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Non-destructive testing (NDT) with high-resolution cone-beam X-ray computed tomography (XCT) plays a crucial role in revealing the 3D distribution and morphology of defects within an object. It’s necessary to generalise an intelligent and customised XCT-based NDT protocol, providing solutions for reconstruction quality improvement and complex defect quantitative analysis for users. We analysed the key techniques of XCT and developed the systematic software for data acquisition, reconstruction, intelligent super-resolution and segmentation in our advanced imaging and data mining laboratory (AIDM-CT). The experimental results demonstrated the software efficiently achieves the control of XCT scanning, artefact correction algorithms related to cone-beam geometrics, beam hardening, ring and radial artefacts, deep-learning based slice super-resolution and feature segmentation, and quantitative analysis. The software is programmed by Python language to provide the friendly multi-function graphical user interface (GUI), in which the programming of reconstruction and corrections combined with Computer Unified Device Architecture (CUDA) acceleration to guarantee the high efficiency of XCT task. Particularly, the multi-functional super resolution module (MF-GAN) is proposed to optimise the quality of CT image and the improved U-Net segmentation module (CBAM U-Net) is also developed to fulfill the high-precision quantitative non-destructive testing of homogeneous and variform microdefects in our AIDM-CT software. [ABSTRACT FROM AUTHOR]

This study explores the enhancement of Internet of Things (IoT) product design and development through the integration of ethnography and big data analytics. A new model is proposed, recognizing the limitations of existing big data approaches in capturing nuanced and complex user needs. This model combines in-depth analysis of user attitudes with extensive review data from current IoT product users, aiming to uncover a more comprehensive understanding of the user experience beyond typical quantitative insights provided by big data. An IoT product named 'Zipband' has been developed as a practical application of this integrated research methodology. A combination of qualitative and quantitative research methods, including interviews and term frequency–inverse document frequency (TF-IDF) scheme analysis, was utilized to identify diverse user needs and uncover new opportunities for user experience improvement. This research contributes to the field by introducing a data-driven ethnographic approach that has the potential to inspire convergence research in IoT product design and user experience areas. [ABSTRACT FROM AUTHOR]

Extensive scientific investigation is necessary because every government wants to construct smart cities. This is why examining how researchers approach this area of study is critical. This study investigates global research trends in smart cities and the Internet of Things (IoT) by analyzing 14,309 articles from the Scopus database (2010–2024). Using text mining and latent semantic analysis (LSA) via the KNIME tool, the research identifies key areas and trends in smart city development. The k-means clustering algorithm predicts future research directions, highlighting the most active countries, influential authors, and significant sources in the field. The results underscore the need for additional research in sectors where IoT applications for smart cities are still in their early stages. This analysis also offers insights into fostering international collaborations among institutions and researchers. The findings suggest that future research should focus on developing secure, scalable solutions to address challenges across various industries. Overall, this study provides a comprehensive overview of smart city research, offering valuable guidance for researchers and policymakers aiming to advance the integration of IoT technologies into urban environments. Article Highlights : This study identifies key global trends in smart city development and IoT integration using text mining and semantic analysis. Security and privacy challenges are significant concerns in the development of smart city technologies. The research emphasizes the importance of secure and scalable solutions to address urban challenges in smart city ecosystems. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to comprehensively assess the safety of rimegepant administration in real-world clinical settings. Methods: Data from the Food and Drug Administration Adverse Event Reporting System (FAERS) spanning the second quarter of 2020 through the first quarter of 2023 were retrospectively analyzed in this pharmacovigilance investigation. This study focuses on employing subgroup analysis to monitor rimegepant drug safety. Descriptive analysis was employed to examine clinical characteristics and concomitant medication of adverse event reports associated with rimegepant, including report season, reporter country, sex, age, weight, dose, and frequency, onset time, et al. Correlation analysis, including techniques such as violin plots, was utilized to explore relationships between clinical characteristics in greater detail. Additionally, four disproportionality analysis methods were applied to assess adverse event signals associated with rimegepant. Results: A total of 5,416,969 adverse event reports extracted from the FAERS database, 10, 194 adverse events were identified as the "primary suspect" (PS) drug attributed to rimegepant. Rimegepant-associated adverse events involved 27 System Organ Classes (SOCs), and the significant SOC meeting all four detection criteria was "general disorders and administration site conditions" (SOC: 10018065). Additionally, new significant adverse events were discovered, including "vomiting projectile" (PT: 10047708), "eructation" (PT: 10015137), "motion sickness" (PT: 10027990), "feeling drunk" (PT: 10016330), "reaction to food additive" (PT: 10037977), etc. Descriptive analysis indicated that the majority of reporters were consumers (88.1%), with most reports involving female patients. Significant differences were observed between female and male patients across age categories, and the concomitant use of rimegepant with other medications was complex. Conclusion: This study has preliminarily identified potential new adverse events associated with rimegepant, such as those involving the gastrointestinal system, nervous system, and immune system, which warrant further research to determine their exact mechanisms and risk factors. Additionally, significant differences in rimegepant-related adverse events were observed across different age groups and sexes, and the complexity of concomitant medication use should be given special attention in clinical practice. [ABSTRACT FROM AUTHOR]

Focused screening on target-prioritized compound sets can be an efficient alternative to high throughput screening (HTS). For most biomolecular targets, compound prioritization models depend on prior screening data or a target structure. For phenotypic or multi-protein pathway targets, it may not be clear which public assay records provide relevant data. The question also arises as to whether data collected from disparate assays might be usefully consolidated. Here, we report on the development and application of a data mining pipeline to examine these issues. To illustrate, we focus on identifying inhibitors of oxidative phosphorylation, a druggable metabolic process in epithelial ovarian tumors. The pipeline compiled 8415 available OXPHOS-related bioassays in the PubChem data repository involving 312,093 unique compound records. Application of PubChem assay activity annotations, PAINS (Pan Assay Interference Compounds), and Lipinski-like bioavailability filters yields 1852 putative OXPHOS-active compounds that fall into 464 clusters. These chemotypes are diverse but have relatively high hydrophobicity and molecular weight but lower complexity and drug-likeness. These chemotypes show a high abundance of bicyclic ring systems and oxygen containing functional groups including ketones, allylic oxides (alpha/beta unsaturated carbonyls), hydroxyl groups, and ethers. In contrast, amide and primary amine functional groups have a notably lower than random prevalence. UMAP representation of the chemical space shows strong divergence in the regions occupied by OXPHOS-inactive and -active compounds. Of the six compounds selected for biological testing, 4 showed statistically significant inhibition of electron transport in bioenergetics assays. Two of these four compounds, lacidipine and esbiothrin, increased in intracellular oxygen radicals (a major hallmark of most OXPHOS inhibitors) and decreased the viability of two ovarian cancer cell lines, ID8 and OVCAR5. Finally, data from the pipeline were used to train random forest and support vector classifiers that effectively prioritized OXPHOS inhibitory compounds within a held-out test set (ROCAUC 0.962 and 0.927, respectively) and on another set containing 44 documented OXPHOS inhibitors outside of the training set (ROCAUC 0.900 and 0.823). This prototype pipeline is extensible and could be adapted for focus screening on other phenotypic targets for which sufficient public data are available. Scientific contribution Here, we describe and apply an assay data mining pipeline to compile, process, filter, and mine public bioassay data. We believe the procedure may be more broadly applied to guide compound selection in early-stage hit finding on novel multi-protein mechanistic or phenotypic targets. To demonstrate the utility of our approach, we apply a data mining strategy on a large set of public assay data to find drug-like molecules that inhibit oxidative phosphorylation (OXPHOS) as candidates for ovarian cancer therapies. [ABSTRACT FROM AUTHOR]

Despite decades of antibody research, it remains challenging to predict the specificity of an antibody solely based on its sequence. Two major obstacles are the lack of appropriate models and the inaccessibility of datasets for model training. In this study, we curated >5,000 influenza hemagglutinin (HA) antibodies by mining research publications and patents, which revealed many distinct sequence features between antibodies to HA head and stem domains. We then leveraged this dataset to develop a lightweight memory B cell language model (mBLM) for sequence-based antibody specificity prediction. Model explainability analysis showed that mBLM could identify key sequence features of HA stem antibodies. Additionally, by applying mBLM to HA antibodies with unknown epitopes, we discovered and experimentally validated many HA stem antibodies. Overall, this study not only advances our molecular understanding of the antibody response to the influenza virus but also provides a valuable resource for applying deep learning to antibody research. • Assembled a dataset of 5,561 published antibodies to influenza HA from 132 donors • Antibodies to HA head and stem domains have distinct convergent sequence features • Developed a lightweight language model (mBLM) for antibody specificity prediction • Discovered HA stem antibodies and key somatic hypermutations using mBLM Predicting antibody specificity based solely on sequence has remained an obstacle in humoral research. Leveraging a curated dataset of >5,000 influenza hemagglutinin (HA) antibodies, Wang et al. develop a lightweight memory B cell language model for antibody specificity prediction. This model identifies unique HA stem antibodies and key antibody sequence features. [ABSTRACT FROM AUTHOR]

Peptides are promising drug development frameworks that have been hindered by intrinsic undesired properties including hemolytic activity. We aim to get a better insight into the chemical space of hemolytic peptides using a novel approach based on network science and data mining. Metadata networks (METNs) were useful to characterize and find general patterns associated with hemolytic peptides, whereas Half-Space Proximal Networks (HSPNs), represented the hemolytic peptide space. The best candidate HSPNs were used to extract various subsets of hemolytic peptides (scaffolds) considering network centrality and peptide similarity. These scaffolds have been proved to be useful in developing robust similarity-based model classifiers. Finally, using an alignment-free approach, we reported 47 putative hemolytic motifs, which can be used as toxic signatures when developing novel peptide-based drugs. We provided evidence that the number of hemolytic motifs in a sequence might be related to the likelihood of being hemolytic. [ABSTRACT FROM AUTHOR]

Risk management (RM) is viewed by organizations from a holistic perspective instead of a silo-based point of view and a change in basic assumptions has occurred in recent years. The comprehensive approach to RM that is developed based on a holistic perspective and is adopted by companies is called enterprise risk management (ERM). From the general point of view, it is expected that companies will have sustainable operations, improved performance and value, and controlled risks by using the ERM concept. In this article, the relations of ERM adaptation with firm performance, firm value, and risks were researched through ordinal variables such as the implementation level of ERM and the sophistication level of the organizational structure of ERM in regression models based on panel data (PD). Empirical evidence depending on a sample of ten banking companies listed on the Borsa Istanbul (BIST) Banks index (XBANK) for the period from 2019 to 2022 confirms the above basic argument for firm performance, firm value, and the insolvency risk of banks in general terms. In addition, the prediction accuracy of PD models is calculated for the performance, value, and risk indicators of banks, and the partial least squares regression model is proposed as the alternative prediction model of data mining. [ABSTRACT FROM AUTHOR]

Objective To explore the risk signals of adverse events (ADE) in the use of exenatide microspheres by the FDA adverse event reporting system (FAERS), and provide reference for clinical rational drug use and drug safety. Methods With exenatide microspheres as the target drug, the search keywords were Exenatide Microspheres for Injection, LY05006, AC 2993 LAR and Bydureon. SAS software was used to extract the ADE report data from January 2, 2012 to March 31, 2023 in the FAERS database and the duplicates were removed. Data mining of exenatide microspheres-related ADE reports was performed by the reporting odds ratio method and the comprehensive standard method. Results A total of 27 248 exenatide microspheres-related ADE reports were retrieved, involving 27 SOCs, of which 4 719 were severe ADE reports. The reporting personnel were mainly consumers (18 435 cases，67.66%), the United States was the mainly reporting country (26 295 cases，96.50%) . A total of 163 ADE risk signals were obtained by reporting odds ratio method and comprehensive standard method, including new adverse reactions such as abnormal blood cholesterol, elevated lipase and mixed hyperlipidemia. Conclusion Based on the FAERS database, the post-marketing ADE of exenatide microspheres was mined and analyzed, which could provide reference for clinical medication safety and improvement of patients' medication compliance. [ABSTRACT FROM AUTHOR]

Artificial, automated, and algorithmic journalism is a current trend that brings about skills and challenges that newsrooms must face. The automation of journalistic tasks such as the writing of texts and the generation of audiovisuals, podcasts, and other resources to tell stories is mediated by artificial intelligence. Therefore, thinking about journalistic work prospectively offers an alternative to focus on the dynamics that will be a trend in the future. This research aims to conduct a systematic review in the Scopus and Web of Science (N = 627) databases on the scientific production of Spain and Latin America, where artificial intelligence, social communication, and journalism converge. Data mining and web scrapping were used, and the PRISMA declaration methodology was implemented to define inclusion and exclusion criteria for academic production (n = 43), integrating bibliometric analysis techniques in collaboration networks and strengths of links between authors, journals, and countries. Finally, we provide information on search trends, the authors who most developed the topic in question and other metrics of interest. In both databases, Spain has the best performance in scientific collaboration networks. [ABSTRACT FROM AUTHOR]

Four types of archaeogenetic data mining are used to investigate the origin of the Minoans and the Uralic peoples: (1) six SNP mutations related to eye, hair, and skin phenotypes; (2) whole-genome admixture analysis using the G25 system; (3) an analysis of the history of the U5 mitochondrial DNA haplogroup; and (4) an analysis of the origin of each currently known Minoan mitochondrial and y-DNA haplotypes. The uniform result of these analyses is that the Minoans and the Uralic peoples had a common homeland in the lower and middle Danube Basin, as well as the Black Sea coastal regions. This new result helps to reconcile archaeogenetics with linguistics, which have shown that the Minoan language belongs to the Uralic language family. [ABSTRACT FROM AUTHOR]

In this paper, we introduce an efficient and effective algorithm for Graph-based Semi-Supervised Learning (GSSL). Unlike other GSSL methods, our proposed algorithm achieves efficiency by constructing a bipartite graph, which connects a small number of representative points to a large volume of raw data by capturing their underlying manifold structures. This bipartite graph, with a sparse and anti-diagonal affinity matrix which is symmetrical, serves as a low-rank approximation of the original graph. Consequently, our algorithm accelerates both the graph construction and label propagation steps. In particular, on the one hand, our algorithm computes the label propagation in closed-form, reducing its computational complexity from cubic to approximately linear with respect to the number of data points; on the other hand, our algorithm calculates the soft label matrix for unlabeled data using a closed-form solution, thereby gaining additional acceleration. Comprehensive experiments performed on six real-world datasets demonstrate the efficiency and effectiveness of our algorithm in comparison to five state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

The maize leafhopper (Dalbulus maidis) is a significant threat to maize crops in tropical and subtropical regions, causing extensive economic losses. While its ecological interactions and control strategies are well studied, its associated viral diversity remains largely unexplored. Here, we employ high-throughput sequencing data mining to comprehensively characterize the D. maidis RNA virome, revealing novel and diverse RNA viruses. We characterized six new viral members belonging to distinct families, with evolutionary cues of beny-like viruses (Benyviridae), bunya-like viruses (Bunyaviridae) iflaviruses (Iflaviridae), orthomyxo-like viruses (Orthomyxoviridae), and rhabdoviruses (Rhabdoviridae). Phylogenetic analysis of the iflaviruses places them within the genus Iflavirus in affinity with other leafhopper-associated iflaviruses. The five-segmented and highly divergent orthomyxo-like virus showed a relationship with other insect associated orthomyxo-like viruses. The rhabdo virus is related to a leafhopper-associated rhabdo-like virus. Furthermore, the beny-like virus belonged to a cluster of insect-associated beny-like viruses, while the bi-segmented bunya-like virus was related with other bi-segmented insect-associated bunya-like viruses. These results highlight the existence of a complex virome linked to D. maidis and paves the way for future studies investigating the ecological roles, evolutionary dynamics, and potential biocontrol applications of these viruses on the D. maidis—maize pathosystem. [ABSTRACT FROM AUTHOR]

This article combines linguistic analysis and data mining methods to explore variations in speakers' evaluative meaning-making in conflict talks. It focuses on conflict style construction through evaluative language, specifically how disputants advance attitudes. The corpus consists of 230 minutes of family mediation talks involving 12 divorcing spouses. The research draws from the Appraisal framework to analyse evaluative meaning-making at a discourse semantics level, capturing both explicit and implicit attitudes, as well as the scaling and dialogic framing of attitudes. Data exploration uses clustering algorithms via RStudio to identify variations in disputants' discursive behaviour. The findings uncover three conflict styles based on disputants' preference for attitude advancement formulations, with varying degrees of assertiveness and forcefulness. This study's contributions include a holistic treatment of evaluative meaning-making, the marriage of digital tools to nuanced linguistic annotation, and a novel interpretation for conflict style. The findings offer fresh insights into disputants' discursive self-presentation in confrontational exchanges. [ABSTRACT FROM AUTHOR]

Studies on hybrid data mining approach has been increasing in recent years. Hybrid data mining is defined as an effective combination of various data mining techniques to use the power of each technique and compensate for each other's weaknesses. The purpose of this study is to present state-of-the-art data mining algorithms and applications and to propose a new hybrid data mining approach for classifying medical data. In addition, in the study, it was aimed to calculate performance metrics of data mining methods and to compare these metrics with the metrics obtained from the hybrid model. The study utilized simulated datasets produced on the basis of various scenarios and hepatitis dataset obtained from the UCI database. Supervised learning algorithms were used. In addition, hybrid models were created by combining these algorithms. In simulated datasets, it was observed that MCC values increased with a higher sample size and higher correlation between the independent variables. In addition, as the correlation between independent variables increased in imbalanced datasets, a noticeable increase was observed in the performance metrics of the group with lower sample size. A similar case was observed with the actual datasets. [ABSTRACT FROM AUTHOR]

In this study, an early warning system predicting first‐year undergraduate student academic performance is developed for higher education institutions. The significant factors that affect first‐year student success are derived and discussed such that they can be used for policy developments by related bodies. The dataset used in experimental analyses includes 11,698 freshman students' data. The problem is constructed as classification models predicting whether a student will be successful or unsuccessful at the end of the first year. A total of 69 input variables are utilized in the models. Naive Bayes, decision tree and random forest algorithms are compared over model prediction performances. Random forest models outperformed others and reached 90.2% accuracy. Findings show that the models including the fall semester CGPA variable performed dramatically better. Moreover, the student's programme name and university placement exam score are identified as the other most significant variables. A critical discussion based on the findings is provided. The developed model may be used as an early warning system, such that necessary actions can be taken after the second week of the spring semester for students predicted to be unsuccessful to increase their success and prevent attrition. [ABSTRACT FROM AUTHOR]

Word frequency has a long history of being considered the most important predictor of word difficulty and has served as a guideline for several aspects of second language vocabulary teaching, learning, and assessment. However, recent empirical research has challenged the supremacy of frequency as a predictor of word difficulty. Accordingly, applied linguists have questioned the use of frequency as the principal criterion in the development of wordlists and vocabulary tests. Despite being informative, previous studies on the topic have been limited in the way the researchers measured word difficulty and the statistical techniques they employed for exploratory data analysis. In the current study, meaning recall was used as a measure of word difficulty, and random forest was employed to examine the importance of various lexical sophistication metrics in predicting word difficulty. The results showed that frequency was not the most important predictor of word difficulty. Due to the limited scope, research findings are only generalizable to Vietnamese learners of English. [ABSTRACT FROM AUTHOR]

Battery lifetime prediction at early cycles is crucial for researchers and manufacturers to examine product quality and promote technology development. Machine learning has been widely utilized to construct data-driven solutions for high-accuracy predictions. However, the internal mechanisms of batteries are sensitive to many factors, such as charging/discharging protocols, manufacturing/storage conditions, and usage patterns. These factors will induce state transitions, thereby decreasing the prediction accuracy of data-driven approaches. Transfer learning is a promising technique that overcomes this difficulty and achieves accurate predictions by jointly utilizing information from various sources. Hence, we develop two transfer learning methods, Bayesian Model Fusion and Weighted Orthogonal Matching Pursuit, to strategically combine prior knowledge with limited information from the target dataset to achieve superior prediction performance. From our results, our transfer learning methods reduce root-mean-squared error by 41% through adapting to the target domain. Furthermore, the transfer learning strategies identify the variations of impactful features across different sets of batteries and therefore disentangle the battery degradation mechanisms and the root cause of state transitions from the perspective of data mining. These findings suggest that the transfer learning strategies proposed in our work are capable of acquiring knowledge across multiple data sources for solving specialized issues. [ABSTRACT FROM AUTHOR]

The aerospace industry and technology are always considered one of the country’s most important and valuable industries. The research area of "Aerospace" is among the priorities of the grand science and technology development strategies, and addressing it is strategically vital. The present research aims to estimate and predict the appropriate algorithm for identifying high-quality aerospace researchers based on Advanced Ensemble Classifier Techniques (AECT) in data mining on the outputs of scientometric analyses and predicting the most essential scientometric-related metrics to identify high-quality researchers. The present study was performed using the protocols of applied research and multiple methods. The studied population includes all aerospace researchers (1945 and 2021) indexed in "The Web of Science Core Collection (WOSCC)". DataLab software and multiple programming languages have been applied in this research. All three algorithms have an accuracy of 0.96 and an F1-score of 0.97, which indicates that the models have high accuracy, validity, sensitivity, and predictive power. The "Blending" algorithm is considered a suitable and predictive model. The output of the LightGBM algorithm is that the most important and robust metric in the evaluation of prominent researchers is a metric from the researchers' effectiveness dimension, the Q parameter. According to the knowledge obtained from the ability to predict AECT in the prediction of highquality researchers, it is possible to use the metrics mentioned in the evaluation of researchers in the field of scientometrics for more accurate and comprehensive prediction. An algorithm that can lead to the optimal and efficient classification of researchers provides the possibility of in-depth analysis of the available data about researchers and smooths the predictive power of the most high-quality researcher. The use of the proposed algorithms in this research, while suggesting the appropriate algorithm, led to reliable and valuable knowledge in classifying high-quality aerospace researchers. [ABSTRACT FROM AUTHOR]

Evaluation of clustering validity to set up an optimal cluster-data space (CDS) is a vital task in many fields related to data mining. Almost existing clustering validity indexes (CVIs) lack stability due to being too sensitive to noise, especially impulse noise. Here, we (1) propose a new CVI named DzI (Dzung Index) or fRisk2 using analysis of fuzzy-set-based accumulated risk degree (FARD), and (2) present a new algorithm named fRisk2-bA for determining the optimal number of data clusters. It is a method of evaluation of the centroid-based fuzzy clustering validity. In essence, the fRisk2 still focuses on enhancing the data compression in each cluster and expanding the separation between cluster centroids. However, these features are exploited indirectly through FARD. As a result, the proposed method not only can avoid the difficulties of the traditional ones relying on the compression and separation properties directly but also can distill better local and global attributes in the data distribution to estimate the CDS more fully. Along with the proved theory basis, surveys, including the ones based on noisy datasets from measurements, showed the compared advantages of fRisk2 as follows. (1) The accuracy, stability, and convergence of the fRisk2 are outstanding. (2) Its total calculating cost is lower than the other surveyed CVIs. [ABSTRACT FROM AUTHOR]

The fuzzy concept lattice is one of the effective tools for data mining, and granular reduction is one of its significant research contents. However, little research has been done on granular reduction at different granularities in formal fuzzy contexts (FFCs). Furthermore, the complexity of the composition of the fuzzy concept lattice limits the interest in its research. Therefore, how to simplify the concept lattice structure and how to construct granular reduction methods with granularity have become urgent issues that need to be investigated. To this end, firstly, the concept of an object granule with granularity is defined. Secondly, two reduction algorithms, one based on Boolean reasoning and the other on a graph-theoretic heuristic, are formulated while keeping the structure of this object granule unchanged. Further, to simplify the structure of the fuzzy concept lattice, a partial order relation with parameters is proposed. Finally, the feasibility and effectiveness of our proposed reduction approaches are verified by data experiments. [ABSTRACT FROM AUTHOR]

Objective To summarize the existing animal models of cholelithiasis, and to explore a pathological model that can better reflect the characteristics of clinical syndromes of traditional Chinese and western medicine and meet the needs of the development of clinical and basic research of traditional Chinese medicine. Methods The animal models for experimental research on cholelithiasis at home and abroad were collected and sorted out by searching CNKI, Wanfang, VIP, SinoMed, PubMed and other databases. The animal types, modeling methods, modeling cycles, detection indicators and positive drugs of the models were statistically analyzed. Results Among the 128 articles included, the animal types of cholelithiasis models were mainly guinea pigs, rabbits and C57BL/6J mice. The most used modeling method is high-fat diet, and the feeding cycle takes eight weeks to complete. High-frequency detection indicators were stone formation rate, total cholesterol, phospholipids, total bilirubin, total bile acid, etc. The commonly used intervention methods are traditional Chinese medicine compound, western medicine and single Chinese medicine or extract. Ursodeoxycholic acid (UDCA) is mainly used for western medicine intervention, whereas the intervention of traditional Chinese medicine mainly includes traditional Chinese medicine compound, acupuncture, exercise, diet and other methods. The positive control drug in the experiment was mostly UDCA. Conclusion With the continuous improvement and development of cholelithiasis animal model, there are many kinds of modeling methods to simulate the clinical manifestations of cholelithiasis in Chinese and western medicine, but there are also some limitations. This paper aims to provide reference for the selection, application and improvement of cholelithiasis model through data mining and characteristic analysis. [ABSTRACT FROM AUTHOR]

Objective To explore the prescription rules of traditional Chinese medicine (TCM) in the treatment of diabetes periodontitis (DP) and the acting mechanisms of core drug combination. Methods Based on the relevant literature retrieved from the CNKI, Wanfang, VIP and Sinomed, a DP prescription database was established. Excel 2021, SPSS Modeler 18.0 and SPSS Statistics 26.0 were used to conduct the statistics of the frequency, efficacy classifications, properties, flavors, and meridian tropism of the included drugs. Association rule analysis and cluster analysis were performed to screen out the core drug combinations. The active components and action targets of core drug combinations were obtained through TCMSP and HERB. The DP related disease targets were predicted using GeneCards. The Venny platform was used to obtain the intersection of disease targets and drug targets. Key components were screened by Cytoscape to establish an “active component-target” network. Based on STRING platform data, PPI network was constructed by Cytoscape to screen core targets. GO functional annotation and KEGG signaling pathway enrichment analysis were carried out for the intersection targets by DAVID. AutoDockVina was applied for molecular docking between core targets and key components. Results A total of 36 articles were included, and 50 prescriptions involving 100 Chinese herbal medicines were extracted. Alismatis Rhizoma, Rehmanniae Radix Praeparata and Astragali Radix were the most common drugs. The most used drug category was deficiency-nourishing drugs. The properties of the herbs were mainly cold and warm, the major flavors were sweet and bitter, and the main meridian tropisms were kidney and liver. Six categories were classified by clustering analysis. Moutan Cortes- Corni Fructus-Rehmanniae Radix Praeparata was screened out as the core drug combination involving 18 active components, 164 drug action targets and 104 intersection of DP targets and drug combination targets. Quercetin, stigmasterol, kaempferol, β-sitosterol, tetrahydroalstonine, and sitosterol were the key components, and AKT1, IL-6, TNF, IL-1B, PTGS2, JUN, TP53, ESR1, and MMP9 were the core targets. GO analysis revealed 3 724 biological processes, 228 cellular components and 404 molecular functions. KEGG analysis showed that DP was treated by the core drug combination through regulating 235 signaling pathways. Molecular docking results showed that there was a good affinity between the core target and the key component. Conclusion Tonifying deficiency is the main treatment methods of TCM for DP, accompanied by clearing heat and removing dampness, activating blood circulation and removing blood stasis, replenishing qi and nourishing yin. Core drug combination (Moutan Cortes- Corni Fructus-Rehmanniae Radix Praeparata) treats DP through multi-component, multi-target and multi-pathway, which provide a reference for clinical diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Objective: To consider the approaches within Web of Science Core Collection (WoSCC) databases for limiting geographically. To compare the limits to an adaptation of NICE's UK MEDLINE filter for use on WoSCC databases. Methods: We tested and appraised the inbuilt functions and search field options that support identification by countries/regions and affiliations. We compared these with an adapted filter to identify healthcare research on or about the UK. We calculated the recall of the inbuilt limits and filter using 177 studies and investigated why records were missed. We also calculated the percentage reduction of the overall number-needed-to-screen (ONNS). Results: Inbuilt limits within WoSCC enable identification of research from specific countries/regions or affiliations if there is data in the address field. Refining by affiliations allows retrieval of research where affiliations are in the 200 or 500 most frequent for a set of results. An adaptation of the UK MEDLINE filter achieved an average of 97% recall. ONNS was significantly reduced using the filter. However, studies where the countries or regions are only mentioned within the full text or other non-searchable fields will be missed. Conclusion: Information specialists should consider how inbuilt geographic limits operate on WoSCC and whether these are suitable for their research. The adapted filter can sensitively limit to the UK and could be useful for systematic reviews due to its high recall and ability to significantly reduce ONNS. Geographic filters can be feasible to adapt for use on WoSCC databases (where similar search fields are used between platforms). [ABSTRACT FROM AUTHOR]

Deep‐time Digital Earth (DDE) is an innovative international big science program, focusing on scientific propositions of earth evolution, changing Earth Science by coordinating global geoscience data, and sharing global geoscience knowledge. To facilitate the DDE program with recent advances in computer science, the geoscience knowledge graph plays a key role in organizing the data and knowledge of multiple geoscience subjects into Knowledge Graphs (KGs), which enables the calculation and inference over geoscience KGs for data mining and knowledge discovery. However, the construction of geoscience KGs is challenging. Though there have been some construction tools, they commonly lack collaborative editing and peer review for building high‐quality large‐scale geoscience professional KGs. To this end, a data service system or tool, DDE KG Editor, is developed to construct geoscience KGs. Specifically, it comes with several distinctive features such as collaborative editing, peer review, contribution records, intelligent assistance, and discussion forums. Currently, global geoscientists have contributed over 60,000 ontologies for 22 subjects. The stability, scalability, and intelligence of the system are regularly improving as a public online platform to better serve the DDE program. [ABSTRACT FROM AUTHOR]

Climate change no longer involves and affects just a few people or communities. However, most of them need climate change detection studies to adapt to the current and future climate conditions efficiently. The present research aimed to detect climate changes by considering the shift in climate conditions from one region to another over different periods based on a similarity index in the Carpathians basin using the new ClimShift toolbox, specially created for this purpose. Developed in R, based on the cosine similarity index and using a set of 32 climate indices (temperature and precipitation), ClimShift uses NC raster format (NetCDF files) as input data. The application is compatible with Microsoft and Unix/Linux environments. The toolbox allows the detection of forward and backward climate shifts. The results can be employed as a Climate Service and are extremely helpful for an efficient process of adaption to climate changes at a local/regional scale. A user‐friendly interface and a tutorial on how to use the toolbox are also available. The toolbox was tested for four locations in the Carpathians Basin (Vienna, Bekes, Cluj‐Napoca and Kosice) using 1961–1990 as a base period and 1991–2021 as an analysis period for the forward climate shift analysis. For Cluj‐Napoca, the application was also tested for the backward climate shift, using 1991–2021 as the base period and 1961–1990 as the analysis period, identifying the region where present climate conditions were specific during the older period. The scientific results indicated a significant shift towards the east and northeast from the older period to the most recent one and a low percentage (6%–10%) in the overlapping area with highly similar conditions between the two periods. [ABSTRACT FROM AUTHOR]

The fast charging of electric vehicles (EVs) has stood out prominently as an alternative for long-distance travel. These charging events typically occur at public fast charging stations (FCSs) within brief timeframes, which requires a substantial demand for power and energy in a short period. To adequately prepare the system for the widespread adoption of EVs, it is imperative to comprehend and establish standards for user behavior. This study employs agglomerative clustering, kernel density estimation, beta distribution, and data mining techniques to model and identify patterns in these charging events. They utilize telemetry data from charging events on highways, which are public and cost-free. Critical parameters such as stage of charge (SoC), energy, power, time, and location are examined to understand user dynamics during charging events. The findings of this research provide a clear insight into user behavior by separating charging events into five groups, which significantly clarifies user behavior and allows for mathematical modeling. Also, the results show that the FCSs have varying patterns according to the location. They serve as a basis for future research, including topics for further investigations, such as integrating charging events with renewable energy sources, establishing load management policies, and generating accurate load forecasting models. [ABSTRACT FROM AUTHOR]

Farmland shelterbelts are aimed at farmland protection and productivity improvement, environmental protection and ecological balance, as well as land use planning and management. Farmland shelterbelts play a vital role in determining the structural integrity and overall effectiveness of farmland, and assessing the dynamic changes within these protective forests accurately and swiftly is essential to maintaining their protective functions as well as for policy formulation and effectiveness evaluation in relevant departments. Traditional methods for extracting farmland shelterbelt information have faced significant challenges due to the large workload required and the inconsistencies in the accuracy of existing methods. For example, the existing vegetation index extraction methods often have significant errors, which remain unresolved. Therefore, developing a more efficient extraction method with greater accuracy is imperative. This study focused on Youyi Farm in Heilongjiang Province, China, utilizing satellite data with spatial resolutions ranging from 0.8 m (GF-7) to 30 m (Landsat). By taking into account the growth cycles of farmland shelterbelts and variations in crop types, the optimal temporal window for extraction is identified based on phenological analysis. The study introduced a new index—the Re-Modified Anthocyanin Reflectance Index (RMARI)—which is an improvement on existing vegetation indexes, such as the NDVI and the improved original ARI. Both the accuracy and extraction results showed significant improvements, and the feasibility of the RMARI was confirmed. The study proposed four extraction schemes for farmland shelterbelts: (1) spectral feature extraction, (2) extraction using vegetation indexes, (3) random forest extraction, and (4) RF combined with characteristic index bands. The extraction process was implemented on the GEE platform, and results from different spatial resolutions were compared. Results showed that (1) the bare soil period in May is the optimal time period for extracting farmland shelterbelts; (2) the RF method combined with characteristic index bands produces the best extraction results, effectively distinguishing shelterbelts from other land features; (3) the RMARI reduces background noise more effectively than the NDVI and ARI, resulting in more comprehensive extraction outcomes; and (4) among the satellite images analyzed—GF-7, Planet, Sentinel-2, and Landsat OLI 8—GF-7 achieves the highest extraction accuracy (with a Kappa coefficient of 0.95 and an OA of 0.97), providing the most detailed textural information. However, comprehensive analysis suggests that Sentinel-2 is more suitable for large-scale farmland shelterbelt information extraction. This study provides new approaches and technical support for periodic dynamic forestry surveys, providing valuable reference points for agricultural ecological research. [ABSTRACT FROM AUTHOR]

Machine learning, as an increasingly prominent method in recent years, has introduced new methodologies and perspectives for extracting geological alteration information. To enhance the accuracy of remote-sensing-alteration mineral information, this study focuses on the extraction of alteration information from hyperspectral remote sensing data using the Kernel-Based Extreme Learning Machine (KELM) optimized with the Sparrow Search Algorithm (SSA). The ideal parameters of the Kernel Extreme Learning Machine model were successfully acquired by utilizing the sparrow optimization method for continuous search and iteration, avoiding the blindness and arbitrariness associated with parameter selection by humans. Spectral Angle Mapper (SAM) technology was used to extract sample data from hyperspectral imagery, which were then used to train the machine learning model for alteration information extraction. The experimental results show that, when compared to the Random Forest and the Support Vector Machine algorithms, the Kernel-Based Extreme Learning Machine algorithm achieved the highest accuracy and the best effect in the extraction results. It closely matches the known mineral points and geochemical anomalies in the area, confirming that the method has a clear advantage in the extraction of hyperspectral alteration information. [ABSTRACT FROM AUTHOR]

Knowledge maps are teaching tools that can promote deeply learning and avoid knowledge loss by helping students plan learning paths. Mining potential association rules of concepts from student exercise data was a common method to construct knowledge maps automatically. While manual conditions should be set to filter the association rules future to improve the accuracy of knowledge maps, which made the construction of the knowledge map can not automatic totally. So, the study proposed a knowledge map construction method that combined knowledge tracking and association rule mining expanding with interaction frequencies based on exercise data to achieve rules cleaning automatically. The method first predicted students' knowledge mastery sequences by a deep knowledge tracking model and discovered clustering relations to represent potential structures between concepts by fuzzy cluster analysis. Meanwhile, the method investigated association rule mining expanding with interaction frequencies to discover association rules between concepts. Finally, the clustering relations were used to filter the mined association rules automatically. To verify the effectiveness of the method, we constructed a knowledge map based on 34,350 online exercise data of 117 students in a computer programming course. Experimental results proved that the map was valid. Our implementations are available at . [ABSTRACT FROM AUTHOR]

The paper delves into social media mining in the context of medical education programs in the information age. It explores the adaptability of Social Media Analytics (SMA) apps within the structure of online courses in medicine and proposes a conceptual framework for a learning process. This process includes practical exercises based on search and social media mining in the healthcare industry, relying on technology solutions. An online course, "Managing Digital Content for Health Professionals", was developed at I.M. Sechenov First Moscow State Medical University to expand the understanding of digital content management processes, the specific details of in-depth social media analysis, and transforming social data into valuable knowledge for health professionals. The study group consisted of 108 participants. Throughout the course, students were tasked to ascertaining the effects gained during practical training and evaluating them. Participants identified the key professional and socio-personal effects of the practical training. Following the evaluation of the online apps' feature sets, the authors concluded that social media analytics requires a comprehensive approach, the synergy of digital tools, a strategy for adapting the mining to the field of expertise, and the paradigm of data synthesis and use. [ABSTRACT FROM AUTHOR]

Aiming at the problem of traditional high utility itemset mining algorithms reporting false positive high utility itemsets in transactions with class labels, this paper proposed two high utility itemset mining algorithms called FHUI and PHUI. The FHUI and PHUI firstly found all the candidates and grouped them by length. Then, the FHUI established null distributions with the frequency distributions, while the PHUI established null distributions by the permutation strategy within or between transactions. Finally, the FHUI and PHUI calculated the p values from the null distributions and exploited the false discovery rate to eliminate the false positive high utility itemsets. The experiments on the benchmark data sets show that the FHUI and PHUI can eliminate a large number of false positive itemsets, which allows them to achieve higher accuracy rates in the classification tasks. The experiments on synthetic data sets reveal that the proportions of false positive itemsets reported by FHUI and PHUI are lower than 4.8% and the average utility values are higher than 39 000. Experimental results prove that the statistically significant high utility itemsets reported by the FHUI and PHUI are more reliable and practical in transactions with class labels. [ABSTRACT FROM AUTHOR]

River floods disrupt communication and transportation networks, damage buildings and infrastructure, destroy agricultural products and livestock, cause capital losses and endanger human life. Accurate and proper flood prediction and forecasting are major challenges in hydrology and water resources management. The aim of this study was to forecast and estimate the daily flows of three rivers in Iran using four tree-based data-mining methods, two ensemble bagging methods and the stochastic time series model Arima (auto-regressive integrated moving average). A comparison of these different methodologies is the main contribution of this work. Five statistical measures were used to evaluate the accuracy of these models based on 4 years of daily discharge flow data. The hold-out method was used to divide the data into training (70%) and testing (30%) sets. It was found that the ensemble tree-based chi-square automatic interaction detector provided the most precise forecasts. The overall results indicate that the data-mining methods of ensemble models and tree-based models improved the average accuracy of the models by 25.0% and 15.5% compared with the stochastic Arima model, respectively, indicating the superiority of their potential in capturing the non-linear behaviour of flow discharges. [ABSTRACT FROM AUTHOR]

Randomized experiments are a powerful methodology for data-driven evaluation of decisions or interventions. Yet, their validity may be undermined by network interference. This occurs when the treatment of one unit impacts not only its outcome but also that of connected units, biasing traditional treatment effect estimations. Our study introduces a framework to accommodate complex and unknown network interference, moving beyond specialized models in the existing literature. Our framework, termed causal message-passing, is grounded in high-dimensional approximate message-passing methodology. It is tailored for multiperiod experiments and is particularly effective in settings with many units and prevalent network interference. The framework models causal effects as a dynamic process where a treated unit's impact propagates through the network via neighboring units until equilibrium is reached. This approach allows us to approximate the dynamics of potential outcomes over time, enabling the extraction of valuable information before treatment effects reach equilibrium. Utilizing causal message-passing, we introduce a practical algorithm to estimate the total treatment effect, defined as the impact observed when all units are treated compared to the scenario where no unit receives treatment. We demonstrate the effectiveness of this approach across five numerical scenarios, each characterized by a distinct interference structure. [ABSTRACT FROM AUTHOR]

Billions of genomic sequences and records of species occurrence are available in public repositories (e.g. National Center for Biotechnology Information, NCBI and the Global Biodiversity Information Facility, GBIF). By implementing analytical tools from different scientific disciplines, data mining these databases can aid in the global surveillance of zoonotic pathogens that circulate among wildlife. We illustrate this by investigating the Hantavirus–rodent system in the Americas, i.e. New World Hantaviruses (NWH). First, we considered the circulation of pathogenic NWH among Cricetidae rodents, by inferring the phylogenetic links among 277 genomic samples of the S segment (N protein) of NWH found in 55 species. Second, we used machine learning to assess the impact of land use on the probability of presence of the rodent species linked with reservoirs of pathogenic Hantaviruses. Our results show that hosts are widely present across the Americas. Some hosts are present in the primary forest and agricultural land, but not in the secondary forest, whereas other hosts are present in secondary forest and agricultural land. The diversity of host species allows Hantavirus to circulate in a wide spectrum of habitats, in particular rural rather than urban. We highlight that public repositories of genomic data and species occurrence are very useful resources for monitoring potential enzootic transmission and spillover of zoonotic viruses in relation with the changes that humans produce in the biosphere. [ABSTRACT FROM AUTHOR]

Climate change is expected to cause important changes in precipitation patterns in Iran until the end of 21st century. This study aims at evaluating projections of climate change over Iran by using five climate model outputs (including ACCESS‐ESM1‐5, BCC‐CSM2‐MR, CanESM5, CMCC‐ESM2 and MRI‐ESM2‐0) of the Coupled Model Intercomparison Project phase 6 (CMIP6), and performing bias‐correction using a novel combination of quantile mapping (QM) and random forest (RF) between the years 2015 and 2100 under three shared socioeconomics pathways (SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5). First, bias‐correction was performed on ERA5‐Land reanalysis data as reference period (1990–2020) using the QM method, then the corrected ERA5‐Land reanalysis data was considered as measured data. Based on the corrected ERA5‐Land reanalysis data (1990–2020) and historical simulations (1990–2014), the future projections (2015–2100) were also bias‐corrected utilizing the QM method. Next, the accuracy of the QM method was validated by comparing the corrected ERA5‐Land reanalysis data with model outputs for overlapping years between 2015 and 2020. This comparison revealed persistent biases; hence, a combination of QM‐RF method was applied to rectify future climate projections until the end of the 21st century. Based on the QM result, CMCC‐ESM2 revealed the highest RMSE in both SSP2‐4.5 and SSP3‐7.0 amounting to 331.74 and 201.84 mm·year−1, respectively. Particularly, the exclusive use of the QM method displayed substantial errors in projecting annual precipitation based on SSP5‐8.5, notably in the case of ACCESS‐ESM1‐5 (RMSE = 431.39 mm·year−1), while the RMSE reduced after using QM‐RF method (197.75 mm·year−1). Obviously, a significant enhancement in results was observed upon implementing the QM‐RF combination method in CMCC‐ESM2 under both SSP2‐4.5 (RMSE = 139.30 mm·year−1) and SSP3‐7.0 (RMSE = 151.43 mm·year−1) showcasing approximately reduction in RMSE values by 192.43 and 50.41 mm·year−1, respectively. Although each bias‐corrected model output was evaluated individually, multi‐model ensemble (MME) was also created to project the annual future precipitation pattern in Iran. By considering that combination of QM‐RF method revealed the lower errors in correcting model outputs, we used the QM‐RF technique to create the MME. Based on SSP2‐4.5, the MME climate projections highlight imminent precipitation reductions (>10%) across large regions of Iran, conversely projecting increases ranging from 10% to over 20% in southern areas under SSP3‐7.0. Moreover, MME projected dramatic declines under SSP5‐8.5, especially impacting central, eastern, and northwest Iran. Notably, the most pronounced possibly decline patterns are projected for arid regions (central plateau) and eastern areas under SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5. [ABSTRACT FROM AUTHOR]

Aims: The primary goal of this study is to evaluate the capabilities of Large Language Models (LLMs) in understanding and processing complex medical documentation. We chose to focus on the identification of pathologic complete response (pCR) in narrative pathology reports. This approach aims to contribute to the advancement of comprehensive reporting, health research, and public health surveillance, thereby enhancing patient care and breast cancer management strategies. Methods: The study utilized two analytical pipelines, developed with open-source LLMs within the healthcare system's computing environment. First, we extracted embeddings from pathology reports using 15 different transformer-based models and then employed logistic regression on these embeddings to classify the presence or absence of pCR. Secondly, we fine-tuned the Generative Pre-trained Transformer-2 (GPT-2) model by attaching a simple feed-forward neural network (FFNN) layer to improve the detection performance of pCR from pathology reports. Results: In a cohort of 351 female breast cancer patients who underwent neoadjuvant chemotherapy (NAC) and subsequent surgery between 2010 and 2017 in Calgary, the optimized method displayed a sensitivity of 95.3% (95%CI: 84.0–100.0%), a positive predictive value of 90.9% (95%CI: 76.5–100.0%), and an F1 score of 93.0% (95%CI: 83.7–100.0%). The results, achieved through diverse LLM integration, surpassed traditional machine learning models, underscoring the potential of LLMs in clinical pathology information extraction. Conclusions: The study successfully demonstrates the efficacy of LLMs in interpreting and processing digital pathology data, particularly for determining pCR in breast cancer patients post-NAC. The superior performance of LLM-based pipelines over traditional models highlights their significant potential in extracting and analyzing key clinical data from narrative reports. While promising, these findings highlight the need for future external validation to confirm the reliability and broader applicability of these methods. [ABSTRACT FROM AUTHOR]

Students in the 21st century are expected to possess the ability to solve ill-defined complex problems (ICPs). One challenge to understanding students’ ability to solve ICPs is the lack of methods for measuring noncognitive and metacognitive behaviors and relating those behaviors to cognitive behaviors with the goal of investigating differences in student performance across ability levels. Based on the principles of the synthetic intelligence (PSI) framework, this study utilized a computerized interactive assessment platform to design a multidimensional evaluation framework (including the four dimensions of perception, cognition, metacognition, and motivation) and analyzed log file data collected from 132 elementary students with regard to solving ICPs. The results revealed new problem-solving strategies among students in the high-achievement group, who spent more time constructing problem models. Due to their ability to exercise goal-oriented self-control, students in the high-achievement group were able to fully explore the information they needed to optimize their solutions. The results also revealed three types of behaviors that characterized differences in motivation, the most notable of which characterized students who succeeded after relentless attempts. This study also explains the interaction mechanism underlying mental processes based on the PSI framework. The findings suggested that educators can highlight differences between environmental stimuli and students’ internal assumptions, encourage students to adopt strategies that disambiguate the task goal and object, and strengthen their ability to search for relevant information to improve their performance in solving ICPs. The results also provide a new paradigm for assessing problem-solving capabilities based on the PSI framework. [ABSTRACT FROM AUTHOR]

This article proposes a unilateral Adaptive update strategy based Interval Prediction (AIP) model for short‐term load prediction, which is developed based on lower and upper bound estimation (LUBE) architecture. In traditional LUBE interval prediction model, the model training is usually trained by heuristic algorithms. In this article, the model training is formulated as a bi‐level optimization problem with the help of proposed unilateral adaptive update strategy and cost function. In lower‐level problem, a simplified biased convex cost function is developed to supervise the learning direction of basic prediction engines. The basic prediction engine utilizes Gated Recurrent Unit (GRU) to extract features and Full connected Neural Network (FNN) to generate interval boundary. In upper‐level problem, a unilateral adaptive update strategy with unilateral coverage rate is put forward. It iteratively tunes hyper‐parameters of cost function during training process. Comprehensive experiments based on residential load data are implemented and the proposed interval prediction model outperforms the tested state‐of‐the‐art algorithms, achieving a 15% reduction in prediction error and a 20% decrease in computational time. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to enhance current practices in business-to-business (B2B) customer churn prediction modelling. Following the recent trend from accuracy-based to profit-driven evaluation business-to-customer churn prediction, we present a novel expected maximum profit measure for B2B customer churn (EMPB), which is used to demonstrate how current practices are suboptimal due to large discrepancies in customer value. To directly incorporate the heterogeneity of customer values and profit concerns of the company, we propose an instance-dependent profit maximizing classifier based on gradient boosting, named B2Boost. The main innovation of B2Boost is the fact that it considers these differences and incorporates them into the model construction by maximizing the objective function in terms of the EMPB. The results indicate that the expected maximal profit gains made in our analyses are substantial. This study arguments towards both deploying models based on customer-specific profitability differences, as well as evaluating based on our instance-dependent EMPB measure. [ABSTRACT FROM AUTHOR]

Rationale: Clinical abbreviations pose a challenge for clinical decision support systems due to their ambiguity. Additionally, clinical datasets often suffer from class imbalance, hindering the classification of such data. This imbalance leads to classifiers with low accuracy and high error rates. Traditional feature‐engineered models struggle with this task, and class imbalance is a known factor that reduces the performance of neural network techniques. Aims and Objectives: This study proposes an attention‐based bidirectional long short‐term memory (Bi‐LSTM) model to improve clinical abbreviation disambiguation in clinical documents. We aim to address the challenges of limited training data and class imbalance by employing data generation techniques like reverse substitution and data augmentation with synonym substitution. Method: We utilise a Bi‐LSTM classification model with an attention mechanism to disambiguate each abbreviation. The model's performance is evaluated based on accuracy for each abbreviation. To address the limitations of imbalanced data, we employ data generation techniques to create a more balanced dataset. Results: The evaluation results demonstrate that our data balancing technique significantly improves the model's accuracy by 2.08%. Furthermore, the proposed attention‐based Bi‐LSTM model achieves an accuracy of 96.09% on the UMN dataset, outperforming state‐of‐the‐art results. Conclusion: Deep neural network methods, particularly Bi‐LSTM, offer promising alternatives to traditional feature‐engineered models for clinical abbreviation disambiguation. By employing data generation techniques, we can address the challenges posed by limited‐resource and imbalanced clinical datasets. This approach leads to a significant improvement in model accuracy for clinical abbreviation disambiguation tasks. [ABSTRACT FROM AUTHOR]

Panic disorder (PD) is considered one of the destructive ailments, with various individuals experiencing a critical functional disorder. As the range of remission for PD is achieved only between 20% and 50% with the help of regular pharmacotherapy, modern solutions are expected to deal with this issue. By taking the advantage of Internet of Things (IoT), a novel IoT-inspired behaviour monitoring framework is proposed for the analysis of panic disorder in a particular context. A quantum probability-inspired quantification measure is calculated to determine the scale of health irregularity. In addition, Temporal Data Mining (TDM) is performed for the formulation of temporal data granules to measure Individual Health Index (IHI) by utilising the Multi-scaled Gated Recurrent Unit (M-GRU) technique of deep learning. Moreover, a two-phased alert generation approach is proposed for notifying the current health condition of an individual to the concerned caretaker or medical specialist for assistive or medical services. In the comparative analysis, the proposed framework has outperformed the state-of-the-art approaches by achieving a considerable classification accuracy of 96.89% for event determination and 94.14% accuracy for health severity determination. Similarly, a considerable improvement with respect to Specificity, Sensitivity, and F-measure has been observed for the proposed framework. [ABSTRACT FROM AUTHOR]

Game-based learning researchers have been investigating various means to maximise learning in educational games. One promising venue in recent years has been the use of learning analytics in online game-based learning environments. However, little is known about how different elements of learning analytics (e.g. data types, techniques methods, and stakeholders) contribute to game-based learning practices within online learning environments. There is a need for a comprehensive review to bridge this gap. In this systematic review, we examined the related literature in five major international databases including Web of Science, Scopus, ERIC, IEEE, and compiled Proceedings of the International Conference on Learning Analytics and Knowledge. Twenty relevant publications were identified and analysed. The analysis was conducted using four core elements of learning analytics, namely the types of data that the system collects (what), the methods used for performing analytics (how), the reasons the system captures, analyzes, and reports data (why), and the recipients of the analytics (who). This study synthesises the existing literature, provides a conceptual framework as to how learning analytics can enhance online game-based learning practices in higher education, and sets the agenda for future research. [ABSTRACT FROM AUTHOR]

Scene perception allows humans to extract information from their environment and plan navigation efficiently. The automatic extraction of potential paths in a scene, also referred to as navigational affordance, is supported by scene-selective regions (SSRs) that enable efficient human navigation. Recent evidence suggests that the activity of these SSRs can be influenced by information from adjacent spatial memory areas. However, it remains unexplored how this contextual information could influence the extraction of bottom–up information, such as navigational affordances, from a scene and the underlying neural dynamics. Therefore, we analyzed ERPs in 26 young adults performing scene and spatial memory tasks in artificially generated rooms with varying numbers and locations of available doorways. We found that increasing the number of navigational affordances only impaired performance in the spatial memory task. ERP results showed a similar pattern of activity for both tasks, but with increased P2 amplitude in the spatial memory task compared with the scene memory. Finally, we reported no modulation of the P2 component by the number of affordances in either task. This modulation of early markers of visual processing suggests that the dynamics of SSR activity are influenced by a priori knowledge, with increased amplitude when participants have more contextual information about the perceived scene. Overall, our results suggest that prior spatial knowledge about the scene, such as the location of a goal, modulates early cortical activity associated with SSRs, and that this information may interact with bottom–up processing of scene content, such as navigational affordances. [ABSTRACT FROM AUTHOR]

We study a setting where a receiver must design a questionnaire to recover a sequence of symbols known to a strategic sender, whose utility may not be incentive compatible. We allow the receiver the possibility of selecting the alternatives presented in the questionnaire, and thereby linking decisions across the components of the sequence. We show that, despite the strategic sender and the noise in the channel, the receiver can recover exponentially many sequences, but also that exponentially many sequences are unrecoverable even by the best strategy. We define the growth rate of the number of recovered sequences as the information extraction capacity. A generalization of the Shannon capacity, it characterizes the optimal amount of communication resources required while communicating with a strategic sender. We derive bounds leading to an exact evaluation of the information extraction capacity in many cases. Our results form the building blocks of a novel, non-cooperative regime of communication involving a strategic sender. • An uninformed receiver aims to recover information from an informed strategic sender • No assumption of incentive compatibility or external incentives • Information-theoretic analysis shows a fundamental limit on recovered information • Real-world setting of screening of travellers using questionnaires can be studied • Novel theory combining Shannon's information theory and Myerson's mechanism design [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]

The sustainability of enterprises in an increasingly competitive environment is proportional to their ability to use the resources efficiently. Maintenance departments are critical to ensure that resources are ready and operational. Increasing the efficiency of maintenance departments depends on reducing the number of failures, performing planned maintenance on time and sustaining availability of spare parts needed. Therefore, it is vital that businesses consider predictive maintenance and spare parts jointly. In this study, predictive maintenance and spare parts integration studies are carried out using gearbox failure data and spare parts consumption data of a bus fleet. This study aims to contribute to the reduction of failure costs by finding failure patterns and predicting the subsequent failures and spare parts to be used. The sequential pattern mining approach was used to determine failure patterns and the traditional frequent itemset mining approach was used to predict spare parts. As a result, 45 failure patterns were found. Rules with a reliability of up to 79% were obtained. In addition, spare part clusters with a support value of approximately 40% were created. With this valuable information, businesses are able to investigate root causes, take precautions against future failures, make predictions about the spare parts that will be needed, and develop joint maintenance planning and inventory management policies. [ABSTRACT FROM AUTHOR]

Understanding the evolution of interconnected cultures and individuals, resembling a complex network, cultural routes serve as bridges between the past and the future, offering the key to cultural richness, diversity, and change. Defined as paths gathered around a specific theme, expressing a common value, cultural routes aim to preserve cultural heritage and support sustainable development and local economic growth. The concept of cultural routes, increasingly present in the literature, is addressed by various researchers through publications in different categories. This study aims to conduct a bibliometric analysis by examining publications related to cultural routes to reveal the dimensions of interaction between researchers and the impacts of their work. Utilizing the Web of Science database with data obtained in September 2023 using the keywords "cultural routes" and "cultural route", a bibliometric analysis was conducted using the VosViewer program, examining a total of 236 publications. The study is considered significant in determining the direction of the trend in the literature regarding cultural routes. [ABSTRACT FROM AUTHOR]

The purpose of this study is to examine research on school dropout in open and distance education in the Web of Science (WoS) database using bibliometric analysis and to reveal trends in this area. In line with this goal, a total of 1,615 studies published between 1980 and 2022 were identified in the Web of Science (WoS) indexes. Descriptive and evaluative bibliometric methods were employed in the analysis of these publications, and the results were visualized using VOSviewer software. According to the research findings, studies on school dropout in open and distance education intensified in 2019. The analysis revealed that E.T. Pascarella is the most cited author, and F.D. Pereira has the highest co-authorship network. An examination based on institutions showed that the Universitat Autònoma de Barcelona has the highest number of publications, while the University of Michigan ranks first in terms of citation numbers. Furthermore, in terms of publication productivity and citation numbers, the United States is ranked first, followed by Spain. Looking at the keywords used in articles on the topic, early studies emphasized concepts such as "distance education", "student retention", "attrition", "student success", "social integration", "academic integration", "survival analysis", "education policy", "graduation", and "financial aids". In subsequent years, these were replaced by terms such as "learning analytics", "educational data mining", "systematic review", "engagement", "drop prediction", "decision tree", "student performance", "gamification", "massive open online course-MOOC", and "artificial intelligence". In this context, discussions were conducted within the framework of the literature, and various recommendations were provided based on the obtained findings. [ABSTRACT FROM AUTHOR]