Increasing greenhouse gas (GHG) emissions and environmental issues have heightened the demandfor renewable energy sources (RES) and prompted a swift transition to electric vehicles (EVs) in thetransportation sector. This shift underscores the need to address the challenges of electricity supplyand continuity for electric vehicle charging stations (EVCS). This study aims to determine the mostsuitable hybrid systems to ensure the electricity supply to EVCSs in the Çukurova region of Adana,Turkey. Six different scenarios involving components such as photovoltaic (PV) panel, wind turbine(WT), biomass generators (BG), electrolyzer (Elz), hydrogen tank (HT), fuel cell (FC), batteries (Bat),inverter (Inv), and the grid were analyzed using HOMER Pro microgrid analysis tool version 3.14.2software. The optimization results indicated that the most feasible system was Scenario 4, comprisingthe PV, BG, Elz, HT, FC, Inv, and grid components. This scenario’s total net present cost (NPC) was$611,283.50, with a levelized cost of energy (LCOE) of $0.0215. The annual energy productionand consumption were 1,507,169 kWh and 1,420,714 kWh, respectively. The fact that the energygenerated from exceeds the energy sourced from the grid reduces the payback period of the system.These findings highlight the economic and sustainable potential of renewable hybrid systems forenhancing the performance of EVCS in solar-rich regions.Keywords: Energy cost efficiency, Renewable energy integration, Electric vehicle charging stations, Hybridsystems, Optimization, Energy sustainability

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

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

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

The goal of natural language processing (NLP), which has recently gained popularity, is to improve the ca-pacity of computers to comprehend and interact with human language. Consequently, to converse usingnatural language, it is crucial that spoken language be grammatically correct, especially for Geéz language.Geéz language sentences must follow certain norms of agreement in terms of number, person, gender, tense,and other factors to be considered grammatically correct. If the input sentence in Geéz language is improper,then it can have problems with subject-verb agreement, object-verb agreement, adjective-noun agreement,and adverb-verb agreement. The goal of the proposed work is to provide a neural machine translation ap-proach for detecting and correcting grammar errors in Geéz sentences. We have prepared manually 11,490Geéz parallel corpuses (Geéz language grammatically incorrect and grammatically correct sentences). Afterwe have prepared a parallel Geéz sentence, we have used normalization, tokenization, padding, and one hotencoding as preprocesses. We have used two deep learning algorithms, including a bidirectional long short-term memory encoder-decoder and a long short-term memory encoder-decoder, for training the proposedmodel. Keras and TensorFlow were used for importing the required libraries, and we used the Python 3.7 en-vironment for implementation. Two test cases are used for the evaluation technique. The first one is for thelong short-term memory encoder-decoder model, and the second one is for the bidirectional long short-termmemory encoder-decoder model. Finally, the bidirectional long short-term memory encoder-decoder modelachieved best results with an accuracy of 82%, recall of 82%, precision of 85%, and F1-measure of 83% withbalanced error type classes.CCS Concepts: • Computing methodologies → Machine translation;Additional Key Words and Phrases: NMT, GSD, GGEH, NLP, DL, LSTM, BILSTM

Now adays people express and share their opinions on various events on the internet thanks to socialmedia. Opinion mining is the process of interpreting user-generated opinion data on social media.Aside from its lack of resources in opinion-mining tasks, Amharic presents numerous difficultiesbecause of its complex structure and variety of dialects. Analyzing every comment written in Amharicis a challenging task. Significant advancements in opinion mining have been achieved using deeplearning. An opinion-mining model was used in this study to classify user comments written in Amharicas positive or negative. The domains that we focus on in this study are YouTube and Facebook. Fromthe Ethiopian broadcasts YouTube and Facebook official pages, we gathered 11,872 unstructured datafor this study using www.exportcomment.com, and Facebook page tools. Text preprocessing andfeature extraction techniques were used, in addition to manual annotation by linguistic specialists.The dataset was prepared for the experiment after annotation, preprocessing, and representation.LSTM, GRU, BiGRU, BiLSTM, and a hybrid of CNN with BiLSTM classifiers from the TensorFlow Kerasdeep learning library were used to train the model using the dataset, which was split using the 80/20train-test method, which proved effective for classification problems. Finally, we achieved of 94.27%,95.20%, 95.49%, 95.62%, and 96.08% using GRU, BiGRU, LSTM, BiLSTM, and CNN with BiLSTM,respectively, in word2vec embedding model.Keywords: Opinion mining, Deep learning, Recurrent neural network, Word2vec, Fast text

The groundwater level in the Chemoga watershed has been declining due to an increase in water demand, anthropogenicactivities, and climate change effects. This paper uses the WetSpass-MODFLOW coupling to evaluate the groundwater rechargein the Chemoga watershed. The MODFLOW groundwater flow simulation model is then used to simulate the hydraulic headdistribution based on these findings. The input data of WetSpass models are soil, land cover, topography, slope, and ground-water depth, as well as monthly meteorological characteristics (such as temperature, wind speed, and rainfall). The long-termspatial and temporal average annual precipitation of 1,453 mm is distributed as 169 mm (11.63%) groundwater recharge and879 mm (60.5%) surface runoff, while 405 mm (27.87%) is lost through evapotranspiration. In such seasonal variations, thegroundwater head due to the wet/summer stress period varied from 4 to 41 m. While in the dry/winter stress period ground-water head varied from 3.5 to 39.8 m, and also the groundwater head due to the annual stress period varied from 3.7 to 40 m.The findings are extensive and can be applied to water resource management and groundwater resource development in asustainable manner by safeguarding high groundwater recharge locations, and reevaluating allowable groundwater abstractionrates.Key words: ArcGis, Chemoga watershed, groundwater recharge, hydraulic head, MODFLOW, WetSpass-M model

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

Accurate regulation of the liquid level in a quadruple tank system (QTS) is not easy and imposes higherrequirements on control strategies, so the design of controllers in these systems is challenging dueto the difficulty of dynamic analysis of its nonlinear characteristics and parametric uncertainties.To overcome these problems in liquid level regulation and increase the robustness to the pumpcoefficients, this article proposes and investigates the use of an optimal hybrid fractional-ordertype-2 fuzzy-PID (OH-FO-T2F-PID) regulator using a combination of two bio-inspired evolutionaryoptimizers, namely augmented grey wolf optimizer and cuckoo search optimizer, which gives rise tothe new hybrid A-GWOCS algorithm. This control mechanism was chosen to facilitate the convergenceof the water liquids in the two tanks as quickly as possible to the corresponding required values. Inaddition, a collaborative optimization technique with several objectives is used to adjust the regulatorparameters. The capability and efficiency of the suggested regulator is first investigated throughcomputer simulation results and then confirmed by real-time control experimental results on the QTSbased on dSPACE 1104 computation engine. The findings showed that the suggested OH-FO-T2F-PIDregulator significantly outperformed both the optimized ADRC and the OH-FO-T1F-PID regulators.Specifically, it reduced the rising time by 17.02% and 95.21%, respectively, and the settling time by25.13% and 74.28%. Additionally, the designed OH-FO-T2F-PID regulator successfully eliminatedthe steady-state error and overshoot, enabling precise regulation of the QTS, and maintenance theliquid level at the desired set point under a wide range of working situations. The robustness of therecommended regulator is also studied by considering − 50% disturbance in the QTS parameters, andthe findings showed that the OH-FO-T2F-PID regulator is less susceptible to variations in parameters.Keywords: Quadruple tank system (QTS), Optimal hybrid fractional order type 2 fuzzy PID regulator,Hybrid A-GWOCSO algorithm, Multi-objective optimization, dSPACE 1104 computation engine