bluuebunny/crossref_metadata_2025 huggingface dataset:
Experimental Evaluations of Model Piles Subject to Inclined Loading in Sand
Authors: Al-Fahham Nabaa A. , Al-Jazaairry Ali A.
Published: April 2024

The structures that subjected to varying loads are supported by pile foundations. These loads consist of inclined and vertical loads. The main objective of this work is to experimentally investigate the effects of an inclined load on the lateral pile reaction and axial pile displacement for piles found in cohesionless soil. For this purpose, the slenderness ratios of model steel piles are varied by values of 11, 14, and 17. Moreover, the pile shapes involved in this study are H-pile, closed end circular and square. The experimental tests were conducted in sandy soil. However, the load inclination angle included in assessing the pile behavior were 0, 30, 45, and 60 from the vertical. In order to study the above parameters on the behavior of model pile, thirty-six experimental tests were carried out. Several findings indicated that the lateral pile deflection was highly influenced by increasing the load inclination. Further, the load inclination effect on the pile's ultimate bearing capacity is high which leads to a reduction in the pile's capacity. Additionally, the vertical displacement of pile is less than horizontal displacement when the pile is subjected to an inclined load of 45° and the model fails by horizontal displacement.
Control of Hybrid Wind Turbine and Diesel Generators using PLC
Authors: Abbas Hasan Wadah , Alhakeem Zaineb M.
Published: April 2024

This paper shows path of controlling hybrid system with wind and diesel generators via a programmable logic controller, these systems produce the required electrical power from different sources. This system can be used in different areas that have wind speeds between (10-180) km/h. When the winds are within this range, then the wind turbine generator is starting and suppling the electrical power to the load, if there is any fault is occurred or the wind is not within the working range then the wind turbine generator ceases and the diesel generator will start and supply the electrical power to the load.
Lung Cancer Detection Using Wavelet Transform with Deep Learning Algorithms
Authors: Thaeir Ali Najwan , El Abbadi Nidhal K. , Mohamad Ghandour Ahmad
Published: April 2024

Lung cancer is a leading cause of mortality among all cancer-related illnesses. The primary method of diagnosis is conducting a scan examination of the patient’s lungs. The scanning analysis can encompass X-ray, CT scan, or MRI techniques. The automated categorization of lung cancer poses a formidable challenge, primarily because of the diverse imaging techniques employed to capture images of a patient’s lungs. Image processing and machine learning methodologies have demonstrated significant promise in the identification and categorization of lung cancer. We present a very efficient model in this study that accurately detects lung cancer and categorizes it as either benign or malignant. The initial phase involves the execution of many procedures to carry out the picture preprocessing process. During the second stage, the image undergoes Wavelet Transform to divide it into three levels. This division allows for the extraction of distinct properties from each level. The third step involves employing an auto-encoder technique to effectively decrease dimensions and eliminate noise, while also identifying any anomalies within the recovered features. The MLP algorithm was employed in the final section. The suggested method underwent testing on a total of 9541 photos, which were categorized into two distinct types: benign, consisting of 4044 images, and malignant, consisting of 5497 images. The proposed approach attained a remarkable accuracy rate of 100%.
Forecasting Cryptocurrency Market Trends with Machine Learning and Deep Learning
Authors: Fadhil Heba M. , Makhool Noor Q.
Published: April 2024

Cryptocurrency became an important participant on the financial market as it attracts large investments and interests. With this vibrant setting, the proposed cryptocurrency price prediction tool stands as a pivotal element providing direction to both enthusiasts and investors in a market that presents itself grounded on numerous complexities of digital currency. Employing feature selection enchantment and dynamic trio of ARIMA, LSTM, Linear Regression techniques the tool creates a mosaic for users to analyze data using artificial intelligence towards forecasts in real-time crypto universe. While users navigate the algorithmic labyrinth, they are offered a vast and glittering selection of high-quality cryptocurrencies to select. The ability of the tool in analyzing past data on historical prices combined with machine learning, orchestrate an appealing scene of predictions equipped with choices and information, users turn into the main characters in a financial discovery story conducted by the cryptocurrency system. The numerical results also support the effectiveness of the tool as highlighted by standout corresponding numbers such as lower RMSE value 150.96 for ETH and minimized normalized RMSE scaled down to under, which is. The quantitative successes underline the usefulness of this tool to give precise predictions and improve user interaction in an entertaining world of cryptocurrency investments.
Analytical review study of the Grid connected Micro grid Energy Management System
Authors: Hussien Saleh Ayat , Obaid Afta Ahmed
Published: April 2024

As an environmentally friendly method of distributing energy production, the integration of photovoltaic systems into micro grids has drawn in significant focus on. Our goal in doing is to examine features regarding micro grid that is linked to the power grid, with a focus on photovoltaic energy management in particular. Finding the optimal micro grid capacity for the solar system seeks to increase energy efficiency, decrease dependence on main grid, and promote an utilization of green power. The outlined optimization approach evaluates the micro grid’s dynamic interactions using state-of-the-art modelling and simulation tools. These components include photovoltaic panels, energy storage systems, alongside the main grid. The refinement method takes into account crucial factors including patterns of load demand, costs of the grid electricity, and variations in solar irradiation. Finding a happy medium between increasing the amount of power generated by renewable sources and decreasing overall energy costs is the objective. That study takes a multi-scenario approach to determining how various micro grid sizes affect overall system efficiency. Using scenario-based simulations and techno-economic criteria, the appropriate size of the photovoltaic system was determined. Factors like payback time, ROI, and system reliability are taken into account here. The study’s findings provide light on grid-connected micro grids, particularly in regards to photovoltaic energy management, which is crucial for their planning and implementation. In order to make educated decisions towards more robust and ecologically friendly power systems, stakeholders, lawmakers, and decision-makers can use the optimal micro grid size as a benchmark for future renewable power projects. This paper reviews the relevant literature and proposes a division and performance strategy based on its findings. By classifying energy management into three groups according to grid connection, configuration, and control method, this article provides a description of the performance, application, advantages, and disadvantages of algorithms that may be used as a reference for selecting an appropriate algorithm. Also included is a comparison table for the control strategies that were used to regulate a micro grid system that is connected to the grid.
A review of Chaotic Maps used for Generating Secure Random Keys
Authors: Abdulwahid Hameed Bahaa , Gbashi Ekhlas K.
Published: April 2024

The fusion of chaos theory and cryptography has yielded a dynamic landscape of innovative solutions for safe random key generation. This paper presents a comparison of several studies conducted in this field, aiming to distill key insights and discern common threads. Amidst the diversity of proposals, a consistent architectural framework emerges, while the true differentiators lie in the selection, configuration, and utilization of chaotic maps. These maps, harnessed for their inherent unpredictability, have a significant impact on how reliable and secure cryptographic systems are. Thus survey highlights the enduring relevance of chaotic maps as versatile tools in the cryptography arsenal. The interplay between mathematical complexity and computational expediency stands as a central theme, illustrating the delicate equilibrium researchers must navigate. As chaos-based cryptographic systems continue to evolve, this analysis serves as a compass for both practitioners and theoreticians, offering insights into the evolving landscape of safe key generation, and the challenges and opportunities that lie ahead.
Mega bracing system with RC multistory based on plate-frame method by sap2000
Authors: Sahib Ali Saja , Ahmed Mazin A. , Zahra Adel A.
Published: April 2024

This study investigates the optimal arrangement of steel braces for a 15-story reinforced concrete building. The analysis is based on a 0.1 mm plate model at the exterior face of the building, which is subjected to nonlinear time history analysis of Elcentro ground motion earthquake. The study focuses on the internal forces path induced by seismic loads and aims to identify the most preferable results for different configurations of steel bracing using sap2000 software. By selecting the moment that led to the maximum internal drift, the study determined the maximum load concentrations. The results indicate that model 4 and 6 produced the optimal results, with a significant increase of 32% and 37% in base shear, as well as a notable improvement of 60.64% and 60.5% in roof displacement, respectively.
Improving credit card fraud detection using machine learning and GAN technology
Authors: Thair Ali Najwan , Jasim Hasan Shahad , Ghandour Ahmad , Salam Al-Hchimy Zainab
Published: April 2024

The motivation behind this study stems from identifying contemporary challenges associated with prosecuting electronic financial crimes. Highlights ongoing efforts to identify and address credit card fraud and fraud as there are many credit card fraud issues in the financial industry. Traditional methods are no longer able to keep up with modern methods of tracking the behavior of credit card users and detecting suspicious cases. Artificial intelligence technology offers promising solutions to quickly detect and prevent future fraud by credit card users. Datasets used to detect financial anomalies are affected by imbalances in financial transactions, and this study aims to address the imbalance of financial fraud datasets using adversarial algorithm techniques and compare them with the most commonly used methods in the scientific literature.The results showed that the function of the adversarial algorithm is consistent in several ways, including allowing researchers and interested parties to determine data growth rates, which helps bring the dataset closer to real-time data from financial markets and banks. This study proposes a hybrid machine learning model consisting of three machine learning algorithms: decision trees, logistic regression, and Naive Bayes algorithm, and calculates performance metrics such as accuracy, specificity, precision, and F1 score. Experimental results reveal varying degrees of accuracy in fraud detection. Model testing using the SMOTE method recorded an accuracy of 98.1% and an F-score of 98.3%. On the other hand, the oversampling and under sampling test methods showed similar performance, with the two methods recording an accuracy of 94.3 and 95.3 and an F-score of 94.7 and 95.1, respectively. Finally, the GAN method excelled, receiving a test score and accuracy of 99.9%, as well as exceptional precision, recall, and F1 score. As a result, we conclude that the GAN method is able to balance the data set, which in turn is reflected in the performance of the model in training and the accuracy of predictions when tested. Historical transaction analysis identifies behavioral patterns and adapts to evolving fraud techniques. This approach enhances transaction security and protects against potential financial losses due to fraud. This contribution allows financial institutions and companies to proactively combat fraudulent activities.
A Survey Study of the Deep Learning for Convolutional Neural Network Architecture
Authors: Mohammed Alkhaldi Tabark , Essam Noor , Ali Talib Al-Khazaali Ahmed , Ali Alhamdany Marwa , Assam Hataf Baqar , Ramadhan Ali J. , TaeiZadeh Ali
Published: April 2024

The deep learning (DL) computer paradigm has been the industry standard for machine learning (ML) during the past few years. It has gradually become the most widely used computational technique in machine learning. One of the benefits of DL is its ability to learn massive amounts of data. Deep learning has seen tremendous growth in the last several years and has been successfully used for many traditional applications. More importantly, DL has outperformed popular machine learning algorithms in several domains, such as cybersecurity, bioinformatics, robotics, etc. The field remains mostly uneducated although it has been contributed to several works reviewing the State-ofthe- Art on DL, each of which only covered a specific aspect of the field. We thus propose a more holistic approach to this contribution, providing a more suitable basis upon which to construct a thorough understanding of DL. Concerning the most significant DL features, including the most recent advancements in the field, this evaluation specifically aims to provide a more thorough survey. This study specifically describes the kinds of DL networks and techniques, as well as their significance. The most common type of DL network, convolutional neural networks (CNNs), is then presented, and the evolution of it.
Attack graph-based security metrics: Concept, taxonomy, challenges and open issues
Authors: Al-Araji Zaid. J. , Sakinah Syed Ahmad Sharifah , Farhood Hussein M. , Awad Mutlag Ammar , Al-Khaldee Mahmood S.
Published: April 2024

Context: Security issues have increased recently because of the increased use of networking. The researchers have proposed many models, approaches, and models, for example, attack graphs. The attack graph model is a valuable tool for vulnerability analysis as well as for displaying all network paths. In general, attack graphs can be utilized for a variety of purposes, including the calculation of security metrics. Nonetheless, in order to sufficiently safeguard networks, a technique for gauging the security degree provided by these activities is required, as “you cannot improve what you cannot measure.” The security level of a system or network is typically represented by network security metrics in qualitative and quantitative ways. The network security metrics are typically employed to evaluate a system's security level and meet security objectives. Aim: This study aims to present a review of attack graph-based security metrics and analyse the previous work. Provides the limitations and issues the researchers faced to improve this important research area. Methodology: The attack graph security metrics field was thoroughly investigated in all research, and four databases—ScienceDirect, Web of Science (WoS), Scopus, and IEEE—were used to collect data between 2001 and 2022. Results: 46 papers were founded on attack graph security metrics with different methods and techniques based on the exclusion and inclusion criteria. The results of the taxonomy created three significant categories: proposed, implemented, reviewed, and surveyed. We believe this study will aid in highlighting research ability, which will subsequently broaden and establish new research topics.
Optimizing Software-Defined Networks with Fuzzy Logic-Based Enhancement of Openflow Protocol
Authors: Hasan Sajjad H.
Published: April 2024

Today, humans have a strong need to control their devices from a distance so that they can control the world more than before and explore it for various purposes such as how the universe came into being, discovering the way of creation, observing the events in Global situation and so on. Communication with remote devices can be possible in various ways. SDN networks provide a possibility to exchange information between heterogeneous nodes. Considering that in SDN networks, the nodes are very expensive and these nodes themselves are performing many tasks and various vital tasks; Therefore, the cost of each byte of memory occupied on these nodes is very expensive and must be managed in such a way that they have the highest efficiency. Therefore, to solve this problem, it is very necessary and costly to carry out large projects. In the proposed method of this research, by improving the OpenFlow protocol in software-based networks, it is tried to avoid the cooperation of nodes in the directional distribution (not dissemination) of a small data, from the accumulation of extra information in the nodes' memories. Finally, after the simulation, it was observed that the improvement rate of the proposed method has improved by 0.38%, 0.05%, and 0.04%, respectively, compared to RD, FLCFP, and LEACH2013 methods. The improvement rate of the proposed method compared to RD, FLCFP, and LEACH2013 methods was 0.65%, 0.059%, and 0.331%, respectively.
Statistical Analysis of Total Replacement of Coarse Aggregate by Sustainable Recycled Aggregates
Authors: Ahmed Abed Zainab , Abd Al-Hussein Kazem Mustafa , Al-Khafaji Zainab
Published: April 2024

Since building materials have been increasingly chosen based on their ecological attributes, contemporary development is strongly tied to preserving natural resources through avoiding environmental deterioration. Nevertheless, it was argued that concrete cannot be considered environmentally friendly because of its destructive, resource-consuming nature and the potential for severe environmental impact after its use. Concrete is among the most adaptable materials in construction and contributes significantly to expanding the infrastructural and industrial segments. Nevertheless, it will continue to be the dominant building material utilized everywhere. Even though recycled aggregates (RAs) were still less often employed in the construction sector, much effort was conducted to recycle hardened concrete, and further improvements are needed to maximize this. Some investigations on recycled aggregate's characteristics, applications, drawbacks, and behavior were also discussed. The current research aims to present a statistical analysis of coarse aggregate replacement with different water/cement ratios utilizing Single and two-way ANOVA for different mechanical features (tensile and compressive strengths) and durability features (Chloride ion penetrability) at 28 and 90 curing days. The results show no significant differences in one-way ANOVA and two-way ANOVA for compressive strength and chloride ion penetrability at 28 and 90 curing days.
Microservices Boundary Determination Migration in DevOps: A Case Study
Authors: TaeiZadeh Ali , Lotfi Zahra , Ramadhan Ali J.
Published: April 2024

The microservice architecture (MSA) is a widely used and researched approach in industry and academia. However, designing the migration to MSA is a complex and challenging task, and there is a lack of clear guidelines on how to address both business and technical issues during the process. This paper presents a step-by-step method for determining the boundaries of microservices, which is a critical activity in MSA migration for both practitioners and academics.We conducted a case study of SHAMIM, a nationwide project serving over 120,000 students, to demonstrate the proposed method. The microservice boundaries were determined by utilizing both business and technical input types, including domain-driven design (DDD), business processes, service call numbers, and data access patterns in databases.Our findings suggest that MSA migration can be conducted more reliably by using change documents maintained by the DevOps team. The proposed method leads to clear improvements in the determination of each microservice boundary, and it can be useful for practitioners and academics involved in MSA migration.
Unmasking deceptive profiles: A deep dive into fake account detection on instagram and twitter
Authors: Dheyaa Radhi Ahmed , Obeid Huda Noman , Al-Attar Bourair , Fuqdan AL-Ibraheemi , A Hakim Baqer , Ali Hussein Al Naffakh Hussein
Published: April 2024

The rise of online social networks, also known as OSNs, has captured the attention of younger generations and made them an integral part of social life. As a result, the use of various social media platforms has increased significantly, greatly impacting individuals' social connections. These platforms offer a wide range of features, such as news distribution, contributing to their widespread use. However, with the rapid growth of social media, the prevalence of fake accounts has become a major problem, posing a threat to both the security of users and the integrity of these platforms. In response, this article explores the effectiveness of machine learning algorithms (ML). Detect and identify fraudulent accounts on popular social media platforms, especially Instagram and Twitter. Our methodology involves analyzing user activity and account information to develop fine-tuned machine-learning models. Our approach takes into account important parameters such as number of followers, number of posts, and engagement.
Use of Random Forest Regression Model for Forecasting Food and Commercial Crops of India
Authors: Ramadhan Ali J. , Priya S. R. Krishna , Naranammal N. , Suman  , Lal Priyanka , Mishra Pradeep , Abotaleb Mostafa , Alkattan Hussein
Published: April 2024

Agriculture is the backbone of Indian Economy. Proper forecast of food crops and cash crops are necessary for the government in policy making decisions. The present paper aims to forecast Wheat and Sugarcane yield using Random Forest Regression. For the development of Random Forest models, Yield has been taken as dependent variable and variables like Gross Cropped Area, Maximum Temperature, Minimum Temperature, Rainfall, Nitrogen, Phosphorous Oxide, Potassium Oxide, Minimum Support Price and Area under Irrigation are taken as independent variables for both Wheat and Sugarcane crop. Values of R2for Wheat and Sugarcane is 0.995 and 0.981 which indicates that the model is a good fit and other performance measures are calculated and results are satisfactory.
An overview of machine learning classification techniques
Authors: Alnuaimi Amer F.A.H. , Albaldawi Tasnim H.K.
Published: April 2024

Machine learning (ML) is a key component within the broader field of artificial intelligence (AI) that employs statistical methods to empower computers with the ability to learn and make decisions autonomously, without the need for explicit programming. It is founded on the concept that computers can acquire knowledge from data, identify patterns, and draw conclusions with minimal human intervention. The main categories of ML include supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning. Supervised learning involves training models using labelled datasets and comprises two primary forms: classification and regression. Regression is used for continuous output, while classification is employed for categorical output. The objective of supervised learning is to optimize models that can predict class labels based on input features. Classification is a technique used to predict similar information based on the values of a categorical target or class variable. It is a valuable method for analyzing various types of statistical data. These algorithms have diverse applications, including image classification, predictive modeling, and data mining. This study aims to provide a quick reference guide to the most widely used basic classification methods in machine learning, with advantages and disadvantages. Of course, a single article cannot be a complete review of all supervised machine learning classification algorithms. It serves as a valuable resource for both academics and researchers, providing a guide for all newcomers to the field, thereby enriching their comprehension of classification methodologies.
Emad-israa transform a new integral transform of two parameters with applications
Authors: Obaid Saud Israa , Kuffi Emad A. , Hamza Talib Sarai
Published: April 2024

In the presented paper, authors introduced a new integral transform technique of two parameters “Emad –Israa Transform” with its fundamental properties and solved linear ordinary differential equations with constant coefficients. Numerical problems have taken for visualizing the complete procedure of solution. Results of these problems suggest that the present transform technique of two parameters “Emad- Israa” transform technique provides the outstanding results without dowing tedious computational work.
Dual soft condensed sets
Authors: Rasheed Janat Y. , Jabar L. A. A.
Published: April 2024

The work here was based on some topological concepts and studied within dual soft set theory where three types of condensed were identified within these sets and their topological effects were highlighted.
The modification conjugate coefficient for conjugate gradient method to solving unconstrained optimization problems
Authors: Hassan Abed Ahmad , Saeed Hamsa Th.
Published: April 2024

In this manuscript, a novel conjugate coefficient is derived through the utilization of equality conjugate gradient direction in conjunction with a modified quasi-Newton (QN) direction. The demonstrated results establish that this innovative approach ensures both sufficient descent and global convergence, meeting the criteria of the Wolf line search condition and inexact line search. Through numerical experiments, we employ the proposed method on a diverse set of established test functions, clearly demonstrating its enhanced efficiency. A comparative analysis is conducted against the FR CG method using the Dolan-More performance profile based on the number of function evaluations (NOF), number of iterations (NOI), and CPU time.
On some types of nano contra βpc-continuous functions
Authors: Mahdi Saif Saleh , Jamil Jamil Mahmoud
Published: April 2024

The purpose of this paper is to define and examine two novel forms of Nano βPC- continuity, namely Nano contra βPC- continuity and Nano almost contra βPC- continuity, and to provide several fundamental characteristics and attributes related to these mappings. Moreover, , paper is done on Nano almost continuously. The connections between these mappings and other well-known functions are found.
Organic Rankin and Brayton-Joule, a Comprehensive Library for Integrating Conventional and Renewable Energy Generation
Authors: Salah Al-Haidari Zaid , Razzaq Abbas Noor , Abdullah Al-Sadiq Kazem Fahd Abu , Ramadhan Ali J. , Al-Mahdawi H. K.
Published: April 2024

The thermodynamic cycles for energy generation are the baseline for development in different types of energy generation, and they are the primary principle for the mix of fossil and renewable energy. In this paper, a detailed description of the organic Rankine and Brayton-Joule cycles has been presented as a comprehensive reference library for linking renewable energy and fossil energy, and an explanation of the governing and basic equations that formulate When developed to be flexible in the use of the research methodology used in this field. This paper provides a comprehensive reference library for linking renewable energy and fossil energy by presenting a detailed description of the organic Rankine and Brayton-Joule cycles. It includes an explanation of the governing equations and basic formulations, as well as a flexible research methodology for use in this field.
Mathematical modeling of tumors growth: Stable and mutated cells competition based model
Authors: Jabbar Aya Khamis , Al-Saedi Hayder M.
Published: April 2024

In this paper, the process for formulating the competition between stable and mutated cells is introduced. The fitness for each stable and mutated cells are employed to determine which type of cell will win in the competition. The numerical solution of the dynamical system for this model which we got is identical with the fitness cells results.
A Modern Technique of Encryption Using The Integral Sadik Transform With The Taylor Series
Authors: A.Kuffi Emad , Mohammed Nada Sabeeh
Published: April 2024

This paper presented a new technique for encrypting original texts based on multiplying the coefficients defined in the Taylor series of the logarithmic function by its equivalent index. As for decryption, a Sadik integral transform and its inverse were applied. Through this new technique, new mathematical techniques for encryption and decryption were discovered. This paper includes a practical example in order to demonstrate the efficiency of this technique.
Stand-alone solar optic device based on aps-photodeciver
Authors: Quldashov Obbosjon , Ergashev Shohbozjon , Kochkorova Gulnara , Tishabayeva Irodaxon , Тeshaboyev Muhiddinjon , Tashlanova Nigora
Published: April 2024

This paper discusses a detailed description of the design of a special physical device of a helioptocoupler aimed at receiving solar radiation with its subsequent conversion, as well as output in the form of an anomalously high photo-voltage. All processes are considered from the point of view of physical and mathematical modeling and detailed analysis of the physical processes used in the design. The analysis from the calculation and design side coincides with the general description of the design of the solar optron device.
Analysis of various operating modes of the microhpp asynchronous generator using the matlab simulink program
Authors: Pirmatov Nurali , Matqosimov Muhammadsodiq , Mahamadjonov Sodiqjon , Sayitov Shavkatjon
Published: April 2024

This article analyzes changes in efficiency and power factor, as well as various operating modes in the capacitor element excitation method, which is increasingly used in asynchronous generators. In this case, the sinusoidal voltage and frequency produced by the induction generator are within the rated values and different operations are compared. The studies were carried out along with the parameters obtained using the simulation model as well as real experimental verification. The simulation model was built in the Matlab Simulink package, which consisted of a rectifier, filter circuit, IGBT transistors and included a control system. In the real experiment, an asynchronous generator with a low-power squirrel-cage rotor was chosen, and sensors for measuring current and voltage were installed on the experimental stand. As a result of the analysis of transient processes occurring in different operating modes of the generator, it was found that the values of voltage, current and frequency changed from the nominal value. The results were achieved by adjusting the speed through a variable speed drive to obtain the rated speed taking into account the speed supplied to the rotor from the hydraulic turbine. At the end of the study, it was concluded that the induction generator has a number of advantages over other types of generators, including simplicity of design, low cost, and the fact that it does not consume a direct current source.
Alternative approach to evaluate the data of ecological monitoring of ecotopes for highways in Ukraine
Authors: Kuzminets Mykola , Dubovenko Yurii , Lampeka Mykola , Tymenko Volodymyr
Published: April 2024

We propose alternative approach to evaluate data from ecological monitoring of ecotopes along the roadways of Ukraine, taking into account raised accuracy of treating the ecological impact on the roadside environment. Methodology includes sources analysis, development of a workflow model for monitoring and identifying the main components of input data and their optimal features by system analysis. The monitoring of ecotopes of green plantations along highways in the steppe zone of South Ukraine increases the accuracy of evaluating the ecological trace on roadside environment and facilities. Scientific novelty of study combines various data sampling, physical equipment, and road measurements with information services (computing, analyzing and treating input data, forecasting of system development, and monitoring management through feedback control). The system analysis of data deals with the optimal linear constraints of the roadside ecotope capacity, leading to a simple mathematical model of the monitoring. The problem of processing environmental monitoring data with such data is reduced to the linear programming problem. Specifically, this mathematical model allows for the consideration few factors influencing the ecological situation and provides the ability to analyze and forecast the development of ecosystems along roads. Conclusions of study are criteria for an objective assessment of the degree of pollution of the environment and its impact on soil cover, vegetation and road equipment. For effective monitoring it is necessary data-based quality management system on highways. It helps evaluate the predicted degree of environmental pollution by degradation of soils, vegetation, and road facilities.
Self-learning system and methods of selection of weight coefficients of neural network
Authors: Khalilov Durbek , Bozorova Sevarakhon , Khonturaev Sardorbek , Khoitkulov Abdumalik , Sotvoldieva Dildorakhon , Toshmatov Sherzod
Published: April 2024

The present work is devoted to the problems of determining the parameters of neural networks. The methodology, algorithm and solution paths of finding the gradient descent, one of the main parameters in a neural network during machine learning, are presented. It is very important to minimize the error of finding the weight parameters of the neuron.
Optimal cubature formulas for approximate integrals of functions defined on a sphere in three-dimensional space
Authors: Bozarov Bakhromjon , Daliyev Bakhtiyor , Tukxtasinov Dadakhon , Nasriddinov Otadavlat , Ruzimatova Makhfuza , Botirova Nasiba
Published: April 2024

The development of effective methods of approximate calculation of integrals using optimal cubature formulas and optimal quadrature formulas with trigonometric weights for defined functions on a sphere, the creation of new algorithms for approximate calculation of trigonometric weighted integrals in different classes of functions, as well as the assessment of their errors, is the goal of this work.
Operation of reinforced concrete beams along an inclined section under conditions of one-sided heating
Authors: Umarov Shodiljon , Mirzababayeva Saxiba , Abobakirova Zebuniso , Goncharova Natalya , Davlyatov Shokhrukh
Published: April 2024

This article presents the results of experimental and theoretical studies of the operation of inclined sections of bendable elements operating under conditions of unilateral heating. A method for calculating such structures for transverse forces is proposed, taking into account the actual stress-strain state of the element
Calculation of geometric dimensions and hydrodynamic characteristics of venturi pipes of a self-draining solar circuit
Authors: Ismoilov Ma'murjon , Obidova Gulmira , Juraeva Muqaddamxon , Meliqo'ziev Azimjon , Maxkamova Dildora , Toshpo'latova Maxbuba
Published: April 2024

This research paper provides an increase in the energy efficiency and reliability of self-draining solar plants by optimizing their hydrodimatic operating modes. Based on this goal, the objectives of the study were to analyze the existing methods of protecting solar collectors of water heating and hot water supply systems from freezing and to identify the most promising methods. Also, the identification of the regularities of the hydrodynamics of a self-draining solar circuit.
Analysis of the quantity of particles emitted to the environment as a result of the brake linings wear of specialized transport vehicles
Authors: Karimova K. , Boltayeva M. , Ziyayeva M.
Published: April 2024

This article examines the composition of particles emitted into the environment as a result of brake lining degradation during the braking of specialized cars. The amount of particles emitted into the environment has been theoretically studied, taking into account that the resource duration of brake linings on specialized vehicles varies based on the adhesion coefficient of wheel contact with the road. The distribution of metal concentrations in particles created by the brake linings wear during the braking of specialized vehicles is extensively studied. Traffic-related sources contribute significantly to particulate matter in cities. It was discovered that the samples collected during the braking of specialized vehicles contain high levels of metal elements such as iron, copper, zinc, chromium, tin and antimony. Currently, there is information about the negative impact of the particles formed during braking of cars on the environment.
Stabilization of embankments and coastal bridges with reinforced concrete piles
Authors: Mekhmonov Mashkhurbek , Makhamadjonov Shukhratjon , Uralov Akmal
Published: April 2024

The article presents the main types of problems in the interface zone of the coastal bridge support with the railway roadbed, the causes of their occurrence. At the transition sections, there is a significant increase in the resulting pressure (Ea) on the shore support of the bridge, under the influence of the train load as a result of an increase in the active soil pressure. The pressures acting on the upper and lower points of the coastal support are determined depending on the height of the embankment, the fictitious height of the train load and the specific gravity of the soil in the transitional sections. The change in the resulting pressure arising from the embankment soils in different soils is calculated on the basis of theoretical calculations and is shown on the graph.
Aircraft fleet optimization by the simplex method
Authors: Bieliatynskyi Andrii , Volkovska Hanna , Akmaldinova Victoria , Steniakin Ivan
Published: April 2024

The transport system efficiency and the quality of its operation is organically connected with the improvement of air transportation technical means on the basis of modern science and technology. One of the directions of further aviation transport system development is the creation of methodological basis for optimal aircraft operation. Therefore, the increase in aircraft efficiency is one of the important tasks, as its improvement is associated with savings in material and financial resources, which affects the fleet structure and the process of passenger traffic. The article is intended to substantiate the method of optimizing the use of airline aircraft by means of mathematical apparatus. It has been found that the mathematical model structure is required to form an optimal aircraft fleet. A variant of aircraft fleet formation has been proposed taking into account the mathematical model, built on the basis of linear programming, namely, using the simplex method. It has been determined that the choice of optimal aircraft options is greatly influenced by the value of the parametric series. To obtain a reliable result, the aircraft loading must be 100%, otherwise the error grows due to coefficients at unknown target function. The proposed method of determining the optimal fleet with different types of aircraft and air traffic routes, taking into account the conditions of their usage, allows to recommend airlines to use aircraft with the highest efficiency.
Force analysis of the mechanism for moving materials of high-speed sewing machines
Authors: Aliyev Shavkatjon , Kengbaev Sirojiddin , Safarov Nazirjon
Published: April 2024

The article is devoted to considering the features of carrying out force analysis of lever mechanisms, in particular, the mechanism for moving materials of high-speed sewing machines. In the research process, it is reasonable to calculate and analyze various objects or mechanisms. This system makes it possible to obtain many new useful solutions when developing an engineering computational and graphical environment. Force analysis was carried out using the example of the mechanism for moving materials of high-speed sewing machines. A comparative force analysis of the mechanism for moving materials of high-speed sewing machines made it possible to determine the nature of the change in loads in kinematic pairs. It made it possible to identify the most loaded links, as well as the force ratio for typical schemes of mechanisms for moving materials of high-speed sewing machines.
Joint impact of service efficiency and salvage value on the manufacturer’s shared vehicle-type strategies
Authors: Feng Jian , Wang Yujie , Liu Zhenfeng
Published: April 2024

With the rapid development of the sharing economy, many traditional automobile manufacturers have been choosing to provide the car sharing service. Some manufacturers share GVs, while others introduce EVs in the sharing market. We develop a model that a monopoly manufacturer who simultaneously sells GVs and EVs and discuss which type of vehicles should the manufacturer launch in the sharing market considering the service efficiency and the salvage value. Our findings are that no matter which type of vehicles the manufacturer shares, EV sales remain the same, but GV sales are reduced. This means that the manufacturer’s EV-sharing strategy always promotes EVs’ adoption. It is found that when both the service efficiency ratio of EV to GV and the salvage value gap between them are low or high, the manufacturer launches EVs; otherwise, the manufacturer launches GVs. We also find that the equilibrium vehicle-type strategy can maximize the manufacturer’s profit while being the most environmentally friendly only if the valuation of shared product is high. Through numerical analysis, we know that, although the manufacturer’s GV-sharing strategy worsens the environment, it always improves the social welfare. Notably, the manufacturer’s EV-sharing strategy is not always beneficial for the environment, especially if the service efficiency ratio is relatively high. Similarly, the manufacturer’s EV sharing does not always improve the social welfare, especially if the service efficiency ratio is in the middle range. The findings not only contribute to guiding the manufacturer’s vehicle-type strategies for car sharing, but also providing potential policy implications for the government’s effort in promoting EVs’ adoption.
Equilibrium balking strategies in unobservable queues with multiple vacations and an optional service
Authors: Yang Dong-Yuh , Dai Yin-Ying , Wu Chia-Huang
Published: April 2024

This paper examines equilibrium mixed strategies in unobservable Markovian queues featuring a second optional service with server vacations, where arriving customers may choose to join or balk the system. All customers arriving at the system receive the essential service, and some customers opt for the second service after the first service has been completed. Once all customers in the system have been served, the server takes the first of multiple vacations. If no customers are waiting upon from the vacation, then the server takes another vacation. In unobservable queues, arriving customers cannot know the queue length; however, the information pertaining to the server state may be available. By examining unobservable queues (fully unobservable and almost unobservable cases), it is possible to formulate an equilibrium joining strategy as well as the socially optimal probability of joining a fully unobservable queue. This paper also presents numerical examples illustrating how system parameters affect mixed equilibrium and socially optimal balking strategies.
Management von Patienten mit akutem Koronarsyndrom und koronarer Mehrgefäßerkrankung
Authors: Feistritzer Hans-Josef , Jobs Alexander , Thiele Holger
Published: April 2024

Der Anteil an Patienten mit koronarer Mehrgefäßerkrankung bei Patienten mit akutem Koronarsyndrom (ACS) variiert je nach Alter und Risikokonstellation. Bei Patienten mit ST-Strecken-Hebungsinfarkt (STEMI) wurde der prognostische Vorteil der vollständigen Revaskularisation in mehreren randomisierten Studien gezeigt. Entsprechende Daten fehlen bisher bei Patienten mit akutem Koronarsyndrom ohne ST-Strecken-Hebungen (NSTE-ACS), eine große randomisierte Studie ist jedoch vor Kurzem gestartet. Im infarktbedingten kardiogenen Schock ist die frühe Revaskularisation die einzige, mit in randomisierten Studien belegten Daten, prognoseverbessernde Therapie, allerdings sollte sich in der Akutphase die perkutane Koronarintervention (PCI) auf die Culprit Lesion beschränken.
Zwischen Reels und Insights – Social-Media-Strategie für Praxen
Authors: Liers Jessica , Kampe Swantje
Published: April 2024

In der digitalen Ära gewinnt die Rolle von Social Media für Ergo- und Physiotherapiepraxen zunehmend an Bedeutung. Doch welche Chancen und Möglichkeiten eröffnen sich, wenn Therapeut*innen gezielt Social-Media-Plattformen wie Facebook und Instagram nutzen? Wie kann das Potenzial der sozialen Medien bestmöglich genutzt werden?
Jeder Sticker motiviert – Verhaltenstherapeutische Methoden in der Pädiatrie
Authors: Schulz Laura
Published: April 2024

Viele Bestandteile der Verhaltenstherapie bieten Ergotherapeut*innen ein evidentes Handwerkszeug im Umgang mit verhaltensauffälligen Kindern und ihren Bezugspersonen. Die bekanntesten sind die Tokensysteme, deren adäquaten Einsatz Laura Schulz näher beleuchtet.
Imaging in double-casing wells with convolutional neural network based on inception module
Authors: Zhang Siqi , Zeng Zhoumo , Wang Xiaocen , Chen Shili , Liu Yang
Published: April 2024

The evaluation of well integrity in double-casing wells is critical for ensuring well stability, preventing oil and gas leaks, avoiding pollution, and ensuring safety throughout well development and production. However, the current predominant method of assessing cementing quality primarily focuses on single-casing wells, with limited work conducted on double-casing wells. This study introduces a novel approach for evaluating the cementing quality using the Inception module of convolutional neural networks. First, the finite-difference method is employed to generate borehole sonic data corresponding to a variety of model configurations, which are used to train a neural network that learns spatial features from the borehole sonic data to reconstruct the slowness model. By adjusting the network architecture and parameters, it is discovered that a neural network with two blocks and 4096 nodes in the fully connected layer demonstrated the best imaging results and exhibited strong anti-noise capabilities. The proposed method is validated using practical wellbore size models, demonstrating excellent results and offering a more effective means of evaluating wellbore integrity in double-casing wells. In addition, dipole acoustic logging data are used to conduct slowness model imaging of the compressional (P-) wave and shear (S-) wave in double-casing wells to verify the feasibility of cementing quality evaluation. The developed method contributes to more accurate evaluations of wellbore integrity for the oil and gas industry, leading to improved safety and environmental outcomes.