Important Keywords Research Articles

Deep Learning Approach for Negation and Speculation Detection for Automated Important Finding Flagging and Extraction in Radiology Report: Internal Validation and Technique Comparison Study

Negation and speculation unrelated to abnormal findings can lead to false-positive alarms for automatic radiology report highlighting or flagging by laboratory information systems. This internal validation study evaluated the performance of natural language processing methods (NegEx, NegBio, NegBERT, and transformers). We annotated all negative and speculative statements unrelated to abnormal findings in reports. In experiment 1, we fine-tuned several transformer models (ALBERT [A Lite Bidirectional Encoder Representations from Transformers], BERT [Bidirectional Encoder Representations from Transformers], DeBERTa [Decoding-Enhanced BERT With Disentangled Attention], DistilBERT [Distilled version of BERT], ELECTRA [Efficiently Learning an Encoder That Classifies Token Replacements Accurately], ERNIE [Enhanced Representation through Knowledge Integration], RoBERTa [Robustly Optimized BERT Pretraining Approach], SpanBERT, and XLNet) and compared their performance using precision, recall, accuracy, and F1-scores. In experiment 2, we compared the best model from experiment 1 with 3 established negation and speculation-detection algorithms (NegEx, NegBio, and NegBERT). Our study collected 6000 radiology reports from 3 branches of the Chi Mei Hospital, covering multiple imaging modalities and body parts. A total of 15.01% (105,755/704,512) of words and 39.45% (4529/11,480) of important diagnostic keywords occurred in negative or speculative statements unrelated to abnormal findings. In experiment 1, all models achieved an accuracy of >0.98 and F1-score of >0.90 on the test data set. ALBERT exhibited the best performance (accuracy=0.991; F1-score=0.958). In experiment 2, ALBERT outperformed the optimized NegEx, NegBio, and NegBERT methods in terms of overall performance (accuracy=0.996; F1-score=0.991), in the prediction of whether diagnostic keywords occur in speculative statements unrelated to abnormal findings, and in the improvement of the performance of keyword extraction (accuracy=0.996; F1-score=0.997). The ALBERT deep learning method showed the best performance. Our results represent a significant advancement in the clinical applications of computer-aided notification systems.

Role of Molecular Targeted Therapeutic Drugs in Treatment of Glioblastoma: A Review Article.

Glioblastoma is remarkably periodic primary brain tumor, characterizing an eminently heterogeneous pattern of neoplasms that are utmost destructive and threatening cancers. An enhanced and upgraded knowledge of the various molecular pathways that cause malignant changes in glioblastoma has resulted in advancement of numerous biomarkers and the interpretation of various agents that pointedly target tumor cells and microenvironment. In this review, literature or information on various targeted therapy for glioblastoma is discussed. English language articles were scrutinized in plentiful directory or databases like PubMed, ScienceDirect, Web of Sciences, Google Scholar, and Scopus. The important keywords used for searching databases are "Glioblastoma," "Targeted therapy in glioblastoma," "Therapeutic drugs in glioblastoma," and "Molecular targets in glioblastoma."

Tacit Knowledge Management in Engineering Industries: Systematic Literature Review and Development of a Conceptual Model

Knowledge of every organisation is the most important resource, often not fully leveraged by organisations. Moreover, the value of tacit knowledge of an individual is much less understood and not exploited to the desired levels in organisations. It is quite evident when an experienced employee retires from an organisation, he carries away a very valuable chunk of knowledge with him, which is a strategic resource of the organisation. The COVID-19 pandemic and the great resignation times created a sudden loss of experts which troubled many organisations and made the topic of tacit knowledge management very relevant to current times. While PRISMA framework was used for selection of articles among the vast literature using justifiable validation criteria, a combination of bibliometric and narrative methods is used for the process of analysis and summarisation. The relevant papers are obtained using keywords “Tacit knowledge”, “Tacit knowledge management”, “Knowledge sharing”. Overall, 50 relevant and quality papers are picked after systematic filtration and study. The important keywords from these papers are picked and analysed to arrive at six major themes under which all the papers can be categorised. The six themes are tacit knowledge concept evolution, barriers to knowledge sharing, tacit knowledge acquisition process — various methods, role of context in knowledge sharing, enablers for tacit knowledge sharing, and IT for explicit Knowledge capture and reuse. Each of these themes also has sub-themes under which the papers are categorised. The gist of papers under each of these themes/sub-themes is captured in detail, providing key insights under each theme. While literature review papers focus on a certain domain or industry, this work developed a conceptual model using these themes which represents the current literature in tacit knowledge management as a system model, thus making this work complementary to the current body of knowledge. This model is useful in placing any future literature under one of the themes. Future work also can focus on adding additional themes as well as making this model more insightful through continuous improvement. Each of the themes in the model created can be colour coded to represent high and least researched areas, which can be a valuable guide for the future researchers. Future research directions relevant for the current engineering industries are collated from the literature reviewed, detailed under the above themes in chronological order.

키워드 네트워크 분석을 통한 조기개입 관련 연구 동향 분석: 국내 학술논문 중심으로

The purpose of his study was to analyze research trends related to early intervention for infants and toddlers through keyword network analysis. To do this, this study investigated the tendency of articles published in domestic journals by year and journal. The research conducted network analysis on the keywords that appeared in studies. Results First, 142 studies related to early intervention for infants and toddlers were published by 2022 since 1993. The largest number of studies were published in the journal of Korean Journal of Early Childhood Special Education. Secondly, the main keywords were identified as ‘infants and toddlers’, ‘disabilities’, ‘early intervention’, ‘developmental delays’, ‘young children’, ‘autism spectrum disorder’ in order of high frequency. The result of keyword network analysis showed that ‘developmental delays’ and ‘autism spectrum disorder’ were identified as important group keywords having high connection centrality. This study’s implications for the research trends of domestic journals related to early intervention with infants and toddlers were examined through keyword network analysis.

Technological forecasting based on estimation of word embedding matrix using LSTM networks

There are a vast number of quantitative and qualitative technological forecasting methods. In the last decade, advanced quantitative technological forecasting methods based on the various applications of data science approaches have been proposed. Text mining is one of the key approaches used to examine large datasets consisting of scientific publications and patent documents with the aim of offering foresight for a selected area. However, the existing related studies either perform a qualitative approach by analysing the recent data to identify the emerging topics or use extrapolation techniques to predict the future values of some statistical terms or the future frequency of some important keywords. In this study, different from such related studies, we propose a deep learning-based framework to predict future co-similarity matrix representing the possible new and disappearing interactions between the words in the future. For this purpose, word vectors are generated using a word embedding technique and the temporal changes of the associations between the words are modelled using Long Short-Term Memory networks for the future estimation of the word embedding matrix. The text mining area is chosen as a case study. The clusters of the terms extracted from the predicted word embedding matrices were analysed and potentially emerging areas were identified for different prediction horizon lengths. The accuracy of the proposed model was analysed based on a set of evaluation metrics that measure the amount of overlapping between the actual and predicted word maps. The quantitative analysis showed that the proposed system can successfully identify the emerging and disappearing areas and can be used as a decision-making tool for the future projection of other areas.

The multilingual Twitter-discourse on vaccination in Germany during the COVID-19 pandemic

There is evidence that specific segments of the population were hit particularly hard by the Covid-19 pandemic (e.g., people with a migration background). In this context, the impact and role played by online platforms in facilitating the integration or fragmentation of public debates and social groups is a recurring topic of discussion. This is where our study ties in, we ask: How is the topic of vaccination discussed and evaluated in different language communities in Germany on Twitter during the Covid-19 pandemic? We collected all tweets in German, Russian, Turkish, and Polish (i.e., the largest migrant groups in Germany) in March 2021 that included the most important keywords related to Covid-19 vaccination. All users were automatically geocoded. The data was limited to tweets from Germany. Our results show that the multilingual debate on Covid-19 vaccination in Germany does not have many structural connections. However, in terms of actors, arguments, and positions towards Covid-19 vaccination, the discussion in the different language communities is similar. This indicates that there is a parallelism of the debates but no social-discursive integration.

A hunger-based scheduling strategy for distributed crawler

Distributed crawling is one of the mainstream text data collection technologies, which is essential for mining boundless data available on the Internet for users. Internet information is clustered by the correlation of keywords, and users employ search engines to retrieve relevant keywords to get the information they care about. Distributed crawlers are used to mine the Internet information by simulating users’ behavior, the more important keywords that users care about, the higher the correlation of data to keywords. In order to preferentially collect information that users care about with minimal resource consumption, in this paper, we design a scheduling framework and propose a novel scheduling strategy based on hunger for distributed crawler. We first define the load capacity of distributed crawler as hunger which reflects the ability to complete tasks and divide keywords queues into sub-queues based on the hunger of distributed crawlers. Then, we use a vector space model and cosine similarity algorithm to learn the correlation of keywords to text data and apply the optimized logistic algorithm to measure the importance of keywords. Meanwhile, we design a comprehensive evaluation algorithm to quantify the contribution of keywords, so that updating sub-queues order. Finally, new sub-queues are used in the deeper scheduling to preferentially get data that users desire and sacrifice the least number of resources. Experimental results demonstrate that our method optimizes the scheduling procedures and makes crawling more efficient with less run time.

Research Hotspots and Trends of Eco-chemometrics in Lakeside Zone

In this study, the visualization tools CiteSpace and VOSviewer were used to visually analyze the research on ecostoichiometry in the lakeside zone, and the knowledge map of the research hotspots and trends of ecostoichiometry in the lakeside zone was drawn through the main research hotspots, research institutions and authors in the field. The method was to search the relevant journal literature collected in the past 20 years from CNKI and Web of Science databases, and finally retrieved the relevant literature that met the criteria, including 162 Chinese literature and 61 English literature. The retrieved English journals were mapped using VOSviewer software, while Chinese journals were mapped using CiteSpace. The visual results show that the number of publications in the field of ecological chemometrics in the lakeside zone at home and abroad shows a fluctuating upward trend. The most influential institution in China is Fujian Normal University, and abroad it is dominated by Qingdao University of Science & Technology. In the past 20 years, the research hotspots at home and abroad in this field have mainly been wetland restoration, nutrient storage, cultivated land protection, cultivated land ecological compensation and ecosystem service value. Research at home and abroad has roughly gone through two stages from the beginning to the rise of fluctuations. Through the literature analysis of influential authors, important research institutions, keywords and other fields of ecological stoichiometry in the lakeside zone, it can provide reference and reference for further research in this field.

Biological Potential and Pharmacological Activity of Columbianetin in Chronic Diseases.

Herbal medicine is widely used in different systems of traditional and complementary medicine including. People believe that herbal medicines are safe and more cost-effective than other synthetic medicines. Herbal medicines are also used as a promising source of new drug discovery molecules in modern medicines. Coumarins are polyphenols class phytochemical that naturally occurs in higher plants and are used in medicine for the treatment of human disorders and associated secondary complications. Columbianetin is a coumarin class phytochemical found in Angelica archangelica L. The aim of the present work is to review the medicinal importance and pharmacological activities of columbianetin and to provide a summary of the medicinal importance and pharmacological activities of columbianetin in medicine. Further scientific progress of columbianetin in the fields of Ethnopharmacology, Phytochemistry and Pharmacology has been analyzed and discussed in the present work. However, possible future research on columbianetin has been also discussed in the present work. Scientific information on columbianetin was collected from PubMed, Elsevier, Google, Google Scholar, and Europe PMC using herbal medicine, columbianetin and coumarin as important keywords. Other published books and journal data have also been included in the present work to know the therapeutic potential of columbianetin in medicine. Scientific data analysis of columbianetin signified the biological importance of phytochemicals belonging to the coumarins. Biological effectiveness of coumarins for their antioxidative, cytotoxic, and anti-inflammatory potential has been discussed in the research fields. Columbianetin has analgesic, antioxidative, anti-inflammatory, anti-proliferative, and anti-allergic activities in medicine. However, its biological effectiveness on neuroprotection, keratinocyte damage and platelet aggregation has also been discussed in the present work. Further tissue distribution of columbianetin in different biological tissue has been estimated through different analytical methods and scientific data are also presented in the present work. The present work summarized the biological importance and pharmacological activities of columbianetin in medicine. Analytical methods developed for the quantitative and qualitative estimation of columbianetin have also been reviewed.

A Bibliometric Study of Scientific Production on Self-Sovereign Identity

Context: Self-sovereign identity (SSI) enables the creation of user-centric, privacy-by-design, secure, and decentralized identity-management systems. The aim of this paper is to carry out a bibliometric analysis of the scientific production on SSI during the period 2017-2022.Method: A complete bibliometric analysis of all publications on SSI indexed in Scopus and Web of Science was carried out. A corpus of 143 articles was examined by processing their bibliographic metadata on a bibliometric tool. In order to do this, the Bibliometrix packageand the R programming language were used.Results: A bibliometric characterization of the publications on SSI was obtained for the 2017 − 2022 period. The most important keywords used in these publications were identified, as well as their use tendencies throughout this period. Moreover, the most influential authors in the area and the most relevant publication sources were also identified.Conclusions: The results of the bibliometric analysis show that Lotka’s and Bradford’s laws apply for academic publications on SSI, which means that the most relevant publications in this area are concentrated in a relatively small group of authors and journals. Paul Jenkins, Nitin Naik, Yang Liu and Aijun An were the most impactful authors, and Lecture notes in computer science, Frontiers in blockchain and IEEE were the most influential journals. Finally, the keyword analysis showed that Blockchain, Authentication, Identity management, Electronic document identification systems, and Digital identity are currently the most relevant concepts for SSI.

Research Trends of the Internet of Things in Relation to Business Model Innovation: Results from Co-Word and Content Analyses

In the era of the Internet of Things, innovative business model initiatives continue to deepen, and the trend of search domains continues to expand. This paper aims to scientifically analyze research trends of the Internet of Things in relation to Business Model Innovation through bibliometric studies. The data were collected using the Clarivate Web of Science (WoS) Core Collection (SSCI and SCI indexed) from 2005 to 2022 (November). However, the publications for the research domains started in 2015. The results show that scientific publications on the Internet of Things in relation to Business Model Innovation have increased gradually since 2019. The WoS database is utilized for analyses because it contains journals and conference proceedings deemed more relevant by the academic domain and highly reputable sources for bibliometric studies. The VOS viewer, R Language, and Microsoft Excel were also used to analyze and complete the study. Bibliometric and scientometric analyses were conducted to identify publication patterns, text analysis, most important keywords (co-word, word cloud, and co-occurrence), trends for the topicality, and content clustering for the publication periods. The visualization of the research trends of the Internet of Things in relation to Business Model Innovation resulted in four co-occurrence clusters leading to some of the topic areas mentioned as follows: (1) The Internet of Things, (2) Business model innovation, (3) Technology infrastructure, and (4) Digital transformation and capabilities. The results of this study will assist academics in identifying worldwide research trends related to the Internet of Things and Business Model Innovation as well as recommending future research areas.

An Improved Corpus-Based NLP Method for Facilitating Keyword Extraction: An Example of the COVID-19 Vaccine Hesitancy Corpus

In the current COVID-19 post-pandemic era, COVID-19 vaccine hesitancy is hindering the herd immunity generated by widespread vaccination. It is critical to identify the factors that may cause COVID-19 vaccine hesitancy, enabling the relevant authorities to propose appropriate interventions for mitigating such a phenomenon. Keyword extraction, a sub-field of natural language processing (NLP) applications, plays a vital role in modern medical informatics. When traditional corpus-based NLP methods are used to conduct keyword extraction, they only consider a word’s log-likelihood value to determine whether it is a keyword, which leaves room for concerns about the efficiency and accuracy of this keyword extraction technique. These concerns include the fact that the method is unable to (1) optimize the keyword list by the machine-based approach, (2) effectively evaluate the keyword’s importance level, and (3) integrate the variables to conduct data clustering. Thus, to address the aforementioned issues, this study integrated a machine-based word removal technique, the i10-index, and the importance–performance analysis (IPA) technique to develop an improved corpus-based NLP method for facilitating keyword extraction. The top 200 most-cited Science Citation Index (SCI) research articles discussing COVID-19 vaccine hesitancy were adopted as the target corpus for verification. The results showed that the keywords of Quadrant I (n = 98) reached the highest lexical coverage (9.81%), indicating that the proposed method successfully identified and extracted the most important keywords from the target corpus, thus achieving more domain-oriented and accurate keyword extraction results.

키워드 네트워크를 통한 아동학대 국내 연구의 동향 분석

The purpose of this study was to visualize the paradigm and flow of child abuse studies in Korea, and to examine the concepts that were important. In this study, by examining trends in child abuse studies through keywords network analysis, the frequency trends and networks of keywords presented in previous studies were reviewed. Through this, I tried to provide implications for children's rights and comparison with other trend analysis researches. For this purpose, 5,813 keywords of 450 types from 1,203 research articles published from 2001 to 2022 were selected and analyzed using the UCINET6 program and the NetDraw program. As a result of the study, first, 'child', 'abuse', 'adolescent', 'parent', 'abuse experience', 'neglect', and 'depression' frequently appeared as the keywords in child abuse research. And, there was a slight difference in the frequency trend depending on the time period. Second, the entire network had a structure including 448 nodes, 7,588 links, and 2 components. Third, the keywords 'adolescence', 'parent', 'abuse experience', 'neglect', 'depression', 'emotional abuse', 'sexual abuse', and 'physical abuse' were keywords with high centrality. Fourth, the ego network of ‘adolescence’, ‘parent’, ‘abuse experience’, and ‘neglect’ had high connection strength between the four words, and was also strongly bound with ‘misconduct’, ‘depression’, and ‘school adjustment’. And the 'adolescence' ego network was connected to 'runaway', 'peer', 'suicidal ideation', 'suicide', and 'family', and the 'parent' ego network was connected to 'children', 'relationship', 'attachment', ' teacher', 'parenting attitude', and 'recognition' were connected. The 'abuse experience' ego network is connected to 'suicide', 'emotion', 'school violence', 'social support', and 'art therapy', and the 'neglect' ego network is linked to 'interpersonal relationships', 'teacher', ' post-traumatic stress' and 'ego-resilience' were strongly linked. Like previous studies, this study also found that the number of child abuse studies had increased since the middle of 2010, and there was a focus on exploring influencing variables. In addition to this, in this study, important keywords that penetrate the period were derived from child abuse studies, and a network of concepts connected to the keywords was identified. In addition, by discovering the focus of studies by period, it provided implications for future research and policy implementation on child abuse.

Knowledge landscape of tumor-associated macrophage research: A bibliometric and visual analysis.

Tumor-associated macrophage (TAM) is a highly abundant immune population in tumor microenvironment, which plays an important role in tumor growth and progression. The aim of our study was to explore the development trends and research hotspots of TAM by bibliometric method. The publications related to TAM were obtained from the Web of Science Core Collection database. Bibliometric analysis and visualization were conducted using VOSviewer, CiteSpace and R software. A total of 6,405 articles published between 2001 and 2021 were included. The United States and China received the most citations, whereas the University of Milan, the university of California San Francisco and Sun Yat-sen University were the main research institutions. Mantovani, Alberto from Humanitas University was the most productive authors with the most citations. Cancer Research published the most articles and received the most co-citations. Activation, angiogenesis, breast cancer, NF-κB and endothelial growth factor were important keywords in TAM research. Among them, PD-1/L1, nanoparticle, PI3Kγ, resistance and immune microenvironment have become the focus of attention in more recent research. The research on TAM is rapidly evolving with active cooperation worldwide. Anticancer therapy targeting TAM is emerging and promising area of future research, especially in translational application. This may provide guidance and new insights for further research in the field of TAM.

Identifying chronological and coherent information threads using 5W1H questions and temporal relationships

Due to the massive volume of articles produced online every day, it is challenging for online platforms (e.g., news agencies) to present the information about an event, activity or discussion to their users in an easily digestible format. Therefore, there is a need for automatic methods to extract related and time-ordered information about events (i.e., information threads) from large unstructured collections of documents. In this work, we propose a novel unsupervised hierarchical agglomerative clustering (HAC) based information threading approach to generate chronological and coherent threads of information in a collection. Unlike, the well-known tasks of topic detection and tracking or event threading that focus on grouping information by important keywords and/or entities, our proposed approach identifies threads based on temporal relations and diverse information about an event, i.e., who did what, why, where, when and how (aka the 5W1H questions). In particular, our proposed approach, deploys a tailored similarity function for HAC by leveraging extracted answers to 5W1H questions along with time decay between documents. We evaluate our proposed HAC 5W1H information threading approach on two large expert-annotated collections of news articles, i.e., NewSHead and Multi-News (over 112k and 32k articles, respectively). Our experiments show that HAC 5W1H markedly improves the number of, and quality of, threads that are generated compared to existing state-of-the-art approaches from the literature, e.g., 100.98% more threads and +213.39% improvement in Normalised Mutual Information compared to the best evaluated baseline on the larger NewSHead collection. We also conducted a user study that shows that our proposed HAC 5W1H information threading approach is significantly (p<0.05) preferred by users in terms of coherence, diversity and chronological correctness compared to the existing state-of-the-art approaches.

Fracture research from India between 1989 to 2022: A scientometric study

Objective. Research on fractures has increased rapidly in India in recent years, but no bibliometric study has been performed on this subject. From the Scopus database, we aimed to examine the bibliometric characteristics, trends, and current status of India’s fracture research and publications between 1989 and 2022. Design/Methodology/Approach. We identified the key organizations, authors, journals, and important keywords, besides studying and visualizing their collaborative interactions using VOSviewer and Biblioshyn software. Results/Discussion. The 1046 India fractures research publications were identified and cited 8927 times. External funding was received by 1.91%, and 11.95% had international collaboration. The 894 authors from 304 organizations and publishing in 88 journals were involved in India’s fracture research. The most productive organization was AIIMS, New Delhi. The most impactful organization was JIPMER, Pondicherry; the most productive author was V. Trikha. The most productive journal was the Indian Journal of Orthopaedics (n=257), and the most impactful journal was Acta Orthopaedica. Femur was the top anatomical location studied for fracture research, followed by Humerus and Tibia. Hip fractures were the most emphasized fracture research areas in older people, in contrast to Humerus, and Femur fracture research in adolescents and children. Delhi was the epicentre of research. Conclusion. This study is the first comprehensive bibliometric analysis of India’s fracture research over 30 years. It provided an insight into its current and past research status and hopes to guide scholars to understand research frontiers and directions in fracture-related research.

Secular

This essay argues that secular is an important keyword for Victorian studies because it foregrounds the particularity of universal concepts. Victorian narratives of secularization and colonial regimes of religious toleration can all be shown to have roots in the Protestant conception of religion as private individual belief and voluntary association. They therefore raise the question of how and whether such political conceptions might transcend their particularist origins. To make this point I begin by exploring the difference between secularism and secularization as critical terms. I then suggest how the recent wave of work on secularism has illuminated the link between the two—namely, by showing how attempts to imagine a secular world in fact depend upon specific ideas of what religion is and where it belongs.

Digital inclusion: A mixed-method study of user behavior and content on Twitter

Objective This study is the first to explore user behavior and characterize the content shared about digital inclusion on Twitter. Methods This mixed-methods research consists of 14,000 tweets featuring the hashtag “#digitalinclusion,” posted on Twitter over 15 months. A machine learning technique, latent Dirichlet allocation, was utilized to discover abstract topics within the tweets statistically. The algorithm identified important keywords and text associated with each topic by modeling the underlying word co-occurrence patterns in the dataset. A manual qualitative content analysis was applied to the qualitative data (1000 tweets). Results Tweets containing #digitalinclusion are driven by four motives: 1) warning against the risks of digital exclusion; 2) tweets that promote actions to increase digital inclusion; 3) tweets that call for others to take action to improve digitalization; and 4) tweets that are neutral but fuel the debate by being active. Quantitative analysis revealed that users discussing digital inclusion come from various continents, including the USA, Europe, Africa, and Asia. There were 3931 unique user accounts, with individuals posting between one and 368 tweets. Approximately half of the tweets contained some embedded media. Conclusion The study concludes that digital inclusion is a subject that engages Twitter users worldwide. Tweets that were associated with community and local initiatives and sustainable development had the highest engagement in terms of the number of retweets and likes. The interpretation is that digital inclusion is crucial for achieving equity in living conditions and enhancing access to health information and services. While initiatives to increase digital inclusion are underway, Twitter users call for more efforts to prevent growing digital exclusion. Twitter, as a social media platform, is valuable for studying the motivations that drive digital inclusion and help counter digital exclusion.

Culturèmes polonais extraits de Proszę państwa do gazu de Tadeusz Borowski et leurs traductions en français

My research interests include culturemes, including those related to the Second World War, analysed in terms of translation within the Polish/French language pair. In my earlier work I examined culturemes related to the Warsaw Uprising of 1944. In this article, I propose to compare a few of the culturemes that emerged in the German Nazi Concentration and Extermination Camp Auschwitz-Birkenau, which I have chosen from the Polish short story Proszę państwa do gazu (This Way for the Gas, Ladies and Gentlemen) by Tadeusz Borowski and its French versions, in two French translations entitled: Au gaz, messieurs-dames! translated by Geneviève Daude (Editions Polonia, 1960) and Aux douches, Mesdames et Messieurs! translated by Laurence Dyèvre and Éric Veaux (Christian Bourgois Editeur, 1992). In addition to comparing strategies for translating the selected culturemes into French, I reached for authentic documents to see if Polish culturemes specific to Auschwitz had French equivalents outside of literature. Culturemes, according to the definition in the Dystynktywny słownik synonimów by Alicja Nagórko, Marek Łaziński and Hanna Burkhardt (2004), are “important keywords for the self-identification of a community, characterising both its attitude to tradition, inherited values, as well as its coping with present time, its current experience of the world”. I include culturemes, created in the Polish language in the reality of the German occupation, among such lexical units of cultural and emotional character, as words of collective memory.

Network Mapping Interface Analysis between Carbon and Trees Corresponding to Climate Change

Background and objective: There have been increasing correlations between the keywords 'carbon' and 'tree' in response to issues related to climate change adaptation. Given this, a study that explores how these keywords relate to issues on climate change adaptation would be significant contribution to the literature. Therefore, this study aims to determine the correlation between these keywords and their network.Methods: To address the aim, the study employed a bibliometric analysis using the VOSviewer software to conduct network-mapping interface analysis using co-occurrence-based visualization and quantification. A two-step approach was applied: the first approach identifies recent driver changes of the keywords 'carbon' and 'tree' and further meaningful keywords. The second approach conducts an in-depth network mapping analysis based on the results drawn by the first approach.Results: The results suggested that, first, 'carbon' and 'tree' were shown to reflect the assessment system and contemporary issues based on the keywords 'carbon sequestration' and 'climate change.' In addition, 'biomass,' 'tree growth,' 'tree ring' and 'trade-off' were shown to be important keywords, which indicated the need for an in-depth correlation analysis. Second, 'biomass' was the key factor in the assessment system for 'carbon sequestration,' in which 'tree growth' and 'tree ring' were included. Third, the network analysis did not report varying results on 'trade-off' compared to 'carbon' and 'tree' with respect to the basic issues on climate change.Conclusion: The conventional assessment system based on the keywords 'carbon' and 'tree' should be improved by incorporating the issues drawn from the keywords 'biomass' and 'trade-off' so that it may reflect the latest contemporary needs. The network analysis on the keywords 'carbon' and 'tree' nevertheless indicated that the two keywords were at the centre of the latest contemporary issues and the assessment system.