Text Keywords Research Articles

Big Data Analysis on Consumer Perception of Pressure Injuries: Text Mining and Semantic Network Analysis

Background: With the ultimate goal of developing chatbot content to address consumer inquiries about pressure injuries (PIs), this study analyzed consumer perceptions of PI using big data.Methods: This study collected text data, with PI as the central word, from three search engines (Naver, Daum, Google) from January 2019 through December 2022, using Textom version 4.5. The words were refined through text mining, keyword analysis, and TF-IDF (term frequency-inverse document frequency) analysis. N-gram analysis and centrality visualization were conducted using Ucinet 6.0. The keywords and frequencies were clustered based on the frequency of words used in CONCOR (convergence of iteration correlation) analysis.Results: Consumers for PI showed a high perception of common sites for PI, concept of PI, healthcare facility for PI, PI products, PI care, PI-related life, and PI-related disease.Conclusion: Development of chatbot content customized to consumers’ needs, based on seven clusters associated with consumers’ perception of PI obtained through extensive data analysis with PI as the central word, is expected to make a significant contribution to improving consumers’ understanding of PI and enhancing the quality of PI management.

Few-shot intent detection with mutual information and contrastive learning

Few-shot intent detection is a challenging task. Most existing methods only focus on acquisition of generalization knowledge in known classes, or on the adaptation situation of meta-learning tasks. However, these methods lack both the learning of beneficial information for intent detection and the learning of discriminative representation of intent keywords. Solving these problems can effectively improve the model’s ability to recognize text intent and thus effectively execute user commands. Therefore, in this paper, we propose a few-shot intent detection framework based on Mutual Information and Contrastive Learning (MICL). It improves the robustness of the model learning of intent keywords through mutual information maximization, thus improving the accuracy of intent detection. Additionally, maximization of mutual information enhances the intent representation consistency on two text views,11In this paper, the “two text views” of the text represent the original text and its corresponding data augmentation text.which helps to promote the efficiency of sample contrastive learning. Specifically, we use a neural network to approximately estimate the joint probability distribution and marginal probability distribution of two text views and maximize the mutual information through back-propagation. In order to enable the model to focus on intent keywords in text, we introduce a sample-level contrastive learning. It helps the model to learn discriminative representations of intent keywords by comparing the similarity of text from two text views. In addition, to prevent overfitting of the model on known classes, we introduce a class-level contrastive learning. We regard it as regularization of known classes, aiming to improve the model generalization performance on unknown classes. Our method achieves state-of-the-art performance on four public datasets.22Our method code is available: https://github.com/YS19999/MICL.

HGATT_LR: transforming review text classification with hypergraphs attention layer and logistic regression

Text classification plays a major role in research such as sentiment analysis, opinion mining, and customer feedback analysis. Text classification using hypergraph algorithms is effective in capturing the intricate relationships between words and phrases in documents. The method entails text preprocessing, keyword extraction, feature selection, text classification, and performance metric evaluation. Here, we proposed a Hypergraph Attention Layer with Logistic Regression (HGATT_LR) for text classification in the Amazon review data set. The essential keywords are extracted by utilizing the Latent Dirichlet Allocation (LDA) technique. To build a hypergraph attention layer, feature selection based on node-level and edge-level attention is assessed. The resultant features are passed as an input of Logistic regression for text classification. Through a comparison analysis of different text classifiers on the Amazon data set, the performance metrics are assessed. Text classification using hypergraph Attention Network has been shown to achieve 88% accuracy which is better compared to other state-of-the-art algorithms. The proposed model is scalable and may be easily enhanced with more training data. The solution highlights the utility of hypergraph approaches for text classification as well as their applicability to real-world datasets with complicated interactions between text parts. This type of analysis will empower the business people will improve the quality of the product.

The design of advertising text keyword recommendation for internet search engines

As the growth of internet technology, human life is full of various advertisements. It is possible for individuals to obtain the advertising information they require, whether in an online or offline context. A research proposal is presented with the objective of enhancing the precision of online advertising recommendations. The proposal is based on the design of internet search engine advertising text keyword recommendation models, which integrate entity naming recognition models to facilitate tasks such as text classification and feature extraction. A recommendation algorithm based on content similarity is used to achieve keyword recommendation. Under the similarity calculation method of continuous bag-of-words model, when K is 100, the model weighted precision of the feature extraction method based on graph sorting and inverse text frequency index is 0.88, the weighted recall is 0.76, and the weighted F1-score is 0.82. In offline simulation testing, 85 % of the keyword recommendation model's recommendation time is less than 1 s, 99 % of the recommendation time is less than 2 s, and the recommendation cost can be significantly reduced by 75 %. In practical applications, the recommendation efficiency of this method can reach 96.3 %, and the recommendation precision can reach 95.8 %. The recommended satisfaction rate can reach 99.5 %. The results demonstrate that this method can provide highly accurate keyword recommendations and reduce the cost of advertising placement. Furthermore, it has been recognized and praised by users.

Using word embedding to detect keywords in texts modeled as complex networks

Using word embedding to detect keywords in texts modeled as complex networks

온라인 과학 교수학습에 대한 초등 예비교사들의 자기효능감 탐색

Purpose: The purpose of this study was to explore the self-efficacy of elementary pre-service teachers regarding online science instruction. Methods: A survey was conducted with 89 elementary pre-service teachers enrolled at Education University A, located in a non-metropolitan area. Among them, 3 participants agreed to additional interviews. Descriptive statistical analysis was conducted for each item, t-test were performed based on gender and major, and qualitative text (keywords) analysis was conducted for open-ended items. Interviews were conducted to elicit responses for open-ended items and were interpreted inductively through keyword analysis. The survey items were measured on a Likert scale for 8 subcategories, and one open-ended item allowed participants to freely describe their thoughts. Results: The results interpreted that elementary pre-service teachers have relatively low self-efficacy regarding online science instruction. There were no significant differences based on gender, but there were statistically significant differences in some items based on major. Ultimately, this suggests that different approaches may be needed for online science instruction based on pre-service teachers' backgrounds. This study can be interpreted as an indicator of pre-service teachers' perceptions and readiness for online science instruction before entering the school field. Conclusion: It emphasizes the importance of specific instructional methods and strategies for online science instruction and proposes the need for practical education.

Autophagy in Osteoarthritis: A Double-Edged Sword in Cartilage Aging and Mechanical Stress Response: A Systematic Review.

Background: Although osteoarthritis (OA) development is epidemiologically multifactorial, a primary underlying mechanism is still under debate. Understanding the pathophysiology of OA remains challenging. Recently, experts have focused on autophagy as a contributor to OA development. Method: To better understand the pathogenesis of OA, we survey the literature on the role of autophagy and the molecular mechanisms of OA development. To identify relevant studies, we used controlled vocabulary and free text keywords to search the MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials, Web of Science, and SCOPUS database. Thirty-one studies were included for data extraction and systematic review. Among these studies, twenty-five studies investigated the effects of autophagy in aging and OA chondrocytes, six studies examined the effects of autophagy in normal human chondrocytes, and only one study investigated the effects of mechanical stress-induced autophagy on the development of OA in normal chondrocytes. Results: The studies suggest that autophagy activation prevents OA by exerting cell-protective effects in normal human chondrocytes. However, in aging and osteoarthritis (OA) chondrocytes, the role of autophagy is intricate, as certain studies indicate that stimulating autophagy in these cells can have a cytotoxic effect, while others propose that it may have a protective (cytoprotective) effect against damage or degeneration. Conclusions: Mechanical stress-induced autophagy is also thought to be involved in the development of OA, but further research is required to identify the precise mechanism. Thus, autophagy contributions should be interpreted with caution in aging and the types of OA cartilage.

Why Are PPP Projects Stagnating in China? An Evolutionary Analysis of China’s PPP Policies

The Public–Private Partnership (PPP) model has significantly contributed to global infrastructure and public service provision. The evolution of the PPP model closely aligns with policy directives. China’s PPP policy evolution has included five stages: budding (1986–2000), fluctuating (2001–2008), steady (2009–2012), expanding (2013–2018), and stagnating (2019–present). This study employs bibliometric analysis and co-word analysis to examine 407 policies enacted by the Chinese government from 1986 to 2018. By extracting policy text keywords at various stages and constructing a co-word network matrix, this study delineates the distinctive characteristics of Chinese PPP policies across different epochs. It can be found that critical areas such as “government credit”, “contract spirit”, and “power supervision” are still underappreciated. The challenges confronting China’s PPP model are multifaceted, stemming from policy gaps that have led to substantial project difficulties. Although the government proposed a new mechanism for franchising in 2023, the new mechanism is only for new PPP projects, and the difficulties of existing PPP projects have not been solved. This study advocates for enhancements in project bankability, regulatory clarity, institutional environment improvement, contract spirit defense, and the development of the PPP-REITs model to address these issues.

AI-assisted literature exploration of innovative Chinese medicine formulas.

Objective: Our study provides an innovative approach to exploring herbal formulas that contribute to the promotion of sustainability and biodiversity conservation. We employ data mining, integrating keyword extraction, association rules, and LSTM-based generative models to analyze classical Traditional Chinese Medicine (TCM) texts. We systematically decode classical Chinese medical literature, conduct statistical analyses, and link these historical texts with modern pharmacogenomic references to explore potential alternatives. Methods: We present a novel iterative keyword extraction approach for discerning diverse herbs in historical TCM texts from the Pu-Ji Fang copies. Utilizing association rules, we uncover previously unexplored herb pairs. To bridge classical TCM herbal pairs with modern genetic relationships, we conduct gene-herb searches in PubMed and statistically validate this genetic literature as supporting evidence. We have expanded on the present work by developing a generative language model for suggesting innovative TCM formulations based on textual herb combinations. Results: We collected associations with 7,664 PubMed cross-search entries for gene-herb and 934 for Shenqifuzheng Injection as a positive control. We analyzed 16,384 keyword combinations from Pu-Ji Fang's 426 volumes, employing statistical methods to probe gene-herb associations, focusing on examining differences among the target genes and Pu-Ji Fang herbs. Conclusion: Analyzing Pu-Ji Fang reveals a historical focus on flavor over medicinal aspects in TCM. We extend our work on developing a generative model from classical textual keywords to rapidly produces novel herbal compositions or TCM formulations. This integrated approach enhances our comprehension of TCM by merging ancient text analysis, modern genetic research, and generative modeling.

Mining user reviews, crowdsourced noise measurements, and metadata to inform design thresholds for acoustic conditions in restaurants

High levels of noise are a well-known source of occupant discomfort in restaurants. While much previous work has focused on assessing subjective perceptions of restaurant soundscapes through survey instruments, less attention has been given to mining online user reviews for keywords related to psychoacoustic predictors. In this study, a dataset of online user reviews for over 2,500 restaurants, bars, and coffee shops in New York City has been obtained and compared against crowd-sourced measurements of noise levels within the same establishments. Analyses of review scores and the occurrence rate of acoustically relevant keywords in review text have been conducted using machine learning techniques to suggest design thresholds for restaurants across different categorizations. Results from this work can be used by consultants and owners to design restaurant soundscapes which may be more favorably reviewed.

How Does ChatGPT Use Source Information Compared With Google? A Text Network Analysis of Online Health Information.

How Does ChatGPT Use Source Information Compared With Google? A Text Network Analysis of Online Health Information.

Research Trends in the Study of Acceptability of Digital Mental Health-Related Interventions: A Bibliometric and Network Visualisation Analysis

The acceptability of digital health interventions is a multifaceted concept that is central to user engagement. It is influenced by cultural and social norms and it is, also, a key consideration for intervention development and evaluation. For this reason, it is important to have a clear overview of how research in digital interventions’ acceptability has evolved, what type of measures or assessments have been most frequently utilised, and what may be the implications for the knowledge area and future research directions. The purpose of this bibliometric and network visualization analysis was to explore the main research patterns in the study of the acceptability of digital mental health interventions and highlight the key characteristics of knowledge production on this topic. The Web of Science was searched for relevant primary studies, with 990 documents selected for inclusion in this bibliometric analysis. Publications’ metrics, text and author keyword analysis, and bibliographical coupling of the documents provided insights into how technological developments, specific research interests, research priorities, and contexts have shaped research in the field. The main differentiation in acceptability approaches emanated from the studies’ research designs, the stage of intervention development and evaluation, and the extent to which there was a focus on user attitudes, experience, and engagement. These differentiations further indicate the importance of having clarity as to what concepts or elements of acceptability a study addresses as well as approaches that have the potential to address the complexities of acceptability.

Increased risk of hearing loss associated with macrolide use: a systematic review and meta-analysis

The increased risk of hearing loss with macrolides remains controversial. We aimed to systematically review and meta-analyze data on the clinical risk of hearing loss, tinnitus, and ototoxicity following macrolide use. A systematic search was conducted across PubMed, MEDLINE, Cochrane, and Embase databases from database inception to May 2023. Medical Subject Heading (MeSH) terms and text keywords were utilized, without any language restrictions. In addition to the electronic databases, two authors manually and independently searched for relevant studies in the US and European clinical trial registries and Google Scholar. Studies that involved (1) patients who had hearing loss, tinnitus, or ototoxicity after macrolide use, (2) intervention of use of macrolides such as azithromycin, clarithromycin, erythromycin, fidaxomicin, roxithromycin, spiramycin, and/or telithromycin, (3) comparisons with specified placebos or other antibiotics, (4) outcomes measured as odds ratio (OR), relative risk (RR), hazard ratio (HR), and mean difference for ototoxicity symptoms using randomized control trial (RCT)s and observational studies (case–control, cross-section, and cohort studies) were included. Data extraction was performed independently by two extractors, and a crosscheck was performed to identify any errors. ORs along with their corresponding 95% confidence intervals (CIs) were estimated using random-effects models. The Preferred Reporting Items for Systematic Reviews and Meta-analyses reporting guidelines for RCTs and Meta-Analysis of Observational Studies in Epidemiology guidelines for observational studies were followed. We assessed the hearing loss risk after macrolide use versus controls (placebos and other antibiotics). Based on data from 13 studies including 1,142,021 patients (n = 267,546 for macrolide and n = 875,089 for controls), the overall pooled OR was 1.25 (95% CI 1.07–1.47). In subgroup analysis by study design, the ORs were 1.37 (95% CI 1.08–1.73) for RCTs and 1.33 (95% CI 1.24–1.43) for case–control studies, indicating that RCT and case–control study designs showed a statistically significant higher risk of hearing loss. The group with underlying diseases such as multiple infectious etiologies (OR, 1.16 [95% CI 0.96–1.41]) had a statistically significant lower risk than the group without (OR, 1.53 [95% CI 1.38–1.70] P = .013). The findings from this systematic review and meta-analysis suggest that macrolide antibiotics increase the risk of hearing loss and that healthcare professionals should carefully consider this factor while prescribing macrolides.

Тайнопись на стальных магистралях: перехват донесений, дешифровка и защита сведений железнодорожными жандармами

The article examines the activities of the gendarmerie railway police in the Russian Empire during the peri-od from the last decade of the 19th century to the February Revolution, focusing on their efforts to intercept anti-government correspondence and enemy reports, decrypt them, and protect information about the transportation of military goods. The author uses extensive previously unpublished records from the gendarmerie police de-partments of the railways to analyze examples of secret writing (cryptograms) used by revolutionaries. The dis-advantages of the ciphers used include a logical connection between letters in the source text and positions in conditional alphabets, as well as a specific arrangement of letters that ensures that only one position of the condi-tional alphabet can correspond to each encrypted value. Militant groups of political radicals actively tried to re-cruit railway employees, especially telegraph operators, to cooperate in order to receive and transmit information about the passage of trains with valuable cargo. This information was crucial for planning and implementing expropriation actions. During the First World War, the critical importance of communication increased the im-portance of cryptography and cryptanalysis. To encrypt messages about the state of Russian railways, the enemy used an extensive arsenal of techniques, such as replacing keywords in texts by established methods; using ci-pherbooks containing various code combinations, and a combination of values replacement and rearrangement according to the developed matrices. The war gave an impetus to the development of cryptography, as ciphers became more complicated for cryptanalysts to decipher. The concealment of the contents of spy reports was en-sured not only through the transformation of texts but also through sophisticated methods of packaging infor-mation carriers in luggage, among food products, and the use of special ink. In charge of decryption, the railway gendarmes largely depended on the special department of the Police Department of the Ministry of Internal Af-fairs and the Ministry of War. These agencies provided the necessary keys. In turn, the railway gendarmerie gained valuable experience in intercepting letters containing secrets when they were transported by rail.

Use of diabetes technologies in the primary school environment: a scoping review protocol.

The objective of this scoping review is to map the literature on the use of diabetes technologies in the primary school environment by children with type 1 diabetes (T1D) and/or their parents. T1D is a complex chronic disorder that is one of the fastest growing diseases in childhood. Technological advances in recent times have seen a growth in the use of diabetes technologies. Despite these advances, having T1D still creates challenges for parents' and children's school experiences. Furthermore, the literature on the use of diabetes technologies during the primary school day is under-investigated. This review will consider peer-reviewed primary research studies or systematic reviews that include children with T1D aged 6 to 12 years who use diabetes technologies in the primary school environment and/or their parents. This review will be conducted in accordance with JBI methodology for scoping reviews and will be reported in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. Following an initial limited search, a full search strategy was developed using index terms and key text words. This strategy will be used across relevant databases, including Embase, MEDLINE (Ovid), CINAHL (EBSCOhost), and Web of Science Core Collection for the full scoping review. There will be no limitations on language or year. Two reviewers will independently screen titles, abstracts, and full-text articles and extract relevant data using the JBI data extraction instrument. Data will be presented in a descriptive manner, supported by tables and charts, and accompanied by a narrative summary.

Unveiling the Therapeutic Potential and Healthcare Applications of Marine Therapy: A Systematic Review with Meta-Analysis and Meta-Regression.

This study was conducted to estimate the effectiveness of marine-derived resources for treating specific diseases, as well as identify the most effective methods for applying such resources in therapeutic applications. Bibliographic databases (PubMed, Embase, and Cochrane) were searched from their inception until May 2023 using Medical Subject Headings terms and text keywords related to seawater, mineral water, or ocean therapy. Fifteen eligible studies were included, involving 1325 participants aged 42.7-63.0 years. In the subgroup analysis based on treatment type, the mean difference was -1.581 (95% CI: -1.889, -1.274) for seawater with sun exposure and -1.210 (95% CI: -1.417, -1.002) for seawater with sun exposure, mud pack application, and sulfur pool therapy. The pooled standardized mean difference was calculated for different outcomes; the results were -1.110 (95% CI: -3.028, 0.806) for osteoarthritis severity, -0.795 (95% CI: -0.982, -0.607) for arthritis pain, -1.623 (95% CI: -2.036, -1.209) for fibromyalgia pain, and -1.498 (95% CI: -1.888, -1.108) for quality of life. Marine therapy is, therefore, promising for treating chronic skin issues, easing musculoskeletal discomfort, and enhancing the quality of life among patients with musculoskeletal pain.

Exploring Technology- and Sensor-Driven Trends in Education: A Natural-Language-Processing-Enhanced Bibliometrics Study

Over the last decade, there has been a large amount of research on technology-enhanced learning (TEL), including the exploration of sensor-based technologies. This research area has seen significant contributions from various conferences, including the European Conference on Technology-Enhanced Learning (EC-TEL). In this research, we present a comprehensive analysis that aims to identify and understand the evolving topics in the TEL area and their implications in defining the future of education. To achieve this, we use a novel methodology that combines a text-analytics-driven topic analysis and a social network analysis following an open science approach. We collected a comprehensive corpus of 477 papers from the last decade of the EC-TEL conference (including full and short papers), parsed them automatically, and used the extracted text to find the main topics and collaborative networks across papers. Our analysis focused on the following three main objectives: (1) Discovering the main topics of the conference based on paper keywords and topic modeling using the full text of the manuscripts. (2) Discovering the evolution of said topics over the last ten years of the conference. (3) Discovering how papers and authors from the conference have interacted over the years from a network perspective. Specifically, we used Python and PdfToText library to parse and extract the text and author keywords from the corpus. Moreover, we employed Gensim library Latent Dirichlet Allocation (LDA) topic modeling to discover the primary topics from the last decade. Finally, Gephi and Networkx libraries were used to create co-authorship and citation networks. Our findings provide valuable insights into the latest trends and developments in educational technology, underlining the critical role of sensor-driven technologies in leading innovation and shaping the future of this area.

Optimizing and interpreting the latent space of the conditional text-to-image GANs

Text-to-image generation intends to automatically produce a photo-realistic image, conditioned on a textual description. To facilitate the real-world applications of text-to-image synthesis, we focus on studying the following three issues: (1) How to ensure that generated samples are believable, realistic or natural? (2) How to exploit the latent space of the generator to edit a synthesized image? (3) How to improve the explainability of a text-to-image generation framework? We introduce two new data sets for benchmarking, i.e., the Good & Bad, bird and face, data sets consisting of successful as well as unsuccessful generated samples. This data set can be used to effectively and efficiently acquire high-quality images by increasing the probability of generating Good latent codes with a separate, new classifier. Additionally, we present a novel algorithm which identifies semantically understandable directions in the latent space of a conditional text-to-image GAN architecture by performing independent component analysis on the pre-trained weight values of the generator. Furthermore, we develop a background-flattening loss (BFL), to improve the background appearance in the generated images. Subsequently, we introduce linear-interpolation analysis between pairs of text keywords. This is extended into a similar triangular ‘linguistic’ interpolation. The visual array of interpolation results gives users a deep look into what the text-to-image synthesis model has learned within the linguistic embeddings. Experimental results on the recent DiverGAN generator, pre-trained on three common benchmark data sets demonstrate that our classifier achieves a better than 98% accuracy in predicting Good/Bad classes for synthetic samples and our proposed approach is able to derive various interpretable semantic properties for the text-to-image GAN model.

Analyzing the Integration Interpretation of Civics and Family Poetry Culture in University Language Programs Based on Deep Learning Models

Abstract In this paper, firstly, we start from the imagery in the poem, excavate the resources of the ideology and politics in the university language poetry, and put forward the feasible path of the integration of the ideology and politics of the university language course and the poetry culture of the family training. The research on poetry culture in university language is conducted using a deep learning model. Based on the deep learning model, keywords in the poetry text are extracted and extended, taking into account the issue of word splitting and lexical annotation. The sequence coding of word vectors is needed so as to construct a poetry generation model based on RNNPG, and then the research and analysis on the concept of ideology and politics of the poetry culture in the university language are carried out. The results show that in terms of model performance, the RNNPG model is 1.17 overall, and the overall generation effect is more ideal compared to other single discriminator generation models. The acceptance level of sophomores is generally above 0.29, which is better than that of freshmen, and the teaching of ancient poems under the perspective of “curriculum politics” is especially necessary to improve their cultural taste and aesthetic interest. This study is conducive to the personalized development of students and the formation of correct concepts.

Suicidality among university students in the Eastern Mediterranean region: A systematic review.

The prevalence of suicide attempts and suicidal ideation among university students is a global concern. Cultural values, social determinants, religion, and especially growing stress all play an important role in this. This systematic review aimed to identify potential protective and risk factors thought to be associated with suicidal ideation among students in the Eastern Mediterranean region and highlight the importance of developing an effective health care response. MEDLINE, CINAHL, Embase, PsycINFO, WHO Global Health Library, IMEMR, Web of Science Core Collections and Farsi and Arabic databases were searched for papers in English, Farsi, and Arabic. A combination of validated filters, free text keywords, and Mesh and Non-Mesh terms were used to retrieve relevant literature. A total of 2774 papers were found after the search, 257 selected for full-text review, and 72 papers included in the final review. Family and peer support play a potential protective role in the development of suicidal ideation among university students, while adverse life events, bullying, depression, anxiety, and other mental health conditions were identified as risk factors. Suicidality was likely under-reported due to stigma around social and cultural factors. Factors involving religion and culture may act as both protective and risk factors and require more in-depth investigation. The student population in the Eastern Mediterranean region face many challenges. The common theme of suicidality emerged as an indicator of an imbalance of resources and stress, which needs to be addressed proactively, given a most likely underreporting of suicidal ideation and attempts due to stigma.