bluuebunny/crossref_metadata_2025 huggingface dataset:
Streamlining Software Development in Enterprises: The Power of Metrics-Driven Development
Authors: B Gomathi Et al.
Published: January 2024

In today's fiercely competitive market, software companies must prioritize the activities in the Software Development Life Cycle (SDLC) to produce high-quality software and stay ahead of competitors. The exponential growth of Cloud computing enabled technologies has prompted companies to adapt their software development processes based on Cloud, as it offers instant access to essential resources. However, traditional software metrics-based approaches fall short when applied to Cloud computing-based software development. In order to address this challenge head-on, this paper presents an approach called Metrics-Driven Development (MDD) tailored specifically for enterprise Cloud development which is also known as full-stack development. In order to support informed decision-making in the age of Cloud computing, the main goal of this research is to evaluate the functionality and quality of software using several metrics. Furthermore, MDD plays a pivotal role in improving the software quality, performance, and efficiency within the realm of Cloud computing. Based on the empirical experiments and observation, it is evident that Metrics-Driven Development is an invaluable approach for enhancing efficiency and effectiveness in enterprise software development.
Novel Approach for Job Offloading Technique in Mobile Cloud Computing
Authors: Nitinkumar Rajnikant Pandya Et al.
Published: January 2024

User preferences for computing have evolved as a result of recent advancements in mobile computing technologies. Mobile devices (SMDs) are still low potential computer platforms with capped memory sizes, CPU speeds, and battery life. Computationally heavy mobile apps may be run on SMDs thanks to Mobile Cloud Computing (MCC), which uses computational offloading. In both the business and academic worlds, there is rising interest in the mobile cloud computing is a new computing paradigm. Lack of a comprehensive experimental framework to use in their experiments and to evaluate their proposed work is a serious issue for mobile cloud computing researchers. Through MCC, mobile devices will be able to serve a wider variety of resource-intensive tasks while maintaining and growing their resource pools. In order to improve the mobile user experience, it places a high priority on improving energy efficiency, storage capacity, computational power, and data security. Since both the mobile device and the Cloud must determine energy-time trade-offs and decisions made on one side have an influence on the performance of the other, designing MCC systems is a challenging task. According to an examination of the MCC literature, all present models are centred on mobile devices, with the Cloud viewed as a system with infinite resources. Furthermore, no MCC-specific simulation tool is currently known to exist. To fill this need, we present in this study a Novel Approach for Job Offloading in Cloud Environments such as Google Cloud, using OCR application, while attempting to reduce energy use (Power). We are measuring the results of this experiment on both Cloud Computing and Mobile Device Computing.
Scrutinization of Video posts on Social Media for Authenticity
Authors: Neelakantam Pavani Et al.
Published: January 2024

Multimedia posts, especially video posts are easy to understand, attractive and are considered as proof of evidence for an event that occurred. Videos are one of the frequently shared posts on social media. Videos and images speak louder than words. They are very much believed by people and forwarded. Videos reach every person despite of language or literacy barriers. Technological improvements lead to easy generation of fraudulent videos to create a negative impact on people and society. Celebrities and politicians are highly affected because of fake video generation and dissemination. The video posts uploaded and forwarded on social media should be analyzed and identified to stop propagation before they create harm to the society. The proposed work converts the video clips into a sequence of frames. The keyframes are then identified by the use of histogram comparison. A CNN model is built with optimum layers for image classification. The keyframes or images are then classified using the CNN model to identify fraudulent content. The proposed work is light in terms of processing when compared to existing or conventional video classification. The work uses FaceForensics++, DeepFake and Celeb-DF V2 datasets and achieved 99.7%, 99.8% and 98.01% accuracy to identify fraudulent video posts.
A Secure IoT-Enabled Machine Learning Framework for Brain Tumor Classification and Prediction Using MR Image Data
Authors: Satyaprakash Swain Et al.
Published: January 2024

Brain tumor identification and classification have improved due to the quick development of medical imaging and machine learning technology. This paper presents two approaches to secure image transmission in the Internet of Things (IoT): a comprehensive approach for brain tumor prediction and classification using a strong IoT infrastructure with cutting-edge machine learning models and a security approach with the implementation of the AES-ECC hybrid model in the MQTT communication protocol for image data encryption and decryption. We make use of a heterogeneous dataset that we sourced from the Kaggle Dataset platform, which includes four different types of MRI scans of brain tumors from 2870 patients. Our proposed methodology starts with the safe acquisition and transfer of MRI images through an IoT protocol infrastructure to a cloud-based platform. CNN, DenseNet, ResNet and G-Net are some of the sophisticated machine learning models that are used to interpret and analyse these pictures. The computer is trained to identify photos of brain tumors into the appropriate groups using all above four models. According to the data, our suggested CNN model performs better than the others, obtaining an amazing 89% accuracy rate. Nonetheless, we want to achieve even greater improvement in forecast precision by utilising ensemble boosting methodologies. Boosting the CNN model with Ada-Boost, Gradient Boost, XG Boost and Cat Boost algorithms aims to maximize prediction performance. We find that the CNN algorithm combined with XG Boost outperforms all other ensemble methods with an amazing accuracy rate of 97%. This encouraging result highlights how combining cutting-edge machine learning algorithms with IoT infrastructure can lead to better brain tumor classification and prognosis. The creation of more precise and effective diagnostic instruments for the identification of brain tumors is one of our study's many implications, one that will ultimately improve patient outcomes and the healthcare industry.
Air Quality Data Acquisition Through Temporal Data Modeling
Authors: Lalit Gandhi Et al.
Published: January 2024

The paper presents a novel temporal data model for acquiring air quality data. The proposed model uses the free and open-source Postgres database with tuple time stamping, resulting in rapid and effective data extraction. The conventional relational database SQL server for data extraction is used in this paper as the foundation for an air quality temporal database model. The air quality data set collected for MDU Rohtak (source: Central Control Room for Air Quality Management - All India) is utilized to test and determine the outcomes of the proposed model. Valid time is used as the time dimension for capturing data values, together with tuple timestamping. The Postgres "tsrange" datatype is used to record the timestamp. To eliminate data duplication, the dataset is cleaned up and normalized. Using the air quality temporal data model, AQI data at various time points is effectively extracted. The present model data extraction is compared with the conventional relational data model using SQL Server database. The air quality data extraction process takes a remarkably minimal amount of time, with an average of 23% lesser time than SQL server database. It takes only a few milliseconds to extract the AQI data, and the results of the experiment are ready immediately after that. The suggested model is helpful in decision-making and offers decision-makers useful information and the tool they need to enhance air quality. The results are relevant to the study of the AQI components by different pollution management agencies like central and state pollution control boards, national green tribunal and Ministry of the Environment for efficient air pollution control measures.
Segmenting Roads from Aerial Images: A Deep Learning Approach Using Multi-Scale Analysis
Authors: Nadeem Akhtar Et al.
Published: January 2024

Road map generation requires frequent map updates due to the irregular infrastructural changes. Updating a manual road map is a lengthy process, whereas using aerial or remote sensing (RS) requires less time for the update. However, road extraction becomes more complex due to the similar texture appearance of building top roofs, shadows, and occlusion due to trees. The occluded roads appear as discontinuous road patch in segmented image of updated maps. In this paper, we propose a deep learning method that uses multi-scale analysis for road feature extraction. The dilated inception module (DI) in the up and down sampling paths of network extracts the local and global texture patterns of the road. Furthermore, we also utilize the pyramid pooling module (PP) which has average and max pooling to study the global contextual information under the shadow regions. In the proposed architecture, first, the road in the aerial images is segmented along with the tiny non-road segments. Next, the post processing, which exploits the geometrical shape features, is utilized for filtering the tiny non-road noises. The performance of proposed network is validated on using the publicly available Massachusetts road data by comparing with the other models available in literature.
Development of IoT Based Industrial Automation SCADA System
Authors: Rashmi Keote Et al.
Published: January 2024

The term "Supervisory Control and Data Acquisition" is abbreviated as SCADA. Basically, SCADA systems are utilized in industry to automate processes. Process automation includes both controlling and acquiring data. This study presents an Internet of Things (IoT)-based SCADA system for a remote BTS station utilizing an Arduino IoT Cloud and ESP32. In order to measure important parameters of interest, temperature, gas, float (fluid level), are programmed. Using a Wi-Fi network communication channel, data is processed and parsed before being sent to the Arduino IoT Cloud. On the Arduino IoT Cloud, a dashboard with widgets is built to track and manage the system. In addition, a mobile application is put into use to facilitate remote control and monitoring. Without physically visiting the site, the plant manager may check the status of a parameter and operate industrial equipment as needed through the app. As a result, this technology lessens the need for regular industry monitoring and the associated maintenance costs. As a result, this technology improves the Plant's performance and production.
Handwritten OCR for Indic Scripts: A Comprehensive Overview of Machine Learning and Deep Learning Techniques
Authors: Shaik Moinuddin Ahmed Et al.
Published: January 2024

The potential uses of cursive optical character recognition, commonly known as OCR, in a number of industries, particularly document digitization, archiving, even language preservation, have attracted a lot of interest lately. In the framework of optical character recognition (OCR), the goal of this research is to provide a thorough understanding of both cutting-edge methods and the unique difficulties presented by Indic scripts. A thorough literature search was conducted in order to conduct this study, during which time relevant publications, conference proceedings, and scientific files were looked for up to the year 2023. As a consequence of the inclusion criteria that were developed to concentrate on studies only addressing Handwritten OCR on Indic scripts, 53 research publications were chosen as the process's outcome. The review provides a thorough analysis of the methodology and approaches employed in the chosen study. Deep neural networks, conventional feature-based methods, machine learning techniques, and hybrid systems have all been investigated as viable answers to the problem of effectively deciphering Indian scripts, because they are famously challenging to write. To operate, these systems require pre-processing techniques, segmentation schemes, and language models. The outcomes of this methodical examination demonstrate that despite the fact that Hand Scanning for Indic script has advanced significantly, room still exists for advancement. Future research could focus on developing trustworthy models that can handle a range of writing styles and enhance accuracy using less-studied Indic scripts. This profession may advance with the creation of collected datasets and defined standards.
Construction of Communication Protocol Using Ring-LWE-Based Homomorphic Encryption in Iot-Cloud Environment
Authors: Ch Jayanth Babu Et al.
Published: January 2024

The rapid development of wireless communication and sensor networks is the basis for forming an Internet of things(IoT) infrastructure. In IoT-based applications, the cryptographic encryption and access control at cloud must be robust to withstand current attacks. The majority of security protocols are based on integer factorization and discrete logarithm problems, which are proved vulnerable to quantum attacks. In this paper, we proposed a scheme for the security and privacy of the user data in a cloud environment. Various types of homomorphic encryption schemes are studied for data privacy in the cloud. The Ring-LWE-based encryption scheme is presented for privacy protection in the cloud which meets the homomorphic properties. The scheme is analysed for security, privacy, reduced messaging overhead and computation overhead. The objective of this paper is to Design and Construct a Ring-LWE-based homomorphic encryption(HE) communication protocol for authenticated user message encryption in a IoT cloud computing environment. The evaluation function in holomorphic encryption defined

based on Ring-LWE encryption for a practical sharing-enabled cloud storage. Then, formally proving the security of the proposed protocol for classical and quantum attacks in cloud environment like Manin-the-middle (MITM) attack, Denial of Service (DoS) and Replay Attack.
Stock Prediction using GAN and Sentiment Analysis
Authors: Shubham Shah Et al.
Published: January 2024

Tweet sentiment analysis can be used to forecast stock market movements. Examining the attitude expressed in tweets about a company or industry might reveal more about how the public perceives and thinks about it. This information can then be utilised to forecast potential changes in the stock price of the firm or industry. This paper proposes a method for forecasting the stock market based on sentiment analysis of Twitter-sourced messages. To create the dataset, information about various companies via the Twitter API is mapped and linked with their stock prices. This paper employs a Generative Adversarial Networks-based framework that integrates three GAN models with Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). To evaluate and compare the efficacy of the proposed model, the Root Mean Square Error (RMSE) is employed as a metric. The stock price and sentiment score of each company were individually assessed to determine which of the three GAN models generated the most accurate outcomes.
Comparative Analysis of Privacy Preservation Mechanism: Assessing Trustworthy Cloud Services with a Hybrid Framework and Swarm Intelligence
Authors: Himani Saini Et al.
Published: January 2024

Cloud computing has emerged as a prominent field in modern computational technology, offering diverse services and resources. However, it has also raised pressing concerns regarding data privacy and the trustworthiness of cloud service providers. Previous works have grappled with these challenges, but many have fallen short in providing comprehensive solutions. In this context, this research proposes a novel framework designed to address the issues of maintaining data privacy and fostering trust in cloud computing services. The primary objective of this work is to develop a robust and integrated solution that safeguards sensitive data and enhances trust in cloud service providers. The proposed architecture encompasses a series of key components, including data collection and preprocessing with k-anonymity, trust generation using the Firefly Algorithm, Ant Colony Optimization for task scheduling and resource allocation, hybrid framework integration, and privacy-preserving computation. The scientific contribution of this work lies in the integration of multiple optimization techniques, such as the Firefly Algorithm and Ant Colony Optimization, to select reliable cloud service providers while considering trust factors and task/resource allocation. Furthermore, the proposed framework ensures data privacy through k-anonymity compliance, dynamic resource allocation, and privacy-preserving computation techniques such as differential privacy and homomorphic encryption. The outcomes of this research provide a comprehensive solution to the complex challenges of data privacy and trust in cloud computing services. By combining these techniques into a hybrid framework, this work contributes to the advancement of secure and effective cloud-based operations, offering a substantial step forward in addressing the critical issues faced by organizations and individuals in an increasingly interconnected digital landscape.
Effective Policies to Protect Information Resources: Identity Management and Organizational Access Studies X
Authors: Rudolf Sinaga Et al.
Published: January 2024

Information resources are an important asset for organizations, both government and private. Information resources can be data, systems, or applications used to support organizational activities. Therefore, it is important to protect information resources from various threats, such as cyberattacks, misuse, or loss. Identity and access management (IAM) is one of the efforts to protect information resources. IAM is the process of managing the identity of users and their access to information resources. Effective IAM can help organizations ensure that only authorized users can access information resources, detect and prevent unauthorized access to information resources, and facilitate user identity management. This study aims to examine effective IAM policies to protect the information resources of Organization X. The results show that an effective IAM policy must meet the following criteria: comprehensive; IAM policies should cover all aspects of identity and access management, from user identity creation to access review, risk-oriented; IAM policies should be designed to minimize risks to information resources, Flexible; IAM policies must be adaptable to the changing needs of the organization, Management support; IAM policies must be supported by the organization's management to be implemented effectively. Based on the results of this study, it is advisable to ensure that information security policies remain effective over time, so organizations should carry out regular evaluations to protect their information resources.
Optimization Algorithms with Machine Learning to Improve Security of Internet of Things
Authors: Zakiya Manzoor Khan, Et al.
Published: January 2024

The IOT network traffic classification is the approach which helps to analyse IOT network traffic. The network traffic analysis can to various network activities. The network traffic analysis process has various steps which include data input, pre-processing, feature extraction, classification and performance analysis. The various machine learning algorithms is proposed in the previous years but those algorithms are unable to achieve high accuracy. The algorithms which are already proposed is unable to extract features from the dataset. To propose algorithm which can extract features from the dataset and achieve high accuracy for the network traffic classification is the motivation this research work. To achieve high accuracy hybrid optimization algorithm is proposed in this paper which is the combination of genetic and PSO algorithm. The hybrid optimization algorithm extract features and later it will be classified using Random Forest. The proposed model is implemented in python and results is achieved in terms of accuracy, precision, recall.
Genomic Prediction yield of Oryza Sativa Using Machine Learning and Deep Learning
Authors: B.Kiranmai, Et al.
Published: January 2024

Breeding value prediction plays a crucial role in improving crop breeding by accurately anticipating the genetic value of phenotypic traits. However, existing methods often lack accuracy in predicting genomic estimated breeding values (GEBVs) and do not sufficiently focus on regression-based approaches. To address this challenge, we propose a novel methodology for genomic prediction of phenotypic trait yield using a two-level classification approach.

In the first phase of our methodology, termed Genomic Prediction of Phenotypic Trait Yield using Two-Level Classification , we perform classification on the biological sequences of a subpopulation of Oryza sativa (rice). These sequences are then clustered based on the leaves of a phylogenetic tree, utilizing the Unweighted Pair Group Method with Arithmetic Mean (UPGMA) algorithm.

In the second phase, we employ machine learning techniques such as Multiple Linear Regression (MLR) to predict GEBVs and achieved remarkable accuracy ranging from 99 to 100 percent on the subpopulations of rice. By integrating the phylogenetic clustering approach and MLR-based prediction, our methodology demonstrates promising results for accurately predicting GEBVs, which can be passed on as genetic value to subsequent generations of offspring.

This research highlights the potential of our two-level classification approach in improving the accuracy of genomic prediction for phenotypic trait yield in rice breeding programs. The findings contribute to the development of enhanced breeding strategies, enabling more efficient selection of desired traits and facilitating the development of genetically improved crop varieties.
Edge Information based Seed Placement Guidance to Single Seeded Region Growing Algorithm
Authors: Rajendra V. Patil Et al.
Published: January 2024

The split of the picture into regions that represent various items or parts of an object has been referred to as image segmentation. An image is split with the objective of studying each object and getting some higher-level information. A wide range segmentation approaches are either region-based or edge-based. By using likeness criteria among candidate sets of pixels, the region-based division split an image into distinct regions of related pixels. The choice of the starting seed points is the primary challenge for this algorithm in order to achieve attractive image subdivision. The region expansion process is an extremely effective and trusted approach for segmenting images. In this paper, an autonomous method based on edge information is proposed to predict initial region development seed for single seeded region growing algorithm. Illumination invariant Log Gabor wavelet based phase congruency method is employed for edge detection. Line segment processing technique is utilized to determine region-growing seed for single seeded region growing algorithm.
Non-Isolated Interleaved Bidirectional DC-DC Converter for Electric Vehicles : A Review
Authors: R. Tamilamuthan Et al.
Published: January 2024

Power conversion plays a significant part in the functioning of electric vehicle systems. During the design process of the electric vehicle, careful consideration was given to the implementation of a multiple energy power management system in order to optimise power consumption efficiency. Addition of battery and capacitor provides the quick acceleration and deceleration like normal IC engine. The efficient DC-DC power conversion was obtained by different converters. Every converter had different specifications and applications. The optimized power transmission was depending on selection of converter. The ripple was produce by achieving high power conversion system. The efficient power management system is obtained by selection of converter with ripple reduction.
Role of Artificial Intelligence, Automation, and Machine Learning in Sustainable Plastics Packaging markets: Progress, Trends, and Directions
Authors: Yarlagadda Sri Charan
Published: January 2024

The optimisation of manufacturing processes in terms of resource consumption, waste minimization, and pollutant emissions is gaining prominence, especially in small and medium-sized enterprises (SMEs). The advent of digital technology and the subsequent explosion in data volume is another key factor. There is great potential in the data collected from a wide variety of devices and systems, which is why even smaller businesses require access to clever, dynamic analytic models. Sustainable packaging solutions are gaining significance as the world struggles to address global environmental issues. These solutions are being developed with a growing contribution from artificial intelligence (AI). Artificial intelligence is being used to create sustainable and environmentally friendly packaging. Artificial intelligence (AI)-driven technology can be used, for instance, to determine which packing materials and designs are optimal for a given product. Artificial intelligence can also be used to determine the best methods of packaging, such as those that make the most of recyclable materials or that optimise packaging lines. The term "plastic production automation" refers to the use of automated systems and machines in the production of plastic goods. Computer-aided design (CAD), robots, and other cutting-edge technologies are used to automate and optimise production. In this article, we describe the findings of a study that aimed to determine whether or not small and medium-sized enterprises (SMEs) in the plastics processing industry may benefit from the use of machine learning methods in order to optimise energy consumption and reduce the number of wrongly made plastic parts. The machine data in a plastics manufacturing facility for the automobile industry were recorded and analysed in terms of the material and energy fluxes for this purpose. To find areas for optimisation, these data were trained using machine learning techniques. The project also sought to solve the challenge of analysing manufacturing processes with significant non-linearities and time-invariant behaviour by employing Big Data techniques and self-learning controls. Machine learning can help with this if there is enough data to train the system.
Identification of indicators used to assess needs for telemedicine consultations in various profiles of medical care
Authors: Samsonova Elena S. , Mikhailov Ilya A. , Omelyanovsky Vitaly V. , Avksentieva Maria V. , Zheleznyakova Inna A. , Lebedenko Georgy G.
Published: January 2024

BACKGROUND: A unified system for assessing the results and real contributions of telemedicine consultations to improving medical care quality in the healthcare system of the Russian Federation has not yet been developed.
AIM: To develop a system of indicators for differentiated assessment of the needs for telemedicine consultations in the provision of medical care.
MATERIALS AND METHODS: In the first stage, reports on the results of on-site activities of national medical research centers in regions of the Russian Federation and their annual public reports (2020–2022) were analyzed to identify indicators that determine the need for telemedicine consultations. The identified indicators were clarified and validated in an open interview with the representatives of the national medical research centers. In the second stage, the value of each indicator was determined based on the expert survey: 18 experts assessed each indicator on a scale of 1–5. Then, the weight coefficient of each indicator was calculated for their subsequent use in planning the coverage of telemedicine consultations.
RESULTS: Three groups of indicators that determined the need for telemedicine consultations for different medical care profiles were as follows: (1) indicators that affect the planned volumes of telemedicine consultations, (2) indicators that characterize the efficiency and effectiveness of telemedicine consultations, and (3) indicators that characterize the validity of requests for telemedicine consultations. Group 1 included indicators of lethality, disability, hospital mortality, frequency of emergency/urgent consultations, and frequency of consultations of patients requiring intensive care. Group 2 included indicators for assessing the effectiveness and efficiency of telemedicine consultations, both subjective (result satisfaction) and objective (number of positive and negative treatment and hospitalization outcomes for cases that received where telemedicine consultations). Group 3 included indicators that characterize the validity of requests for telemedicine consultations: thoroughness of a patient’s examination before a telemedicine consultation and accuracy of the diagnosis. The weight coefficients of group 1 indicators ranged from 0.05 to 1.61 and varied for different profiles.
CONCLUSION: A system of indicators was proposed for the differentiated assessment of the needs for telemedicine consultations when providing medical care.
Corporate Legal Relations in a Commercial Organization: Analysis of the Essence and Legal Nature
Authors: Minakova Irina R.
Published: January 2024

The article analyzes corporate relations according to the current classification groups of civil law relations. The author relates corporate relations to different types of classification groups while determining their essential features. The analysis reveals that the legal nature of corporate relations is independent.
Problems of the Theory of Interpretation of the Will of the Legislator
Authors: Zhigitov Alexandr A.
Published: January 2024

This article discusses the theory of interpretation of law that considers the will of the legislator the main object of interpretation. The main purpose is to recheck the axiomatic statements of this theory of interpretation about the will of the legislator and an adequate assessment of its scientific potential from the standpoint of hermeneutics and phenomenology. To achieve this goal, the main postulates of the theory of interpretation of the will of the legislator are considered, and its problem areas and heuristic characteristics are analyzed. In particular, insufficient and incomplete justification is fixed, namely the real presence of the figure of the legislator or the existence of the author of the legal norm, the possibility of expressing the collective will, and the identity between the implanted meaning available in the text and the understood. Arguments in favor of a critical understanding of the theory of interpretation of the will of the legislator are investigated both in applied and theoretical manners and in the private and public spheres of relations. The conclusion emphasizes the limited cognitive significance of the theory of the will of the legislator in the process of interpreting law.
Criminal Responsibility for Violation of Regulatory Legislation
Authors: Guzeeva Olga S.
Published: January 2024

The article examines the problems of the constitutional legitimacy of criminal law norms with a blanket disposition. The norms of the criminal law on liability for violation of regulatory prescriptions formulate the corpus delicti using a blanket method of describing it, referring to a violation of sectoral or cross-sectoral regulatory norms or institutions. The design and application of criminal law norms with blanket dispositions, repeatedly legitimized by the Constitutional Court of the Russian Federation, still pose particular theoretical and applied problems. Their analysis reveals the following conclusions.
1) When there is a mismatch between the blanket disposition and blanket legislation, it is necessary to distinguish between cases of a true absence of regulatory norms from the imaginary uncertainty of blanket criminal law. This can occur in two cases: a) when the law describes the act as “unlawful”, provided that the regulatory legislation does not know the legal forms of such behavior (such rules cannot be considered blanket); b) when there are no definitions of the concepts used in the text of the Criminal Code of the Russian Federation in the regulatory acts (such a regulatory deficiency can be overcome through legal or special technical advice).
2) The blanket disposition may contain or imply references exclusively to regulatory legal acts, and not to technical norms and rules and provisions of these acts that contain mandatory and not recommended prescriptions. Furthermore, the normative legal acts may occupy a different place in the hierarchical system of sources of law — from international treaties to regulations. Two circumstances are noted in this regard: a) a constitutionally admissible blanket reference to by-laws does not exclude situations in which regulations contradict laws and formulate prohibitions and restrictions not provided for by them; in this regard, the law enforcement officer must ensure that the bylaw blanket content of the norm is legitimate, both in terms of content and form; b) references in the blanket part of the criminal legal norm to regional normative legal acts is considered constitutionally justified to the extent that the principle of equality of citizens before the law is guaranteed: in the material composition — an indication of the consequences that are the main criminogenic sign, regardless of the type violated rules, and in formal offenses — the hierarchical and consistent nature of the regulatory legislation adopted on the joint jurisdiction of the federation and its subjects.
Problems of Legal Regulation of Transactions Not Subject to Value-Added Tax
Authors: Paul Aleksei G.
Published: January 2024

The paper analyzes the features of the legal regulation of transactions not subject to value added tax, analyzes the problems that arise here, and provides a way to resolve them.
Analysis of the early results of using the direct anterior approach with a skin incision “bikini” in primary hip arthroplasty
Authors: Eremin Ivan K. , Daniliyants Armen A. , Ogarev Egor V. , Zagorodniy Nikolay V.
Published: January 2024

BACKGROUND: The use of less invasive techniques in hip arthroplasty is on the rise, which has led to an increased interest in direct anterior access and contributed to a significant expansion of its use over the past two decades. From an anatomical point of view, the use of direct anterior access in hip arthroplasty is associated with less soft tissue trauma.
AIM: To evaluate the results of primary hip arthroplasty using direct anterior access with a "bikini" skin incision.
MATERIAL AND METHODS: 163 patients with coxarthrosis were enrolled in the study, including 71 men and 92 women, who were then randomised into 2 groups: group 1 (comparison) — 78 patients in whom a standard (lateral) access was used for primary hip arthroplasty, and group 2 (main) — 85 patients in whom a direct anterior access was used for primary hip arthroplasty. To study the effectiveness of the proposed approach to hip arthroplasty, we used the results obtained by dynamic assessment of complaints (severity of pain syndrome) and functional status of the affected joint using Harris Hip Score and Western Ontario and McMaster Universities osteoarthritis Index WOMAC scales. When analysing the surgical treatment safety, the incidence of postoperative complications was taken into account.
RESULTS: In total, 3 cases of complications (3.9%) were noted in the comparison group, whereas in the main group the number of complications in the early postoperative period after endoprosthesis was lower — 1 case (1.2%). The assessment of inpatient treatment duration showed that in the comparison group the value of this index was equal to 5–6 days, whereas in the main group it was lower, being 2–3 days. Studying the dynamics of the pain syndrome assessment index by the patients according to the visual analogue scale showed that a day after the operation the index value in group 2 was 7.94±0.41 points which was statistically significantly lower (p 0.05) than the corresponding index in group 1 — 8.21±0.39 points.
CONCLUSION: In the postoperative period, the use of direct anterior access in hip arthroplasty is associated with a lower severity of pain syndrome, faster recovery of hip joint functionality, and shorter in-patient stay compared with anterolateral access.
Revision surgeries following ankle arthrodesis fixed with retrograde intramedullary nails affected by mechanical complications
Authors: Grazhdanov Konstantin A. , Zuev Pavel P. , Kauts Oleg A. , Baratov Andrey V. , Norkin Igor A.
Published: January 2024

BACKGROUND: 25 percent of all musculoskeletal diseases that involve joints of the lower extremities accrues to ankle arthrosis. Late stages of this disease are indications for ankle arthrodesis. To date, surgeons who perform ankle arthrodesis do not agree about both the treatment technique for ankle articular surfaces and the method of their fixation, but other studies and our own observations suggest that 8 to 20 percent of the surgeries performed with retrograde locking rods lead to revision interventions due to the disorder of ankle ankylosis formation caused by mechanical complications associated with the implanted fixators (aseptic loosening of their parts, stress fracture of the intramedullary nail or locking screws, peri-implant tibia fracture).
CLINICAL CASES DESCRIPTION: The aim of this work was to study and demonstrate the results of revision surgical interventions performed for failed ankylosis after ankle arthrodesis with intramedullary rod fixation against the development of mechanical complications. Clinical observations of revision interventions for failed ankylosis of the ankle joint against the complications due to mechanical origin using a retrograde blockable intramedullary rod as the primary fixator were carried out. Our work presents the technology of revision surgical interventions in case of compromised processes of ankle joint bone block formation associated with aseptic loosening of the structural elements, stress fracture of the intramedullary rod and blocking screws, tibial peri-implant fracture.
CONCLUSION: Revision surgery for complications due to mechanical origin after ankle arthrodesis with intramedullary rod fixation requires an individual approach in each clinical case.
Joint-sparing surgery in patients with acetabular dysplasia complicated by sphericity of the femoral head
Authors: Teplenky Mikhail P. , Bunov Vyacheslav S. , Fozilov Jonibek Т.
Published: January 2024

BACKGROUND: Acetabular dysplasia combined with ischaemic deformity of the femoral head contributes to rapid progression of coxarthrosis in young adults. The issues of treatment tactics for this pathology remain unresolved.
AIM: Analysis of the immediate and mid-term results of extra-articular and intra-articular combined reconstructive interventions performed in patients with acetabular dysplasia combined with femoral head sphericity disorder.
MATERIALS AND METHODS: Treatment outcomes were studied in 23 patients with acetabular dysplasia associated with femoral head deformity. The average age at the operation was 19.0±1.2 years (14–34). The mean follow-up period was 3.4±0.4 years. Anatomical and functional results were evaluated according to the criteria of D’Aubigne-Postel, Tonnis and according to the Russian Scientific Center “Restorative Traumatology and Orthopedics” system. Extra-articular reconstructive interventions on both articular components were performed in 14 patients. In 9 cases, intra-articular operations were performed.
RESULTS: Functional results after extra-articular interventions: good (15–17 points) — 8 joints, satisfactory (12–14 points) — 6 joints. The average score was 5.05±0.06 points. Functional results after intra-articular interventions: good (15 points) — 3 joints, satisfactory (13–14 points) — 5 joints, unsatisfactory (7 points) — 1 joint. The average score is 4.3±0.07 points. Rapid progression of arthrosis was noted in one case. In other cases, the degree of arthrosis did not change. Anatomical results of treatment after extra-articular interventions: good (2.6±0.03) — 8 joints, satisfactory (1.95±0.06) — 6 joints. Anatomical results of treatment after intra-articular interventions: good (2.55±0.01) — 2 joints, satisfactory (2.1±0.04) –— 6 joints, unsatisfactory (1.6) — 1 joint.
CONCLUSION: Differential application of extra-articular and intra-articular joint-saving interventions makes it possible to slow down the progression of the pathological process in the joint in conditions of sphericity disturbance of the articular surfaces, which makes it possible to consider them as a temporary alternative to endoprosthesis in adolescents and young adults.
Diagnostic value of magnetic resonance imaging using contrast enhancement, high-field tomographs in the detection of Morton’s neuroma
Authors: Bolshakova Daria A. , Kardanov Andrey A. , Maysigov Musa N. , Korolev Andrey V.
Published: January 2024

BACKGROUND: Magnetic resonance imaging (MRI) is becoming more and more widespread in our country, being one of the leading methods in diagnosing injuries and diseases of the ankle joint and foot. From year to year the number of tomographs and studies is steadily increasing, and the absence of ionising radiation, the possibility of visualising both soft tissue and bone structures makes this procedure more attractive for patients. Reproducibility of the study, the possibility of obtaining a second opinion distinguish MRI as a leading tool in the diagnosis of neuromas. Despite its relatively frequent use, the significance of MRI diagnosis in patients with suspected intertarsal neuroma using paramagnetic imaging, 3 tesla and 1.5 tesla MRI has not been fully reported.
СLINICAL CASES DESCRIPTION: The article presents a series of illustrative studies of patients with intermetatarsal neuroma: using paramagnetic imaging tools, without the use of paramagnetic imaging tools, with large slice thickness, with high-field tomography.
CONCLUSION: Despite the obvious advantages of using high-field imaging in the diagnosis of peripheral nervous system diseases, in our opinion, the informative value of this study in the routine diagnosis of Morton's neuroma does not exceed that of the currently most common 1.5 tesla MRI.
Features of treatment of hip fractures in children with severe forms of cerebral palsy
Authors: Tomov Akhmed D. , Gabidullina Lilia F. , Shlyapnikova Natalya S. , Hachubarov Has-Magomed H. , Popkov Dmitriy A.
Published: January 2024

BACKGROUND: The increased incidence of fractures in children with severe spastic paralysis due to low-energy trauma is associated with poor bone mineral density, lack of adequate verticalization, and use of antiepileptic drugs. The most frequent localization of fractures is the lower extremities, especially the femur. The increased frequency of traumatization at this level correlates with a high level of impairment of global motor functions — levels IV–V according to the Gross Motor Function Classification System.
CLINICAL CASE DESCRIPTION: Surgical treatment of a femur fracture in a child with cerebral palsy was performed. Given the ineffectiveness of repositioning due to spasticity of the adductor and iliopsoas muscles, one-stage repositioning of the fragments, myotomy and osteosynthesis were performed. Treatment of fractures in 3 more children with cerebral palsy was also described in the article. The results were evaluated at least 6 months after the end of treatment. Surgical treatment is preferable, providing sufficient stabilization of the fracture, reducing the time of plaster immobilization and the probability of associated risks, as well as preventing delayed consolidation and nonunions.
CONCLUSION: Elastic intramedullary reinforcement used in the treatment of osteopenic fractures is a method of preventing fracture recurrence in the distant period or preventing significant fragment displacement.
Osteochondritis dissecants of the femur in children. Treatment. Modern tendencess
Authors: Yeltsin Alexander G. , Ivanov Iaroslav A. , Mininkov Dmitry S. , Gushchina Daria A.
Published: January 2024

Osteochondritis dissecants is a pathological condition characterized by the manifestation of subchondral osteonecrosis, which subsequently leads to the separation of the cartilage plate from the femoral condyle. It is a serious condition that can significantly affect patients' quality of life. Despite a significant amount of research, there is still a need to better understand this pathology and to develop effective diagnostic and treatment strategies to achieve optimal clinical outcomes. The aim of this work was to analyze the literature on osteochondritis dissecants of the knee joint and to substantiate modern treatment methods for this disease, as well as to find out the preferred treatment options depending on the age group of patients. Literature sources were analyzed. The search was carried out in the databases RSCI, NCBI Pubmed, Medline for the last 10 years. Osteochondritis dissecants is a disease requiring a modern approach to treatment. With the advent of new technologies and techniques, various therapeutic options have emerged, such as collagen membrane replacement of the defect, mosaic chondroplasty, etc. However, the question of the preferred treatment method remains relevant, especially in view of the age-related characteristics of patients. An individualized approach, taking into account their age, stage of injury and clinical features, is an important aspect for effective treatment of osteochondritis dissecants. Further research and clinical follow-up are needed to better define the most effective treatment strategies and prevent complications.
VII Congress of Traumatologists and Orthopedists of the Far Eastern Federal District
Authors: Shigaev Eugene S. , Davydov Sergey O. , Miromanov Alexander M.
Published: January 2024

The brief message provides information about the VII Congress of Traumatologists and Orthopedists of the Far Eastern Federal District held on August 25–26, 2023 in Ulan-Ude together with the interregional scientific and practical conference with international participation “Topical Issues of Traumatology and Orthopedics of the Far East”. The work of the VII Congress was held in a dynamic and fruitful format, which made it possible to achieve the goals set — modern issues of prevention, diagnosis, treatment of injuries, diseases and conditions of the musculoskeletal system, medical rehabilitation of patients were considered, which made it possible to improve existing or master new knowledge, skills, and abilities and the competence of students to carry out high-quality professional activities in the specialty “Traumatology and Orthopedics”, which will ultimately form labor functions in accordance with the professional standard “traumatologist-orthopedist”. At the end of the Congress, the delegates adopted a resolution reflecting the most problematic aspects of the service, the solution of which will significantly increase the availability and quality of the provision of traumatological and orthopedic care in the Far Eastern Federal District.