Issues Of Interpretation Research Articles

Conflicting data and new ways of digitising Byzantine prosopography

AbstractThis paper aims to illustrate the abilities and practicability of digital prosopographical databases through the case of historical dates. Numerous historical databases were created in the last few decades, and they applied different models to record and store historical data in an effective way. Although the developers and researchers of these projects faced several problems due to a conflict between the characteristics of structured databases and the nature of historical evidence and historical studies. The paper compares the methodologies of two databases, PBW and RELEVEN through the cases of the death of John of Bari and Bisantius Guirdeliku. The primary sources provide conflicting dates of these events. Thus, the analysis can address the issues of interpretation and authority behind a piece of information.

Application of Artificial Intelligence Advances in Athletics Industry: A Review

ABSTRACTWith the rapid development of Artificial Intelligence (AI) technology, the athletics field is undergoing profound changes. This transformation is reflected not only in the ways of training and competition but also in the overall enhancement of athletes' performance and the efficiency of event management. The introduction of AI has made data analysis feasible, enabling coaches and athletes to gain deeper insights and make more informed decisions. This paper reviews the current applications of AI in the athletics domain and its enhancement of athlete performance and competitive strategies, focusing on the practical applications of AI in event management, performance analysis, injury detection, and personalized training. Furthermore, AI systems support coaches in intelligent analysis by integrating historical data with real‐time data, thereby improving the efficiency of tactical decision‐making. However, despite the significant achievements in AI applications, a series of challenges remain, including the lack of high‐quality datasets, insufficient model interpretability, and ethical and privacy issues. In light of these challenges, we also propose viewpoints on future development directions aimed at promoting the intelligent transformation of the athletics industry.

Issues of Interpretation and Application of the Notion of “International Legal Legitimation”

INTRODUCTION. In the process of adapting to the changing realities of geopolitics and international law, collective legitimation has become one of the main political functions of the UN as a means of politically significant approval (or disapproval) of the behaviour and positions of states as consistent with international law and at the same time meeting the needs of the present, a means of recognising them as legitimate (or illegitimate). Awareness of the dynamic nature of such a phenomenon of contemporary international law as “international legal legitimation”, competent mastery of international legal argumentation skills, their constant improvement and updating, innovative approach to the formulation of international legal positions that could really claim to be dominant and progressive, development of an optimal mechanism for their articulation and legitimation will allow to ensure the competitiveness of Russia’s international legal policy among such rigid, wilful, but, it should be admitted, calculating and shrewd strategies of legitimation, which are applied primarily by the United States of America. Against the background of Washington’s generally successful and effective international legal legitimation of its interventions, as a result of which not a single resolution was adopted either by the UN Security Council or the UN General Assembly (UNGA) that would have qualified the American invasions of Iraq, Libya or Syria as “aggression”, the international legal support for the Russian Federation’s special military operation in the Ukraine (SVO) needs to be improved, since precisely this qualification was given to it by the UNGA. The relevance and significance of the issue increases in view of the need to enhance the quality of the international legal justification of the SVO, which should be aimed at the recognition, approval and acceptance by the majority of members of the international community of Russia’s international legal position on this issue, i. e. at its international legal legitimation, capable of reversing the currently prevailing pro-Western international legal consciousness. Therefore, this phenomenon encapsulates a tremendous applied value in the context of the increasing number of hybrid threats to the national security of our country and the emergence of new sources of such threats.MATERIALS AND METHODS. The theoretical and empirical basis of the research is constituted by domestic and mainly foreign international legal literature with an emphasis on the latest scientific developments and also with reference to the relevant to the topic law enforcement practice (practice of the International Court of Justice) and material of specialised dictionaries. On the basis of integrative (multidimensional) approach to scientific legal research, taking into account such principles of scientific cognition as theoretical novelty and scientific relevance, the study was guided, in particular, by hermeneutic, formal-legal, formallogical, structural-functional and systematic methods, as well as methods of analysis and synthesis, legal modelling, legal construction and strategic planning.RESEARCH RESULTS. The article reviews relevant studies on the term “legitimation” in international law, outlines various approaches to the definition and interpretation of this notion, reveals the content and structural elements of the process of international legal legitimation aimed at achieving, recognising or confirming the legitimacy of an international legal position of a state. Forming her own vision of the notion of “international legal legitimation”, the author has devised a number of classifications (structural schemes): a typology of approaches to the definition of the term “legitimation” (nihilistic, idealistic and compromise), the structure of the process of international legal legitimation, the types of international legal legitimation according to the time and will criteria.DISCUSSION AND CONCLUSIONS. Amid the large-scale and powerful “legal aggression” of the West against Russia, the phenomenon of international legal legitimation of the state’s actions often raises the question of the need to be bold, inventive and even creative in formulating international legal positions in a particular area. Our hypothesis is that in the real world, in which international law is a “product of a game of powers and interests”, the direct and active involvement of the Russian Federation in the process of constructing a common model of international legal legitimation of states’ positions would help to increase the effectiveness of the protection of Russian interests. The first step on this path could be the development and approbation of an international legal mechanism of legitimation of Russia’s international legal position on the SVO. Besides, it is important to analyse the international legal legitimation practices of particular states not only in order to study and potentially absorb their experience, but also to learn how to predict and anticipate further international legal manoeuvres of a certain country so as to be able to respond to them in a timely and appropriate manner.

The Issue of Attribution of Acts Committed in the Information Space

INTRODUCTION. Due to the recent trend related to the use of information and communication technologies by certain states with the aim to assert their geopolitical superiority, study of the issue of attribution of responsibility for acts committed in the information space as well as possible of responses to such attacks becomes even more relevant. Therefore, in the article the problems related to international responsibility, particularities of attacks in information space and ways to attribute such attacks, as well as effectiveness of international law are analyzed.MATERIALS AND METHODS. The research examines treaties, international customs, general widely recognized principles of international law, as well as resolutions, materials and documents of specialized working groups, and doctrine. General scientific and specific scientific methods of enquiry (methods of analysis and synthesis, deduction, induction, dialectical and formal legal methods) were used in the preparation of the study.RESEARCH RESULTS. The result of the conducted research is the conclusion that it is impossible to effectively regulate issues arising from the issue of attribution of acts committed in the information space exclusively with the current international law. Therefore, there is a need to develop a relevant international treaty taking into account the provisions of the concept of the UN Convention on International Information Security. At the same time, in order to effectively attribute an act, it is important to take into account the political, technical and legal elements of attribution of responsibility.DISCUSSION AND CONCLUSIONS. The article considers approaches to the issue of attribution of attacks in the information space as well as of possible ways of responding to such attacks within the framework of current international law. The problem of possible application of international humanitarian law, especially issue of interpretation of “attack” for the purposes of attacks in information space was also examined. In addition, the author analyzes the provisions of the concept of the UN Convention on International Information Security.The author concludes that current international law is not sufficient for effective attribution of an act committed in the information space. Elaboration of additional international legal regulation that would take into account the particularities of the information space is required. It is also crucial to develop international cooperation in this area, including through the creation of a global register of contact points.

The Enhancing Cybersecurity with AI Algorithms and Big Data Analytics: Challenges and Solutions

As digital transformation accelerates across industries, cybersecurity faces escalating challenges due to increasingly sophisticated cyber threats. This study explores the integration of artificial intelligence (AI) algorithms and big data analytics to enhance cybersecurity systems, focusing on addressing data integration and interpretability issues. Employing a descriptive-qualitative methodology, the research analyzes literature, case studies, and secondary data to evaluate the effectiveness of AI and big data in detecting and mitigating cyber threats. Key findings reveal that deep learning algorithms, such as artificial neural networks, achieved an accuracy of 93% in anomaly detection, outperforming traditional rule-based approaches by 18%. Additionally, big data platforms like Spark demonstrated superior efficiency, processing 500 GB of data in 35 seconds compared to Hadoop’s 60 seconds. However, the study identifies challenges related to the interpretability of AI models and the complexity of integrating diverse datasets, which impede real-time threat detection. Periodic updates to AI training datasets were found to improve detection accuracy by up to 15%, emphasizing the importance of adaptive learning models. This research contributes to the field by proposing strategies to enhance system resilience, including adopting Explainable AI (XAI) for transparency and advanced data integration techniques. The findings underscore the potential of AI and big data to revolutionize cybersecurity, offering organizations a proactive approach to combating evolving cyber threats. Future studies should focus on sector-specific implementations and optimizing response mechanisms for comprehensive security frameworks.

OECD:n ohjeiden kasvava merkitys kansainvälisessä verotuksessa

The development of international tax law is increasingly based on different OECD models and guidelines. Originally OECD models and guidelines were followed only as recommendations, but more and more often they are implemented as binding EU directives or domestic legislation. This development raises difficult issues of application and interpretation. This article discusses the changing status of the OECD guidelines and the application and interpretation issues related to the development. The issues are illustrated using transfer pricing adjustment provisions and the provisions concerning minimum taxation of large groups of companies as examples. The author concludes that despite difficult interpretation and application issues, there are good reasons to implement OECD models and guidelines in the European Union in the form of binding EU Directives.

A smart CardioSenseNet framework with advanced data processing models for precise heart disease detection

Heart diseases remain one of the leading causes of death worldwide. As a result, early and accurate diagnostics have become an urgent need for treatment and management. Most of the conventional methods adopted tend to have major drawbacks: the issues of accuracy, interpretability, and feature representation. This work, therefore, proposes CardioSenseNet, which may provide a new framework that can improve accuracy and efficiency in heart disease detection. Firstly, the approach introduces a few new methods: DGPN for data preprocessing, STHIO for feature selection, and SADNet for prediction. DGPN normalizes the data depending on the distribution characteristic, which improves the quality of the feature representation. STHIO adopts the Sheep Flock Optimization method for the exploration of features and Tuna Swarm Optimization for the exploitation of features, guaranteeing the optimality in feature selection. SADNet is one such deep learning model that tries to find the complicated pattern in high-dimensional data for better prediction accuracy. Extensive experiments on benchmark datasets such as Cleveland and CVD endorse the efficiency of CardioSenseNet with a high accuracy of 99 % and at an minimum loss of 0.12 %. The results thus indicate that CardioSenseNet is a promising solution for the detection of heart diseases with high accuracy and at an early stage; therefore, it will contribute significantly to cardiovascular healthcare developments.

‘If parody does not prickle it does not work’: Reflections on the Interpretive Challenges of Dark Parody in the Dutch and South African Courts

Focusing on the nexus of humour and the right to free speech, this article introduces the concept of ‘dark parody’ to address parodies that reuse existing material with a dark, humorous twist and explores how courts deal with two of the main interpretive challenges presented by a ‘dark parody’: (1) interpreting the distance between the parody and the copyright/trademark protected work it parodies, and (2) its use of dark humour. Using a metahermeneutic approach, the article analyses how the main features of parody, the legal criteria applied to parody in courts of law, and the role of dark humour influence the judicial interpretation process regarding (dark) parody in the context of copyright and trademark law. Based on this analysis, the article proposes the theoretical framework of ‘parodic distance’ to address the interpretive challenges systematically presented by dark parody via six main categories—content, style, context, message, function and affect—from which the distance between a parody and the original can be interpreted. Applying this framework to two case studies [Mercis c.s. v. Punt.nl, Court of Appeal of Amsterdam; Laugh It Off Promotions v. South African Breweries, Constitutional Court of South Africa], the article provides a comparative analysis of how these courts currently deal with interpretive issues surrounding dark humour in copyright and trademark law and shows how the framework of parodic distance can be a potentially useful conceptual tool that provides a shared vocabulary to complement judicial interpretation and legal discourse at the nexus of dark parody and the law.

Role of Image Segmentation and Deep Learning in Medical Imaging

The rapid advancements in medical imaging technologies have significantly enhanced diagnostic accuracy and clinical decision-making in modern healthcare. Image segmentation and deep learning have emerged as transformative tools among these advancements. This article explores the pivotal role of image segmentation and deep learning in medical imaging, detailing their methodologies, applications, challenges, and future directions. Deep learning, particularly Convolutional Neural Networks (CNNs), has revolutionized medical imaging by automating the analysis of complex datasets and improving diagnostic precision. Image segmentation, a fundamental component of medical imaging, allows for delineating specific structures such as organs, tissues, and pathological regions. Together, these technologies have been applied in diverse fields, including oncology, cardiology, neurology, and ophthalmology, enabling applications such as tumor detection, organ segmentation, disease progression monitoring, and treatment planning. However, despite its transformative potential, the integration of deep learning into medical imaging faces several challenges. These include data scarcity, privacy concerns, interpretability issues, and regulatory hurdles. The article discusses various strategies to address these challenges, such as data augmentation, transfer learning, and the development of explainable AI models to ensure transparency and trustworthiness. Evaluation metrics, such as accuracy, sensitivity, specificity, and Dice Similarity Coefficient (DSC), are essential for assessing model performance. Rigorous clinical validation and regulatory approval are crucial to integrating deep learning systems into clinical workflows effectively. Looking ahead, the future of deep learning in medical imaging holds immense promise. Innovations like multimodal imaging, personalized medicine, and AI-driven automation are set to further revolutionize the field, enhancing the efficiency and accuracy of diagnostics. Collaborative efforts between clinicians, researchers, and AI developers will play a vital role in overcoming current limitations and driving progress. This article concludes by emphasizing the transformative potential of deep learning and image segmentation in medical imaging, highlighting their ability to improve diagnostic accuracy, streamline clinical workflows, and ultimately, enhance patient care. By addressing current challenges and continuing to innovate, these technologies are poised to redefine the landscape of medical diagnostics and treatment in the years to come.

Фортепианные пьесы М.П. Мусоргского. Статья I. Танцы, оперные сцены, скерцо

The article is devoted to the little–studied part of the piano heritage of M.P. Musorgsky — dance genres (Polka and Hopak), a Fair scene from the opera Sorochinskaya Fair and several plays written in the genre of Scherzo and Schercino. An attempt has been made to comprehensively present and analyze Musorgsky’s piano opuses, which are not part of large cyclic forms. The issues of genre specificity, piano stylistics and performance interpretation are discussed. The formal structure of the plays is considered by referring to the theoretical systems of A.B. Marx, I.V. Sposobin and L.A. Mazel. Textual issues are an important component of the article, since referring to the composer’s autographs makes it possible to identify features lost in publications of the XIX–XX centuries. It is concluded that the specific symphonic pianism was one of the characteristic essential properties of Mussorgsky’s piano style, expressed in various compositions (or versions of one composition) with varying degrees of strength. The study and publication were initiated by the ongoing work at the State Institute of Art Studies on the publication of the Complete Academic Collection of Works by M.P. Musorgsky.

Kazakhstani Simultaneous Interpreters’ Perceptions Concerning Interpretation Techniques and Tools

This study examines the key issues of simultaneous interpretation from the practitioners’ viewpoint. It is framed within the context of interpreters’ competences and the main tools and strategies like probability forecasting, segmentation, simultaneous interpreters deal with under Kazakhstani multilingual conditions. To analyze those issues, the authors used such data collection tools as semi-structured interviews with the practitioners as well as interpreters’ introspection which unveil some techniques used by interpreters consciously and unconsciously. The validity and reliability of the research were verified by ANOVA. Consequently, the main research objective is to show the interpreters’ opinion concerning the main issues of simultaneous interpretation process, so it will be valuable for both in-service and pre-service interpreters as they conduct the introspective analysis of the working process and results; also, it reveals some problems interpreters face to while doing their work. Such phenomena as probability forecasting, compression technique, segmentation of the source texts and others related to simultaneous interpretation are observed by practitioners who demonstrate both their attitude and necessity of their academic upgrading. Also, one of the main findings of our study is related to the interpreters’ reflection concerning such tool of their work as auditory speech recognition.

Contribution in evaluation the procedures for dealing with financial crime and corruption

This paper deals with an especially important and topical issue: tackling financial crime and corruption. It is important to record and evaluate the legislative framework, which appears to be multilevel, as there are a variety of provisions, which seek to cover all the issues raised. The research is of particular interest in comparatively assessing the changes that have taken place with the new Penal Code, which in general has resolved multiple interpretative issues, leaving some intact. The work is structured in three main sections: (1) in the protection of the Greek state from external intrusions by analyzing the provisions on fraud against public property, (2) in the protection of the Greek state from internal intrusions by analyzing the provisions for infidelity and embezzlement at the expense of public property and (3) in the fight against corruption in the public sector by analyzing the provisions of bribery in the public sector and the influence of trade. At the end, some structural conclusions for the overall legislative framework are recorded.

A Temporal Network Based on Characterizing and Extracting Time Series in Copper Smelting for Predicting Matte Grade.

Addressing the issues of low prediction accuracy and poor interpretability in traditional matte grade prediction models, which rely on pre-smelting input and assay data for regression, we incorporate process sensors' data and propose a temporal network based on Time to Vector (Time2Vec) and temporal convolutional network combined with temporal multi-head attention (TCN-TMHA) to tackle the weak temporal characteristics and uncertain periodic information in the copper smelting process. Firstly, we employed the maximum information coefficient (MIC) criterion to select temporal process sensors' data strongly correlated with matte grade. Secondly, we used a Time2Vec module to extract periodic information from the copper smelting process variables, incorporates time series processing directly into the prediction model. Finally, we implemented the TCN-TMHA module and used specific weighting mechanisms to assign weights to the input features and prioritize relevant key time step features. Experimental results indicate that the proposed model yields more accurate predictions of copper content, and the coefficient of determination (R2) is improved by 2.13% to 11.95% and reduced compared to the existing matte grade prediction models.

Automated lung cancer detection using novel genetic TPOT feature optimization with deep learning techniques

Lung cancer remains a leading cause of cancer-related deaths globally. Early and accurate detection is crucial for improving patient outcomes. Traditional methods, relying on manual interpretation of medical images, are time-consuming and prone to errors. Deep learning, particularly convolutional neural networks (CNNs), offers an automated alternative capable of learning intricate patterns from medical images. However, previous deep learning models for lung cancer detection have faced challenges such as limited data, inadequate feature extraction, interpretability issues, and susceptibility to data variability. This paper presents a novel deep learning methodology that addresses these limitations. Our approach leverages expansive datasets, incorporates advanced feature extraction techniques, improves interpretability, and accommodates the diverse nature of lung cancer. Specifically, we develop dedicated models for both chest X-ray and CT images utilizing publicly available datasets from Kaggle. Through the integration of feature selection and model selection techniques—such as employing a genetic algorithm in conjunction with the tree-based pipeline optimization tool (TPOT)—we achieved remarkable accuracy. Our X-ray model attains an overall accuracy of 95.47%, the CT model achieves an accuracy of 98.70%, and the combined model achieves an impressive overall accuracy of 98.93%. Our methodology significantly enhances the performance and efficiency of lung cancer detection and is a valuable tool for early diagnosis and intervention.

From Submission to Love: Understanding the Motivations in Romans 13:1-10

This paper studies Rom 13:1-10, analyzing the inner connection between Paul’s exhortation to submit to governing authorities and his admonition to love one another. It addresses interpretative and exegetical issues, including possible textual interpolations and the meanings of key terms. The analysis demonstrates that Paul’s argument hinges on the incrementum (αὔξησις) of underlying rationales, moving from general and external incentives to personal and internal motivations. This study highlights that the coherence of the passage relies on understanding both its imperatives and motivations, ultimately suggesting a deeper moral imperative.

Comparative analysis of traditional and transfer learning algorithms for coal spoil classification via close-range imagery

ABSTRACT The characterisation of materials is a prerequisite for evaluating and predicting the stability of mining waste dumps. Over the past three decades, the BHP Mitsubishi Alliance Coal framework has been a cornerstone in Australian coal mines for characterising waste dump materials. However, its reliance on subjective human observations has introduced potential inaccuracies and subjectivity into the process. In response to these limitations, this study proposes an innovative approach to classify coal spoil attributes by remotely acquiring images through phones/tablets. Automated image-based classification relies on feature extraction and a substantial amount of data. Nevertheless, the inherent complexity of geological factors contributing to the formation of both rare and dominant materials leads to imbalanced data. Recognising the need for classification mechanisms to overcome these challenges in spoil classification, the study explores and compares the use of convolutional neural networks, hybrid deep learning, and traditional techniques. Among the sixteen models evaluated in this study, the ResNet18-support vector machine model emerges as a powerful tool in geotechnical characterisation. However, it is essential to address issues of interpretability and adaptability to diverse datasets. As this study evolves, the field of geotechnical characterisation of spoil can anticipate the development of more robust methods in the future.

Challenging underground geophysical, geological and topographical surveys in the Borna Maggiore di Pugnetto karst collapse cave to delineate its genesis and actual structure

AbstractElectric resistivity surveys in karst environments are commonly employed to establish parameters that can help in the evaluation of collapse risk related to sinkhole or cave formation. However, these surveys are often executed from the surface with consequent limits in resolution and identification potential as a function of coverage thicknesses. Application of these methodologies directly inside known caves, for a better understanding of their formation mechanisms, is uncommon due to accessibility problems, the nontrivial referencing issues that arise when operating in an underground environment and the challenging 2D/3D interpretation issues emerging from the presence of the cavity itself. This paper reports on the application of electric resistivity tomography along with specific geological and topographic mapping, inside the Borna Maggiore di Pugnetto karst collapse cave. Comprehensive knowledge of this cave, developed in mica‐rich and carbonate‐rich calcschists, is problematic with traditional investigations, due to the cave breakdown that masks its structure. In this study, the 3D geometry of the cave is reconstructed using a topographical survey. This reconstruction is then utilised to perform a 3D inversion of the electrical resistivity tomography (ERT) dataset. The results of both 2D and 3D inversions are compared and discussed, focusing on the survey's ability to identify resistivity anomalies within the 3D volume surrounding the cave. Additionally, an open‐source script is provided to facilitate the replication of this 3D modelling and inversion in similar underground contexts. Results of the paper show the effectiveness of the proposed surveys in the delineation of genesis and actual structure of the cave. The paper also proposes a methodological approach that can be adopted in similar contexts to enhance the understanding of speleogenesis.

Control of foreign investment in the fisheries sector

The object of the study is the relations connected with foreign investment in strategic sectors of the economy, the subject is the relations connected with foreign participation in the sphere of fisheries. The following topics are considered in detail: the grounds for increased control over foreign investments in the sphere of fisheries; the concepts of «strategic economic entity engaged in fishing», «foreign investor», «group of persons, which includes a foreign investor», used for the purposes of regulating relations in the sphere of fisheries; legislative restrictions on foreign participation in the sphere of fisheries; consequences of violation of the rules on restricting foreign participation. Particular attention is paid to the issues of interpretation of the concept of «strategic economic entity», and to the interpretation of the law when applied to the foreign investor using the right to extract (catch) aquatic bioresources. The methodological basis of the study was formed by general logical methods of theoretical analysis, special formal-legal, empirical methods and the method of technical-legal analysis. The made the following conclusions regarding the specifics of foreign participation control in the fisheries sector: 1) it is justified to use a broad approach to the concept of a «strategic economic entity», which allows recognizing a company as such regardless of whether it has the right to extract (catch) aquatic biological resources; 2) the legal regime of a single economic entity applies to a group of persons and the same restrictions apply as to an investor acting autonomously; 3) in the fisheries sector, stricter barriers to foreign participation are established, compared to other strategic industries; 4) in case of violation of the legislation in the sector under consideration, measures of both civil and administrative nature are applied simultaneously.

Interpretation Issues with the ‘Seat Allocation Party’ Provision for Proportional Representation in the Public Official Election Act: A Challenge to the National Election Commission’s Interpretation

Interpretation Issues with the ‘Seat Allocation Party’ Provision for Proportional Representation in the Public Official Election Act: A Challenge to the National Election Commission’s Interpretation

The Existence of an Intra-Amniotic Microbiome: Assessing a Controversy.

The existence of intra-amniotic and placental microbiomes during pregnancy has been the source of considerable debate, with research yielding conflicting evidence. This study evaluated investigations into the putative presence of these microbiomes in healthy pregnancies by identifying design and data interpretation issues, particularly concerning contamination in samples with low-density bacterial DNA. Positive findings from diverse populations suggest a consistent presence of microbiota in the intra-amniotic space. Negative conclusions regarding the existence of these prenatal microbiomes emphasise the impact of contamination in the analysis of samples with low-density bacterial DNA. This study concluded that there is no definitive evidence to refute the existence of intra-amniotic microbiomes in healthy pregnancies. Also, it provides suggestions for controlling potential contamination factors in future research on intra-amniotic and placental microbial populations.