Occurrence Of Words Research Articles

Bag of Feature-Based Ensemble Subspace KNN Classifier in Muscle Ultrasound Diagnosis of Diabetic Peripheral Neuropathy

Muscle ultrasound quantification is a valuable complementary diagnostic tool for diabetic peripheral neuropathy (DPN), enhancing physicians’ diagnostic capabilities. Quantitative assessment is generally regarded as more reliable and sensitive than visual evaluation, which often necessitates specialized expertise. This work develops a computer-aided diagnostic (CAD) system based on muscle ultrasound that integrates the bag of features (BOF) and an ensemble subspace k-nearest neighbor (KNN) algorithm for DPN detection. The BOF creates a histogram of visual word occurrences to represent the muscle ultrasound images and trains an ensemble classifier through cross-validation, determining optimal parameters to improve classification accuracy for the ensemble diagnosis system. The dataset includes ultrasound images of six muscles from 53 subjects, consisting of 27 control and 26 patient cases. An empirical analysis was conducted for each binary classifier based on muscle type to select the best vocabulary tree properties or K values for BOF. The result indicates that ensemble subspace KNN classification, based on the bag of features, achieved an accuracy of 97.23%. CAD systems can effectively diagnose muscle pathology, thereby addressing limitations and identifying issues in individuals with diabetes. This research underscores muscle ultrasound as a promising diagnostic tool to aid physicians in making accurate diagnoses, streamlining workflow, and uncovering muscle-related complications in DPN patients.

Using word clouds to reveal patients' perceptions about their arthritis.

Patients' perceptions about their illness have the power to influence health outcomes. However, existing illness belief measures can be burdensome. Using word clouds to illustrate patients' experiences is potentially a novel solution, but research is lacking in this area. This study aimed to explore whether word clouds illustrate patients' perceptions about their illness and to determine commonalities and differences in experiences of rheumatic diseases. A cross-sectional study was conducted with 323 patients with rheumatoid arthritis, ankylosing spondylitis, psoriatic arthritis, or gout in New Zealand. Participants reported two words describing their inflammatory arthritis experience and completed the Brief Illness Perception Questionnaire. Word clouds were generated from participants' responses, and analysis of variance models were used to explore perceptions between groups. Eleven themes were identified, with each constituting a percentage of total word occurrence and weighting in word clouds. Symptom/sensation was the most prominent theme across all conditions (35%-61%), followed by affect/emotional experience (16%-25%). Theme prevalence varied across word clouds for each condition. Social impact was more prevalent for gout, and burden/intrusion was more prominent for ankylosing spondylitis. Functioning was less prevalent in the gout word cloud than in other conditions. There was moderate overlap between word cloud themes and illness perception domains. Word clouds uniquely identified themes related to burden/intrusion, functioning, opportunity, and social impact of illness. This study supports using word clouds as a low-burden method of examining the experiences of patients with inflammatory arthritis and could be tested with other patient groups. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

EXPRESS: Stratified Distributional Analysis - a Novel Perspective on RT Distributions.

Response times and their distributions serve as a powerful lens into cognitive processes. We present a novel statistical methodology called Stratified Distributional Analysis (SDA) to quantitatively assess how key determinants of response times (word frequency and length) shape their distributions. Taking advantage of the availability of millions of lexical decision response times in the English Lexicon Project and the British Lexicon Project, we made important advances into the theoretical issue of linking response times and word frequency by analysing RT distributions as a function of word frequency and word length. We tested these distributions against the lognormal, Wald, and Gamma distributions and three measures of word occurrence (word form frequencies obtained from subtitles and contextual diversity as operationalized as discourse contextual diversity and user contextual diversity). We found that the RT distributions were best described by a lnorm distribution across both megastudies when word occurrence was quantified by a contextual diversity measure. The link between the lnorm distribution and its generative process highlights the power of SDA in elucidating mechanisms that govern the generation of RTs through the fitting of probability distributions. Using a hierarchical Bayesian framework, SDA yielded posterior distributions for the distributional parameters at the single-participant level, enabling probabilistic predictions of response times as a function of word frequency and word length, which has the potential to serve as a diagnostic tool to uncover idiosyncratic features of word processing. Crucially, while we applied our parsimonious methodology to lexical decision response times, it is applicable to a variety of tasks such as word-naming and eye-tracking data.

Tracing neoliberal discourse in school documentation. The analysis of educational projects in Barcelona state schools

An extensive literature has been produced to interpret the transformations that have taken place in education as an expression of the expansion of neoliberal rationality in public policies. This study adopts a mixed method, combining quantitative (text mining) and qualitative approaches (computer-assisted discourse analysis), with the aim of determining/verifying/analysing the permeability of the educational projects of state secondary schools in the city of Barcelona with regard to the neoliberal educational paradigm. Taking into account the occurrence of specific node words (such as work, competition, quality, and company), the results highlight the construction of an educational discourse focused on teaching oriented towards the needs of the new labour market, thus allowing schools to become one of the institutions that contribute to shaping a new type of subject: the flexible, emotionally stable, self-enterprising person who always needs to acquire new skills to increase her ‘human capital’.

A corpus-based study of subtitling religious swear words from English into Arabic

Abstract Subtitling Hollywood films with religious taboo words for conservative and closed societies, such as an Arab society, is a difficult task. This study investigates the dominant religious terms and functions used in Hollywood films and to identify the dominant translation strategies used for them in Arabic subtitles and determines whether these strategies are source language-oriented or target language-oriented (domesticating or foreignising). A corpus of 90 Hollywood films released between 2000 and 2018 is used to answer these questions; insights from descriptive translation studies are also taken (Toury, 2012). The corpus is analysed quantitatively and qualitatively using a self-designed, parallel and aligned corpus of 90 films and their Arabic subtitles. Findings reveal that the functions of religious taboo words have significant impacts on the choices of subtitling strategies. Foreignization strategies are used in roughly two-thirds of all occurrences of religious taboo words despite the cultural distance between English and Arabic. Using Modern Standard Arabic in Arabic subtitles limits subtitlers’ linguistic options. Furthermore, the nature of audio-visual translation influences subtitler choices because the meaning of a word can appear on the screen as a gesture, image or sound.

A METHOD FOR SYNTHESIZING TOPOS OF HASHES IN THE CATEGORICAL cN-SCHEME OF THE MARKOV ALGORITHM. Part 1

In organizational systems, economic interactions are studied. The purpose of this study is to synthesize a problem-oriented management subsystem for combating money laundering and terrorist financing. To achieve this goal, the problem of synthesizing a constructive way of processing such interactions is solved. The method is based on the development of the N-scheme of the Markov algorithm with an output that cannot be considered normal according to Markov. This algorithm scheme allows us to consider a quadruple consisting of words in the ordered and extended Markov alphabet A2, morphism numbers N, source numbers i, and the alphabet M. From the N-scheme of the Markov algorithm, its categorical version is synthesized. The need for this scheme of the Markov algorithm is due to the solution of the problem of folding Markov occurrences of words into each other of large length for their more compact representation. The idea of organizing a convolution for the entire topos is tested separately. The term “topos of hashes” is introduced into scientific circulation. This topos is considered as an alternative way to represent blocks of chains of Markov occurrences. The hash topos is formed from hash trees known in blockchain technology. This idea follows from previous studies of classifiers in topos, as well as the possibility of splitting chains of Markov occurrences into blocks. The convolution is considered using gamma and hash functions. In the course of the study, it was possible to theoretically show the connection between Markov algorithms and blockchain technology, taking into account the fact that each chain of occurrences of words in each other is considered as a category, and these chains are formed in different sources, otherwise worlds, and the way they are synthesized may have signs of a creative process, then a connection was found between the theories of categories, algorithms, blockchain technology and intuitionistic models by S. Kripke and L. E. Ya. Brouwer.

Changes in the Language of the Reformed Church in the Second Half of the 20th Century: The Convergence of Oral History and Big Data

The author’s journey begins with a deep immersion in his traditional Protestant faith, where he was taught to thoroughly know both the Old and the New Testament. As he reflects on his faith, the concept of being a “stranger and sojourner” comes to the forefront, highlighting the feeling of alienation within the secular world. The narrative takes an interesting turn when the author discusses the impact of changing political language on his perspective. The introduction of new political ideas and discourse challenges the previously straightforward worldview, leading to a sense of uncertainty in decision-making. Believers find themselves in a world with a multiplicity of perspectives that require a re-evaluation of what it means to “deny” or to make choices. In a changing social and political context, the narrative continually explores the intricate connection between faith and language. The author’s experiences and reflections provide a compelling insight into the complexities of navigating a shifting spiritual landscape while grappling with the ever-evolving meaning of words and beliefs. The second half of the article presents the results of a computer analysis of the vocabulary of two Bible translations that defined the language usage of the Reformed Church in the second half of the 20th century: the 1908 Károli and the 1975 New Translation. The analysis focuses on words that present difficulties in understanding due to their archaic or religious nature. As a point of comparison, it also showcases the vocabulary of the Gospel of John, where a mere 199 word roots constitute 80% of word occurrences. Keywords: language of the church, Bible translation, vocabulary of the Bible, letter and soul, protestant language use

Text sentiment analysis and classification based on bidirectional long and short term memory networks

In this study, we explored the method of text sentiment analysis and classification using bi-directional long and short-term memory networks, which brings new ideas to the field of sentiment classification. In the text pre-processing stage, we processed chat phrases, non-alphanumeric characters, deactivated words, numbers, special characters, blanks, and addresses to convert the raw text data into numerical data that can be processed by the model. Subsequently, word frequency statistics were conducted for texts of different mood types to analyse the frequency of occurrence of different words in texts of different mood types. By introducing a bi-directional long and short-term memory network model for training, we observed the accuracy and loss value performance on the training and validation sets. The accuracy of the training set increases from the initial 94.6% to 99.5%, and the accuracy of the validation set gradually grows to 99.4%; at the same time, the loss value of the training set decreases from the initial value of 0.2075 to 0.0194, and the loss value of the validation set gradually increases from the initial value of 0.0538 to 0.0372. This indicates that the bi-directional long short-term memory network achieves a significant and stable performance improvement. The confusion matrix results for the test set show that our model achieves more than 99% prediction accuracy on the test set. This implies that our proposed method can efficiently classify textual sentiment with high prediction accuracy. This research result helps to improve the effectiveness of the application of sentiment analysis techniques in the fields of social media opinion monitoring and product review analysis, and provides a useful reference for further deepening the research on text sentiment analysis. In conclusion, this study achieves the optimisation of the text sentiment classification task by means of a bidirectional long and short-term memory network model, and achieves satisfactory results. In the future, the model performance and generalisation ability can be further improved by combining more corpus and optimising the model structure, so as to be better applied in real-world scenarios.

Research in an intercultural context: mediator-investigators of epidemiological health studies, bridges between two worlds.

ContextFrench Guiana is a French overseas department where very different linguistic, cultural and societal populations live together in a small area. Health issues and their specific epidemiological profiles call for research projects crossing several cultures. In this context, health mediators have a role to play in research. The aim of this study was to describe the perceptions, attitudes and opinions on research of mediators and researchers collaborating on research projects, and to describe the strengths and difficulties encountered during this cooperation.MethodsThe Inter-med project was conducted in French Guiana between February 2022 and April 2023 on the base of semi-directive interviews with mediators, or researchers, all working in health research in the intercultural context of French Guiana. The socio-demographic characteristics of the participants were described. An inductive thematic analysis was carried out on all the interviews, and word occurrence analysis on certain themes. The information was triangulated with field coordination notebooks from two epidemiological surveys conducted in French Guiana between 2021 and 2022.ResultsA total of 26 semi-structured interviews were conducted and 1,328 notebook pages analyzed. Mediation was described as an indispensable interface between the world of research and that of the population targeted by a survey. Mediators have a role to play at different stages of projects, in respect of good clinical practice, ethics and legislation. They act as interfaces between languages, concepts and representations. Their profession remains under-defined and under-dimensioned. The jobs offered are often precarious. Mediation work is emotionally costly, calls on soft skills and requires a combination of rigor and flexibility. All these aspects are implemented in the specific world of research, where there are common concepts and divergent perceptions. Researchers and mediators converge on a common goal: improving health.ConclusionThis study covers several aspects of the development and implementation of research projects. Respect for good clinical practice and people, transparency and data quality are redundant concerns, and this study touches on ethnocentrism, stigmatization and cultural representations. This study points out that the integration and recognition of mediators could be beneficial in research conducted in a cross-cultural context.

Areas of Interest in Dental Education: A Bibliometric Analysis of the Last Decade.

This study aimed to perform a comprehensive bibliometric analysis of journals focused on dental education (Journal of Dental Education and European Journal of Dental Education) from 2014 to 2023. An ISI Web of Science Search was performed in October 2023 with no filters for language or keywords. Published articles between 2014 and 2018, 2019 and 2023, and 2014-2023, along with the top 100 cited articles published within this period were exported as txt files. Keyword and title word network maps and occurrences were generated using VOS Viewer software. Author-affiliated countries with the most publications were tabulated from the Web of Science. Dental education and dental students and education were consistently in the top six keywords and title word occurrences in all periods and top 100 cited articles. Similar trends were observed for keyword and title word network maps with an emphasis on dental education and students. However, the 2019-2023 period saw the emergence of coronavirus disease 2019, three-dimensional printing, virtual reality, and education technology, with the earlier period (2014-2018) showing clusters around students, perceptions, dental hygiene education, and assessment. The United States ranked top of the list for most published author-affiliated countries, with England, Canada, Australia, and Saudi Arabia in the top six for all periods analyzed. In conclusion, within the limitations of this study, areas of interest in dental education journals in the last decade were identified along with the countries with most publications.

THE METHOD OF MARKING ITS CHAINS OF OCCURRENCES WITH A MORPHISM NUMDER IN THE N-SCHEME OF THE MARKOV ALGORITHM

To detect and classify λ-voids, we study a way of marking own chains of occurrences of words in each other by the morphism number determined in the N-scheme of the Markov algorithm. In the framework of the category-theoretic approach, the N-scheme of the Markov algorithm is considered as a topos form of the Markov algorithm. The topos explores additional properties of classifiers. The idea of introducing the Markov control flow algorithm into the N-schema is subjected to a comprehensive theoretical analysis. The control flows in the Turing algorithm and the topos form of the Markov algorithm are subjected to a comparative analysis. From the standpoint of the theoretical provisions of the constructivist approach of the Soviet (Leningrad) school, the conditions for equivalence and non-equivalence of the Markov and Turing algorithms are considered. The form of the Markov algorithm equivalent and non-equivalent to the Turing algorithm is synthesized, substantiated and analyzed. To detect the final occurrences in its chain of occurrences, necessary for the detection and classification of λ-voids, the composition of the elements of the N-scheme of the Markov algorithm, which is responsible for this, is specified. For some unknown algorithm for numbering its chains of occurrences of words in each other using the database of the N-scheme of the Markov algorithm, the method of its implementation by the imitating Turing algorithm is considered. The idea of Turing's algorithm imitating some of the algorithms used for the synthesis of artificial intelligence is analyzed. In the context of this study, it is shown that the imitation of the Turing algorithm is due to the need for a second pass through the data, organized by accessing the database in the N-scheme of the Markov algorithm. A description is given for the method of marking own chains of occurrences with the morphism number in the N-scheme of the Markov algorithm. It is concluded that it is necessary to decompose the instruction for checking whether the chain of occurrences belongs to its own chain into fragments, the length of which will be relatively small, for which we consider the method of marking the chains of occurrences with a word, as well as convolution.

Implementasi Algoritma Naive Bayes Multimonial dalam Menganalisa Sentimen Masyarakat di Twitter (Studi Kasus : Perpindahan Ibu Kota)

The move to Indonesia's capital city received different responses from the public, pros and cons. This has an influence on the development of the capital itself. The impact of the move will be felt by the community, especially the people of the Kalimantan area or private developers who want to invest. This research will discuss the classification of public opinions regarding the transfer of the capital which are categorized into 3 categories, namely neutral, negative and positive. The aim of this research is to obtain the results of sentiment analysis or public responses to the move of the nation's capital. The benefit for the government or development is as input regarding the direction of development, the economy and policies regarding the development of the nation's capital city. Data collection was obtained from Twitter using scrapping techniques. The sentiment analysis stage is carried out by preprocessing first, namely by labeling and grouping. At the analysis stage, the Multinomial Naïve Bayes algorithm was used. This method was chosen as an analysis method because this method is able to measure the frequency of occurrence of words in documents more effectively. The classification results obtained from the 1049 data used were that 25.54% stated that public sentiment was neutral, 35.08% stated that public sentiment was negative, and 39.37% stated that public sentiment was positive. The accuracy rate was also obtained at 92.67%.

Wildfires and social media discourse: exploring mental health and emotional wellbeing through Twitter.

The rise in global temperatures due to climate change has escalated the frequency and intensity of wildfires worldwide. Beyond their direct impact on physical health, these wildfires can significantly impact mental health. Conventional mental health studies predominantly rely on surveys, often constrained by limited sample sizes, high costs, and time constraints. As a result, there is an increasing interest in accessing social media data to study the effects of wildfires on mental health. In this study, we focused on Twitter users affected by the California Tubbs Fire in 2017 to extract data signals related to emotional well-being and mental health. Our analysis aimed to investigate tweets posted during the Tubbs Fire disaster to gain deeper insights into their impact on individuals. Data were collected from October 8 to October 31, 2017, encompassing the peak activity period. Various analytical methods were employed to explore word usage, sentiment, temporal patterns of word occurrence, and emerging topics associated with the unfolding crisis. The findings show increased user engagement on wildfire-related Tweets, particularly during nighttime and early morning, especially at the onset of wildfire incidents. Subsequent exploration of emotional categories using Linguistic Inquiry and Word Count (LIWC) revealed a substantial presence of negative emotions at 43.0%, juxtaposed with simultaneous positivity in 23.1% of tweets. This dual emotional expression suggests a nuanced and complex landscape, unveiling concerns and community support within conversations. Stress concerns were notably expressed in 36.3% of the tweets. The main discussion topics were air quality, emotional exhaustion, and criticism of the president's response to the wildfire emergency. Social media data, particularly the data collected from Twitter during wildfires, provides an opportunity to evaluate the psychological impact on affected communities immediately. This data can be used by public health authorities to launch targeted media campaigns in areas and hours where users are more active. Such campaigns can raise awareness about mental health during disasters and connect individuals with relevant resources. The effectiveness of these campaigns can be enhanced by tailoring outreach efforts based on prevalent issues highlighted by users. This ensures that individuals receive prompt support and mitigates the psychological impacts of wildfire disasters.

A Comparison of Expressive Vocabulary in Spontaneous Language Sample and Parent Checklist of Young Children with Autism Spectrum Disorder

Objectives: This study aims to examine the differences in vocabulary abilities in children with autism spectrum disorder (ASD) using a parent checklist and spontaneous language analysis.Methods: Participants included 10 children with ASD, aged 18-30 months, and 10 typically developing children matched for language age. Data were collected using the Korean version of the MacArthur-Bates Communicative Development Inventory (K M-B CDI) vocabulary checklist and spontaneous language analysis. The total number of vocabulary and the number of 5 word classes of vocabulary were measured and a repeated measures analysis of variance (ANOVA) and Pearson correlation analysis was performed to compare the two groups.Results: Children with ASD demonstrated a higher total vocabulary according to the parent checklist compared to typically developing children. However, there was no significant difference in the results of spontaneous language analysis. While the parent checklist indicated higher vocabulary across all five word classes for ASD children, spontaneous language analysis revealed that they predominantly used nouns and verbs, with fewer occurrences of determiners, independent words, and relational words. Moreover, the vocabulary overlap between the two methods was lower in the ASD group compared to the typically developing group. Conclusion: This study highlights the challenges faced by children with ASD in appropriately using their known vocabulary. It emphasizes the importance of considering not only the parent checklist but also analyzing spontaneously used vocabulary in real communication situations. This integrated approach may provide valuable insights for developing personalized interventions and therapeutic strategies for language development in children with ASD.

Management’s tone change in MD&A and tax avoidance

Purpose The purpose of this paper is to explore how variations in management’s tone within management’s discussion and analysis (MD&A) sections of 10-K reports can serve as an indicator of tax avoidance and highlight the complex relationship between such linguistic shifts and the tax avoidance decisions within firms. Design/methodology/approach The paper uses a textual analysis approach to identify linguistic cues in MD&A sections of 10-K filings related to tax avoidance, going beyond traditional quantitative measures. The study uses differences in negative word occurrences in MD&A to measure management’s tone change and examines various measures of tax avoidance. The sample covers the period from 1993 to 2017 and comprises all firms with 10-K filings available on EDGAR, totaling over 30,000 firm-year observations. Findings The findings indicate a complementary relationship between tax avoidance and other drivers of firm performance. When firms have more negative management’s tone, they are less willing to engage in tax avoidance and vice versa. The study’s approach with management’s tone change provides a different and statistically significant improvement in model fit for detecting tax avoidance. Practical implications This paper provides actionable insights for detecting tax avoidance through the analysis of management’s tone in corporate disclosures, offering a new tool for researchers, investors and tax authorities. It highlights the importance of linguistic cues as indicators of tax avoidance behavior, complementing traditional financial metrics. Originality/value The paper contributes to the literature by using management’s tone change as a time-varying factor to explain tax avoidance behavior. It uncovers a larger set of linguistic cues in MD&A that can be used to detect tax avoidance. This research provides a complementary approach to traditional quantitative tax avoidance measures and offers insights into the overall relationship between tax avoidance and firm performance, going beyond one-dimensional measures typically used in prior literature.

Case study identification with GPT-4 and implications for mapping studies

Context:Rainer and Wohlin showed that case studies are not well understood by reviewers and authors and thus they say that a given research is a case study when it is not. Objective:Rainer and Wohlin proposed a smell indicator (inspired by code smells) to identify case studies based on the frequency of occurrences of words, which performed better than human classifiers. With the emergence of ChatGPT, we evaluate ChatGPT to assess its performance in accurately identifying case studies. We also reflect on the results’ implications for mapping studies, specifically data extraction. Method:We used ChatGPT with the model GPT-4 to identify case studies and compared the result with the smell indicator for precision, recall, and accuracy. Results:GPT-4 and the smell indicator perform similarly, with GPT-4 performing slightly better in some instances and the smell indicator (SI) in others. The advantage of GPT-4 is that it is based on the definition of case studies and provides traceability on how it reaches its conclusions. Conclusion:As GPT-4 performed well on the task and provides traceability, we should use and, with that, evaluate it on data extraction tasks, supporting us as authors.

The care process for users of alcohol and other drugs in primary health care: a qualitative study

To understand the process of caring for alcohol and other drug users by Primary Health Care professionals. This is a qualitative exploratory study with professionals from two Family Health Teams. Data was collected through focus groups with guiding questions and recorded interviews, which were later transcribed and analyzed by Bardin. IRaMuTeQ® software was used for categorization, identification and coding, as well as Reinert's method for classifying elementary context units. Eight professionals took part in this study. 1,869 occurrences of words were identified in the corpus of this research, divided into 381 active forms. The analysis resulted in two categories: "The user of alcohol and other drugs: the challenges of everyday life" and "The Psychosocial Care Network and ways of optimizing care for users". Professionals face various difficulties in caring for alcohol and other drug users, such as lack of qualifications, lack of a support network, lack of motivation on the part of the person and fragmentation of the Psychosocial Care Network. However, overcoming the adversities experienced by users is also an essential strategy, as these people face stigma and prejudice in health services. The care process proved to be fragile in the face of the adversities that influence it, thus highlighting the importance of investments in services and ongoing training for professionals working in the RAPS, in order to enable assertive, continuous, resolutive and humanized care.

Political Protest through the Prism of the British Press: Strategies for Managing the Perception of Journalistic Texts

Introduction. The purpose of this paper is to identify similarities and differences in discursive strategies aimed at managing the perception of journalistic texts describing similar protest events in different countries.Materials and methods. The material for the pa­per includes articles from “The Economist” describ­ing protest events. Protests in August 2019 in Mos­cow and “yellow vest” protests in autumn – spring 2018 – 2019 in France were chosen as reference events. The methodological tools include the following general scientific methods: analysis, synthesis, sys­temic and functional approaches. Critical discourse analysis is used as a specific method. Discursive strat­egies form the subject of the research. The referential strategy includes an event-factual plan of the events being described. The connotative strategy works with emotionally expressive and evaluative vocabulary. The meta-discursive strategy chooses text frames and fills them with referential and evaluative components which together form the pragmatic functionality of the discourse..Research results. The research involves the fol­lowing stages. At the first stage the central frames of the articles on the basis of the titles and subtitles of the articles are identified. At the second stage the referential and evaluative components in the vocabu­lary of the frames are examined. At the third stage a comparative analysis of the meta-discursive strategies of the articles under research is carried out, which in­cludes comparing the pragmatic functionality of the headings, titles and subtitles of the articles; contrast­ing the articles in terms of the composition of frames and identifying what is common and specific in the content of the main frames of the articles. The results of the work show that such linguistic tools as selecting facts and events for the text, framing the content of the text, choosing language that produce an emotional-evaluative, suggestive and persuasive effect on read­ership, frequent occurrence of words that are fit for the goals of the text are used to manage text perceptionDiscussion and conclusion. Professionals in the fi of international relations, international law, political science, public administration must know the linguistic methods used to analyze journalistic and political texts and be able to identify language tools aimed to drive text perception in the target direction.

METHODS FOR SYNTHESIZING SIGNS OF THE RISKS OF LAUNDERING PROCEEDS FROM CRIME

Methods for synthesizing risk features are being explored in the interest of creating a methodology for classifying MLB and TF risk typologies in big data from multiple data sources of organizational systems. A new space of risk features is investigated, consisting of words and their morphisms, represented by diagrams of Markov occurrences of words in each other, obtained by synthesis using the N-scheme of the Markov algorithm. Typologies of individual risks of money laundering and terrorist financing are considered as separate risk knowledge. When considering knowledge, it is taken into account that they are of a changeable nature, therefore, they must be reviewed and refined in the process of counteraction. Knowledge is treated with the assumption that some of them are quite far apart when viewed through the structure of the categories they form. The study shows synthesized methods for excluding and suspending risk features in a promising risk classifier. The method for excluding risk features from a promising classifier is considered and illustrated by an example of a valid feature in the classifier of the Central Bank of the Russian Federation, introduced by the Bank of Russia Regulation No. 375-P dated March 2, 2012 (as amended on October 20, 2020), which requires exclusion from it. The way to suspend the action of a risk attribute, without excluding it from a promising classifier, is presented in the study, using the example of a risk from the classifier of the Central Bank of the Russian Federation. Examples of refinement of a risk attribute, without excluding it from the classifier and synthesizing a risk typology for it, are presented in the form of categorical formulas with restrictions on some morphism parameters. The presented methods of excluding and suspending the action of risk features in the risk classifier allow a wider use of the acquired knowledge, which is understood as typologies of individual risks, and take into account the changing nature of this knowledge, understood as a specification of knowledge.

Trends in Aflatoxin M1 Global Research: A Bibliometric Analysis Study.

Fungal metabolites known as aflatoxin M1 (AFM1) are linked to contaminated milk and milk products. Consuming food contaminated with AFM1 poses major health risks and may even be fatal. The retrieved publications were categorized in this bibliometric study using the Web of Science (WoS) database Jan 1, 1970 to Nov 30, 2022 based on a variety of factors, including the time of publication of articles, citation totals, languages, research areas, countries, affiliations, funding agencies, journals, and keywords analysis to identify any hot and developing subjects. Additionally, VOSviewer software version 1.6.18 provided the bibliometric analysis of the global collaboration network and hot research themes. Overall, 679 published documents were detected. Food Control was the top-line journal in publications on AFM1 research with 540 published articles, while the USA was the best productive country in AFM1 publications as well as the major country with the maximum co-authorship collaboration. This study ensures quantitative and qualitative analyses of the top 25 journals, most cited published articles, most relevant authors and title word occurrences in published documents on AFM1 publications. Over the past two decades, there has been an enormous rise for research conducted on global AFM1. The assessment of the historical state and development trend in AFM1 scientific research can serve as a roadmap for future research and eventually, serve as a foundation for bettering management practices for territorial decisions, healthcare, and dairy industries.