Classification Issues Research Articles

The concept of a legal person of public law in the doctrine and legislation of Georgia and Ukraine

The article analyzes the doctrine and legislation of Georgia and Ukraine on the legal status of legal entities of public law. The issue of classification of legal entities under the Civil Code of Georgia and Ukraine is studied. According to the legislation of Georgia, only three entities can establish legal entities of public law: the government acting on behalf of the state, a regional authority, in cases provided for by law, and a municipality; Tbilisi City Assembly. The author considers the concept, legal nature, and types of legal entities of public law in Georgia. The article examines the legal status of municipalities, the legal status of higher educational institutions, religious organizations as legal entities of public law in Georgia. The author analyzes the provisions of the legislation of Georgia on the procedure for the establishment, structure of bodies, and legal regime of property of municipalities. The structure of local self-government bodies consists of the following bodies: municipal assembly, mayor (gamgebeli). The property separation of a municipality as a legal entity of public law consists of property that is fixed by law for the municipality, property that is transferred by the state into the ownership of the municipality on a free basis. The article analyzes the legal status of a higher educational institution as a legal entity of public law. The author examines the structure, legal regime of property, the procedure for the transfer and use of property of a higher educational institution. The governing bodies of a higher educational institution include the following: academic council, council of representatives, rector, head of administration and quality control department. The property of a higher educational institution founded by the state consists of property transferred to it by state, legal and natural persons, as well as property acquired by it at its own expense. The author analyzes doctrinal and legislative sources regarding the legal regulation of religious organizations in Georgia. Religious organizations in Georgia can carry out activities in two main organizational and legal forms: in the form of a non-entrepreneurial legal entity, in the form of a legal entity of public law.

Characterization of path Loss in the tetra band for smart city project using sparse convAutoNet-driven regression random machines

ABSTRACT TETRA communication system, a flexible system, effectively and optimally transfers voice and data; however, path loss in the tetra band was affected by landscape contours, environmental factors, and propagation medium due to an inadequate distance between the two ends of the cable and the height and position of the transmitter and receiver. Hence, a novel, Characterization of Path Loss in the Tetra Band for Smart City Project is developed. It uses a Sparse ConvAutoNet-driven Regression Random Machines approach to improve antenna quality by mitigating signals coming from certain low elevation directions and moving the antenna away from reflection objects. Traditional techniques have shown that higher frequencies do not provide a longer range, affecting the geographic coverage. Furthermore, previous methods suffered from slow cellular network and data transfer speeds due to classification and clustering issues in pattern recognition. To overcome these limitations, Support Vector Regression (SVR) and Regression Random Machines (RRMs) are employed, utilising a free kernel choice function to enhance the stability of the cellular network and data transfer, eliminating classification and clustering issues. As a result, the suggested model results in a lower RMSE of 1.05, lower MSE of 0.9, higher R-squared of 0.97, lower MAPE of 0.037.

Патогенетические и клинические аспекты неоваскулярной и факогенной вторичной (рефрактерной) глаукомы

The purpose of this review is to consider neovascular and phacogenic types of secondary glaucoma that ophthalmologists have to deal with in their daily clinical practice for effective identification of the risk group, for the purpose of prevention, slowing down progression, as well as maintaining visual functions and quality of life of the patient. The review was written by using the domestic RSCI database and the international PubMed database, the search keywords were «secondary glaucoma», «neovascular glaucoma», «phacogenic glaucoma», «phacotopic glaucoma», «phacomorphic glaucoma», «phacotoxic glaucoma», «phacolytic glaucoma». An analysis of 44 literature sources was performed. The search depth was 23 years (2001–2024). The article highlights the issues of classification of secondary phacogenic and neovascular glaucoma. It was revealed that practicing opthalmologists more often use the clinical classification of secondary glaucomas proposed by A.P. Nesterov. Emphasis is placed on the issues of pathogenesis, clinical presentation and differential diagnostic features of neovascular and phacogenic glaucoma, which are distinguished by a variety of etiopathogenetic mechanisms of development and clinical manifestations. Key words: secondary glaucoma, classification of secondary glaucoma, neovascular glaucoma, phacogenic glaucoma

Creating Refined Datasets for Better Chaos Detection.

In recent years, the analysis of signal properties (especially biomedical signals) has become an important research direction. One interesting feature of signals is their potential to be chaotic. This article concerns the issues of classification of real signals or synthetic ones in the context of detecting chaotic properties. In previous works, datasets of synthetic signals were created based on well-known chaotic and non-chaotic dynamical systems. They were published and used to train classifiers. This paper extends the previous studies and proposes a method for obtaining/extracting signals to force classifiers to learn to detect chaos. The proposed method allows the generation of groups of signals with similar initial conditions. The property of chaotic dynamical systems was used here, which consists of the strong dependence of the signal courses on a small change in the initial conditions. This method is based on reconstructing multidimensional phase space and data clustering. An additional goal of the work is to create referential datasets with so-called refined signals using the described method and to make them publicly available. The usefulness of the new datasets was confirmed during a simple experiment with the usage of the LSTM neural network.

ADET MODEL: Real time autism detection via eye tracking model using retinal scan images.

Deficits in concentration with social stimuli are more common in children affected by autism spectrum disorder (ASD). Developing visual attention is one of the most vital elements for detecting autism. Eye tracking technology is a potential method to identify an early autism biomarker based on children's abnormal visual patterns. Eye tracking retinal scan path images can be generated by eyeball movement during the time of watching the screen and capture the eye projection sequences, which helps to analyze the behavior of the children. The Shi-Tomasi corner detection methodology uses open CV to identify the corners of the eye gaze movement in the images. In the proposed ADET model, the corner detection-based vision transformer (CD-ViT) technique is utilized to diagnose autism at an early stage. Generally, the transformer model divides the input images into patches, which can be fed into the transformer encoder process. The vision transformer is fine-tuned to resolve binary classification issues once the features are extracted via remora optimization. Specifically, the vision transformer model acts as the cornerstone of the proposed work with the help of the corner detection technique. This study uses a dataset with 547 eye-tracking retinal scan path images for both autism and non-autistic children. Experimental results show that the suggested ADET frameworkachieves a better classification accuracy of 38.31%, 23.71%, 13.01%, 1.56%, 18.26%, and 44.56% than RM3ASD, MLP, SVM, CNN, SVM, and our proposed ADET methods. This screening method strongly suggests that it be used to assist medical professionals in providing efficient and accurate autism detection.

Improving Multiclass Rainfall Prediction with Multilayer Perceptron and SMOTE: Addressing Class Imbalance Challenges

Rainfall is a key climate element that affects weather patterns and human activities, especially in agriculture and daily life. Therefore, accurately classifying rainfall is crucial for predicting future rainfall amounts. This study uses the Multilayer Perceptron (MLP) classification method, a neural network algorithm, to classify rainfall. The dataset, sourced from the BMKG website, has a class imbalance, requiring using the SMOTE (Synthetic Minority Over-sampling Technique) technique. The research compares the performance of MLP with and without SMOTE. The results show that the best model was achieved with SMOTE. MLP without SMOTE achieved an accuracy of 75%, sensitivity of 40.34%, specificity of 86.15%, and an AUC of 63.25%. In comparison, MLP with SMOTE achieved an accuracy of 71.27%, sensitivity of 71.14%, specificity of 90.30%, and an AUC of 80.72%. Although accuracy decreased, the overall evaluation, particularly the AUC, improved significantly. Therefore, the SMOTE technique effectively addresses the class imbalance issue in rainfall classification.

Bank Customer Churn Prediction Using Machine Learning

The banking sector is fiercely competitive in the present difficult time. Banks concentrate on both customer retention and customer turnover to raise the caliber and degree of service. The classification issue in the banking sector is examined in this essay. It detects possible churners from among potential customers and primarily focuses on bank customers' worries around churn. The bank uses supervised machine learning to identify and forecast which of its clients are most likely to leave. Since it is necessary to define churn and non-churn clients, customer churn prediction can be used in this situation. To address the distinctions between churn and non-churn clients, this study uses logistic regression, decision trees, and random forest classifiers. Accuracy levels can be attained via several classifiers. The Kaggle dataset for bank customer churn modeling is used for the experiment. To identify an appropriate model with more accuracy and predictability, the outcomes are compared. The findings demonstrate that, upon oversampling, in terms of accuracy, the decision tree model outperforms other models.

A text classification method by integrating mobile inverted residual bottleneck convolution networks and capsule networks with adaptive feature channels.

This study proposes a novel text classification model, MBConv-CapsNet, to address large-scale text data classification issues in the Internet era. Integrating the advantages of Mobile Inverted Bottleneck Convolutional Networks and Capsule Networks, this model comprehensively considers text sequence information, word embeddings, and contextual dependencies to capture both local and global information about the text effectively. It transforms from the original text matrix to a more compact and representative feature representation. A Capsule Network is designed to adaptively adjust the importance of different feature channels, including N-gram convolutional layers, selective kernel network layers, primary capsule layers, convolutional capsule layers, and fully connected capsule layers, aiming to enhance the model's ability to capture semantic information of text across different feature channels. The use of the sparsemax function instead of the softmax function for dynamic routing within the Capsule Network directs the network's focus more on capsules contributing significantly to the final output, reducing the impact of noise and redundant information, and further improving the classification performance. Experimental validation on multiple publicly available text classification datasets demonstrates significant performance improvements of the proposed method in binary classification, multi-classification, and multi-label text classification tasks, exhibiting better generalization capability and robustness.

Experiences of Indonesian Nurses Facing Recognition Barriers in the Saudi Commission for Health Specialties: A Qualitative Study

Background: Many Indonesian nurses working in Saudi Arabia failed to get the professional classification from the Saudi Commission for Health Specialties (SCFHS) based on their educational nursing background. Purpose: This research aims to explore the experiences of Indonesian nurses facing recognition barriers in the SCFHS policy on nursing bachelor’s degrees obtained before 2014. Methods: A qualitative study was used in this research with a case study approach. Data was collected through focus group discussions with semi-structured interviews. The research subjects were 12 Indonesian nurses as participants with inclusion criteria being nurses still active and had been terminated from government and private hospitals in Saudi Arabia. Data analysis was guided by Colaizzi’s method, followed by Taguette transcripts. Results: Three themes were identified from the data analysis: perceived unfairness, professional recognition, and classification issues. The first theme consisted of three sub-themes: disappointment, work contract, and salary. The second theme consisted of three sub-themes: nursing background, upgrading, and Indonesian nurses, and the third theme consisted of two sub-themes: failure and nurse specialist. Conclusion: Indonesian nurses who obtained bachelor’s degrees in nursing before 2014 were rejected by SCFHS in terms of professional classification recognition. To minimize these obstacles, the Indonesian government must evaluate the implementation of the educational nursing system that is not recognized by SCFHS, while the Kingdom of Saudi Arabia could implement the SCFHS regulatory equity for Indonesian nurses. Implications for Nursing: The future of nurses’ careers depends on the acceptance of the nursing education and passing the professional body policy to recognize the professional classification. Keywords: Indonesian nurses, Saudi Arabia, SCFHS, Health policy, Recognition barriers

Condition monitoring and multi-fault classification of hydraulic systems using multivariate functional data analysis.

Condition monitoring and multi-fault classification of hydraulic systems using multivariate functional data analysis.

On the Issue of Classification of Institutional Rent as a Socio-Economic Category of Rent Relations: Methodological Aspect

В научных исследованиях использование социально-экономических категорий позволяет анализировать и выявлять тенденции или зависимости для принятия решений по разработке мер государственного реагирования на социально-экономические риски и вызовы. В настоящем исследовании институциональная рента рассматривается как социально-экономическая категория, отражающая наиболее существенные свойства и связи рентных отношений, которая формируется и реализуется в процессе организационного взаимодействия между субъектами рентных отношений при производстве, потреблении и перераспределении прав собственности на социально-значимые блага. Наличие институциональной среды социально-экономического развития позволяет субъектам рентных отношений использовать институты для снижения трансакционных издержек их организационного поведения. В этих условиях институт становится ресурсом рентных отношений, а умение снижать организационные издержки за счет эффективного применения институциональных факторов — самостоятельным конкурентным преимуществом, позволяющим субъектам рентных отношений извлекать монопольный доход в виде институциональной ренты. Применение классификации как общенаучного метода систематизации знаний об институциональной ренте позволяет расширить теоретическую базу исследований рентных отношений и предложить новые направления использования институциональных инструментов ее анализа, что определяет актуальность настоящего исследования. Целью статьи является выявление методологических особенностей классификации институциональной ренты как социально-экономической категории рентных отношений. В работе уточнено определение институциональной ренты, а также обоснованы методологические особенности ее классификации, критерием которой является возможность субъектов рентных отношений формировать свои конкурентные преимущества за счет использования формальных институтов и неформальных ограничений в процессе снижения различных видов трансакционных издержек. В статье предложена общая классификация формальной институциональной ренты по уровню иерархии формальных институтов и неформальной институциональной ренты по виду неформальных ограничений, созданных институтами семьи, культуры, религии и образования.

Nonclassical dynamical behavior of solutions of partial differential-difference equations

<p>For partial differential-difference equations, a review of results regarding the relation between the type of the equation and dynamical properties of its solutions is provided. This includes the case of elliptic equations with timelike independent variables: Their solutions acquire dynamical properties (more exactly, behave as solutions of parabolic equations). The following approach to classify differential-difference equations into types, based on the property of differential-difference operators to be Fourier multipliers is applied in the following manner: An operator is treated to be elliptic if the real part of its symbol is positive, while the parabolic and hyperbolic types are defined correspondingly. It is shown that the proposed approach (being a natural extension of the classical notion of the ellipticity) is reasonable. On the other hand, fundamental novelties (compared with the classical theory of partial differential equations) occur as well. We provide conditions which guarantee the following results. For the half-space (half-plane) Dirichlet problem for elliptic equations, integral representations (of the Poisson type) of solutions are constructed, which are infinitely differentiable outside the boundary hyperplane (plane), and the asymptotic closeness of solutions (as the timelike independent variable unboundedly increases) takes place. For the Cauchy problem for parabolic equations, the same is valid, but we deal with the classical time instead of the timelike independent variable. For hyperbolic equations, multiparameter families of infinitely smooth global solutions are constructed. The said (sufficient) conditions restrict the sign of the real part of the symbol for the differential-difference operator with respect to spatial or spacelike independent variables. In a number of special cases, they might be weakened such that symbols with sign-changing real parts are admitted. The objective of the study is to observe the current stage of the classification issues for partial differential-difference equations in terms of the aforementioned approach: The ellipticity of a Fourier multiplier is defined by means of the sign (of there real part) of its symbol. Since both differential and translation operators are Fourier multipliers, methods of Fourier analysis are applicable in this study: We apply the Fourier transformation to the original partial differential-difference equation, solve the obtained ordinary differential equation, and apply the inverse Fourier transformation to the obtained solution. The main contribution obtained within this study is an efficient (workable) type concept for the fundamentally new extension of the class of partial differential equations, which is the class of partial differential-difference equations.</p>

ALL IN HOW YOU LOOK AT IT: ATTITUDES, AFFECT, AND ABILITY IN THE ASSESSMENT OF SUBJECTIVE COGNITION

Abstract Subjective cognitive decline (SCD)—self-perceived persistent cognitive decline in the absence of objective cognitive impairment—is a risk factor for dementia. Despite the potential for SCD to aid in the detection of future dementia, differentiating trajectories of normative from non-normative decline among people with SCD is complicated for several reasons, including that self-perceived cognition is associated with socioemotional and general health factors in addition to objective abilities. Moreover, operational definitions of self-perceived cognition vary across the research literature and overlap with subjective aging. A robust understanding of the relationships among self-perceived cognition and aging, objective cognition, mood, attitudes, and health will strengthen the characterization and differentiation of SCD. This symposium will explore measures of perceived cognition and describe their relationships with mood, health, and aging attitudes among healthy older adults. The first presenter will describe the differential impacts of negative and positive affect on perceived memory difficulties, highlighting the complex relationships among depression, affective states, and subjective memory decline. The second presenter will expand on these findings by contrasting a measure of subjective memory decline with related measures of subjective memory and subjective age, and explore differential impacts of perceived health and objective memory. The third presenter will focus on perceived cognitive ability more broadly to describe its relationship with attitudes toward aging, depression, and objective cognition. The Discussant will compare and contrast findings with regard to operational definitions of self-perceived cognition and consider implications of the findings as they relate to emerging issues of clinical classification and management of SCD.

Implementing A Naïve Bayes Classifier on Iris Data Using MATLAB, A Classification Method by Using Grid Parameters Optimization

Data mining categorization is crucial for predicting outcomes. Naive Bayes Classification (NBC) is a prominent approach used in data mining for classification purposes. It has the ability to anticipate outcomes and is generally more efficient than other techniques of categorization. Several Naive Bayes classification methods exhibit subpar performance when used to classification and regression issues. One of the factors contributing to the success of NBC is the reliance on assumptions of independence among predictors and the initial hyperparameters. Nevertheless, this robust assumption results in a decrease in precision. This work introduces a novel approach to enhance the precision of NBC. The suggested approach employs a grid search to enhance the accuracy of Naïve Bayes classification. The results obtained indicate that the technique utilised achieved a significant degree of accuracy in forecasting compared to the conventional built-in Matlab NBC. Thus, by assuming conditional independence, the accuracy of the NBC may be enhanced.

Participants in criminal proceedings: problematic issues of classification

The article analyzes the conceptual approach that was used by the legislator to classify participants in criminal proceedings. It is noted that the current classification into participants in criminal proceedings and participants in judicial proceedings looks very illogical, since the concept of criminal proceedings also includes judicial proceedings as a separate stage. In addition, the specified classification groups do not include a court and an investigating judge, but the secretary of the court session and the court administrator are present. The purpose of the article is to formulate proposals for improving the classification of participants in criminal proceedings on the basis of clarifying the functions they perform and taking into account the rights granted to them and the duties assigned to them. To achieve the set goal, the following main scientific tasks were solved: to analyze the norms of the Code of Criminal Procedure of Ukraine regarding the determination of the status and classification of participants in criminal proceedings; to analyze scientific publications after the adoption of the Code of Criminal Procedure of Ukraine in 2012, devoted to participants in criminal proceedings; using a systemic approach to determine the connections between participants in criminal proceedings with their distribution by stages of criminal proceedings and functions performed. Taking into account the fact that the institute of participants in criminal proceedings with their distribution into parties and other participants is a novelty in domestic criminal procedural legislation, it is proposed to consider the problem of their classification based on the application of a systemic approach. In particular, a classification of participants in criminal proceedings is proposed, built on the basis of the use of two bases – stages of criminal proceedings and the nature of the functions performed by participants in criminal proceedings. In the author's view, this makes sense from several points of view: 1) clarity in determining the regulatory and legal status of participants in criminal proceedings; 2) use of a systemic approach in scientific research; 3) performance of didactic tasks when teaching a training course on criminal proceedings.

EDCLoc: a prediction model for mRNA subcellular localization using improved focal loss to address multi-label class imbalance

BackgroundThe subcellular localization of mRNA plays a crucial role in gene expression regulation and various cellular processes. However, existing wet lab techniques like RNA-FISH are usually time-consuming, labor-intensive, and limited to specific tissue types. Researchers have developed several computational methods to predict mRNA subcellular localization to address this. These methods face the problem of class imbalance in multi-label classification, causing models to favor majority classes and overlook minority classes during training. Additionally, traditional feature extraction methods have high computational costs, incomplete features, and may lead to the loss of critical information. On the other hand, deep learning methods face challenges related to hardware performance and training time when handling complex sequences. They may suffer from the curse of dimensionality and overfitting problems. Therefore, there is an urgent need for more efficient and accurate prediction models.ResultsTo address these issues, we propose a multi-label classifier, EDCLoc, for predicting mRNA subcellular localization. EDCLoc reduces training pressure through a stepwise pooling strategy and applies grouped convolution blocks of varying sizes at different levels, combined with residual connections, to achieve efficient feature extraction and gradient propagation. The model employs global max pooling at the end to further reduce feature dimensions and highlight key features. To tackle class imbalance, we improved the focal loss function to enhance the model’s focus on minority classes. Evaluation results show that EDCLoc outperforms existing methods in most subcellular regions. Additionally, the position weight matrix extracted by multi-scale CNN filters can match known RNA-binding protein motifs, demonstrating EDCLoc’s effectiveness in capturing key sequence features.ConclusionsEDCLoc outperforms existing prediction tools in most subcellular regions and effectively mitigates class imbalance issues in multi-label classification. These advantages make EDCLoc a reliable choice for multi-label mRNA subcellular localization. The dataset and source code used in this study are available at https://github.com/DellCode233/EDCLoc.

Factors of mild and moderate course of COVID-19 in non-immunized persons

Severity of COVID-19 is known to be related to various factors. The aim of the study was to perform comparative analysis of the features associated with mild and moderate course of infection caused by the SARS-CoV-2 virus in the absence of immunization against a new coronavirus infection, and looking for opportunities of binary classification. We performed the analysis of clinical and laboratory data of 59 medical staff members who had a mild (group No. 1), or moderate (group No. 2) forms of COVID-19. The results were analyzed using STATISTICA and GraphPad Prism programs. We have found that the patients with moderate course of the disease were 12% older, which numerically corresponded to a difference of 5 years (p = 0.019). Males were 1.5 times more likely to have a moderate course of the disease (p = 0.000). A part of respondents used non-specific immunoprophylactic agents. It was found that the intake of cholecalciferol, riamilovir/triazaverin did not affect the severity of disease, and the usage of human recombinant interferon alpha-2b in order to prevent SARS-CoV-2 virus infection was associated with subsequent mild course of COVID-19 (p = 0.000). More severe clinical course of COVID-19 was associated with simultaneous usage of several nonspecific immunoprophylactic agents, in particular, ascorbic acid, zinc compounds, umifenovir hydrochloride monohydrate (p = 0.000). Higher body mass index was also associated with a severe course of the disease (p = 0.034). The analysis showed that the issue of binary classification for the patients with mild and moderate COVID-19 in the absence of vaccination may be resolved (recognition accuracy is 90.9%, p 0.0001). The risk of moderate COVID-19 in non-immunized individuals is influenced by advanced age, gender factor, increased body mass index, presence of hypertension (70% hazard increase), as well as simultaneous use of several non-specific immunoprophylactic agents to prevent infection. Presence of allergic reactions and frequency of seasonal acute respiratory viral infections do not affect the severity of COVID-19. Prophylactic usage of human recombinant IFNa-2b is associated with milder course of infection caused by the SARS-CoV-2 virus.

ResDenseNet:Hybrid Convolutional Neural Network Model for Advanced Classification of Diabetic Retinopathy(DR) in Retinal Image Analysis

Preventing vision loss in diabetic retinopathy (DR) requires early and precise detection. Although strong feature extraction is required and there is class imbalance in the current methods, deep learning (DL) techniques have showed promise in DR classification. With components from both the ResNeXt and DenseNet designs, a unique DL architecture for DR classification is proposed in this work. A unique DL architecture that integrates DenseNet and ResNeXt components is proposed in this work.To address unique issues in DR classification, the proposed method integrates channel-wise masking with an attention mechanism. The network is able to learn from the less frequent DR stages because the channel-wise masking reduces the influence of the majority class and the attention method concentrates the network on important features. To improve interpretability and confidence in the model's predictions, the incorporation of Explainable AI (XAI) approaches is also covered.Our findings show that the suggested approach outperforms current architectures, achieving better sensitivity for differentiating DR phases at 0.82 and accuracy at 0.87. This shows that this new method has promise for improving DR categorization, which could result in earlier diagnoses and better patient outcomes.

Current concepts of chronic gastritis and gastropathies: new international consensus RE.GA.IN

Recent decades have been marked by several important milestones in the field of gastritis. New epidemiological reports were received, especially from countries with a high incidence of gastric cancer, and H.pylori infection was recognized as the most important cause of gastritis, due to which it was quickly included in the primary infectious oncogenic agents. Clinical strategies for the detection and eradication of H.pylori infection were developed, the previously unsuspected clinical and pathological significance of gastric microbiota was established, and extraordinary progress was made in the technology of esophagogastroduodenoscopy. Histolo­gical determination of gastritis in terms of stage (that is, risk stratification of stomach cancer) was implemented into clinical practice. In addition to the Sydney and Houston gastritis classifications, the six editions of the Maastricht Consensus (1996–2022) and the Kyoto Global Consensus deserve special attention, thanks to which broad agreements and significant scientific achievements became possible in the most controversial issues of the entire spectrum of gastritis, with a special emphasis on H.pylori gastritis, which accounts for more than 90 % of all forms of gastritis worldwide. The Real-world Gastritis Initiative (RE.GA.IN) marks the next step in the ongoing search to achieve a better understanding of various gastric conditions. The RE.GA.IN consensus was concluded in Venice in November 2022 after 8 months of intensive global scientific reasonings. RE.GA.IN mission itself has been focused on critically reviewing, updating, sharing and building consensus on the current scientific knowledge of inflammatory gastric lesions. It includes eight sections on clinicopathological topics, each consisting of preamble, which presents brief statements and corresponding explanatory texts. For each statement, the level of evidence (assessed according to a predefined four-level scale) and the strength of recommendations on the GRADE system are reported. The following topics were considered in the consensus: 1) definition and classification issues of gastritis; 2) spectrum of H.pylori gastritis; 3) key diagnosis of H.pylori gastritis; 4) H.pylori gastritis: clinical results; 5) autoimmune gastritis; 6) gastritis of low prevalence; 7) gastritis and gastric microbiota; 8) epidemiology of gastritis and associated precancerous and neoplastic lesions. Thus, the most controversial aspects of gastritis were addressed, a comprehensive and diverse body of knowledge was brought together to use patient-oriented evidence to assist physicians in their real-world clinical practice.

Analysis of legislation and judicial practice regarding the legal status and social guarantees of employees of digital platforms in EU countries: challenges and perspectives

In today’s world, the rapid development of digital technologies and the gig economy has led to the emergence of new forms of employment that significantly change traditional labor relations. The platform economy, based on short­term contracts and freelance work, has become an important element of the labor market, offering new opportunities for workers, but at the same time presenting them with numerous challenges. The legal status of platform workers remains uncertain in many countries. Platforms often classify their workers as «independent contractors», «economically dependent independent workers» or «self-employed», which allows them to avoid obligations related to labor laws. This leads to legal gaps that jeopardize the social protection of workers on digital platforms. Different approaches to the legal status and social guarantees of workers in different countries create additional complexities in regulation. The issue of legal classification of digital platform workers remains unresolved, affecting their labor rights and social security. The authors consider the challenges that arise in connection with new forms of employment that displace traditional labor relations. The main attention is paid to the legal uncertainty of the status of gig workers, their social protection and the lack of a unified approach to the regulation of this sphere in different EU countries. The article analyzes legislative initiatives and case law in France, Germany, Austria and Spain, where the legal status of platform workers varies from employed to self-employed. Particular attention is paid to recent court decisions that affect the classification of platform workers and their social rights. The study reveals significant differences in the national legislation and judicial practice of different EU countries, which indicates the need to develop a unified European approach to the regulation of new forms of work. The authors emphasize the need to adapt national legislation to the new realities of the labor market, in light of the new EU Directive (Directive of the European Parliament and of the Council on improving working conditions in platform work), which aims to ensure the correct classification of employment status and the protection of the rights of gig workers.