Data Quality Research Articles

AI-driven anomaly detection in cloud computing environments

The rapid adoption of cloud computing has changed the way businesses manage and store data, but it has also introduced new security challenges. One of the most pressing concerns in cloud environments is the detection of anomalies, which can signal potential security breaches, system failures, or performance issues. Traditional anomaly detection methods often fall short due to the complexity, scalability, and dynamic nature of cloud infrastructures. In recent years, Artificial Intelligence (AI)-driven anomaly detection techniques, particularly those leveraging machine learning and deep learning, have shown promise in overcoming these limitations. This paper reviews AI-driven approaches to anomaly detection in cloud computing environments, exploring their applications in enhancing cloud security, optimizing performance, and ensuring efficient resource management. The paper examines the strengths of various AI techniques, including supervised and unsupervised learning, deep learning, and hybrid models, highlighting their capacity to detect complex, previously unknown anomalies. Despite their advantages, implementing AI-based systems in cloud environments presents challenges, including data quality issues, scalability concerns, and computational resource requirements. Solutions such as federated learning and model optimization techniques are explored as methods to address these challenges. Furthermore, the paper discusses future research directions, including the integration of AI-driven anomaly detection with emerging technologies like blockchain and IoT, and the potential for advancements in self-supervised learning and explainable AI (XAI). This review concludes by emphasizing the critical role of AI in securing cloud infrastructures and the promising future of anomaly detection in the cloud computing landscape.

No differences between remote and laboratory-based testing of cardiac interoceptive accuracy using the phase adjustment task

AbstractIn recent years, there has been an increased interest in remote testing methods for quantifying individual differences in interoception, the perception of the body’s internal state. Hampering the adoption of remote methods are concerns as to the quality of data obtained remotely. Using data from several studies, we sought to compare the performance of individuals who completed the Phase Adjustment Task—a new measure of cardiac interoceptive accuracy that can be administered via a smartphone application—supervised in a laboratory against those who completed the task remotely. Across a total sample of 205 individuals (119 remote and 86 laboratory), we observed no significant differences in task performance between the two groups. These results held when matching groups on demographic variables (e.g., age) and considering only individuals who had successfully completed a screener task. Overall, these data attest to the suitability of the Phase Adjustment Task for remote testing, providing an opportunity to collect larger and more diverse samples for future interoception research.

The Role of AI in Data Engineering and Integration in Cloud Computing

This article presents a comprehensive analysis of the transformative role of Artificial Intelligence (AI) in revolutionizing data engineering and integration processes within cloud computing environments. The article examines the implementation of AI-driven solutions across multiple dimensions, including automated pipeline generation, intelligent schema matching, anomaly detection, and real-time data integration. Through a mixed-methods approach incorporating both quantitative and qualitative analyses, the article demonstrates significant improvements in data processing efficiency, with organizations achieving up to 67% reduction in processing time and 89% enhancement in accuracy. The article encompasses case studies from financial services, healthcare, and e-commerce sectors, providing concrete evidence of practical applications and scalability. Key findings reveal that AI-powered systems substantially outperform traditional approaches in cost efficiency, scalability, and data quality management, while simultaneously reducing operational overhead. The article also addresses implementation challenges, including legacy system integration and initial deployment complexities, offering strategic insights for organizations pursuing AI integration in their data engineering workflows. These article contribute to the broader understanding of how AI technologies can be effectively leveraged to address the growing challenges of data management in cloud computing environments, while providing a framework for future developments in this rapidly evolving field.

Transforming Tax Compliance with Machine Learning: Reducing Fraud and Enhancing Revenue Collection

The integration of machine learning (ML) in tax administration has the potential to revolutionize tax compliance, enhancing fraud detection and optimizing revenue collection. This literature review explores the application of ML in tax systems, emphasizing its transformative role in addressing the limitations of traditional, labor-intensive compliance methods. Justification for adopting a literature review approach is rooted in the need to consolidate diverse perspectives, address research gaps, and provide an informed synthesis of existing findings. The study highlights the criteria used for selecting case studies and research papers, ensuring a robust analysis of ML’s ability to automate detection processes, improve risk assessment, and enable predictive analytics for efficient tax administration. Despite its potential, ML adoption is challenged by data quality issues, privacy concerns, technical infrastructure demands, and ethical considerations, which must be systematically addressed. This paper also identifies literature gaps, particularly the lack of balanced discourse, and provides recommendations for overcoming barriers, including enhancing data management practices, adopting ethical frameworks, and fostering cross-border collaboration. By addressing these challenges, ML can equip tax authorities with tools for creating efficient, adaptive, and fair systems. This research underscores ML’s growing importance in transforming global tax compliance, setting the stage for a future of more responsive and effective revenue administration.

Habitat Assessment of Bocachico (Prochilodus magdalenae) in Ciénaga de Betancí, Colombia, Using a Habitat Suitability Index Model

This study evaluates the habitat of the Bocachico fish (Prochilodus magdalenae) in the Ciénaga de Betancí, Colombia, using a habitat suitability index (HSI) model. Wetlands like the Ciénaga de Betancí are under significant pressure from anthropogenic activities, affecting biodiversity and ecosystem health. The Bocachico, a species of immense cultural and economic importance, faces habitat degradation and fragmentation. Using hydrodynamic and water quality data, a numerical model (EFDC+ Explorer 11.5), and field data collected from multiple sampling campaigns, we assessed habitat suitability based on five key parameters: water temperature, dissolved oxygen, ammonia nitrogen, velocity, and depth. The model results indicated that environmental conditions in the wetland remained relatively stable during the dry season, with an average HSI score of 0.67, where 9% of the wetland area displayed acceptable conditions, and the remaining 91% displayed medium conditions. The wet season, on the other hand, had an average HSI score of 0.64, with 7.2% of the area in the acceptable suitability range, and the remaining 92.8% in the medium category. Variations in HSI were primarily driven by ammonia nitrogen levels, water velocity, and depth. Despite limited fluctuations in the HSI, areas of low suitability were identified, particularly in regions impacted by human activities. These findings have practical implications for conservation strategies, providing valuable insights for the sustainable management and conservation of the Ciénaga de Betancí, informing strategies for improving habitat conditions for the Bocachico, and supporting wetland restoration efforts.

How Infants Direct Their Gaze to Faces in the Presence of Other Objects: The Development of Face Preference Between 4 and 7 Months After Birth

ABSTRACTFrom early in development, infants process faces in their environment differentially from other items. By around 6 months of age, they are able to orient toward faces in the presence of distractor items. This paper aimed to assess whether this preferential looking toward faces was observable prior to 6 months of age, and whether there were developmental trends. We assessed this using the face pop‐out task, a free viewing eye‐tracking experiment in which infants viewed arrays containing an image of a face, alongside four distractor items. We assessed whether infants at 4, 5, 6 and 7 months (n = 1585 participants) differed in the proportion of first looks, total dwell time, and frequency of fixations to faces compared to other items. All three outcome variables were significantly higher toward faces than toward any of the other items in all the age groups. Moreover, there were age‐related differences across all measures—the older the infants were, the more pronounced their face preferences were. These age‐related differences could not be attributed to differences in data quality, and thus suggest that face preference is observable at 4 months of age but shows a strong development until 6 months.

The Impact of Incentivization on Recruitment, Retention, Data Quality, and Participant Characteristics in Ecological Momentary Assessments

Ecological Momentary Assessment (EMA) study participation is usually incentivized using monetary (e.g., fixed or performance-contingent payment) or non-monetary (e.g., feedback) compensation. This study investigates the impact of this incentivization on recruitment, retention, data quality, and participant characteristics in a sample of 74 students. For this purpose, an EMA study (time-based sampling) was conducted in participants’ daily life using a 2 Payment (fixed/ performance-contingent) x 2 Feedback (yes/ no) experimental between-subjects design. Offering feedback increased the likelihood of participation and reduced the likelihood of participants receiving fixed payment to drop out. Offering feedback additionally improved data quality. Furthermore, offering feedback attracted participants with higher interest in research and the study topic. Offering fixed vs performance-contingent payment had little effect on the outcomes of interest. Offering feedback as compensation in EMA studies may facilitate recruitment and increase data quality; however, it may also risk higher selection bias. Conclusions are drawn from a relatively small student sample; the results thus need to be replicated in larger and more diverse samples.

Plasma metabolomic characteristics of atrial fibrillation patients with spontaneous echo contrast.

The spontaneous echo contrast (SEC) in patients with atrial fibrillation (AF) indicates a prethrombic state that ultimately progresses into thrombus formation. A comprehensive understanding of specific plasma metabolomics characteristics may protect AF patients from thrombus, particularly in the early stage. Through the investigation of metabolic pathways, we endeavor to uncover the metabolomic characteristics associated with SEC states, and to examine the differential metabolites by which may exert their influence on thrombotic states. Patients with AF were enrolled, and the participants were divided into three groups based on the results of the echocardiogram: non-SEC, low-SEC and high-SEC group. Samples were collected and subjected to non-targeted metabolomics analysis. The analytical process included data quality control, metabolite difference analysis, component analysis, Kegg cluster analysis, etc. RESULTS: Our metabolic phenotype revealed a clear differential metabolic pattern between the SEC and non-SEC. Specifically, we identified 35 and 142 significantly differential metabolites in venous and atrial plasma, respectively, suggesting that SEC may be involved in pervasive metabolic dysregulation and that the degree of metabolic dysregulation in atrial plasma is more severe than that in venous blood. Patients with SEC have a significantly different metabolic pattern compared to those without SEC. Our work promoted the understanding of mechanism of the occurrence and development of SEC, facilitated the screening of the target metabolites for its therapeutic intervention, and provided evidence for the prevention and treatment of SEC or thrombosis in AF. Our work also provided new directions for subsequent research in related fields. In conclusion, our study not only provides a theoretical basis for understanding the occurrence and development of SEC in AF, but also provides recommendations for the daily diet of AF patients with SEC, such as a balanced intake of essential amino acids, avoiding excessive intake of benzoic acid, and intake of appropriate inositol. Not applicable.

Validation of water quality parameters using ion balancing and electrochemical approaches: a case study

ABSTRACT The present study validates the accuracy of water quality analysis for twenty geo-referenced water samples from a watershed of Gaya district, Bihar, using ion balancing and electrochemical approaches. Analytical data were presented using box plots, Gibbs diagram and descriptive statistics. Regression equations of various chemical parameters and ionic strength with electrical conductivity (EC) are reported for routine conversion and outlined mathematical equations for data validation. The pH and EC of the water samples varying from 4.9 to 8.7 and 190 to 800 µS cm−1, respectively. Calcium (Ca2+), magnesium (Mg2+), sodium (Na+) and potassium (K+) varied from 0.88 to 2.53, 0.4 to 1.6, 0.8 to 3.75, and 0.01 to 0.22 meq L−1 whereas, bicarbonate (HCO3 –), chloride (Cl–) and sulphate (SO4 2–) ranged from 1.3 to 6.0, 0.25 to 1.5 and 0.01 to 0.6 meq L−1, respectively. Consistency between calculated and measured values judged the correctness based upon relative error (RE %) of the water analysis data. Overall RE on an average of ca. 6% and 5% is reported regarding consistency between the calculated and measured specific conductivity ( calc. and .) and ionic strength ( calc. and .), respectively and difference was statistically non-significant. The ion balancing method showed very good to good analytical data quality for 80% of water samples, while electrochemical approaches (EC and ionic strength) indicated good to moderately good analytical data quality for 85% of the investigated samples. Taking cognisance of the outcome of all validation approaches, an overall 20% rechecking of water samples including (sample ID S3, S8, S13 and S14) is proposed for better analytical data quality for further data mining operations.

Fraudulent Participation in Online Qualitative Studies: Practical Recommendations on an Emerging Phenomenon.

Fraudulent participation is defined in the following as participation in research by individuals who, for one reason or another, intentionally provide false responses. Qualitative studies are at an increased risk of fraudulent participation when online recruitment and participation are used, and monetary incentives offered. Fraudulent participation threatens data quality and subsequent evidence-based practice, yet validated guidance on how to tackle it is lacking. This paper offers a critical reflection thereon by three separate qualitative research groups that experienced fraudulent participation in collaboration with a patient representative, a bioethicist, a legal expert, a journal deputy editor, and a chief executive of a national charity. The Prevent FRaudulent Online STudy participation (P-FROST) recommendations provide advice on (1) Study set-up (including team members and study design), (2) Monetary incentives and recruitment, (3) Data collection (screening and interview considerations), and (4) Analysis, reporting, and support. The reflection which balances the diverse perspectives of patients, researchers, funders, ethics boards, and legal teams puts forward the P-FROST recommendations to identify and prevent fraudulent participation throughout the design, ethical approval, and implementation of online qualitative research.

Are AI tools better than traditional tools in literature searching? Evidence from E-commerce research

This article examines the potential implications of Artificial Intelligence (AI) for literature search, comparing AI-based tools to conventional research methods. It also addresses the scarcity of academic literature on specific AI tools for research writing, posing four critical questions regarding accuracy, quality, uniqueness, and qualified uniqueness. Employing Algorithmic Theory and Data Dependency Theory, this project scrutinizes AI performance in algorithms, machine learning models, and data quality. Testing nine e-commerce topics using Scopus, Web of Science, Elicit, and SciSpace, the authors conclude that while conventional methods excel in accuracy and quality, AI tools show promise in uniqueness, complementing literature reviews. The findings also emphasize the judicious integration of AI tools and advocate for further research into new applications and diverse fields. Ultimately, this research offers highly relevant insights into leveraging AI tools to enhance conventional literature search practices in research and professional domains.

Artificial intelligence, medications, pharmacogenomics, and ethics.

Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing various scientific and clinical disciplines including pharmacogenomics (PGx) by enabling the analysis of complex datasets and the development of predictive models. The integration of AI and ML with PGx has the potential to provide more precise, data-driven insights into new drug targets, drug efficacy, drug selection, and risk of adverse events. While significant effort to develop and validate these tools remain, ongoing advancements in AI technologies, coupled with improvements in data quality and depth is anticipated to drive the transition of these tools into clinical practice and delivery of individualized treatments and improved patient outcomes. The successful development and integration of AI-assisted PGx tools will require careful consideration of ethical, legal, and social issues (ELSI) in research and clinical practice. This paper explores the intersection of PGx with AI, highlighting current research and potential clinical applications, and ELSI including privacy, oversight, patient and provider knowledge and acceptance, and the impact on patient-provider relationship and new roles.

Bots in web survey interviews: A showcase

Cost- and time-efficient web surveys have progressively replaced other survey modes. These efficiencies can potentially cover the increasing demand for survey data. However, since web surveys suffer from low response rates, researchers and practitioners start considering social media platforms as new sources for respondent recruitment. Although these platforms provide advertisement and targeting systems, the data quality and integrity of web surveys recruited through social media might be threatened by bots. Bots have the potential to shift survey outcomes and thus political and social decisions. This is alarming since there is ample literature on bots and how they infiltrate social media platforms, distribute fake news, and possibly skew public opinion. In this study, we therefore investigate bot behavior in web surveys to provide new evidence on common wisdom about the capabilities of bots. We programmed four bots – two rule-based and two AI-based bots – and ran each bot N = 100 times through a web survey on equal gender partnerships. We tested several bot prevention and detection measures, such as CAPTCHAs, invisible honey pot questions, and completion times. The results indicate that both rule- and AI-based bots come with impressive completion rates (up to 100%). In addition, we can prove conventional wisdom about bots in web surveys wrong: CAPTCHAs and honey pot questions pose no challenges. However, there are clear differences between rule- and AI-based bots when it comes to web survey completion.

Optimizing Multi-Omics Data Imputation with NMF and GAN Synergy.

Integrating multiple omics datasets can significantly advance our understanding of disease mechanisms, physiology, and treatment responses. However, a major challenge in multi-omics studies is the disparity in sample sizes across different datasets, which can introduce bias and reduce statistical power. To address this issue, we propose a novel framework, OmicsNMF, designed to impute missing omics data and enhance disease phenotype prediction. OmicsNMF integrates Generative Adversarial Networks (GANs) with Non-Negative Matrix Factorization (NMF). NMF is a well-established method for uncovering underlying patterns in omics data, while GANs enhance the imputation process by generating realistic data samples. This synergy aims to more effectively address sample size disparity, thereby improving data integration and prediction accuracy. For evaluation, we focused on predicting breast cancer subtypes using the imputed data generated by our proposed framework, OmicsNMF. Our results indicate that OmicsNMF consistently outperforms baseline methods. We further assessed the quality of the imputed data through survival analysis, revealing that the imputed omics profiles provide significant prognostic power for both overall survival and disease-free status. Overall, OmicsNMF effectively leverages GANs and NMF to impute missing samples while preserving key biological features. This approach shows potential for advancing precision oncology by improving data integration and analysis. Source code is available at: https://github.com/compbiolabucf/OmicsNMF.

Automatic Identification and Suppression of Random Noise and Methods for Profile Splicing in the Sub-Bottom Profile of Deep Water

The complex topography of deep sea presents numerous challenges for the accurate exploration of sub-bottom profiles. These include real-time tracking of seafloor reflectors, acquisition and storage of deep-sea long-term reflection data, and splicing of successive profiles. Based on the actual survey data of deep sea, we have developed automatic positioning and noise suppression algorithms, namely the double-difference threshold of proximity points. Furthermore, we have created automatic algorithms, namely content expansion and group data moving, based on extremum in seafloor’s depth. These have been designed to automatically suppress the random noise and effectively splice the sub-bottom profile data in deep water. The aforementioned processing techniques facilitate the enhancement of the quality of deep-water sub-bottom profile data, thereby enabling the provision of a comprehensive and successively long profile for interpretation in the context of deep-water sub-bottom profile data.

Filtered partial differential equations: a robust surrogate constraint in physics-informed deep learning framework

Embedding physical knowledge into neural network (NN) training has been a hot topic. However, when facing the complex real world, most of the existing methods still strongly rely on the quantity and quality of observation data. Furthermore, the NNs often struggle to converge when the solution to the real equation is very complex. Inspired by large eddy simulation in computational fluid dynamics, we propose an improved method based on filtering. We analysed the causes of the difficulties in physics-informed machine learning, and proposed a surrogate constraint (filtered partial differential equation, FPDE) of the original physical equations to reduce the influence of noisy and sparse observation data. In the noise and sparsity experiment, the proposed FPDE models (which are optimized by FPDE constraints) have better robustness than the conventional PDE models. Experiments demonstrate that the FPDE model can obtain the same quality solution with 100 % higher noise and 12 % quantity of observation data of the baseline. Besides, two groups of real measurement data are used to show the FPDE improvements in real cases. The final results show that the FPDE still gives more physically reasonable solutions when facing the incomplete equation problem and the extremely sparse and high-noise conditions. The proposed FPDE constraint is helpful for merging real-world experimental data into physics-informed training, and it works effectively in two real-world experiments: simulating cell movement in scratches and blood velocity in vessels.

Accuracy of transabdominal ultrasound to diagnose functional constipation and fecal impaction in children: a systematic review and meta-analysis.

Functional constipation is common in children and accurate diagnostic methods are essential for early diagnosis and effective management. The diagnostic accuracy of transabdominal ultrasound to diagnose functional constipation is unclear. To evaluate the diagnostic accuracy of transverse rectal diameter measurement via transabdominal ultrasound in diagnosing children with functional constipation and in identifying fecal impaction. Electronic databases were searched from inception to March 2023. Original studies investigating the diagnostic accuracy of measuring transverse rectal diameter via transabdominal ultrasound, including children with and without functional constipation, or with and without fecal impaction were included. Data extraction and quality assessment were performed independently by two reviewers. Sixteen studies were included (n = 1,801 children, 0-17years). Thirteen studies investigated the diagnostic accuracy for functional constipation, and five for fecal impaction. High risk of bias was found across the majority of studies mainly due to un-blinded case-control designs. Cut-off transverse rectal diameter values to diagnose functional constipation ranged from 2.4cm to 3.8cm. Meta-analysis (seven studies, n = 509 children) estimated mean sensitivity and specificity to diagnose functional constipation were 0.68 (95% confidence interval (CI) 0.55-0.78) and 0.81 (95% CI 0.71-0.88), respectively. Meta-analysis of diagnostic accuracy of identifying fecal impaction was not feasible. Studies reported a sensitivity and specificity ranging between 68-100% and 83-100%, respectively. Transabdominal ultrasound may be a valuable non-invasive diagnostic tool to diagnose functional constipation by measuring transverse rectal diameter and identifying fecal impaction in children. Heterogeneous study methods and lack of age-dependent normal values impair current clinical recommendations. Future research should focus on separating age groups and developing a standardized protocol.

Effects of relaxation techniques in reducing stress and anxiety among infertile women: A systematic review

Infertility affects millions of women worldwide and can lead to increased levels of stress and anxiety, impacting overall well-being and quality of life. Relaxation techniques, such as Mindfulness-Based Stress Reduction (MBSR), Yoga, Progressive Muscle Relaxation Technique (PMR), and Guided Imagery, have emerged as potential interventions to alleviate emotional distress in infertile women. A comprehensive review of the existing literature is needed to assess the effectiveness of these techniques in addressing stress and anxiety in the context of infertility.Following PRISMA guidelines, a systematic review was conducted by searching electronic databases for relevant studies published up to September 2021. The search used keywords related to relaxation techniques, infertility, stress, and anxiety. Two reviewers screened the identified articles based on predefined criteria. Included studies examined the effectiveness of relaxation techniques in reducing stress and anxiety among infertile women and used validated measures as outcomes. Data extraction and quality assessment were independently performed.The review included 18 studies that explored various relaxation techniques' effectiveness in reducing stress and anxiety among infertile women. MBSR, Yoga, PMR, and Guided Imagery interventions consistently showed significant reductions in perceived stress and anxiety levels. MBSR interventions proved particularly promising in promoting emotional well-being and coping with infertility challenges. Yoga offered a holistic approach, incorporating physical postures, breath control, and meditation to address multifaceted stress. PMR provided practical coping tools, and Guided Imagery offered guided visualization for anxiety management.This systematic review highlights the potential of relaxation techniques like MBSR, Yoga, PMR, and Guided Imagery in reducing stress and anxiety among infertile women. These interventions offer accessible and cost-effective strategies to enhance emotional well-being and coping skills in infertility contexts. Healthcare providers and counselors can consider incorporating these techniques into treatment plans to address the emotional needs of infertile women and improve their overall well-being and quality of life.

Assessment of the reliability of sociological research on the Internet

The purpose of research. The main goal of assessing reliability is to quantitatively substantiate the measure of confidence in a particular process. The article discusses the methodological and practical aspects of assessing the reliability of sociological research conducted on the Internet. Emphasis is placed on analyzing the specific problems encountered when using online surveys and other digital data collection methods. Issues of sample representativeness and a number of other quantitative indicators that affect the reliability of the assessment of sociological research are discussed.Methods. The article is devoted to methodological and practical aspects of assessing the reliability of sociological research conducted on the Internet. Issues of sample representativeness, weighting coefficients, data analysis, testing for systematic errors, and calculation of Cronbach’s alpha coefficient are discussed.Results. The main steps for calculating the reliability of sociological research conducted on the Internet are presented. Key steps include proper sample selection and adjustment, use of weights, performing statistical analysis, and estimating confidence intervals. These methods help minimize errors and increase the reliability of the data obtained.Conclusion. The proposed methods for assessing the reliability of the Internet will help improve the quality of data and the reliability of the results, namely, Online surveys can be subject to various types of biases, including self-selection of participants, when only certain groups of people decide to participate in the survey. Credibility assessment will promote fair and transparent research, which strengthens the trust of respondents and the public in research projects.

Investigating Tropical Cyclone Warm Core and Boundary Layer Structures with Constellation Observing System for Meteorology, Ionosphere, and Climate 2 Radio Occultation Data

The Constellation Observing System for Meteorology, Ionosphere, and Climate 2 (COSMIC-2) collects data covering latitudes primarily between 40 degrees north and south, providing abundant data for tropical cyclone (TC) research. The radio occultation data provide valuable information on the boundary layer. However, quality control of the data within the boundary layer remains a challenging issue. The aim of this study is to obtain a more accurate COSMIC-2 radio occultation (RO) dataset through quality control (QC) and use this dataset to validate warm core structures and explore the planetary boundary layer (PBL) structures of TCs. In this study, COSMIC-2 data are used to analyze the distribution of the relative local spectral width (LSW) and the confidence parameter characterizing the random error of the bending angle. An LSW less than 20% is set as a data QC threshold, and the warm core and PBL composite structures of TCs at three intensities in the Northwest Pacific Ocean are investigated. We reproduce the warm core intensity and warm core height characteristics of TCs. In the radial direction of the typhoon eyewall, the impact height of the PBL increases from 3.45 km to 4 km, with the tropopause ranging from 160 hPa to 100 hPa. At the bottom of the troposphere, the variations in the positive and negative bias between the RO-detected and background field bending angles correspond well to the PBL heights, and the variations in the positive bias between the RO-detected and background field refractivity reach 14%. This research provides an effective QC method and reveals that the bending angle is sensitive to the PBL height.