Identification Of Duplicates Research Articles

Strengthening Health Security at Ground Border Crossings: Key Components for Improved Emergency Preparedness and Response-A Scoping Review.

Background: Ground crossing borders are considered a threat to health security due to their complex and challenging environments. The objective of this scoping review is to identify the main themes crucial for implementing effective health management at ground crossing borders to maintain health security. Methods: This scoping study was conducted following the methodological framework developed by Arksey and O'Malley and the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). Documents published from January 2005 to December 2023 were searched for using PubMed, Scopus, Web of Science, and UN databases. Two reviewers screened and reviewed eligible studies in three stages: duplicate identification and elimination, title and abstract screening, and full-text assessment. Data were charted and grouped into themes, the frequency of each theme and its percentage was calculated, and then thematic analysis was conducted. Results: Forty-five studies met the inclusion criteria. Areas of research were grouped into eight themes: "Infection Prevention and Control measures (IPC) (17%)", "Collaboration, Coordination, and Partnership (17%)", "Research and Data Sharing (15%)", "Build Border Health Capacity (13.5%)", "Planning (13.5%)", "Communication (13%)", "Legislations and Frameworks (7%)", and "Services and Assistance for At-Risk Groups (4%)". Through this scoping review, we found that the eight themes are interconnected, and are crucial for implementing effective health management at the ground crossing borders and for better emergency preparedness responses among countries.

DC_OCEAN: an open-source algorithm for identification of duplicates in ocean databases

A high-quality hydrographic observational database is essential for ocean and climate studies and operational applications. Because there are numerous global and regional ocean databases, duplicate data continues to be an issue in data management, data processing and database merging, posing a challenge on effectively and accurately using oceanographic data to derive robust statistics and reliable data products. This study aims to provide algorithms to identify the duplicates and assign labels to them. We propose first a set of criteria to define the duplicate data; and second, an open-source and semi-automatic system to detect duplicate data and erroneous metadata. This system includes several algorithms for automatic checks using statistical methods (such as Principal Component Analysis and entropy weighting) and an additional expert (manual) check. The robustness of the system is then evaluated with a subset of the World Ocean Database (WOD18) with over 600,000 in-situ temperature and salinity profiles. This system is an open-source Python package (named DC_OCEAN) allowing users to effectively use the software. Users can customize their settings. The application result from the WOD18 subset also forms a benchmark dataset, which is available to support future studies on duplicate checks, metadata error identification, and machine learning applications. This duplicate checking system will be incorporated into the International Quality-controlled Ocean Database (IQuOD) data quality control system to guarantee the uniqueness of ocean observation data in this product.

Psychometric Properties of questionnaires for assessing musculoskeletal disorders in the ankle: a systematic review protocol

Introduction: Ankle musculoskeletal disorders are common among various populations, including athletes and the elderly. However, there’s a lack of specific instruments in the literature that address factors influencing the progression of these disorders. Objective: This review aims to identify and describe questionnaires focusing on these factors, emphasizing their psychometric characteristics, feasibility, and applicability across different populations. Methods: This study is a systematic review protocol developed according to PRISMA-P recommendations. Searches were conducted in various databases, including Medline, Embase, LILACS, PEDro, SCOPUS, CENTRAL, CINAHL, and Web of Science, without time limitation. The results will be analyzed using the Rayyan®️ software, where two researchers will conduct the identification and removal of duplicates, selection, and screening of studies, with discrepancies resolved by a third examiner. Results: The first phase will comprise reading titles and abstracts, followed by a full manuscript analysis. Methodological quality will be assessed using the COSMIN checklist. Psychometric evidence will follow Terwee et al. criteria. The overall synthesis score will combine the consistency of psychometric evidence with methodological quality, categorizing studies according to levels of evidence proposed by the Cochrane Musculoskeletal Group. The article eligibility process will be represented through the PRISMA flowchart, and results will be presented in tables and figures accompanied by descriptive and narrative analysis. Dissemination will include peer-reviewed publication and conferences in rehabilitation-specific contexts. Conclusion: This study aims to compile existing research to streamline the analysis and application of ankle joint assessment questionnaires recommended for clinical use. It will focus on identifying high-quality questionnaires and examining their ability to explore barriers and facilitators in rehabilitation processes. Keywords: Ankle fractures; ankle injuries; patient-reported outcome measures; surveys and questionnaires.

Morphological, biochemical and molecular characterization of short-day tropical Indian garlic (Allium sativum L.)

Garlic, an asexually propagated bulbous crop, displays a wide diversity based on its morphological traits and biochemical compositions. This study investigated the genetic variability of Indian garlic through morphological, biochemical, and molecular markers. Twenty-nine genotypes along with three Allium species as outgroup were included in the present study. Observations were recorded on 14 quantitative traits, 17 qualitative traits, and 9 biochemical traits in fresh garlic. Significant variability was observed among genotypes for different characters. All the morphological and biochemical traits showed higher phenotypic coefficient of variation (PCV) than genotypic coefficient of variation (GCV) revealing the role of environment in trait expression. High to moderate heritability and genetic advance as percent mean were recorded for different traits except dry matter and Total Soluble Solids (TSS). Correlation analysis revealed the highest positive correlation between total yield, marketable yield, Ferric Reducing Antioxidant Potential (FRAP) and 2,2-diphenyl-1-picrylhyrazyl (DPPH). Cluster analysis differentiated all the genotypes into three major clusters based on morphological and biochemical traits. 214 Simple Sequence Repeats (SSRs) were screened and nine markers exhibited polymorphism. Cluster analysis using molecular markers revealed 4 distinct clusters. The observations from this study will help in the identification of diverse garlic germplasm for its efficient management and duplicate identification of germplasm resources.

Morpho-Physiological Characterization of Potato (Solanum Tuberosum) Genotypes of the Andigenum and Phureja Group from the Working Collection of the Universidad De Nariño

This study presents the morpho-physiological characterization of potato (Solanum tuberosum) genotypes from the Andigenum and Phureja groups, cultivated at the Nariño, Colombia. The research addresses the vital role of genetic diversity in agricultural sustainability and food security, highlighting the importance of genetic resources for crop improvement and ecosystem stability. The study utilized a comprehensive morphological characterization based on both qualitative and quantitative descriptors to assess genetic variability within the university's potato collection. This approach allowed for the identification of duplicates, the enhancement of a morphological database, and insights into the genetic diversity of the collections. Using techniques like Multiple Correspondence Analysis (ACM) and Principal Component Analysis (PCA), distinct clusters of genotypes were identified, which were highly influenced by their ploidy levels. Results indicated significant morphological variation between diploid Phureja and tetraploid Andigenum genotypes. Phureja genotypes showed greater similarity among themselves compared to the more diverse Andigenum group. This morphological diversity is importante for the identification of specific traits beneficial for breeding programs, such as resistance to environmental stresses and diseases. The study underscores the importance of maintaining genetic diversity within potato germplasm collections, facilitating the selection of genotypes with desirable agronomic traits. The findings contribute significantly to the understanding of the genetic and morphological foundations necessary for the future genetic improvement of the potato, a staple food crop with profound implications for global food security. The geographic distribution of the genotypes also suggested a correlation between morphological traits and geographic locations, reinforcing the role of local adaptation in crop diversity and resilience.

Better entity matching with transformers through ensembles

In this paper, we introduce AttendEM, a framework for entity matching (EM), i.e., pairwise identification of duplicates across databases. Eschewing the prevalent focus on text cleaning and training data augmentation of other transformers-based EM solutions, AttendEM leverages intra-transformer ensembling of distinctively rearranged text, additional aggregator tokens, and extra self-attention to enhance the base transformer architecture. Against state-of-the-art (SOTA) solutions on the ER-Magellan benchmark datasets, AttendEM achieved higher F1 scores in most cases. These SOTA solutions are Ditto (mean improvement of 0.21% with Ditto’s own reported results, 3.93% with DAEM’s Ditto replication, 2.99% with HierGAT’s Ditto replication), DAEM (0.53%), and HierGAT (0.54%). AttendEM’s improvements are comparable to solutions that claimed to have outperformed Ditto, HierGAT (Yao et al., 2022) (2.46% compared to AttendEM’s 2.99%) and DAEM (Huang et al., 2022) (3.42% compared to AttendEM’s 3.93%), when calculated using results from their respective Ditto replications.

OCCUR Shiny application: A user‐friendly guide for curating species occurrence records

Abstract Ecology increasingly relies on a massive volume of biodiversity occurrence records to draw insights into large‐scale biogeographical, ecological and evolutionary phenomena. This often involves defining a set of criteria that guides the collection, filtering and standardising of available records. These curation processes are often neither described in detail nor well documented. This hampers the comparability and reproducibility of studies and thus undermines the robustness of any ecological result. Yet, to date, there is no guide providing a friendly way to reason and document a complete data curation process. We reviewed the available literature on data curation, including tools such as R packages and workflows. From this assessment, we created a complete guide organised into five modules that allows users to consciously select and validate occurrence records based on these curation criteria. This is presented in the user‐friendly Shiny R OCCUR application, available at https://ecoinformatic.shinyapps.io/OCCUR/. OCCUR application provides a guide for researchers to deal with the trade‐off between data certainty and coverage generated in each data curation step. An interactive graph of these changes is provided within each module. OCCUR also produces a custom‐made final report including all steps used for data filtering. This report helps to streamline the writing of the methods section of manuscripts and technical reports, thus promoting the reproducibility of data curation processes. This Shiny application provides an interactive overview of the data curation methods and their use for handling occurrence records from public repositories. It brings together the taxonomic, temporal and spatial dimensions of data and also the identification of duplicates of the records. OCCUR can be applied for multiple purposes, such as teaching R to ecologists or enhancing reproducibility of macroecology and biogeography.

High‐throughput genotyping in estimating genetic resources and detecting pathogens in aquaculture

High‐throughput genotyping in estimating genetic resources and detecting pathogens in aquaculture

Duplicate Bug Report Detection Using an Attention-Based Neural Language Model

<i>Context:</i> Users and developers use bug tracking systems to report errors that occur during the development and testing of software. The manual identification of duplicates is a tedious task especially with software that have large bug repositories. In this context, their automatic detection becomes a necessary task that can help prevent frequently fixing the same bug. <i>Objective:</i> In this article, we propose <i>BERT-MLP</i>, a novel pretrained language model using bidirectional encoder representations from ransformers (BERT) for duplicate bug report detection (DBRD) with the aim of improving the detection rate compared to existing works. <i>Method:</i> Our approach considers only unstructured data. These are fed into the BERT model in order to learn the contextual relationships between words. The output is fed into a multilayer perceptron (MLP) classifier, representing our base DBRD. <i>Results:</i> Our approach was evaluated on three projects: Mozilla Firefox, Eclipse Platform, and Thunderbird. It achieved an accuracy of 92.11, 94.08, and 89.03&#x0025;, respectively, for Mozilla, Eclipse, and Thunderbird. A comparison with a dual-channel convolutional neural network (DC-CNN) model and other pretrained models, including RoBERTa and Sentence-Bert has been conducted. Results showed that <i>BERT-MLP</i> outperformed, the second best performing models (DC-CNN and Sentence-BERT) by 12&#x0025; in accuracy for Eclipse and 9&#x0025; for both Mozilla and Thunderbird, respectively.

Abstract A23: Less is more: Ultra-low input sequencing allows for evaluation of stem cells as a predictor of post-transplant relapse in the myelodysplastic syndromes

Abstract Malignant hematopoietic stem cells (HSCs) initiate the myelodysplastic syndromes (MDS) and drive treatment failure through their resistance to all non-transplant therapies. As a result, allogeneic stem cell transplantation (allo-SCT) is the only curative therapy for MDS. However, relapse remains the leading cause of treatment failure after allo-SCT. Improved tools to predict relapse in this setting are necessary to allow for pre-emptive therapeutic interventions, which may be more effective when the burden of disease is still low. Given prior evidence that HSCs serve as the functional unit of resistance to non-transplant therapies, we asked whether relapse after transplant originates from MDS HSCs, and whether their persistence can predict relapse. To overcome the limiting number of HSCs available in clinical bone marrow (BM) samples, we adapted a workflow developed for assessment of cell-free DNA and utilized a hybridization capture-based targeted sequencing panel covering 260 myeloid malignancy-associated genes. Unique dual index primers (UDIs) were utilized to tag input DNA, improving PCR duplicate identification, which overcome issues with amplification bias typically faced by low-input sequencing. This ultimately allowed us to consistently capture evaluable read depths from as few as 10 sorted cells. We assembled an initial cohort of 21 patients who underwent allo-SCT for MDS or secondary acute myeloid leukemia. Inclusion criteria were follow-up time greater than 1.5 years (or relapse in this timeframe), BM sampling prior to Day+120 post-transplant and relapse, and biospecimens allowing for sorting of greater than 10 HSCs. We sequenced sorted CD34+CD38-CD45RA- HSCs and multipotent progenitors (MPPs), CD34+CD38- CD45RA+ lymphoid-primed multipotent progenitors (LMPPs), and CD34+CD38+ progenitors, as well as bulk BM. With a median follow-up of two years, 9 of the 21 patients relapsed, with an average time to relapse of 362 days. Detection of disease mutations in HSCs/MPPs in any BM at D+30-D+120 post-transplant was 100% specific and 88% sensitive for relapse, showing improvements in both metrics over bulk BM assessment (22% sensitive and 66% specific), despite far lower cell input and read depth. Average time from detection to relapse was 6.9 months. In patients destined to relapse, disease mutations are often present in HSCs at a variant allele frequency of greater than 45%, while simultaneously undetectable on high-depth sequencing of bulk cells (&amp;gt;400x coverage). While even higher depth error-corrected sequencing bulk BM could likely detect such mutations, it would require &amp;gt;20x the sequencing depth, and could not identify the involved cell populations. In conclusion, we show for the first time that relapse of MDS after allo-SCT is driven by failure to eradicate MDS HSCs. Detection of MDS HSCs early post-transplant is highly predictive for relapse and opens new doors for pre-emptive interventions to prevent relapse. These findings are being validated in an independent cohort of 46 patients and updated data will be presented at the meeting. Citation Format: Benjamin Kroger, Yi Huang, Prapti Patel, Aimaz Afrough, Gurbakhash Kaur, Larry Anderson, Farrukh Awan, Praveen Ramakrishnan, Kunal Verma, Russell Vittrup, Robert H Collins, Madhuri Vusirikala, Yazan F Madanat, David Sallman, Carmelo Gurnari, Jaroslaw Maciejewski, Roni Tamari, Stephen Chung. Less is more: Ultra-low input sequencing allows for evaluation of stem cells as a predictor of post-transplant relapse in the myelodysplastic syndromes [abstract]. In: Proceedings of the AACR Special Conference: Acute Myeloid Leukemia and Myelodysplastic Syndrome; 2023 Jan 23-25; Austin, TX. Philadelphia (PA): AACR; Blood Cancer Discov 2023;4(3_Suppl):Abstract nr A23.

GENETIC DIVERSITY ANALYSIS AMONG SEVERAL APPLE GENOTYPES USING ISSR MARKERS

Apple (Malus domestica Borkh.) is one of the most cultivated fruit trees species worldwide, its fruits being consumed not only for their organoleptic attributes, but also for their nutraceutical properties. Therefore, the genetic variability of the species is extremely important to insure a large enough pool of cultivars to accommodate consumer demands for various fruit traits, such as taste, flavor, color, shape, etc., as well as to preserve cultivars that have less desirable organoleptic properties but are resistant/tolerant to biotic and/or abiotic stress and could be good genitors for these traits. The current study presents the use of ISSR method to assess the genetic variability among seven Romanian apple cultivars from the orchard collection of University of Agronomic Sciences and Veterinary Medicine of Bucharest, Romania: ‘Florina’, ‘Remar’, ‘Ciprian’, ‘Iris’, ‘Rebra’, ‘Generos’, and ‘Redix’. In addition, the method proved to be useful in identifying closely related individual genotypes, allowing the identification and elimination of duplicates from collections, without compromising the collection’s genetic variability.

More extreme duplication in FDA Adverse Event Reporting System detected by literature reference normalization and fuzzy string matching.

Literature reports of adverse drug events can be replicated across multiple companies, resulting in extreme duplication (defined as a majority of reports being duplicates) in the FDA Adverse Event Reporting System (FAERS) database because they can escape legacy duplicate detection algorithms routinely deployed on that data source. Literature reference field, added to in 2014, could potentially be utilized to identify replicated reports. FAERS does not enforce adherence to the Vancouver referencing convention, thus the same article may be referenced differently leading to duplication. The objective of this analysis is to determine if variations of the same literature references observed in FAERS can be resolved with text normalization and fuzzy string matching. We normalized the literature references recorded in the FAERS database through the first quarter of 2021 with a rule-based algorithm so that they better conform to the Vancouver convention. Levenshtein distance was then utilized to merge sufficiently similar normalized literature references together. Normalization of literature references increases the percentage that can be parsed into author, title, and journal from 61.74% to 93.93%. We observe that about 98% of pairs within groups do have a Levenshtein similarity of the title above the threshold. The extreme duplication ranged from 66% to 87% with a median of 72% of reports being duplicates and often involved addictovigilance scenarios. We have shown that these normalized references can be merged via fuzzy string matching to improve enumeration of all the individual case safety reports that refer to the same article. Inclusion of the PubMed ID and adherence to the Vancouver convention could facilitate identification of duplicates in the FAERS dataset. Awareness of this phenomenon may improve disproportionality analysis, especially in areas such as addictovigilance.

FDup: a framework for general-purpose and efficient entity deduplication of record collections.

Deduplication is a technique aiming at identifying and resolving duplicate metadata records in a collection. This article describes FDup (Flat Collections Deduper), a general-purpose software framework supporting a complete deduplication workflow to manage big data record collections: metadata record data model definition, identification of candidate duplicates, identification of duplicates. FDup brings two main innovations: first, it delivers a full deduplication framework in a single easy-to-use software package based on Apache Spark Hadoop framework, where developers can customize the optimal and parallel workflow steps of blocking, sliding windows, and similarity matching function via an intuitive configuration file; second, it introduces a novel approach to improve performance, beyond the known techniques of “blocking” and “sliding window”, by introducing a smart similarity matching function T-match. T-match is engineered as a decision tree that drives the comparisons of the fields of two records as branches of predicates and allows for successful or unsuccessful early-exit strategies. The efficacy of the approach is proved by experiments performed over big data collections of metadata records in the OpenAIRE Research Graph, a known open access knowledge base in Scholarly communication.

G-DIRT: a web server for identification and removal of duplicate germplasms based on identity-by-state analysis using single nucleotide polymorphism genotyping data.

Maintaining duplicate germplasms in genebanks hampers effective conservation and utilization of genebank resources. The redundant germplasm adds to the cost of germplasm conservation by requiring a large proportion of the genebank financial resources towards conservation rather than enriching the diversity. Besides, genome-wide-association analysis using an association panel with over-represented germplasms can be biased resulting in spurious marker-trait associations. The conventional methods of germplasm duplicate removal using passport information suffer from incomplete or missing passport information and data handling errors at various stages of germplasm enrichment. This limitation is less likely in the case of genotypic data. Therefore, we developed a web-based tool, Germplasm Duplicate Identification and Removal Tool (G-DIRT), which allows germplasm duplicate identification based on identity-by-state analysis using single-nucleotide polymorphism genotyping information along with pre-processing of genotypic data. A homozygous genotypic difference threshold of 0.1% for germplasm duplicates has been determined using tetraploid wheat genotypic data with 94.97% of accuracy. Based on the genotypic difference, the tool also builds a dendrogram that can visually depict the relationship between genotypes. To overcome the constraint of high-dimensional genotypic data, an offline version of G-DIRT in the interface of R has also been developed. The G-DIRT is expected to help genebank curators, breeders and other researchers across the world in identifying germplasm duplicates from the global genebank collections by only using the easily sharable genotypic data instead of physically exchanging the seeds or propagating materials. The web server will complement the existing methods of germplasm duplicate identification based on passport or phenotypic information being freely accessible at http://webtools.nbpgr.ernet.in/gdirt/.

The Effectiveness of Extracorporeal Shockwave Therapy for Midportion Achilles Tendinopathy: A Systematic Review.

Achilles tendinopathy is one of the most common lower limb injuries in both athletes and the general population. Despite the plethora of conservative treatment options available for the management of Achilles tendinopathy, as many as one in four patients will go on to require surgery. Extracorporeal shockwave therapy (ESWT) has emerged as a promising treatment option and has been successful in the management of other common musculoskeletal injuries such as plantar fasciitis. However, the evidence for ESWT in the management of Achilles tendinopathy remains inconclusive. Therefore, the aim of this systematic review was to evaluate the current evidence for the use of ESWT in the management of midportion Achilles tendinopathy.A comprehensive literature search was conducted using the databases MEDLINE (Pubmed), AMED, EMBASE, CINAHL, and CENTRAL. The databases were searched from their inception to December 2021. This was conducted to identify randomised control trials (RCTs) evaluating the effectiveness of ESWT versus control treatment in the management of midportion Achilles tendinopathy.Following a comprehensive search of the literature, a total of 283 articles were identified. Following the screening of titles and abstracts, 236 articles were excluded. The main reasons for exclusion were the identification of duplicates, non-randomised studies, and the use of ESWT on other pathology. Following the exclusion of 236 articles, 47 articles were retrieved for full-text review. Of these 47 articles, 40 were excluded leaving a total of 7 RCTs eligible for inclusion in this review. There was consistent evidence from 4 RCTs that ESWT is effective in the management of midportion Achilles tendinopathy.This review suggests that ESWT is a safe and effective modality for treating midportion Achilles tendinopathy as it reduces pain and improves function. The best available evidence suggests that a combination of ESWT with eccentric exercises and stretching may be even more effective than ESWT alone. Further research is required to confirm this and to determine the optimum ESWT treatment protocol.

Automating Duplicate Detection for Lexical Heterogeneous Web Databases

Introduction: The need for efficient search engines has been identified with the everincreasing technological advancement and huge growing demand for data on the web. Method: Automating duplicate detection over a query results in identifying the records from multiple web databases that point to a similar real-world entity and return non-matching records to the end-users. The proposed algorithm in this paper is based on an unsupervised approach with classifiers over heterogeneous web databases that return more accurate results with high precision, Fmeasure, and recall. Different assessments have also been executed to analyze the efficacy of the proposed algorithm for the identification of duplicates. Result: Results show that the proposed algorithm has greater precision, F-score measure, and the same recall values as compared to standard UDD. Conclusion: This paper aims to introduce an algorithm that automates the process of duplicate detection for lexical heterogeneous web databases. Discussion: This paper concludes that the proposed algorithm outperforms the standard UDD.

Identification of duplicate accessions in the pea (Pisum sativum L.) collection at VIR

Background. Identification of duplicates in the collections of genetic resources is the most important problem of seed gene bank management. Duplicate accessions expand the collection size, thus raising the costs of germplasm maintenance without broadening the genetic diversity.Materials and methods. The studied material included 17 pairs of Pisum sativum L. accessions from the VIR collection which presumably had been erroneously registered twice in the VIR catalogue; however, they had identical introductory numbers. The accessions entered the collection in 1922–1996 and to date they have been reproduced 2 to 16 times. After a field assessment, 15 pairs of putative duplicate accessions of various uses were selected for molecular analysis. A RAPD analysis was performed using five primers from the Operon Series. Total seed proteins were analyzed by SDS-polyacrylamide gel electrophoresis.Results. The following criteria were used to ascertain identity of the accessions or their difference: 1) similarity of morphological characters (habitus, and anthocyanin pigmentation of flowers and vegetative organs) and flowering dates; 2) identity or polymorphism of RAPD profiles; and 3) identity or difference in electrophoretic banding patterns of seed storage proteins. Seven pairs of duplicates were identified according to the results of a comparative analysis. Among them, the accessions in the pairs k-81/k-1199, k-8331/k-8645, k-8719/k-8760, and k-8757/k-8825 turned out to be completely identical, while k8464/k-8472, k-8740/k-8873, and k-8689/k-8723 were heterogenic, but had similar RAPD profiles and seed proteins patterns.Conclusions. An integrated assessment involving in-field plant phenotyping and analyzing polymorphism of amplified DNA fragments and components in electrophoretic banding patterns of seed proteins is promising for detecting identical or heterogenic accessions in genebank collections.

Nearest Neighbor Method for Discriminating Aftershocks and Duplicates When Merging Earthquake Catalogs

Early aftershocks contain important information about the physics of earthquake occurrence and postseismic relaxation processes. However, the standard catalogs of early aftershocks are usually incomplete. Many events can be missed in the main shock coda, some of which are strong enough due to the extremely high noise level. Under these conditions, the process of event identification becomes largely stochastic. Due to different network configurations and record processing methods, different agencies may register/miss different events, thus merging catalogs can improve the completeness of the aftershock sequence. When merging catalogs, the problem of identifying duplicates (records related to the same seismic event) arises. The main difficulty is discriminating aftershocks and duplicates, since both are events close in space and time. The problem is analogous to the problem of discriminating aftershocks and independent events. The solution methods are usually similar too. In this paper, we apply the nearest neighbor method modified for our problem. This method has become widespread in recent years in the problem of identifying aftershocks, and a probabilistic metric in the space of network errors in determining the epicenters and times of seismic events. It is applied for automatic identification of duplicates when merging catalogs of aftershocks for the Tohoku earthquake. An analysis of the space-time structure of duplicates and aftershocks shows their significant difference, which makes it possible to successfully solve the problem. In a sample from the global Advanced National Seismic System (ANSS) catalog (M&gt; 4), were found more than 700 events missed by the Japan Meteorological Agency (JMA) seismic network, which is one of the best in the world. Among the misses, there are several events with M&gt; 6 in the first hours after the main shock. Duplicate identification reliability is &amp;gt;97%. The method can be used to improve the completeness of aftershock sequences. The reliable identification of duplicates allows, in addition, to study the correspondence of the magnitudes determined by different agencies. Therefore the present method is an effective tool for creating merged catalogs of earthquakes with a uniform magnitude.

Entity Matching of Shop Accounts in Online Commerce Portals

Currently, online marketplace data are valuable data sources to be analyzed forvarious purposes. In the data collecting phases, duplication of shop accounts was found, resulting in biased analysis. This study examines the development of a mechanism to identify duplicate entities, i.e. store accounts, between different online marketplaces, or commonly known as entity matching. Word similarity algorithms were adopted as the core elements of our approach. Additionally, we present an entity matching model by examining logisticregression, naive Bayes, and random forest to find the best model for classifying store account similarities. Top online marketplaces in Indonesia are the object of our study, limited to one developing municipality, i.e. Sleman, DI Yogyakarta. The results show the best model has an accuracy value of 0.961, precision of 0.963, a recall of 0.958, and an F1-score of 0.962. Therefore, these results are acceptable for duplicate identification.

Feature Extraction of Plant Leaf Using Deep Learning

Half a million species of plants could be existing in the world. Classification of plants based on leaf features is a critical job as feature extraction (includes shape, margin, and texture) from binary images of leaves may result in duplicate identification. However, leaves are an effective means of differentiating plant species because of their unique characteristics like area, diameter, perimeter, circularity, aspect ratio, solidity, eccentricity, and narrow factor. This paper presents the extraction of plant leaf gas alongside other features from the camera images or a dataset of images by applying a convolutional neural network (CNN). The extraction of leaf gas enables identification of the actual level of chlorophyll (Ch) and nitrogen (N) which may help to interpret future predictions. Our contribution includes the study of texture and geometric features, analyzing ratio of Ch and N in both healthy and dead leaves, and the study of color‐based methods via CNN. Several steps are included to obtain the results: image preprocessing, testing, training, enhancement, segmentation, feature extraction, and aggregation of results. A vital contrast of the results can be seen by considering the kind of image, whether a healthy or dead leaf.