Popular Topics Research Articles

Fraudulent Financial Reporting: How far has it been Researched?

This study aims to determine the development and trend map of fraudulent financial reporting research that is published by a reputable journal in the theme of economics and finance. The data analyzed were more than 91 publications of indexed research publications. The export data is then processed and analyzed using the R Biblioshiny application program to find out the bibliometric map of the development of the research in this topic. The results showed that the number of publications on the fraudulent financial reporting research experienced a significant increase. The results show that the most popular authors are Groove H and Carcello JV, and the most popular keyword topics are Financial, Reporting, and Fraudulent. Apart from that, there are several trend topics related to fraudulent financial reporting.

CiteSpace-based Visualization and Analysis of Knowledge Graph in International Supply Chain Domain

Abstract Along with globalization and technological advances, the complexity and dynamics of international supply chains are increasing. Using CiteSpace to visualize and analyze the knowledge map of the global supply chain helps to deeply understand the industry’s development trends and essential elements. The knowledge map of the international supply chain field is visualized and analyzed using CiteSpace software in this paper, which uncovers the key components and development trends in the field. The study involves collecting and analyzing relevant literature from 2009 to 2021, and applying techniques such as word frequency analysis, co-occurrence network construction, and spectral clustering algorithms to map the knowledge map. Supply chain management, supply chain resilience and risk management are the most popular topics in the supply chain field, in which the word “supply chain management” has a co-occurrence frequency of 485 times, with a centrality of 0.457. Using the spectral clustering algorithm, we identify several key research clusters and find that “supply chain security” has become a recent research topic. Security” has become a hot research topic recently. Research in the field of supply chain is gradually focusing on improving chain flexibility and reducing risks. There is increasing interest in the trend of supply chain futurization and intelligence.

CSP in 40 years of scientific publications

In 2024, Cadernos de Saúde Pública (CSP) celebrates 40 years of uninterrupted publications. This article analyzes the trajectory of the journal and projects its future considering contemporary challenges in scientific publishing in the field of Public Health. This study was based on the analysis of main editorial policy milestones and the identification of the most popular topics. Three periods were recognized. In the "beginning" (1985 to 1990), the journal had restricted circulation, with a quarterly frequency. The word "epidemiology", used in a descriptive context, is highlighted. Then, in the period of "consolidation" (1991 to October 2012), CSP was indexed in the Index Medicus-MEDLINE database and had its online collection published by the Scientific Electronic Library Online (SciELO), expanding the reach of articles. In this period, the words that characterize the field of policy, planning, and management were clearly seen, and terms related to the epidemiological method emerged. The period of "diversity" (November 2012 to November 2023) introduced the requirement of an external editor to the Oswaldo Cruz Foundation's staff among the three co-editors-in-chief, aiming to ensure editorial independence. New activities and a similar frequency among the five most common terms highlight such "diversity". Several issues are observed in scientific publishing in 2024: non-commercial Open Science, artificial intelligence, appreciation of science, scientific dissemination, among others. Addressing new challenges in an ethical and transparent manner will allow future advances, maintaining CSP's credibility among authors and readers and its commitment to improvements in the living and health conditions of populations.

Estimation of Operation Time with Digital Twin in Manufacturing

There are two most important factors that are taken into consideration in businesses. One of them is time and the other is cost. In order to save time and cost, planning of production subcomponents involves a series of critical activities. Making more effective plans in the field of production has become possible with industry 4.0. Industry 4.0 includes the digitalization of production. One of the most popular topics in this process is the Digital Twin [DT]. The DT philosophy has enabled businesses to better understand the sub-processes of production. In this way, they can optimize them. With the development of this philosophy, more detailed models have been created. Enterprises keep their data under control in order to control, manage and optimize processes. These data are then utilized in the model building process. The aim of this study is to estimate the time from the moment a product enters the process to the moment it leaves the process by using the data obtained through time study etc. studies. Automated machine learning (AutoML) method is used to build the best model. Machine learning (ML) algorithms, which are popularly used in the literature, may not always give the best result. In order to prevent this, starting from the data preprocessing step, including hyperparameter optimization, the aim is to find the algorithm and parameters that give the best performance. It will contribute to DT studies by estimating the operation time. The study used a 115-row dataset from CNC machines. The dataset consists of velocity, motion and actual time. The actual time is tried to be estimated using motion/speed. It is aimed to achieve the best results with AutoML. lazy predict and tpot library were used in the study. As a result, an estimation of the duration of 100% was realized.

Physicochemical and Structural Investigation of L-Threonine/Glycerol-based Deep Eutectic Solvent Using Experimental and Molecular Modelling Approaches.

One of the most popular topics in sustainable chemistry has been the creation of new eco-friendly solvents. Deep eutectic solvents (DES) have established themselves as accessible and affordable substitutes for...

Thyroid Nodules: Emerging Trends in Detection and Visualization based on Citespace.

Thyroid nodule (TN) is a highly prevalent clinical endocrine disease. Many countries have formed guidelines on the prevention and treatment of TN based on extensive research. However, there is a scarcity of TN-related literature based on bibliometrics. This study aimed to evaluate the scientific achievements and progress of TN research from a global perspective by investigating the literature for 20 years through bibliometrics. We searched the literature on TN in the core collection of the Web of Science database from 2002 to 2021 and used the Citespace software to analyze the co-authorship, co-citation, and co-occurrence of countries, institutions, authors, keywords, and co-cited literature. We retrieved 12319 documents related to TN. The literature on TN has been growing since 2002. The United States has contributed the largest proportion of TN papers (20.64%), followed by China, Italy, and South Korea. The United States ranked first in terms of centrality (0.38). Haugen BR, Gharib H, and Cibas ES are the top three most cited authors. The papers published in Thyroid were cited most frequently (7952 times). The most prominent keywords were management, cancer, fine needle aspiration, diagnosis, malignant tumor, thyroid cancer, ultrasound, biopsy, benign, surgery, ablation, and cytology. All keywords could be divided into three categories: diagnosis stratification, treatment, and cancer. As far as potential hot spots are concerned, the keywords that have recently burst strongly and are still continuing are: "Association Guideline" (2018-2021), "Radiofrequency Ablation" (2017-2021), "Classification" (2019-2021), and "Data System" (2017-2021). Based on the current trends, the number of publications on TN will continue to increase. The United States is the most active contributor to research in this field. Previous literature focused on stratification, cancer, surgery, and ablation, and there were different opinions on the stratification of diagnosis. There were relatively few studies on pathogenesis and treatment using medicine. More focus will be placed on association guidelines, radiofrequency ablation, classification, and data system, which may be the next popular topics in TN research.

A novel electrochemical flow-cell for operando XAS investigations in X-ray opaque supports.

Improvement of electrochemical technologies is one of the most popular topics in the field of renewable energy. However, this process requires a deep understanding of the electrode-electrolyte interface behavior under operando conditions. X-ray absorption spectroscopy (XAS) is widely employed to characterize electrode materials, providing element-selective oxidation state and local structure. Several existing cells allow studies as close as possible to realistic operating conditions, but most of them rely on the deposition of the electrodes on conductive and X-ray transparent materials, from where the radiation impinges the sample. In this work, we present a new electrochemical flow-cell for operando XAS that can be used with X-ray opaque substrates, since the signal is effectively detected from the electrode surface, as the radiation passes through a thin layer of electrolyte (∼17 μm). The electrolyte can flow over the electrode, reducing bubble formation and avoiding strong reactant concentration gradients. We show that high-quality data can be obtained under operando conditions, thanks to the high efficiency of the cell from the hard X-ray regime down to ∼4 keV. We report as a case study the operando XAS investigation at the Fe and Ni K-edges on Ni-doped γ-Fe2O3 films, epitaxially grown on Pt substrates. The effect of the Ni content on the catalytic performances for the oxygen evolution reaction is discussed.

National Image Construction Based on Multimodal Translation in the Context of Big Data

Abstract This study focuses on the public’s perception of a specific country’s image, and adopts comment texts from multimodal translated materials as the object of analysis. A country image text perception model was established by combining the TextRank algorithm and the LDA topic model. The model extracts and categorizes themes from the collected comment data, and discusses in depth the different themes of country image and their degree of attention. It is found that the most popular theme is “economic dimension”, with a heat degree of 42.59%, followed by “regional development” and “political dimension”. This reflects the high level of public interest in these areas. In addition, the study quantified the public’s emotional disposition towards the country’s image and found that most people have a positive attitude towards the country, with a high percentage of positive sentiment of 84.89%, but most of them are mildly positive. This suggests that although the public’s overall perception tends to be positive, there is room to improve the country’s image and enhance the public’s positive perception.

Navigating the asthma network on Twitter: Insights from social network and sentiment analysis.

Asthma is a condition in which the airways become inflamed and constricted, causing breathing difficulties, wheezing, coughing, and chest tightness. Social networks can have a substantial effect on asthma management and results. However, no studies of social networks addressing asthma have been undertaken. The aim of this research was to identify the significant social network structures, key influencers, top topics, and sentiments of asthma-related Twitter conversations. All the tweets collected for this study included the keyword "asthma" or were mentioned in or in replies to tweets that were performed. For this study, a random sample of Twitter data was collected using NodeXL Pro software between December 1, 2022, and January 29, 2023. The data collected includes the user's display name, Twitter handle, tweet text, and the tweet's publishing date and time. After being imported into the Gephi application, the NodeXL data were then shown using the Fruchterman-Reingold layout method. In our study, SNA (Social Network Analysis) metrics were utilized to identify the most popular subject using hashtags, sentiment-related phrases (positive, negative, or neutral), and top influencer by centrality measures (degree, betweenness). The study collected 48,122 tweets containing the keyword "asthma" or mentioned in replies. News reporters and journalists emerged as top influencers based on centrality measures in Twitter conversations about asthma, followed by government and healthcare institutions. Education, trigger factors (e.g., cat exposure, diet), and associated conditions were highly discussed topics on asthma-related social media posts (e.g., sarscov2, copd). Our study's sentiment analysis revealed that there were 8427 phrases associated neutral comments (18%), 12,582 words reflecting positive viewpoints (26%), and 27,111 words reflecting negative opinions (56%). This study investigates the relevance of social media influencers, news reporters, health experts, health organizations, and the government in the dissemination and promotion of asthma-related education and awareness during public health information.

Detective Fiction in Different Cultures

In the wake of Postmodernism, detective fiction has become one of the most popular subjects among the critics and scholars. Apart from being a preferred topic for study and analysis by critics, this genre is also enjoying popularity among the readers. This paper seeks to attempt a study of detective fiction created in different culture like French, Japanese, Chinese etc and explore detective fiction in Urdu and Bengali language. This paper would also discuss the subgenres of detective fiction.

Constructing and Experimental Research on the Feedback Mechanism of Mathematics Teaching Based on Error Analysis

Abstract In the current teaching context, mathematics has become the most popular subject among all disciplines, and the establishment of mathematics teaching evaluation and feedback mechanisms is particularly important. The study constructs a feedback mechanism for mathematics teaching based on error analysis using the improved hierarchical analysis method and fuzzy comprehensive evaluation method. The mathematics teaching feedback mechanism and the traditional manual feedback mechanism are used to compare the performance of error analysis. Completely evaluating math teachers in Z-school using the math teaching evaluation index system. Compare the students’ math scores before and after the implementation of the math teaching feedback mechanism to explore the application effect of the feedback mechanism. The results show that the overall error of the math teaching feedback mechanism (1.12) is better than that of the traditional manual feedback mechanism (3.19), and the error fluctuation of the former is also smaller than that of the latter. The feedback results from the math teaching feedback mechanism are more accurate than the actual feedback results. The fuzzy comprehensive evaluation results of Teacher A are in the following order from highest to lowest: teaching method (93.069) > teaching content (92.531) > teaching objectives (91.645) > teaching effect (90.537) > teaching attitude (88.543). After the experiment of the math teaching feedback mechanism, the mean score of math achievement of the experimental group increased by 12 points. The control group decreased by 1.32 points, and the teaching feedback mechanism achieved significant results.

Research on Hot-spots and Trends of Human Resource Management under Digitalization

A new era of digital technology, characterized by the advent of mobile internet, cloud computing, big data, and artificial intelligence, is reshaping human production and lifestyle, heralding a paradigm shift in the way we engage with the world around us. As a matter of fact, digital transformation is no longer a distant future but an ongoing reality faced by all enterprises alike. Within this evolving landscape, human resources management, being a pivotal component of organizational management, is undergoing profound changes driven by digitalization. This paper combs the literature from all over the world in the field of digital human resource management in the past ten years, and extracts the key discoveries. This paper sums up the four most popular research topics: digital natives, service-oriented human resource management, updated connection mode, and ensuring the status of humanity. On the basis of the review, this paper looks forward to the future by pointing out possible research directions and making relevant recommendations.

Research trends on parasocial interactions and relationships with media characters. A review of 281 English and German-language studies from 2016 to 2020.

Parasocial phenomena are among the most popular and best-researched topics in media reception and effects research. The research can now look back on a history of over 65 years and has experienced another significant boom in recent years. Between 2016 and 2020, more studies were published than in the entire previous 60 years. This descriptive review builds directly on the seminal review by Liebers and Schramm for the years 1956 to 2015 and is based on 281 English-and German-language studies published from 2016 to 2020. The following three research questions guided the review: How are the studies from 2016 to 2020 positioned in terms of the media contexts examined, the parasocial phenomena investigated, the methods and measurements used and the samples? (RQ1) What has changed in the studies from 2016 to 2020 compared to the studies from 1956 to 2015 in terms of the media contexts investigated, the parasocial phenomena studied, the methods and measurements used and the samples? (RQ2) What substantive perspectives and key insights can be gained from the studies conducted between 2016 and 2020 and what gaps in research can still be observed? (RQ3). The results show, for instance, that the largest proportion of studies from 2016 to 2020 focused on the area of social and new media, on non-fictional characters and amicable forms of parasocial relationships, using surveys and existing measurement instruments or adapted versions of them (RQ1). Compared to the studies from 1956 to 2015, parasocial phenomena are increasingly being researched experimentally, as mediating and moderating factors and using established scales, with a slight increase in the proportion of studies specifically investigating parasocial relationships or relationship breakups (RQ2). The research focus has evolved from film and television to social media and cross-media settings. Most empirical studies of parasocial phenomena still rely on young and predominantly female samples (RQ2). The neglect of negatively valenced parasocial phenomena, comparative cultural perspectives, comparative media perspectives, long-term effects and less educated and older people as samples can still be identified as major research gaps (RQ3) and opens up numerous opportunities for future research.

A Brief Survey of Distribution Robust Graph Neural Networks

Graph neural network is a powerful tool for solving various graph tasks, such as node classification and graph classification. However, there is increasing evidence suggesting that it is sensitive to distribution shifts and its performance is reduced by at least 10% when distribution shifts happen. In the last three years, to solve the wide existing distribution shifts, some distribution robust strategies have been designed specifically for graph neural networks. It is also one of the popular research topics in graph neural networks in recent years. Therefore, in this paper, we divide these strategies into three categories depending on the specific problems they solve: distribution shift for semi-supervised node classification, distribution shift for unsupervised node classification, and out-of-distribution shift for graph classification. Furthermore, we discuss and summarize the strategies for each type of problem. After investigating these strategies, we put forward future development direction.

Ранняя поэма Чосера «Книга о Герцогине»: особенности топики

The article discusses the meaning of the poem The Book of the Duchess by Geoffrey Chaucer, which is considered as a multi-layered structure. Traditionally popular topics in Chaucerian studies related to this work are those that involve discussion of questions about the validity of the publishers’ choice of one of the surviving titles for the poem, about the correlation of the images of the main characters, including the Dreamer, in the plot and narrative, about the motivation for the two-part type of composition (The Proemium and The Dream) and the function of using in the plot of the retelling from Ovid’s Metamorphoses (the story of Ceyx and Alcyone), and finally, about the connection of this retelling with the “Boethian” poems of Guillaume de Machaut. The author of the article consistently touches on the essence of the listed issues, in order to finally present his own concept of the form and meaning of Chaucer’s early poem. In particular, the primacy of the image of the lady lost by the knight is questioned. The author of the article proposes to consider the structure of the work as a voluminous, multi-layered object, under the outer layer of which (the praising of the queen stolen by Fortune, whose prototype was Blanche the Duchess of Lancaster) there is a layer with a focus on the complaining knight (and the implicit function inherent in the elements of this layer of praising this knight for his courtliness, education, and qualities of a lover), which, in turn, hides the third layer, the semantic core of which is in the theme of Chaucer’s own competition with Guillaume de Machaut and Ovid in the realm of poetic fantasies.

Perspective of ecocycles for human well-being and health: A bibliometric analysis

The ecocycle is essential in extending the sustainable development cycle for human health and well-being. It needs to be highlighted to see what issues are behind the crossing of issues circulating so far. This research aims to explore ecocycle studies for human well-being and health during 2000-2023. A suitable bibliometric study has been conducted that visualizes the evolution and development trend of the selected studies, themes distribution, trails, keywords, and other metrics, which have been highlighted using the Biblioshiny tool derived from the R-studio package. The results found that the ecocycle for human well-being and health study has fluctuated in its evolution of publication trends, but impacts have been addressed each annual year. Prescott S.L. is a scholar who actively publishes papers, and an article by Miller K.E published in 2010 is the most cited article (n=867). The journal “International Journal of Environmental Research and Public Health” was the leading source of topics, and “University of Toronto” had the most affiliates. Furthermore, the United States served as a prolific country; trending topics such as sustainability, ecology, and mental health were among the top three. Likewise, well-being was a popular theme of research, whereby variable factors of physiology were closely coordinated. In addition, depression, inflammation, biophilosophy, and zoonosis are also featured in ecocycle studies for human well-being and health for recent publications and are expected to become future study directions.

Saint to Slut: The Representation of Sex Workers in Art

Sex work is one of the oldest professions that remains in society today. With its long history, sex workers have repeatedly found themselves in the limelight, society contemplating and prescribing meanings to their bodies. As a result, sex workers have become a popular subject of art throughout the years. This article specifically looks at their depiction in the Renaissance/Late-Renaissance and the present platform of OnlyFans to understand the evolution of the representation of sex workers. An analysis of their portrayal is done through a comparison of artworks in an effort to look at society's varying perceptions of sex workers and, more importantly, the role of agency within these works. While the discussion of agency in sex work is an ongoing and delicately complex one, this article aims to explore the possibility of reclamation and empowerment within the profession by asking the question: When depicting a sex worker who is owning her body? The sex worker, artist, or viewer?

An Analysis of the Evaluation of Islamic Laws in Pakistan from 1988 to 1999

In Pakistan, there has always been a strong desire for Islamic law, or Sharia. The Islamic nature of Pakistan's political structure was discussed as soon as the country was established. One of the primary reasons that the process of drafting a constitution was delayed was the place of religion in the political structure. Islam emerged as the primary determining element in 1949 with the ratification of the Objectives Resolution. Still, there was a persistent push for further Islamization of the legislation. Different Islamic laws were introduced at different times. In this regard, the General Zia era is more notable. This article aims to highlight the Islamic legislation that was implemented following the reign of Zia. According to the paper, Zia's death did not mark the end of the process of Islamization. It remained a popular subject, especially as a potent campaign slogan. IJI, which Nawaz Sharif led, and later PML-N were more interested in employing religious rhetoric during elections; PPP, on the other hand, also occasionally employed religious cards.

Importance, Popularity and Elements of Educational Platforms – A Study of the Opinions of Students from Poland, Ukraine and Kazakhstan

This article presents a comparative international research study analyzing the opinions of students from Poland, Ukraine, and Kazakhstan regarding the importance, popularity, and elements of educational platforms in the field of computer science. The study employed the Kruskal-Wallis test for statistical analysis. The research questions addressed are as follows: RQ1: What are educational platforms in general, and which specific platforms are most popular among students from Poland, Ukraine, and Kazakhstan? RQ2: Which topics are most frequently searched for on educational platforms, and what motivates students from these countries to engage in learning through educational platforms? RQ3: How do students from Poland, Ukraine, and Kazakhstan evaluate the usefulness of different types of content posted on educational platforms, and do they use various platforms to share their code? Two hypotheses are proposed: firstly, that the country of origin of the student does not significantly affect the frequency of use of educational platforms, and secondly, that the country of origin does influence the choice of courses students are interested in on educational platforms. The most popular topics on these platforms are programming and computer networks, with students from Kazakhstan also displaying a keen interest in subjects related to artificial intelligence and computer graphics. Additionally, this study analyzes the conditions of learning and teaching in specialized modules at each university, including teacher requirements, curriculums, and the potential for practical implementation of new knowledge by students. Furthermore, the study examines course elements that are most essential to students, such as video recordings of lectures, tutorials/instructional videos, practical tasks, and solutions to tasks. The article concludes with a discussion and summary of the findings.

Systematic Literature Review terhadap Klasifikasi Emosi pada Lirik Lagu Berbahasa Ambon menggunakan Metode Bidirectional LSTM dengan Glove Word Representation Weighting

One form of text that can express emotions is lyrics. Lyrics are a type of literary work expressed in the form of words, the contents of which can express the songwriter's personal feelings, thoughts, and emotions. Therefore, the lyrics can be used as an object of research on the classification of emotions. The classification of song lyrics really requires bi-LSTM to be the input value when classifying data in the form of song lyrics in order to get high accuracy results. This research was carried out systematically and the results were measurable. Descriptive qualitative research was used in this research. The results of identification based on case studies and statistics show that the reviews of popular topics are identical. The classification of song lyrics really requires bi-LSTM to be the input value when classifying data in the form of song lyrics in order to get high accuracy results.