Classification Of Entities Research Articles

The child as a party to tax proceedings

There are two categories of entities in legal-tax relationships. These include tax authorities, the active party in the legal-tax relationship, and taxpayers, the passive party in the legal-tax relationship. While there is no ambiguity in identifying and defining the rights and obligations of tax authorities, this issue can become more complex concerning the taxpayer. Especially when it comes to taxpayers who are natural persons and have not attained the age of majority. It should be noted that the attendance of minors in economic events, such as advertisements or competitions, is becoming increasingly significant. Minors often become parties to donation agreements, leading to the acquisition of certain assets. It is essential to mention that each of these events, if associated with income generation, results in the emergence of tax liability. This necessitates accurately determining the liability and initiating tax proceedings if the relevant factual conditions are present. This article seeks to answer whether a child can be a party to tax proceedings and what rights they have in this respect. To explore this issue, the definitional scope of the term 'child' was first outlined, followed by examining their legal-tax capacity and, ultimately, considering the child as a party to tax proceedings.

Types of legal entities under public law in German doctrine and legislation

This article examines the concepts and types of legal entities under public law in German doctrine and legislation. The legal status of public legal entities is regulated by the provisions of the German Constitution, the German Civil Code, the Law on Administrative Procedures, the Law «On the Procedure for Consideration of Cases Under the Management of Administrative Bodies», the Law «On Administrative Courts». The author examines the issue of determining the criteria for the classification of legal entities, so legal entities under public law are created in an executive order, act to satisfy the public or public interest, have authority, operate in organizational and legal forms of an open type, are responsible for the activities of their officials, legal the regime of property has certain features, in particular, such property can be transferred for use by legal entities under public law. As a rule, legal entities under public law have a special scope of legal capacity, unlike legal entities under private law, in this regard, the article analyzes the doctrine of ultra vires. The author examines the procedure for creating legal entities under public law in Germany, the types of organizational and legal forms of legal entities under public law that carry out their activities in the form of a public corporation, public institution, public fund. The article considers the sources of property formation of legal entities under public law, in particular, it can be income from entrepreneurial activity, budget funds, contributions of members of public corporations, donations (for example, the German State Bank, the German Chamber of Commerce and Industry, the Prussian Cultural Heritage Fund). The article considers four types of public corporations: territorial, united, personal, real. A feature of the activity of public corporations in Germany is the presence of membership, unlike public institutions.

TarKG: a comprehensive biomedical knowledge graph for target discovery.

Target discovery is a crucial step in drug development, as it directly affects the success rate of clinical trials. Knowledge graphs (KGs) offer unique advantages in processing complex biological data and inferring new relationships. Existing biomedical KGs primarily focus on tasks such as drug repositioning and drug-target interactions, leaving a gap in the construction of KGs tailored for target discovery. We established a comprehensive biomedical KG focusing on target discovery, termed TarKG, by integrating seven existing biomedical KGs, nine public databases, and traditional Chinese medicine knowledge databases. TarKG consists of 1143313 entities and 32806467 relations across 15 entity categories and 171 relation types, all centered around 3 core entity types: Disease, Gene, and Compound. TarKG provides specialized knowledges for the core entities including chemical structures, protein sequences, or text descriptions. By using different KG embedding algorithms, we assessed the knowledge completion capabilities of TarKG, particularly for disease-target link prediction. In case studies, we further examined TarKG's ability to predict potential protein targets for Alzheimer's disease (AD) and to identify diseases potentially associated with the metallo-deubiquitinase CSN5, using literature analysis for validation. Furthermore, we provided a user-friendly web server (https://tarkg.ddtmlab.org) that enables users to perform knowledge retrieval and relation inference using TarKG. TarKG is accessible at https://tarkg.ddtmlab.org.

„Sukob nadležnosti“ kod sukoba nadležnosti u upravnom postupku

A conflict of jurisdiction in administrative proceedings occurs when two authorities declare themselves competent (positive conflict) or not competent (negative conflict) to act in the same administrative matter (case). The regulations that determine the authorities responsible for resolving conflicts of jurisdiction contain certain ambiguities, which can be interpreted as overlapping jurisdiction for resolving conflicts of jurisdiction, on the one hand, and gaps in terms of jurisdiction for resolving conflicts of jurisdiction between certain categories of administrative entities, on the other hand. In this sense, the paper talks about the ‘conflict of jurisdiction’, in the first case ‘positive’, and in the second ‘negative’. We are not talking about real conflicts of jurisdiction, because they can arise only with respect to a specific administrative matter, but about vague norms that regulate or fail to adequately regulate jurisdiction for resolving conflicts of jurisdiction, which can lead to problems in determining the authority responsible for resolving conflicts of jurisdiction.

ToxDAR: A Workflow Software for Analyzing Toxicologically Relevant Proteomic and Transcriptomic Data, from Data Preparation to Toxicological Mechanism Elucidation.

Exploration of toxicological mechanisms is imperative for the assessment of potential adverse reactions to chemicals and pharmaceutical agents, the engineering of safer compounds, and the preservation of public health. It forms the foundation of drug development and disease treatment. High-throughput proteomics and transcriptomics can accurately capture the body's response to toxins and have become key tools for revealing complex toxicological mechanisms. Recently, a vast amount of omics data related to toxicological mechanisms have been accumulated. However, analyzing and utilizing these data remains a major challenge for researchers, especially as there is a lack of a knowledge-based analysis system to identify relevant biological pathways associated with toxicity from the data and to establish connections between omics data and existing toxicological knowledge. To address this, we have developed ToxDAR, a workflow-oriented R package for preprocessing and analyzing toxicological multi-omics data. ToxDAR integrates packages like NormExpression, DESeq2, and igraph, and utilizes R functions such as prcomp and phyper. It supports data preparation, quality control, differential expression analysis, functional analysis, and network analysis. ToxDAR's architecture also includes a knowledge graph with five major categories of mechanism-related biological entities and details fifteen types of interactions among them, providing comprehensive knowledge annotation for omics data analysis results. As a case study, we used ToxDAR to analyze a transcriptomic dataset on the toxicology of triphenyl phosphate (TPP). The results indicate that TPP may impair thyroid function by activating thyroid hormone receptor β (THRB), impacting pathways related to programmed cell death and inflammation. As a workflow-oriented data analysis tool, ToxDAR is expected to be crucial for understanding toxic mechanisms from omics data, discovering new therapeutic targets, and evaluating chemical safety.

Acupuncture indication knowledge bases: meridian entity recognition and classification based on ACUBERT.

In acupuncture diagnosis and treatment, non-quantitative clinical descriptions have limited the development of standardized treatment methods. This study explores the effectiveness and the reasons for discrepancies in the entity recognition and classification of meridians in acupuncture indication using the Acupuncture Bidirectional Encoder Representations from Transformers (ACUBERT) model. During the research process, we selected 54 593 different entities from 82 acupuncture medical books as the pretraining corpus for medical literature, conducting classification research on Chinese medical literature using the BERT model. Additionally, we employed the support vector machine and Random Forest models as comparative benchmarks and optimized them through parameter tuning, ultimately leading to the development of the ACUBERT model. The results show that the ACUBERT model outperforms other baseline models in classification effectiveness, achieving the best performance at Epoch = 5. The model's "precision," "recall," and F1 scores reached above 0.8. Moreover, our study has a unique feature: it trains the meridian differentiation model based on the eight principles of differentiation and zang-fu differentiation as foundational labels. It establishes an acupuncture-indication knowledge base (ACU-IKD) and ACUBERT model with traditional Chinese medicine characteristics. In summary, the ACUBERT model significantly enhances the classification effectiveness of meridian attribution in the acupuncture indication database and also demonstrates the classification advantages of deep learning methods based on BERT in multi-category, large-scale training sets. Database URL: http://acuai.njucm.edu.cn:8081/#/user/login?tenantUrl=default.

Segmentation-aware relational graph convolutional network with multi-layer CRF for nested named entity recognition

AbstractNamed Entity Recognition (NER) is fundamental in natural language processing, involving identifying entity spans and types within a sentence. Nested NER contains other entities, which pose a significant challenge, especially pronounced in the domain of medical-named entities due to intricate nesting patterns inherent in medical terminology. Existing studies can not capture interdependencies among different entity categories, resulting in inadequate performance in nested NER tasks. To address this problem, we propose a novel Layer-based architecture with Segmentation-aware Relational Graph Convolutional Network (LSRGCN) for Nested NER in the medical domain. LSRGCN comprises two key modules: a shared segmentation-aware encoder and a multi-layer conditional random field decoder. The former part provides token representation including boundary information from sentence segmentation. The latter part can learn the connections between different entity classes and improve recognition accuracy through secondary decoding. We conduct experiments on four datasets. Experimental results demonstrate the effectiveness of our model. Additionally, extensive studies are conducted to enhance our understanding of the model and its capabilities.

Cervical Spine Injuries

Around a third of all cervical spine injuries occur in the upper cervical spine in the area between the occiput and the second cervical vertebra. The latter being the most common location of the injury with around 70%. But also atlas fractures, occipital condyle fractures, traumatic spondylolisthesis of C2, atypical fractures in the corpus area as well as atlantooccipital and atlantoaxial ligamentous lesions should be mentioned in connection with injuries in this area. In many cases, conservative therapy regimen is possible. In unstable or displaced injuries, however, surgical intervention is required, with various surgical procedures being used. The frequency, diagnostics, classification, and standard therapy of the individual entities are presented in detail in this continuing medical education article.

Имущественная обособленность как признак юридического лица: теория и практика (по страницам классики и судебных актов)

The authors consider the essence of the concept of ‘property isolation of a legal entity’, different approaches to the definition of property isolation are given and analysed. The authors conclude that property isolation is characteristic not only of legal entities, but also of individuals and contractual associations, while for legal entities the value of property isolation is high, due to the fact that it allows to participate in turnover and bear property responsibility. Since the degree of property isolation of different types of legal entities differs, the authors propose the respective classification of legal entities. The authors conclude that there are no universal criteria of property isolation for civil, criminal, administrative and other forms of legislation. Thus, direct application of concepts and rules of civil legislation on property isolation to the areas of public law is unreasonable.

Towards digital-twin-enabled facility management: the natural language processing model for managing facilities in buildings

ABSTRACT Facility management is an essential part of the building which encompasses multiple service areas and processes to ensuring optimum functionality of the facility. Digital twin (DT) has been increasingly introduced to the FM domain to facilitate FM tasks such as decision-making. Despite the advancements in DT-enabled FM, there still exists a problem in the disconnections between DT academics and FM practitioners. The emergence of knowledge management can act as a bridge between academics and FM practitioners. To mine knowledge in the DT-enabled FM industry for a better communication between different stakeholders and timely decisions in the case of emergency, this study proposes a natural language processing (NLP)-based approach. A detailed classification of FM-related entities is first presented, which divides the FM into five categories. The NLP model is used to extract entities and knowledge from a large volume of text data. The results indicate that an NLP-based approach can accurately extract FM-related knowledge. The findings of this study not only provide a basis for automatic DT-enabled FM-related knowledge extraction but also support the downstream tasks such as knowledge graph construction and FM-related emergency decision-making.

HpGraphNEI: A network entity identification model based on heterophilous graph learning

Network entities have important asset mapping, vulnerability, and service delivery applications. In cyberspace, where the network structure is complex and the number of entities is large, effectively obtaining the relevant attributes of entities is a difficult task. Graph neural network-based approaches focus on target IP node messaging from neighboring nodes; however, the graph learning task ignores the heterophilous relationship of network entity identification (NEI) tasks in the graph structure and fails to effectively message from non-neighboring nodes. To address the limitations of the existing task, we propose a NEI model based on heterophilous graph learning (HpGraphNEI); HpGraphNEI converts heterophilous graphs under the NEI task into homophilous graphs and uses the graph learning mechanism to carry out attribute completion task for incomplete entity attributes. First, the acquired dataset is feature-extracted by network measurement, and the clustering algorithm is employed to divide the target nodes into communities. Second, the network topology graph is constructed to embed the node attribute information and neighborhood structure information into the graph in the form of feature vectors. Then, the global attention in the community is calculated according to the attention results, the edges with strong correlation in the network are filtered, the adjacency matrix is reconstructed, and then the updated node information is aggregated to complete the incomplete attribute completion. Fourth, the updated nodes are categorized to output network entity categories and construct network entity portraits based on the attribute completion nodes. We conducted a 2-month data collection in three real regions and successfully identified 6 types of network entities. Compared with the optimal baseline, all the metrics have significantly improved, with NEI accuracy above 93.74% and up to 96.28%, improved 2.27% to 2.69%.

A Question Answering Chatbot for Gastric Cancer Patients After Curative Gastrectomy: Development and Evaluation of User Experience and Performance.

Postoperative gastric cancer patients have many questions about managing their daily lives with various symptoms and discomfort. This study aimed to develop a knowledge-based question answering (QA) chatbot for their self-management and to evaluate the user experience and performance of the chatbot. To support the chatbot's natural language processing, we analyzed QA texts from an online self-help group, clinical guidelines, and refined frequently asked questions related to gastric cancer. We developed a named entity classification with seven superconcepts, 4544 subconcepts, and 1415 synonyms. We also developed a knowledge base by linking the users' classified question intents with the experts' answers and knowledge resources, including 677 question intents and scripts with standard QA pairs and similar question phrases. A chatbot called "GastricFAQ" was built, reflecting the question topics of the named entity classification and QA pairs of the knowledge base. User experience evaluation (N = 56) revealed the highest mean score for usefulness (4.41/5.00), with all other items rated 4.00 or higher, except desirability (3.85/5.00). The chatbot's accuracy, precision, recall, and F score ratings were 85.2%, 87.6%, 96.8%, and 92.0%, respectively, with immediate answers. GastricFAQ could be provided as one option to obtain immediate information with relatively high accuracy for postoperative gastric cancer patients.

Power data quality assessment and verification governance based on knowledge graph

In addressing the challenges of scattered data and limited professional knowledge in traditional power data quality assessment and verification governance, our approach leveraged natural language processing (NLP) technology for text preprocessing, incorporating power system research findings and case analyses. Named entity recognition enhanced entity identification accuracy, while relationship weighting technology facilitated entity classification and relationship weight assignment. The resulting power system knowledge graph was seamlessly integrated into a graph database for real-time updates. Through the synergistic use of relationship weighting technology and graph convolutional networks (GCN), our method achieved precise representation and modeling of power system knowledge graphs. The research outcomes underscored the method’s exceptional performance in real-time anomaly detection, maintaining an anomaly detection rate between 1% and 10% and accuracy fluctuating from 80% to 98%. This method stands as a testament to its efficacy in processing power data and validating its robustness in power system assessment and verification.

Nested Entity Recognition Method Based on Multidimensional Features and Fuzzy Localization

Nested named entity recognition (NNER) aims to identify potentially overlapping named entities. Sequence labeling method and span-based method are two commonly used methods in nested named entity recognition. However, the linear structure of sequence labeling method results in relatively poor performance, and span-based method requires traversing all spans, which brings very high time complexity. All of them fail to effectively leverage the positional dependencies between internal and external entities. In order to improve these issues, this paper proposed a nested entity recognition method based on Multidimensional Features and Fuzzy Localization (MFFL). Firstly, this method adopted the shared encoding that fused three features of characters, words, and parts of speech to obtain a multidimensional feature vector representation of the text and obtained rich semantic information in the text. Secondly, we proposed to use the fuzzy localization to assist the model in pinpointing the potential locations of entities. Finally, in the entity classification, it used a window to expand the sub-sequence and enumerate possible candidate entities and predicted the classification labels of these candidate entities. In order to alleviate the problem of error propagation and effectively learn the correlation between fuzzy localization and classification labels, we adopted multi-task learning strategy. This paper conducted several experiments on two public datasets. The experimental results showed that the proposed method achieves ideal results in both nested entity recognition and non-nested entity recognition tasks, and significantly reduced the time complexity of nested entity recognition.

On Some Issues of Modeling the Economic Efficiency of Measures Implemented by Subjects of Public Control as a Criterion for Evaluating the Effectiveness of Their Activities

Annotation. This article is devoted to the analysis of the need to model a system for assessing the economic efficiency of activities implemented by subjects of public control. In the modern world, the existence and development of individual independent and sovereign states is determined by the level of democracy in the organization and functioning of the apparatus of public power, which in its activities must take into account the interests and needs of the entire population of a particular country, or its overwhelming majority. In this regard, the role and importance of the entire set of civil society institutions in ensuring democratic regimes in individual national states is increasing. In the Russian Federation, the most important institution of civil society at the present stage is the institution of public control. The work substantiates the place and role of this institution of civil society as a legal guarantee of the system of constitutional rights, freedoms and legitimate interests of individuals and legal entities, the rights and legitimate interests of certain categories of legal entities. The authors formalized and analyzed the main factors influencing the effectiveness of the processes of organizing and conducting public control events. The work examines the main problems and prospects for increasing the performance of subjects of public control in the Russian Federation. It is substantiated that one of the main conditions for increasing the effectiveness of the functioning of the institution of public control is the need to assess the effectiveness of the activities of the entire system of subjects of public control in Russia. The work has developed and substantiated the author’s definition of the concepts "effectiveness of activities of subjects of public control", "model for assessing the economic efficiency of activities implemented by subjects of public control". The main features of these concepts, their essence and content are investigated and analyzed. The article formulates and substantiates a model for assessing the economic efficiency of activities implemented by subjects of public control (since it is the most important criterion for assessing the performance of subjects of public control in particular, as well as the institution of public control itself in general), which is a systemic set of quantitative and qualitative criteria.

Integrating remote sensing and field investigation for lithological mapping of Per-Eonile to Neonile sequences west of Sohag city, Egypt: Impact on urban development

In the domains of geological study, natural resource exploitation, geological hazards, sustainable development, and environmental management, lithological mapping holds significant importance. Conventional approaches to lithological mapping sometimes entail considerable effort and difficulties, especially in geographically isolated or inaccessible regions. Incorporating geological surveys and satellite data is a powerful approach that can be effectively employed for lithological mapping. During this process, contemporary RS-enhancing methodologies demonstrate a remarkable proficiency in identifying complex patterns and attributes within the data, hence facilitating the classification of diverse lithological entities. The primary objective of this study is to ascertain the lithological units present in the western section of the Sohag region. This objective will be achieved by integrated Landsat ETM+ satellite imagery and field observations. To achieve our objectives, we employed many methodologies, including the true and false color composition (FCC&TCC), the minimal noise fraction (MNF), principal component analysis (PCA), decoration stretch (DS), and independent component analysis (ICA). Our findings from the field investigation and the data presented offer compelling evidence that the distinct lithological units can be effectively distinguished. A recently introduced geology map has been incorporated within the research area. The sequence of formations depicted in this map is as follows: Thebes, Drunka, Katkut, Abu Retag, Issawia, Armant, Qena, Abbassia, and Dandara. Implementing this integrated technique enhances our comprehension of geological units and their impacts on urban development in the area. Based on the new geologic map of the study area, geologists can improve urban development in the regions by detecting building materials “aggregates”. This underscores the significance and potential of our research in the context of urban development.

Effectiveness of Audit Committee Features to Asymmetric Information with Audit Quality as Moderating

Purpose: The study aims to explore the effectiveness of audit committee features, such as the total members, meeting frequency, financial competence, and independence, as a determinant of asymmetric information with audit quality as moderating. Design/Method/Approach: We employed a regression model with panel data, analyzing 260 firm-year observations for entities whose shares traded on the Indonesia Stock Market from 2017 to 2021, all of which fall under the category of public interest entities. Findings: Our examination uncovered meeting frequency was statistically significant in reducing asymmetric information, and audit quality enhanced the interrelation of the meeting frequency, reducing asymmetric information. In contrast, our examination could not uncover any correlation between the total members, financial competence, and independence with asymmetric information. In addition, the study demonstrated no relationship between audit quality enhancing correlation between the total members, financial competence, and independence to mitigate asymmetric information. Theoretical Implications: The study supports agency and asymmetric information theories by demonstrating the significant role of oversight through frequent meetings in mitigating conflicts between agents and principals. This academic support enriches the existing literature and provides valuable insights for future research in the field. Practical Implications: Our findings suggest that the regular occurrence of dialogue between audit committees and management needs to be increased to provide better oversight and ultimately improve organizational performance, benefiting stakeholders and the board. Finally, our study implies the current regulations in Indonesia have not succeeded in encouraging the efficacy of audit committee monitoring through other features of audit committees as mandated by Indonesian regulation. Hence, regulatory breakthroughs by policymakers are essential to foster the performance of audit committees. Originality/Value: Our study provides a novel perspective on how audit committee features influence asymmetric information by providing insights by investigating the involvement of audit quality as a moderating factor. Research Limitations/Future Research: While our study may be limited in its applicability due to the constrained data sourced from the Indonesian Stock Market, the potential insufficiency of proxy variables in apprising audit committee efficacy, and the restricted use of control variables, addressing these constraints should be a focus of future studies to achieve a more comprehensive understanding. Paper Type: Empirical JEL Classification: G34, O16

Использование технологий искусственного интеллекта в процессе функционирования института общественного контроля в России

This article analyzes the current problems and prospects of using artificial intelligence technologies in the func-tioning of public control in the Russian Federation. The work elucidates the place and role of this civil society institution in securing legal guarantees for the implementation, protection, and defense of the system of consti-tutional principles, alongside the rights, liberties, and lawful interests of the populace and distinct legal entity categories. The authors analyze various points of view on the limits and prospects of using artificial intelligence technologies in the field of public administration. The necessity of modernizing approaches in the organization and activities of subjects of public control through the use of artificial intelligence technologies in their activities is substantiated. The article outlines and scrutinizes the primary challenges hindering the utilization of the above-mentioned technologies by subjects of public control. A set of solutions is created and justified to ad-dress these issues, including through the introduction of amendments and additions to the Russian legislation on public control.

Body alteration: On the mental function of body modification and body decoration

Decorating the body as well as (semi-)permanent bodily modifications are a longstanding human practice, together with spending a great amount of time and effort on such body alterations. The present article reviews the mental functions of body-altering behavior. The primary aim is to identify and elucidate the predominant mental function underpinning these body alterations. Following several guidelines for reviews, we synthesized the literature, including several categories of body-altering entities from different eras. We argue that there are two crucial commonalities for individuals engaging in body-altering behavior that comprise the mental functions for doing so: aesthetics and group affiliation (dynamics), including the latter’s subfunctions of supporting individuality, resistance, personal narrative, physical endurance, and sexual motivation. Incorporating the existing literature, we find that aesthetic motivation takes precedence over group affiliation, thereby establishing aesthetics as the primary mental function of body-altering behavior; factors substantiating this conclusion are explored in detail within the article.

Span Graph Transformer for Document-Level Named Entity Recognition

Named Entity Recognition (NER), which aims to identify the span and category of entities within text, is a fundamental task in natural language processing. Recent NER approaches have featured pre-trained transformer-based models (e.g., BERT) as a crucial encoding component to achieve state-of-the-art performance. However, due to the length limit for input text, these models typically consider text at the sentence-level and cannot capture the long-range contextual dependency within a document. To address this issue, we propose a novel Span Graph Transformer (SGT) method for document-level NER, which constructs long-range contextual dependencies at both the token and span levels. Specifically, we first retrieve relevant contextual sentences in the document for each target sentence, and jointly encode them by BERT to capture token-level dependencies. Then, our proposed model extracts candidate spans from each sentence and integrates these spans into a document-level span graph, where nested spans within sentences and identical spans across sentences are connected. By leveraging the power of Graph Transformer and well-designed position encoding, our span graph can fully exploit span-level dependencies within the document. Extensive experiments on both resource-rich nested and flat NER datasets, as well as low-resource distantly supervised NER datasets, demonstrate that proposed SGT model achieves better performance than previous state-of-the-art models.