop algorithms for pain recognition. Two physiological indicators, electrocardiogram and electrodermal activity were utilized. Different kinds of machine learning algorithms were implemented to establish the framework for more advances in the development of complex pain classification algorithms. CatBoost and AdaBoost performed significantly better than other methods, with average performance accuracy of 83.68% and 82.68% respectively for fusion of electrocardiogram and electrodermal activity signals. The binary classification experiment discriminates between the baseline and the pain tolerance level (T0 vs. T4).

Overcurrent protective relays for electric distribution systems

A. E. Anyalebechi — 2025-02-01

This paper presents over-current protective relays for electric distribution systems. The objective is to determine the reliability of the system by comparing the performance of numerical and electromechanical relays. Electromechanical over-current protective relay on 33kV Itire-Ijesha distribution feeder was analyzed and modeled using MATLAB/Simulink. A numerical relay was proposed and also modeled using the real data of the existing Itire-Ijesha 33kV distribution feeder. While electromechanical relay was simulated as an instantaneous relay, the numerical relay was programmed and simulated as an IEEE moderately inverse definite minimum time (IDMT) relay. The simulation was for single line-to-ground fault with the red phase, double line-to-ground fault using red and blue phase as well as line-to-line fault using red and blue phase. Comparing the performances of the two different models, numerical relay tripped in mini-seconds while the electromechanical relay model tripped in seconds. In conclusion, therefore, the numerical relay proved to be superior, smarter, and more reliable than electromechanical relays.

Development and evaluation of a cost-effective aeration-filtration solar disinfection system for water treatment

E. J. Nwankwo — 2025-02-01

Solar disinfection (SODIS) of drinking water involves storing water in small transparent containers and exposing it to sunlight, but it has not yet gained widespread use despite being simple and low-cost. This study aimed to improve the acceptability of SODIS by developing and evaluating the effectiveness of a pilot-scale, semi-continuous flow, aeration-filtration SODIS (AF-SODIS) system that combines aeration, filtration, and solar disinfection in a single process. Over two weeks, the system was tested with feed water of 100 NTU turbidity and over 1.1 × 10⁵ MPN/100 mL of pathogens. The results showed that the system effectively removed over 97% of turbidity and 99.99% of E. coli. When compared to previously developed flow SODIS systems, this new system is more affordable and scalable, which could encourage greater adoption and sustained use. However, responses from potential users suggested that a substantial promotional and educational initiative would be required to establish SODIS treatment as a regular practice in rural areas of developing countries

Hydrological properties of agricultural soil under treatment with different levels of biochar-based nanoparticle

O. G. Dayo-Olagbende — 2025-02-01

Soil hydrology plays a crucial role in many fields of study, including agriculture, due to the variation in the length of the rainy season brought about by erratic weather patterns. To mitigate crop failure, sustainable and drought-resistant agricultural techniques must be developed. This can be achieved by comprehending the transformative effects of nanoparticles on soil structure, water availability, and nutrient dynamics. Hence, this study aims to explore the impact of biochar-based nanoparticles on the hydrological properties of agricultural soil. Six (6) levels of biochar-based nanoparticles were used as treatments at 0 g, 100 g, 200 g, 300 g, 400 g, and 0 g with constant water supply as control applied to 20 g of soil. The hydrological properties considered are clay flocculation, dispersion ratio, structural stability, void ratio, sodium percentage, and water retention ability, among others, are relevant to agriculture. Biochar addition initially increased soil moisture retention and soil aggregation, but impaired stability at excessive levels due to nanoparticle toxicity. Clay flocculation dramatically improved with 100 g of biochar nanoparticle, yet severely declined beyond 200 g due to toxicity inhibiting natural aggregation. Lower biochar application rates increased aggregate stability compared to control samples, but stability did not increase proportionately as biochar application increased. Compared to higher amounts, the void ratio significantly changed with 100 g biochar addition, .patterns in stability and sodium content indicate biochar nanoparticles profoundly altered soil structure, highlighting the narrow threshold between benefits and ecosystem damage from excessive application. It was concluded that as much as biochar-based nanoparticles can help improve the hydrological properties of the agricultural soil, application beyond 200 g could have a counter effect and hence has to be monitored.

Effect of critical batch variables on the efficiency and kinetics of adsorption of Ni (II) onto carbonized and uncarbonized palm kernel chaff

C. C. Nnaji — 2025-02-01

A series of batch experiments were conducted for refined CPKC (carbonized sample) and UCPKC (uncarbonized sample) using 10, 30, 50, 70, 100 and 120 mg/L of Ni (II) solution at 30ᵒC, 35ᵒC and 40ᵒC at pH 3 and pH 9. Adsorption kinetics were investigated by determining the concentration of Ni (II) removed by the adsorbent at precise moments of time of 5, 10, 20, 30, 40, 60, 90 and 120 minutes. Standard kinetic equations were used to model data obtained from the experiments. For the range of conditions studied, the best improvement in the rate of adsorption and the adsorption capacity was observed for the carbonized sample at a temperature of 35oC. The rate constants of PKC were notably higher in an alkaline solution when the initial pH at the start of mixing was measured at 9, compared to the acidic solution with an initial pH of 3, across all studied temperatures. The rate constants of PSO₁, PSO₂, PSO₃, PSO4, PSO₅ and PSO₆ for CPKC ranged from 0.06 – 0.16, 0.14 – 0.44, 0.13 – 0.42, 0.13 – 0.30 and 0.09 – 0.59 g/mg.min^-1. In nearly all cases, PSO₁ model showed better correlation than other kinetic models with R² values ranging from 0.8 to 1.0. The average increase in adsorption rate constant for CPKC (pH 3), CPKC (pH 9), UCPKC (pH 3) and UCPKC (9) for 1oC increase in temperature was 0.31, 0.98, 0.51 and 0.43 mg/g.min respectively. The kinetic models were ranked as follows: /PSO₁/>/PFO/>/PSO_{2 - 5}/>/PSO₆/.

Smart multi-purpose farm disease monitoring and notification model

N. I. Ezeani — 2025-02-01

Disease has remained a threat to the existence of every living thing on earth. Particularly in agriculture, disease has remained a major constraint to the success of crop yield and demands urgent solutions, starting with early detection. While many studies have been presented on plant disease detection and control, despite their success, the research gap resides in creating a balance between plant disease detection and farm disease detection. This is because detecting disease in a plant does not necessarily imply that the farm is infected with diseases, and this has resulted in issue of false alarm in the existing system. To address this challenge, YOLOV-5 model was trained with a plant disease dataset considering diverse classes of plants such as corn, waterleaf, tomato, pepper, and cassava. The plant disease model generated was used to develop a farm disease monitoring, detection and notification algorithm, which was converted into mobile application software using Python programming for real-time monitoring notification of farm diseases. This multi-purpose system when tested, reported an average precision mean of 0.95. In addition, experimental validation of the model in maize and watermelon farms reported real-time disease detection and notification which facilitates rapid response and control of the disease by the farmer.

SWAT hydrological model of Zamfara watershed of Sokoto-Rima river catchment, North West Nigeria

A. M. Shuaibu — 2025-02-01

This study employs the Soil and Water Assessment Tool (SWAT) hydrological model to comprehensively analyze the water dynamics within the Zamfara watershed. The model's efficacy in assessing diverse hydrological processes is established through meticulous calibration and validation processes. A sensitivity analysis scrutinizes the model's responsiveness to various thresholds in sub-basin delineation and the definition of Hydrologic Response Units (HRUs). The study's outcomes reveal discernible patterns in annual precipitation, groundwater recharge, and evapotranspiration within the basin. These findings shed light on crucial factors that influence the basin's water balance. They underscore the vital necessity of a thorough hydrological understanding to effectively manage water resources in the Zamfara watershed. The assessment of the catchment's hydrology indicates an average annual precipitation of 876.61 mm. Groundwater recharge accounts for 25.07% of the total groundwater system input, averaging 219.77 mm annually. Notably, only 5% of the overall groundwater recharge contributes to replenishing deep groundwater recharge storage, while the remaining portion refills the shallow aquifer. Consequently, evapotranspiration emerges as the most substantial constituent of the water balance, representing 57% with an average of 500.79 mm per year. Runoff constitutes a mere 12.44% of the total basin output, while the remaining components of precipitation are lost. It is essential to highlight that evapotranspiration serves as the primary mechanism for water loss from the catchment. The calibration and validation phase exhibited by the SWAT model within the confines of the study area showcased exceptional efficacy, achieving notably high R-squared (R²) values surpassing the threshold of 0.80 for the designated gauging sites. This remarkable achievement underscores the SWAT model's commendable aptitude and reliability in conducting precise hydrological assessments within the intricate dynamics of the Zamfara watershed. The total groundwater reserve of the watershed catchment area is estimated at 24,767,082 m³. This suggests high amount of groundwater reserve within the study area for both agricultural and domestic usage.

Leveraging emerging technologies for operational optimization and business model innovation in African enterprises

N. David — 2025-02-01

The African economy has experienced substantial growth in recent years, which has increased the number of businesses created annually in the region, and this can translate into significant economic growth over the next few years. With technological advancements and digitization, African businesses can optimize their operations and improve efficiency by leveraging new and disruptive technology for innovation and development. This study discusses emerging technologies like the Internet of Things and Blockchain. It extensively analyzes the use of Radio Frequency Identification systems in Supply Chain Management as a case study. In addition, it also proposes a blueprint for leveraging emerging technologies and discusses some of the challenges these new technologies may bring. Furthermore, we highlighted how some new technologies could improve efficiency for African businesses when successfully integrated into creating business models and discussed examples of African businesses already on this path. Africa must embrace technology for economic transformation, and African businesses are tools to achieve this transformation.

Systematic review - innovative approaches in artificial intelligence development

O. Blahodelskyi — 2025-02-01

This study analyses current advances and challenges of artificial intelligence (AI) development through a systematic literature review. The literature search was conducted across major databases including IEEE, ScienceDirect and Scopus. Peer-reviewed articles published between 2016-2023 were included based on relevance to the research topic. Out of 152 initial search results, 48 articles were selected for in-depth review using PRISMA guidelines. The results were analyzed to identify key focus areas, techniques and applications of AI over the selected period. The study found a significant increase in AI-related publications in 2022-2023 (62% of selected articles), indicating growing research interest. Key application areas identified are smart cities, education, image processing and medicine. Machine learning methods like neural networks and deep learning were frequently applied for tasks like classification, prediction and pattern recognition. Along with opportunities, ethical concerns like privacy, security, transparency and bias were major AI challenges discussed. Developing standards, regulations and testing mechanisms to ensure reliability and fairness of AI systems was highlighted. In summary, the systematic review demonstrates the rising significance of AI across industries, while underscoring the need to proactively address risks for its safe and ethical development. Overall, the study confirms that AI has great potential in various industries, but its implementation requires the development of ethical standards and data security to maximise benefits and minimise risks. The practical significance of the study is to deepen knowledge about AI and its impact on modern society.

Oil deposits in the field of Turkmenistan: industrial evaluation by statistical method

A. Deryaev — 2025-02-01

The purpose of this study is to analyse and evalute the effectiveness of the statistical method in estimating oil reserves on the example of a field in Turkmenistan. Various methods such as analytical, statistical, correlation tables, moving average, calculation were used to arrive at the conclusion. Residual oil reserves were estimated taking into account different types of wells and cumulative production, which provided a more complete picture of the field’s potential. The results of the study showed that the use of statistical method for estimating oil reserves in the Goturdepe field in Turkmenistan is of high importance for effective production management. The analysis of well production data and the application of statistical methods allowed the identification of important trends and patterns in oil production, which is a key step for optimizing the field development process. One of the key findings of the study is the need to account not only for current production, but also to forecast future production. This helps to optimize the field development process and make informed decisions about further investment. Within the framework of this study, it was noted that, given the complex geology of the region, the use of statistical methods is critical to ensure the accuracy and reliability of reserve estimates. This approach allows for the consideration of many factors, including variations in production and geological features, providing more accurate estimation results and informed decision-making in the management of oil production in Turkmenistan. The results of the study confirm the effectiveness of the statistical method for estimating oil reserves in the Goturdepe field, which contributes to effective production management and also allows optimization of the field development process, helping to improve the efficiency and reliability of production in the region.