bluuebunny/crossref_metadata_2025 huggingface dataset:
Fault‐tolerant control of current residual vector three‐phase four‐switch motor drive system based on MLD model
Authors: Chen Dezhi , Zhao Wenbo , Sun Yun , Zhao Wenliang , Zhang Zhixiang , Xin Ziyuan
Published: April 2024

Aiming at the problem of single‐phase switch tube open circuit fault in inverters, a current residual vector three‐phase four‐switch fault‐tolerant control strategy based on the mixed logic dynamic (MLD) model of permanent magnet synchronous motor (PMSM) drive system is proposed. Firstly, the MLD and three‐phase four‐switch mathematical models of the motor drive system are established and a calculation method of DC bus capacitance is proposed. Secondly, to improve the dynamic performance of the motor, an improved vector control based on the MLD model is proposed. Finally, by setting the threshold and dividing the area of the current residual vector, the accuracy of inverter fault diagnosis is improved, and the fast and smooth switching of the drive system after the fault is realized. Simulation and experimental results show that this strategy can effectively reduce the influence of calculation error and system noise, and has strong robustness for the change of motor speed and load.
Smart binary phase‐coding for pulse‐compression radar: An electronic protection technique against repeater jamming
Authors: Yıldırım Alper , Kiranyaz Serkan
Published: April 2024

Nowadays, modern radar systems increase their target detection capabilities by processing pulses coherently. On the other hand, digital radio frequency memory‐based modern jammers have the ability to work coherently and can deceive radars even with a very low effective radiated power. These jammers, which have the capability of storing the radar's pulse, can use the previous pulses that they have stored in their memory during the electronic deception, without waiting for the last pulse of the radar, in other words, before the new pulse is received. If the radar does not change its parameters from pulse to pulse, such smart jamming techniques applied in this way can be very effective. In this article, the authors propose to use a smart binary phase‐coding method for pulse compression radar as an electronic protection technique against repeater jamming. This approach further improves the target detection capability of modern radar systems, which use coherent integration in the receiver. The proposed method can provide high protection against digital radio frequency memory‐based repetitive range deception techniques without compromising the radar's target detection capability. In the simulations, the traditional approach in which the same code is used without changing from pulse‐to‐pulse, and the approach using code sets obtained by the smart binary phase‐coding method in intra‐pulse modulation are compared. The results show that the proposed method can significantly improve the isolation against deception jamming and the target detection capability simultaneously.
A Survey of Deep Learning Techniques and Computer Vision in Robotic and Drone with Applications
Authors: Khazaal Abbas Maaroof Maysoon , Bouhlel Med Salim
Published: April 2024

The methods of deep learning have lately demonstrated outstanding outcomes of robotic objects such as imagination, localization and striping. Its exceptional abilities in order to learn idealizations from complicated data gathered in the real world ambiance make it perfect for a high range of independent applications of robot. Simultaneously, unmanned aerial vehicles are becoming more used for a variety of civilian stints ranging from security, superintending, and disaster relief, extraditing of package and repository arrangement. A thorough exegesis one of the primary deep learning techniques is also supplied. A set of the main difficulties in using deep learning with UAV-based solutions. Even still, autonomous navigation remains a challenge where computer vision technologies can shine. As a result, development the forecast made by the network and the ground-truth attention distribution, increased the use of vision systems and algorithms, been a major focus of studies conducted recently. An organized mapping investigation is conducted to gain a broad perception of subject. Some studies provide a thorough examination of addressing computer vision in relation to the following independent unmanned aerial vehicles vision establish chores such as navigation, control, back trace and sense.
Anomaly Detection in Cloud Network: A Review
Authors: Al-Mazrawe Amer , Al-Musawi Bahaa
Published: April 2024

Cloud computing stands out as one of the fastest-growing technologies in the 21st century, offering enterprises opportunities to reduce costs, enhance scalability, and increase flexibility through rapid access to a shared pool of elastic computing resources. However, its security remains a significant challenge. As cloud networks grow in complexity and scale, the need for effective anomaly detection becomes crucial. Identifying anomalous behavior within cloud networks poses a challenge due to factors such as the voluminous data exchanged and the dynamic nature of underlying cloud infrastructures. Detecting anomalies helps prevent threats and maintain cloud operations. This literature review examines previous works in anomaly detection in the cloud that employ various strategies for anomaly detection, describes anomaly detection datasets, discusses the challenges of anomaly detection in cloud networks, and presents directions for future studies.
A Survey of Wireless Charging Methods and Optimization Techniques of Electric Vehicles
Authors: Kama Zahraa Niema , Jasim Hawraa Neama
Published: April 2024

Since the 1990s, electric vehicles have been extensively utilized, with a focus on extending the life of storage systems, lowering costs, and providing flexible grid connectivity—a feature that is currently being studied. The substitution of alternative energy sources for conventional fuels has resulted in significant advancements in energy conservation and greenhouse gas emission reduction. Electric vehicle power sources are discovered in power plants. Traditionally, the power grid supplies the power plant with its electricity. A power plant that uses a hybrid solar-wind system can save more energy and reduce greenhouse gas emissions more significantly. An overview of the latest wireless charging methods and optimization techniques is given in this article. Firstly, the essential characteristics of an electric car (EV), wireless charging techniques, and the type of charging system that is dependent on the location of the vehicle charging have been provided in detailed descriptions. After that, a few of the most common optimization techniques to determine the location and size of EV charging stations are presented. With a focus on its explicit application in electric vehicles, the paper provided researchers and scholars with an in-depth knowledge of the fundamentals of WPT and its mechanism of operation.
Review of Electrical methods for Enhancing crude oil Viscosity and Flowability
Authors: Aneed Taqi S. , Najim Sarmad T.
Published: April 2024

This review is aimed to explain a comparative survey on the most applicable techniques and methods for increasing the flowability (viscosity reduction) of crude oil since it has defects on transportation, processing, and production due to high power consumption for pumping and corrosion as well as depositing in the pipeline and facilities it is related to the troubles that may occur in processing stream lines in addition to the financial cost associated with transporting crude oil. In this review several techniques have been surveyed including heating, dilution, additives, emulsification, shearing, ultrasonic, and electrical treatment. Based on the present survey electrical methods were reported to be very effective and efficient.
Effect of the hollow electrode on the material removal rate of titanium alloy (grade 1) in the electrochemical machining (ECM) process
Authors: Maktuf Montadher M. , Shwaish Raed R. , Aghdeab Shukry H.
Published: April 2024

This study examined the material removal rate (MRR) affected by the variables of the electrochemical process including electrode type, voltage, electrolyte type, electrolyte concentration, and gap. Titanium alloy was employed as the workpiece, while copper was utilized as the cylindrical electrode. The input parameters were established according to Taguchi’s methodology. An analysis of variance (ANOVA) using a linear regression model was dependent on making sense of the experiment’s findings. the solid electrode achieved the maximum material removal rate (MRR) value (0.0857) g/min, while the hollow electrode with (1500 l/hour) flow rate achieved the minimum material removal rate (MRR) value (0.00002) g/min. the material removal rate (MRR) was directly proportional to the voltage. Maximum material removal rate (MRR) occurred at a concentration of (75 g/l) for the (NH4C) electrolyte, while minimum material removal rate (MRR) occurred at a concentration of (50 g/l) for the (NH4Cl) electrolyte. It has been observed that the contributing factors in controlling material removal rate (MRR) were as follows; (66.67%) for the voltage, (5.89%) for the electrode, (5.72%) for the electrolyte concentration, (3.24%) for the type of electrolyte, and (3.07%) for the gap.
A Review Load balancing algorithms in Fog Computing
Authors: Mahdi Roa’a Mohammed , Hassan Hassan Jaleel , Abdulsaheb Ghaidaa Muttasher
Published: April 2024

With the rapid advance of the Internet of Things (IoT), technology has entered a new era. It is changing the way smart devices relate to such fields as healthcare, smart cities, and transport. However, such rapid expansion also challenges data processing, latency, and QoS. This paper aims to consider fog computing as a key solution for addressing these problems, with a special emphasis on the function of load balancing to improve the quality of service in IoT environments. In addition, we study the relationship between IoT devices and fog computing, highlighting why the latter acts as an intermediate layer that can not only reduce delays but also achieve efficient data processing by moving the computational resources closer to where they are needed. Its essence is to analyze various load balancing algorithms and their impact in fog computing environments on the performance of IoT applications. Static and dynamic load balancing strategies and algorithms have been tested in terms of their impact on throughput, energy efficiency, and overall system reliability. Ultimately, dynamic load balancing methods of this sort are better than static ones for managing load in fog computing scenarios since they are sensitive to changing workloads and changes in the system. The paper also discusses the state of the art of load balancing solutions, such as secure and sustainable techniques for Edge Data Centers (EDCs), It manages the allocation of resources for scheduling. We aim to provide a general overview of important recent developments in the literature while also pointing out limitation where improvements might be made. To this end, we set out to better understand and describe load balancing in fog computing and its importance for improving QoS. We thus hope that a better understanding of load balancing technologies can lead us towards more resilient and secure systems.
Intelligent Particle Swarm Optimization Method for Parameter Selecting in Regularization Method for Integral Equation
Authors: Al-Mahdawi H.K. , Hatem Khorsheed Farah , Subhi Alhumaima Ali , Ramadhan Ali J. , Hussien Kilan M , Alkattan Hussein
Published: April 2024

We use the Tikhonov method as a regularization technique for solving the integral equation of the first kind with noisy and noise-free data. Following that, we go over how to choose the Tikhonov regularization parameter by implementing the Intelligent Piratical Swarm Optimization (IPOS) technique. The effectiveness of combining these two approaches IPOS and Tikhonov is demonstrated to be highly practicable.
Heart Disease Prediction System using hybrid model of Multi-layer perception and XGBoost algorithms
Authors: Nadheer Israa
Published: April 2024

Multi-layer perceptron (MLP) algorithms play a critical role in improving the accuracy and effectiveness of heart disease diagnosis in the context of the machine learning research. This paper presents an approach of heart disease prediction involves RReliefF-based feature importance assessment then MLP-based classification of features into three groups based on importance scores is proposed. The study employs three feedforward neural networks to classify effectively the clustered groups. Furthermore, an integrated approach utilizes XGBoost ensemble classification, leveraging boosted ensemble learning to enhance overall classification of the outputs of FNN models. By partitioning Cleveland dataset into 70% training and 30% testing sets creates independent datasets, the incorporation of MLP outputs into the XGBoost model yields satisfied testing performance. The confusion matrix showcases accurate classifications, with 96.67% accuracy, 95.92% sensitivity, and 97.92% precision. The F1-Score, at 96.91%, validates the model's balanced performance in precision and recall. This study exemplifies the efficacy of integrating data processing, feature engineering, and ensemble learning techniques for robust cardiovascular disease prediction, providing a reliable and efficient methodology for healthcare applications.
The Application of Random Forest to the Classification of Fake News
Authors: Thair Ali Najwan , Falih Hassan Karrar , Najim Abdullah Muataz , Salam Al-Hchimy Zainab
Published: April 2024

Fake News is one of the most widespread phenomenon with significant consequences on our daily life, particularly in the political realm. Due to the increasing use of the internet and social media, it is now much simpler to propagate false information. Therefore, the identification of elusive news is a significant issue that must be addressed, mostly due to obstacles such as the limited number of benchmark datasets and the volume of news produced per second. This study suggested using comparative data analysis based on random forest machine learning algorithm to identify bogus 4news. In this study the size of the whole dataset is 20.761 fake news record, whereas the size of it is 4.345 records. The first step in the data preparation process is to remove any unnecessary special characters, numbers, English letters, and whitespace. Before implementing the proposed classification algorithms, the most prevalent feature extraction approach (TF-IDF) is used. The data indicate that the highest level of accuracy attained was 88.24%.
Grey Wolf Optimization-based Neural Network for Deaf and Mute Sign Language Recognition: Survey
Authors: Hussein Zahraa A. , Mosa Qusay O. , Hussein Hammadi Alaa
Published: April 2024

Recognizing sign language is one of the most challenging tasks of our time. Researchers in this field have focused on different types of signaling applications to get to know typically, the goal of sign language recognition is to classify sign language recognition into specific classes of expression labels. This paper surveys sign language recognition classification based on machine learning (ML), deep learning (DL), and optimization algorithms. A technique called sign language recognition uses a computer as an assistant with specific algorithms to evaluate basic sign language recognition. The letters of the alphabet were represented through sign language, relying on hand movement to communicate between deaf people and normal people. This paper presents a literature survey of the most important techniques used in sign language recognition models
Localizing the Thickness of Cortical Regions to Descriptor the Vital Factors for Alzheimer’s Disease Using UNET Deep Learning
Authors: Kadhim Karrar A. , Mohamed Farhan , Najjar Fallah H. , Ahmed Salman Ghalib , Ramadhan Ali J.
Published: April 2024

Alzheimer’s disease (AD) stands as a formidable global health challenge, impacting millions of lives. Timely detection and localization of affected brain regions are pivotal for understanding its progression and developing effective treatments. This research introduces a cutting-edge approach to address these critical concerns. Traditionally, exploring the influence of AD on the human brain has been a complex task. Existing methods often face limitations in accurately localizing the most affected brain regions, impeding our understanding of the disease's focal impact. Additionally, the need for efficient and precise cortical thickness analysis techniques has driven the quest for innovative solutions. In this paper, we proposed the DL+DiReCT method, a high-precision strategy that integrates deep learning-based neuroanatomy segmentations with Diffeomorphic Registration-based Cortical Thickness (DiReCT). This approach streamlines the measurement of cortical thickness, enabling rapid and precise localization of AD-affected regions within the brain. Our method significantly contributes to enhancing our understanding of the localized effects of Alzheimer’s disease. Our extensive study, involving 434 subjects from the ADNI dataset and rigorous data augmentation and optimization, has yielded remarkable outcomes. This approach has far-reaching implications for discerning the specific regions of the brain affected by AD, shedding light on their consequences for essential physiological factors.
Yield Forecast of Sugarcane Using Two Different Techniques in Discriminant Function Analysis
Authors: Ramadhan Ali J. , Krishna Priya S. R. , Keerti Balambiga R. , Othman Ali J. , Yadav Shikha , Mishra Pradeep , Abotaleb Mostafa , Alkattan Hussein , Albadran Zainalabideen
Published: April 2024

The present study aims to develop yield forecast models for the Sugarcane crop of the Coimbatore district in Tamilnadu using two different techniques namely Variables and Months in Discriminant function analysis. For this, the Sugarcane yield data for 57 years along with the monthly data on seven weather variables have been taken. For applying discriminant analysis, the yield data of sugarcane has been divided into two categories namely two groups and three groups. The discriminant scores from the two and three-group discriminant functions were employed as independent variables in the development of yield forecast models. The yield forecast models for both strategies were created utilizing scores and trend values as independent variables. The first 52 years of yield data (1960-2012) were used to create the model, and the last five years of data (2012-2016) were used for validation. The comparison has been made between two and three groups for both techniques. The results indicate the technique using the variable-wise method gives better results based on goodness of fit. Among the two categories in the variable-wise method, three groups performed better.
Comparison Study Using Arima and Ann Models for Forecasting Sugarcane Yield
Authors: Ramadhan Ali J. , Krishna Priya S. R. , Naranammal N. , Pavishya S. , Naveena K. , Ray Soumik , Mishra P. , Abotaleb Mostafa , Alkattan Hussein , Albadran Zainalabideen
Published: April 2024

Sugarcane is the largest crop in the world in terms of production. We use sugarcane and its byproducts more and more frequently in our daily lives, which elevates it to the status of a unique crop. As a result, the assessment of sugarcane production is critical since it has a direct impact on a wide range of lives. The yield of sugarcane is predicted using ARIMA and ANN models in this study. The models are based on sugarcane yield data collected over a period of 56 years (1951-2017). Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) have been used to analyze and compare the performance of different models to obtain the best-fit model. The results show that the RMSE and MAPE values of the ANN model are lower than those of the ARIMA model and that the ANN model matches best to this data set.
Effect of main reinforcement on box girder flexural and shear behavior
Authors: Abood Rawa N. , Mubarak Huda M. , Hameed Yahyia M. , Abdulrazzaq Nawras T. , Abdul-Razzaq Khattab S. , Hasan Baida N.
Published: April 2024

The primary goal of the current study is to determine, in a lab setting, how the main longitudinal reinforcement ratio affects the behavior and load capacity of single-cell box girders made of reinforced concrete. Three specimens were cast and tested, each with a length of 1600 mm, top and bottom slab has a uniform thickness of 50 mm, an upper flange width of 430 mm, a bottom flange width of 300 mm, and a height of 230 mm. The three box girders are reinforced by having three distinct ACI 318–19 reinforcing ratios: the minimum, the maximum, and average of them, i.e., the reinforcing ratios were 0.0033, 0.0184, and 0.00591, respectively. All three specimens were subjected to 2-concentrated forces. For the tested box girders, the cracking load, crack pattern, failure load, deflection, average strain values on the concrete surface, strain values in steel bars, and failure mode were recorded and discussed. Experimental work has proven that increasing the ratio of the main steel reinforcement from minimum to maximum, passing through the average between them, i.e. 79-425%, leads to higher load capacity by about 66.67-99% and midspan deflection decrease by about 28-36%. It was also concluded that steel reinforcement yield occurred at both minimum and average main reinforcement resulting in flexural failure. While at the maximum of the main reinforcement, yield did not urge but rather a failure occurred in another place which is the shear, whose stirrups suffered from yield this time
Energy Efficient Clustering Using Improved Particle Swarm Optimization in Wireless Sensor Networks
Authors: Idan Zainab S. , Al-Fatlawi Ahmed
Published: April 2024

The Wireless Sensor Network (WSN) includes many low-cost nodes that have the capacity to perceive, operate, and communicate wirelessly. WSN can spread the information to all around through a cooperative node approach. It also has many advantages in terms of both cost and cooperative intelligence. In a wireless sensor network, nodes have limited energy resources, so their life cycle is considered as one of the main concerns about wireless sensor networks. Energy efficiency grouping and routing are two well-known issues in optimization that have been widely studied in order to increase the lifetime of wireless sensor networks. In this paper, an improved particle swarm optimization (IPSO) clustering algorithm for energy efficiency network management is introduced in order to find a route for creating optimal clusters. To evaluate the efficiency of the proposed clustering algorithm, this algorithm is simulated and compared with the particle swarm optimization(PSO) algorithm based on parameters such as network energy, number of live nodes and network life.
Exploring chatgpt's performance in news recommendation: A multi-faceted analysis
Authors: Ali Hussein Al Naffakh Hussein , Dheyaa Radhi Ahmed , A Hakim Baqer , Fuqdan AL-Ibraheemi , Al-Attar Bourair
Published: April 2024

Using complex language models has been a common strategy as personalized news recommendation systems are adopted by online news sites more and more. Before the epoch of GPT-3, news recommendation systems underwent a progression from rule-based and collaborative filtering approaches in the pre-2010s, through the integration of neural networks in the 2010s, to the emergence of earlier iterations of large language models like GPT-2 in 2019. Pre-trained language models have ushered in a new era of recommendation paradigms, thanks to the emergence of huge language models like GPT-3 and T-5. With its easy-to-use interface, ChatGPT is becoming more and more popular for text-based jobs. Focusing on news provider fairness, individualized news recommendations, and fake news identification, this study starts an inquiry into ChatGPT's efficacy in news recommendations. We acknowledge that ChatGPT's output sensitivity to input phrasing is a limitation, and our goal is to investigate these limitations from each angle. We also investigate whether certain prompt formats can help to alleviate these constraints or if more research is needed. To go beyond set assessments, we create a webpage where ChatGPT's performance on the examined activities and prompts is tracked once a week. Through the use of big language models, this work seeks to improve news recommendation performance and stimulate more research in this area.
Assessment of Municipal and Industrial Wastewater Impact on Yamuna River Water Quality in Delhi
Authors: Ramadhan Ali J. , Yadav Shikha , Anand Subhash , Pratap Singh Aditya , Atta Kousik , Abotaleb Mostafa , Alkattan Hussein , Albadran Zainalabideen
Published: April 2024

Delhi's Yamuna River serves as a notable illustration of an ecologically compromised system that has undergone a transition into a conduit for sewage due to pervasive pollution and escalating anthropogenic influences. Delhi, being the primary contributor to pollution, is responsible for over 70% of the total pollutant load in the Yamuna. The city's drainage systems discharge a substantial Biological Oxygen Demand load into the river daily, resulting in severe pollution. This research utilizes pre-existing data to examine diverse factors, evaluating the quality of water at distinct observation locations along the Yamuna. The utilization of correlation analysis aids in recognizing connections among elements influencing the pollution of river water. The outcomes of the correlation analysis disclose a notable link between COD-BOD factors, whereas the connections among alternative factors like BOD-DO, BOD-pH, COD-DO, COD-pH, and DOpH range from moderate to negligible. The majority of observed parameters exceed hazardous levels deemed acceptable for river water utilization. The evaluation of Sewage Treatment Plants highlights the imperative to augment capacity in terms of treatment, storage, reactivation of closed plants, and efficient operation to meet the growing demand for fresh water. Additionally, there is a pressing need to generate demand for wastewater in diverse urban sectors.
Proposed framework for enhancing integrity technique using distributed query operation
Authors: Farooq Khattak Umar , Alı Aıtızaz , Hussein  , Hussein Al Naffakh Ali
Published: April 2024

Database growth and Storage problems are main concern for large and small enterprises nowadays, which significantly influences the efficiency of database applications. Database archiving is one of the solutions available for management. Many issues have been identified as a result of the use of archive databases, including the elimination of inactive data from online transaction processing systems (OLTP), performance and integrity management. The aim of this paper is to propose a framework for storing OLTP and archiving data in a distributed database environment as part of an integrated system. Maintaining the integrity between an OLTP database and an archiving database is crucial, but even more critical is the performance of the required query. The main aspect of the proposed framework is that it will not only ensure data integrity for primary and unique keys between OLTP and archive database, but it will also improve query performance by introducing parallel processing and query execution plans to maintain that integrity.
A description of linear mappings in semiprime rings with involution
Authors: Horan Angham Shaban , Atteya Mehsin Jabel
Published: April 2024

The main purpose of this paper is to descriptive the action of the linear mappings in semi-prime rings and prime ring with involution. More precisely, we establish some results for centralizer mappings (resp. commuting mappings) of a semi-prime *- ring and *-prime ring ℛ.
On subgroups product graph of finite groups
Authors: Abd Shakir Jawad , Shelash Hayder B.
Published: April 2024

This paper explores Subgroup Product Graphs (SPG) in cyclic groups, presenting a Vertex Degrees Formula based on the prime factorization of a positive integer n. The Isolated Vertex Property asserts that for a positive integer n, the SPG γ_sp(G) lacks isolated vertices. The Matrix Degree and Edge Formula provide a matrix representation and calculate the edges in SPG. Additionally, a Subgraph Relation identifies the complete graphKπ(n) as a subgraph in γ_sp(G). Specific Examples illustrate vertex degrees for different n values. In essence, the study contributes isomorphisms, characterizes properties, and computes degrees and edges for diverse subgroups in Subgroup Product Graphs.
A Salp Swarm Algorithm for Interpreting Model Predictions
Authors: Hussein Alia A. , Ramadhan Ali J. , TaeiZadeh Ali , Hussein Issa Mohand
Published: April 2024

The Internet of Things (IoT), is changing practically every aspect of modern life. The proliferation of IoT has led to a rise in the frequency of cyber catastrophes. The threat landscape that security professionals face is dynamic, complex, and diversified. This paper proposes a novel approach to enhance Internet of Things applications by fusing the swarm intelligence of Salp Swarm Algorithms (SSA) with the predictive power of Random Forest (RF) and Decision Tree (DT) models Even though there is a lot of interest in the topic of explainable Artificial Intelligence (XAI) these days, more research is still needed to fully understand how successful XAI is at finding attack surfaces and vectors when implemented in cyber security applications. The growing use of machine/deep learning models in cyber defense, especially anomaly-based IDS, requires understanding the architecture of the models and providing evidence for their predictions to determine the probability of intrusions. Numerous approaches to interpretation have been proposed. They help researchers comprehend things like which variables have influenced the machine learning predictions. In this paper, we primarily address two popular local interpretation methods in machine learning: Shapley values and Local Interpretable Model-Agnostic Explanations (LIME).
Stabilization of parameters of optoelectronic devices on semiconductor emitters
Authors: Kuldashov Obbosjon , Rayimdjanova Odinakhon , Djalilov Bahrom , Ergashev Shohbozjon , Toxirova Sarbinoz , Muhammadjonov Azamatjon
Published: April 2024

The current state of using LEDs for gas analysis, environmental monitoring, hygrometry, medical diagnostics, and communication systems is analyzed. Existing methods for ensuring the stability of LED parameters, their significance, and application in automatic control devices are considered, along with the advantages and disadvantages of building multichannel optical schemes (thermostabilization, thermocompensation, thermostating). It is identified that little attention has been paid to ensuring the stabilization of LED radiation fluxes used in optoelectronic devices for automatic control. A method for stabilizing parameters (radiation power, radiation intensity, radiation flux, peak spectrum, direct current, direct voltage) of LEDs with optical negative feedback has been developed to enhance the measurement accuracy and reliability of optoelectronic devices on semiconductor emitters. A structural diagram of optoelectronic devices on semiconductor emitters has been developed to ensure its temporal stability.
Research of the semiconductor sensors based on anomal photovoltaic effect with highly sensitive parameters of the deformation
Authors: Rayimdjanova Odinakhon , Mukhtarov Farrux , Ismoilov Ma'murjon , Abdusamatov Hasanboy , Abdullaeva Mohigul , Madaminov Muhriddin
Published: April 2024

Optical parameters of thin polycrystalline semiconductor films during deformation (t, n, 𝜆, R, T 𝑣a, 𝜑 𝜎) changes. An optical elastic effect has been created. Its physical basis is that deformation affects the shape of the zonal diagram by changing the crystal lattice constants. This condition gives rise to various optical and other electronic phenomena in semiconductor thin films .
Modelling solar water desalination system using natural convection
Authors: Kuchkarov Akmaljon , Mamatov Olmosbek , Juraboev Nodir , Obidjonov Zafar , Muzaffarova Nodira , Qosimov Shoxrux
Published: April 2024

In recent times, solar energy has emerged as one of the most promising energy sources. Numerous scholars and researchers are exploring various applications of solar energy. This presented work focused on solar water desalination system to evaluate its thermal performance in natural convection mode. Three-dimensional Computational fluid dynamics CFD model is built in Comsol Multiphysics 6.1. Numerical model simulations have been carried out by using Heat transfer in Solid &Fluid and Laminar flow interfaces. The surface temperature varies between 19.5℃ and 102℃ while the velocity magnitude was between 0.05 m/s and 0.5 m/s. Variations of the surface temperature, velocity magnitude, pressure, convective and conductive heat flux magnitudes in Solar water desalination system are presented in the discussion section of the article.
Forecasting the development of renewable national energy in the tourism sector of Ukraine
Authors: Zaichenko Stefan , Trachuk Andrii , Shevchuk Nataliia , Pochka Kostiantyn , Shalenko Vadym
Published: April 2024

The article is devoted to a systematic review of modern trends in the use of renewable energy sources and their impact on the country's energy system. The basis of the analysis is the assessment of the degree of technological maturity of renewable energy technologies, the effectiveness of their implementation in comparison with traditional energy sources. Thanks to the wide scope of the analysis, the article will make an important contribution to the understanding of the prospects and determinants of the effective implementation of renewable national energy in the context of modern energy challenges and can serve as a basis for further research in this area. This article employs Holt's method to construct predictive models for four key components of Renewable Energy Sources (RES) in Ukraine - wind, solar, hydro, and biomass. Based on the forecasting results, the authors have determined the prospects for the development of renewable energy resources in Ukraine. The findings underscore the strategic importance of intensifying development efforts, attracting investments, and revisiting energy policies to align with global trends towards carbon-neutral economies. The results prompt recommendations for revising the Energy Strategy of Ukraine, and optimizing regulatory frameworks for effective energy service supervision. In light of these projections, the article emphasizes the need for proactive measures to ensure sustainable and environmentally friendly growth in Ukraine's renewable energy sector.
Conventional and renewable energy sources and the carbon footprint
Authors: Dimov Neno
Published: April 2024

It is accepted that energy is among the main, if not the most important, anthropogenic source of carbon dioxide (CO2) emissions. It is for this reason that many efforts are aimed at finding alternative, non-CO2-emitting energy sources. For several decades, large-scale projects have been invested in the construction of solar and wind power plants, which are claimed to be the most environmentally friendly way to obtain energy. Today, the European Green Deal and international climate policy are currently among most actual topical political and economic topics. How true is this and is this energy capable of solving the “greenhouse effect” problem? Is it capable of meeting our needs for reliable, consistent and cheap energy? Is the “greenhouse effect” caused by human activity at all? These are questions that this article seeks to answer. I am well aware that I am going to violate the purportedly consensual, common understanding of these matters I am also aware of the existence of certain, albeit unwritten, limitations in expressing opinions on these issues. But I am convinced that science is a sequence of stated hypotheses, their denial, challenge, improvement, modification and so on. That is, science is not a place for searching for consensus, but for a productive dispute leading to the correct conclusions.
Experience of using Google cloud services in Ukrainian universities: survey results
Authors: Sharov Sergii , Tereshchuk Serhii , Sharova Tetiana , Spanatii Oleksandr , Kolomoiets Halyna
Published: April 2024

The research analyzes a survey of Ukrainian teachers regarding the advantages of cloud technologies provided by Google Workspace for Education. We surveyed 167 respondents from different regions of Ukraine. They were asked to complete a specially designed questionnaire that included three closed-ended multiple-choice questions and two open-ended questions that required extended responses. It was found out that during distance or blended learning teachers together with students most often use Google Sheets, Google Meet, Google Forms and Google Documents. The most significant advantages of Google Workspace for Education services are the convenience and usability, as well as a possibility of distance learning.
Applications of attack mitigation mechanisms in energy networks, attack detection and preventive methods for security in the smart grid
Authors: Simeonidis Dimitrios
Published: April 2024

The Smart Grid promotes energy and environmental sustainability through the integration of vast distributed energy resources. The implementation of such a green system has huge economic and social benefits. The new generation of sensors, the smart one's meters and electronic devices are integral elements of the smart network. However, the upcoming development of smart devices in different levels and their integration into communication networks may cause cyber threats. Failure to address these problems will create difficulties in modernizing the existing electrical system. Another key concern is the protection of privacy associated with collecting and using energy consumption data.
Algorithm of the electronic sign-code signature on the basis of the composition of existing complexities
Authors: Akbarov Davlatali , Umarov Shukhratjon , Turdimatov Mamirjon , Sotvoldiyev Khusniddin , Ibrokhimov Nodirbek , Sadirova Khursanoy
Published: April 2024

The article developed a new algorithm for electronic digital signature in the composition of existing difficulties: discrete logarithm in a finite field, addition of points with rational coordinates of the elliptic curve. Based on a combination of the complexities of a discrete logarithm on a finite field with a large number characteristic, decomposition of a sufficiently large odd number into prime factors, and addition of points of an elliptic curve, an electronic digital signature algorithm is developed for generating. The generally recognized model of electronic sign-code signature covers three processes: generation of digital signature keys; ESS formation; confirmation of authenticity of electronic sign-code signature. Firmness offered AESS is based on several complexities: calculations of the discrete logarithming in a finite field, decisions of the task of expansion of enough big odd number on simple factors, executions of operation of addition of points of the elliptic curve set in a finite field. It considerably raises crypto algorithm strength.
Determination of the stress-strain state of models of steel cylindrical tanks using the “ANSYS” program
Authors: Davlyatov Shokhrukh , Jakhongirov Ilimdorjon , Abdurakhmonov Abdukholik , Solijonov Foziljon , Abobakirova Zebuniso
Published: April 2024

In the world, metal cylindrical shell constructions occupy a leading position in the construction of reservoirs for various purposes, bodies of water pressure generating towers, television towers, chimneys, lighting masts, tower cranes and similar engineering structures. In this direction, in developed countries such as the USA, Germany, Japan, and Russia, special attention is paid to increasing the load-carrying capacity of cylindrical shell structures, ensuring earthquake resistance and priority, reducing metal consumption, preventing the resulting deformations, and thereby ensuring their reliable operation. In this regard, one of the important tasks is to increase the priority of various construction structures, optimize their shape and size, improve existing calculation methods, and develop modern advanced methods of construction preparation and assembly.
Nonlinear systems control algorithm with backstepping method
Authors: Siddikov Isamidin , Khalmatov Davronbek , Khushnazarova Dilnoza , Khujanazarov Ulugbek
Published: April 2024

The article gives the possibilities of applications of the backstepping method for solving the problem of synthesis of a control system for the nonlinear dynamic plant with indefinably changing parameters. Has been modified the well-known method of backstepping inclusion in the control system of the state observer, allows for the restoration of unmeasured coordinates with a differentiating unit, due to which substantially simplified the definition of customizable parameters of control device and provides the desired dynamic accuracy of control.
Building and predicting a neural network in python
Authors: Zulunov Ravshan , Akhundjanov Umidjon , Musayev Khurshid , Soliyev Bakhrom , Kayumov Ahror , Asraev Mukhammadmullo
Published: April 2024

The article examines the practical implementation of Artificial Intelligence technologies, Machine Learning and Deep Learning technologies. Methods of construction and prediction of neural networks are considered using special libraries of Python language. Linear regression and NumPy it is suggested to use libraries.
The compelled fluctuations of the rectangular two-layer a piecewise homogeneous plate of the constant thickness
Authors: Djalilov Mamatisa , Burxonova Malohat , Suyumov Jurabek , Polvonov Alimirzo , Abduvaliyev Izzatjon
Published: April 2024

In the given work it is solved private applied problems of fluctuation of rectangular and infinite two-layer plates on the basis of the approached equation of the sixth order received in article. Frequencies of own fluctuations, influences of parameters of making plates on these frequencies, viscosity influences etc. are investigated The received theoretical results for the decision of dynamic problems of cross-section fluctuation a piecewise homogeneous two-layer plates of a constant thickness when a material of the top layer piecewise ‒ a homogeneous two-layer plate elastic, and another satisfies models of Marksville, that is viscoelastic, allow to count more precisely the is intense-deformed condition of plates at non-stationary external loadings.
Bayesian estimation of generalized long-memory stochastic volatility
Authors: Gonzaga Alex C.
Published: April 2024

We propose a Bayesian approach to estimating the parameters of a Generalized Long-Memory Stochastic Volatility (GLMSV) model, a versatile framework designed to address both persistent (long-memory) and seasonal (cyclic) behaviors across various frequencies. This provides an alternative method incorporating prior information about the model parameters, and allows for relatively computationally efficient sampling from the posterior distribution by a reparametrization of the model parameters. The practical applicability of this methodology is demonstrated through the analysis of intraday volatility in Microsoft stock prices.
Physical and thermal insulation properties of foam concrete with the use of lime sludge (chemical waste)
Authors: Abdullaev Ibroxim , Mamatov Khamidullo , Abdullaev Ulugbek , Otaqulov Bakhromjon , Аbduganiev Numanjon , Solijonov Hojiakbar , Umirdinov Ikhtiyor , Vakhobov Abobakir
Published: April 2024

The article investigates the physical and thermal insulation, the physical display of sand and lime sludge (waste), the electron microscopic analysis of foam concrete of the application of lime sludge (waste) to foam concrete. The results of a study of the physical and thermal insulation of foam concrete with lime waste and the compressive strength of structural foam concrete are presented.. For more efficient use in the development of new energy-saving foam concrete building products, laboratory experiments have established that lime slurry can be used as a filler for foam concrete.
Analysis of the process of heat transfer in space
Authors: Bilolov Inomjon , Otajonov Jamshid , Isroilov Sharobiddin , Mavlonova Dilnozaxon , Abdurakhmonov Sultonali , Aliev Ibratjon
Published: April 2024

In work the general principles of transfer of thermal energy in limited space are theoretically considered. To simplify the task, the tasks are defined in the Cartesian coordinate system. The dependence of the transfer on the coefficients of thermal conductivity, heat capacity and eigencoefficients of differential partial differential equations of an inhomogeneous and homogeneous nature are analyzed. Modeling sets initial and boundary conditions. In solving the problem, the method of multiple separation of variables is used. In conclusion, the final form of the function of the phenomenon of heat transfer in a limited space is derived from 4 variables.
The effect of slip on the development of flow separation due to a bump in a channel based on a two-fluid turbulence model
Authors: Madaliev Murodil , Usmonov Mavlonbek , Otajonov Jamshidbek , Bilolov Inomjan , Otakhanova Zamira , Rajabova Khurshida , Israilov Sharabiddin
Published: April 2024

In the paper, two-fluid turbulence model simulations were used to calculate the flow of a statistically two-dimensional turbulent boundary layer over a hump. In this work, the finite element method is used for the numerical implementation of the turbulence equations. Galerkin least squares (GLS) stabilization was used to stabilize the discretized equations. The results of the two fluid models were compared with experimental measurements by Matai, R., Durbin P. A. and the SST turbulence model which exists in the Comsol Multiphysics software package. The implementation of the Comsol Multiphysics software package showed the stability and good accuracy of the two-fluid turbulence model than the SST turbulence model.
Research on the properties of Wide Span Vehicle in Controlled Traffic Farming
Authors: Kuvachov Volodymyr , Ihnatiev Yevhen , Mitkov Vasil , Boltianskyi Boris , Sharova Tetiana
Published: April 2024

The purpose of the article is to investigate the traction, energy, and coupling properties of Wide Span Vehicles (WSV) during their operation in Controlled Traffic Farming (CTF) conditions. Based on the analysis, systematization, and synthesis of theoretical data, the advantages of using WSVs in CTF conditions have been identified, along with the absence of mechanical and technological foundations for their functioning and utilization. The conducted research has determined that the necessary specific power of WSVs should be 23.5 W·kg‒1 at 1 km·h‒1, Their movement on CTF tracks is accompanied by a lower slip value (at the level of 0.15…0.17) compared to their movement on agricultural background, where the wheel realizes the maximum traction contact force, allowing it to develop pulling forces at the level of 6.37 kN per ton of its operational mass. In future studies, we plan to explore the technological properties of WSVs, characterizing their compliance with technological requirements across the entire range of agricultural operations for which they are intended as part of the equipment.