COMPARISON OF THE TERMS CREATIVE ACCOUNTING, EARNINGS MANAGEMENT AND FRAUDULENT ACCOUNTING THROUGH BIBLIOGRAPHIC ANALYSIS

Research background: The article focuses on the issues of creative accounting, earnings management, and fraudulent accounting, which are global phenomena. These concepts are well known globally, as they are dealt with by many world-renowned authors. In this study, we applied bibliometric analysis to these concepts to reveal their interconnectedness. The research was conducted on a sample of more than 19,000 articles. Purpose of the article: The main goal of the study is to use the VosViewer design and visualisation program to capture and record the most common terms associated with the terms, ‘creative accounting’, ‘revenue management’, and ‘fraudulent accounting’, and to show a biometric network of the most commonly used terms. Methods: To capture and illustrate important words associated with the above terms, the VosViewer program was used, which drew mind maps that represented the words and expressions that were closest to the topic. Scientific articles from the Web of Science database, which contains many world-class articles related to the topic, were used as input data. Findings & Value added: The results of the study provided an interesting insight into the keywords associated with the issues of creative accounting, revenue management, and fraudulent accounting. The results show that the keywords and phrases are related, as several of them are repeated in each of the terms mentioned. This means that, although these terms are different in nature, they are nevertheless connected by many words and phrases. However, it remains necessary to observe that each of the given terms appears on a different colour of fraud (white, grey, or black fraud).

Government plans in the 2016 and 2021 Peruvian presidential elections: A natural language processing analysis of the health chapters

Background: While clinical medicine has exploded, electronic health records for Natural Language Processing (NLP) analyses, public health, and health policy research have not yet adopted these algorithms. We aimed to dissect the health chapters of the government plans of the 2016 and 2021 Peruvian presidential elections, and to compare different NLP algorithms. Methods: From the government plans (18 in 2016; 19 in 2021) we extracted each sentence from the health chapters. We used five NLP algorithms to extract keywords and phrases from each plan: Term Frequency–Inverse Document Frequency (TF-IDF), Latent Dirichlet Allocation (LDA), TextRank, Keywords Bidirectional Encoder Representations from Transformers (KeyBERT), and Rapid Automatic Keywords Extraction (Rake). Results: In 2016 we analysed 630 sentences, whereas in 2021 there were 1,685 sentences. The TF-IDF algorithm showed that in 2016, 22 terms appeared with a frequency of 0.05 or greater, while in 2021 27 terms met this criterion. The LDA algorithm defined two groups. The first included terms related to things the population would receive (e.g., ’insurance’), while the second included terms about the health system (e.g., ’capacity’). In 2021, most of the government plans belonged to the second group. The TextRank analysis provided keywords showing that ’universal health coverage’ appeared frequently in 2016, while in 2021 keywords about the COVID-19 pandemic were often found. The KeyBERT algorithm provided keywords based on the context of the text. These keywords identified some underlying characteristics of the political party (e.g., political spectrum such as left-wing). The Rake algorithm delivered phrases, in which we found ’universal health coverage’ in 2016 and 2021. Conclusion: The NLP analysis could be used to inform on the underlying priorities in each government plan. NLP analysis could also be included in research of health policies and politics during general elections and provide informative summaries for the general population.

“Lemongrass” and its applications for the treatment of hypertension

Hypertension (high blood pressure) is the pre-symptom of cardiovascular disease. The number of people living with hypertension has doubled to 1.28 billion and proportionally increased until today. This is a long-term disease and requires continuous monitoring. A traditional Chinese herbal, “Lemongrass”, might be a good choice for the mainstay of hypertension. Some library search engines are used, such as SCI/SCIE, PubMed, and Scopus, within ten to twenty years, from 1999-2020. The searched keywords and phrases are “lemongrass”, “formulation”, “traditional Chinese medicine”, “hydrogel”, “hypertension”, “lemongrass + tea formulation”, “lemongrass + hydrogel”, “Lemongrass + Hypertension”, “Lemongrass + traditional Chinese medicine” etc. This minireview discusses the background of hypertension, lemongrass, research progress, mechanism, lemongrass tea formulations, lemongrass with Traditional Chinese Medicine (TCM) formulations, and the lemongrass hydrogel application in the treatment of hypertension.

Algorithm for torticollis diagnosis in children of younger age groups

BACKGROUND: Torticollis is a common term for abnormal head or neck positions. Torticollis can be due to a wide variety of pathological processes, from relatively benign to life-threatening. This syndrome is of particular relevance in pediatric practice and is often underestimated at the primary care level. AIM: To analyze the data of domestic and foreign literature on the etiopathogenesis and clinical features of various types of torticollis in children and develop algorithms for the differential diagnosis of torticollis in children of younger age groups. MATERIALS AND METHODS: A literature search was conducted in the open information databases of eLIBRARY and Pubmed using the keywords and phrases: torticollis, congenital muscular torticollis, non-muscular torticollis, acquired torticollis, and neurogenic torticollis, without limiting the depth of retrospection. RESULTS: Based on the literature data generalization, the classification of torticollis and the key directions of its differential diagnosis are systematized in tabular form. The range of differential diagnosis of torticollis is quite wide and has its characteristics in newborns and children of the first years of life, contrary to older children. The most common is congenital muscular torticollis. Concurrently, non-muscular forms of torticollis in the aggregate are not uncommon, more often with a more serious etiology, and require careful examination. Based on the analyzed literature, differential algorithms for torticollis diagnosis in children of younger age groups have been compiled. CONCLUSIONS: Increasing the level of the knowledge of pediatric clinicians in the etiopathogenesis of torticollis syndrome will improve the efficiency of early diagnosis of dangerous diseases that lead to pathological head and neck positions in children.

Government plans in the 2016 and 2021 Peruvian presidential elections: A natural language processing analysis of the health chapters

Background: While clinical medicine has exploded, electronic health records for Natural Language Processing (NLP) analyses, public health, and health policy research have not yet adopted these algorithms. We aimed to dissect the health chapters of the government plans of the 2016 and 2021 Peruvian presidential elections, and to compare different NLP algorithms. Methods: From the government plans (18 in 2016; 19 in 2021) we extracted each sentence from the health chapters. We used five NLP algorithms to extract keywords and phrases from each plan: Term Frequency–Inverse Document Frequency (TF-IDF), Latent Dirichlet Allocation (LDA), TextRank, Keywords Bidirectional Encoder Representations from Transformers (KeyBERT), and Rapid Automatic Keywords Extraction (Rake). Results: In 2016 we analysed 630 sentences, whereas in 2021 there were 1,685 sentences. The TF-IDF algorithm showed that in 2016, nine terms appeared with a frequency of 0.10 or greater, while in 2021 43 terms met this criterion. The LDA algorithm defined two groups. The first included terms related to things the population would receive (e.g., ’insurance’), while the second included terms about the health system (e.g., ’capacity’). In 2021, most of the government plans belonged to the second group. The TextRank analysis provided keywords showing that ’universal health coverage’ appeared frequently in 2016, while in 2021 keywords about the COVID-19 pandemic were often found. The KeyBERT algorithm provided keywords based on the context of the text. These keywords identified some underlying characteristics of the political party (e.g., political spectrum such as left-wing). The Rake algorithm delivered phrases, in which we found ’universal health coverage’ in 2016 and 2021. Conclusion: The NLP analysis could be used to inform on the underlying priorities in each government plan. NLP analysis could also be included in research of health policies and politics during general elections and provide informative summaries for the general population.

On the Resolution of Compositional Datasets into Convex Combinations of Extreme Vectors

<p>Large compositional datasets of the kind assembled in the geosciences are often of remarkably low approximate rank. That is, within a tolerable error, data points representing the rows of such an array can approximately be located in a relatively small dimensional subspace of the row space. A physical mixing process which would account for this phenomenon implies that each observation vector of an array can be estimated by a convex combination of a small number of fixed source or 'endmember' vectors. In practice, neither the compositions of the endmembers nor the coefficients of the convex combinations are known. Traditional methods for attempting to estimate some or all of these quantities have included Q-mode 'factor' analysis and linear programming. In general, neither method is successful. Some of the more important mathematical properties of a convex representation of compositional data are examined in this thesis as well as the background to the development of algorithms for assessing the number of endmembers statistically, locating endmembers and partitioning geological samples into specified endmembers. Keywords and Phrases: Compositional data, convex sets, endmembers, partitioning by least squares, iteration, logratios.</p>

On the Resolution of Compositional Datasets into Convex Combinations of Extreme Vectors

<p>Large compositional datasets of the kind assembled in the geosciences are often of remarkably low approximate rank. That is, within a tolerable error, data points representing the rows of such an array can approximately be located in a relatively small dimensional subspace of the row space. A physical mixing process which would account for this phenomenon implies that each observation vector of an array can be estimated by a convex combination of a small number of fixed source or 'endmember' vectors. In practice, neither the compositions of the endmembers nor the coefficients of the convex combinations are known. Traditional methods for attempting to estimate some or all of these quantities have included Q-mode 'factor' analysis and linear programming. In general, neither method is successful. Some of the more important mathematical properties of a convex representation of compositional data are examined in this thesis as well as the background to the development of algorithms for assessing the number of endmembers statistically, locating endmembers and partitioning geological samples into specified endmembers. Keywords and Phrases: Compositional data, convex sets, endmembers, partitioning by least squares, iteration, logratios.</p>

Challenges for the fishing community on nutritional perspective in West Bengal coastal areas during the COVID-19 pandemic

Covid 19 pandemic has been affecting from March 2020 to 2021 onwards to all types of people globally. The fishing community is one of the most affected communities depend on their limited income. Their health, mental condition, and lifestyles are all adversely affected by this pandemic situation, mainly for middle or lower-level income groups, including India. Additionally, covid 19 also has disrupted the livelihood of fishing communities in socio-economic and nutrition, especially health perspectives. To review and analyze current literature trends on covid 19 concerning fishing communities’ life, food habits, and challenges. The study was designed based on current literature from 2020 March till 2021 August, focussing mainly on the West Bengal coastal areas. ‘Fishing communities,’ ‘West Bengal coastal areas,’ ‘covid-19 crisis’, ‘social and economic challenges,’ ‘nutrition crisis’ are significant keywords and phrases used for the online searches in open access all databases including Pubmed, Google scholars. Fishing communities are highly affected due to the COVID-19 pandemic economically and on nutritional perspectives due to various factors. Presently, there are scant data; hence more research and views are needed about the fishing community in Indian and global perspective as well.

Students’ view on supportive co-teaching in medical sciences: a systematic review

Background Supportive co-teaching (SCT) is the practice of employing two or more experts whose knowledge and experiences are needed simultaneously to make a connection across different disciplines in a classroom. Although this interdisciplinary approach seems to be beneficial, there are many features which need further examination. This study was conducted to systematically review studies addressing the use of this approach and learners’ views on SCT in medical sciences. Methods We searched for the studies addressing students’ views on SCT in medical sciences from January1st 2000 to June 31st, 2019. All the studies, both quantitative and qualitative published in English language, investigating the students’ views on SCT, in non-clinical courses in the setting of medical sciences were included. We searched electronic databases of PubMed, Scopus, Embase, Web of Science, WHO Global Health Library, Health Systems Evidence, and ERIC with the keywords and phrases related to the topic which were: “co-teaching”, “team teaching”, “collaborative teaching”, “peer-to-peer co-teaching”, “partnership teaching”, and“ teacher collaboration”. Results By the initial search, 9806 studies were found and after deletion of duplicates and screening, 111 remained for selection. Upon the independent review by two researchers, we were able to discern 12 studies eligible to be included for data extraction. All the studies reported positive views of the students towards SCT although some identified concerns and drawbacks. The students stated that they could better perceive the relationship between basic and clinical sciences, were more engaged in the learning process, and their learning experience was optimized in a course directed by SCT. Conclusion Overall, the students showed positive views of this approach of teaching, and their grades indicated they learned better than expected. However, mismatch and lack of coordination between instructors would make the class distracting, confusing and even disturbing. Further studies investigating different variables related to teachers and students in SCT classes are suggested.

Can Cultured Meat Be an Alternative to Farm Animal Production for a Sustainable and Healthier Lifestyle?

Producing animal proteins requires large areas of agricultural land and is a major source of greenhouse gases. Cellular agriculture, especially cultured meat, could be a potential alternative for the environment and human health. It enables meat and other agricultural products to be grown from cells in a bioreactor without being taken from farm animals. This paper aims at an interdisciplinary review of literature focusing on potential benefits and risks associated with cultured meat. To achieve this goal, several international databases and governmental projects were thoroughly analyzed using keywords and phrases with specialty terms. This is a growing scientific domain, which has generated a series of debates regarding its potential effects. On the one hand the potential of beneficial effects is the reduction of agricultural land usage, pollution and the improvement of human health. Other authors question if cultured meat could be a sustainable alternative for reducing gas emissions. Interestingly, the energy used for cultured meat could be higher, due to the replacement of some biological functions, by technological processes. For potential effects to turn into results, a realistic understanding of the technology involved and more experimental studies are required.