This paper addresses the smart management and control of an independent hybrid system based onrenewable energies. The suggested system comprises a photovoltaic system (PVS), a wind energyconversion system (WECS), a battery storage system (BSS), and electronic power devices that arecontrolled to enhance the efficiency of the generated energy. Regarding the load side, the systemcomprises AC loads, DC loads, and a water pump. An Adaptive Neuro-Fuzzy Inference System (ANFIS)-based MPPT technique is suggested to enhance the efficiency of the PVS and WECS. This technologyprovided good performance compared with the Perturb and Observe (P&O) algorithm and MPPT-basedfuzzy logic controller (FLC). The use of the ANFIS-PI proposed to control the bidirectional converteraccomplished voltage stabilization for the DC bus. This work also came with a fuzzy logic-basedalgorithm to manage the load side that depends on battery charge ratio, solar radiation, and windspeed. According to results obtained in the MATLAB/Simulink environment, the proposed technologieswere found to have performed well. The goal we were also pursuing was achieved through the fulluse of the energy generated by the proposed algorithm. The proposed study holds great potential forremote regions.Index terms: Renewable energy, Hybrid system, MPPT, ANFIS controller, Management

While Model Predictive Control (MPC) has been widely studied in power electronics, its real-time imple-mentation on DC-DC boost converters—particularly under variable loading conditions—remains limited. Thispaper proposes a new real-time implementation of the Model Predictive Control (MPC) law for a DC-DC boostconverter connected to variable loads. This implementation ensures precise current regulation through accurateduty cycle control updates, enabling the inverter’s frequency switching to be activated or deactivated as needed.This is achieved by proposing a predictive model of the current occurring in the first channel of the convertermodel, where a fitness function—comprising reference tracking and control effort—is minimized. Compared tothe proportional-integral (PI) controller, the MPC law proves more efficient, particularly in preventing oscilla-tions in both transient and steady-state output current responses. This advantage is validated through experi-mental tests for either a current inductance load or a resistive load. Since this type of real-time implementationhas not been previously applied on this converter, it constitutes the main contribution of this paper.
Keywords: PI controller, DC-DC boost converters, Model predictive control (MPC), Experimental validation

Study region: The study area is located in the East Gojjam zone, Amhara, Ethiopia; the area coversthe Choke Mount and is surrounded by the Abbay River.Study focus: The primary focus of the study was assessing the possible groundwater sites in theselected area using the Analytical Hierarchy Process (AHP) with the Geographic InformationSystem (GIS) approach for groundwater exploration and investigation.New hydrological insights for the region: Water is a very important resource used to the day-to-dayactivities in our life, which is found naturally on the surface and subsurface of the Earth. Thestudy area is a part of a nation-wide economically significant region in Ethiopia and the Horn. Thearea is the primary water supply (Choke Mountain) for the Ethiopian Grand Ethiopian Renais-sance Dam (GERD) receives the highest water supply from this region. The results of the studyshow that the groundwater potential zones in the area are mapped as poor, moderate, high, andvery high groundwater potential areas. The Validations of the results were made using theborehole log data, and reasonably accepted the rationality of the adopted methodology. Theconsidered parameters, as well as their evaluation of the production of the groundwater potentialMap, were confirmed. The produced Groundwater potential map is very important for IrrigationEngineers, domestic water supply studies, agricultural studies, environmentalists, and futuregroundwater conservation strategies.
Keywords: GIS, AHP, Groundwater potentials, East Gojjam, Ethiopia

This study presents a novel approach for optimizing UAV (unmanned aerial vehicle) Multicircularflight control by developing a fractional order proportional integral derivative (FOPID)-based hybridEagle strategy particle swarm optimization ant lion optimizer (HESPSOALO). The proposed algorithmcombines the strengths of particle swarm optimization (PSO) and the ant lion optimizer (ALO), whichare enhanced by the Eagle strategy to systematically fine-tune the FOPID controller parameters.This hybrid optimization method aims to improve system stability, responsiveness, and disturbancerejection in UAVs, particularly in challenging dynamic flight conditions. The proposed approachwas validated against traditional control methods that utilize FOPID (Base), the Base HESPSOALOalgorithm, the FOPID-based HPSOGWO (Hybrid Particle Swarm Optimization-Gray Wolf Optimizer),and the FOPID-based HGWOALO (Hybrid Gray Wolf Optimization-Ant Lion Optimizer) with a setof benchmark functions used in the analysis. The results demonstrate a minimization of positionand angular errors, reduced oscillations, and overall improved control stability for the FOPID-basedHESPSOALO compared with the other methods. Furthermore, a multicriteria decision-making(MCDM) framework is applied to evaluate the overall performance of alternative control strategiesutilizing the CRiteria importance through intercriteria correlation (CRITIC) and technique of orderpreference by similarity to ideal solution (TOPSIS) techniques. The MCDM analysis demonstratesthat among the evaluated criteria, Kp has the highest importance, with a weight of 0.244019,whereas Kd is deemed the least significant, with a weight of 0.161023. The ranking results revealthat the HESPSOALO algorithm (Base) is the best-performing controller method, with a rankingscore of 0.571161, indicating its superior control performance across major metrics. In contrast, theFOPID + HPSOGWO controller method ranks the lowest, with a score of 0.282794. The findings havesignificant industrial implications, particularly in sectors where UAVs are critical for precision tasks,such as logistics, agriculture, surveillance, and environmental monitoring. By optimizing the FOPIDcontroller parameters, the HESPSOALO algorithm enhances UAV stability, responsiveness, andreliability in dynamic environments, resulting in more precise control and robust performance undervarying conditions. This improvement may reduce operational risks and maintenance costs whileincreasing efficiency, prolonging UAV service life, and achieving energy savings. This study provides arobust solution for UAV control based on the potential of hybrid optimization algorithms to improveUAV precision and reliability in autonomous flight.Keywords: UAV multicircular flight control, FOPID, Hybrid optimization, CRITIC, TOPSIS

This paper provides an in-depth analysis and performance evaluation of four Solar Radiance (SR)prediction models. The prediction is ensured for a period ranging from a few hours to several days ofthe year. These models are derived from four machine learning methods, namely the Feed-forwardBack Propagation (FFBP) method, Convolutional Feed-forward Back Propagation (CFBP) method,Support Vector Regression (SVR), and the hybrid deep learning (DL) method, which combinesConvolutional Neural Networks and Long Short-Term Memory networks. This combination results inthe CNN-LSTM model. Additionally, statistical indicators use Mean Squared Error (MSE), Root MeanSquared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), andNormalized Root Mean Squared Error (nRMSE). Each indicator compares the predicted output by eachmodel above and the actual output, pre-recorded in the experimental trial. The experimental resultsconsistently show the power of the CNN-LSTM model compared to the remaining models in terms ofaccuracy and reliability. This is due to its lower error rate and higher detection coefficient (R2 = 0.99925).Keywords: Artificial neural networks, Convolutional neural network, Convolutional feed-forward backpropagation, Deep learning, Feed-forward back propagation, Long short-term memory, Solar radianceforecasting

Enhancing heat transfer in thermal systems is crucial for energy efficiency. The use of Nano-fluids and twistedtape inserts in circular pipes are the most widely used passive heat transfer improvement techniques. Whilenanofluids, especially CuO-water, enhance thermal conductivity, twisted tapes create swirl flow to disturbboundary layers. The Nusselt number, friction factor, and thermal performance parameters of a circular pipecontaining Nano-fluids and twisted tapes at 180 and 120 degrees are studied numerically in this work. Thetwisted tape inserts are modeled as idealized helical baffles to induce secondary swirl flows, thereby disruptingthermal boundary layers and improving heat exchange. The research yields findings for a strip twist ratio of threeand a turbulent flow range of Re 4000–20,000. The RNG k–ε model is utilized to solve the governing equationsand a steady heat flux of 30,000 W/m2 is supplied. The highest simulation findings of Nusselt number for Nano-fluid are 5.25, 9.85, and 12.5 % higher in comparison to Gnielinski relations of water for plain tube and twistedtape inserts at 180 and 120 degrees respectively. However, increased pressure drop is noted as a trade-off, theoverall thermal performance factor of 1.42 was achieved for Nano-fluid flow in a pipe with a 120◦ twisted tapeinsert which yields a significant heat transfer improvement.
Keywords: CuO-water nano-fluid, Turbulent flow, Twisted tape, Heat transfer enhancement, CFD

Integration of nonlinear loads in modern power systems has led to many issues arising mainly dueto the generation of harmonic currents and the presence of reactive power, both having adverseeffects on power quality and grid stability. Harmonic currents cause increased losses, overheatingof equipment, and voltage distortions, while reactive power imbalances result in inefficiencies inpower delivery and compromised system performance. To overcome these problems, a Shunt ActivePower FIlter design and an optimal control strategy for harmonic mitigation and reactive powercompensation are proposed in this paper. The design incorporates an optimized anti-windup PIcontroller for DC-link voltage regulation and an optimized output filter to enhance the quality of theinjected current. This design is formulated as an optimization problem and solved using the GoldenJackal Optimizer. MATLAB/Simulink simulations validate the proposed method under differentoperating conditions, covering dynamic change of loads and unbalanced grid conditions. The resultshows a remarkable reduction in Total Harmonic Distortion (THD) of grid current, and reactive powercompensation meanwhile maintaining the stability of the grid.Keywords:  Golden Jackal optimization, Shunt active power filter (SAPF), Optimal control, Power quality,Current harmonics compensation

The work presented in this paper focuses on formulating the development of time-dependent electromagneticfield laws through the application of Gauss’s divergence theorem. The first part of the discussion looks at thebasic ideas of electromagnetism. It focuses on how classical formulations of the laws of electromagnetism can beadapted to account for non-stationary conditions, especially regarding magnetic fluids that don’t conduct elec-tricity. It is suggested that employing Gauss’s divergence theorem could help improve the computational analysisof these generalized equations, which would make them more useful in magnetic fluid dynamics. The paperexamines the intricate interactions between non-conductive particles and conductive fluids under magneticfields. By putting these interactions into a single theoretical framework, this work aims to help us understandnon-stationary electromagnetic phenomena and how they affect many different scientific and engineering fields.The concluding section of the study examines the prospective practical applications of these extended equations.They could enable the development of more advanced electromagnetic devices and systems. Creating a strong setof analytical tools that can find new scientific paths and useful applications is the main goal of the study,particularly in the areas of electromagnetic induction and fluid dynamics. This research offers potential forsubstantial progress in both theoretical comprehension and technological advancement, The proposed method isapplicable to real-world systems such as ferrofluid-based cooling, magnetic dampers, plasma generators, andsmart electromagnetic devices. These applications demonstrate the practical benefits of coupling field behaviorwith boundary dynamics using Gauss’s theorem.
Keywords: Gauss theorem, Non-conductive;Magnetic, Non-stationary, Fluids, Induction

Hate speech has been disseminated more frequently on social media sites like Facebook in recent years. OnFacebook, hate speech can proliferate through text, image, or video. We suggested a deep learning approach toidentify offensive memes posted on Facebook in case of Amharic language’. The research process commenced bymanually gathering memes posted by Facebook users. Next came textual data extraction, annotation, pre-processing, splitting, feature extraction, model development and assessment Amharic OCRs were employed toextract textual data. Character normalization, stop word removal, and unnecessary character removal make upthe text-preprocessing step. Using Stratified KFold the textual dataset is split into the train set (80 %), thevalidation set (10 %) and the test set (10 %). Vectors are created from the preprocessed texts using the Bog ofwords (BOW), TFIDF and word embeddings. Following that, the vectors are fed into Machine learning algo-rithms: NB, DT, RF, KNN, LSVM and LR, and deep learning models that are based on Dense, BiGRU, and BiLSTMalgorithms. The model with the optimal parameters is chosen after numerous experiments. With an accuracy rateof 94 %, the BiLSTM + Dense model, the suggested technique identified nasty meme posts on Facebook written inAmharic.
Keywords: Deep learning, BILSTM, BIGRU, Amharic language hate speech

Habesha kemis, an Ethiopian attire traditionally donned by women belonging to the Habeshacommunity, has undergone variations of designs over time. Initially, it comprised a lengthy dresswith a fitted bodice and sleeves extending to the ankles. In the Amhara region, various ethnic groupssuch as Gojjam, Gondar, Shewa, Agew, and Wollo uphold their distinct cultural customs. While theseHabesha garments may appear similar outwardly, their embroidered motifs exhibit unique patterns,shapes, and hues, symbolizing the rich cultural legacy of Gojjam, Gondar, Shewa, Agew, and Wollo.The study aimed to identify the most appropriate model for recognizing and classifying the quality ofHabesha kemis embroidery design. Digital image processing methods and CNN models incorporatingVGG16, VGG19, and ResNet50v2 classifiers were used. Following the gathering of datasets,image preprocessing and segmentation were employed to enhance the model’s performance. Insegmentation, we used canny edge detection, local binary pattern, and dilation with contour detectionfor segmenting and automatically cropping each habesha kemis. After applying the segmentationprocess, the individual habesha kemis and foreign matters are placed in a folder based on theircorresponding categories. This resulted in 320 images before augmenting for each class amountrepresentative. The performance of VGG16, VGG19, and ResNet50v2 for Agew, Gojjam, Gonder,Shewa, and Wollo was evaluated. This process resulted in an image size of 224 × 224 in the CNNmodel with a VGG16 architecture and a SoftMax classifier of course we try also 64 × 64 and 128 × 128.Augmentation techniques were applied to increase the dataset size from 1600 to 3,270. Finally, themodel was evaluated and achieved an accuracy of 95.72% in test data and 99.62% in training datacompared to the VGG19 and ResNet50v2 models.Keywords Ethiopian cultural cloth, Habesha kemis, Embroidery design, Shemma