Relevant Words Research Articles

Assessing national medicines policies: a protocol for systematic review of access to medicine assessment metrics and indicators

IntroductionEfforts to improve access to essential medicines globally include the implementation and assessment of national medicine policies. Although existing assessment tools may identify gaps, they do not link these to...

Pseudo-labeling with keyword refining for few-supervised video captioning

Video captioning generate a sentence that describes the video content. Existing methods always require a number of captions (e.g., 10 or 20) per video to train the model, which is quite costly. In this work, we explore the possibility of using only one or very few ground-truth sentences, and introduce a new task named few-supervised video captioning. Specifically, we propose a few-supervised video captioning framework that consists of lexically constrained pseudo-labeling module and keyword-refined captioning module. Unlike the random sampling in natural language processing that may cause invalid modifications (i.e., edit words), the former module guides the model to edit words using some actions (e.g., copy, replace, insert, and delete) by a pretrained token-level classifier, and then fine-tunes candidate sentences by a pretrained language model. Meanwhile, the former employs the repetition penalized sampling to encourage the model to yield concise pseudo-labeled sentences with less repetition, and selects the most relevant sentences upon a pretrained video-text model. Moreover, to keep semantic consistency between pseudo-labeled sentences and video content, we develop the transformer-based keyword refiner with the video-keyword gated fusion strategy to emphasize more on relevant words. Extensive experiments on several benchmarks demonstrate the advantages of the proposed approach in both few-supervised and fully-supervised scenarios.

Does Government Environmental Concern Affect Enterprise Sustainable Development? Evidence from China

As the executor and agent of China’s environmental policy, local governments’ environmental concern reflects local governments’ determination in environmental governance. To figure out how the strengthening environmental concerns affect enterprises’ long-term activities, this study focuses on pharmaceutical manufacturing enterprises due to the enormous and complex composition of emissions. We apply bag of words to summarize relevant environmental words from the annual work reports in local governments to measure environmental concern. The empirical results of the OLS method reveal that the increasing environmental concerns of local governments did decrease the growth rate of chemical oxygen demand (COD) emission authentically. At the same time, it will inhibit the research and experimental development (R&D) activity intensity, but promote production efficiency of pharmaceutical manufacturing enterprises. After that, we discuss the heterogeneity of enterprise ownership, corporate social responsibility and regional regulatory strength of enterprises. Overall, we conclude that environmental concern did reduce COD emission and promote production efficiency, but it also has negative spillover effects. The novel contribution of this paper is that it enriches the trade-off between strengthening environmental compliance costs and long-term production and innovation activities. These results indicate that pharmaceutical manufacturing enterprises prioritize optimizing existing production processes instead of adopting efficient technology when complying with stricter environmental regulation. The reduction of R&D activities may pose risks to the long-term sustainable development of enterprises.

Users’ self-description on social media: a methodology to integrate labels and textual information

AbstractTo analyze how groups of people self-describe on social media, existing studies separately or partly exploited labels and textual self-descriptions. In this paper, we leveraged both sources of information about the users in an integrated procedure, by training a language model on a sample of text documents with balanced characteristics, and then, by using a list of both domain-specific and statistically relevant words as a guide, similarities between word and document representations were explored to analyze group differences in self-describing. Eventually, a bootstrap procedure was employed to assess the reliability of the results. The methodology proposed was applied on data from StockTwits platform, where people write a bio and declare their experience, approach, and primary holding period in trading. We found that groups of traders differ in semantics. In addition, the relationship between the approach in trading and the holding period still holds in self-descriptions, whereas the experience transversely influences bio writing, with professionals using words strictly specific to the domain of stock trading.

Effect of sodium-glucose cotransporter-2 inhibitor (SGLT2-i) in patients with LVAD: a systematic review and meta-analysis

Abstract Introduction Sodium-glucose cotransporter-2 inhibitor (SGLT2-i) have been shown to reduce risks of clinical events in patients with heart failure (HF). However, data on the use of SGLT2-i in patients with left ventricular assist devices (LVAD) are scarce. We thought to assess the efficacy and safety of SGLT2-i in patients with LVAD Methods A systematic search was done in PubMed, Scopus, Web of Science, Embase, and Cochrane from inception to December 2023. We used all relevant words for "SGLT2-i" and "LVAD" to search in databases and we included studies and published abstracts in peer-reviewed journals of studies assessed SGLT2-i in patients with LVAD. The efficacy and safety data were extracted across the studies. RevMan 5.4.1 was used for meta-analysis. Results Three studies and eight abstracts totaling 209 patients using SGLT2-i were included. Empagliflozin, Dapagliflozin, and Canagliflozin were the used SGLT2-i across the included studies. SGLT2-i effectively decreased hemoglobin A1c (HbA1c) (Mean = -0.44, 95% CI [-0.79, -0.09], p = 0.01) and diuretic dose (Mean=-2.54, 95% CI [-3.56, 1.51], p< 0.00001) from baseline. No significant improvement was found for glomerular filtration rate (GFR), B-type natriuretic peptide (BNP), or mean pulmonary artery pressure (MPAP). Regarding safety, the pooled percentage of people with driveline infection was 9.33%, 95% CI [2.05, 16.60], p=0.01. Conclusion SGLT2-i effectively improves HbA1c and diuretic dose in patients using LVAD. Further randomized studies with a large number of participants may be warranted.

Outcome Measures in intellectual disability: A Review and narrative synthesis of validated instruments.

Outcome measurement is essential to determine the effectiveness of health interventions and improve the quality of services. The interplay of social, individual, and biological factors makes this a complex process in the psychiatry of people with intellectual disability (PwID). Review of outcome measures which are validated in PwID. A PRISMA-guided review was conducted, using a predefined criteria and a relevant word combination on four databases: EMBASE, Medline, CINAHL and PsycINFO. Each included study was examined for relevance to intellectual disability psychiatry. The psychometric data of each tool was critically assessed. Findings were narratively synthesised. Of 1,548 articles, 35 met the inclusion criteria. Several outcome measures were identified relevant to intellectual disability psychiatry, including tools for challenging/offending behavior, specific neurodevelopmental/clinical conditions such as ADHD, epilepsy, and dementia however, psychometric properties, validity and reliability varied considerably. The tools identified were largely clinician rated, with a dearth of measures suitable for completion by patients or their family carers. Most outcome measures used for PwID lack suitable psychometric properties including validity or reliability for use within the ID population. Of importance, those with alternative expression or are non-verbal have been excluded from the research developing and reporting on measurement instruments. There is an underserved population who risk being left behind in the era of value-based medicine and increasing use of outcome measurement when assessing the effectiveness of healthcare interventions on individual and population levels. This is the first of its kind review in this area.

Discrete diffusion models with Refined Language-Image Pre-trained representations for remote sensing image captioning

RS image captioning (RSIC) utilizes natural language to provide a description of image content, assisting in the comprehension of object properties and relationships. Nonetheless, RS images are characterized by variations in object scales, distributions, and quantities, which make it challenging to obtain global semantic information and object connections. To enhance the accuracy of captions produced from RS images, this paper proposes a novel method referred to as Discrete Diffusion Models with Refined Language-Image Pre-trained representations (DDM-RLIP), leveraging an advanced discrete diffusion model (DDM) for nosing and denoising text tokens. DDM-RLIP is based on an advanced DDM-based method designed for natural pictures. The primary approach for refining image representations involves fine-tuning a CLIP image encoder on RS images, followed by adapting the transformer with an additional attention module to focus on crucial image regions and relevant words. Furthermore, experiments were conducted on three datasets, Sydney-Captions, UCM-Captions, and NWPU-Captions, and the results demonstrated the superior performance of the proposed method compared to conventional autoregressive models. On the NWPU-Captions dataset, the CIDEr score improved from 116.4 to 197.7, further validating the efficacy and potential of DDM-RLIP. The implementation codes for our approach DDM-RLIP are available at https://github.com/Leng-bingo/DDM-RLIP.

Retelling the Story of the Birds and the Bees in the Age of Biodiversity Extinction

John Lovejoy coined the term biological diversity in 1980, made the first projection of global extinction rates, and 43 years later we are still discussing biodiversity and extinction in an inconsequential fashion. Extinction signs include the loss of millions of birds in the UK since 1970 and the decline in insects. Goulson summarizes in detail the scientific and biological evidence for the many species extinctions. Although most people do not notice the declines in insects, the loss of bees has been noted when bees’ use as pollinators began to be harnessed as a corporate and commercial activity. This is linked to intensive agricultural practices. The lobbying power of agrochemical companies shapes agricultural practices that directly impact the well-being of all species. Critical ecological discourse analysis of insect decline and the issues related to it is employed, going back to the famous speech given by Michael Halliday. Then corpus linguistic methods scrutinize material from the website of Syngenta, an agrochemical company. We ask whether the website of such a firm can uncover the necessary circumstances for such biodiversity. A corpus-assisted critical analysis of Syngenta’s business report, looks at computer-generated concordances of some of the relevant content words, like ‘crop’, ‘sustainable’, ‘soil’, ‘control’, ‘biodiversity’ and ‘water’. Hopefully, this study will encourage researchers to provide more indications of the disappearance of so many species, and not just birds and insects. But, to really achieve effective protection of biodiversity much more is needed.

PROCESSAMENTO DE LINGUAGEM NATURAL PARA RECONHECIMENTO DE ENTIDADES NOMEADAS EXTRAÍDAS DE CORPUS

This article presents a code implementation to automate the reading, analysis and generation of summaries of unstructured web texts, highlighting the crucial role of Natural Language Processing (NLP). The process was divided into two stages: data collection and pre-processing. In the first stage, a scientific article was selected and relevant data was extracted via web scraping, organized into an HTML page hosted online. In pre-processing, tokenization, normalization, stopword removal and word counting were performed. Using NLTK, the most important sentences were identified and ranked based on keyword frequency, allowing the selection of the most relevant sentences from the introduction, methodology, results/dicussion and conclusion sections. Discrepancies such as links and figure descriptions have been removed to improve the clarity of the abstract. The “most_common()” method was used to select the most relevant words in each section. After additional processing to delete unnecessary words, the final summary was clear and understandable, despite minor flaws. This automated approach provides an efficient way to synthesize information from scientific articles, optimizing the textual analysis process.

Bottom Copy Pronunciation in Japanese Passives

AbstractOn the basis of Abe's (2016) proposal that in the case of an A‐chain, any member can be the target for pronunciation, I argue that Japanese passives may have the bottom copies of the produced A‐chains pronounced. When the passivized subjects appear to occupy their original θ‐positions, it is standardly claimed that the relevant word order is derived by scrambling other material sentence‐initially. Contrary to this claim, I demonstrate that there are cases, including those of what Miyagawa (1989) calls the causative‐passive construction, where the passivized subjects actually occupy their original θ‐positions. I then provide evidence that those passivized subjects undergo “covert” A‐movement, hence giving support to my bottom copy pronunciation analysis.

MATHEMATICAL MODELING OF THE LEXICAL-SEMANTIC FIELD OF ANDREY SHEPTYTSKYI'S PASTORAL LETTERS USING MACHINE LEARNING ALGORITHMS

This article explores the lexical and semantic field of Andrey Sheptytsky's pastoral letters using machine learning algorithms. To study the lexical and semantic field, such algorithms as Word2Vec, TF-IDF, CBOW, Bag-of-Words and Skip-Gram are used. The texts of this prominent figure are studied through the prism of modern methods of natural language processing, which allows for a more detailed identification of the peculiarities of their vocabulary and semantics. The article discusses the process of creating computer models for text analysis, as well as the use of machine learning algorithms for automatic processing and classification of lexical items in pastoral letters. The results obtained allow us to better understand the specifics of the author's language and mentality, as well as to identify patterns in the use of relevant words and expressions in his work. This work opens up new possibilities for the study of pastoral texts and deepening our understanding of their semantic properties.

The analysis of firm web data for predicting company innovativeness: a comparison across different types of innovation

ABSTRACT This paper investigates which of the core types of innovation can be best predicted based on ’firms’ website data. In particular, we focus on four distinct key standard types of innovations in firms: product, process, organisational, and marketing innovation. Web mining of textual data on the websites of firms from Estonia, combined with the application of artificial intelligence (AI) methods, turned out to be a suitable approach to predict firm-level innovation indicators. The key novel addition to the existing literature is the finding that web mining is more applicable to predicting marketing innovation than the other three core types of innovation. As AI-based models are often black-box in nature, for transparency, we use an explainable AI approach (SHAP - SHapley Additive exPlanations), where we look at the most important words predicting a particular type of innovation. Our models confirm that the marketing innovation indicator from survey data was clearly related to marketing-related terms on the ’firms’ websites. In contrast, the results on the relevant words on websites for other innovation indicators were much less clear. Our analysis concludes that the effectiveness of web-scraping and web-text-based AI approaches in predicting cost-effective, granular and timely firm-level innovation indicators varies according to the type of innovation considered.

A Scoping Review on Recent Progress on Purpurin and its Derivatives

Background: Purpurin is being used as a red dye for many decades. But recently, due to its pharmacological properties, purpurin and its derivatives have attracted a lot of researchers for the treatment of various ailments, such as cancer, Alzheimer’s disease, depression, etc. Objectives: The objective of this study is to provide an overview of its pharmacological properties, pharmacokinetic studies, synthesis, isolation, quality assurance, and patent studies. Method: A systemic scoping review was undertaken. Three databases (Pubmed, Scopus, and Google Scholar) and patent websites were searched using relevant words (e.g., purpurin, purpurin derivatives, anticancer, toxicity, etc.). All outcomes for studies that met the inclusion criteria were included in the review. Extracted data were accumulated using tables, figures, and accompanying narrative descriptive summaries. The review was reported using the preferred reporting items for scoping review (PRISMAScR) guidelines. Sixty-eight studies and eighty-six patents met the inclusion criteria, mostly preclinical (in vitro, in vivo, and in silico) studies performed in rats, mice, dogs, and zebrafish, followed by one clinical trial study. Results: The potent antioxidant nature of purpurin is the main reason behind its vast pharmacological properties. It acts by decreasing mitochondrial stress and by acting on the endoplasmic reticulum. It also crosses the BBB barrier, has high GI absorption, and follows the Lipinski rule, which makes it a good drug for various neurodegenerative disorders. It inhibits various CYP-450, CYP 1A2, and CYP 3A4 enzymes, which are responsible for causing mutations. It gets photosensitized by UV light and causes ROSdependent apoptosis in cancer cells. Conclusion: This scoping review highlights purpurin and its derivatives as highly prized moieties in the treatment of various neurological conditions and cancer. The unique nature of purpurin is responsible for its pharmacological properties, which are due to the presence of hydroxyl and keto groups at specific positions. It gets photosensitized by UV and laser light and acts as an anticancer drug. But the lack of robust evaluation in clinical studies is another major concern. Purpurin can be seen in the prescription in the future, although a lot of work still needs to be done.

VOCABULARY OF THE LEXICAL MINIMUM WHEN LEARNING UKRAINIAN AS A FOREIGN LANGUAGE (LEVEL A1) (A review of the textbook by Ksenia Borodin and Oksana Turkevych “1000 and 1 Words. Lexical minimum in Ukrainian (A1)” – Slavic Language Education 3 (LANGUAGE-LAB), 2023. – 58 p.)

The review characterizes the first edition of the lexical minimum for Ukrainian as a foreign language at the A1 level in accordance with the “Standardized Requirements: Levels of Proficiency in Ukrainian as a Foreign Language A1-C2” (Kyiv, 2020). The structure, grammatical description, and examples of the use of language units contained in the textbook are analyzed. The author explains the criteria used to form the content of the A1 lexical minimum. The lexical material is organized and described with grammatical comments on the alternation of vowels or consonants, the formation of plural forms from other bases, the use of dative and accusative forms of personal pronouns, personal or imperative verb forms, etc. The authors also see this as an element of self-testing for knowledge of the relevant word forms, as well as in the case of forming a context for each vocabulary unit, which demonstrates to users – both students and teachers and scholars in the field of linguistics – the practical application of the lexical minimum. Also noteworthy are the didactic tips on how to work with this educational material (translation tasks, making word cards by topic group, creating tests to check the acquired knowledge at A1 level, etc.) The textbook is an extremely important and relevant work that has been prepared in accordance with modern requirements, didactic achievements and scientific research in the study and teaching of Ukrainian as a foreign language. Undoubtedly, this work will help beginner students, teachers, and all those interested in mastering the Ukrainian language to develop the lexical skills necessary for the realization of communicative activities as soon as possible and in a structured way. Key words: Ukrainian as a foreign language, lexical minimum, beginner level A1, Standardized requirements for levels of proficiency in Ukrainian as a foreign language.

Informal learning of Japanese as a foreign language through watching anime

ABSTRACT With the rise of global interest in anime among young adults, several studies have examined the acquisition of Japanese through watching anime in formal settings; however, studies focusing on learners with no formal training in Japanese are scarce. The present study focuses on this latter population of anime viewers and examines their L2 listening development and whether it is impacted by years of viewing and anime genre. This study also investigates the extent to which relevant vocabulary can be incidentally acquired from watching anime. Native English-speaking anime viewers in the US, who had never formally studied Japanese, completed a questionnaire and took the listening comprehension portion of the Japanese-Language Proficiency Test. They also viewed 12 subtitled episodes of an action/adventure anime series and took pre-tests and post-tests assessing meaning-recall of 40 relevant target words. 38% of the viewers correctly answered at least half of the questions on the listening test, yet years of viewing and anime genre appeared to have no significant impact on the listening scores. The vocabulary post-test also showed significant learning gains. These findings emphasise the potential benefits of extensive viewing – more specifically anime viewing – in L2 development, even in the absence of formal language study.

An intelligent error correction model for English grammar with hybrid attention mechanism and RNN algorithm

Abstract This article proposes an English grammar intelligent error correction model based on the attention mechanism and Recurrent Neural Network (RNN) algorithm. It aims to improve the accuracy and effectiveness of error correction by combining the powerful context-capturing ability of the attention mechanism with the sequential modeling ability of RNN. First, based on the improvement of recurrent neural networks, a bidirectional gated recurrent network is added to form a dual encoder structure. The encoder is responsible for reading and understanding the input text, while the decoder is responsible for generating the corrected text. Second, the attention mechanism is introduced into the decoder to convert the output of the encoder into the attention probability distribution for integration. This allows the model to focus on the relevant input word as it generates each corrected word. The results of the study showed that the model was 2.35% points higher than statistical machine translation–neural machine translation in the CoNLL-2014 test set, and only 1.24 points lower than the human assessment score, almost close to the human assessment level. The model proposed in this study not only created a new way of English grammar error correction based on the attention mechanism and RNN algorithm in theory but also effectively improved the accuracy and efficiency of English grammar error correction in practice. It further provides English learners with higher-quality intelligent error correction tools, which can help them learn and improve their English level more effectively.

An Evolutionary Approach to Motivation and Learning: Differentiating Biologically Primary and Secondary Knowledge

The ubiquity of formal education in modern nations is often accompanied by an assumption that students’ motivation for learning is innate and self-sustaining. The latter is true for most children in domains (e.g., language) that are universal and have a deep evolutionary history, but this does not extend to learning in evolutionarily novel domains (e.g., mathematics). Learning in evolutionarily novel domains requires more cognitive effort and thus is less motivating. The current study tested the associated hypothesis that learning will feel easier and more motivating for evolutionarily relevant (e.g., “mother,” “food”) than evolutionarily novel (e.g., “computer,” “gravity”) word pairs and that a growth mindset emphasizing the importance of effort in learning might moderate this effect. Specifically, 144 adults were presented with 32 word pairs (half evolutionarily relevant and half evolutionarily novel) and were randomly assigned to a growth mindset or a control condition. Evolutionarily relevant words were better remembered than evolutionarily novel words (d = 0.65), and the learning was reported as more enjoyable (d = 0.49), more interesting (d = 0.38), as well as less difficult (d = − 0.96) and effortful (d = − 0.78). Although the growth mindset intervention fostered a mindset belief, compared to the control condition, it did not lead to improved recall performance or changes in motivational beliefs. These results are consistent with the prediction of higher motivation and better learning of evolutionarily relevant words and concepts than for evolutionarily novel words and concepts. Implications for future research and educational practice are discussed.

Use of tDCS Alongside Speech Language Training for Personally Relevant Words Promotes Naming in Individuals with PPA (P7-11.004)

Use of tDCS Alongside Speech Language Training for Personally Relevant Words Promotes Naming in Individuals with PPA (P7-11.004)

Data Pre-processing of Website Browsing Records: To Prepare Quality Dataset for Web Page Classification

The increased usage of the internet worldwide has led to an abundance of web pages designed to supply information to internet users. The use of web page classification is becoming increasingly necessary to organize the growing number of web pages. This classification model serves as a tool to restrict internet usage to specific categories of web pages. To develop the classification model, it’s crucial to check the quality of the dataset, as it determines the performance of the web page classification model. Raw datasets are typically unreliable and subject to noise, which complicates data analysis. This is why data pre-processing is necessary to prepare the dataset properly. In this study, website browsing records serve as the dataset. The primary goal of this paper is to investigate data pre-processing techniques for website browsing records, focusing on Game and Online Video Streaming web pages. Data pre-processing involves two main steps: data cleaning and web content pre-processing. After completing the data cleaning process, the datasets are reduced from the original. This demonstrates that many datasets can be eliminated due to their inactivity or unsuitability as the datasets for Game and Online Video Streaming web pages. Meanwhile, web content pre-processing removes noise from an HTML document, retaining only relevant words that can represent the web page by creating a word cloud image. Convolutional Neural Networks (CNN) will be used to construct a model for categorizing web pages to determine whether they fall under Game or Online Video Streaming. The pre-processed data will be used as the input for this model.

Math Word Problem Generation via Disentangled Memory Retrieval

The task of math word problem (MWP) generation, which generates an MWP given an equation and relevant topic words, has increasingly attracted researchers’ attention. In this work, we introduce a simple memory retrieval module to search related training MWPs, which are used to augment the generation. To retrieve more relevant training data, we also propose a disentangled memory retrieval module based on the simple memory retrieval module. To this end, we first disentangle the training MWPs into logical description and scenario description and then record them in respective memory modules. Later, we use the given equation and topic words as queries to retrieve relevant logical descriptions and scenario descriptions from the corresponding memory modules, respectively. The retrieved results are then used to complement the process of the MWP generation. Extensive experiments and ablation studies verify the superior performance of our method and the effectiveness of each proposed module. The code is available at https://github.com/mwp-g/MWPG-DMR .