Occurrence Of Terms Research Articles

Global research trends on educational investment: A bibliometric analysis

The theme of educational investment has gained increasing attention from scholars, leading to a substantial rise in related publications. At the macro level, almost every country in the world is experiencing an increase in expenditure on education investment while at the micro level, families are placing greater emphasis on education, with household spending on education rising annually as well. As education plays a crucial role in shaping human capital and driving socioeconomic development, investments in this field have become a prominent focus in academic discourse. Over the past two decades, research on educational investment has expanded rapidly, making it challenging to fully grasp the scope of the developments. Numerous studies have explored various aspects of the topic, such as the impact of parental involvement, policy interventions, and market dynamics on education outcomes. The objective of this study is to compile and analyze research on educational investment from 2004 to 2024. To achieve this, a bibliometric analysis was employed, providing a comprehensive overview of the subject. The results reveal a significant growth in the number of academic articles and citations over the last 20 years. Furthermore, the study identifies the most prolific authors and academic journals contributing to the discourse on educational investment. The keyword co-occurrence analysis highlights five major thematic clusters: education, human capital, parental involvement, policy, and market. The frequent occurrence of certain terms suggests strong interconnections among key concepts. This study offers valuable insights for future scholars and policymakers, laying the groundwork for addressing emerging challenges and shaping future directions in the field of educational investment. By mapping thematic trends and identifying influential contributors, this research serves as a guide for deeper academic exploration and more informed policy decision-making.

Comparative Study among Term Frequency-Inverse Document Frequency and Count Vectorizer towards K Nearest Neighbor and Decision Tree Classifiers for Text Dataset

Background: Text classification techniques are increasingly important with the exponential growth of textual data on the internet. Term Frequency-Inverse Document Frequency (TF-IDF) and Count Vectorizer(CV) are commonly used methods for feature extraction. TF-IDF assigning weights to terms based on their frequency. CV simply counts the occurrences of terms. The performance of CV as well as TF-IDF are evaluated and compared with KNN and DT classifiers across text datasets. Methodology: The investigation begins with preprocessing. The feature vectors are created using both TF-IDF and CV. Feature vectors are passed into the KNN and DT classifiers at in training stage. Experiments are executed the usage of Kaggle's public database Ukraine 10K tweets sentiment_analysis dataset and the Womens ecommerce clothing reviews dataset. Findings: The average of precision, recall, f1 score and accuracy of KNN with TF-IDF were 84.5%, 87%, 83%, 87% respectively and KNN with CV were 83.5%, 87%, 83.5%, 87% respectively. Similarly, average of precision, recall, f1 score and accuracy of DT with TF-IDF were 89%, 89%, 89%, 89% respectively and DT with CV were 89%, 89.5%, 89.5%, 89.5% respectively. The results obtained in this research is consistent with previous similar research result. Conclusions: The performance of TF-IDF is almost similar as CV for a particular dataset and a particular classifier in this study. Novelty: The experiment performed using these classifiers and feature extraction methods on the datasets is a novelty and contribution of this research.

Short term clinical outcomes of Complex Percutaneous Coronary Intervention in patients with intermediate to high SYNTAX score: Ain Shams complex PCI Registry

Abstract Background Risk stratification is an important and essential component in proper patient counseling when dealing with complex coronary anatomy and decision making between either coronary artery bypass graft (CABG) or percutaneous coronary intervention (PCI). The SYNTAX score was pioneered as an anatomical-based risk score that aided in this decision-making process. Objective To develop an electronic registry for patients with complex coronary anatomy with intermediate to high SYNTAX score undergoing non-emergent PCI who were indicated for CABG but either they were rejected by the surgeons or they refused it. Patients and Methods Our registry was conducted on 193 patients presented during eight months from the first of July 2021 till the end of February 2022 to Ain Shams University Hospitals and underwent PCI. Results comparing the intermediate to high SYNTAX score groups, there is a statistically significant increase in the in hospital and short term MACE and mortality with high SYNTAX score patients, while there was no difference regarding in hospital CIN. Regarding MACE it significantly increases in patients with anemia, CKD, LV dysfunction. For multivariate analysis it is shown that both incomplete revascularization and clinical SYNTAX II are independent predictors for the occurrence of short term MACE. Conclusion Both incomplete revascularization and clinical SYNTAX II score are independent predictors of short term (6 months) occurrence of MACE.

Concepts that fit in a (Roman) hand

Abstract The Roman author Lucius Anneus Seneca (4 BC–65 AC), the main representative of Stoic philosophy in Latin literature, wrote several tragedies in verse in which the Latin noun manus ‘hand’ has a remarkable incidence, almost doubling the occurrence of other terms more related to tragic themes, such as scelus ‘crime’ or mors ‘death’. This paper is based on the hypothesis that this high frequency is linked to the concept of embodiment as well as on the metonymies and metaphors used in Seneca’s figurative language to encode abstract concepts. The occurrences of the term manus in a corpus composed of Seneca’s dramatic and philosophical texts have been analysed, paying attention to the metonymic and metaphorical contexts where it appears. As a result, it has been observed that this word can refer to multiple realities such as individuals, actions, identity, control, or power.

The Optimization of n-Gram Feature Extraction Based on Term Occurrence for Cyberbullying Classification

The Optimization of n-Gram Feature Extraction Based on Term Occurrence for Cyberbullying Classification

Cluster Analysis of Japanese Whiskey Product Review Using K-Means Clustering

Since 2008, the Japanese whiskey business has grown steadily. Overall, the whiskey market (at factory price) is expected to reach $2.95 billion in 2019, accounting for 8.6 percent of the entire alcoholic beverage industry. The rise in popularity of Japanese whiskey is associated with the country's growing international reputation. Founded 1985 as an independent bottler, Master of Malt was the first company to service clients who ordered single malt whiskey through the mail-order system. Master of Malt's omnichannel approach encompasses all channels available to the company. Known as their 'omnichannel,' this refers to the organization's capability to provide speed and precision from any place at any time. As their brand has grown over the years, they have used various marketing strategies, including a website redesign and rebuild that involved the creation of all relevant content and designing and constructing landing pages for their website. Following a clustering technique, we discovered that the data is being divided into four distinct groups and that these clusters may serve as a recommender system based on the occurrence of terms in each of the categories. Our summarizing component combined phrases related to the exact subtopics and provided users with a concise summary and sentimental information about the group of phrases.

Antineoplastic agents associated with neutropenic enterocolitis in patients with malignancy: A quantitative safety signal analysis.

The use of certain chemotherapy agents is associated with the development of a condition called "chemotherapy-associated neutropenic enterocolitis" (CANE). To determine the risk of CANE associated with the use of each antineoplastic agent. The FDA FAERS database of spontaneous adverse reactions was searched for the occurrence of the MedDRA preferred term "neutropenic colitis." The search resulted in 1134 records of patients (535 [47.3%] females, 479 [42.2%] males, sex not specified in 120 [10.6%]) with neutropenic colitis receiving immunosuppressive chemotherapy. The mean age of patients was 47 (SD 22). 22 antineoplastic agents were found to have a strong association (reported odds ratio [ROR] > 100) with the occurrence of CANE; 9 had ROR < 2. Drug databases have several limitations in providing updated information about newly approved pharmaceutical adverse events. Signal detection is a diagnostic method recognized as practical in pharmacovigilance. It may be utilized in the FDA's adverse event reporting database and has demonstrated a reasonable predictive performance in signaling adverse events. Our study emphasized the substantial knowledge gap between what we know about the potential risk of CANE caused by antineoplastic agents and the reports of the FDA on their new approved products.

Construction and analysis of uncertainty indices based on multilingual text representations

The work by Baker et al. (2016), who propose a dictionary based method and estimate the level of economic policy uncertainty (EPU) based on the occurrence of specific terms in ten leading newspapers in the USA, is among the first ones to detect the potential of text data in economic research. Following this line of research, this paper proposes automated approaches to construction of EPU indices for different countries based on newspapers’ texts. Multilingual fastText word embeddings, (S)BERT embeddings, and a novel multilingual topic modeling approach are used to construct EPU indices for Germany, Russia, and Ukraine. It is shown that constructed EPU indices based on multilingual word embeddings are Granger causal to the economic activity in all of the considered countries.

Simulating urban scaling with a term linkages network of a university

In this paper, we make an attempt to increase our understanding of the urban scaling phenomenon. The aim is to investigate how superlinear scaling emerges if a network increases in size and how this scaling depends on the occurrence of elements that constitute the network. To this end, we consider a city as a complex network structure and simulate this structure by the network of all publications of a research intensive university. In this simulation, the publications take the role of the city inhabitants and the concepts (terms and keywords) in the publications represent all kinds of abilities and qualities of the inhabitants. We use in this experiment all author- and database-given terms of the scientific publications of Leiden University from 2022. We calculate the co-occurrence of terms, and on the basis of these connections, we create a network and let this network grow by successively adding publications from the total set of publications. In this way, we get a series of networks with different sizes and this simulates a series of cities with different number of inhabitants. This procedure is performed for different values of the term occurrence threshold. We then analyze how four important network parameters, namely, number of terms, number of clusters, number of links, and total link strength increase with increasing size of the network. Particularly the number of network links and the total network linkage strength are in our opinion the parameters that dominate the scaling phenomenon and can be considered as a simulation of the socioeconomic strength of a city, that is, its gross urban product. We find a significant power law dependence of these network parameters on network size and the power law exponents for the lowest occurrence threshold are within the range that is typical for urban scaling. In our approach, the number of clusters can be interpreted as a measure of complexity within the network. Since the occurrence threshold determines the diversity of terms, we may expect a special relation between the occurrence threshold and the number of clusters. This is indeed the case: whereas for the three other network parameters the scaling exponent increases with increasing occurrence threshold, the number of clusters is the only network parameter of which the scaling exponent decreases with increasing occurrence threshold. Finally, we discuss how our publication term network approach relates to scaling phenomena in cities.

The Scale and Diversity of References to Emotion in Polish Case Law on Personal Injury

This article is intended as a systematic, quantitative study of the occurrence of emotion-related terms in the discourse of Polish case law on personal injury. My purpose is to show the scale (prevalence) and range (variety) of references to terms related to feelings and affect in the decisions of Polish courts, and to compare this data with rudimentary psychological nomenclature. I am interested not only in how often Polish courts refer to affective phenomena in general, but also how they distinguish various kinds of emotions and, to some extent, what role they assign to them in their reasoning about personal injury. This data is placed in the context of basic psychological models. This is supplemented by preliminary remarks on what roles most notable emotions fulfill, aiming at creating a rudimentary taxonomy of emotions in Polish case law on personal injury. In this way the study aims to answer the questions whether Polish courts notice emotions while adjudicating on non-pecuniary harm, what range of emotions they refer to, and how this data shows the perceived function of affect-related terms in case law. In short, the aim of this article may be described as drawing up a tentative “map” of the affective legal terrain, as well as a taxonomy of functions that emotions may play in personal injury law.

Geopolitical risks and the banking sector

Geopolitical risks have come into the foreground recently as their effects on macroeconomy are analysed. This said, the banking sector is key due to its embeddedness in the economy and because risk management is an important part of the stability of financial systems. Banking regulations have quantified different types of risks over the past decades, but several types of risks can be considered in the model side by side with economic changes. One experiences the highest number of obstacles in preparing for risks that, one the one hand, are difficult to measure and, on the other hand, occur relatively infrequently but may exert a major impact on individual businesses or for the whole economy. Geopolitical risks belong to that group. In this paper, we survey the most significant geopolitical risks of the modern age and examine the corporate reports of banks in the US, Europe, and Asia to analyse the occurrence of terms relating to geopolitics. Then, we attempt to build an index which can help compare the regions more. Risk indexes have been on the rise recently following the conflicts of the past years. The risk index formed from corporate reports also correlates with geopolitical indexes found in other types of the literature.

FunPredCATH: An ensemble method for predicting protein function using CATH

MotivationThe growth of unannotated proteins in UniProt increases at a very high rate every year due to more efficient sequencing methods. However, the experimental annotation of proteins is a lengthy and expensive process. Using computational techniques to narrow the search can speed up the process by providing highly specific Gene Ontology (GO) terms. MethodologyWe propose an ensemble approach that combines three generic base predictors that predict Gene Ontology (BP, CC and MF) terms from sequences across different species. We train our models on UniProtGOA annotation data and use the CATH domain resources to identify the protein families. We then calculate a score based on the prevalence of individual GO terms in the functional families that is then used as an indicator of confidence when assigning the GO term to an uncharacterised protein. MethodsIn the ensemble, we use a statistics-based method that scores the occurrence of GO terms in a CATH FunFam against a background set of proteins annotated by the same GO term. We also developed a set-based method that uses Set Intersection and Set Union to score the occurrence of GO terms within the same CATH FunFam. Finally, we also use FunFams-Plus, a predictor method developed by the Orengo Group at UCL to predict GO terms for uncharacterised proteins in the CAFA3 challenge. EvaluationWe evaluated the methods against the CAFA3 benchmark and DomFun. We used the Precision, Recall and Fmax metrics and the benchmark datasets that are used in CAFA3 to evaluate our models and compare them to the CAFA3 results. Our results show that FunPredCATH compares well with top CAFA methods in the different ontologies and benchmarks. ContributionsFunPredCATH compares well with other prediction methods on CAFA3, and the ensemble approach outperforms the base methods. We show that non-IEA models obtain higher Fmax scores than the IEA counterparts, while the models including IEA annotations have higher coverage at the expense of a lower Fmax score.

Personalized Query Expansion with Contextual Word Embeddings

Personalized Query Expansion, the task of expanding queries with additional terms extracted from the user-related vocabulary, is a well-known solution to improve the retrieval performance of a system w.r.t. short queries. Recent approaches rely on word embeddings to select expansion terms from user-related texts. Although promising results have been delivered with former word embedding techniques, we argue that these methods are not suited for contextual word embeddings, which produce a unique vector representation for each term occurrence. In this article, we propose a Personalized Query Expansion method designed to solve the issues arising from the use of contextual word embeddings with the current Personalized Query Expansion approaches based on word embeddings. Specifically, we employ a clustering-based procedure to identify the terms that better represent the user interests and to improve the diversity of those selected for expansion, achieving improvements of up to 4% w.r.t. the best-performing baseline in terms of MAP@100. Moreover, our approach outperforms previous ones in terms of efficiency, allowing us to achieve sub-millisecond expansion times even in data-rich scenarios. Finally, we introduce a novel metric to evaluate the expansion terms’ diversity and empirically show the unsuitability of previous approaches based on word embeddings when employed along with contextual word embeddings, which cause the selection of semantically overlapping expansion terms.

Assessing the Impact of International Financial Reporting Standards (IFRS) through Bibliometric Research

The present study evaluates the influence of International Financial Reporting Standards (IFRS) on scholarly literature through the utilization of bibliometric analysis. This study examines the frequency of occurrence of terms in a dataset consisting of renowned publications, thereby uncovering significant themes and areas of focus in IFRS research. Prominent terminology such as "Financial Reporting Standard," "Reporting," and "Accounting" indicate an exhaustive examination of the practical implementation of reporting standards and accounting principles. Underappreciated terminologies such as "Mandatory Adoption" and "Earnings Management" underscore intricate domains of inquiry, addressing the complexities and ramifications linked to the implementation of IFRS. The analysis reveals overarching themes such as sustained exploration, strategic considerations, and practical application, which offer valuable insights for both researchers and practitioners. The research contributes to the delineation of research priorities, the provision of instructional materials, the orientation of policy deliberations, and the resolution of pragmatic obstacles within the ever-evolving domain of IFRS research.

Analysis of Research Development on the Use of Internet of Things (IoT) Technology in Health Monitoring

Health monitoring is one of the many industries that has been transformed by the Internet of Things' (IoT) explosive growth. A thorough bibliometric analysis, cluster analysis, and term occurrences analysis are used in this study to reveal the complex world of IoT technology in health monitoring. Thematic groups are shown by cluster analysis, essential insights are provided by highly cited publications, and key concepts are clarified by word occurrences. The multidisciplinary aspect of research is exemplified by collaborative networks, prominent contributions, and rising trends within the literature. The amalgamation of results offers a comprehensive comprehension of the present condition of IoT in health monitoring, steering forthcoming research directions and promoting sustained innovation at the nexus of technology and healthcare.

A Bibliometric Analysis of Social Justice Movements and Popular Culture Intersection on Social Media

Using a bibliometric analysis, this study conducts a thorough investigation of the relationship between social justice movements, popular culture, and social media. Utilizing citation analysis, co-authorship networks, keyword co-occurrence, and temporal trends analysis, the study draws from a wide spectrum of scholarly publications, including seminal texts and modern contributions. The complex relationships found in the literature are made easier to show with the help of the visualization application VOSviewer. The analysis identifies important research areas and highlights topics including the effects of youth internet use, the contribution of popular culture to activism, and social network dynamics. The intellectual landscape is also shaped by important works by writers like Tarrow, McPherson, and Collins. Along with highlighting justice, race, and gender, the study also uncovers rising themes like diversity and group action through term occurrences. All things considered, this bibliometric analysis offers a thorough grasp of the intellectual landscape, generating a nuanced knowledge of the complex convergence of social justice, popular culture, and social media and providing ideas for future research.

INVESTIGATING THE COLLOCATION OF THE WORDS 'MOM' AND 'DAD' IN ALEC GREVEN'S NOVELS: A CORPUS LINGUISTIC ANALYSIS OF SEMANTIC PREFERENCES AND PROSODY

This research delves into the collocation and semantic analysis of the words "Mom" and "Dad" in two novels, "How to Talk to Mom?" and "How to Talk to Dad?", authored by a teenage male writer, Alec Greven. The primary objective is to investigate the frequency of occurrence of these parental terms and explore their semantic preferences and prosody within the context of dictions used in the novels. The study uses a mixed-method design, incorporating both quantitative and qualitative approaches to achieve a comprehensive understanding of the research topic. Using corpus linguistic methods, the study examines the frequency of the words "Mom" and "Dad" in each novel, revealing that "Mom" occurs more frequently than "Dad." Through concordance hits, the collocational patterns of the parental terms are analyzed, shedding light on the contexts in which they are commonly used. The collocation analysis demonstrates that "Dad" is frequently associated with family, friendship, and engaging activities, while "Mom" is linked to family, household chores, and maternal preferences. Furthermore, the research explores the semantic prosody of "Mom" and "Dad" revealing that "Dad" is portrayed more positively compared to "Mom."

Using Social Media to Help Understand Patient-Reported Health Outcomes of Post-COVID-19 Condition: Natural Language Processing Approach.

While scientific knowledge of post-COVID-19 condition (PCC) is growing, there remains significant uncertainty in the definition of the disease, its expected clinical course, and its impact on daily functioning. Social media platforms can generate valuable insights into patient-reported health outcomes as the content is produced at high resolution by patients and caregivers, representing experiences that may be unavailable to most clinicians. In this study, we aimed to determine the validity and effectiveness of advanced natural language processing approaches built to derive insight into PCC-related patient-reported health outcomes from social media platforms Twitter and Reddit. We extracted PCC-related terms, including symptoms and conditions, and measured their occurrence frequency. We compared the outputs with human annotations and clinical outcomes and tracked symptom and condition term occurrences over time and locations to explore the pipeline's potential as a surveillance tool. We used bidirectional encoder representations from transformers (BERT) models to extract and normalize PCC symptom and condition terms from English posts on Twitter and Reddit. We compared 2 named entity recognition models and implemented a 2-step normalization task to map extracted terms to unique concepts in standardized terminology. The normalization steps were done using a semantic search approach with BERT biencoders. We evaluated the effectiveness of BERT models in extracting the terms using a human-annotated corpus and a proximity-based score. We also compared the validity and reliability of the extracted and normalized terms to a web-based survey with more than 3000 participants from several countries. UmlsBERT-Clinical had the highest accuracy in predicting entities closest to those extracted by human annotators. Based on our findings, the top 3 most commonly occurring groups of PCC symptom and condition terms were systemic (such as fatigue), neuropsychiatric (such as anxiety and brain fog), and respiratory (such as shortness of breath). In addition, we also found novel symptom and condition terms that had not been categorized in previous studies, such as infection and pain. Regarding the co-occurring symptoms, the pair of fatigue and headaches was among the most co-occurring term pairs across both platforms. Based on the temporal analysis, the neuropsychiatric terms were the most prevalent, followed by the systemic category, on both social media platforms. Our spatial analysis concluded that 42% (10,938/26,247) of the analyzed terms included location information, with the majority coming from the United States, United Kingdom, and Canada. The outcome of our social media-derived pipeline is comparable with the results of peer-reviewed articles relevant to PCC symptoms. Overall, this study provides unique insights into patient-reported health outcomes of PCC and valuable information about the patient's journey that can help health care providers anticipate future needs. RR2-10.1101/2022.12.14.22283419.

Social Activism and Societal Transformation: Assessing the Impact of Human Rights Movements

This research employs bibliometric analysis to examine the impact of the Human Rights Movement on social activism and societal transformation. Through the utilization of the VOSviewer software, a systematic assessment of scholarly publications was conducted to uncover trends, collaborations, and influential works in this dynamic field. The analysis revealed distinct thematic clusters, including those centered around conflict, culture, business, health, and basic human rights. Seminal works, such as "Activists Beyond Borders" and "Racial Formation in the United States," were identified as pivotal in shaping the discourse. The distribution of term occurrences highlighted a focus on practice, human rights activists, traditional justice, and education. This study contributes to a comprehensive understanding of the multidimensional relationships between human rights, activism, and societal transformation, providing insights for academics, policymakers, and activists alike.

Gendered Discourses and Actor Representation in Eoin Colfer’s Artemis Fowl Series: A Corpus-Based Study

The Artemis Fowl series (2001-2012) is described by its Irish author Eoin Colfer as “Die Hard with Fairies”. Artemis Fowl is an-eight book series of adventures and supernatural actions. The starring character, Artemis Fowl, is a twelve-year- old criminal mastermind. His primary goal is to pursue money, which makes him kidnap the fairy leader. This results in a continuing fight between two worlds: Artemis’s and the fairies’. The Fowl adventures present many themes, of which gendered discourses and actor representation inspire this research. This paper examines the eight-series child books via Corpus Linguistics apparatus. AntConc is a corpus analysis software utilized to generate wordlists of each book so that words with higher frequency may be investigated in their context of utterance via the concordance toolkit. The next step aims to detect the occurrence of search terms pertaining to gendered discourse and actor description. In this concern, the contributions presented by Tannen (1993, 1999), Edley (2001), Weatherall and Gallois (2003), Sunderland (2004), and Wetherell and Edley (2014) are considered in the detection of gendered language. Van Leeuwen’s taxonomy (1996, 2008) of social actor representation is also considered to detect actor descriptions. Accordingly, the eight-series adventure novels are linguistically analyzed so that the main themes, gender identity markers and actor representation may be uncovered. The study supports the discursive psychologists’ belief that gender is created and enhanced via discourse. The study is original in incorporating corpus linguistic toolkits, discursive psychology and discourse analysis to child Irish fantasy literature to expose the gendered-identities negotiated and the features representing social actors.