Journal of Advances in Science, Engineering and Technology
Journal of advances in Science, Engineering and Technology brings to fore scientific publications in all spheres that embrace scientific methods in reporting or researching. It enhances our understanding of the way in which frontier research/studies in science and technology influence society and vice versa. It is the first and most dependable journal that integrates all spheres of science, in the realization of the interrelationship in all scientific fields. The publications are in the form of original research articles, case reports/case series, and technical notes etc. It covers areas like physical, chemical, biological, both pure and applied. It also publishes engineering and technology findings, and therefore promotes interdisciplinary perspectives drawing upon a number of "hard core" science disciplines.
Masinde Muliro University of Science and Technology
Evaluation of the performance of Improved Indigenous Chicken genotypes in Kenya
The Kenyan poultry industry contributes significantly to food security, nutrition, and economic development, with indigenous chicken constituting more than 84% of the country's chicken population. The low productivity of these indigenous breeds, primarily due to poor genetics, has hindered their potential. The Kenya Agricultural and Livestock Organization (KALRO) developed three breeding lines of chicken: KC1, KC2, and KC3. This study aimed to evaluate the effect of genetics on the performance of two KALRO breeding lines, KC1 and KC3, and the control. This study employed a completely randomized design with four replicates per genotype. Daily feed intake and weekly body weight were recorded for 135 chicks per genotype (KC1, KC3, and control F2) from hatch to 18 weeks. General Linear Model (GLM) determined the effects of genetics, temporal factors, and their interaction on chicken performance during the chick and grower stages. Results from the study showed genetics played a major role in the amount of feed KC3 consumed, while the KC1 breeding line achieved better live weight than the control group (). Growth was substantially affected by both time and genetics. The performance evaluation in the breeding lines revealed that the KC1 and KC3 breeding lines showed a superior outcome than the control. Therefore, farmers can use the KC1 and KC3 lines as they enable better production levels. Future studies should consider evaluating the adaptability and performance of KC1 and KC3 breeding lines across diverse agroecological zones to support widespread adoption.
Copyright (c) 2026 Journal of Advances in Science, Engineering and Technology
Determination of Optical, Electrical, Structural, and Morphological Properties of MAPbI3 Perovskite Films for Photovoltaic Applications
Perovskite devices based on methylammonium have been promising in the photovoltaic industry. A major challenge encountered with these devices lies in correlating their processing conditions, microstructure, and the overall photovoltaic performance. In particular, this paper addresses the challenge of accuracy in relating these properties by employing an automated SEM image Python segmentation approach that quantifies grain morphologies. The results derived from the segmentation technique assure reproducibility and bridge the gap between film fabrication, microstructure, and the properties. The resulting films were prepared by two-step spin coating and two-stage thermal annealing procedures, resulting in a planar inverted perovskite with architecture ITO/ PTAA/MAPbI3/C60 /BCP/Ag. The fabricated films exhibit a tetragonal structure with high crystallinity, which is characterized by a conspicuous (110) plane. The results display fairly good homogeneity and large grain sizes averaging 212 nm, indicating a wide direct band gap of 1.52eV as displayed by the Tauc plots. The films show values of absorption coefficients exceeding 104 cm-1. Generally, the resulting device has a high power conversion efficiency of 13.91%, demonstrating that the automated SEM segmentation technique is a powerful tool useful in optimizing perovskite device processing parameters. Insights in this study, therefore, provide information on the enhancement of overall morphology-performance correlation leading to high-quality-film photovoltaic devices.
Effect of Biochar Feedstock Types on Physical and Chemical Properties of Humic Nitisols in Kakamega County, Kenya
In Kenya, inorganic fertilisers have been extensively promoted despite concerns regarding their high cost, limited farmer knowledge, and potential environmental risks. This study evaluated the potential of biochar as a soil amendment to enhance the physical and chemical properties of humic Nitisols in Kakamega County, Kenya. A field experiment was conducted in 2022 at the Kenya Agricultural and Livestock Research Organization (KALRO) Non-Ruminant Institute, Kakamega, using a randomized Complete Block Design with three replications. Biochar (5 t ha⁻¹), derived from sugarcane bagasse, coffee husk, coffee pulp, and timber offcut, was applied alone or in combination with NPK (23:23:0) at planting and calcium ammonium nitrate (200 kg ha⁻¹) as topdressing. Maize (Hybrid 624) was grown under field conditions. Soil physical properties (bulk density, porosity, and water holding capacity) and chemical properties were analyzed using standard laboratory procedures. Sugarcane bagasse biochar combined with fertiliser reduced bulk density from 1.215 g cm⁻³ to 0.776 g cm⁻³ and enhanced soil moisture from 21.78% to 30.13%. Coffee husk biochar with fertiliser significantly increased exchangeable potassium from 0.177 cmol kg⁻1 to 1.708 cmol kg⁻1, while timber offcut biochar applied alone increased total nitrogen from 0.454% to 1.307%. Coffee pulp and sugarcane bagasse biochars raised total organic carbon from 0.798% to 2.98% and soil pH from 5.6 to 6.38. Overall, integrating biochar with inorganic fertiliser consistently outperformed sole applications, demonstrating a climate-smart and sustainable strategy for restoring humic Nitisols and enhancing maize production in Western Kenya.
Zinc Sulphide Nanoparticles as a Viable Alternative to Platinum Counter Electrode for Low-Cost Dye Sensitized Solar Cells
Dye-sensitized solar cell (DSSC) is considered one of the most promising and economical emerging solar energy conversion technologies because of its low production cost and potential efficiencies. However, an expensive and scarce platinum (Pt) is used in their counter electrode (CE). Additionally, Pt CE corrodes as a result of the oxidized electrolyte's chemical attack and requires high manufacturing temperatures. This makes the production of DSSC relatively expensive. This research focused on preparing and utilizing zinc sulphide (ZnS) nanoparticles as the CE in DSSC. The nanoparticles were synthesized using the low-cost chemical reduction method. The X-Ray diffraction (XRD) measurement showed high crystallinity of the nanocomposite. The Fourier Transform Infrared (FTIR) measurements showed the presence of Zn-S bond stretching vibrations. As compared to Pt-based CE, ZnS-based CE demonstrated slightly lower conductivity of 54.41 S/m to 379.85 S/m for Pt. Power conversion efficiency (PCE) was noted to improve from 0.07% for unannealed ZnS-based CE to 1.1 % for ZnS-based CE annealed at 400 0C. The improvement is associated with enhanced crystalline quality of ZnS nanoparticles upon annealing the CE at 400 0C. Therefore, ZnS nanoparticles show great promise as a cost-effective alternative counter electrode for DSSC with further material properties optimization.
Advances in Beamline in situ Spin-Coating for Perovskite Thin Films and Beyond
This research letter presents a beamline-integrated in situ spin coater that enables real-time observation of thin-film formation and crystallization during spin coating and annealing. The system integrates temperature-stable heating, low-vibration motors, and a helium-purged inert chamber with synchronized anti-solvent injection and detector acquisition at millisecond resolution. Controlled through a LabView interface, synchronized triggers coordinate spin speed, anti-solvent delivery, and data collection within the beamline environment. Applied to Cs0.1FA0.9PbI3 films passivated with PEACl, time-resolved grazing-incidence X-ray scattering (GIWAXS) reveals transient Pb–Cl–I intermediates that vanish as 3D perovskite phases form. The evolution of quasi-2D domains, lattice contraction, and crystallite orientation directly correlates with nucleation and passivation dynamics. This approach provides a general framework for linking processing conditions to structural evolution in perovskite and other functional thin films.
Investigation of the Amine-Functionalized Silica Material for Capture of CO₂ by Adsorption
CO₂ emissions are of serious concern due to their negative effect on climate change. As emissions from various industrial processes continue to rise, the need to capture and utilize CO₂ becomes increasingly important. This paper examined the applicability of amine-functionalized silica material for the capture of CO₂. Commercially sourced silica gel was modified with an amino group and used as a solid support substrate. The substrate, silica, was suspended in dimethylformamide solution and reacted with a chlorinating reagent, phosphorous pentachloride, followed by substitution of the chloride atom with the amino group from ethylenediamine. The modified silica and CO₂-treated samples were analyzed using Fourier-transform infrared spectroscopy and thermogravimetric analysis. It was observed from the Thermal analysis that the silica framework was stable between 0°C and 100°C, and the mass loss observed near this temperature is due to the decomposition of the grafted ethylenediamine and not of the silica support. The modified silica was applied for the removal of a laboratory-prepared CO₂ gas. An excess of CO₂ was bubbled into a 1M calcium hydroxide solution, and turbidity measurements were recorded to determine removal efficiency. The regeneration of the amino-modified silica was confirmed using dilute hydrochloric acid, which stripped the attached CO₂. The contact time was evaluated, and an optimum duration of 35 minutes yielded the highest removal efficiency. Adsorption followed pseudo-first-order kinetics, indicating a chemisorption process. This study reported an adsorption capacity of 0.3558 g/g of CO₂, demonstrating that the modified material is effective for CO₂ capture.
Development of an Agricultural Machinery Rental System for Small and Medium-Scale Farmers in Tanzania using a Mobile Application
Nowadays, many online services are trending and greatly supporting the development of a mobile app aimed at improving agricultural machinery rentals for farmers in Tanzania. This app also helps equipment owners maximize their machinery utilization. The initiative addresses key issues in traditional machinery rentals, especially limited access and lack of transparent pricing. The app offers a centralized, easy-to-use platform that allows registered users to browse, select, and book equipment tailored to their specific farming needs. The system consists of four modules: login and registration, equipment listing, machinery selection, and booking and payment. It is built using the Flutter framework for a responsive interface, Dart as the primary programming language, and Firebase for dependable backend services. Thorough testing, including Unit, Integration, System, and Acceptance tests, was carefully conducted. This extensive validation ensured the system’s functionality, seamless communication between modules, and stability across various Android devices. The app achieved positive results, with an average score of 4.47 for user interface design and 4.36 for usefulness, ease of use, and overall usability. The standard deviation values ranged from 0.6 to 0.8, indicating that most user ratings were close to the average while allowing for some individual differences in perception. This demonstrates its effectiveness as a practical and innovative solution for agricultural machinery rental in Tanzania.
Copyright (c) 2025 Journal of Advances in Science, Engineering and Technology
Ionospheric Response to Intense Geomagnetic Storms of 17 March 2015 and 22 June 2015 over the East African region
The variation of Total Electron Content (TEC) derived from seven International Global Navigation Satellite System (GNSS) stations (IGS) over the East African low latitude region for intense geomagnetic storms of 17 March 2015 and 22 June 2015 was investigated. The O/N2 maps were accessed from the GUVI website. Daily variation of Dst, IMF Bz, IEF Ey, and solar wind speed for the period was analyzed. The results showed during the storm of 17 March 2015 a positive storm effect was noticed over the station during the initial and main phase of the storm across the stations which was attributed to the prompt penetration of high–latitude electric fields to lower latitudes and the effect of Equatorial Ionization Anomaly (EIA) and negative storm effect which was attributed to the downward E × B drift and depletion of O/N2 over the East African region. During the storm of 22 June 2015, there was a positive storm effect during the recovery period across all the stations, which was also attributed to the prompt penetration of high–latitude electric fields to lower latitudes and the effect of EIA. The study also revealed that the storm impacts varied with local time and season, with greater effects during the equinox period, indicating that local time plays a role in the ionospheric response to geomagnetic storms.
Hybrid Precoding for Multi-Cell mmWave Multiuser Massive MIMO 5G Networks
In recent times, there has been a rapid growth in the data traffic demand. The 5G millimeter wave technology has been deployed to offer a large bandwidth resource for cellular systems. However, the millimeter wave communication suffers from high path losses. Therefore, there is a need to implement a high number of antennas to overcome the high propagation losses and offer beam-forming gain. Multiple-Input Multiple-Output is a wireless technology that uses multiple antennas at both the transmitter and receiver to enhance performance by increasing reliability and data speeds. The Multiple-Input Multiple-Output technology has the potential of meeting the need for high network capacity in the next generation cellular networks since it can offer improved power and spectrum efficiency. When massive Multiple-Input Multiple-Output is deployed in a multi-cell environment, inter-cell interference goes up as the cell edge users are approached. It also results in pilot contamination due to the frequent reuse of the pilots in each cell. In this paper, a Single Value Decomposition-based hybrid precoding scheme was formulated as a spectral efficiency maximization problem for multi-cell multi-user millimeter wave massive Multiple-Input Multiple-Output by minimizing channel estimation errors and inter-cell interference. The model was simulated in MATLAB, and the results for the full and partial connection scenarios. The results showed a 1.6381 times improvement in performance in terms of spectrum efficiency and normalized minimum square error when compared to the compressive sensing algorithm.
Early Detection and Classification of Power Systems Faults in Medium Voltage Distribution Lines Based on Sparse Representation
The increasing demand for reliable and resilient electrical power necessitates advanced fault detection strategies in Medium Voltage Distribution Lines (MVDLs), which account for over 80% of power outages. This study proposes a novel approach for the early detection and classification of Short Circuit Faults (SCFs) in MVDLs using a Sparse Representation (SR) framework combined with Mel-Frequency Cepstral Coefficients (MFCCs). The method was validated through simulations on an IEEE 5-Bus System, where Line-to-Ground (L-G), Line-to-Line (L-L), and Double Line-to-Ground (L-L-G) faults were induced under both noise-free and noisy (25 dB SNR) conditions. Feature vectors derived from voltage and current signals were transformed into MFCCs, and SR was applied via dictionary learning and Orthogonal Matching Pursuit to extract fault-relevant signatures. Classification was performed using Support Vector Machines trained on sparse codes. Two models were developed: one for fault type classification and another for combined fault-type and localization using zonal segmentation. Results show that the SR-based classifier significantly outperformed conventional models (ANN, SVM) in both accuracy and noise robustness. The SR model achieved 98.7% accuracy in noise-free and 96.8% in noisy settings, with F1-scores exceeding 96%. Localization accuracy ranged from 97.5% (2-zones) to 90.5% (4-zones). The proposed SR-based classifier demonstrated high computational efficiency and scalability, enabling real-time applicability for medium-voltage grid monitoring. This research demonstrates the efficacy of integrating SR and MFCCs for robust MVDL fault diagnostics, with potential applications in enhancing grid stability, enabling predictive maintenance, and minimizing service disruptions.
Optimal Placement and Sizing of Battery Energy Storage System for Loss Reduction in A Power Distribution System Using Chaotic Coyote Optimization Algorithm
The growing global electricity demand and increasing integration of renewable energy have intensified the need for efficient and resilient power distribution systems. Power losses in distribution networks remain a persistent challenge, increasing operational costs and reducing efficiency. This study investigates the optimal placement and sizing of battery energy storage systems to minimize losses in a 148-bus distribution network. A Chaotic Coyote Optimization Algorithm is proposed, which enhances the conventional Coyote Optimization Algorithm by incorporating chaotic maps to improve exploration, prevent local optima trapping, and accelerate convergence. Two scenarios were evaluated: (i) a grid-connected system and (ii) a grid-connected system with solar photovoltaic integration. For each scenario, single-unit and dual-unit battery energy storage configurations were tested using simultaneous and sequential optimization strategies. The Chaotic Coyote Optimization Algorithm consistently outperformed the Coyote Optimization Algorithm, Whale Optimization Algorithm, and Particle Swarm Optimization, achieving faster convergence and greater solution accuracy. Results showed that dual-unit battery energy storage configurations significantly reduced losses compared to single-unit setups, with the best case (dual-unit plus solar photovoltaic integration) achieving a 51.4 percent reduction in active power losses and an improvement in minimum bus voltage from 0.927 per unit to 0.975 per unit. The study demonstrates that combining battery energy storage with renewable integration not only reduces distribution losses but also enhances voltage stability. The proposed Chaotic Coyote Optimization Algorithm framework provides a robust methodology for energy storage optimization, offering valuable insights for utilities in planning sustainable, cost-effective, and resilient smart grids.
The frontier researches in science, engineering and technology are rapidly developing day by day. These frontier topics in research today are at the intersection of multiple disciplines, and a breakthrough in one area of research often drives progress in others. The rapid pace of technological innovation that we often hear about today means that these frontier topics will continue to evolve, potentially shaping the future in unimaginable ways. More so, the technologies under development are interrelated and integrated (Duran & Sengil, 2019). Their application in research and industry often requires the use of more than one technology. Therefore, this brings a new era in technologically interrelated research, where researchers must take an interdisciplinary approach in solving problems. The current trend and the direction frontier topical research in science, engineering and technology is taking is difficult to predict. The aim of this editorial is therefore to highlight the future frontiers of science, engineering and technology that we would all do well to know more about. Fig. 1 shows the interdependence and linkage between frontier research topics in science, engineering, and technology. It can be observed that AI and machine learning links with many research disciplines for supportive purposes and integration. The parting question for this article is “which of these topics interests you? How can research advances in these topics be used to enhance your work? Alternatively, do you have a specific area that you would wish to explore further?
Feeding Deterrence and Insecticidal Activity of Selected Plant Essential Oils Against the Larger Grain Borer Prostephanus truncatus in Stored Maize Grains
In our screening program for botanical pesticides from local plants, the powders and essential oils from Plectranthus marrubioides (Hochst ex Benth), Tetradenia riparia (Hochst Codd), Ocimum suave (Wild), Lippia javanica (Burm, Spreng), and Ocimum lamiifollium (Hochst ex Benth) were found to possess insecticidal activity against the maize weevil, Prostephanus truncatus. The essential oils from aerial parts of the plants were obtained by hydro-distillation and tested under laboratory conditions (25 ± 1ºC, 70–75% R.H.) for their ability to protect the grains from damage by P. truncatus. The insects were reared and tested on whole maize grain (variety DK 8031). Bioassays were conducted using five dilution levels of essential oils in hexane (10, 5, 2.5, 1.25, and 0.625% wt/wt), each tested on ten unsexed adult large grain borers per replicate, with four replicates per treatment. The mortality data of the insects at each level of dilution were collected, and the mean values were computed and subjected to Student-Newman-Keuls (SNK) t-test. Essential oils were also tested as antifeedants of the insect in dried maize. It was observed that within the 0.625% level of dilution, the oil extracts showed a significant difference (p<0.05) in the antifeedant test done using the different plant essential oils. Of the five plant oils, P. marrubioides and T. riparia showed the highest insecticidal activity with the lowest mean of 0.55 ± 0.15 and 0.40 ± 0.11, respectively, as compared to O. lamiifolium (2.4 ± 0.11), O. suave (2.55 ± 0.11), and L. javanica (2.5 ± 0.15) after a period of six months. The chemical constituents of the two most potent oils were analyzed by GC-MS. The main compounds in P. marrubioides essential oil were δ-2-carene (10.84%), o-cymene (8.72%), 1,8-cineole (7.89%), and camphor (5.65%). T. riparia essential oil was found to be rich in fenchone (24.74%), β-pinene (5.23%), o-cymene (5.16%), and 1,8-cineole (10.89%). These results show that these plant oils, and particularly P. marrubioides and T. riparia, have the potential to protect dry stored grain products from damage by the larger grain borer. Consequently, the plant oils can be developed into post-harvest control agents of the larger grain borer in stored grain products.
Chemical metabolite profiling and in vitro antibacterial activities of selected medicinal plants of Kakamega tropical forest, Kenya
Plants play a major role in the management of diseases, either exclusively or in combination with conventional medicines. This study investigates crude extracts of Conyza floribunda, Zanthoxyllum gilletii, Olea capensis, Warburgia ugandensis, and Lantana trifolia for antibacterial phytochemicals. Extraction of the phytochemicals was done by cold percolation in hexane, followed by 50% methanol in dichloromethane. The chemical composition of the non-polar hexane extracts was established using gas chromatography –mass spectrometry (GC-MS). The major chemical components were found to be; 1H-cycloprop[e]azulen-7-ol (8.41%, C. floribunda), 2,4-decadienamide (38.89%, Z. gilletii) isopimarol (29.08%, O. capensis), 2,2,5,7,8-pentamethyl-6-hydroxychroman (8.54%, W. ugandensis leaf), (1R,4aS,6R,8aS)-8a,9,9-trimethyl-1,2,4a,5,6,7,8,8a-octahydro-1,6-methanonaphthalen-1-ol (10.17%, W. ugandensis bark) and tetrapentacontane (13.63%, L. trifolia). The in vitro antibacterial activities of the plant extracts were evaluated by agar well diffusion. Although each extract showed selective inhibition towards tested microbes, W. ugandensis stem-bark hexane extract showed the highest activities with zones of inhibition (ZI) of 16.0, 13.0, 11.0, 11.0, 10.0, and 9.0 mm against S. aureus, E. coli, methicillin-resistant S. aureus (MRSA), P. aeruginosa, P. aeruginosa (7533), and S. aureus (29213), respectively. This activity could be attributed to the major constituent (1R,4aS,6R,8aS)-8a,9,9-trimethyl-1,2,4a,5,6,7,8,8a-octahydro-1,6-methanonaphthalen-1-ol in addition to the synergistic effects of sesquiterpenes, which accounted for 80% of the total phytochemicals in the extract. This supports the ethno-medicinal use of W. ugandensis in the management of bacterial infections, further recommending its full utilization for the development of antimicrobial drugs.
Performance Evaluation of Optimized IEEE 802.11ax in IIoT Environments: Throughput, Latency, and Packet Loss Improvements
This paper presents a comprehensive performance evaluation of optimized IEEE 802.11ax networks in Industrial Internet of Things (IIoT) environments focusing on three critical performance metrics: throughput, latency, and packet loss. Using a Deep Reinforcement Learning (DRL)-based optimization strategy, the researchers configured MAC layer parameters to meet the demands of heterogeneous industrial traffic demands. Justification for the use of DRL is provided by its ability to handle complex, stochastic environments effectively. MATLAB simulations modelled and analyzed network performance under harsh IIoT environments, characterized by electromagnetic interference (EMI), fluctuating traffic loads, high-density device deployments, and significant physical obstructions such as metal structures. The results showed that the optimized network significantly improved system throughput, average transmission delay, and packet retransmission rate. The peak throughput increased from 1420 Mbps to 2150 Mbps, and the highest packet loss ratio decreased from 32.5% to 23%. Latency also saw notable improvement, with the number of nodes experiencing latency greater than 0.1 seconds decreased from 5 to 1. These findings demonstrated that the proposed optimization strategy for IEEE 802.11ax systems can significantly enhance performance in IIoT environments, making them more reliable and efficient for industrial applications.
Simulation of the Effects of Land Cover on Groundwater Dynamics in Yala Catchment Kenya
Groundwater depletion is on an increasing trend due to land use changes. Soil water content is important in plant growth, nutrient transport, and oxygen balance, influencing various physiological and biochemical processes necessary for plant development and ecosystem stability. This study aimed to simulate soil water dynamics and assess the effects of land use changes on groundwater in the Middle Yala Catchment. The objectives were to determine effects of land use on moisture retention, infiltration and water availability for sustainable resource management. This was achieved through field experiments and modelling using HYDRUS-1D. Primary data, including soil moisture and groundwater levels, were collected under different environmental conditions such as tree canopy and grassland plantations. Climate data was collected from the weather station in Vihiga County. HYDRUS 1D model was used to simulate the relationship between land use scenarios under varying soil texture. The findings revealed that grassland areas maintained higher and more stable soil moisture levels than eucalyptus plantations, which experienced significant moisture fluctuations and harmed groundwater recharge. Seasonal rainfall patterns affected soil moisture in diverse ways across land uses, with eucalyptus plantations causing significant depletion due to high water uptake and evapotranspiration. The findings suggest that grasslands are more effective at retaining soil moisture, while eucalyptus plantations may contribute to water scarcity due to high water uptake and. The model accurately captured soil moisture trends in grassland but met challenges with eucalyptus interactions. The study emphasizes the importance of selecting vegetation types that enhance groundwater recharge and advocates for regulated eucalyptus expansion and sustainable agricultural practices.
Optimal Sizing and Analysis of Sustainable Solar PV-Biomass Hybrid Energy System for Learning Institutions
Energy access is a significant challenge globally due to limited access to electricity together with the unreliability of grid extension. The main backup system is often diesel generators which suffer from high maintenance, running costs and harmful emissions. The abundance of solar irradiation in the equatorial region and biomass due to the high population and agricultural activities provides a green energy solution to improve access to electricity through distributed generation. This study presents a design and analysis of a sustainable, and optimal configuration of a hybrid power system based Solar PV-Biomass energy system. The proposed system was designed and optimized using Hybrid Optimization of Multiple Energy Resources with the most optimal system being solar PV, Biomass Generator, and Diesel generator with storage. A sensitivity analysis using inflation rate and solar irradiation established that with increasing solar irradiation and reducing inflation rate, the Least Cost of Energy decreases. A sustainability analysis based on Multi-Criteria Decision-Making techniques showed that the most sustainable energy alternatives was solar PV, Biomass Generator with storage of specifications 360 kW of solar PV array, 540 kW of Biomass Generator, 142 kW converter and 576 strings of 1 kW lead-acid batteries storage bank with least cost of energy of 0.1026 USD/kWh. If this proposed hybrid energy system is implemented, it will improve energy access, reliability and reduce costs of energy compared to conventional grid extension.
Machine Learning Based Spectrum Sensing for Interference Reduction in 5G Cognitive Radio Networks
The rapid increase in global population has driven a surge in users of radio technology, leading to a shortage of available frequency spectrum for wireless systems. To optimize the use of limited spectrum, secondary (unlicensed) users can access the spectrum of primary (licensed) users when it is temporarily unused. These unused portions of spectrum are called spectrum holes or white spaces. Cognitive radios play a key role by performing spectrum sensing to detect when the spectrum is available for secondary users. Real-time spectrum detection is essential for allowing secondary users to access the spectrum without interfering with primary users. However, existing spectrum sensing methods often suffer from poor detection accuracy due to channel fading and noise. This research focuses on the design and evaluation of machine learning-based spectrum sensing algorithms for Cognitive Radio 5G networks. A hybrid sequential clustering algorithm, which combines Particle Swarm Optimization (PSO) with the K-means algorithm, is proposed. In this approach, PSO, a population-based optimization technique, determines the initial centroids and provides an optimal starting point for clustering. K-means then partitions the sensed spectrum into two clusters: occupied and unoccupied. Extensive simulations in Python were conducted to evaluate the performance of the PSO-K algorithm in various 5G network scenarios. Analysis of detection accuracy demonstrated a 9.3% improvement compared to traditional energy detection techniques.
The Effect of Sunflower Oil and Water on Thermal Storage Parameters of a Flat Plate Solar Water Heating Collector
Solar thermal energy conversion by flat solar collectors can be used for water heating to a temperature of 80℃. However, the thermal performance of these solar collectors is very much affected by varying weather conditions. Heat retention can effectively be achieved by the use of thermal storage fluids like sunflower oil. The aim of the study is to assess the effects of sunflower oil and water on the thermal storage capacity parameters of a flat plate solar water heater. Specific objectives were to determine the effect of water and sunflower oil on the peak output temperature, heat removal factor, heat loss coefficient, and efficiency of the flat plate solar water heating systems. Flat plate solar water heating collectors containing sunflower oil and water as thermal storage fluids were designed and constructed for this study. The absorber plate was made of mild steel welded to galvanized iron riser pipes. The temperatures were measured using K-type thermocouples connected to a data logger and a computer. Simulation and theoretical modeling were done using KOLEKTOR 2.2 software, while experimental data computation and analysis were done using MATLAB. Research findings showed that sunflower oil and air attained a peak temperature of 75℃, while that of water was 65℃ from 12 noon to 3.30 pm. Sunflower oil has the longest stagnation (steady) temperature duration. Both experimental and theoretical results showed that sunflower oil has a higher removal factor FR and efficiency factor F1 than water. From the KOLEKTOR model, efficiency factor F1 values of sunflower oil, water, and air are 0.922, 0.916, and 0.818, respectively. Sunflower oil is also a better thermal storage fluid than water since it has a lower heat loss coefficient than water. Other studies have also shown that sensible heat storage media improve efficiency by reducing thermal losses. The information from this study would be useful for the effective utilization of intermittent solar energy for heating applications. Thus, reducing water heating expenses and conserving our environment.
Dynamic Performance Analysis of Grid-Tied Hybrid Renewable Energy Systems under Fault Conditions: An IEEE 14-Bus system.
The global energy landscape is rapidly evolving towards increased reliance on renewable energy sources, driven by concerns over climate change and the finite nature of fossil fuels. However, the integration of variable and intermittent renewable energy sources into the electricity grid presents significant challenges to grid stability and reliability. This study presents a comparative analysis of Solar PV-Wind HRES Gid-Tied System and Solar PV-Wind-Battery HRES Gid-Tied System based on transient and steady-state stability analysis. The systems were simulated using the Electrical Transient Analyzer Program (ETAP®) and Grid-tied to an IEEE 14-bus system. For transient analysis, two common grid disturbances were explored, i.e., Line to Ground faults on buses and faults on the transmission line at different fault positions i.e., 0%, 10%, 25%, 50%, 75%, 90%, and 100% with a fault clearance time of 1.05 s, 1.50 s, and 2.00 s, and the fault set to occur at 1.00 s with a simulation time of 50 s. Lastly, ANOVA analysis was performed to analyze the effects of fault position on a transmission line, fault clearance time, and the presence of a battery energy system on the system setting time after a fault occurred based on generator speed, voltage, and frequency. For the Solar PV-Wind HRES Gid-Tied System, it was established that the voltage and frequency profiles at the Bus with the fault dropped significantly during the fault but recovered to their pre-fault level after the fault was cleared for all the explored fault clearance time. However, a larger fault clearance time (2.00 s) had a longer settling time. Furthermore, there were statistically significant differences between the explored fault clearance times. For the Solar PV-Wind-Battery HRES Gid-Tied System, it was established that the BESS enhanced the stability and efficiency of an HRES with voltages ranging from 0.98772 to 1.000 p.u. Moreover, there was a statistically significant interaction between the effects of fault clearance time and battery on the settling time for voltage, frequency, and generator speed. In comparison, the settling time for the Solar PV-Wind-Battery HRES Gid-Tied System was lower than that for the Solar PV-Wind HRES Gid-Tied System. Therefore, the battery energy system effectively compensates for the inherent variability of renewable energy sources preventing cascading failures and ensuring system robustness.
Phytochemicals in Mondia whitei Confer Fungistatic Activity Against Alternaria solani and Phytophthora infestans and Improves Tomato (Solanum lycopersicum) Performance.
Tomato is a significant vegetable, but is adversely attacked by several fungal diseases that lower yield. Bio-extracts from plants, as fungicides are safe to human and the environment. This study assessed the impact of Mondia whitei extracts on tomato plant growth and antifungal activity in vitro and in vivo and screened for fungistatic phytochemicals in the plant’s extract. The extracts were obtained from M. whitei roots using methanol, dichloromethane, and ethyl acetate solvents and tested against Alternaria solani and Phytophthora infestans at concentrations of 10% and 20% in vitro and 2.5%, 10%, and 20% in vivo. Ridomil was used as positive control and blank as negative control. In vitro and in vivo experiments were replicated three and four times, respectively, in a completely randomized design. The extracts completely inhibited in vitro growth of the pathogens. In vivo M. whitei-treated plants showed a significant reduction (p≤0.05) in A. solani and P. infestans disease incidences which were recorded at 18.57% and 35% respectively for 20% bio-extract. Tomato plants treated with negative control recorded 57.86% and 80% for A. solani and P. infestans, respectively. Disease severity index in A. solani inoculated plants was 4.43 for 20% bio-extract compared to negative control that recorded an index of 8.29. For P. infestans, 20% bio-extract recoded disease severity index of 7.14 compared to 10.42 in the negative control plants. Plant height increased with increase in extract concentration. Negative control, 2.5%, 10% and 20% bio-extract concentrations recorded heights of 93.49 cm, 119.04 cm, 128.72 cm and 133.96 cm respectively for A. solani inoculated plants and 99.30 cm, 99.97 cm, 111.42 cm and 117.71 cm respectively for P. infestans inoculated plants. Phytochemicals such as flavonoids, tannins, and steroids among others were detected. This study presents M. whitei as a potentially safe and affordable alternative to synthetic fungicides against fungal diseases in tomato crops.