Extra Data Research Articles

Enhanced fish species classification using dynamic multilayer perceptron and transformer encoders with extra distribution data

Enhanced fish species classification using dynamic multilayer perceptron and transformer encoders with extra distribution data

Improvement of the Surveillance Protocols After Experiencing a Pseudo-Epidemic After 2018 Central Sulawesi Earthquake in Indonesia.

Following a disaster, a pseudo-epidemic can occur due to redundant and duplicated data caused by infrastructure and information system disruptions. This study aims to investigate whether there have been improvements the post-disaster surveillance system in the comparison of diarrhea incidents between Central Sulawesi, Indonesia, and Cianjur, West Java, Indonesia. We conducted an analysis of the epidemic-prone disease diarrhea before and during disasters, comparing the data with secondary data from the Early Warning Alert and Response System (EWARS) and the District Health Information System V.02 (DHIS-2). In central Sulawesi in 2018 and Cianjur in 2022, we observed an upsurge in diarrhea cases in the first week after the disaster. Although diarrhea cases increased after the disaster, they remained within acceptable outbreak criteria. Multiplication and redundant data were detected in the DHIS-2 system in Central Sulawesi, likely leading to erroneous overreporting. Changes in surveillance officers and their personal experiences during the disaster contributed to data inconsistencies. As compared to Central Sulawesi, the DHIS-2 reporting form in Cianjur was simplified as an individual form to enhance efficiency and accuracy. Enhancing valid assessment and conducting thorough investigations are essential to improve surveillance protocols for epidemic-prone diseases following disasters.

Refining Cellular Data for Accurate Trip Chain Identification: A Novel Approach for Urban Travel Analysis

Cellular data play a crucial role in supporting travel demand assessment and urban traffic planning because of their cost-effectiveness and extensive coverage. However, inherent inaccuracies, such as imprecise positioning, data duplication, and abnormal communication frequencies, hinder their ability to depict travelers’ trips accurately. In this paper, we present a novel approach to mitigate these issues by employing base station mapping to refine positioning data, eliminating duplicates and abnormal frequency records. This refinement enables more effective utilization of cellular data for trip chain identification. We address the ping-pong handover effect by employing a finite automaton machine and an approximate nearest neighbor searching method with carefully selected seeds to identify activities. Our method’s accuracy is validated through ground truth value analysis, focusing on permanent resident population estimation and metro passenger flow estimation. Furthermore, we demonstrate the practical effectiveness and value of our proposed method through a series of representative applications in a real-world case study in the city of Nanning, Guangxi Province, China.

Commercialization of scientific misconduct: the challenge of paper mills

Abstract According to an estimate, there are at least 400,000 papers in the scientific literature originating from paper mills, which are for-profit companies that guarantee the publication of low-quality papers. There is evidence that such papers are accompanied by various forms of scientific misconduct, including plagiarism, image duplication, falsification, and fabrication of data. These papers are cited and included in systematic reviews and meta-analyses, which have negative consequences for science and related disciplines. The scientific community has fragmented knowledge on paper mills, and many paper mills go undetected. Current resources of stakeholders are not sufficient to detect and prevent journals from publishing fraudulent papers. The goal of this presentation is to raise awareness of the paper mills problem and to discuss general ‘red flags’ of such papers, as well as possible measures to prevent editors and journals from publishing them. These measures may include open peer review, editor training, research on paper mills, improvement of detection methods, and united actions by stakeholders.

Impact of breast reconstruction on quality of life in women undergoing mastectomy for breast cancer: a systematic review and meta-analysis using the Breast-Q questionnaire

Introduction: Post-mastectomy breast reconstruction is crucial in breast cancer treatment; however, its impact on women’s quality of life (QOL) remains inadequately understood. The BREAST-Q questionnaire is used for assessing QOL related to breast surgery. Therefore, this study aimed to determine whether there are differences in QOL between women undergoing mastectomy with and without breast reconstruction using the BREAST-Q questionnaire. Methods: We conducted a systematic review and meta-analysis following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines and registered in PROSPERO. Articles in English, Portuguese, or Spanish were included, and studies on prophylactic mastectomy or breast-conserving surgery, duplicate studies, duplicate data, articles without original data (reviews, editorials, case reports, technical reports) and those without breast reconstruction were excluded. A comprehensive search was conducted across nine scientific databases. The defined search strategy was related to the terms standardized by the MeSH/PubMed controlled vocabularies, including Medical Subject Headings (MeSH), Embase subject headings (Emtree), and their synonyms. Statistical heterogeneity was assessed using the chi-squared and inconsistency tests. Statistical analyses were performed using the R software. A meta-analysis was performed for each BREAST-Q domain when data from multiple studies were available. Results: Of 1266 studies identified, three with 611 participants were included in the meta-analysis. Of these participants, 248 underwent mastectomy without reconstruction and 363 underwent post-mastectomy breast reconstruction. The meta-analysis revealed significantly higher scores in the domains “satisfaction with the breast,” “physical well-being of the chest,” “psychosocial well-being,” and “physical well-being” in women who underwent breast reconstruction compared with those who did not. Conclusion: This study demonstrates a positive association between breast reconstruction and enhanced QOL in women undergoing breast cancer treatment and mastectomy. Patient-reported outcome measures using questionnaires, such as Breast-Q, can elucidate satisfaction levels with the reconstruction, which will likely improve continuously with advances in modern plastic surgery.

Measuring IT Governance in ERP Systems: a COBIT 2019 Evaluation of SAP Material Management Module

Information technology systems such as Enterprise Resource Planning (ERP) are crucial for companies to meet their business needs. However, as companies grow, challenges arise, particularly in the Material Management (MM) module within SAP. This study aims to identify issues such as data inaccuracies and material duplication during the interfacing process with SAP MM and assess IT governance capabilities using the COBIT 2019 framework. The methods used include interviews and literature review, focusing on the domains of APO12 (Managed Risk), BAI03 (Managed Solutions Identification & Build), and DSS03 (Managed Problems). The results show that APO12 reached capability level 4, while BAI03 and DSS03 are at level 3, with a target of level 4. Recommendations include enhancing proactive system design evaluation, developing systematic procedures to address data issues, and implementing permanent solutions to prevent data duplication and inaccuracies during interfacing.

Investigating Methods to Generate Models for Turbine Maps Using Machine Learning

<div>For turbocharged engine design, manufacturer-provided turbocharger maps are typically used in simulation analysis to understand key engine performance metrics. Each data point in the turbocharger map is generated by physically testing the hardware or through CFD analysis—both of which are time-consuming and expensive. As such, only a modest set of data can be generated, and each data map must be interpolated and extrapolated to create a smooth surface, which can then be used for engine simulation analysis.</div> <div>In this article, five different machine learning algorithms are described and compared to experimental data for the prediction of Cummins Turbo Technologies (CTT) fixed geometry turbines within and outside of the experimental data range. The results were validated against xxx-provided test data. The results demonstrate that the Bayesian neural networks performed the best, realizing a 0.5%–1% error band. In addition, it is extrapolatable when suitable manually created extra data points are incorporated within the dataset at low and high turbine speeds.</div>

A Novel DC GCN with Attention Mechanism for Accurate Near Duplicate Video Data Cleaning

There has been a steady emergence of nearly identical recordings in the last several decades, thanks to the exponential development of video data. The use of regular videos has been impacted by data quality difficulties produced by near-duplicate movies, which are becoming increasingly noticeable. While there has been progress in the field of near-duplicate video detection, there is still no automated merging method for video data characterised by high-dimensional features. As a result, it is challenging to automatically clean near-duplicate videos in advance video dataset data quality. Research on removing near-duplicate video data is still in its early stages. The precision of near-duplicate video data cleaning is severely compromised by the delicate issues of video data organization besides initial clustering centres in the current research, which arise when the previous distribution is unknown. In tackle these problems, we offer a new kind of Graph Convolutional Neural Network (GCN) that uses dense influences and a categorization attention mechanism. Deeply connected graph convolutional networks (DC-GCNs) learn about faraway nodes by making GCNs deeper. By using dense connections, the DC-GCN is able to multiplex the small-scale features of shallow layers and generate features at diverse scales. Finally, an attention mechanism is incorporated to aid in feature combination and importance determination. Sparrow Search Optimisation Algorithm (SSA) is used to pick the parameters of the given model in the most optimal way. In the end, experiments are carried out using a coal mining video dataset and a widely known dataset called CC_WEB_VIDEO. The simulation findings show that the suggested strategy performs better than certain previous studies.

Building a Fingerprint Recognition Structure as a Security Mechanism to Verify the Identities of Users in Cloud Environment through Resnet-18

Objectives: To authenticate users employing fingerprint recognition thereby strengthening a security mechanism in a cloud environment. Methods: This study presents a novel approach for fingerprint detection in cloud systems by combining deep learning with metaheuristic optimization. A novel approach based on ResNet-18 for feature extraction and Thermal Exchange Optimization (TEO) classification is presented. This method aims to reduce data duplicity and provide safe access rights. When tested on 80% of the NIST and BDAL datasets, the proposed ResNet-18-TEO method outperforms existing models concerning recognition rates of 95.3% and 94.3% respectively. Findings: The experimental results reveal a False Acceptance Rate (FAR) spanning 0.6% to 4.8% and a False Rejection Rate (FRR) spanning 1.1% to 5.3%. Novelty: The proposed method would help cloud-based security since its fingerprint recognition time is substantially less than 25 seconds in training and 15 seconds in testing that of existing processes. In future work, this research will improvise using several deep learning techniques which are adopted using real time or research dataset. Keywords: Fingerprint recognition, Cloud, Security, Authentication, Privacy

Cluster partition-fuzzy broad learning-based fast detection and localization framework for false data injection attack in smart distribution networks

The distributed renewable energy generations, as accessible and easily targets for attackers, introduce an extra false data injection attack (FDIA) threat in the smart distribution networks. Scattered attack points and complex attack features hinder the elimination of potential threats. In this context, an FDIA fast detection and pinpoint localization framework is proposed. This framework identifies abnormal signals and attacked nodes from the unique topology structure and status contiguity of smart distribution networks, namely, spatial-temporal correlations of power grids, by using a cluster partition-fuzzy broad learning system (CP-FBLS). Unlike most existing FDIA detection methods, which are dedicated to high accuracy but neglect the urgent need for rapid detection in smart distribution networks, the proposed CP-FBLS framework maintains the fast computational nature of a fuzzy broad learning system (FBLS), while avoiding the accuracy degradation caused by high-dimension of data in large-scale smart distribution networks. Moreover, the multi-layer structure of the proposed framework recognizes the location of FDIA, bridging the research gap of attack localization. To comprehensively evaluate the proposed strategy, datasets containing various FDIA types are constructed. Numerical simulations based on the above datasets in modified IEEE 34-bus and 123-bus distribution systems are implemented. The results of the case studies showed that the proposed method can achieve 98.43 % accuracy with 0.34 ms detection time, realizing rapid detection and localization of various FDIAs with satisfactory accuracy.

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.

Healthy nutrition – prevention of chronic non-communicable diseases. Overview of recommendations

The article provides an overview of the National Recommendations on healthy nutrition available in Europe, Asia (China), the USA and the Russian Federation, assesses the compliance of the presented guidelines with the principles of a healthy diet according to the recommendations of the World Health Organization, and analyzes their role in the prevention of the development of non-infectious diseases, including number of diseases of the cardiovascular system. A search of relevant literature for the review was conducted in PubMed (www.pubmed.gov) using English keywords (“national dietary guidelines”, “WHO diet”, “FBDG”, “dietary guidelines”, “cardiovascular factors”), the website of the National Project of the Russian Federation “Healthy Nutrition” (https://здоровое-питание.рф), eLIBRARY.RU (https://elibrary.ru/defaultx.asp) using keywords in Russian (“ESSE-RF”, “risk factors for non-infectious diseases”, “nutrition”). Publications published between 1995 and 2022 were considered. The review included the National Recommendations in force for 2023, as well as the results of cohort and multicenter studies, the purpose of which was to assess the impact of current recommendations on the development of traditional cardiovascular risk factors, associated clinical conditions (myocardial infarction, stroke, etc.), as well as the impact on general mortality. Publications with completely duplicate data and conclusions were excluded, and in case of partial overlap, both sources were indicated. As a result, a list was compiled of articles, newsletters, and links with up-to-date information on the National Guidelines.

Covalent Labeling Automated Data Analysis Platform for High Throughput in R (coADAPTr): A Proteome-Wide Data Analysis Platform for Covalent Labeling Experiments.

Covalent labeling methods coupled to mass spectrometry have emerged in recent years for studying the higher order structure of proteins. Quantifying the extent of modification of proteins in multiple states (i.e., ligand free vs ligand-bound) can provide information on protein interaction sites and regions of conformational change. Though there are several software platforms that are used to quantify the extent of modification, the process can still be time-consuming, particularly for proteome-wide studies. Here, we present an open-source software for quantitation called Covalent labeling Automated Data Analysis Platform for high Throughput in R (coADAPTr). coADAPTr tackles the need for more efficient data analysis in covalent labeling mass spectrometry for techniques such as hydroxyl radical protein footprinting (HRPF). Traditional methods like Excel's Power Pivot (PP) are cumbersome and time-intensive, posing challenges for large-scale analyses. coADAPTr simplifies analysis by mimicking the functions used in the previous quantitation platform using PowerPivot in Microsoft Excel but with fewer steps, offering proteome-wide insights with enhanced graphical interpretations. Several features have been added to improve the fidelity and throughput compared to those of PowerPivot. These include filters to remove any duplicate data and the use of the arithmetic mean rather than the geometric mean for quantitation of the extent of modification. Validation studies confirm coADAPTr's accuracy and efficiency while processing data up to 200 times faster than conventional methods. Its open-source design and user-friendly interface make it accessible for researchers exploring intricate biological phenomena via HRPF and other covalent labeling MS methods. coADAPTr marks a significant leap in structural proteomics, providing a versatile and efficient platform for data interpretation. Its potential to transform the field lies in its seamless handling of proteome-wide data analyses, empowering researchers with a robust tool for deciphering complex structural biology data.

Content-Defined Chunking Algorithms in Data Deduplication: Performance, Trade-Offs and Future-Oriented Techniques

In the digital era, the exponential growth of data presents significant challenges for storage efficiency and processing speed. This paper reviews Content-Defined Chunking (CDC), a cornerstone in data deduplication technology, aimed at addressing these challenges. We systematically examine various CDC algorithms, categorising them into hashing-based and hash-less methodologies, and evaluating their performance in deduplication processes. Through a critical analysis of existing literature, the study identifies the balance between chunking speed and deduplication efficacy as a pivotal area for enhancement. Our findings reveal the need for innovative CDC algorithms to adapt to the evolving data landscape, proposing future research directions for improving storage and processing solutions. This work contributes to the broader understanding of data deduplication techniques, offering a pathway towards more efficient data management systems.

AF-Dedup: Secure Encrypted Data Deduplication Based on Adaptive Dynamic Merkle Hash Forest PoW for Cloud Storage

AF-Dedup: Secure Encrypted Data Deduplication Based on Adaptive Dynamic Merkle Hash Forest PoW for Cloud Storage

Blockchain-Based Data Deduplication and Distributed Audit for Shared Data in Cloud-Fog Computing-Based VANETs

Blockchain-Based Data Deduplication and Distributed Audit for Shared Data in Cloud-Fog Computing-Based VANETs

High hospitalization rates and risk factors among frail patients with cirrhosis: A 10-year population-based cohort study

Cirrhosis-related inpatient hospitalizations have increased dramatically over the past decade. We used a longitudinal dataset capturing a large metropolitan area in the US from 2011-2021 to evaluate contemporary hospitalization rates and risk factors among frail patients with cirrhosis. We conducted a retrospective, longitudinal cohort study using the Chicago Area Patient-Centered Outcomes Research Network (CAPriCORN) database, an electronic health record (EHR) repository that aggregates de-duplicated data across seven healthcare systems in the Chicago metropolitan area, from 2011-2021. The primary outcome of our study was the rate of hospitalization encounters. Frailty was defined by the Hospital Frailty Risk Score. Hospitalization rates were reported per 100 patients per year and a multivariable logistic regression analysis identified predictors of annual hospitalization probability. During the study period, of 36,971 patients, 16,265 patients (44%) were hospitalized (compensated: 18.4%, decompensated: 81.6%). Hospitalization rates were highest in patients with decompensated cirrhosis, reaching nearly 77.3 hospitalizations/100 patients per year. Hospitalization rates among patients with compensated cirrhosis were also high (14.2 vs. 77.3 hospitalization/100 patients per year), with odds of annual hospitalization three times (OR 3.1; 95%CI 2.9-3.4) as high among compensated patients with intermediate frailty and five times (OR 5.2; 95%CI 4.5-6.0) as high among those with severe frailty (compared to compensated patients with low frailty). Compensated and decompensated cirrhosis patients with intermediate to severe frailty face a substantially increased odds of annual hospitalizations compared to those with low frailty. Future work should focus on targeted interventions to incorporate routine frailty screenings into cirrhosis care and to ultimately minimize high hospitalization rates.

High-Precision Analysis Using μPMU Data for Smart Substations

This paper proposes a correction technique for bad data and high-precision analysis based on micro-phasor measurement unit (μPMU) data for a stable and reliable smart substation. First, a high-precision wide-area monitoring system (WAMS) with 35 μPMUs installed at Korea’s Yeonggwang substation, which is connected to renewable energy sources (RESs), is introduced. Time-synchronized μPMU data are collected through the phasor data concentrator (PDC). A pre-processing program is implemented and utilized to integrate the raw data of each μPMU into a single comma-separated values (CSV) snapshot file based on the Timetag. After presenting the technique for identification and correction of event, duplicate, and spike bad data of μPMU, causal relationships are confirmed through the voltage and current fluctuations for a total of five states, such as T/L fault, tap-up, tap-down, generation, and generation shutdown. Additionally, the difference in active power between the T/L and the secondary side of the M.Tr is compared, and the fault ride through (FRT) regulations, when the fault in wind power generation (WP), etc., occurred, is analyzed. Finally, a statistical analysis, such as boxplot and kernel density, based on the instantaneous voltage fluctuation rate (IVFR) is conducted. As a result of the simulation evaluation, the proposed correction technique and precise analysis can accurately identify various phenomena in substations and reliably estimate causal relationships.

Dynamic and efficient device collaborations in 5G‐advanced and 6G networks

AbstractCollaborative transmission, comprising multiple devices owned by a single user, is progressively evolving into an essential strategy to meet the stringent demands of burgeoning collaborative scenarios in 5G‐advanced and 6G networks. This paper proposes three novel use cases for device collaboration, namely data duplication, data splitting and wireless backup, to address these requirements. To provide dynamic and efficient collaboration, both non‐transparent mode via the medium access control layer collaboration and transparent mode via the physical layer collaboration are proposed. The paper further introduces a comprehensive design framework including protocol stack design, user equipment capability reporting, user equipment pairing, scheduling mechanism and transmission mechanism for different collaborative use cases with different collaborative modes. Evaluation outcomes reveal that the recommended methods could decrease the resources consumed for data duplication while increasing the user perceived throughput for data duplication and data splitting. The proposed methods also augment transmission reliability for both data duplication and wireless backup.

Linkability measures to assess the data characteristics for record linkage.

Accurate record linkage (RL) enables consolidation and de-duplication of data from disparate datasets, resulting in more comprehensive and complete patient data. However, conducting RL with low quality or unfit data can waste institutional resources on poor linkage results. We aim to evaluate data linkability to enhance the effectiveness of record linkage. We describe a systematic approach using data fitness ("linkability") measures, defined as metrics that characterize the availability, discriminatory power, and distribution of potential variables for RL. We used the isolation forest algorithm to detect abnormal linkability values from 188 sites in Indiana and Colorado, and manually reviewed the data to understand the cause of anomalies. We calculated 10 linkability metrics for 11 potential linkage variables (LVs) across 188 sites for a total of 20680 linkability metrics. Potential LVs such as first name, last name, date of birth, and sex have low missing data rates, while Social Security Number vary widely in completeness among all sites. We investigated anomalous linkability values to identify the cause of many records having identical values in certain LVs, issues with placeholder values disguising data missingness, and orphan records. The fitness of a variable for RL is determined by its availability and its discriminatory power to uniquely identify individuals. These results highlight the need for awareness of placeholder values, which inform the selection of variables and methods to optimize RL performance. Evaluating linkability measures using the isolation forest algorithm to highlight anomalous findings can help identify fitness-for-use issues that must be addressed before initiating the RL process to ensure high-quality linkage outcomes.