This study investigates the performance of hemispherical solar stills (HSS) enhanced with date kernelsand olive kernels as heat storage materials to improve water distillation efficiency. By utilizing thesenatural and sustainable materials, the research highlights an alternative to synthetic options. Rigorousexperimentation and detailed analysis under identical conditions reveal that both kernels significantlyimprove heat retention and water production rates. The HSS with date kernels (HSSDK) achieved adaily water productivity of 6.66 kg/m2 day, representing an efficiency increase of 10.87%, while theHSS with olive kernels (HSSOK) produced 8.00 kg/m2 day, enhancing efficiency by 13.54%. The cost perm3 of distilled water for HSSDK is approximately USD 4.65, while HSSOK costs USD 3.89, comparedto USD 7.83 for the conventional CHSS system. These results demonstrate that the inclusion of heatstorage materials has significantly reduced the cost of water production, with reductions of about 40%for HSSDK and 50% for HSSOK compared to the conventional system. These results are attributedto the high thermal conductivity and specific heat capacities of the kernels, enabling effective heatstorage and gradual release. This study demonstrates the potential of agricultural by-products ascost-effective and sustainable solutions for solar water distillation. Further research is recommendedto optimize the quantities and configurations of these materials, as well as to explore their integrationwith other renewable energy systems to enhance overall efficiency and sustainability.Keywords: Hemispherical solar still, Date kernels, Olive kernels, Heat storage materials, Distillation efficiency

This paper presents a novel approach to modeling and controlling a solar photovoltaic conversionsystem(SPCS) that operates under real-time weather conditions. The primary contribution is theintroduction of an uncertain model, which has not been published before, simulating the SPCS’sactual functioning. The proposed robust control strategy involves two stages: first, modifying thestandard Perturb and Observe (P&O) algorithm to generate an optimal reference voltage usingreal-time measurements of temperature, solar irradiance, and wind speed. This modification leadsto determining and linearizing the nonlinear current-voltage (I-V) characteristics of the photovoltaic(PV) array near standard test conditions (STC), resulting in an uncertain equivalent resistance used tosynthesize an overall model. In the second stage, a robust fixed-order H∞ controller is designed basedon this uncertain model, with frequency-domain specifications framed as a weighted-mixed sensitivityproblem. The optimal solution provides the controller parameters, ensuring good reference trackingdynamics, noise suppression, and attenuation of model uncertainties. Performance assessments atSTC compare the standard and robust P&O-MPPT strategies, demonstrating the proposed method’ssuperiority in performance and robustness, especially under sudden meteorological changes andvarying loads. Experiment results confirm the new control strategy’s effectiveness over the standardapproach.

This paper introduces the Efficient Metaheuristic BitTorrent (EM-BT) algorithm, aimed at optimizingthe placement and sizing of photovoltaic renewable energy sources (PVRES) and capacitor banks(CBs) in electric distribution networks. The main goal is to minimize energy losses and enhance voltagestability over 24 h, taking into account varying load profiles, solar irradiance, and temperature effects.The algorithm is rigorously tested on standard distribution networks, including the IEEE 33, IEEE69, and ZB-ALG-Hassi Sida 157-bus systems. The results reveal that EM-BT outperforms establishedmethods like Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Whale OptimizationAlgorithm (WOA), demonstrating its effectiveness in reducing energy losses and maintaining stablevoltage profiles. By effectively combining PVRES and CBs, this research highlights a robust approach toenhancing both technical performance and operational reliability in distribution systems. Additionally,the consideration of temperature effects on PVRES efficiency adds depth to the study, making it avaluable contribution to the field of power system optimization.Keywords: Efficient Metaheuristic BitTorrent (EM-BT) algorithm, Photovoltaic renewable energy sources(PVRES), Capacitor banks (CBs), Energy loss minimization, Particle Swarm Optimization (PSO), Grey WolfOptimizer (GWO), Whale Optimization Algorithm (WOA), Operational reliability

This study suggests an enhanced version of the adaptive fuzzy fast terminal synergetic controller(AF-FTSC) for controlling the uncertain DC/DC buck converter based on the synergetic theory ofcontrol (STC) and newly developed terminal attractor technique (TAT). The benefits of the proposedSC algorithm involve the features of finite-time convergence, unaffected by parameter variations, andchattering-free phenomenon. A type-1 fuzzy logic system (T1-FLS) make the considered controllermore robust and is utilized to estimate the undefined converter nonlinear dynamics without resortingto the usual linearization and simplifications of the converter model. Taking a switching DC-DC buckconverter as a demonstration, the suggested AF-FTSC is thoroughly analyzed and executed on adSPACE ds1103 controller board. The outcomes of the experiment confirm the competence andapplicability of the suggested regulator.Keywords: Synergetic control, Fuzzy logic system, Fast terminal method, Finite-time convergence, DC/DCbuck converter

One of the biggest global challenges to development and public health is road traffic accidents (RTAs). As aresult, this study focuses on analysing road traffic accident determinant factors using the Wrapper Feature Se-lection Method in case of East Gojjam Zone located in Amhara region, Ethiopia, sub-Saharan. To do this, EastGojjam Road traffic office RTA data classified as simple injury, major injury, and death is gathered. The gatheredinformation is pre-processed before being used using machine learning classification algorithms includingNearest Neighbour (KNN), Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), and NaïveBayes (NB). Using the wrapper feature selection approach, the most significant factor was identified using themachine-learning algorithm KNN, which obtained the best classification score with an accuracy of 99.5 %. Thus,the type of vehicle, the reason for the accident, the location of the accident, and the licence level were identifiedas crucial RTA factors. Finally, the variables, Sino track, unfavourable weather, Dolphin, and Debre Elias rated100 %, 100 %, 85 %, and 82.35 % for fatality in relation to the factors licence driver, cause of accident, type ofvehicle, and accident location, respectively.
Keywords: Road traffic accident, East Gojjam, Amhara region, Ethiopia, Machine learning, Feature selection, Filter, Wrapper method, Embedded method, Data mining

Natural fiber-reinforced polymer composites are the most widely used materials and preferable interms of biodegradability, cost production, recyclability, and low density. The main aim of thisstudy is to conduct an experimental investigation on tensile strength and impact strength ofpalmyra palm leaf stalk fiber (PLSF) and sisal fiber reinforced polymer hybrid composite. Thecomposite material was fabricated using hand lay-up techniques. The working parameters aremass fraction ratio of PLSF/sisal fiber and volume fiber fraction with the matrix. Tensile strengthand impact energy resistance tests were experimentally conducted according to the ASTM stan-dard dimensions. The results revealed that the addition of sisal fiber to PLSF enhanced the tensilestrength by 12.850 %, 26.540 %, and 30.630 % respectively compared to pure Palmyra palm leafstalk fiber reinforced composite (PPFRC). Whereas, the addition of PLSF to sisal fiber improvedthe impact of energy by 20.980 %, 13.610 %, and 11.880 % compared to pure sisal fiber rein-forced composite (PSFRC). The tensile strength with 20 % fiber volume fraction is improved by53.996 % and 12.188 % compared to 10 % and 15 % of fiber respectively. The impact strengthwas also enhanced by 24.931 % and 10.030 % compared to 10 % and 15 % of volume fiberfraction respectively. The tensile strength and impact energy of the treated fiber compositeincreased by 62.243 % and 22.478 % respectively compared to the untreated hybrid Palmyrapalm leaf stalk and sisal hybrid fiber reinforced composite (UHPSFRC). Generally, the HPSFRC-2(Palmyra palm leaf stalk/sisal fiber) (P/S ratio 50/50 % ratio with 20/80 % ratio of fiber/matricpercentage reinforced polymer hybrid composite) has good tensile strength and impact energy.Therefore, the mechanical property of the (Palm/Sisal) hybrid composite can be used for themanufacturing of the automotive interior parts like door panel, dash board, seat back, andautomotive roof.
Keywords: Handy lay-up, Hybrid fiber, Mechanical properties. unsaturated polyester resin

Induction motor drives with variable speed applications that employ vector control are quitepopular nowadays because they provide strong dynamic performance and flexible speed control.By decoupling the torque-producing current components of stator current from the rotor flux,Indirect Field Oriented Control is recognized for generating excellent performance in inductionmotor drives. This investigation is being done to show the effectiveness of the novel FPI input-output data-trained ANFIS controller and compare the three controllers’ performance in termsof load variation capabilities, motor parameter variation, and speed tracking. Consequently, acomparison of the three controllers is important to select which controller performs high in in-duction motor drive. Indirect Field Oriented Control of induction motor with Fractional Pro-portional Integral (FPI), Integer Proportional Integral (IPI), and Adaptive Neuro-Fuzzy InferenceSystem (ANFIS) controllers are all discussed in this work along with their designs and compar-ative analysis. The square of error was used as a fitness function to genetically optimize the FPIand IPI controller parameters. The suggested Adaptive Neuro-Fuzzy Inference System (ANFIS)controller uses a hybrid learning approach. It is trained by the FPI controller’s input-output data.Using the results of MATLAB simulations under various operating situations, the performance ofthe ANFIS controller was compared with FPI and IPI controllers. Because of FPI controller in-cludes an extra parameter for adjustment, namely integration order, it performed better than IPIcontroller for speed control of the induction motor. According to the simulation findings, thepercentage peak overshoots while employing ANFIS, FPI, and IPI controllers were 0.495 %,12.062 %, and 14.699 % respectively. As a result, ANFIS exhibits a drastic reduction in overshoot.Additionally, with the ANFIS controlled induction motor drive, the speed achieves the requiredset value at 0.14 s. For no load, constant, and changing loads, the induction motor drive’s per-formance has been examined.
Keywords: Induction motor, Indirect field oriented control, Fractional PI, ANFIS, Integer PI

Given the Amharic language has a lot of perplexing terminology since it features duplicate homophone letters,fidel’s ሀ, ሐ, and ኀ (three of which are pronounced as HA), ሠ and ሰ (both pronounced as SE), አ and ዐ (bothpronounced as AE), and ጸ and ፀ (both pronounced as TSE). The WSD (Word Sense Disambiguation) model, whichtackles the issue of lexical ambiguity in the context of the Amharic language, is developed using a deep learningtechnique. Due to the unavailability of the Amharic wordnet, a total of 1756 examples of paired Amharicambiguous homophonic words were collected. These words were ድህነት(dhnet) and ድኅነት(dhnet), ምሁር(m’hur)and ምሑር(m’hur), በአል(be’al) and በዢል(be’al), አቢይ (abiy) and ዐቢይ(abiy), with a total of 1756 examples.Following word preprocessing, word2vec, fasttext, Term Frequency-Inverse Document Frequency (TFIDF), andbag of words (BoW) were used to vectorize the text. The vectorized text was divided into train and test data. Thetrain data was then analysed using Naive Bayes (NB), K-nearest neighbour (KNN), logistic regression (LG), de-cision trees (DT), random forests (RF), and random oversampling technique. Bidirectional Gate Recurrent Unit(BiGRU) and Bidirectional Long Short-Term Memory (BiLSTM) improved to 99.99 % accuracy even with limiteddatasets.
Key Words: Amharic language, Homophone, Machine learning, Deep learning, Bidirectional, BiLSTM, BiGRU, TFIDF, BoW, Word embedding, Amharic word sense disambiguation