Research Tasks Research Articles

"Political Romanticism" and "Aesthetic Politics": Content, Comparison, Criticism

Relevance. In Western political philosophy and political theory, the judgment that modern democracy does not have a satisfactory general ("synoptic") theory, has the status of (almost) universal significance ‒ the philosophical interpretation of this "situation" is an urgent research task for modern political philosophy.Purpose. The representation of the political in the "concept of the political" and the "economic paradigm of the political" reveals identical characteristic features; the configuration of their unity and the assessment attributed to them differ ‒ the goal is to understand the different meanings attributed to the same "phenomena" and "processes" of modernity in conceptual frameworks defined by opposite paradigms.Objectives. The method of determining the actual policy in the horizon of "metaphysical principles" is demonstrated. The fundamentals of the paradigmatic representation of the political and the principles of constructing the narrative of modern liberal democracy are revealed. The procedure of giving credibility to theoretical judgments about the political is analyzed. Methodology. The paradigmatic representation of the political can be understood, and only as a result explained; understanding refers to the real concept, explanation – to reality; the combination of the "real" and "real" distinguished in the tradition of Western philosophy takes place in a philosophical concept; the formation of the concept presupposes philosophical interest; philosophical hermeneutics and deduction provide tools suitable for understanding the political in unity: from "metaphysical foundations" to practical politics. Results. The statement about the romantic nature of the modern representation of the political in the liberal economic paradigm looks reliable. It is proved that historical and aesthetic representation, more than «mimetic», fulfills the imperatives attributed to the modern form of political – Western representative democracy. The evidence of the necessary replacement of the «center of political ontology» from the sovereign to an «empty place» is demonstrated.Conclusions. Representative democracy, romantic and postmodern, is a necessary and legitimate form of the modern liberal way of representing the political. The main task of aesthetic politics is supposed to ensure security: "keep to the middle and avoid extremes", exclude an event, normalize the political, that is, to banalize politics.

Fast computation of General SimRank on heterogeneous information network

Similarity computation is a fundamental aspect of information network analysis, underpinning many research tasks including information retrieval, clustering, and recommendation systems. General SimRank (GSR), an extension of the well-known SimRank algorithm, effectively computes link-based global similarities incorporating semantic logic within heterogeneous information networks (HINs). However, GSR inherits the recursive nature of SimRank, making it computationally expensive to achieve convergence through iterative processes. While numerous rapid computation methods exist for SimRank, their direct application to GSR is impeded by differences in their underlying equations. To accelerate GSR computation, we introduce a novel approach based on linear systems. Specifically, we transform the pairwise surfer model of GSR on HINs into a new random walk model on a node-pair graph, establishing an equivalent linear system for GSR. We then develop a fast algorithm utilizing the local push technique to compute all-pair GSR scores with guaranteed accuracy. Additionally, we adapt the local push method for dynamic HINs and introduce a corresponding incremental algorithm. Experimental results on various real datasets demonstrate that our algorithms significantly outperform the traditional power method in both static and dynamic HIN contexts.

Genesis and Current State of the Forensic Prediction Theory

Predictive activities in the field of forensic science are one of the most relevant practice-oriented areas of research within the framework of forensic science. Predicting development trends of any subject, process, and system based on existing information about them and the achieved level of science allows us to obtain advanced knowledge that is relevant given the high speed of emergence, processing and use of information in modern society. The digitalization process causes the emergence of new objects and new tasks of expert research, changes in their methods and methodology, which significantly affects the formation and development of new types of forensic examinations. The theory of forensic forecasting should become the foundation for scientifically based forecasts in this area. The work scrutinizes the genesis of this theory, its close connection with scientific forecasting in other sciences of the criminal legal cycle. Particular attention is given to its connection with forensic forecasting. The paper states the relatively low level of development of the theory of forensic forecasting, and identifies factors that complicate forecasting in the field of forensic activity. The relevance of further developments in this area is noted. It is indicated that the timely organization of methodological developments, updating of professional and educational requirements for experts and other practically significant activities require theoretical predictive information for their planning.

Housing conditions in European one-person households.

This study addresses the satisfaction of housing-related needs in single-person households across European countries. The primary objective is to assess the housing satisfaction of single-person households in European countries, specifically within the Visegrad Group. The study seeks to identify trends in housing conditions, create a ranking of countries based on these conditions, and categorize countries with similar levels of unmet housing needs. The study employs statistical measures and methods to achieve its objectives. Time series are constructed for European countries, and linear trends are analyzed to identify statistically significant changes in selected housing aspects from 2005 to 2022. Various research tasks, including ranking countries and grouping them based on housing conditions, are accomplished using established methods like linear ranking and Ward's cluster analysis. Key findings include significant variations in financial burdens related to housing costs, thermal comfort, environmental pollution, and safety issues across European countries. The study reveals both improvements and challenges in housing conditions from 2005 to 2022 in one-person households. For instance, financial stress due to housing costs decreased in some countries, while thermal comfort issues improved in several nations. The results also highlight the heterogeneity within the Visegrad Group. The study concludes that there is a need for targeted actions to address housing-related issues in single-person households. The findings underscore the importance of investments in building energy efficiency, initiatives for affordable housing construction, and environmental policies. The research emphasizes the impact of housing conditions on health, well-being, and overall community life, urging policymakers to consider these factors for holistic improvement in the housing sector.

Avoiding basic mistakes when programming the use of artificial intelligence in soil and environmental science research

In this discussion text (proposed as an opinionated exposition on a relevant scientific issue, with the aim of stimulating further discussion in a broader scientific forum) the author comments on mistakes that should be avoided when trying to use artificial intelligence (AI) in research, with special focus on soil science and environmental sciences. The author indicates aspects of research where it would not be reasonable (and/or correct) to use AI, while showing other aspects where an appropriate use of AI tools could be of real help for researchers in these fields and the whole society. The use of AI in investigation is a cutting-edge theme needing reflection and proposals to extract its best without causing an inappropriate deviation of resources, as well as a waste of time for people involved in direct research tasks or assessment, and without provoking undesirable side effects.

Assessment of Phytomass and Carbon Stock in the Ecosystems of the Central Forest Steppe of the East European Plain: Integrated Approach of Terrestrial Environmental Monitoring and Remote Sensing with Unmanned Aerial Vehicles.

The rapid and accurate estimation of aboveground forest phytomass remains a challenging research task. In general, methods for estimating phytomass fall mainly into the category of field measurements performed by ground-based methods, but approaches based on remote sensing and ecological modelling have been increasingly applied. The aim is to develop the scientific and methodological framework for the remote sensing estimation of qualitative and quantitative characteristics of forest stands, using the combination of surveys and machine learning models to determine phytomass of forest stands and calculate the carbon balance. Even-aged stands of different tree species growing in the forest steppe zone of the East European Plain were chosen as test objects. We have applied the modernized methodological approaches to compare and integrate forest and tree stand characteristics obtained by ground-based and UAV-based comprehensive surveys; additionally, we developed computer vision models and methods for determining the same characteristics by remote sensing methods. The key advantage of the proposed methodology for remote monitoring and carbon balance control over existing analogues is the minimization of the amount of groundwork and, consequently, the reduction inlabor costs without loss of information quality. Reliable data on phytomass volumes will allow for operational control of the forest carbon storage, which is essential for decision-making processes. This is important for the environmental monitoring of forests and green spaces of various economic categories. The proposed methodology is necessary for the monitoring and control of ecological-climatic and anthropogenic-technogenic transformations in various landscapes. The development is useful for organizing the management of ecosystems, environmental protection, and managing the recreational and economic resources of landscapes with natural forests and forest plantations.

ОДНОВИМІРНА ЗГОРТКОВА МОДЕЛЬ НЕЙРОННОЇ МЕРЕЖІ ДЛЯ ОБРОБЛЕННЯ СИГНАЛІВ З АМПЛІТУДНОЮ МОДУЛЯЦІЄЮ БАГАТЬОХ СКЛАДОВИХ

The processing of radio signals using artificial neural networks (ANNs) has great potential for research, which can be explained by the adaptability of ANNs to various transmission conditions and the ability to detect abstract patterns of changes in signal parameters. The article reviews the works of other authors devoted to different ways of using ANNs for processing radio signals. Taking into account the information in the reviewed works, the research task was formed, which consists in developing an optimized ANN model for radio signal processing. Signals with amplitude modulation of many components (AMMC) were chosen to form training samples for ANN. The choice of modulation type is justified by greater energy efficiency compared to other widely used digital modulation types, such as quadrature amplitude modulation. Mathematic basis of AMMC signal generation is described. The process of finding the coordinates of three component 8-AMMC signal constellation is explained, the formation of signals in the time plane based on the found coordinates is explained as well as their discretization and the addition of white noise. An iterative algorithm for generating initial data for ANN based on the described ratios is proposed. The general structure of one-dimensional convolutional neural network is considered. Functions of individual neurons, connections between them, the formation of layers and the convolution operation are described mathematically. On the basis of the previously given ratios, a final display of the network was formed. Specific dimensions and activation functions for layers are selected. The use of convolutional layers is justified by time invariance. Based on the reviewed mathematical models, selected activation functions and dimensions, a neural model was formed. The process of validating the effectiveness of the formed neural model is described, which is based on comparing the symbolic error probabilities of the proposed and reference models at different signal-to-noise ratios. The validation results are presented. The advantages of the obtained model over the previously proposed purely recurrent model and the AMMC reference receiver are explained.

Drawing human pedigree charts with DrawPed.

Creating pedigree charts is a recurring task in biomedical research, but there are few online tools for drawing complex human pedigrees available and even fewer are free. With DrawPed we aim to close this gap. DrawPed automatically draws pedigree charts from standard PED format pedigree files. Users can also create pedigrees from scratch and interactively edit existing pedigrees. The application can display conditions not captured in a PED file such as deceased persons or suspected consanguinity of parents. Pedigree charts are displayed as SVGs, which are scalable and hence publication-ready. Pedigrees can be exported as PED files for storage, exchange, or use in other applications. DrawPed is open source and freely available at https://www.genecascade.org/DrawPed/.

XTime: A general rule-based method for time expression recognition and normalization

Time expression (a.k.a., timex) recognition and normalization (TERN) is a crucial task for downstream research. However, previous studies have overlooked the critical characteristics of timexes that significantly impact the task. To gain deeper insights, we conduct an analysis across four diverse English datasets to examine the key attributes of timex constituents. Our analysis reveals several noteworthy observations, such as: timexes tend to very short; the majority of timexes contain time tokens; there exist strong mapping relationships between time tokens and timex types; there exists a priority relationship among timex types; and timex values exhibit only some standard formats. Based on these insights, we propose a novel general rule-based method termed XTime11Source codes and datasets are available at https://github.com/xszhong/XTime. to recognize timexes from free text and normalize them into standard formats. Notably, XTime’s rules are designed in a general and heuristic manner, enabling its independence of diverse domains and text types. Experimental evaluations conducted on both in-domain and out-of-domain English datasets demonstrate that XTime consistently outperforms or performs comparably to representative state-of-the-art methods.

Specific Grammatical Aspects of Translating the Iaea Regulatory Documents into the Russian Language

The article discusses some grammatical features of the IAEA safety requirements and guides in the English language and in their Russian translations. Considering that these documents make a foundation of the global nuclear safety system, and grammar is the backbone of the language and an essential constituent of the communication content, the paper is focused on the analysis of the ways of translating specific grammatical structures, which determines current importance of this study. The aim of the work is to compare typical grammatical constructions used in the texts of the Russian translations of the IAEA publications under consideration with the grammatical features of similar Rostechnadzor’s regulatory documentation. The main tasks of the authors are to compare the ways of translating English modal and passive constructions, English verbals and verbal structures into Russian and to verify the translator’s choice of a certain grammatical structure in the translations of the regulations and guidelines under consideration. Taking research tasks predetermined the choice of the following research methods: comparative analysis; linguistic and statistical analysis; contextual analysis; inductive and deductive analysis. To obtain linguistic and statistical analysis data, the authors applied such software tools as SDL Trados, Adobe Acrobat, Excel and MS Word. In order to ensure the correctness of the results, only the substantive part of the documents under consideration was analyzed.The analysis has brought to the following results: the need to comply with the regulatory requirements narrows the choice of Russian modal constructions in the translation of "should" statements used in IAEA documents. The issue of changing the regulatory requirement that prescribes to use the Russian verb "следует" has to be treated very carefully, considering the established practice of both translating the IAEA safety guides and their understanding by the target users; the translation of English passive constructions and verbals used in the IAEA safety guides gives clear preference to the use of Russian indefinite-personal structures with verbs in the active voice in the 3rd person plural; the grammatical structures used in the translation of the IAEA publications in question generally correspond to the grammatical structures used in the Rostechnadzor regulatory documents, which is predetermined by the information nature and ensures a fairly high similarity of their style.

Ile ojca, ile matki jest w polskiej ojczyźnie?

HOW MUCH OF A FATHER, HOW MUCH OF A MOTHER IS THERE IN THE POLISH OJCZYZNA [FATHERLAND]? ONCE AGAIN ABOUT THE ROLE OF ETYMOLOGICAL DATA IN THE RECONSTRUCTION OF THE LINGUISTIC WORLDVIEW The author assumes that (1) etymological data are ethnolinguistically relevant, (2) the task of etymological research is to detect the original meaning of a word (to find the original semantic motivation) and to reach a world that disappeared, but, at the same time, remained a part of the present, (3) the focus of the research should shift from formal interests to broadly understood semantics (reconstruction of the conceptualization processes that accompany the nominations and of the linguistic image of the words hiding behind words). Based on this, the author undertakes the analysis of the names for the fatherland in the Polish language. To do this, she compares the Polish lexis and its onomasiological basis with the corresponding vocabulary of other Slavic languages. According to the author, studying the semantics of a whole group of words allows us to discover the basic cognitive situation, i.e. the generalized experience of a specific linguistic and cultural community. Using dictionary data and studies of the concept of the “fatherland” in Slavic languages, the author analyses the names related to the Polish concept of OJCZYZNA: lexical and semantic nests such as “ojczyzna i patria” [homeland and patria], “macierz [“motherland”], “państwo” [“state”] and “kraj” [country], and she extracts the underlying cultural “codes”: (1) kin and family, (2) home, (3) power and dominion, and (4) land. The author also shows the coherence of these codes and the image of the fatherland that emerges from them.

The Specific Application and Meaning Expression of Folk Art in Urban Landscape Design Under the Optimized Interactive Genetic Algorithm

With the rapid and stable growth of China's economy and the rapid development of urban construction, urban complexes have sprung up in major cities. The lag in urban ecological environmental protection, maintenance and construction has brought great pressure to the needs of today's urban residents in terms of life, safety, and sense of belonging. In the major cities with many high-rise buildings in contemporary China, land resources are becoming more and more scarce, and the urban ecological environment is in urgent need of recycling, and due to the blind imitation of Western culture and design mode and the neglect of my country's traditional regional culture, the urban landscape lacks interaction, resonance, and sense of belonging with citizens, and the phenomenon of landscape similarity emerges in various cities, focusing on the landscape space of urban complexes. There are also these problems. Urban residents urgently need a third space that can adjust their physical, mental, and spiritual needs. How to design an urban complex landscape that meets the aesthetic needs and humanistic needs of contemporary cities and has regional characteristics has become the first important task of my research. Folk art is an artistic treasure created by the working people in their production and life. Folk art is the embodiment of cultural regionality and the foundation of national culture. It awakens people's awareness of the importance of local culture, awakens people's sense of belonging, and is closer to the local public life. Today, the living soil and social and humanistic environment of folk art are in the process of urbanization in our country, and there is a trend of gradual disappearance of lifestyle changes. How to make the contemporary urban complex landscape an organic soil for the survival, expression, application, and development of folk art is an important task in contemporary urban landscape design. Based on optimization, related concepts such as symbols, folk art symbols, urban complexes, urban complex landscape design, etc. have been sorted out. The relevant experimental results show that the construction land accuracy of the logistic regression model based on genetic algorithm has increased from 78.0% to 85.3%. kappa increased from 74.5% to 81.2%. Research shows that the logistic regression parameter optimization method based on genetic algorithm has better simulation effect than the conventional logistic regression method and is more suitable for the situation of unbalanced data distribution and many data features in the simulation of large-scale urban land dynamic changes.

Digital multimodal composing in L2 classrooms: A research agenda

Abstract Research on digital multimodal composing (DMC) in second language (L2) classrooms has proliferated considerably in recent years, to a large extent in response to the changing digital and multimodal communication landscape. This article offers a research agenda on DMC in L2 classrooms. We begin with a theoretically oriented overview of DMC scholarship. We then examine seven research themes for future research inquiry, from which we draw seven research tasks. The seven themes are: (1) the effectiveness of DMC for L2 writing development; (2) DMC task design; (3) L2 teacher education/training for implementing DMC; (4) feedback practice for DMC; (5) DMC assessment; (6) collaborative DMC as a translanguaging space; and (7) the deployment of DMC for critical digital literacies. Throughout the article, we refer to interdisciplinary scholarship and methods from multimodality, L2 writing, composition studies, new literacy studies, language teacher education, and computer-assisted language learning. The seven research tasks represent what we see as the essential next steps for understanding DMC, which is a young domain that has great potential to advance L2 language and literacy education in the digital age.

Instructional design of blended learning in higher education: an analytical review of international research

Relevance. The relevance of this study lies in the fact that in modern conditions the huge potential of constantly updated digital educational technologies is not sufficiently used in blended learning. Purpose. The purpose of the study was to rethink traditional learning in accordance with the requirements of digital time, to consider the possibilities and contradictions of online learning, as well as to study blended learning in more depth. Methodology. The research tasks involved analysing literature at the intersection of pedagogy, psychology, information technology, and technical sciences. Also, comparative analysis, modelling, and synthesis were used. As a research methodology, interdisciplinary, systemic, activity, synergetic approaches, project methods, and blended learning didactics were implemented, the results of the experiment were analyzed and its results were summarized, and the differentiation of digital resources in education was compared. Results. It was determined that a methodology for assessing the formation of general and professional competencies of students through blended learning can be achieved by creating tools for measuring expected competencies. The results of previous studies have revealed the lack of resources that can provide a link between online and offline learning, and the need to develop a blended learning design using digital communication in learning. The analysis of the process of transformation of education during the pandemic and after it revealed the lack of didactic and technological justification for blended learning. Conclusions. The study provides a comprehensive analysis of blended learning in the digital era, offering practical tools and highlighting areas for further development in educational practices. Keywords: blended learning; instructional design; digital communications; information technology; educational trends; new directions in learning; multimedia

Reliable Out-of-Distribution Recognition of Synthetic Images.

Generative adversarial networks (GANs) and diffusion models (DMs) have revolutionized the creation of synthetically generated but realistic-looking images. Distinguishing such generated images from real camera captures is one of the key tasks in current multimedia forensics research. One particular challenge is the generalization to unseen generators or post-processing. This can be viewed as an issue of handling out-of-distribution inputs. Forensic detectors can be hardened by the extensive augmentation of the training data or specifically tailored networks. Nevertheless, such precautions only manage but do not remove the risk of prediction failures on inputs that look reasonable to an analyst but in fact are out of the training distribution of the network. With this work, we aim to close this gap with a Bayesian Neural Network (BNN) that provides an additional uncertainty measure to warn an analyst of difficult decisions. More specifically, the BNN learns the task at hand and also detects potential confusion between post-processing and image generator artifacts. Our experiments show that the BNN achieves on-par performance with the state-of-the-art detectors while producing more reliable predictions on out-of-distribution examples.

Using product profiles for retrospective case studies in SGE – system generation engineering

AbstractSupporting product developers in innovating is an important task of design research. An invention and a valid need situation described through a product profile are necessary elements of innovation. But how can we derive recommendations on how to develop “innovative” products if the success of a product can only be measured retrospectively? Retrospective case studies are one approach to investigate relationships between context factors, variations in systems and product profiles, and innovation success. To investigate these, we use product profiles in case studies across system generations.

The role of generation Z media literacy in confronting the destructive value messages of digital media

Introduction. Today, the Internet environment has become a usual place of living and working for Generation Z. Digital media have created a unique digital environment adapted to the most diverse requests and needs of the new generation. However, in addition to its constructive capacity and boundless opportunities for education, the digital environment has a negative impact on the value orientations and priorities of the younger generation. The aim of this article is to review the studies on the role of media literacy in countering the negative effects and destructive value messages in digital media, as well as to summarize current trends and educational solutions in teaching media literacy. Materials and methods. The methodological basis of the research includes general scientific methods such as analysis, synthesis, induction, deduction, description, philosophical methods (hermeneutics, dialectics), social and pedagogical methods. Materials of the research are articles from Russian and foreign periodicals published mainly for the last 5 years. The main source of data was the analysis of publications in the search engine for world scientific publications Google Scholar. The search for publications was additionally carried out through electronic scientific libraries Elibrary. ru, CyberLeninka, and ResearchGate. Research results. The increasing complexity and growth of the destructive influence of digital media on the young generation determines the principles of developing and shaping the content and focus of modern media education. The development of media literacy should be carried out according to the age, value, and motivational features of personal development at all levels of education, combining technological, cognitive, social, and ethical components of media competence. Conclusion. The development and execution of media literacy projects and educational programs aimed at developing media literacy and increasing resistance to manipulative and destructive content of digital media is an urgent research and practical task, especially in the context of the experimental-evidence-based paradigm. Today, under conditions of increasing complexity and improvement of digital media, literacy and information culture become the key to the safe psychological and physical growth of a person in the digital world, including in the context of increasing civil involvement of young people. Media literacy is a sophisticated set of abilities, including the ability to think critically about a diverse range of digital content (news, entertainment, commercial, educational, viral) and to understand its consequences. It also includes the ability to use digital tools and technologies to create content, the ability to reflect on one's state and behavior and to manage emotional reactions, and the ability to build socially acceptable and ethically correct communication and interaction with other users. There is a shortage of Russian studies aimed at investigating the educational effects related to literacy education of different age groups in accordance with the user's goals and motivations for digital content consumption.

Integrated machine learning reveals aquatic biological integrity patterns in semi-arid watersheds

Semi-arid regions present unique challenges for maintaining aquatic biological integrity due to their complex evolutionary mechanisms. Uncovering the spatial patterns of aquatic biological integrity in these areas is a challenging research task, especially under the compound environmental stress. Our goal is to address this issue with a scientifically rigorous approach. This study aims to explore the spatial analysis and diagnosis method of aquatic biological based on the combination of machine learning and statistical analysis, so as to reveal the spatial differentiation patterns and causes of changes of aquatic biological integrity in semi-arid regions. To this end, we have introduced an innovative approach that combines XGBoost-SHAP and Fuzzy C-means clustering (FCM), we successfully identified and diagnosed the spatial variations of aquatic biological integrity in the Wei River Basin (WRB). The study reveals significant spatial variations in species number, diversity, and aquatic biological integrity of phytoplankton, serving as a testament to the multifaceted responses of biological communities under the intricate tapestry of environmental gradients. Delving into the depths of the XGBoost-SHAP algorithm, we discerned that Annual average Temperature (AT) stands as the pivotal driver steering the spatial divergence of the Phytoplankton Integrity Index (P-IBI), casting a positive influence on P-IBI when AT is below 11.8 °C. The intricate interactions between hydrological variables (VF and RW) and AT, as well as between water quality parameters (WT, NO3–N, TP, COD) and AT, collectively sculpt the spatial distribution of P-IBI. The fusion of XGBoost-SHAP with FCM unveils pronounced north-south gradient disparities in aquatic biological integrity across the watershed, segmenting the region into four distinct zones. This establishes scientific boundary conditions for the conservation strategies and management practices of aquatic ecosystems in the region, and its flexibility is applicable to the analysis of spatial heterogeneity in other complex environmental contexts.

Germany's national public health gets reorganized: A new institute shall take center stage

In the aftermath of the COVID-19 pandemic, the German federal government recently orchestrated a fundamental change to its public health infrastructure. This reconstruction centers around the founding of a National Institute for Prevention and Education in Medicine (Bundesinstitut für Prävention und Aufklärung in der Medizin, BIPAM) at the cost of two federal institutions, the Robert Koch-Institute (RKI) and the Federal Center for Health Education (Bundeszentrale für gesundheitliche Aufklärung, BzGA). Thus, the Federal Ministry of Health (Bundesministerium für Gesundheit, BMG) plans to dissolve the BzGA and integrate its personnel into the future BIPAM. Further, all RKI research and surveillance activities related to non-communicable diseases, including AI methods development will be transferred into the BIPAM. The RKI responsibilities will solely focus on infectious diseases. According to announced plans of the BMG the primary objective for establishing the BIPAM is to address non-communicable diseases and enhance overall population health. However, the medical specialist training for public health remains non-academic at a state institution. Simultaneously the BMG already replaced two thirds of experts of the permanent commission on vaccination (Ständige Impfkommission, STIKO) and determined new procedures for appointing future expert commissioners. With these changes, Germany embarks on an extraordinary reshuffling of its national public health organizations and responsibilities, by fundamentally separating all issues around non-communicable diseases from those of infectious diseases. Germany's unraveled research tasks of public health authorities however remains unmet. Thus, 2024 marks a pivotal caesura for public health in the modern history of Germany.

A Survey on Deep Learning Event Extraction: Approaches and Applications.

Event extraction (EE) is a crucial research task for promptly apprehending event information from massive textual data. With the rapid development of deep learning, EE based on deep learning technology has become a research hotspot. Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey. This article fills the research gap by reviewing the state-of-the-art approaches, especially focusing on the general domain EE based on deep learning models. We introduce a new literature classification of current general domain EE research according to the task definition. Afterward, we summarize the paradigm and models of EE approaches, and then discuss each of them in detail. As an important aspect, we summarize the benchmarks that support tests of predictions and evaluation metrics. A comprehensive comparison among different approaches is also provided in this survey. Finally, we conclude by summarizing future research directions facing the research area.