Research Tasks Research Articles

Reforming the Electoral System of the Republic of Benin in the 2020s

Relevance. Presidential elections in the Republic of Benin are scheduled for April 2026. Against this background, certain institutional adjustments took place. Back in 2018, a new legal institution emerged – the Repressive Economic Crimes and Terrorism Court CRIET. And on March 15, 2024, a new electoral code was adopted, amending and supplementing the electoral code of November 15, 2019.The purpose of the study is to analyze the political content of the electoral reform in the modern Republic of Benin.Research objectives: to analyze the formation and development of the political and electoral systems of the Republic of Benin from the moment of independence to the present day; to study of the political and legal support for the reform of the electoral system of the Republic of Benin; to determine the significance of the newly formed judicial body "CRIET" as a political resource for the political elites of the Republic of Benin.Methodology. In solving the research tasks the following methods were used: concrete historical method, method of political and legal analysis, institutional method, comparative method.The results of the study. The institutional features of the upcoming 2026 presidential elections in the Republic of Benin are determined exclusively by two instruments: the new Electoral Code and the CRIET court used to regulate electoral processes.Conclusion. The reasons for updating the Electoral Code on November 15, 2019 and the emergence of the CRIET court are as follows: ensuring stability at the institutional level, ousting political opponents and maintaining power. The political context of the electoral reform in the Republic of Benin is marked by the authorities' tendency to use CRIET as a political resource for the country's political elite.

Modern features of coronary thrombosis imaging in non-ST-elevation acute coronary syndrome

Cardiovascular diseases, in particular ST-elevation (STE-ACS) and non-ST-elevation (NSTE-ACS) acute coronary syndrome remain one of the main causes of death worldwide. Published data indicate that in a number of patients, the cause of NSTE-ACS is infarct-related artery occlusion. In modern medicine, there are both invasive and non-invasive coronary thrombosis imaging methods. The aim of the study was to analyze and systematize the literature data on coronary thrombosis imaging methods. The review was prepared using publications indexed in the PubMed, Google Scholar, Web of Science, and Cyberleninka databases. The search depth was 5 years, starting from 2020. The review is based on summarized data from the most relevant clinical studies, reports, and systematic reviews. The conducted literature analysis showed that both invasive and non-invasive imaging methods have a number of advantages and disadvantages. In this regard, improving these techniques remains an urgent research task.

Algorithms for Integrated Object Detection in Wireless Sensor Networks

Introduction. Wireless sensor networks can be used to solve various economic problems, including detection of objects (phenomena) of interest. Most of the well-known information processing algorithms in such networks are built according to a radial architecture. Such an approach assumes each sensor to have a direct access to the central node responsible for making the final decision. At the same time, this approach cannot always be implemented in practice, largely due to the complicated topography of the area. In this connection, the development of an integrated detection algorithm for sequential transmission of information from sensor to sensor is a relevant research task. This algorithm will contribute to improving the efficiency of decision making, extending the detection area and increasing the operation duration of the power sources of sensors.Aim. Synthesis and analysis of an integrated algorithm for object detection in wireless sensor networks with a linear topology.Materials and methods. The detection algorithm was synthesized based on the statistical theory of optimal signal detection and, specifically, on the following a priori information: the probability of errors in the detection of each sensor object and the probability of errors in communication channels. The efficiency of the synthesized algorithm was evaluated numerically in the MATLAB environment.Results. An algorithm for integrated detection of objects in wireless sensor networks was proposed. The efficiency of the developed algorithm was evaluated and the influence of such parameters as the signal/noise ratio and the number of sensors in the system on the detection efficiency was analyzed.Conclusion. The analysis of the synthesized algorithm can be performed with sufficient accuracy, with the algorithm parameters and the probability of errors when moving from sensor to sensor being determined by fairly simple recurrent expressions. Future research directions should address the influence of communication channels with fading and scattering on the detection efficiency, as well as the development of integrated detection algorithms with unknown target coordinates.

Faceted technology as the basis for the development of a set of stochastic research tasks for a hybrid intelligent learning system

Introduction. Modern education involves the integration of innovative teaching methods and digital technologies. In the context of the new digital paradigm, the issues of ensuring quality management of educational systems that fully meet the needs of each student in self-actualization, activation of his cognitive and motivational processes become relevant. In this aspect, the problem of organizing and accompanying the research activities of schoolchildren in the field of mathematics through intellectual management acquires particular importance. Intellectual educational systems can provide the processes of self-regulation of personality and individualization of learning during the development of complex mathematical knowledge. Materials and methods. A facet classification method proposed by Sh.R. Ranganathan assuming a parallel partitioning of a set of objects on a single feature into distinct, mutually independent subsets. Two facet features are defined as independent classification parameters when filling the database of research tasks – the level of development of research activity and the profile of thinking. KEYWORDS The results of the study. The didactic possibilities of facet classification are revealed, which provides the construction of a wide range of problem-oriented tasks, the construction of an individual educational trajectory, the management of the depth of intra-subject, interdisciplinary and inter-conceptual connections, as well as integrative thought processes in general. The database is presented in the form of a classification matrix that considers the gradation of the levels of research activity of schoolchildren and thinking profiles. Conclusion. The inclusion of schoolchildren in the activities of mastering mathematical concepts and procedures in a hybrid intellectual learning environment is an important factor in increasing the level of educational motivation and research culture, ensuring harmonious personal development through increasing intellectual and creative potential.

ChatGPT in English Learning : A Machine Learning Perspective

This study explores the transformative role of ChatGPT in education, focusing on its impact on language learning, writing support, and academic research through an analysis of English-language papers published from November 2022, when ChatGPT was developed, to 2024. Specifically, 20 research articles were selected and analyzed to examine how ChatGPT has been applied and discussed in educational contexts, with most of them published in journals indexed in Web of Science and Scopus. Using word frequency, similarity analysis, and clustering based on Principal Component Analysis (PCA) and K-means, the results highlight that ChatGPT is a multifaceted tool that enhances personalized learning experiences. The frequency analysis reveals strong associations with terms like “student,” “learning,” “education,” and “AI,” emphasizing ChatGPT’s role in supporting language acquisition, academic writing, and student engagement. The similarity analysis further demonstrates its contributions to writing, feedback, and research tasks. Clustering results identified four key areas of ChatGPT's application: AI and Technology in Education; Research and Data Utilization; Writing and Feedback; Language Learning and Education. These findings suggest that ChatGPT is a valuable resource in educational settings, though further research is needed to explore its long-term effects and ethical considerations in language learning contexts.

ІНТЕГРАЦІЯ ЕКСПРЕСИВНОГО СИНТАКСИСУ В МЕТОДИ НАВЧАННЯ ГРАМАТИКИ УКРАЇНСЬКОЇ МОВИ ДЛЯ МАЙБУТНІХ УЧИТЕЛІВ-СЛОВЕСНИКІВ: ТЕОРЕТИКО-ПРИКЛАДНИЙ АСПЕКТ

Introduction. The reform of higher education in Ukraine and its orientation towards international standards put forward new requirements for the formation of grammatical competence of future philology teachers. The ability of a future teacher of the Ukrainian language to creatively and synergistically approach the study of grammar is of great importance for the development of professional skills. The learning process requires a combination of theoretical and practical components, since grammatical knowledge detached from real speech practice acquires the status of officialdom and does not contribute to the full formation of language competence. At the same time, expressive syntax, as a part of the language system, opens a wide range of research, creative and analytical tasks that develop thinking and professional skills of the acquirer. Integrating expressive syntax into teaching methods improves grammar teaching and deepens cultural understanding of language. Purpose. The purpose of the article is to substantiate the theoretical foundations and determine practical approaches to the integration of expressive syntax into the methods of teaching the grammar of the Ukrainian language for future teachers of the Ukrainian language. Methods. The methods of theoretical analysis, comparison, systematization and generalization were used to obtain the research results. Results.The article examines the integration of expressive syntax into the methods of teaching the grammar of the Ukrainian language for future teachers of the Ukrainian language. It was noted that the introduction of means of expressive syntax into teaching methods contributes to the thorough formation of grammatical competence of future teachers of the Ukrainian language. It was found that expressive syntax integrated into various teaching methods (oral presentation of the material (explanatory-illustrative); teacher's conversation with students (heuristic); language observation (problematic); work with a textbook (reproductive); exercise method (partially exploratory); exploratory (formative), allows to ensure a holistic approach to the learning of grammar and the development of speech. The theoretical foundations and practical approaches to the integration of expressive syntax are substantiated, which allow structuring and systematizing the knowledge of learners, optimizing the educational process, and increasing the effectiveness of teaching the Ukrainian language. It is concluded that the analyzed methods of learning the grammar of the Ukrainian language with the interaction of expressive syntax demonstrate the effectiveness of the development of critical analysis, language skills and the conscious use of stylistically colored language tools in professional activities. Originality. The scientific novelty of the research results lies in the substantiation of the theoretical foundations and the determination of practical approaches to the integration of expressive syntax into the grammar teaching methodology of future teachers of the Ukrainian language. Practical recommendations for improving the educational process have been developed, which contribute to the harmonious development of professional skills, critical thinking and speech culture of future teachers of the Ukrainian language. Conclusion. On the basis of the theoretical justification of the methods of teaching the Ukrainian language and practical approaches to the implementation of expressive syntax, it was established that such integration allows for a comprehensive approach to the formation of the grammatical competence of future philology teachers. It was noted that the introduction of expressive syntax into each method contributes to the increase in the efficiency of grammar learning and the development of speech and practical knowledge in students. In this way, the integration of expressive syntax into teaching methods promotes the mastery of traditional grammatical constructions and the functional capabilities of various syntactic elements in the context of stylistic changes.

The Role and Implications of Artificial Intelligence in Higher Education: Opportunities and Challenges

Artificial Intelligence (AI) is transforming society, industries, and the education sector by automating tasks across the knowledge economy. AI in higher education presents significant opportunities for personalized learning, improved efficiency, and addressing inequalities, but also challenges ethics, data privacy, and the potential for over-reliance on technology. AI can enhance teaching and learning through intelligent tutoring systems, chatbots, and adaptive learning platforms, while aiding in research, data analysis, and administrative tasks. However, concerns about biased information, data security, and the impact on student autonomy and cognitive development need careful consideration. This paper highlights the crucial role of AI in higher education with its own set of opportunities and challenges in a qualitative manner.

Determining the influence of drum mixer parameters on the change in dough components concentration at the initial mixing stage

The object of this study is the process of mixing flour components and the liquid phase in a mixer. The research task is to analyze features of the technological process of dough mixing and establish regularities of uniform distribution of components based on theoretical and experimental studies of physical-mechanical, colloidal, and biochemical processes. Mathematical models and conceptual approaches have been proposed for modeling the transfer of flour components and liquid during dough mixing. A conceptual representation of an improved model of the first stage of dough mixing has been considered, taking into account the influence of design parameters of the drum working body and the multifaceted working chamber of the mixer and technological modes. Additionally, a modified equation was built for numerical modeling of the initial stage of mixing. This makes it possible to effectively calculate spatial variables and make preliminary predictions of the process dynamics. It has been established that the duration of interaction among components before the formation of a liquid-viscous structure is 60–65 seconds. At this step of the stage, it is important to enable high mixing intensity to achieve rapid and uniform distribution of components to ensure the required quality of the mixture. A feature of the results is the determination of the time and spatial parameters of the process, as well as the establishment of fundamental regularities of the pre-mixing stage. The proposed approaches could be used at the stages of design calculations to assess the efficiency of mixers and select their rational parameters, which would contribute to increasing the productivity and quality of the finished product

An attention-enhanced few-shot model for event detection in online social networks

The rapid expansion of digital communication has driven online social networks (OSNs) to surpass conventional news outlets, seamlessly integrating into our daily lives. This dependence on social network data has led to the emergence of various research paths, including event detection. However, such research tasks require large scale labelled social network data, and annotating such vast amounts of data is nearly impractical and often considered unfeasible. In addition, established literature encounters significant hurdles when it comes to detect unseen or new events, even after acquiring a substantial amount of training data. This marks a new era, emphasizing the importance of leveraging minimal data and embracing broader generalizations, much like human understanding of information. To this end, we proposed AttendFew, a model to detect events in X (formely X) with limited data (Few-shot learning) which detects events while mitigating data dependency, even for unseen events, aligning with the current need for less data and greater adaptability. The proposed method encodes the posts (also known as “posts”) with BERTweet and Graph Attention Networks to capture both contextual and structural aspects of social network data and further, stacked attention will be applied to make the model attend local and global context effectively and enhances the ability to understand complex data of social network. Furthermore, the amalgamation of the feature score and Multi-layer Perceptron (MLP) facilitates class matching, aligning the derived features with their associated classifications. This operation emphasizes pivotal dimensions within the feature space while addressing data sparsity. AttendFew is evaluated on real-world datasets and exhibits significantly better performance than state-of-the-art (SOTA) and other baseline methods in terms of accuracy, F1-score. This study represents an attempt to utilize a few-shot learning model in addressing the challenges posed by the sparsity and dynamism of online social networks for event detection.

The Influence of Ivan Kotliarevskyi on the Formation of Symbolic Topos in the Cultural Landscape of Naddniprianshchyna in the Second Half of the 19th Century

The purpose of this study is to conduct a cultural analysis of Ivan Kotliarevskyi’s legacy, ideas, and persona through the lens of their connections with topographical markers and memorial sites, which later became well-known tourist destinations. This analysis makes it possible to identify their role in shaping Ukrainian national identity and cultural heritage. The research methodology employs a wide range of general and specialised methods, including analysis and synthesis, comparison, generalisation, as well as genetic and biographical approaches. Additionally, historical-cultural and contextual frameworks are applied. These methods facilitate a detailed examination of Ivan Kotliarevskyi’s contributions, the impact of his figure and legacy, and their connections to the development of the cultural and social environment, tourist sites, and national identity within the social context of Naddniprianshchyna in the second half of the 19th century. The scientific novelty of the results presented in this article lies in addressing a significant research task: analysing the role of Ivan Kotliarevskyi in the development of symbolic topos, memorial and tourist sites, as well as in the formation of modern Ukrainian identity and its expression in the topographical markers of Naddniprianshchyna. This analysis provides a deeper understanding of the influence of the local cultural and educational context on the formation of Ukraine’s national and cultural values. Conclusions. As a result of this study, it was established that Ivan Kotliarevskyi holds one of the key positions not only in the formation of Ukrainian literature and education but also in the development of culture, symbolic topos, memorial sites, and Ukrainian national identity. It is worth noting that Kotliarevskyi’s literary works, legacy, ideas, and persona had a significant influence on the evolution of sociocultural and political processes in Ukraine during the second half of the 19th century, contributing to the development of modern national self-identification. It has been established that Kotliarevskyi’s work and legacy were based on the ideas of spiritual revival, the preservation of historical memory, and the formation of new social trends, and the development of Ukrainian society. Furthermore, his figure and memory became an important educational and unifying factor in various social processes of the second half of the 19th century. These processes were rooted in the study and preservation of ethnocultural traditions and the formation of the Ukrainian nation-building idea.

ФІНАНСОВІ ІНСТРУМЕНТИ ПУБЛІЧНО-ПРИВАТНОГО ПАРТНЕРСТВА В УМОВАХ ВІЙНИ РОСІЇ ПРОТИ УКРАЇНИ ТА ВІДБУДОВИ

Introduction. The article substantiates the necessity of using the possibilities of such a new component of public finance as public-private partnership finance for the reconstruction of the Ukrainian economy. Purpose. The purpose of the study is to determine the peculiarities of using the forms and types of public-private partnerships and their instruments in the implementation of partnership projects at all levels of government and by all its actors. Methods. The methodological basis of the study is the theoretical provisions of financial science, scientific works of well-known economists in the field of public-private partnership. To solve the research tasks, the paper uses the dialectical method of scientific knowledge to determine the content of public-private partnership finance and identify its instruments, conceptual and logical analysis and a systematic approach to the structure of this partnership, as well as the factor method, which allowed to identify and substantiate the determinants of public-private partnership development in Ukraine. Results. The research resulted in the identification of dialectical relations between different subjects of public-private partnership finance, the study of the unity of theory and practice in the implementation of the principles of public-private partnership and its instruments. It is substantiated that the fundamental characteristic of the methodology for cognizing the essence of public-private partnership finance and its instruments is the determinism of phenomena. Conclusions. The theoretical foundations for the use of financial instruments of public-private partnerships have been identified, which allows us to more clearly establish the methodological basis for their application in the context of the reconstruction of the Ukrainian economy, taking into account the experience of democratic countries. It is proved that public-private partnership finance can become a significant source of sustainable development investment in Ukraine in the context of war and reconstruction.

ВІДНОВЛЕННЯ ЗЕМЕЛЬНИХ РЕСУРСІВ ПІСЛЯ ВІЙНИ: ЕКОНОМІКО-ПРАВОВИЙ АНАЛІЗ ТА ПЕРСПЕКТИВИ

Introduction. The war launched against Ukraine by the Russian Federation on February 24, 2022, has become the largest military conflict in the European space since World War II. Military operations cause great damage to the environment. Direct hits of shells, burnt military equipment and oil products destroy the ecosystem and pollute soils and water with heavy metals and toxic elements. Purpose. The purpose of the article is a comprehensive study of lands disturbed as a result of hostilities, and a set of works on the restoration and use of disturbed territories is proposed. Methods. The methodological basis of the study is the theoretical provisions of science, scientific works of famous scientists in the field of use of disturbed lands. To solve the research tasks in the work, statistical, monographic methods, logical analysis and a systematic approach were used. Results. The article analyzes the main negative consequences of hostilities. Information data on the presence of toxic heavy metals in soils after bombings and artillery shelling is provided. The main set of works for the restoration of the damaged territory is proposed. Conclusions. The result of the study was the discovery that a quick solution to the current situation with the contamination of territories as a result of the war of the Russian Federation against Ukraine at the state level is possible only if effective regulatory legal acts and institutions are developed and applied. One of the first conditions is the development and approval at the legislative level of the National Program for the Protection and Restoration of Lands in Ukraine in the Post-War Period, which should include a detailed plan for the restoration of the lands of Ukraine.

Smoking status and voting behaviour and intentions in countries of the former Soviet Union

Smokers experience multiple disadvantages throughout their lives, yet there is another disadvantage, political, that is less widely recognised. Smokers are less likely to vote but only so far in studies conducted in Western democratic regimes. This cross-sectional study aimed to examine the association between current smoking and voting behaviour and intentions in nine countries of the former Soviet Union (FSU). Data were analysed from 18,000 individuals aged ≥ 18 in Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Moldova, Russia and Ukraine, collected in the Health in Times of Transition (HITT) survey in 2010/11. Information was obtained on smoking status and voting behaviour and intentions. In a fully adjusted logistic regression analysis, current smoking was associated with significantly higher odds of ‘never voting’ (not having voted in the past or intending to vote in future) in the pooled sample (OR: 1.29, 95% CI 1.13–1.47). In stratified analyses, smoking was associated with never voting in women but not men and in young but not middle-aged or older adults. The smoking-never voting association was observed in flawed democracies (OR: 1.57, 95% CI 1.07–2.32) and hybrid regimes (OR: 1.31, 95% CI 1.08–1.59) but not in authoritarian regimes (OR: 1.02, 95% CI 0.81–1.29). Smoking is associated with never voting in these FSU countries although not in all population subgroups or types of political regime. A necessary task for future research will be determining the factors associated with not voting among smokers in these countries.

Role of artificial intelligence in advancing immunology.

Artificial intelligence (AI) has revolutionized various biomedical fields, particularly immunology, by enhancing vaccine development, immunotherapies, and allergy treatments. AI helps identify potential vaccine candidates and predict how the body reacts to different antigens based on a vast number of genomic sequences and protein structures. AI can help cancer patients by analyzing their data and offering personalized immunotherapies. AI has also advanced the field of allergy by identifying potential allergens and predicting allergic reactions based on patient genetic and environmental factors. AI could also help diagnose multiple immunological diseases, including autoimmune diseases and immunodeficiencies, by analyzing patient history and laboratory results. AI has deepened our understanding of the human genome by providing numerous amounts of data from DNA sequences previously believed to be nonfunctional. Through machine learning and deep learning, many laborious research tasks, such as screening for DNA mutations, can be efficiently performed in a short amount of time. AI and machine learning are significantly advancing biomedical science in significant areas, including research and industry. This review discusses the latest AI-based tools that can be utilized in the field of immunology. AI tools significantly advance the field of medical research and healthcare by enabling new scientific discoveries and facilitating rapid clinical diagnosis.

"Do I Need Anything More Specific?": Experiences of Autistic Participants in a Language-Focused Research Study.

Autistic advocates have called for researchers to engage with the needs and experiences of autistic people when planning and designing research studies. The purpose of the present study was to better understand the experiences of autistic adults participating in a language research study and how researchers can design more accessible future studies. The present study was a secondary thematic analysis of data recorded during a larger study of spoken narratives by autistic adults. During virtual research interviews, participants frequently expressed comments about the nature of the research tasks and their experiences of participation in the study. The full interview transcripts were analyzed to identify data relating to participants' subjective experiences of research participation. Thematic analysis was applied to transcripts of all comments not directly elicited by the structured narrative prompts. Four main topics and their subthemes were established based on analysis of the data set: processing strategies, attitudes toward research, awareness of the research process, and self-reflective comments about the narrative tasks. The main topics and their subthemes are discussed to derive insight into the experiences of autistic research participants. The findings are especially relevant to researchers and practitioners who conduct spoken language tasks with autistic people. To make research participation more accessible and affirming for autistic people, researchers can share specific information about what to expect before, during, and after participation.

Augmented and Programmatically Optimized LLM Prompts Reduce Chemical Hallucinations.

Utilizing large Language models (LLMs) for handling scientific information comes with risk of the outputs not matching expectations, commonly called hallucinations. To fully utilize LLMs in research requires improving their accuracy, avoiding hallucinations, and extending their scope to research topics outside their direct training. There is also a benefit to getting the most accurate information from an LLM at the time of inference without having to create and train custom new models for each application. Here, augmented generation and machine learning-driven prompt optimization are combined to extract performance improvements over base LLM function on a common chemical research task. Specifically, an LLM was used to predict the topological polar surface area (TPSA) of molecules. By using augmented generation and machine learning-optimized prompts, the error in the prediction was reduced to 11.76 root-mean-squared error (RMSE) from 62.34 RMSE with direct calls to the same LLM.

Vehicle Trajectory Recovery Based on Road Network Constraints and Graph Contrastive Learning

Location-based services and applications can provide large-scale vehicle trajectory data. However, these data are often sparse due to human factors and faulty positioning devices, making it challenging to use them in research tasks that require precision. This affects the efficiency and optimization of sustainable transportation systems. Therefore, this paper proposed a trajectory recovery model based on road network constraints and graph contrastive learning (RNCGCL). Vehicles must drive on the road and their driving processes are affected by the surrounding road network structure. Based on the motivations, bidirectional long short-term memory neural networks and an attention mechanism were used to obtain the spatiotemporal features of trajectory. Graph contrastive learning was applied to extract the local feature representation of road networks. A multi-task module was introduced to guarantee the recovered points strictly projected onto the road. Experiments showed that RNCGCL outperformed other benchmarks. It improved the F1-score by 2.81% and decreased the error by 8.62%, indicating higher accuracy and lower regression errors. Furthermore, this paper validated the effectiveness of the proposed method by case studies and downstream task performance. This study provides a robust solution for trajectory data recovery, contributing to the overall efficiency and sustainability of transportation.

Integration of research support services (RSS) and research data management (RDM) strategies to enhance research efficiency and impact: a scientometric study

Purpose This study aims to review the scientific landscape of research support services (RSS) and research data management (RDM) provided by libraries, focusing on their application in research and learning contexts. This research seeks to examine the growth of literature in this field, identify the most influential authors, countries, affiliations and journals and assess collaboration across different geographical regions. In addition, the study will identify the most impactful paper based on the highest citation count and Altmetric Attention Score and analyse keywords to determine research trends in this subject area. Design/methodology/approach This study uses a Scientometric and Altmetric approach to analyse a decade (2014–2023) of research outputs from Scopus. Data was retrieved with a keyword-based search strategy and visualized for science mapping (author, country, organization collaborations and keyword co-occurrence) using VOSviewer. Dimension.ai was used to determine Altmetric scores and Mendeley readership of influential works. Findings This study analyses 1,085 RSS and RDM publications (2014–2023), showing global growth but lagging citations. Christina Ribeiro leads with 21 publications. Key sources include the Data Science Journal and the University of Pittsburgh. RDM has more focus than RSS, which needs further study. RSS and RDM simplify research tasks, widely used in social and computer sciences. Top works are mainly in library science. Effective RSS in libraries can enhance research and redefine their academic role. Originality/value This study examines global scientific literature to assess libraries’ awareness of RSS and RDM services for research and learning. To the best of the authors’ knowledge, no previous study has applied Scientometrics and Altmetrics to these aims and objectives before.

Networking Systems for Video Anomaly Detection: A Tutorial and Survey

The increasing utilization of surveillance cameras in smart cities, coupled with the surge of online video applications, has heightened concerns regarding public security and privacy protection, which propelled automated Video Anomaly Detection (VAD) into a fundamental research task within the Artificial Intelligence (AI) community. With the advancements in deep learning and edge computing, VAD has made significant progress and advances synergized with emerging applications in smart cities and video internet, which has moved beyond the conventional research scope of algorithm engineering to deployable Networking Systems for VAD (NSVAD), a practical hotspot for intersection exploration in the AI, IoVT, and computing fields. In this article, we delineate the foundational assumptions, learning frameworks, and applicable scenarios of various deep learning-driven VAD routes, offering an exhaustive tutorial for novices in NSVAD. In addition, this article elucidates core concepts by reviewing recent advances and typical solutions and aggregating available research resources accessible at https://github.com/fdjingliu/NSVAD. Lastly, this article projects future development trends and discusses how the integration of AI and computing technologies can address existing research challenges and promote open opportunities, serving as an insightful guide for prospective researchers and engineers.

СОВЕТСКИЙ СЕВЕР КАК ПЕРЕВАЛОЧНАЯ БАЗА ЛЕНД-ЛИЗА. ЧАСТЬ 1. ТАМОЖЕННАЯ СЛУЖБА: КРАТКИЙ ИСТОРИОГРАФИЧЕСКИЙ ОБЗОР

In the article, the subject of the research is a historiographical review of sources and scientific literature on the topic. It has been revealed that there is a certain research base on the problem, namely: statistical data, publications of research results in articles, materials of scientific events, collections and statistical reports, as well as in monographs. This creates the basis for the implementation of certain research projects. It is shown that the statistical data, characterized by the greatest completeness of information, distinguish the studies of foreign authors, but as a rule, they take into account only the volume of shipments sent. In the publications of domestic authors, the emphasis is placed on the received and officially issued volumes of supplies. As a result, there are discrepancies in the scientific literature and discussions that do not lead to consensus. It is proved that in the array of studies, the results of the activities of the customs of the Soviet Union during the war, including the northern and Arctic ones, occupy an insignificant place, which does not allow us to fully and objectively reveal their place and role in strengthening the material and technical base of the Active army and the rear of the country. The conclusion is formulated that the degree of scientific development of the problem currently prevents the formation of a more complete picture of the level of implementation of the Lend-Lease program. The study of a scientific problem remains one of the scientific research tasks for both domestic and foreign scientists.