Data Discrepancies Research Articles

An Appraisal of the Progress in Utilizing Radiosondes and Satellites for Monitoring Upper Air Temperature Profiles

Upper air temperature measurements are critical for understanding weather patterns, boundary-layer processes, climate change, and the validation of space-based observations. However, there have been growing concerns over data discrepancies, the lack of homogeneity, biases, and discontinuities associated with historical climate data records obtained using these technologies. Consequently, this article reviews the progress of utilizing radiosondes and space-based instruments for obtaining upper air temperature records. A systematic review process was performed and focused on papers published between 2000 and 2023. A total of 74,899 publications were retrieved from the Google Scholar, Scopus, and Web of Science databases using a title/abstract/keyword search query. After rigorous screening processes using relevant keywords and the elimination of duplicates, only 599 papers were considered. The papers were subjected to thematic and bibliometric analysis to comprehensively outline the progress, gaps, challenges, and opportunities related to the utilization of radiosonde and space-based instruments for monitoring upper air temperature. The results show that in situ radiosonde measurements and satellite sensors have improved significantly over the past few decades. Recent advances in the bias, uncertainty, and homogeneity correction algorithms (e.g., machine learning approaches) for enhancing upper air temperature observations present great potential in improving numerical weather forecasting, atmospheric boundary studies, satellite data validation, and climate change research.

Examining Factors Impacting Encounter Length and Missed Appointments at a Student-Run Free Clinic: A Retrospective Analysis

Background: St. Vincent's Clinic (SVC) is a free, student-run clinic affiliated with the University of Texas Medical Branch that has been an invaluable resource in providing free healthcare services to marginalized populations in Galveston, Texas. The clinic offers a wide variety of specialty services along with free resources such as transportation and medication assistance, telehealth options, and interpreter services. Despite these resources, the clinic has faced challenges with consistently high no-show rates and long encounter lengths, impacting overall efficiency and patient care. We aimed to explore factors that may contribute to these challenges and uncover opportunities to improve patient satisfaction and optimize clinic efficiency.
 Methods: A retrospective chart review was conducted on all patients seen at SVC across all specialty clinics between March 2021 and March 2023. Patient demographics, appointment status, encounter length, language spoken, department specialty, and appointment modality were recorded. A series of statistical analyses were conducted on collected variables, including chi-square analysis, unpaired t-tests, and single-factor analysis of variance (ANOVA) tests, to assess significant associations.
 Results: The average encounter length varies significantly across different spoken languages and specialty clinics, but no significance was observed between different appointment modalities. The no-show rates were significantly different depending on the appointment modality, specialty clinic, and patient language spoken. Notably, while the encounter length was significantly shorter for English-speaking patients, Spanish-speaking patients had a lower no-show rate and were more likely to keep scheduled appointments.
 Conclusions: Language barriers and specialty clinic types can impact the encounter lengths and no-show rates, highlighting the need for targeted interventions such as proper resource allocation. Limitations include potential data discrepancies from factors such as human error or variations in documenting appointments. Future research should explore patient perspectives and experiences to improve patient satisfaction and overall optimize clinic operations.

Natural Language Processing For Requirement Elicitation In University Using Kmeans And Meanshift Algorithm

Data Driven Requirement Engineering (DDRE) represents a vision for a shift from the static traditional methods of doing requirements engineering to dynamic data-driven user-centered methods. Data available and the increasingly complex requirements of system software whose functions can adapt to changing needs to gain the trust of its users, an approach is needed in a continuous software engineering process. This need drives the emergence of new challenges in the discipline of requirements engineering to meet the required changes. The problem in this study was the method in data discrepancies which resulted in the needs elicitation process being hampered and in the end software development found discrepancies and could not meet the needs of stakeholders and the goals of the organization. The research objectives in this research to the process collected and integrating data from multiple sources and ensuring interoperability. Conclusion in this research is determining is the clustering algorithm help the collection data and elicitation process has a somewhat greater impact on the ratings provided by professionals for pairs that belong to the same cluster. However, the influence of POS tagging on the ratings given by professionals is relatively consistent for pairs within the same cluster and pairs in different clusters.

Linking crash and breathalyzer data in Connecticut

Objectives To document the process of linking breathalyzer and motor vehicle crash (MVC) data for the State of Connecticut using a unique identifier in the place of personal and private information. Methods Deterministic linkage methodologies were utilized in Microsoft SQL Server to join 5,634 (of 6,650) breathalyzer records to corresponding MVC driver records for the period of January 1, 2017 to December 31, 2022. Differences between the linked and original datasets were documented by comparing the consistency of frequency and proportion distributions of key variables. Results Proportions of annual records, alcohol breath tests, and refusals were nearly unchanged when comparing linked and original breathalyzer data. When examining variables in the original MVC driver records, there were differences in the within-group proportions for sex and age, with an overrepresentation of males and drivers aged 26-to-40 years old. For crash and injury severity, the linked dataset had lower proportions of more severe injury records when compared to the original MVC data. Additionally, 1,007 breathalyzer records were not matched with an associated MVC record. Conclusions Linkage methodology is sound and produced quality matches. The use of a unique identifier provided a strong match qualifier in the absence of personal and private data. Changes in proportions for age, sex, crash and injury severity align with previous research. Potential missed matches may be attributed to several factors outside of the linkage process, including data discrepancies and varied reporting practices. Future studies will further explore these differences and incorporate additional toxicology data as part of a continued effort to fuze crash, citation, toxicology, and public health data. The end result will be a holistic, comprehensive, and multifaceted database for transportation research and education.

Identifying Strengths and Gaps in Data Management and Reporting through Malaria Routine Data Quality Assessment: Results from Two Health Regions in Madagascar.

In 2021, the U.S. President's Malaria Initiative Measure Malaria project provided support to the National Malaria Program to conduct a data quality assessment. The main goal was to help health centers enhance the quality of their malaria data. The assessment involved reviewing data from outpatient registers, monthly reports, and DHIS2 data. Reporting timeliness, completeness, data element completeness, and availability of source documents were assessed. For timeliness, the assessment measured the proportion of reports that were submitted on time out of the expected total. The results showed that the reporting timeliness was inadequate in Atsinanana (85%) and adequate for Atsimo-Andrefana (95%). Data elements completeness, which refers to reports without missing data, was inadequate in Atsinanana (43%) and Atsimo-Andrefana (68%). The availability of source documents, such as records forms, was assessed and found to be 59% in Atsimo-Andrefana and 48% in Atsinanana. The use of standard reporting forms, which ensures consistency and accuracy in reporting, was reported to be 44% in Atsinanana and 54% in Atsimo-Andrefana. Data discrepancies were identified between outpatient registers, monthly reports, and DHIS2 data. A verification factor (VF) was used to compare the figures in these different sources. The VF was 1.2 in Atsinanana and 1.1 in Atsimo-Andrefana for both monthly reports and DHIS2 data, indicating an overreporting of fever cases tested in 6- to 13-year-olds. Overall, the assessment revealed gaps in data elements completeness, reporting accuracy, and availability of data recording guidelines. The findings suggest that regular data quality assessments should be implemented to guide decision making in Madagascar.

Manual Data Collection in Assembly Lines: A Case Study on the Human Factor in Data Accuracy

Due to the growing presence of digital technologies in factories, an increasing number of automated processes systematically record various data such as timestamps, product IDs, quantities, and defects. However, this is still not the case for many manual assembly lines, which often rely on manual data acquisition where workers, in addition to the assembly tasks, are asked to record data manually. Such manual data generation can significantly reduce the data quality and increase the variation of the data. In this paper, the authors study this problem by conducting a case study of a manufacturer of engine components based in Switzerland. The paper examines the antecedents of deviations in manually collected assembly line data—both from a technical and behavioral perspective. Based on the findings, a model is developed for the case company that compares manually collected data to the same data in the planning systems to identify and act on data discrepancies. The paper concludes with practical guidelines on how to mitigate data quality loss in manual assembly.

Insights into COVID-19 Data Collection and Management in Malawi: Exploring Processes, Perceptions, and Data Discrepancies

Insights into COVID-19 Data Collection and Management in Malawi: Exploring Processes, Perceptions, and Data Discrepancies

Blockchain-based secure dining: Enhancing safety, transparency, and traceability in food consumption environment

This research paper seeks to examine the possibilities of blockchain technology. For use in the field of restaurant food tracking and safety. Public health risks and economic costs are at stake when foodborne illness outbreaks occur, making food safety a top priority in the food industry. It can be difficult to quickly identify and address possible concerns about using traditional food traceability systems due to inefficiencies, data discrepancies, and a lack of transparency. In this study, we introduce a novel blockchain-based system developed especially for the purpose of tracking restaurant food. Blockchain decentralised consensus, immutability, and smart contracts are put to use in this system to provide trustworthy and transparent traceable infrastructure. Real-time monitoring and data collection along the food supply chain become possible when the blockchain architecture is combined with the Internet of Things (IoT) devices and RFID technology. We show that our proposed blockchain-based traceability solution is practical and efficient through a thorough assessment and validation procedure. The outcomes show that the system not only improves data quality and reliability but also drastically decreases the time and resources needed for food traceability. In addition, patrons are more likely to return to eateries that place a premium on food safety when they are given more information about the establishment’s practises. Additionally, we discuss scalability, data privacy, and interoperability concerns that may arise in future implementations and provide some initial ideas for overcoming these issues.

Data assimilation of decay heat experiments for feedback on nuclear data

Integral decay heat experiments can provide interesting feedback on particular nuclear data (decay data and fission yields mainly). After ensuring that the C/E discrepancies were mostly due to nuclear data discrepancies, a Bayesian inference approach can be applied. Nevertheless, the results strongly depend on the quality of the experiment and on our capability to estimate realistic experimental correlation matrices when considering several integral experiments in the assimilation process. A former study performed in 2019 was dedicated to the data assimilation of a large C/E dataset from the experimental validation database of fuel inventory calculations with the DARWIN2.3 package in order to provide feedback to the nuclear data evaluators. This paper is an attempt to exploit the General Electric decay heat experiments performed in the USA in the 1980s in order to confirm or not the trends on four particular cumulated fission yields: 235U(nth,f)133Cs, 235U(nth,f)137Cs, 239Pu(nth,f)106Ru and 239Pu(nth,f)144Ce.

Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces.

Training an accurate classifier for EEG-based brain-computer interface (BCI) requires EEG data from a large number of users, whereas protecting their data privacy is a critical consideration. Federated learning (FL) is a promising solution to this challenge. This paper proposes Federated classification with local Batch-specific batch normalization and Sharpness-aware minimization (FedBS) for privacy protection in EEG-based motor imagery (MI) classification. FedBS utilizes local batch-specific batch normalization to reduce data discrepancies among different clients, and sharpness-aware minimization optimizer in local training to improve model generalization. Experiments on three public MI datasets using three popular deep learning models demonstrated that FedBS outperformed six state-of-the-art FL approaches. Remarkably, it also outperformed centralized training, which does not consider privacy protection at all. In summary, FedBS protects user EEG data privacy, enabling multiple BCI users to participate in large-scale machine learning model training, which in turn improves the BCI decoding accuracy.

Experimental and modeling investigation of the laminar flame speeds for ammonia with various oxygen and diluent mixtures

The study utilizes two spherical bombs to explore the Laminar Flame Speed (LFS) and Markstein length of oxygen-enriched ammonia flames and ammonia combined with different oxidizers (N2 with 21% and 30% O2 and He with 21% O2). The experiments cover a range of initial conditions, exploring temperatures from 309 K to 423 K and an equivalence ratio spanning from 0.8 to 1.3, all at an initial pressure of 1 bar. At the same time, three established literature mechanisms (Stagni, Shrestha, and Zhang) are used to simulate the data and perform kinetic analysis. A detailed analysis focuses on how the radius of the domain affects LFS, highlighting the importance of a larger domain, in contrast with findings from previous literature. The findings highlighted that, although LFS increased (as expected) with the increase in the initial temperature, the Markstein length remains largely unaffected in oxygen-enriched ammonia flames. This was also calculated using Bechtold and Matalon's approach, and the results were in a good agreement with the experimental values. The study determines alpha exponents across an equivalence ratio range of 0.8 to 1.3, and also found that the inert gas has varying effects on both LFS and Markstein length. Buoyancy effects were noticeable in the ammonia/air experiments, particularly at lower temperatures, challenging existing literature heavily focused on ammonia blends at temperatures below 400 K, potentially contributing to data discrepancies observed in prior studies.

Digitalization of Bale Kampoeng Radjoet SMEs Stock Opname Recording Systems based on Website with Waterfall Method

Bale Kampoeng Radjoet is an SME in Bandung that markets knitting products from 12 artisans. In the business process, recording product data is still done manually using a notebook. This has an impact on the stock opname process, namely the occurrence of product data discrepancies in the warehouse with records and difficulties in knowing product stock. These problems are caused by the absence of a system that helps record all product data. This is the basis for improving the stock opname recording system by digitalization, namely the use of a website-based information system. The development of the stock opname recording website uses the waterfall method, with system modeling using the Unified Modeling Language (UML). The system verification process uses blackbox testing and validation using User Acceptance Testing (UAT) which refers to ISO 25010. UAT results show the characteristics of functional suitability 89.33%, performance 92%, usability 91.2%, and reliability 93.33%. The results adjusted to the interpretation criteria show that the four characteristics are in the range of 81%-100%, which means that the website can be accepted by users because it fulfills user needs and can be run according to its function.

TELEDENTISTRY IN A JURIDICAL PERSPECTIVE: CRIMINAL, CIVIL AND ADMINISTRATIVE ASPECTS

Introduction: To address the limitations resulting from the implementation of health protocols in providing healthcare services to patients, a solution was found in the utilization of information and communication technology to support healthcare services for patients in the form of telehealth, often known as telemedicine. Review: According to Article 1, Paragraph 21 of Law Number 17 of 2023 regarding Health, telemedicine is defined as the provision and facilitation of healthcare services, including public health, health information services, and self-care services, through telecommunications and digital communication technology. One of the aspects of telemedicine in the field of dentistry is teledentistry, which can be a solution for optimizing dental and oral healthcare services for individuals and the community. However, there are various issues in its implementation, especially related to legal issues such as licensing, patient electronic medical record privacy and confidentiality, clinical guidelines, and others that are highly vulnerable to data discrepancies, falsification, or digital patient data leakage. Conclusion: These issues can potentially lead to medical disputes that may enter the legal realm. On the other hand, teledentistry services also play a crucial role in improving public health, especially in dental and oral health in Indonesia, particularly during the COVID-19 pandemic. Therefore, the development of teledentistry services needs to be continuously improved, both in terms of systems and regulations, to provide security and legal certainty for the Indonesian population.

Aggregation of monitoring datasets for functional diversity estimation

Long-term monitoring data is central for the analysis of biodiversity change and its drivers. Time series allow a more accurate evaluation of diversity indices, trait identification and community turnover. However, evaluating data collected across different monitoring programs remains complicated because of data discrepancies and inconsistencies. Here we propose a method for aggregating datasets using diffusion maps. The method is illustrated by aggregating long-term phytoplankton abundance data from the Wadden Sea and Southern North Sea gathered by two institutions located in Germany and The Netherlands. The aggregated data allowed us to infer species traits, to reconstruct the main trait axis which drives community functionality, ultimately quantifying functional diversity of the individual samples, having used only the co-occurrence of species in samples. Although functional diversity varies greatly among sampling stations, we detect a slight positive trend in German stations, which contrasts with the clear decreasing trend observed in most of the Dutch Wadden Sea stations. At the Terschelling transect, in Southern North Sea, the stations also showed contrasting estimations of functional diversity between off-shore and in-shore stations. Our research provides further evidence that traits and functional diversity can be robustly reconstructed from monitoring data alone, showing that data aggregation can increase the accuracy of this reconstruction, being able to aggregate heterogeneous datasets.

Measuring nurses’ on-shift physical activity and sedentary time by accelerometry or heart rate monitoring: a descriptive case study illustrating the importance of context

BackgroundThere is debate whether nurses are active enough stemming from differences in measurement tools, clinical contexts, and nursing tasks. A descriptive case study concerning the use of device-based measures in combination with direct observation is presented to examine the effect of the nursing context and the discrepancies between different measurement tools for identifying nurses’ on-shift activity levels.MethodsData were collected across seven shifts in medical and surgical wards. Nurses’ activity was assessed using accelerometry and heart rate monitoring, in addition to direct observation. Data graphs were plotted for each shift and measurement device, with direct observations used to contextualise the data and identify discrepancies.ResultsHigher activity levels were recorded on-shift through heart rate monitoring (87%) compared to accelerometry (27%). This pattern was also observed specifically on early, late, and medical ward shifts. Data discrepancies between the two devices stemmed from the shift and (or) ward type, highlighting the importance of understanding the context of nursing duties when assessing nurses’ activity levels.ConclusionsIt is also vital that researchers, policymakers, and practitioners consider how they will measure nurses’ occupational physical activity, which consequently will influence outcomes, and therefore, decisions around the need (or not) for intervention.

Leveraging the functionality of Research Electronic Data Capture (REDCap) to enhance data collection and quality in the Opioid Analgesic Reduction Study.

The Opioid Analgesic Reduction Study is a double-blind, prospective, clinical trial investigating analgesic effectiveness in the management of acute post-surgical pain after impacted third molar extraction across five clinical sites. Specifically, Opioid Analgesic Reduction Study examines a commonly prescribed opioid combination (hydrocodone/acetaminophen) against a non-opioid combination (ibuprofen/acetaminophen). The Opioid Analgesic Reduction Study employs a novel, electronic infrastructure, leveraging the functionality of its data management system, Research Electronic Data Capture, to not only serve as its data reservoir but also provide the framework for its quality management program. Within the Opioid Analgesic Reduction Study, Research Electronic Data Capture is expanded into a multi-function management tool, serving as the hub for its clinical data management, project management and credentialing, materials management, and quality management. Research Electronic Data Capture effectively captures data, displays/tracks study progress, triggers follow-up, and supports quality management processes. At 72% study completion, over 12,000 subject data forms have been executed in Research Electronic Data Capture with minimal missing (0.15%) or incomplete or erroneous forms (0.06%). Five hundred, twenty-three queries were initiated to request clarifications and/or address missing data and data discrepancies. Research Electronic Data Capture is an effective digital health technology that can be maximized to contribute to the success of a clinical trial. The Research Electronic Data Capture infrastructure and enhanced functionality used in Opioid Analgesic Reduction Study provides the framework and the logic that ensures complete, accurate, data while guiding an effective, efficient workflow that can be followed by team members across sites. This enhanced data reliability and comprehensive quality management processes allow for better preparedness and readiness for clinical monitoring and regulatory reporting.

Few-shot learning-based human behavior recognition model

ScopeThe challenges of human behavior recognition based on sensor data often require addressing the needs of various new users in real-world situations, which leads to difficulties in personalizing user data and resolving concerns related to resolution. This relates to the fact that persons with different characteristics who engage in the same behavior inevitably provide varying data discrepancies. Models that are faced with unfamiliar users encounter difficulties in accurately recognizing expected behavior, and it is not practicable to gather a large amount of training data every time the model needs to be reconfigured for new users. AimThis study introduces a novel approach known as the FSLBR model, which combines the domains of few-shot learning and behavior recognition algorithms. Initially, a meta-learning technique focused on optimization is employed to categories datasets according to user categories. NoveltyThe paradigm of few-shot learning has shown significant effectiveness by leveraging a limited amount of data for new tasks. MethodologyThe FSLBR method and an attention-centered Memory module are then smoothly integrated into the FSLBR model. ResultsIn the field of behavior recognition, it is observed that a small selection of data is sufficient for good classification when dealing with new users. The capacity of the model network to extract and extrapolate data features is improved. The empirical experiments conducted on the MEx dataset support the assertion that the proposed FSLBR model demonstrates improved performance under the few-shot learning framework compared to standard deep learning methods.

Cellsig plug-in enhances CIBERSORTx signature selection for multidataset transcriptomes with sparse multilevel modelling.

The precise characterization of cell-type transcriptomes is pivotal to understanding cellular lineages, deconvolution of bulk transcriptomes, and clinical applications. Single-cell RNA sequencing resources like the Human Cell Atlas have revolutionised cell-type profiling. However, challenges persist due to data heterogeneity and discrepancies across different studies. One limitation of prevailing tools such as CIBERSORTx is their inability to address hierarchical data structures and handle nonoverlapping gene sets across samples, relying on filtering or imputation. Here, we present cellsig, a Bayesian sparse multilevel model designed to improve signature estimation by adjusting data for multilevel effects and modelling for gene-set sparsity. Our model is tailored to large-scale, heterogeneous pseudobulk and bulk RNA sequencing data collections with nonoverlapping gene sets. We tested the performances of cellsig on a novel curated Human Bulk Cell-type Catalogue, which harmonizes 1435 samples across 58 datasets. We show that cellsig significantly enhances cell-type marker gene ranking performance. This approach is valuable for cell-type signature selection, with implications for marker gene validation, single-cell annotation, and deconvolution benchmarks. Codes and the interactive app are available at https://github.com/stemangiola/cellsig; and the database is available at https://doi.org/10.5281/zenodo.7582421.

An Improve the Onboarding Process in Trade Finance Applications Using Blockchain Technology

Today, challenges in Know Your Customer (KYC) and Anti-Money Laundering (AML) processes include inefficiencies, data silos, and the risk of fraudulent activities. Integrating blockchain technology offers a transformative solution to these issues. Blockchain's decentralized and tamper-resistant nature ensures a single, verifiable source of truth for customer information, reducing data discrepancies across institutions. Smart contracts can automate AML compliance checks, ensuring real-time monitoring and rapid response to suspicious activities. The immutability of blockchain records enhances auditability, facilitating regulatory compliance. Furthermore, the secure and transparent nature of blockchain instills trust among stakeholders, fostering collaboration in combating financial crimes. By leveraging blockchain in KYC and AML processes, the financial industry can achieve enhanced efficiency, reduced fraud, and strengthened regulatory adherence.

Dual attention-based deep learning network for multi-class object semantic segmentation of tunnel point clouds

Aiming to automatically segment multi-class objects on the tunnel point cloud, a deep learning network named dual attention-based point cloud network (DAPCNet) is developed in this paper to act on point clouds for segmentation. In the developed model, data normalization and feature aggregation are first processed to eliminate data discrepancies and enhance local features, after which the processed data are input into the built network layers based on the encoder-decoder architecture coupled with an improved 3D dual attention module to extract and learn features. Furthermore, a custom loss function called Facal Cross-Entropy (“FacalCE”) is designed to enhance the model's ability to extract and learn features while addressing imbalanced data distribution. To validate the effectiveness and feasibility of the developed model, a dataset of tunnel point clouds collected from a real engineering project in China is employed. The experimental results indicate that (1) the developed model has excellent performance with Mean Intersection over Union (MIoU) of 0.8597, (2) the improved 3D dual attention module and “FacalCE” contribute to the model performance, respectively, and (3) the developed model is superior to other state-of-the-art methods, such as PointNet and DGCNN. In summary, the DAPCNet model exhibits exceptional performance, offering effective and accurate results for segmenting multi-class objects within tunnel point clouds.