bluuebunny/crossref_metadata_2025 huggingface dataset:
Performance of New Control Strategy of Dual Stator Induction Generator System Applied in Wind Power Generation
Authors: Ameur Fatima , Kouzi Katia , Ameur Khadidja
Published: September 2024

In order to improve the quality of energy and reduce the harmonics produced by the power electronics converters, it is proposed and developed in this article the direct torque control, in which the flux and torque are estimated from the only measurable electrical quantities. The direct torque control DTC method, to enhance the dynamic and static performances as well as the robustness of the control of the Wind Energy Conversion System (DSIG). DTC is a control technique that exploits the possibility of imposing torque and flux on alternating current machines in a decoupled manner, once powered by a voltage inverter without current regulation made by a feedback loop, ensuring a decoupling, similar to that obtained from a vector control. The technique involved rapid torque response, insensitivity to parametric variation, in particular the machine's rotor time constant and systematic suitability for control without speed sensor. The main function of the generator side controller is to track the maximum power through controlling the rotational speed of the wind turbine using PI controller. The performance and the effectiveness of the   proposed control system are tested via simulation results in terms of reference tracking, and robustness against parameters variations of the DSIG. Simulation results for 1.5 MW DSIG control show robust with respect to the parametric variation 2 Rs, 1,5 Rs et 0.5 Rs, and fast dynamic behavior of system, with the temps of response is 0.02 s, active power extracted 0.15 MW with lambda 9 and Cp 0,5 that the wind turbine can operate at its optimum power point for a wide range of wind speed and power quality can be greatly improved.
Deep Learning-Based Automated Approach for Classifying Bacterial Images
Authors: Abougarair Ahmed Jaber , Oun Abdulhamid A. , Sawan Salah I. , Ma’arif Alfian
Published: September 2024

Identifying and classifying bacterial species from microscopic images is crucial for medical applications like prevention, diagnosis, and treatment. However, because of their diversity and variability in appearance, manually classifying bacteria is difficult and time-consuming. This work suggests employing deep learning architecture to automatically categorize bacterial species in order to overcome these difficulties and raise the accuracy of bacterial species recognition. We have evaluated our suggested approach using the Digital Images of Bacteria Species (DIBaS), a publicly accessible resource of photographs of tiny bacteria.  This work uses a dataset that differs in terms of bacterial morphology, staining methods, and imaging circumstances. This paper aims to enhance the accuracy and reduce the computational requirements for Convolutional Neural Networks (CNN) based classification of bacterial species using GoogLeNet and AlexNet to train the models. This paper focuses on employing transfer learning to retrain pre-trained CNN models using a dataset consisting of 2000 images encompassing 12 distinct bacteria species known to be harmful to human health.  The concept of transfer learning was utilized to expedite the network's training process and enhance its categorization performance.  The results are promising, with the method achieving an accuracy of 98.7% precision, recall of 99.50%, and an F1-score of 99.45%   with classifier speed. Furthermore, the proposed bacteria classification approach demonstrated strong performance, irrespective of the size of the training data used.  This paper contributes by automating bacterial classification to facilitate faster and more accurate identification of bacterial species, which facilitates the treatment of infections and related diseases, in addition to monitoring public health, and promoting the wise use of antimicrobial drugs. To improve outcomes in the future, researchers can also integrate deep learning techniques with other machine learning methods.
A Combination of INC and Fuzzy Logic-Based Variable Step Size for Enhancing MPPT of PV Systems
Authors: Lamine Ouassa Mohammed , Bessous Noureddine , Abdelhalim Borni , Banakhr Fahd A. , Mosaad Mohamed I. , Oussama Mammeri , Mahmoud Mohamed Metwally
Published: September 2024

The significance of using the variable step Incremental Conductance (INC) technique in Maximum Power Point monitoring (MPPT) of photovoltaic (PV) systems resides in its capacity to improve the efficiency of energy conversion. This is accomplished through the constant measurement and comparison of incremental changes in current and voltage, precisely monitoring the maximum power point amidst changing environmental conditions. This traditional INC-MPPT approach has two primary disadvantages. Initially, it employs a predetermined scaling factor that necessitates human adjustment. Furthermore, it adjusts the inclination of the PV characteristics curve to modify the step size. This implies that even little changes in PV module voltage will have a substantial impact on the total step size. As a result, it shifts the operating point away from the intended reference maximum power point. The objective of this work is to improve the efficiency of traditional INC by overcoming the constraints associated with step size modifications. This is achieved by using a fuzzy logic (FL) technique to adjust the step size adaptively in response to environmental changes. The presented INC-FL-MPPT successfully achieves MPPT for a PV system under enhanced steady-state and transient-state settings. The results demonstrate the superiority of the suggested approach compared to three distinct MPPT strategies, namely Perturb and Observe (PO), Classical INC, and PO-FL technique.
Design and Manufacturing Using 3D Printing Technology of A 5-DOF Manipulator for Industrial Tasks
Authors: Sharkawy Abdel-Nasser , Nazzal Jamal Mahmoud
Published: September 2024

Robotic manipulators have become very necessary in industrial applications all over the world. In this paper, a 5-DOF robotic manipulator is designed and manufactured to simulate a real industrial task. The manipulator is intended to transfer an object with a weight of 30 grams from a known place to another known one, which is a pick and place task. Firstly, all parts of the manipulator are designed using SolidWorks software. During the design, all parts’ dimensions are considered. The end-effector of the manipulator is designed based on gear system. Secondly, 3D printing technology is used to manufacture these designed parts. The manufacturing process is very accurate and efficient. Servo motors are considered to do the motion of the manipulator, which are easily and directly connected to the control circuit. As, 5-DOF manipulator is manufactured, five servo motors are used: one motor for every joint. The motion of the motors is controlled by Arduino Uno unit which is a cheap and easy programming unit. Experiments are executed with the developed robot to show its effectiveness and success by preparing three boxes which the robot effectively transfers from one place to another. Eventually, the challenges during the design and manufacturing of this robot are mentioned in this paper.
Photovoltaic Energy Anomaly Detection using Transformer Based Machine Learning
Authors: Wirawan I Made , Wibawa Aji Prasetya , Widiyanintyas Triyanna
Published: September 2024

This study uses the Anomaly Transformer model to find anomalies in photovoltaic energy generation in Malang, Indonesia. The main background of this study is the lack of satellite monitoring in this region and the importance of annual data for electricity generation forecasting. Temperature scattered direct solar radiation, and hourly electricity production are all part of the dataset used which is only available since 2019. Anomalies were detected at 05.00 and 16.00 WIB, indicating instability in the energy supply due to high temperatures in the morning and heavy rain in the afternoon. Detection of these anomalies is important to improve the efficiency and reliability of photovoltaic systems, reduce operational costs, and reduce the risk of system failure. Indonesia has many challenges for photovoltaic energy generation due to its unique location, with many islands located close to the equator. The use of the Anomaly Transformer algorithm improves the accuracy of anomaly detection over conventional methods. This algorithm helps to find complex patterns in very large time series. The results show that the anomaly transformer model can effectively detect anomalous patterns. It offers ideas to improve the stability and efficiency of photovoltaic systems in Malang and other areas with comparable environmental conditions. Improved energy efficiency and environmental sustainability are the results of anomaly pattern detection.
A Novel Predictive Voltage Control Technique for a Grid Connected Five Phase Permanent Magnet Synchronous Generator
Authors: Mahmoud Hussein , A. Mohamed Mohamed , A. Hassan Ahmed , A. Mossa Mahmoud
Published: September 2024

This study focuses on developing an effective control strategy to enhance the dynamics of a wind turbine grid-connected five-phase permanent magnet synchronous generator (PMSG). To visualize the superior performance of the newly proposed controller, the generator's performance is evaluated with another traditional predictive control scheme: predictive torque control (PTC). However, the vector control principle is applied to the GSC converter. The PTC has limitations such as significant ripple, substantial load commutation, and the inclusion of a weighting element in its cost functions. The proposed predictive methodology aims to overcome limitations, uses a simple cost function, and doesn't require weighting elements to address concerns about stability errors. Comparing the proposed predictive voltage controller (PVC) to the PTC, the findings show that the suggested PVC has many benefits, including faster dynamic response, a simpler control structure, fewer ripples, reduced current harmonics, low computation burdens, and robustness, so the generated power affects system efficiency, leading to improved power quality and reduced switching losses, enhancing power converters efficiency and their switches lifespan, this fact is verified mathematically as the total harmonic distortion (THD) has reduced to 1.346% average percentage for the proposed controller. However, the THD of the PTC is 3.05%. In addition, the study examines the incorporation of pitch angle control (PAC) and maximum power point tracking (MPPT). These controls restrict the consumption of wind energy when the generator speed surpasses its rated speed and optimize the extraction of wind energy during periods of low wind availability. In summary, the proposed PVC-enhanced control system reveals superior performance in dynamic response, control simplicity, current quality, and computational efficiency compared to other methods.
Adaptive Frequency Control of an Isolated Microgrids Implementing Different Recent Optimization Techniques
Authors: Hamid Mohamed Nasr Abdel , Banakhr Fahd A. , Mohamed Tarek Hassan , Ali Shimaa Mohamed , Mahmoud Mohamed Metwally , Mosaad Mohamed I. , Albla Alauddin Adel Hamoodi , Hussein Mahmoud M.
Published: September 2024

In recent years, significant improvements have been made in the load frequency control (LFC) of interconnected microgrid (MG) systems, driven by the growing demand for enhanced power supply quality. However, challenges such as low inertia, parameter uncertainties, and dynamic complexity persist, posing significant hurdles for controller design in MGs. Addressing these challenges is crucial as any mismatch between demand load and power generation inevitably leads to frequency deviation and tie-line power interchange within the MG. This work introduces sophisticated optimization techniques (grey wolf optimization (GWO), whale optimization algorithm (WOA), and balloon effect (BE)) for LFC, focusing on the optimal online tuning of integral controller gain (Ki) for controlled loads. The WOA regulates the frequency of the system so variable loads can be accommodated and 6 MW of PV is added to the MG. A PV and a diesel generator-powered isolated single area MGs with electrical random loads are managed by the adaptive controller by regulating the frequency and power of the PV. Online tuning of integral controllers is possible using the WOA. A comparison is carried out between the WOA+BE and three other optimizers, namely the GWO, GWO+BE method, and the WOA. This paper shows the effect of add BE identifier to standard WOA and GWO. MATLAB simulation results prove that the BE identifier offers a significant advantage to the investigated optimizers in the issue of adaptive frequency stability even when disturbances and uncertainties are concurrent.
Design of Novel STASOSM Controller for FOC Control of Dual Star Induction Motor Drives
Authors: Pham Ngoc Thuy
Published: September 2024

In this paper, a Novel Super-Twisting Algorithm combined with Improved Second - Order Sliding Mode (NSTASOSM) for the Field-Oriented Control (FOC) of high performance SPIM drives is proposed. This structure, on the one hand, effectively solves the weaknesses of traditional backstepping control (BS) and sliding mode (SM) control that are the dependent on the change of parameters, load disturbance and the phenomenon of chattering, on the other hand, increases the convergence speed and the reference tracking ability, enhance the robust and stably of drive systems even when working in conditions of uncertain parameter and load disturbances, eliminates the chattering phenomenon. The obtained results by simulation using the Matlab/ Simulink tool verified the performance of this proposed control structure.
Seasonal Electrical Load Forecasting Using Machine Learning Techniques and Meteorological Variables
Authors: Singh Bali , Shah Owais Ahmad , Arora Sujata
Published: September 2024

Accurate forecasting of seasonal power consumption is crucial for effective grid management, especially with increasing energy demand and renewable energy integration. Weather patterns significantly influence energy usage, making load prediction a challenging task. This study employs machine learning algorithms, including Random Forest (RF), Artificial Neural Networks (ANN), and Decision Tree (DT) models, to forecast electricity consumption using meteorological variables such as solar irradiance, humidity, and ambient temperature. The impact of weather elements on load prediction accuracy across different seasons is explored using seasonal forecasting techniques. The results demonstrate the superior performance of ANN and RF models in forecasting summer and winter loads compared to the rainy season. This discrepancy is attributed to the abundance of data for the summer and winter seasons, and the ability of the models to capture complex patterns within the data for these particular seasons. The study highlights the potential of machine learning techniques, particularly ANN and RF, in conjunction with meteorological data analysis, for enhancing the accuracy of seasonal electrical load forecasting. This can contribute to more effective power grid management and support the transition towards a more sustainable energy landscape. The findings underscore the importance of data quality, quantity, and appropriate model selection for different seasonal conditions.
Simulation and Arduino Hardware Implementation of ACO, PSO, and FPA Optimization Algorithms for Speed Control of a DC Motor
Authors: Najem Adil , Moutabir Ahmed , Ouchatti Abderrahmane
Published: September 2024

This article proposes implementing and comparing the effectiveness of three optimization algorithms (ACO, PSO, and FPA) for tuning a proportional-integral-derivative (PID) controller on an Arduino Mega 2560 board. This relatively unexplored approach aims to evaluate these algorithms through practical experiments. The choice of PID control is due to its design simplicity and widespread industrial use. Similarly, the permanent magnet DC motor (PMDC) was selected because of its crucial role in various industrial sectors. Tuning PID parameters using optimization algorithms has garnered increasing interest due to its demonstrated efficiency. Several studies have validated the stability of ACO, PSO, and FPA algorithms, justifying their selection. In this article, simulation results showed that ACO, with a response time of 0.322s and an overshoot of 0.68%, was more effective than PSO, which had a response time of 0.768s and an overshoot of 13%. FPA had a response time of 0.347s, close to ACO, but a higher overshoot of 6%. In practice, several factors come into play, such as speed ripples caused by the speed sensor, and machine saturation, which must be considered to ensure practical implementation. After adjusting the PID parameters and integrating a low-pass filter in the feedback loop, ACO, with a response time of 0.596s and an overshoot of 1.68%, was very close to FPA, which had a response time of 0.644s and an overshoot of 0.81%. This comparison highlighted the advantages of the FPA algorithm, which is simple to use, requires fewer parameters to adjust, and takes less time than ACO. This study suggests the potential for implementing a hybrid FPA-ACO algorithm, leveraging the strengths of both algorithms.
Study of the Crowbar's Functioning in Doubly Fed Induction Wind Generators: Towards Achieving Fault Ride Through Capability
Authors: Alnami Hashim
Published: September 2024

This work examines the analysis of temporary behaviors and crowbar hardware layout for enhancing the fault ride-through capability (FRTC) in doubly fed induction wind generators (DFIWGs) A crowbar that is linked in parallel to the rotor side converter (RSC) is a feature found on the majority of DFIWGs these days to safeguard the RSC and DC-bus capacitor (DCBC). Previous studies demonstrated that the crowbar resistance has an impact on the DFIWG transient response's oscillations and peak values. In order to satisfy the FRTC criterion, the article initially methodically examines the DFIWG dynamics with and without a crowbar during a 100% voltage dip and studies the effects of two resistance values on the DCBC. It has been demonstrated that choosing a crowbar resistance greater than the permitted range may cause the DFIG FRT performance to decline. By actively addressing grid faults and improving performance, stability, and dependability, this integrated crowbar shows the potential of state-of-the-art control approaches for the dependable and efficient use of DFIWGs. MATLAB/Simulink is used to run robust simulations, and the results unambiguously show that the proposed model may significantly improve the FRTC of DFIWGs.
EEG-Based Lie Detection Using Autoencoder Deep Learning with Muse II Brain Sensing
Authors: Hermawan Arya Tandy , Zaeni Ilham Ari Elbaith , Wibawa Aji Prasetya , Gunawan Gunawan , Hartono Nickolas , Kristian Yosi
Published: September 2024

Detecting deception has significant implications in fields like law enforcement and security. This research aims to develop an effective lie detection system using Electroencephalography (EEG), which measures the brain's electrical activity to capture neural patterns associated with deceptive behavior. Using the Muse II headband, we obtained EEG data across 5 channels from 34 participants aged 16-25, comprising 32 males and 2 females, with backgrounds as high school students, undergraduates, and employees. EEG data collection took place in a suitable environment, characterized by a comfortable and interference-free setting optimized for interviews. The research contribution is the creation of a lie detection dataset and the development of an autoencoder model for feature extraction and a deep neural network for classification. Data preparation involved several pre-processing steps: converting microvolts to volts, filtering with a band-pass filter (3-30Hz), STFT transformation with a 256 data window and 128 overlap, data normalization using z-score, and generating spectrograms from power density spectra below 60Hz. Feature extraction was performed using an autoencoder, followed by classification with a deep neural network. Methods included testing three autoencoder models with varying latent space sizes and two types of classifiers: three new deep neural network models, including LSTM, and six models using pre-trained ResNet50 and EfficientNetV2-S, some with attention layers. Data was split into 75% for training, 10% for validation, and 15% for testing. Results showed that the best model, using autoencoder with latent space size of 64x10x51 and classifier using the pre-trained EfficientNetV2-S, achieved 97% accuracy on the training set, 72% on the validation set, and 71% on the testing set. Testing data resulted in an F1-score of 0.73, accuracy of 0.71, precision of 0.68, and recall of 0.78. The novelty of this research includes the use of a cost-effective EEG reader with minimal electrodes, exploration of single and 3-dimensional autoencoders, and both non-pretrained classifiers (LSTM, 2D convolution, and fully connected layers) and pretrained models incorporating attention layers.
Multi-Objective Particle Swarm Optimization for Enhancing Chiller Plant Efficiency and Energy Savings
Authors: Bhardwaj Yogesh , Shah Owais Ahmad , Kumar Rakesh
Published: September 2024

This study aims to enhance operational efficiency in chiller plants by implementing the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. The primary objectives are to simultaneously reduce energy consumption and increase cooling efficiency, addressing the challenges posed by variable environmental and operational conditions. Employing the MOPSO algorithm, this research conducts a detailed analysis using real-time environmental data and operational parameters. This approach facilitates a dynamic adaptation to changes in ambient temperature and electricity pricing, ensuring that the algorithm's application remains effective under fluctuating conditions. The application of MOPSO has resulted in significant reductions in energy use and improvements in cooling efficiency. These results demonstrate the algorithm's capacity to optimize chiller plant operations dynamically, adapting to changes in environmental conditions and operational demands. The study finds that MOPSO's adaptability to dynamic operational conditions enables robust energy management in chiller plants. This adaptability is crucial for maintaining efficiency and cost-effectiveness in industrial applications, especially under varying environmental impacts. The paper contributes to the field by enhancing the understanding of how advanced optimization algorithms like MOPSO can be effectively integrated into energy management systems for chiller plants. A novel aspect of this research is the integration of real-time data analytics into the optimization process, which significantly improves the sustainability and operational efficiency of HVAC systems. Furthermore, the study outlines the potential for similar research applications in large-scale HVAC systems, where such algorithmic improvements can extend practical benefits. The findings underscore the importance of considering a broad range of environmental and operational factors in the optimization process and suggest that MOPSO's flexibility and robustness make it a valuable tool for achieving sustainable and cost-effective energy management in industrial settings.
Arduino-Controlled Multi-Function Robot with Bluetooth and nRF24L01+ Communication
Authors: Ahmmed Faysal , Rahman Asef , Islam Amirul , Alaly Ajmy , Mehnaj Samanta , Saha Prottoy , Hossain Tamim
Published: September 2024

This paper outlines the design and development of an advanced robotic system that integrates hardware implementation with theoretical simulation to address the need for versatile and user-friendly robotic solutions in various environments. Addressing the issue of limited adaptability in existing robotic systems, we propose a wireless, voice and gesture-controlled robot car with an integrated robotic arm capable of performing complex tasks such as line following, obstacle avoidance, object manipulation, and autonomous navigation over one-kilometer range. To improve operational efficiency and user involvement, this paper designs a multifunctional robotic platform that integrates user-friendly control interfaces with inexpensive, state-of-the-art sensor technologies. To achieve this, we integrate a variety of sensors, including ultrasonic sensors for precise distance measurement, infrared sensors for object detection and line following, an L298 motor driver for controlling geared motors, servo motors for controlling robotic arms, a flex sensor for claw control, and an mpu6050 accelerometer for gesture recognition. The system also uses a custom-made Bluetooth app for remote control, nRF24L01+ for long-range wireless control, and Arduino Mega and Nano for processing and control functions. The results demonstrate the robot functions well in dynamic conditions, and it can be used in hospitals to assist healthcare professionals, in restaurants for food delivery, and in industrial settings for object manipulation. The system’s design proves robust in real-world scenarios, offering significant improvements in accessibility and operational efficiency. This study aligns with Sustainable Development Goals (SDGs) 3 (Good Health and Well-being), 9 (Industry, Innovation, and Infrastructure), and 17 (Partnerships for the Goals). The robotic arm's potential application in healthcare settings advances SDG 3, its contribution to industrial productivity advances SDG 9, and collaborations with tech companies to expand and improve the robot's capabilities promote SDG 17.
Cucumber Disease Image Classification with A Model Combining LBP and VGG-16 Features
Authors: Arifin Miftahol , Yuniarti Anny , Suciati Nanik
Published: September 2024

Cucumber (Cucumis sativus) is a significant horticultural crop worldwide, highly valued for both fresh consumption and processing. However, cucumber cultivation faces challenges due to diseases that can substantially reduce yield and quality. Diseases like leaf spots, stem wilt, and fruit rot are caused by pathogens including viruses, bacteria, and fungi. Traditionally, disease detection in cucumbers is performed manually, which is time-consuming and inefficient. Therefore, developing machine vision-based models using Deep Learning (DL) and Machine Learning (ML) for early disease detection through image analysis is crucial for assisting farmers. While many studies on plant disease classification using various DL and ML models show optimal results, research on cucumbers has mostly focused on leaf diseases. This study aims to optimize cucumber disease image classification by developing a model that combines Local Binary Pattern (LBP) texture features and VGG-16 convolutional features. The dataset used, Cucumber Disease Recognition Dataset consists of 8 classes of cucumber plant disease images covering leaves, stems, and fruits. This study classifies cucumber plant disease images using Random Forest (RF) combined with LBP texture features and VGG-16 visual features and compares its performance with models using VGG-16, LBP+RF, and VGG-16+RF on the same dataset. The results show that the proposed model achieved a precision of 84.7%, recall of 84%, F1-Score of 83.8%, and accuracy of 84%. These results outperform the comparative models, demonstrating the effectiveness of the combined approach in classifying cucumber plant diseases.
Long Short-Term Memory vs Gated Recurrent Unit: A Literature Review on the Performance of Deep Learning Methods in Temperature Time Series Forecasting
Authors: Furizal Furizal , Fawait Aldi Bastiatul , Maghfiroh Hari , Ma’arif Alfian , Firdaus Asno Azzawagama , Suwarno Iswanto
Published: September 2024

Temperature forecasting is a crucial aspect of meteorology and climate change studies, but challenges arise due to the complexity of time series data involving seasonal patterns and long-term trends. Traditional methods often fall short in handling this variability, necessitating more advanced solutions to enhance prediction accuracy. LSTM and GRU models have emerged as promising alternatives for modeling temperature data. This study is a literature review comparing the effectiveness of LSTM and GRU based on previous research in temperature forecasting. The goal of this review is to evaluate the performance of both models using various evaluation metrics such as MSE, RMSE, and MAE to identify gaps in previous research and suggest improvements for future studies. The method involves a comprehensive analysis of previous studies using LSTM and GRU for temperature forecasting. Assessment is based on RMSE values and other metrics to compare the accuracy and consistency of both models across different conditions and temperature datasets. The analysis results show that LSTM has an RMSE range of 0.37 to 2.28. While LSTM demonstrates good performance in handling long-term dependencies, GRU provides more stable and accurate performance with an RMSE range of 0.03 to 2.00. This review underscores the importance of selecting the appropriate model based on data characteristics to improve the reliability of temperature forecasting.
OPTIMIZATION AND COLUMN STUDY ON HEXAVALENT CHROMIUM REMOVAL USING HYDROXYAPATITE BEADS DERIVED FROM MOSAMBI FRUIT PEEL
Authors: Pawar Suman , Theodore Thomas
Published: September 2024

This study elaborates on the preparation of hydroxyapatite particles from mosambi fruit peels by precipitation method. This hydroxyapatite was used as an adsorbent (AB) for the removal of Cr (VI) ion particles from wastewater. The adsorption was performed and optimized by desirability function to be pH 4, initial chromium concentration 200 mg/L, adsorbent dosage 0.425 g, and contact time 32.5 min, respectively using Response Surface Methodology (RSM). The Langmuir adsorption isotherm, pseudo-2nd-order, and Elovich model are well explained. Column experiments gave better results in the removal of Cr (VI) and this method is very effective for the treatment of industry effluent.
On the Jacobsthal numbers which are the product of two Modified Pell numbers
Authors: Daşdemir Ahmet , Varol Mehmet
Published: September 2024

This paper presents an analytic study of determining all the possible solutions of the Diophantine equations such that $ {q_k} = {J_m} {J_n} $ and $ {J_k} = {q_m} {q_n} $. These give intersections of the Modified Pell and Jacobsthal numbers too for the case where $ m = 1 $ or $ n = 1 $.
Second-order Hankel determinant for a subclass of analytic functions satisfying subordination condition connected with modified q-Opoola derivative operator
Authors: Alatawi Abdullah , Darus Maslina
Published: September 2024

This paper introduces a new subclass of analytic functions employing the operator that was recently defined by the authors. The coefficients estimate $|a_s| (s = 2, 3)$ of the Taylor-Maclaurin series in this new class, as well as the Fekete-Szegö functional problems, have been derived. Furthermore, we obtained the sharp upper bound for the functional $|a_2a_4 − a_{3}^2|$ for functions belonging to this new subclass
Biomedical modelling through path analysis approach
Authors: Ahmad Arshed , Sarı Murat , Demir İbrahim
Published: September 2024

Since blood disease markers are one of the most prevalent health problems in this era, the aim of this study is to forecast pathological subjects from a population through biomedical variables of individuals using the currently produced path analysis (PA) model. In terms of the dataset, 539 subjects were used to implement this study. A mathematical approach based on the PA has been used to create a reliable biomedical model in this research that investigates if there exists a relation between the various anemia types and the biomedical variables through observational variables (the blood variables, age, and sex) and anemia types. Other linear approaches were taken into consideration for comparison, in terms of $R^2$ value of the model, which has a value of 0.699. The findings reveal that the model has great predictive potential. It is believed that the developed model, which includes observational variables, will help healthcare providers predictively plan appropriate treatment programs for their patients.
A new approach to curve couples with Bishop frame
Authors: Babadağ Faik , Atasoy Ali
Published: September 2024

This paper presents a detailed study of a new generation of the Bishop frame with components including three orthogonal unit vectors, which are tangent vector, normal vector and binormal vector. It is a frame field described on a curve in Euclidean space, which is an alternative to the Frenet frame. It is useful for curves for which the second derivative is not available. Moreover, the conditions which the Bishop frame of one curve coincides with the Bishop frame of another curve are defined. It would be valuable to replicate similar approaches in the Bishop frame of one curve coincides with the Bishop frame of another curve.
On a class of bi-univalent functions of complex order related to Faber polynomials and q-Sălăgean operator
Authors: Nsar Zeinab , Mostafa A. O. , Mohamed Samar
Published: September 2024

In this paper, we define a new class of bi-univalent functions of complex order $∑_{q}ⁿ(τ,ζ;φ)$ which is defined by subordination in the open unit disc $D$. By using $D_{q}ⁿϜ(ς)$ operator. Furthermore, using the Faber polynomial expansions, we get upper bounds for the coefficients of function belonging to this class.
Spectral properties of a functional binomial matrix
Authors: Bayat Morteza
Published: September 2024

In this article, we consider the definition of the Fibonacci polynomial sequence with the second-order linear recurrence relation, where coefficients and initial conditions depend on the variable $t$. And then, we introduce the functional binomial matrix depending on the coefficients of the second-order linear recurrence relation. In the following, we study the spectral properties of the functional binomial matrix using the Fibonacci polynomial sequence and we obtain a diagonal decomposition for it using the Vandermunde matrix. Finally, by applying some linear algebra tools we obtain a number of combinatorial identities involving the Fibonacci polynomial sequence.
An extended framework for bihyperbolic generalized Tribonacci numbers
Authors: Gürses Nurten , İşbilir Zehra
Published: September 2024

The aim of this article is to identify and analyze a new type special number system which is called bihyperbolic generalized Tribonacci numbers (BGTN for short). For this purpose, we give both classical and several new properties such as; recurrence relation, Binet formula, generating function, exponential generating function, summation formulae, matrix formula, and special determinant equations of BGTN . Also, the system of BGTN is quite a big family and includes several type special cases with respect to initial values and $r,~ s, ~t$ values, we give the subfamilies and special cases of it. In addition to these, we construct some numerical algorithms including recurrence relation and special two types determinant equations related to calculating the terms of this new type special number system. Then, we examine several properties by taking two special cases and including some illustrative numerical examples.
Conformal $\eta$-Ricci-Yamabe solitons on submanifolds of an $(LCS)_n$-manifold admitting a quarter-symmetric metric connection
Authors: Yadav Sunıl , Haseeb Abdul , Yıldız Ahmet
Published: September 2024

This paper presents some results for conformal $\eta$-Ricci-Yamabe solitons (CERYS) on invariant and anti-invariant submanifolds of a $(LCS)_n$-manifold admitting a quarter-symmetric metric connection (QSMC). In addition, we developed the characterization of CERYS on $M$-projectively flat, $Q$-flat, and concircularly flat anti-invariant submanifolds of a $(LCS)_n$-manifold with respect to the aforementioned connection. Finally, we construct an example that appoints some of our inference.
Norm retrieval in dynamical sampling form
Authors: Bozkurt Fatma
Published: September 2024

In this article, we study the construction of norm retrievable frames that have a dynamical sampling structure. For a closed subspace $W$ of $R^n$, we show that when the collection of subspaces $\{A^\ell W\}_{i \in I}$ is norm retrievable in $R^n$ for a unitary or Jordan operator $A$, then there always exists a collection of norm retrievable frame vectors that have a dynamical sampling structure in $R^n$.
Diffraction of sound from semi-infinite perforated and semi-infinite coated duct with ring source
Authors: Tiryakioğlu Burhan
Published: September 2024

This study delves into the analysis of acoustic waves emanating from a ring source in an infinite cylindrical duct. The duct is equipped with an acoustically absorbing lining on its outer surface when $z$ is less than $l$, and is perforated when $z$ is greater than $l$. The inclusion of acoustically absorbing lining results in a substantial increase in the complexity of the equations compared to the scenario without such lining. Through rigorous efforts, these intricate equation systems are numerically solved, and graphs are generated across various parameter values. Furthermore, by adjusting the parameter values, a physical resemblance is established with an existing study in the literature, showcasing impeccable alignment in the results.
Mathematical analysis and numerical simulations for a nonlinear Klein Gordon equation in an exterior domain
Authors: Meral Gülnihal
Published: September 2024

In this study, the finite propogation speed properties investigated for a two dimensional exterior problem defined by nonlinear Klein-Gordon equation. Under some assumptions on the initial data and the nonlinearity, the solution is shown to have a finite propogation speed. Furthermore, it is demonstrated that the problem has a unique solution, and accurate numerical solutions have been produced by the use of the dual reciprocity boundary element approach with linear radial basis functions.
Approximation properties of the univariate and bivariate Bernstein-Stancu operators of max-product kind
Authors: Yeşilnacar Binmar Ayşe Kübra , Acar Ecem , Kırcı Serenbay Sevilay
Published: September 2024

This paper presents the nonlinear maximum product type of univariate and bivariate Bernstein–Stancu operators and uses new definitions to investigate the approximation properties. The order of approximation obtained with the nonlinear maximum product type of operator sequences would be better than the degree of approximation of the known linear operator sequences.
On the Mellin-Gauss-Weierstrass operators in the Mellin-Lebesgue spaces
Authors: Özsaraç Fırat
Published: September 2024

In this paper, we present the modulus of smoothness of a function $f∈X_{c}^{p}$, which the Mellin-Lebesgue space, and later we state some properties of it. In this way, the rate of convergence is gained. Moreover, we elucidate some pointwise convergence results for the Mellin-Gauss-Weierstrass operators. Especially, we acquire the pointwise convergence of them at any Lebesgue point of a function $f$.
On ideal bounded sequences
Authors: Miller-van Wieren Leila
Published: September 2024

In this paper, we study the notion of ideal bounded sequences, related to a given ideal, generalizing an earlier concept known as statistical boundedness of a sequence. We proceed to prove some results connecting ideal boundedness of a sequence to that of its subsequences. For this purpose, we use Lebesgue measure and Baire category to measure size.
PENGARUH KESADARAN KELENGKAPAN KEPEMILIKAN SURAT TANDA KENDARAAN BERMOTOR TERHADAP ASPEK TANGGUNG JAWAB WARGA NEGARA DI KELURAHAN RUMBIH
Authors: Indrawati Cici , Pitoewas Berchah , Adha Muhammad a Mona
Published: September 2024

The influence of awareness of ownership of motorized vehicle certificates is important for the community as a form of citizen responsibility in terms of obeying state rules. Therefore, this study was conducted with the aim of knowing the effect of awareness of the completeness of ownership of motorized vehicle registration certificates in the rumbih village. The research method used is a quantitative approach with descriptive methods, then data collection techniques in the field are carried out by means of observation and questionnaires. . Responsibility to the community which includes the rules, norms that apply in the community. Then the responsibility for religion which includes God related to religion and beliefs. With regard to a sense of responsibility, humans should base the assumptions of each individual by acknowledging the fact that humans in narrow and broad relationships need each other to realize the values of life that are felt to be good and support the existence of each individual. Based on the results and discussion, a regression model has been obtained with an r-square of 7.8% which has fulfilled all the classical assumptions in linear regression. In addition, based on the hypothesis test that has been carried out, it can be concluded that the completeness of ownership of a motorized vehicle registration certificate significantly affects aspects of citizen responsibility. Based on the sign of the regression, it indicates that ownership of a motorized vehicle registration certificate has a positive effect on aspects of citizen responsibility.Keywords: Public Awareness, Citizens, Responsibilities
PENGARUH GLOBALISASI TERHADAP EKSISTENSI SEDEKAH BUMI PADA MASYARAKAT SUNDA PEKON MERBAU
Authors: Rahayu Sri , Adha Muhammad Mona , Putri Devi Sutrisno
Published: September 2024

The existence of Sedekah Bumi has decreased due to globalization. This has become a problem in Pekon Merbau in the social and cultural fields, such as the loss of the original culture of an area such as Sedekah Bumi. The current of globalization has had a major influence on Indonesian culture, including the Sedekah Bumi ceremony. The rapid pace of information and telecommunications can actually lead to a tendency that leads to the waning of the value of cultural preservation. Therefore, this research was conducted with the aim of knowing the influence of globalization on the existence of the Sedekah Bumi tradition in society. The research method used is a quantitative approach with descriptive methods, then data collection techniques in the field are carried out by means of observation and questionnaires. Based on the results of the study indicate that globalization affects the existence of the Sedekah Bumi in Pekon Merbau. This is shown from the results of the simple linear regression analysis test showing the t-count value of 3.136 and the sig value of 0.002. Thus it can be concluded that t hitung > t table is 3.136 > 0.67581 and the value of sig < 0.05 is 0.002 < 0.05 so that H0 is rejected and H1 is accepted. Globalization is a factor that can affect the fading of the existence of the Sedekah Bumi in Pekon Merbau, so that if globalization is positive, the influence will be positive on the existence of the Sedekah Bumi which indicates that the Sedekah Bumi tradition will increasingly fade. On the other hand, if globalization is negative, the impact will be negative, which means the Sedekah Bumi tradition will be stronger and continue to be preserved.Keywords: Sedekah Bumi, Globalization, Existence
TRADISI “NGEMAI MANDI” ANAK KE SUNGAI SEBAGAI WUJUD CINTA BUDAYA PADA MASYARAKAT DAYAK SEBERUANG DI DESA JAYA MENTARI
Authors: Juri Juri , Yanda Yuvita
Published: September 2024

This research is motivated by the concern of researchers towards the fading of regional culture. Regional culture that used to be so thick in the midst of people’s lives as a sign of a fairly harmonius relationship between human beings, human with nature and human with Petara (God). Human at that time believed that if they colud maintain good relations with others, nature and God, the wrath of nature for bad human action could be minimized. This pattern has persisted for centuries. Changing generations, more and more people are inhabiting the earth and the emergence of technology in varoius fields, causes the cultural viscosity that has long been rooted in society, slowly eroding. Before the erosion got worse, we, as a team of researchers who have a strong interest in culture, tried to mix it up return the remains of cultural debris with the hops of generations next you can continue to maintain the culture so that it doest’n just remain a memory. The specific problem in this study includ 1) proses and form of culture playing for the first time bathing in the river, 2) materials of needed to brings children to bathe for the first time in the river, 3) challenges and efforts made by the community in maintaining culture. The results showed: 1) the cultural prosess of bringing a child to take a bath for the first time in the river is still quite thick and is still being preserved, 2) materials needed when taking a child to bathe in the river for the first time include: offerings (pulut rice, white rice, pork, chicken, eth), one white ceramic place, one Gong, one umbrella, one piece of sling cloth, special traditional clothes, for men and woman who charry bathed children, palm wine, and so on, 3) challenges and efforts made, among others: 1) the growing development of humans causes them to be more influenced by foreign culture, the lack of young pople who are interested culture, culture inheritance is more oral, many young pople work in urban areas and others areas, many young people get married, than follow their husbands or wives. The effort made are: involve the younger generation in every traditional activity, make a book about customs, when there are young couples getting married, they are required to marry according to custom and religion. Keywords:Traditions, Bring a children first Time bathing in the River