Windows Environment Research Articles

A study on cloud data access through browser credential migration in Windows environment

Various user data stored in cloud services for data continuity and efficiency is one of the main collection targets in digital forensic investigation. Some forensic tools collect cloud data based on user account and password, or provide data collection functions based on user credentials stored in the web browser. However, because many web services require additional authentication using user devices to protect user data, access using only the account and password is becoming difficult. In the case of credentials generated by auto-login, it does not work or requires re-authentication when moved to the investigator's device. This is so that other devices cannot utilize the credentials that are kept on the device due to security measures. In this paper, we propose a new method to migrate the credentials stored by the web browser to other devices and effectively utilize them, unlike the forensic method that involves using local credentials. Our analysis revealed that the majority of browsers encrypt and store credentials, so we researched credential decryption methods. We proceeded with the migration; move and encrypt the decrypted credentials to the investigator's device, or move the not encrypted credentials simply. As a result, we conducted credentials migration experiments on a total of 28 browsers, among which we have clarified that migration is possible in all browsers except three that do not store data, such as Tor. We verified that it is possible to log in and collect data on 20 types of web services that are frequently used using migrated credentials. Although the approach we propose is straightforward, it allows for effective and efficient cloud data collection in digital forensic investigation.

Modernizing the Legacy Healthcare System to Decentralize Platform Using Blockchain Technology

The use of blockchain technology is expanding in various industries, including finance, supply chain management, food, energy, IoT, and healthcare. The article aims to address the challenges of complex medical procedures, large-scale medical data management, and cost optimization in the healthcare industry. By employing blockchain technology, the article aims to enhance data security and privacy while ensuring the integrity and efficiency of the healthcare system. This article focuses on the application of blockchain technology in the healthcare system by reviewing the existing literature and proposing multiple workflows for better data management. These workflows were implemented using the Ethereum blockchain platform and involve complex medical procedures such as surgery and clinical trials, as well as managing a large amount of medical data. The feasibility of the proposed system is analyzed in terms of associated costs, and a model-driven engineering approach is used to recover the architecture of traditional healthcare systems. The aim is to provide stakeholders in the healthcare system with better healthcare services and cost optimization. The solution being proposed automates interactions between different parties involved. Smart contracts were created using Solidity language, and their functions were tested using the Remix IDE. This paper illustrates that our smart contract code was designed to avoid common security vulnerabilities and attacks. To test the framework, a prototype of the smart contract was deployed on an Ethereum TESTNET blockchain in a Windows environment. This study found that the proposed approach is both practical and efficient.

Research on building material design in circular economy based on improved neural network

Material management technology has always been the basic and core technology of traditional construction projects. But now, because traditional material management consumes more resources and has low efficiency, it has gradually been replaced by modern material management systems represented by computer technology, thus saving a lot of enterprise input costs and reducing the impact of traditional material management technology on enterprise operating income. Based on this background, this paper introduces improved neural network technology to adapt to the circular economy environment, so as to complete the construction and improvement of the building materials data management system. The system can be used to complete building materials management, basic data management, etc., and can be optimized for the system itself. Through network technology, the client can be connected to the server switch, and Ajax technology and JSP programming language can be used for compatibility processing, so that the system can run better in the Windows environment. According to the simulation experiment designed for the system, the output sample value of the system is close to the measured value, with good prediction accuracy, small error and high stability. Therefore, the application of AdaBoost ANN algorithm can effectively realize the prediction of building materials. In addition, other functions of the system, such as interactive page design, operation interface and database, can better adapt to the circular economy environment and actual user needs. In this paper, the improved neural network is applied to the field of building material design under the environment of circular economy, thus completing the system optimization.

Windows malware detection based on static analysis with multiple features.

Malware or malicious software is an intrusive software that infects or performs harmful activities on a computer under attack. Malware has been a threat to individuals and organizations since the dawn of computers and the research community has been struggling to develop efficient methods to detect malware. In this work, we present a static malware detection system to detect Portable Executable (PE) malware in Windows environment and classify them as benign or malware with high accuracy. First, we collect a total of 27,920 Windows PE malware samples divided into six categories and create a new dataset by extracting four types of information including the list of imported DLLs and API functions called by these samples, values of 52 attributes from PE Header and 100 attributes of PE Section. We also amalgamate this information to create two integrated feature sets. Second, we apply seven machine learning models; gradient boosting, decision tree, random forest, support vector machine, K-nearest neighbor, naive Bayes, and nearest centroid, and three ensemble learning techniques including Majority Voting, Stack Generalization, and AdaBoost to classify the malware. Third, to further improve the performance of our malware detection system, we also deploy two dimensionality reduction techniques: Information Gain and Principal Component Analysis. We perform a number of experiments to test the performance and robustness of our system on both raw and selected features and show its supremacy over previous studies. By combining machine learning, ensemble learning and dimensionality reduction techniques, we construct a static malware detection system which achieves a detection rate of 99.5% and error rate of only 0.47%.

Generic Patient-Centered Blockchain-Based EHR Management System

Accessing healthcare services by several stakeholders for diagnosis and treatment has become quite prevalent owing to the improvement in the industry and high levels of patient mobility. Due to the confidentiality and high sensitivity of electronic healthcare records (EHR), the majority of EHR data sharing is still conducted via fax or mail because of the lack of systematic infrastructure support for secure and reliable health data transfer, delaying the process of patient care. As a result, it is critically essential to provide a framework that allows for the efficient exchange and storage of large amounts of medical data in a secure setting. The objective of this research is to develop a Patient-Centered Blockchain-Based EHR Management (PCEHRM) system that allows patients to manage their healthcare records across multiple stakeholders and to facilitate patient privacy and control without the need for a centralized infrastructure by means of granting or revoking access or viewing one’s records. We used an Ethereum blockchain and IPFS (inter-planetary file system) to store records because of its advantage of being distributed and ensuring the immutability of records and allowing for the decentralized storage of medical metadata, such as medical reports. To achieve secure a distributed, and trustworthy access control policy, we proposed an Ethereum smart contract termed the patient-centric access control protocol. We demonstrate how the PCEHRM system design enables stakeholders such as patients, labs, researchers, etc., to obtain patient-centric data in a distributed and secure manner and integrate utilizing a web-based interface for the patient and all users to initiate the EHR sharing transactions. Finally, we tested the proposed framework in the Windows environment by compiling a smart contract prototype using Truffle and deploy on Ethereum using Web3. The proposed system was evaluated in terms of the projected medical data storage costs for the IPFS on blockchain, and the execution time for a different number of peers and document sizes. The findings of the study indicate that the proposed strategy is both efficient and practicable.

Concrelife: A Software to Solve the Chloride Penetration in Saturated and Unsaturated Reinforced Concrete

This paper presents new software (Concrelife) capable of reliably simulating chloride ions penetration in reinforced concrete from different environments in the most common 1-D rectangular geometry scenarios. Its numerical solution is obtained from the simulation of models whose structure is based on Network Simulation Method. These models are generated by the program itself and run in the powerful free code NgSpice. The mathematical model of the problem includes the formation of bound chloride, precipitated chloride, reduction of porosity, saturated and unsaturated conditions, etc. All this allows tackling all kinds of scenarios, such as successive changes in concentration and temperature at the boundary, wet-drying cycles, washing of structures, etc. Concrelife has been developed with a pleasant window environment, intuitive and easy for a user not expert in numerical techniques, both for the introduction of data and for the graphic representation of the results, which include the spatial and temporal concentration of all species of chloride, porosity, water content in pores etc. To test and verify the results of the software, applications are presented to real scenarios.

An Expert System for Breast Cancer Prediction (ESBCP) using Decision Tree

Objectives: Breast cancer is one of the major concerns in present day scenario. Detecting breast cancer at early stage increases the chances of survival. The objective of this research is to propose suitable feature selection method to improve the efficiency of breast cancer prediction at early stages to increase the survival rate. Methods: In this work, an expert intelligent technique has been proposed named “Expert System for Breast Cancer Prediction (ESBCP)” to detect breast cancer. To validate the results, the proposed system determines accuracy, precision, F-measure, and recall. The proposed model introduced a feature selection technique named - Undiluted Feature Set (UFS) to select the most relevant and promising features. The experimental work was carried out using Python 2.8 version in a Windows environment, taking a dataset on breast cancer from the UCI machine learning repository. There were 699 occurrences in the dataset with nine attributes and two classes. The proposed work utilized a decision tree and a new feature selection technique based on a heuristic search and the Stochastic Hill method. The experimental results were evaluated using the 10-fold Cross-Validation (CV). Findings: The experimental findings showed that the suggested model - ESBCP can accurately detect breast cancer at an early stage. As per the result, with simple decision tree the accuracy recorded 93.42 percent whereas ESBCP obtained 94.01 percent. It may seem that the improvement of 0.59 percent is very small, but for a large population even this mere change can have a greater impact. Novelty: The suggested model ESBCP and the feature selection technique - UFS have a lot of potential in the fields of medical research and bioinformatics in terms of classification capability and predictive power. Keywords: Expert System; Decision Tree; Undiluted Feature Set; Breast Cancer; Feature Selection

Blockchain in Healthcare: A Decentralized Platform for Digital Health Passport of COVID-19 Based on Vaccination and Immunity Certificates.

COVID-19 has become a very transmissible disease that has had a worldwide impact, resulting in a huge number of infections and fatalities. Testing is critical to the pandemic's successful response because it helps detect illnesses and so attenuate (isolate/cure) them and now vaccination is a life-safer innovation against the pandemic which helps to make the immunity system stronger and fight against this infection. Patient-sensitive information, on the other hand, is now held in a centralized or third-party storage paradigm, according to COVID-19. One of the most difficult aspects of using a centralized storage strategy is maintaining patient privacy and system transparency. The application of blockchain technology to support health initiatives that can minimize the spread of COVID-19 infections in the context of accessibility of the system and for verification of digital passports. Only by combining blockchain technology with advanced cryptographic algorithms can a secure and privacy-preserving solution to COVID-19 be provided. In this article, we investigate the issue and propose a blockchain-based solution incorporating conscience identity, encryption, and decentralized storage via interplanetary file systems (IPFS). For COVID-19 test takers and vaccination takers, our solution includes digital health passports (DHP) as a certification of test or vaccination. We explain smart contracts constructed and tested with Ethereum to preserve a DHP for test and vaccine takers, allowing for a prompt and trustworthy response from the necessary medical authorities. We use an immutable trustworthy blockchain to minimize medical facility response times, relieve the transmission of incorrect information, and stop the illness from spreading via DHP. We give a detailed explanation of the proposed solution's system model, development, and assessment in terms of cost and security. Finally, we put the suggested framework to the test by deploying a smart contract prototype on the Ethereum TESTNET network in a Windows environment. The study's findings revealed that the suggested method is effective and feasible.

Windows Forensics Analysis

Computer forensics investigates crimes on digital devices hard disk and any other digital storage devices to determine the evidence of any unauthorized access. Essentially, forensic investigators conduct forensic analysis with the help of different approaches (Static and Dynamic) and also tools like ProDiscover, Encase, Accessdata FTK and Autopsy to guarantee the security of a system in an organization. The process requires knowledge of different registry, laws, and regulations related to computer crimes, the operating system (Windows, Linux, etc.) and the network in an organization (e.g., Win NT). In this paper we will use different forensic tools to extract forensic evidence from a windows environment.

Decentralized Patient-Centric Report and Medical Image Management System Based on Blockchain Technology and the Inter-Planetary File System.

Several academicians have been actively contributing to establishing a practical solution to storing and distributing medical images and test reports in the research domain of health care in recent years. Current procedures mainly rely on cloud-assisted centralized data centers, which raise maintenance expenditure, necessitate a large amount of storage space, and raise privacy concerns when exchanging data across a network. As a result, it is critically essential to provide a framework that allows for the efficient exchange and storage of large amounts of medical data in a secure setting. In this research, we describe a unique proof-of-concept architecture for a distributed patient-centric test report and image management (PCRIM) system that aims to facilitate patient privacy and control without the need for a centralized infrastructure. We used an Ethereum blockchain and a distributed file system technology called the Inter-Planetary File System in this system (IPFS). Then, to secure a distributed and trustworthy access control policy, we designed an Ethereum smart contract termed the patient-centric access control protocol. The IPFS allows for the decentralized storage of medical metadata, such as images, with worldwide accessibility. We demonstrate how the PCRIM system design enables hospitals, patients, and image requestors to obtain patient-centric data in a distributed and secure manner. Finally, we tested the proposed framework in the Windows environment by deploying a smart contract prototype on an Ethereum TESTNET blockchain. The findings of the study indicate that the proposed strategy is both efficient and practicable.

ЭВОЛЮЦИОННЫЙ ПОПУЛЯЦИОННЫЙ МЕТОД РЕШЕНИЯ ТРАНСПОРТНОЙ ЗАДАЧИ

The paper considers an evolutionary population method for solving a transport problembased on the metaheuristics of crystallization of a placer of alternatives. We study a closed (orbalanced) model of the transport problem: the amount of cargo from suppliers is equal to the totalamount of needs at destinations. The goal of optimization is to minimize the cost (achieving a minimumof transportation costs) or distances and the criterion of time (a minimum of time is spent ontransportation). The metaheuristics of the crystallization of a placer of alternatives is based on astrategy based on remembering and repeating past successes. The strategy emphasizes «collectivememory», which refers to any kind of information that reflects the past history of development andis stored independently of individuals. An ordered sequence Dk of routes is considered as a codefor solving the transport problem. The objects are routes, the alternatives are the set of positions Pin the list, where np is the number of positions in the list Dk. The set of objects Dk corresponds tothe set of all routes. The set of alternative states P of the object corresponds to the set of alternativeoptions for placing the object in the list Dk. The operation of the population evolutionary algorithmfor the crystallization of a placer of alternatives is based on a collective evolutionarymemory called a placer of alternatives. A scattering of solution alternatives is a data structureused as a collective evolutionary memory that carries information about the solution, includinginformation about the realized alternatives of agents in this solution and about the usefulness ofthe solution. A constructive algorithm for the formation of a reference plan by decoding the list Dkhas been developed. At each step t, the problem of choosing the next route in the sequence Dk anddetermining the amount of cargo transported from the point of departure Ai to the point of destinationBj along this route is solved. The developed algorithm is population-based, implementing thestrategy of random directed search. Each agent is a code for some solution of the transport problem.At the first stage of each iteration l, a constructive algorithm based on the integral placer ofalternatives generates nk decision codes Dk. The formation of each decision code Dk is performedsequentially in steps by sequentially selecting an object and position. For the constructed solutioncode Dk, the solution estimate ξk and the utility estimate δk are calculated. An individual scatteringof alternatives Rk is formed and a transition to the construction of the next solution code is formed.At the second stage of the iteration, the integral placer of alternatives formed at previous iterationsfrom l to (l-1) is summed with all individual placers of alternatives formed at iteration l.At the third stage of iteration l, all integral utility estimates r*αβ of the integral placer of alternativesR*(l) are reduced by δ*. The algorithm for solving the transport problem was implemented inC++ in the Windows environment. Comparison of the values of the criterion, on test examples,with a known optimum showed that in 90% of the examples the solution obtained was optimal, in2% of the examples the solutions were 5% worse, and in 8% of the examples the solutions differedby less than 2%. The time complexity of the algorithm, obtained experimentally, lies within O(n2).

EEMDS: Efficient and Effective Malware Detection System with Hybrid Model based on XceptionCNN and LightGBM Algorithm

The security threats posed by malware make it imperative to build a model for efficient and effective classification of malware based on its family, irrespective of the variant. Preliminary experiments carried out demonstrate the suitability of the generic LightGBM algorithm for Windows malware as well as its effectiveness and efficiency in terms of detection accuracy, training accuracy, prediction time and training time. The prediction time of the generic LightGBM is 0.08s for binary class and 0.40s for multi-class on the Malimg dataset. The classification accuracy of the generic LightGBM is 99% True Positive Rate (TPR). Its training accuracy is 99.80% for binary class and 96.87% for multi-class, while the training time is 179.51s and 2224.77s for binary and multi classification respectively. The performance of the generic LightGBM leaves room for improvement, hence, the need to improve the classification accuracy and training accuracy of the model for effective decision making and to reduce the prediction time and training time for efficiency. It is also imperative to improve the performance and accuracy for effectiveness on larger samples. The goal is to enhance the detection accuracy and reduce the prediction time. The reduction in prediction time provides early detection of malware before it damages files stored in computer systems. Performance evaluation based on Malimg dataset demonstrates the effectiveness and efficiency of the hybrid model. The proposed model is a hybrid model which integrates XceptionCNN with LightGBM algorithm for Windows Malware classification on google colab environment. It uses the Malimg malware dataset which is a benchmark dataset for Windows malware image classification. It contains 9,339 Malware samples, structured as grayscale images, consisting of 25 families and 1,042 Windows benign executable files extracted from Windows environments. The proposed XceptionCNN-LightGBM technique provides improved classification accuracy of 100% TPR, with an overall reduction in the prediction time of 0.08s and 0.37s for binary and multi-class respectively. These are lower than the prediction time for the generic LightGBM which is 0.08s for binary class and 0.40s for multi-class, with an improved 100% classification accuracy. The training accuracy increased to 99.85% for binary classification and 97.40% for multi classification, with reduction in the training time of 29.97s for binary classification and 447.75s for multi classification. These are also lower than the training times for the generic LightGBM model, which are 179.51s and 2224.77s for the binary and multi classification respectively. This significant reduction in the training time makes it possible for the model to converge quickly and train a large sum of data within a relatively short period of time. Overall, the reduction in detection time and improvement in detection accuracy will minimize damages to files stored in computer systems in the event of malware attack.

Blockchain-Enabled Decentralized Secure Big Data of Remote Sensing

Blockchain technology has emerged as a promising candidate for space exploration and sustainable energy systems. This transformative technology offers secure and decentralized strategies to process and manipulate space resources. Remote sensing provides viable potential with the coexistence of open data from various sources, such as short-range sensors on unmanned aerial vehicles (UAVs) or Internet-of-Things (IoT) tags and far-range sensors incorporated on satellites. Open data resources have most recently emerged as attractive connecting parties where owners have shown consent to share data. However, most data owners are anonymous and untrustworthy, which makes shared data likely insecure and unreliable. At present, there are several tools that distribute open data, serving as an intermediate party to link users with data owners. However, these platforms are operated by central authorities who develop guidelines for data ownership, integrity, and access, consequently restricting both users and data owners. Therefore, the need and feasibility of a decentralized system arise for data sharing and retrieving without involving these intermediate limiting parties. This study proposes a blockchain-based system without any central authority to share and retrieve data. Our proposed system features (i) data sharing, (ii) maintaining the historical data, and (iii) retrieving and evaluation of data along with enhanced security. We have also discussed the use of blockchain algorithms based on smart contracts to track space transactions and communications in a secure, verifiable, and transparent manner. We tested the suggested framework in the Windows environment by writing smart contracts prototype on an Ethereum TESTNET blockchain. The results of the study showed that the suggested strategy is efficient, practicable, and free of common security attacks and vulnerabilities.

Stress testing of Docker containers running on a Windows operating system

At the moment, Docker technology is becoming more prevalent in the Windows environment. The pertinent topic in this regard is how Docker containers running on the Windows operating system would behave in the event of a resource deficit. The article discusses circumstances in which there is insufficient processor or RAM to service all running containers. It is proposed to employ stress and volume testing to investigate the stability and reliability of Docker containers. The purpose of stress testing is to determine how the system behaves when the load on the application is greatly increased in comparison to what was initially expected. By delivering enormous amounts of data for processing, volume testing is accomplished. To assess container performance during stress testing, a Prometheus-based technology stack is used. Docker containers running in a Windows system have been shown to operate predictably under extreme load conditions. As the processor load increases, the performance of the containerized program drops proportionately to the decrease in processor time allocated. When there is insufficient RAM to run all containers, Docker uses virtual memory to dynamically distribute memory amongst containers. If a container's execution is halted due to a lack of available RAM on the system, the container's execution is terminated. Other containers continue to function normally.

RESEARCH OF THE QUALITATIVE COMPOSITION OF MILK OF COWS OF SIMMENTAL BREED

Modern dairy breeds of cattle have high milk yields. This is especially true of breeds created using Holstein breed [14]. At the same time, scientists have repeatedly stated that the use of Holstein breeders deteriorates the quality characteristics of milk productivity. Therefore, the influence of conditional blood by Holstein breed significantly affects productivity. However, researchers emphasize that the breed as a genetic factor has a significant impact on milk productivity. This explains the influence of the biochemical composition of milk on the profitability of the dairy industry.
 Dairy breeds that have been created in Ukraine over the past twenty years are characterized by a sufficient level of milk yield and quality indicators. The level of milk yield of animals is more than 7.0 thousand kg of milk with fat and protein content in milk above 3.8% and 3.2%, respectively. Some breeds, which are created on the basis of domestic combined breeds are characterized by a sufficient level of milk components. Their fat content in milk is more than 4.0%, and protein – more than 3.4%. During lactation, the content of milk components changes.
 The aim of our work was to investigate some indicators of the biochemical composition of milk of Simmental cows.
 Materials and methods. The research was conducted on Simmental cows kept in the Mykhailivka PZ of the Lebedyn District of the Sumy Region (n = 46) and the Urozhay SFG of the Romensky District of the Sumy Region (n = 30).
 For sampling of milk used counter – indicator IU-1. The milk sample was stored in a plastic container (25 ml) during the day at a temperature of +3C0, using the preservative chrompic. The content of fat and protein in milk was determined in the laboratory of Sumy National Agrarian University on the equipment Ultrasonic milk analyzer Master Classic manufacturer Milkotester Ltd (Bulgaria).
 The results of the research were processed by the methods of mathematical statistics using the "Statistica-6.1" package in the Windows environment on a PC.
 The animals fully meet the requirements of the breed standard for both the first (standard 2700 kg) and the third lactation (3500 kg).
 In terms of milk yield, Simmental cows met the requirements of the breed standard for both the first and second lactations.
 Biochemical analysis of milk was carried out to determine the level of such indicators as fat, protein, lactose, dry matter and dry nonfat milk residue. According to all the studied features, the first-born were inferior to adult animals. A statistically significant difference was found between the fat and protein content of milk in favor of adult animals. As a result, it can be argued that there is a significant difference in the amount of dry matter and dry skim milk residue between animals of different ages. Adult animals met the breed standard in terms of both fat and protein content, the first-born animals were slightly lower than the standard in terms of protein content.
 There are relationships of different directions and magnitudes between the contents of individual components. Statistically significant correlation coefficients were found between the content of: fat and protein; fat and dry matter; protein and dry matter; protein and skimmed milk powder.
 Conclusions. As a result of research, it was found that both first-born and adult Simmental cows are characterized by a sufficient level of milk productivity with high quality milk. In all respects, they met the requirements regulated by the breed standard, with the exception of the first in terms of protein content. There is a significant positive correlation between individual indicators (0.75 ± 0.03–0.96 ± 0.01).

FORMATION OF ECONOMICALLY USEFUL TRAITS IN COWS OF UKRAINIAN BROWN DAIRY BREED OF DIFFERENT GENOTYPES BY KAPPA-CASEIN

The genes of cattle casein are polymorphic. Today, a large number of variants of kappa-casein protein are known:
 A, B, B2, C, D, E, F1, F2, G1, G2, H, I і J. Scientists pay attention to three main types of genotypes – AA, AB and BB. It has been proven that the most common alleles of kappa-casein in cattle are A and B. Importantly, the allelic variant of kappa-casein B is associated with the production of milk with a more optimal chemical composition and technological parameters for cheese production compared to the A allele.
 According to the results of scientific research, it was established that the level of milk yield in cows with the AA genotype was higher compared to animals of other genotypes. At the same time, animals with the BB genotype are characterized by a higher protein content in milk compared to milk from animals with the AA genotype. Scientists have also proven that the milk of animals with the desired genotype (BB) curdles faster during cheese production, and the yield of cheese is higher than that of animals with the AA genotype. In confirmation of this, other researchers note that animals with the AA genotype have a lower average protein content in milk (by 0.09%) compared to animals with the BB genotype.
 The purpose of the study is to establish the influence of kappa-casein genotypes on economic and useful traits of animals of Ukrainian Brown Dairy breed.
 Research materials and methods. Genotyping of 42 heads of Ukrainian Brown Dairy breed cattle belonging to the Breeding Plant of the State Enterprise "Experimental Farm of Institute of Agriculture of Northern East of NAAS" was carried out. Determination of kappa-casein gene polymorphism was carried out in the genetic laboratory of the Institute of Physiology named after Bogomolets of NAS with the help of molecular biological analysis of allele recognition by polymerase chain reaction (PCR) in real time.
 Blood samples were collected in monovets with a volume of 2.7 ml (Sarstedt, Germany), with subsequent freezing of the samples and their storage at -20ºC. DNA for genotyping was obtained from samples using the Monarch® New England BioLab (USA) genomic DNA purification kit according to the manufacturer's protocol.
 Three sub-experimental groups of Ukrainian Brown Dairy breed animals with genotypes according to kappa-casein AA, AB and BB were formed. The electronic database of SUMS "Orsek" was used to evaluate economic and useful features. Changes in live weight of cattle up to 18 months of age, indicators of reproductive capacity, and milk productivity were evaluated. The research results were processed using the methods of mathematical statistics using the "Statistica-6.1" package in the Windows environment on a personal computer.
 As a result of the conducted research, no significant influence of the kappa-casein genotype on live weight indicators of heifers was established in all studied periods. This indicates that when creating herds of animals with the desired BB genotype, the growth indicators of repair heifers will not deteriorate.
 According to indicators of reproductive capacity, there is no statistically significant difference between animals of different genotypes. At the same time, the youngest age of first insemination was distinguished by animals with the AB genotype, and the highest – with the AA genotype. The duration of the intercalving period and the value of the coefficient of reproductive capacity were the lowest in homozygous (AA) and heterozygous (AB) animals.
 During the first lactation, homozygous AA animals had an advantage in the content of fat and protein in milk. Homozygous BB cows had an advantage in terms of milk yield in the third lactation, in terms of fat content – animals with heterozygous genotype AB, protein – animals with genotype AA. In terms of better lactation, animals with the BB genotype prevailed over other groups in terms of milk yield. Homozygous AA animals had an advantage in terms of fat content, and homozygous BB animals had an advantage in terms of protein content.
 Conclusions. Animals of the Ukrainian Brown Dairy breed corresponded to the standard of the breed in almost all indicators of milk productivity. Between animals of different genotypes, according to kappa-casein, a difference was established for certain economic and useful traits. It should be noted that in different periods and according to different characteristics, it varied greatly, and in some cases it was statistically significant. In the third and best lactation according to milk yield, a statistically significant difference was established between homozygous BB, heterozygous AB genotypes and homozygous AA in favor of the first two genotypes. It can be stated that the formation of herds with the BB genotype for kappa-casein will ensure the preservation of the desired values of productive indicators of dairy cattle.

PUBLIC TRANSPORTATION PORTAL FOR COMMUTERS

Public transportation saves money, makes travel easier, and so enhances opportunities and property value. It also minimizes air pollution, relieves traffic congestion, and provides job opportunities for millions of people. Trains, buses, and other forms of public transportation that operate on regular routes and charge predetermined prices are referred to as public transportation. This mode of transportation is an important aspect of every society because of the role it plays. That is why most municipal governments offer incentives for residents to use public transportation and leave their personal vehicles at home. Apart from being cost-effective, public transportation can alleviate a number of issues that cities confront, particularly those with heavy car dependency. Crude oil prices, which rose well beyond $100 per barrel as a result of the demandsupply gap and the Russia-Ukraine conflict, may also come back faster than they climbed. The main objective of this system is to provide an application that allows residents and visitors of a city to find out more about transportation-related aspects of the city. Aspects include information about public transportation options, schedules, fares, and so forth (education). The site might also provide real-time information about buses and other public transportation information. This application software has been computed successfully and was also tested successfully by taking “test cases”. It is user-friendly and has the required options, which can be utilized by the user to perform the desired operations. The software is developed using Java as the front end and Oracle as the back end in a Windows environment. The goals that are achieved by the software are: Optimum utilization of resources, Efficient management of records, Simplification of the operations, Less processing time and getting required information, User friendly, and Portable and flexible for further enhancement.

Refined descriptive sampling simulated annealing algorithm for solving the traveling salesman problem

Abstract The simulated annealing (SA) algorithm is a popular intelligent optimization algorithm which has been successfully applied in many fields. In this paper, we propose a software component under the Windows environment called goRDS which implements a refined descriptive sampling (RDS) number generator of high quality in the MATLAB programming language. The aim of this generator is to sample random inputs through the RDS method to be used in the Simple SA algorithm with swap operator. In this way, the new probabilistic meta-heuristic algorithm called RDS-SA algorithm will enhance the simple SA algorithm with swap operator, the SA algorithm and possibly its variants with solutions of better quality and precision. Towards this goal, the goRDS generator was highly tested by adequate statistical tests and compared statistically to the random number generator (RNG) of MATLAB, and it was proved that goRDS has passed all tests better. Simulation experiments were carried out on the benchmark traveling salesman problem (TSP) and the results show that the solutions obtained with the RDS-SA algorithm are of better quality and precision than those of the simple SA algorithm with swap operator, since the software component goRDS represents the probability behavior of the SA input random variables better than the usual RNG.

A Study of Symbolic Analysis of Electrical Circuits on the Example of a Radio Frequency Filter

The year 2022 marks the 175th anniversary of Kirchhoff's theorem on finding a symbolic expression of the current of an electric circuit branch from its contour determinant in the form of a fraction with a denominator and numerator. The first part of Kirchhoff's theorem concerning finding the contour determinant (fraction denominator) marked the beginning of graph theory, while the second part of the theorem on finding the numerator of a fraction remains unused to date. A symbolic circuit analysis calculation of transient processes in the radio frequency filter is carried out at the operator's stepwise and harmonic influences. The computer-aided calculation program developed by the authors is based on the modified Kirchhoff rule for finding the symbolic expression of the current of a branch of an electrical circuit from its contour determinant and it can be useful for researchers, engineers and students of mathematical and technical specialties. The program console is written in the Delphi package and opens in the Windows environment under the name "TINS" and it has an intuitive interface. The expressions for the branch currents can be copied without modification to the Maple computer algebra package, and to the Mathcad package – with the replacement of mathematical symbols. The reliability of the calculations was checked by initial and final conditions by comparing the Laplace transform table with the originals found by the “invlaplace” operation built into the Mathcad package. The presented computer program and calculation methodology allow you to quickly understand certain sections of mathematics and radio electronics and easily choose another object as a study. Computer-aided calculation of electromagnetic processes in radio engineering devices using the "TINS" program reduces the analysis time by obtaining results in symbolic form.

Hybridized Model for Data Security Based on Security Hash Analysis (SHA 512) and Salting Techniques

High-profile security breaches and attacks on many organization’s database have been on the increase and the consequences of this, are the adverse effect on the organizations in terms of financial loss and reputation. Many of the security breaches has been ascribed to the vulnerability of the organization’s networks, security policy and operations. Additionally, the emerging technology solutions like Internet-ofThings (IoT), Artificial Intelligence, and Cloud Computing, has extremely exposed many of the organizations to different forms of cyber-threats and attacks. Researchers and system designers have made attempts to proffer solution to some of these challenges. However, the efficacy of the techniques remains a great concern due to insufficient control mechanisms. For instance, many of the techniques are majorly based on a single mode encryption techniques which are not too robust to withstand the threats and attacks on organization’s database. To proffer solution to these challenges, the current research designed and integrated a hybridized data security model based on Secured Hash Analysis (SHA 512) and Salting Techniques to enhance the adeptness of the existing techniques. The Hash Analysis algorithm was used to map the data considered to a bit string of a fixed length and salt was added to the password strings essentially to hide its real hash value. The idea of adding salt to the end of the password is basically to complicate the password cracking process. The hybridized model was implemented in Windows environment using python 3.7 IDE platform and tested on a dedicated Local Area Network (LAN) that was exposed to threats from both internal and external sources. The results from the test show that the model performed well in terms of efficiency and robustness to attacks. The performance of the new model recorded a high level of improvement over the existing techniques with a recital of 97.6%.