Search

In the era of informatization and digitization, the rapid increase in the number of scientific papers has given rise to various challenges, such as lengthy articles, difficulty in information extraction and high time costs associated with reading. Literature reading challenges for researchers are increasingly tedious and time-consuming. By utilizing the language models, the assited reading system of scientific papers has been designed to address these challenges. By adopting machine reading comprehension technology as the core, the system parses scientific texts and offers some common questions to achieve automated response capabilities. By fully utilizing the pre-trained language model PERT, the system enhances its capabilities in semantic understanding and information extraction, effectively resolving various challenges in reading scientific papers and helping readers improve the efficiency of scientific literature review. [ABSTRACT FROM AUTHOR]

Background: In the healthcare domain today, despite the substantial adoption of electronic health information systems, a significant proportion of medical reports still exist in paper-based formats. As a result, there is a significant demand for the digitization of information from these paper-based reports. However, the digitization of paper-based laboratory reports into a structured data format can be challenging due to their non-standard layouts, which includes various data types such as text, numeric values, reference ranges, and units. Therefore, it is crucial to develop a highly scalable and lightweight technique that can effectively identify and extract information from laboratory test reports and convert them into a structured data format for downstream tasks. Methods: We developed an end-to-end Natural Language Processing (NLP)-based pipeline for extracting information from paper-based laboratory test reports. Our pipeline consists of two main modules: an optical character recognition (OCR) module and an information extraction (IE) module. The OCR module is applied to locate and identify text from scanned laboratory test reports using state-of-the-art OCR algorithms. The IE module is then used to extract meaningful information from the OCR results to form digitalized tables of the test reports. The IE module consists of five sub-modules, which are time detection, headline position, line normalization, Named Entity Recognition (NER) with a Conditional Random Fields (CRF)-based method, and step detection for multi-column. Finally, we evaluated the performance of the proposed pipeline on 153 laboratory test reports collected from Peking University First Hospital (PKU1). Results: In the OCR module, we evaluate the accuracy of text detection and recognition results at three different levels and achieved an averaged accuracy of 0.93. In the IE module, we extracted four laboratory test entities, including test item name, test result, test unit, and reference value range. The overall F1 score is 0.86 on the 153 laboratory test reports collected from PKU1. With a single CPU, the average inference time of each report is only 0.78 s. Conclusion: In this study, we developed a practical lightweight pipeline to digitalize and extract information from paper-based laboratory test reports in diverse types and with different layouts that can be adopted in real clinical environments with the lowest possible computing resources requirements. The high evaluation performance on the real-world hospital dataset validated the feasibility of the proposed pipeline. [ABSTRACT FROM AUTHOR]

Purpose: The main objective of this study is to evaluate the impact of Persian Scientific Papers (PSPs) on Persian Wikipedia by studying Wikipedia's citations to these papers. Design/methodology/approach: The present study is applied research, which has been performed by the web-mining method, such as downloading web pages, extracting information (references), identifying papers, detecting peer-review journals and calculating the frequency rates. The statistical population included 10,000 Persian Wikipedia Pages (PWPs) that were analyzed in two rounds with a six-month interval. Findings: The number of pages containing the Persian references section was 3,994 and 4,063 out of the 10,000 pages extracted in the first and second rounds. The ratio of pages that cited scientific sources (58 and 67 pages) to the pages extracted from the PWP was equal to 0.58 and 0.67%. The ratio of pages that cited scientific sources to pages with Persian references in each round was equal to 1.45 and 1.64%. The number of references extracted from the PWP in each round equaled 30,441 and 35,891. Eight titles from reputable Persian journals had received at least three citations from Wikipedia. Originality/value: The present study has determined the extent of interaction between science and society (knowledge flow) in the form of citations from Wikipedia articles to articles in peer-reviewed journals. The study of this issue in Persian Wikipedia in more than 2000 Persian peer-reviewed journals shows the originality of the present paper. Studying citation reliability in a collaborative and openly editable platform is another originality of the work. [ABSTRACT FROM AUTHOR]

Keyword extraction is the task of identifying important terms or phrase that are most representative of the source document. Although the process of automatic extraction of keywords from title is an old method, it was mainly for extraction from a single web document. Our approach differs from previous research works on keyword extraction in several aspects. For those who are non-expert of the scientific fields, understating scientific research trends is difficult. The purpose of this study is to develop an automatic method of obtaining overviews of a scientific field for non-experts by capturing research trends. This empirical study excavates significant term extraction using Natural Language Processing (NLP) tools. More than 15000 titles saved in a .csv file was our dataset and scripts written in Python were our process to compare how far significant terms of scientific title corpus are similar or different to the terms available in the abstract of that same scientific article corpus. A light-weight unsupervised title extractor, Yet Another Keyword Extractor (YAKE) was used to extract the results. Based on our analysis, it can be concluded that these algorithms can be used for other fields too by the non-experts of that subject field to perform automatic extraction of significant words and understanding trends. Our algorithm could be a solution to reduce the labour-intensive manual indexing process. [ABSTRACT FROM AUTHOR]

The growing number of scientific papers and document sources underscores the need for methods capable of evaluating the quality of publications. Researchers who are looking for relevant papers for their studies need ways to assess the scientific value of these documents. One approach involves using semantic search engines that can automatically extract important knowledge from the growing body of text. In this study, we introduce a new metric called "MAATrica," which serves as the foundation for an innovative method designed to evaluate research papers. MAATrica offers a new way to analyze and categorize text, focusing on the consistency of research documents in the life sciences, particularly in the fields of medicinal and nutraceutical chemistry. This method utilizes semantic descriptions to cover in silico experiments, as well as in vitro and in vivo essays. Created to aid in evaluation processes like peer review, MAATrica uses toolkits and semantic applications to build the proposed measure, identify scientific entities, and gather information. We have applied MAATrica to roughly 90,000 papers and present our findings here. [Display omitted] • MAATrica is a novel metric for assessing the coherence of methodologies in research papers within the fields of medicinal and nutraceutical chemistry. • MAATrica utilizes SciWalker (SW) as semantic search engine and ontologies to automate knowledge extraction and evaluate research papers. • MAATrica metric has been tested and validated using a dataset comprising approximately 90,000 papers within the SW platform. • MAATrica's reliability has undergone testing through comparisons with manual evaluations, revealing strong agreement and potential support for peer review. • MAATrica employs a user-controlled and customizable ontology, enabling personalized analysis of research papers in the fields of medicinal and nutraceutical chemistry. [ABSTRACT FROM AUTHOR]

This paper summarizes librarian research on information visualization as well as general trends in the broader field, highlighting the most recent trends, important journals, and which subject disciplines are most involved with information visualization. By comparing librarian research to the broader field, the paper identifies opportunities for libraries to improve their information visualization support services. [ABSTRACT FROM AUTHOR]

An institutional repository (IR) is a system for archiving digital research outputs such as scholarly papers. While using this system is a simple way to return the benefits of academic research to society, the number of papers currently archived in institutional repositories across the world is very small. We have, therefore, developed a system that can increase the number of papers archived in institutional repositories. The main aim of this system is to reduce the workload of researchers and librarians in paper registration for an IR by using external databases. As a result of our trial implementation and evaluation of the system, we found that its application resulted in a fourfold increase in the number of papers registered in a month without decreasing the working efficiency of the participating librarians. Moreover, by analyzing the log data for the system's implementation, we have acquired further knowledge related to researchers who need librarians' support in paper registration with an IR. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

This research study proposes a classification system to identify and prevent occupational accidents in fiber storage warehouses at a pulp and paper facility. The present analysis is based on variables including pedestrian circulation, bobcat, trailer trucks, access, pedestrian circulation zones, and handrails. The proposed methodology defines and trains the system by using occupational accident event data collected at the facility. Three different predicting algorithms are used: J48 decision-making trees, Naive Bayes, and Bayesian nets. The results show that the J48 decision-making tree algorithm accurately identifies possible occupational accidents 90% of the time. It is concluded that identifying variables involved in occupational accidents allows generating a C4.5 (J48) decision-making tree that can be used as a support tool to prevent occupational accidents. [ABSTRACT FROM AUTHOR]

Tech mining is the application of text mining tools to science and technology information resources. The ever-increasing volume of scientific outputs is a boom to technological innovation, but it also complicates efforts to obtain useful and concise information for problem solving. This challenge extends to tech mining, where the development of techniques compatible with big data is an urgent issue. This article introduces a semi-supervised method for extracting layered technological information from scientific papers in order to extend the reach of tech mining. Our method starts with several pre-set seed patterns used to extract candidate phrases by matching the dependency tree of each sentence. Then, after a series of judgements, phrases are divided into two categories: 'main technique' and 'tech-component'. (A technique, for the purposes of this study, is a method or tool used in the article being analysed.) In order to generate new patterns for subsequent iterations, a weighted pattern learning method is also adopted. Finally, multiple iterations of the method are applied to extract technological information from each paper. A dataset from the field of optical switcher is used to verify the method's effectiveness. Our findings are that (1) by two loops of extraction process in each iteration, our method realises the layered technological information extraction, which contains the 'part–whole' relationships between main techniques and tech-components; (2) the recall rate for main techniques is superior to the baseline after iterating 23 rounds; (3) when layering is disregarded, in the aspect of the precision and the volume of techniques, the new method is higher than that for the baseline; and (4) adjusting another two parameters can optimise the efficiency – however, the effect is neither pronounced nor straightforward. [ABSTRACT FROM AUTHOR]

The fact-condition statement plays a significant role in the geological papers, where geological theory or phenomenon as a fact is constrained by its precondition. This constraint relation conveys a lot of key information in geological papers, but it has been ignored in most of studies on the information extraction. Although the rule-based algorithms have been proposed in the previous studies to extract the relationship between facts and conditions, but their low accuracy limit usability. Therefore, this paper introduces a series of mechanisms and methods to improve the extraction accuracy. We firstly utilize three kinds of pooling for extracting the semantic features of fact-condition tuples to find that balanced semantic representations can more accurately reflect their relations. In addition, semantic correlation between facts and conditions is also designed to mine their relations via the measurement of semantic similarity or semantic distance. Based on the above, the Deep Structured Semantic Model with Multi-Grained Representation (MGR-DSSM) is proposed. In this model, the Deep Structured Semantic Model (DSSM) is utilized two individual projections for denoising semantic correlation between facts and conditions, while the Multi-Grained Representation (MGR) is designed for encoding their semantic correlation at multiple granularities. Furthermore, the linear MGR-DSSM with SciBERT achieves the best result with 70.41% of accuracy and 74.55% of F1 score, obviously outperforming other baseline models. Finally, the linear MGR-DSSM with SciBERT is adopted to extract super relation from 722 geological paper abstracts, and the geological knowledge graph with fact-condition statement is built. With more accurate relation between fact and condition, the information validity of geological knowledge graph is enhanced. • The relation between facts and conditions play an essential role in the geological paper, the extraction of which is studied for the first time. • A series of mechanisms are proposed to improve the accuracy of fact-condition relation extraction. • The MGR-DSSM is proposed, which obviously outperforms other baselines in super relation extraction. [ABSTRACT FROM AUTHOR]

Scientific retrieval systems need to be given domain search terms for searching publications, however, as natural language, search terms provided by users are often fuzzy and limited and some relevant terms are always overlooked in searching. Meanwhile, users always desire to be given domain related keywords to enlighten themselves what other terms can be used for their searching. This paper presents a concepts recommendation model in scientific paper retrieval, in which concepts are extracted from keyword in scientific papers, and some data mining algorithms are used to calculate the similarity between search terms and concepts and do recommendation for users. This model is simple and can be used with small dataset, in which all training data used is from meta data of papers that is easy to acquired. Experimental result hold good precision, which shows that this research not only simplifies searching step and improves the searching quality for users, but also lays the foundation for semantic search. [ABSTRACT FROM AUTHOR]

The optimal predictive rule is the conditional response function Graph HT ht . Start with a set of I n i training samples, and divide them into I K i evenly sized and non-overlapping folds Graph HT ht . We have access to a stream independent and identically distributed samples Graph HT ht with Graph HT ht and Graph HT ht . [Extracted from the article]

Objectives: The International Society for Bipolar Disorders Big Data Task Force assembled leading researchers in the field of bipolar disorder (BD), machine learning, and big data with extensive experience to evaluate the rationale of machine learning and big data analytics strategies for BD. Method: A task force was convened to examine and integrate findings from the scientific literature related to machine learning and big data based studies to clarify terminology and to describe challenges and potential applications in the field of BD. We also systematically searched PubMed, Embase, and Web of Science for articles published up to January 2019 that used machine learning in BD. Results: The results suggested that big data analytics has the potential to provide risk calculators to aid in treatment decisions and predict clinical prognosis, including suicidality, for individual patients. This approach can advance diagnosis by enabling discovery of more relevant data‐driven phenotypes, as well as by predicting transition to the disorder in high‐risk unaffected subjects. We also discuss the most frequent challenges that big data analytics applications can face, such as heterogeneity, lack of external validation and replication of some studies, cost and non‐stationary distribution of the data, and lack of appropriate funding. Conclusion: Machine learning‐based studies, including atheoretical data‐driven big data approaches, provide an opportunity to more accurately detect those who are at risk, parse‐relevant phenotypes as well as inform treatment selection and prognosis. However, several methodological challenges need to be addressed in order to translate research findings to clinical settings. [ABSTRACT FROM AUTHOR]

The academic world is driven by scholarly research and publications. Yet, for many fields, the volume of published research and the associated knowledge base have been expanding exponentially for decades. The result is that scientists are literally drowning in data and information. There are strategies and approaches that could help with this problem. The goal of this paper is to demonstrate the power of computer‐based approaches such as data mining and machine learning to evaluate large collections of papers. The objective is to conduct a systematic analysis of research related to the emerging area of groundwater‐related diseases. More specifically, the analysis of information from the database of papers will examine systematics in the research topics, the inter‐relationships among multiple diseases, contaminants, and groundwater, and discover styles of research associated with groundwater and disease. The analysis uses 426 papers (1971 to 2017) retrieved from a MEDLINE bibliographic database, PubMed, given the search terms "groundwater" and "disease." We developed tools that take care of necessary text processing steps, which lead naturally to clustering and visualization techniques that demonstrate published research. The resulting 2D article map shows how the collection of papers is subdivided into 11 article clusters. The cluster topics were determined by analyzing keywords or common words contained in the articles' titles, abstracts, and key words. We found that research on water‐related disease in groundwater primarily focuses on two types of contaminants—chemical compounds and pathogens. Cancer and diarrhea are two major diseases associated with groundwater contamination. According to the systematic analysis, the study of this area is still growing. Article impact statement: Using data mining and machine learning techniques to study the research strands of groundwater‐related disease from the database. [ABSTRACT FROM AUTHOR]

Purpose: Historical newspaper collections provide a wealth of information about the past. Although the digitization of these collections significantly improves their accessibility, a large portion of digitized historical newspaper collections, such as those of KBR, the Royal Library of Belgium, are not yet searchable at article-level. However, recent developments in AI-based research methods, such as document layout analysis, have the potential for further enriching the metadata to improve the searchability of these historical newspaper collections. This paper aims to discuss the aforementioned issue. Design/methodology/approach: In this paper, the authors explore how existing computer vision and machine learning approaches can be used to improve access to digitized historical newspapers. To do this, the authors propose a workflow, using computer vision and machine learning approaches to (1) provide article-level access to digitized historical newspaper collections using document layout analysis, (2) extract specific types of articles (e.g. feuilletons – literary supplements from Le Peuple from 1938), (3) conduct image similarity analysis using (un)supervised classification methods and (4) perform named entity recognition (NER) to link the extracted information to open data. Findings: The results show that the proposed workflow improves the accessibility and searchability of digitized historical newspapers, and also contributes to the building of corpora for digital humanities research. The AI-based methods enable automatic extraction of feuilletons, clustering of similar images and dynamic linking of related articles. Originality/value: The proposed workflow enables automatic extraction of articles, including detection of a specific type of article, such as a feuilleton or literary supplement. This is particularly valuable for humanities researchers as it improves the searchability of these collections and enables corpora to be built around specific themes. Article-level access to, and improved searchability of, KBR's digitized newspapers are demonstrated through the online tool (https://tw06v072.ugent.be/kbr/). [ABSTRACT FROM AUTHOR]

The scientific literature on Al-Si alloy materials is a rich source of factual information about various classes of entities (e.g., alloys, properties, and compositions) as well as various relationships between these entities. Automatically extracting this information using natural language processing (NLP) techniques to generate a knowledge graph for Al-Si alloy materials is a challenging task. In this paper, we propose ASaRE-Net (Al-Si alloy Relationship Extraction Network) to extract entities and relationships from Chinese scientific literature on Al-Si alloy materials, thus forming a triple (Entity 1, Relation, Entity 2) as the basic unit for constructing a knowledge graph. Due to the complexity of overlapping triples and many triples in a text sentence, this paper designs a triple-aware module based on knowledge representation learning, which is used to mine the correlation between entities and relations, and uses a simple and efficient labeling strategy to decode, so as to alleviate the problem of cascading errors. This paper defines 11 types of entity and 13 types of relationship for Al-Si alloy materials, and constructs ASaIED (Al-Si alloy Information Extraction Dataset). Experimental results show that compared to the strongest baseline, the proposed model improves the F1 score by 2.34% on the ASaIED dataset, and demonstrates better performance in scenarios involving SEO overlapping types and many triples. The proposed model can continuously provide credible data for constructing knowledge graphs in the field of Al-Si alloy materials. [ABSTRACT FROM AUTHOR]

In recent years, with the development of 3D digitization of cultural relics, most cultural sites contain a large number of fine 3D data of cultural relics, especially complex geometric objects such as painted cultural relics. At present, how to automatically extract surface damage information from the fine 3D color model of painted cultural relics and avoid the loss of accuracy caused by reducing the dimension using conventional methods is an urgentproblem. In view of the above issues, this paper proposes an automatic and high-precision extraction method for cultural relics surface shedding diseases based on 3D fine data. First, this paper designs a 2D and 3D integrated data conversion model based on OpenSceneGraph, a 3D engine, which performs mutual conversion between 3D color model textures and 2D images. Second, this paper proposes a simple linear iterative clustering segmentation algorithm with an adaptive k value, which solves the problem of setting the superpixel k value and improves the accuracy of image segmentation. Finally, through the 2D and 3D integrated models, the disease is statistically analyzed and labeled on the 3D model. Experiments show that for painted plastic objects with complex surfaces, the disease extraction method based on the 3D fine model proposed in this paper has improved geometric accuracy compared with the current popular orthophoto extraction method, and the disease investigation is more comprehensive. Compared with the current 3D manual extraction method in commercial software, this method greatly improves the efficiency of disease extraction while ensuring extraction accuracy. The research method of this paper activates many existing 3D fine data of cultural protection units and converts conventional 2D data mining and analysis into 3D, which is more in line with the scientific utilization of data in terms of accuracy and efficiency and has certain scientific research value, leading value and practical significance. [ABSTRACT FROM AUTHOR]

Traffic flow prediction plays a critical role in urban traffic management and planning. Accurate prediction of traffic flow can enhance traffic efficiency, improve traffic safety, conserve traffic resources, and promote sustainable urban development. Graph convolutional networks (GCN) have great potential in traffic flow prediction due to their ability to handle complex data with topological structures while considering spatial relationships between nodes in data. However, it is difficult for the traditional GCN-based models to capture dynamic spatiotemporal dependencies between the stations since these models usually use static relationship graph in GCN. This paper presents an enhanced approach called the coupled generative graph convolution model (CGGCN) for capturing dynamic regional traffic relationships. The CGGCN learns the adjacency matrix of graph convolution in a generative manner, thereby improving the ability of previous graph convolution models to effectively capture the dynamic relationships of regional traffic within multi-layer structures. Additionally, the paper optimizes the fusion method of coupling graph convolution in multi-level matrix feature flow information aggregation, leading to the efficient extraction of spatiotemporal information. To validate the effectiveness of the proposed model, extensive experiments were conducted on two real traffic datasets obtained from New York, namely NYCBike and NYCTaxi. The results demonstrate the superior performance of the CGGCN model when compared to other eight baseline models. [ABSTRACT FROM AUTHOR]

Sentiment analysis and opinion mining is an area that has experienced considerable growth over the last decade. This area of research attempts to determine the feelings, opinions, emotions, among other things, of people on something or someone. To do this, natural language techniques and machine learning algorithms are used. This article discusses the problem of extracting sentiment and opinions from a collection of reviews on scientific articles conducted under an international conference on computing in northern Chile. The first aim of this analysis is to automatically determine the orientation of a review and contrast this with the assessment made by the reviewer of the article. This would allow scientists to characterize and compare reviews crosswise and more objectively support the overall assessment of a scientific article. A hybrid approach that combines an unsupervised machine learning algorithm with techniques from natural language processing is proposed to analyze reviews. This method uses part-of-speech (POS) tagging to obtain the syntactic structure of a sentence. This syntactic structure, along with the use of dictionaries, allows determining the semantic orientation of the review through a scoring algorithm. A set of experiments were conducted to evaluate the capability and performance of the proposed approaches relative to a baseline, using standard metrics, such as accuracy, precision, recall, and the F 1 -score. The results show improvements in the case of binary, ternary and a 5-point scale classification in relation to classical machine learning algorithms such as SVM and NB, but they also present a challenge to improve the multiclass classification in this domain. [ABSTRACT FROM AUTHOR]

The ability of evolutionary algorithms (EAs) to manage a set of solutions, even attending multiple objectives, as well as their ability to optimize any kinds of values, allows them to fit very well some parts of the data‐mining (DM) problems, whose native learning techniques usually associated with the inherent DM problem are not able to solve. Therefore, EAs are widely applied to complement or even replace the classical DM learning approaches. This application of EAs to the DM process is usually named evolutionary data mining (EDM). This contribution aims at showing a glimpse of the EDM field current state by focusing on the most cited papers published in the last 10 years. A descriptive analysis of the papers together with a bibliographic study is performed in order to differentiate past and current trends and to easily focus on significant further developments. Results show that, in the case of the most cited studied papers, the use of EAs on DM tasks is mainly focused on enhancing the classical learning techniques, thus completely replacing them only when it is directly motivated by the nature of problem. The bibliographic analysis is also showing that even though EAs were the main techniques used for EDM, the emergent evolutionary computation algorithms (swarm intelligence, etc.) are becoming nowadays the most cited and used ones. Based on all these facts, some potential further directions are also discussed. WIREs Data Mining Knowl Discov 2018, 8:e1239. doi: 10.1002/widm.1239 This article is categorized under: Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Computational Intelligence Technologies > Classification Technologies > Prediction [ABSTRACT FROM AUTHOR]

The number of published findings in biomedicine increases continually. At the same time, specifics of the domain's terminology complicates the task of relevant publications retrieval. In the current research, we investigate influence of terms' variability and ambiguity on a paper's likelihood of being retrieved. We obtained statistics that demonstrate significance of the issue and its challenges, followed by presenting the sci.AI platform, which allows precise terms labeling as a resolution. [ABSTRACT FROM AUTHOR]

This paper comprehends the global research scenario about incorporating cultural components in contemporary urban planning practice. Various urban cultural elements have been analysed under the umbrella of 'Urban Cultural Dynamics' (UCD). This study aims to identify hot spots and explore influential sources through a bibliometric investigation of the evolutionary patterns of cultural dynamics in urban planning research. Data was collected from the Web of Science core collection (1965-2021). The data mining tools VOSviewer, Biblioshiny, KNIME, and WordStat were used to analyse hot topics, keywords, influential journals, core papers, top authors, and cooperation networks of countries and institutions. The Analytical Hierarchy Process (AHP) is employed in journal analysis. The analysis showed that publications on UCD increased exponentially between 2016 and 2022. Italy (32), China (26), and Spain (18) are the top countries in total publications. Technical universities of Dresden and Munich have the most significant influence on UCD's research, with 103 and 101 total citations, respectively. Landscape and Urban Planning, Journal of Cultural Heritage, and Tourism Geographies are the top three consistently influential journals. The top ten core papers were identified. The keywords and topic analysis charted out the evolution of multidisciplinary UCD research topics. The hotspot analysis revealed the significance of cultural dynamics in sustainable urban development. The results highlighted the need for intercontinental cooperation between developed countries and the third world. The future scope of research on UCD topics is discussed in the conclusion. [ABSTRACT FROM AUTHOR]

Recently, research on the circadian rhythm of hypertension has gained popularity. However, few bibliometric analyses have been conducted in this field. In this study, CiteSpace 6.1. R6, VOSviewer 1.6.18, R language (version 4.2.3), R package Bibliometrix (4.1.2), and Microsoft Excel 365 were used to conduct the data mining and knowledge visualization analysis. A total of 1,560 papers from 1,825 institutions in 77 countries were included. Research on the role of circadian rhythms in hypertension is increasing annually. Overall, Chronobiology International published the most literature and Hypertension received the most citations. Ramon Hermida from the Universidade de Vigo in Spain published the most papers and had the most citations. The United States of America and Japan have been the most productive countries. The University of Ferrara, Universidade de Vigo, and the University of California system produced the most publications. Amongst authors, Hermida had the most and longest literature bursts. Keywords such as "chronic kidney disease," "oxidative stress," and "gene expression" have been breakout keywords since 2014. This study revealed the dynamic evolution of research on circadian rhythms in hypertension and provides a knowledge base for researchers. [ABSTRACT FROM AUTHOR]

The literature review has clearly indicated that the scale and characteristics of demand for Geography Earth and Environmental Sciences experts across different countries is unknown. Therefore, there is an urgent need to investigate this issue. This paper presents the results of research on the real demand for GEES specialists. In the paper, real demand is expressed by job vacancies (N = 17 378) published in six European countries over a period of 18 months. To analyse such an extensive body of text data, we used data mining techniques such as: SVD, inter-factor correlation analysis, word frequency analysis and word significance indicators, which allowed us to recognise similarities and differences in the size and structure of demand for these specialists in specific groups of countries. Employers from the UK and Ireland offered the most comprehensive range of positions whereas the expectations of Polish employers were the least diverse. Word frequency analyses for each occupation group demonstrated which components of GEES experts' knowledge and skills are considered universal on the labour market and which are subject to substantial regional variations. Moreover, word significance analyses allowed us to identify the occupations where a wider range of general skill areas was required and the positions for which primarily geographic skills were in demand. [ABSTRACT FROM AUTHOR]

Remote sensing technology in the new era gradually need a new scientific paradigm driven by big data and knowledge. However, in the current research, the use of existing knowledge is slightly insufficient. As for the whole research process, there is a lack of guidance based on the field knowledge. In view of the above problems in the field of remote sensing in agriculture, it is of great significance to organize knowledge based on concept graph and develop knowledge-driven big data analysis theory and methods. This paper focuses on the construction and application of agricultural remote sensing concept graph. Firstly, it explores the core contents, research hotspots and development trends of the current agricultural remote sensing field through reference visualization and analysis methods such as keyword frequency statistics, keyword co-occurrence, word detection with stronger citation burst and so on. Then, this paper summarizes the comprehensive ontology of agricultural remote sensing based on the results of keyword clustering. Thus, the schema layer of concept graph is constructed from top to bottom. Under the guidance of the schema layer, the data layer of the concept graph is artificially filled from the bottom up with different triads stored in Neo4j. In terms of application, retrieval, query and recommendation of the proposed concept graph for agricultural remote sensing tasks is a typical application case. Based on the results of the recommendation, it could guide the process of information extraction such as cultivated land parcel delineation and crop type identification at the parcel scale. The study of concept graph helps to summarize and generalize the relevant knowledge in the field of agricultural remote sensing. Relevant research provide effective solutions for automated and intelligent agricultural information extraction and application. [ABSTRACT FROM AUTHOR]

The goal of the paper is to establish quadrature formulas on combinatorial graphs. Three types of quadrature formulas are developed. Quadrature formulas of the first type are obtained through interpolation by variational splines. This set of formulas is exact on spaces of variational splines on graphs. Since bandlimited functions can be obtained as limits of variational splines we obtain quadrature formulas which are approximately exact on spaces of bandlimited functions. Accuracy of this type of quadrature formulas is given in terms of geometry of the set of nodes of splines and in terms of smoothness of functions which is measured by means of the combinatorial Laplace operator. Quadrature formulas of the second type are obtained through point-wise sampling for bandlimited functions and based on existence of certain frames in appropriate subspaces of bandlimited functions. The third type quadrature formulas are based on the average sampling over subgraphs. Our quadrature formulas which are based on sampling are exact on a relevant subspaces of bandlimited functions. The results of the paper have potential applications to problems that arise in data mining. [ABSTRACT FROM AUTHOR]

Background Method Result Conclusion In the agricultural sector, the early identification of plant diseases presents a pressing challenge. Throughout the growing season, plants remain vulnerable to an array of diseases. Failure to detect these diseases at their early stages can significantly compromise the overall yield, thereby reducing profitability for farmers. To address this issue, several researchers have introduced standard methods that leverage machine learning and deep learning techniques. However, many of these methods offer limited classification accuracy and often necessitate extensive training parameter adjustments.The objective of this study is to develop a new deep learning‐based technique for detecting and classifying plant diseases at earlier stages. Thus, this paper introduces a novel technique known as the deep belief network‐based enhanced kernel extreme learning machine (DBN‐EKELM) that identifies a disease automatically and performs effective classification. The initial phase involves data preprocessing to enhance quality of plant leaf images, facilitating the extraction of critical information. With the goal of achieving superior classification accuracy, this paper proposes the use of the DBN‐EKELM technique for optimal plant leaf disease detection. Given that KELM parameters are highly sensitive to minor variations, proper parameter tuning is essential and introduces a novel binary gaining sharing knowledge‐based optimization algorithm (NBGSK).The efficacy of the proposed DBN‐EKELM method is evaluated by comparing its performance with other conventional methods, considering various measures like accuracy, precision, specificity, sensitivity and F‐measure.Experimental analyses demonstrate that the DBN‐EKELM technique achieves an impressive rate of approximately 98.2%, 97%, 98.1%, 97.4% as well as 97.8%, surpassing other standard methods. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

To reveal complex causes of aircraft events, this paper aims to mine association rules between the trigger probability and relative strength via a modified Apriori algorithm. Clustering is adopted for data preprocessing and TF–IDF value calculation. Causative item sets of aircraft events are obtained based on the accident causation 2–4 model and are coded to establish code indicators. By avoiding the use of statistical methodologies to resolve not-a-number (NaN) values for altering the interrelations among causes, an enhancement in the Apriori algorithm is proposed by considering frequent items. By extracting frequent patterns, in this paper, all the association rules that satisfy three perspectives (support, confidence and lift) are determined by constantly generating and pruning candidate item sets. A network graph is used to visualize the association rules between different unsafe events and all types of causes. Finally, 9835 representative pieces of data, including general unsafe events, general incidents and serious incidents from the Southwest Air Traffic Management Bureau, are selected for analysis. The results show that improper energy allocation, poor conflict resolution ability, inadequate onsite management duties, adoption of a luck mentality, and occurrence of controller oversight are highly correlated with general unsafe events, and failure to rectify incorrect recitation is notably correlated with general incidents, while inadequate manual promotion, lack of conflict judgement and insufficient safety management are strongly correlated with serious incidents. This study quantitatively reveals the potential patterns and characteristics of mutual interactions among various types of historical aircraft events and highlights directions for controllable prevention and prediction of aircraft events. [ABSTRACT FROM AUTHOR]

Background: MicroRNAs (miRNAs) emerge in various organisms, ranging from viruses to humans, and play crucial regulatory roles within cells, participating in a variety of biological processes. In numerous prediction methods for miRNA-disease associations, the issue of over-dependence on both similarity measurement data and the association matrix still hasn't been improved. In this paper, a miRNA-Disease association prediction model (called TP-MDA) based on tree path global feature extraction and fully connected artificial neural network (FANN) with multi-head self-attention mechanism is proposed. The TP-MDA model utilizes an association tree structure to represent the data relationships, multi-head self-attention mechanism for extracting feature vectors, and fully connected artificial neural network with 5-fold cross-validation for model training. Results: The experimental results indicate that the TP-MDA model outperforms the other comparative models, AUC is 0.9714. In the case studies of miRNAs associated with colorectal cancer and lung cancer, among the top 15 miRNAs predicted by the model, 12 in colorectal cancer and 15 in lung cancer were validated respectively, the accuracy is as high as 0.9227. Conclusions: The model proposed in this paper can accurately predict the miRNA-disease association, and can serve as a valuable reference for data mining and association prediction in the fields of life sciences, biology, and disease genetics, among others. [ABSTRACT FROM AUTHOR]

To improve the effect of web page information extraction, this paper proposes an improved information extraction method of mixed text density grids. Under various power constraints of the relay node itself, this paper proposes a design scheme of joint beamforming and artificial noise based on safety and rate maximisation. Furthermore, with the help of semidefinite relaxation techniques and first‐order approximations, it can be approximated as a semidefinite programming problem that is easy to solve. In addition, this paper uses an iterative algorithm based on a continuous convex approximation to process data to improve the accuracy of web page data extraction. The experimental results show that the information extraction method based on improved mixed text density webpages proposed in this paper has a good information extraction effect. [ABSTRACT FROM AUTHOR]

This special issue features the selected works of authors who have presented papers at the 2022 iteration of the Joint Conference on Digital Libraries (JCDL) in Cologne, Germany. The motto of the conference was "Bridging Worlds" and was run as a fully hybrid event. Ten papers covering all aspects of Digital Libraries, namely Natural Language Processing, Information Retrieval, User Behavior, Scholarly Communication, Classification, Information Extraction are included in this issue. [ABSTRACT FROM AUTHOR]

The Jiyang depression in the southeastern part of the Bohai Bay Basin has a relatively large scale set of shale oil in the Paleogene Shahejie Formation, but the complex internal components lead to narrow frequency bands, low resolution and difficulty in reservoir information extraction. Impedance is important information for reservoir characterization, and how to predict high‐resolution impedance using available information is particularly important. Deep learning, known for its effectiveness in addressing non‐linear problems, has found extensive applications in various fields of oil and gas exploration. However, the challenges of overfitting and poor generalization persist due to the limited availability of training datasets. Besides, existing methods often use networks to solve a single problem in fact, deep learning can deal with a series of problems intelligently. In order to partially solve the above problems, an intelligent storage prediction network framework is proposed in this paper. Physical information is introduced to realize data‐driven and model‐based approaches, thus solving the problem of difficult construction of training datasets. The processing part accomplishes the high‐resolution processing of seismic records, thus solving the problems of narrow bandwidth and low resolution. Initial model constraints are introduced so as to obtain more stable inversion results. Finally, the well data is compared and analysed to identify and predict the lithology and complete the intelligent prediction of unconventional reservoirs. The results are compared with the traditional model‐driven inversion method, revealing that the approach presented in this paper exhibits higher resolution in predicting dolomite. This contributes to the establishment of a robust data foundation for reservoir evaluation. [ABSTRACT FROM AUTHOR]

Background: A huge amount of research is carried out nowadays in Artificial Intelligence to propose automated ways to analyse medical data with the aim to support doctors in delivering medical diagnoses. However, a main issue of these approaches is the lack of transparency and interpretability of the achieved results, making it hard to employ such methods for educational purposes. It is therefore necessary to develop new frameworks to enhance explainability in these solutions. Results: In this paper, we present a novel full pipeline to generate automatically natural language explanations for medical diagnoses. The proposed solution starts from a clinical case description associated with a list of correct and incorrect diagnoses and, through the extraction of the relevant symptoms and findings, enriches the information contained in the description with verified medical knowledge from an ontology. Finally, the system returns a pattern-based explanation in natural language which elucidates why the correct (incorrect) diagnosis is the correct (incorrect) one. The main contribution of the paper is twofold: first, we propose two novel linguistic resources for the medical domain (i.e, a dataset of 314 clinical cases annotated with the medical entities from UMLS, and a database of biological boundaries for common findings), and second, a full Information Extraction pipeline to extract symptoms and findings from the clinical cases and match them with the terms in a medical ontology and to the biological boundaries. An extensive evaluation of the proposed approach shows the our method outperforms comparable approaches. Conclusions: Our goal is to offer AI-assisted educational support framework to form clinical residents to formulate sound and exhaustive explanations for their diagnoses to patients. [ABSTRACT FROM AUTHOR]

Background: Emotion is a complex phenomenon that greatly affects human behavior and thinking in daily life. Electroencephalography (EEG), one of the human physiological signals, has been emphasized by most researchers in emotion recognition as its specific properties are closely associated with human emotion. However, the number of human emotion recognition studies using computer games as stimuli is still insufficient as there were no relevant publicly available datasets provided in the past decades. Most of the recent studies using the Gameemo public dataset have not clarified the relationship between the EEG signal's changes and the emotion elicited using computer games. Thus, this paper is proposed to introduce the use of data mining techniques in investigating the relationships between the frequency changes of EEG signals and the human emotion elicited when playing different kinds of computer games. The data acquisition stage, data pre-processing, data annotation and feature extraction stage were designed and conducted in this paper to obtain and extract the EEG features from the Gameemo dataset. The cross-subject and subject-based experiments were conducted to evaluate the classifiers' performance. The top 10 association rules generated by the RCAR classifier will be examined to determine the possible relationship between the EEG signal's frequency changes and game-induced emotions. Results: The RCAR classifier constructed for cross-subject experiment achieved highest accuracy, precision, recall and F1-score evaluated with over 90% in classifying the HAPV, HANV and LANV game-induced emotions. The 20 experiment cases' results from subject-based experiments supported that the SVM classifier could accurately classify the 4 emotion states with a kappa value over 0.62, demonstrating the SVM-based algorithm's capabilities in precisely determining the emotion label for each participant's EEG features' instance. Conclusion: The findings in this study fill the existing gap of game-induced emotion recognition field by providing an in-depth evaluation on the ruleset algorithm's performance and feasibility of applying the generated rules on the game-induced EEG data for justifying the emotional state prediction result. [ABSTRACT FROM AUTHOR]

The paper reports findings from a study that examining how cross-platform social media analysis can help to map the digital discourse on sustainable mobility and sustainable transport, and enhance the understanding of sociotechnical low-carbon transport transitions. Using the hashtag search queries #sustainabletransport and #sustainablemobility, 33,121 Tweets (2013–2021) and 8,089 Instagram images including captions (2017/2018–2021) were scraped using the Python modules Twint and Instaloader. Quantitative text and sentiment analyses were applied to the Tweets and image captions. Additionally, an automated machine learning-based image analysis of the Instagram images was conducted using object detection via OpenCV. Synthesized results formed the base for a cross-platform analysis inspired by Rogers' method comprising hot topics/key themes, user mentions, sentiment polarity, and co-hashtags. Notably, electromobility emerged as a prominent theme, particularly on Instagram, while #sustainabletransport was closely associated with active travel, notably bicycling, and #sustainablemobility showcased a dominance of electromobility discourse. The study demonstrates the investigative potentials of cross-platform social media analysis studies to enhance the understanding of sociotechnical low-carbon transport transitions. Drawing on key results, the paper suggests an adapted version of the Geelsean Multi-Level Perspective on Sociotechnical Transitions. [ABSTRACT FROM AUTHOR]

Developing and executing large-scale data analysis applications in parallel and distributed environments can be a complex and time-consuming task. Developers often find themselves diverted from their application logic to handle technical details about the underlying runtime and related issues. To simplify this process, ParSoDA, a Java library, has been proposed to facilitate the development of parallel data mining applications executed on HPC systems. It simplifies the process by providing built-in scalability mechanisms relying on the Hadoop and Spark frameworks. This paper presents ParSoDA-Py, the Python version of the ParSoDA library, which allows for further support of commonly used runtimes and libraries for big data analysis. After a complete library redesign, ParSoDA can be now easily integrated with other Python-based distributed runtimes for HPC systems, such as COMPSs and Apache Spark, and with the large ecosystem of Python-based data processing libraries. The paper discusses the adaptation process, which takes into consideration the new technical requirements, and evaluates both usability and scalability through some case study applications. [ABSTRACT FROM AUTHOR]

The low-light image enhancement task aims to improve the visibility of information in the dark to obtain more data and utilize it, while also improving the visual quality of the image. In this paper, we propose a dual cross-attention multi-stage embedding network (DCMENet) for fast and accurate enhancement of low-light images into high-quality images with high visibility. The problem that enhanced images tend to have more noise in them, which affects the image quality, is improved by introducing an attention mechanism in the encoder–decoder structure. In addition, the encoder–decoder can focus most of its attention on the dark areas of the image and better attend to the detailed features in the image that are obscured by the dark areas. In particular, the poor performance of the Transformer when the dataset size is small is solved by fusing the CNN-Attention and Transformer in the encoder. Considering the purpose of the low-light image enhancement task, we raise the importance of recovering image detail information to the same level as reconstructing the lighting. For features such as texture details in images, cascade extraction using spatial attention and pixel attention can reduce the model complexity while the performance is also improved. Finally, the global features obtained by the encoder–decoder are fused into the shallow feature extraction structure to reconstruct the illumination while guiding the network for the focused extraction of information in the dark. The proposed DCMENet achieves the best results in both objective quality assessment and subjective evaluation, while for the computer vision tasks working in low-light environments as well, the enhanced images using the DCMENet proposed in this paper show the best performance. [ABSTRACT FROM AUTHOR]

New discoveries and knowledge are summarized in thousands of published papers per year per scientific domain, making it incomprehensible for scientists to account for all available knowledge relevant for their studies. In this paper, we present ACCORDION (ACCelerating and Optimizing model RecommenDatIONs), a novel methodology and an expert system that retrieves and selects relevant knowledge from literature and databases to recommend models with correct structure and accurate behavior, enabling mechanistic explanations and predictions, and advancing understanding. ACCORDION introduces an approach that integrates knowledge retrieval, graph algorithms, clustering, simulation, and formal analysis. Here, we focus on biological systems, although the proposed methodology is applicable in other domains. We used ACCORDION in nine benchmark case studies and compared its performance with other previously published tools. We show that ACCORDION is: comprehensive, retrieving relevant knowledge from a range of literature sources through machine reading engines; very effective, reducing the error of the initial baseline model by more than 80%, recommending models that closely recapitulate desired behavior, and outperforming previously published tools; selective, recommending only the most relevant, contextspecific, and useful subset (15%-20%) of candidate knowledge in literature; diverse, accounting for several distinct criteria to recommend more than one solution, thus enabling alternative explanations or intervention directions. [ABSTRACT FROM AUTHOR]

The Triple Ionosophere PhotoMeter (TRIPM) is designed to make the disk observations of the Earth airglow emissions at OI 135.6 nm and N 2 Lyman-Birge-Hopfield (LBH) band that flew aboard the newly launched early morning satellite Fengyun-3E (FY-3E) which was launched on July 5th, 2021. This is the first attempt to obtain the global twilight airglow data in the far ultraviolet band using Ionosophere PhotoMeter. The seasonal behavior of the twilight airglow at the OI 135.6 nm and N 2 LBH band is exhibited and interpreted in the paper. Validity of the TRIPM data is analyzed by comparison with the simulation results of the Global Airglow (GLOW) and the simulation results reveal good consistency with the observational results. Furthermore, based on the ionospheric contributions calculated by GLOW model, we extract the ionospheric information from the TRIPM 135.6 nm emissions. This means the ionospheric signature in the 135.6 nm radiance at the dawn and dusk times can be provided. Comparison between the ionospheric 135.6 nm radiance due to O+ and electron radiative recombination with GPS TEC data shows a similar morphology of equatorial arcs and seasonal variation of the ionosphere. [ABSTRACT FROM AUTHOR]

Airborne diseases have contributed in an incredible way to increase worldwide morbidity and disability during the last few years. The omicron is an airborne disease that spreads rapidly all over the world, with many infected persons. As a result, detecting and identifying the positive cases of omicron for treatment on time, and preventing the disease from spreading is a constant challenge and difficult process. Several medical tests have been used and analyzed just to identify omicron but all are ineffective and not properly accurate at an early stage. The current study examines the enormous potential of the latest progress of IoT after incorporating a framework using fog-cloud computing for omicron viruses. This paper presented a novel framework based on IoT technology for diagnosis such disease on time. The proposed model uses the weighted naive bayes algorithm that has ability to analyze the health condition and predict probabilistic health state vulnerability of an individual. In addition, the porposed framework indulges the utilization of Long Short-Term-Memory and Enhanced temporal data-based Re-current neural network (LSTM-ERNN) for the predictivity of the spread of such virus. The experimental outcomes indicate that the proposed system achieves an accuracy of 98.7%, a Recall of 97.8%, a precision of 92.8%, and the f-measure consists of 97.4%. The experimental results depicted that the proposed approach has performed better outcomes as compared to other state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Extracting information from scanned business documents, while a necessary commercial task, continues to be mostly done manually, requiring significant human effort. Current solutions for automated document information extraction still have limited capabilities in regards to user-required customizability and extraction of dataset-specific information, leaving the area as a very active field of research. In this paper, we propose modifications and improvements to our previously developed custom pipeline for extracting and tabulating key-value pairs from commercial invoice documents. Our design changes and additions adapt the pipeline to a wider variety of document types and use cases, primarily through the implementation of dataset-specific configuration files that promote customizability along with new technical modules that address both general and dataset-specific complexities. We compare our pipeline's performance against current machine learning and commercial solutions on a real-world dataset, and demonstrate that it is able to extract a wider variety of fields while maintaining competitive or greater accuracies compared to the alternate solutions. [ABSTRACT FROM AUTHOR]

AbstractAdvancements in technology have elevated the prominence of 3D point cloud data, making its analysis increasingly vital across various applications. This need drives the demand for advanced statistical analytic approaches to handle challenges such as size, sparsity, and irregularity in 3D point clouds while ensuring accurate and efficient information extraction. This paper introduces a novel nonparametric distributed (NPD) learning framework that utilizes trivariate spline smoothing over a triangulation of domain. The proposed NPD algorithm features a straightforward, scalable, and communication-efficient implementation scheme that can achieve near-linear speedup. In addition, we provide rigorous theoretical support for the NPD estimation framework and demonstrate that the NPD spline estimators attain the same convergence rate as the global spline estimators obtained using the entire dataset and achieve the optimal nonparametric convergence rate established by Stone (1982) under some regularity conditions. To evaluate the efficacy of the proposed NPD method, we conduct simulation studies comparing it with several global nonparametric estimation methods used to smooth the 3D data. The results demonstrate the superior performance of the NPD method in accurately and efficiently processing and learning from 3D point clouds, highlighting its potential to advance large and complex data analysis. Supplementary material, which contains related technical details, proofs of the theoretical results, and additional results in simulation studies, is available online. [ABSTRACT FROM AUTHOR]

Background: Applying graph convolutional networks (GCN) to the classification of free-form natural language texts leveraged by graph-of-words features (TextGCN) was studied and confirmed to be an effective means of describing complex natural language texts. However, the text classification models based on the TextGCN possess weaknesses in terms of memory consumption and model dissemination and distribution. In this paper, we present a fast message passing network (FastMPN), implementing a GCN with message passing architecture that provides versatility and flexibility by allowing trainable node embedding and edge weights, helping the GCN model find the better solution. We applied the FastMPN model to the task of clinical information extraction from cancer pathology reports, extracting the following six properties: main site, subsite, laterality, histology, behavior, and grade. Results: We evaluated the clinical task performance of the FastMPN models in terms of micro- and macro-averaged F1 scores. A comparison was performed with the multi-task convolutional neural network (MT-CNN) model. Results show that the FastMPN model is equivalent to or better than the MT-CNN. Conclusions: Our implementation revealed that our FastMPN model, which is based on the PyTorch platform, can train a large corpus (667,290 training samples) with 202,373 unique words in less than 3 minutes per epoch using one NVIDIA V100 hardware accelerator. Our experiments demonstrated that using this implementation, the clinical task performance scores of information extraction related to tumors from cancer pathology reports were highly competitive. [ABSTRACT FROM AUTHOR]