Data Quality Research Articles

PEPSI’s non-detection of escaping hydrogen and metal lines adds to the enigma of WASP-12 b

ABSTRACT WASP-12 b is an ultra-hot Jupiter of special interest for atmospheric studies since it is on an inspiraling orbit in an extreme environment of intense radiation and circumstellar gas. Previously claimed detections of active mass-loss from this planet are controversial across the literature. To address this controversy, we obtain two new transit observations of WASP-12 b with the optical high-resolution PEPSI spectrograph on the Large Binocular Telescope. Contrary to previous work, we do not observe planetary H$\alpha$ absorption and rule out the amplitude of previously reported detections. Our non-detection may be limited by the sensitivity of our data or could indicate weaker mass-loss than suggested by previous studies. We conduct injection-recovery experiments to place constraints on the radial extent of WASP-12 b’s escaping atmosphere as probed by Balmer lines, but find that our data do not have the sensitivity to probe down to the planet’s Roche lobe. Using physically motivated models of atmospheric escape, we explore upper limit constraints on the planet’s mass-loss rate and deem the data quality in the wavelength regime of Balmer lines insufficient to determine a physically meaningful constraint. We also conduct a spectral survey of other optical absorbers to trace atmospheric circulation but detect no additional absorption. We conclude that previous claims of H$\alpha$ absorption from the atmosphere of WASP-12 b should be reevaluated. Given the anticipated line strength of Balmer/optical features, observing the atmosphere of this faint target will require stacking more observations even with the largest telescope facilities available.

Advanced Machine Learning Algorithms for Predictive Maintenance in Industrial Manufacturing Systems

In industrial manufacturing systems, predictive maintenance is the process of increasing the rate of productivity and minimizing the time that equipment takes to be out of order through early identification of the equipment that is likely to fail. The main focus of this research is to analyze the possibility of using modern approaches in machine learning to enhance the methods of predictive maintenance. We compare multiple current approaches of deep learning, ensemble methods, and anomaly detection to determine their effectiveness in predicting the maintenance requirements utilizing the sensor and operational data. With the help of a large amount of data, we consider the results of the work of each algorithm for the assessment of the predictive accuracy, the ranking of features, and the detection of anomalies. The findings highlight disparities in the effectiveness of the algorithms in terms of accuracy, precision, and recall, and the deep learning models’ ability to grasp intricate and anomalous patterns. The performance of the maintenance predictions is depicted by the use of visualizations of the performance metrics and feature importance. It also describes the drawbacks of the existing models, such as the problem of data quality and generalization. The study draws attention to the possibility of applying sophisticated machine-learning methods to improve the effectiveness of PM in industrial environments. Possible directions of future research are to enhance the generalization ability of the developed models and to expand the usage of modern trends in the machine learning field to enhance maintenance strategies.

Evaluating AIS implementation to improve accounting information quality: the prospect in Jordanian family SMEs in the post-Covid-19 age

PurposeAs organisations rely more on systems to protect their accounting data, it is critical to investigate whether cutting-edge “Accounting Information Systems (AIS)” technologies increase “Accounting Information Quality (AIQ)” in post-Covid-19 era.Design/methodology/approachThe current study examined how Covid-19 modified the expected links between AIS implementation and AIQ factors. To make the “structural model”, a “web-based survey” was used. A “Multi-group Analysis” (MGA) with “AMOS.26” was employed to test the regulating effect. To look at their regression values, the 412 participants from “Family Small- and Medium-sized Enterprises FSMEs” in Jordan who took part in the MGA were split into two groups: those who had a high experience of Covid-19 impact (N = 143) and those who had a low experience of Covid-19 impact (N = 269). The study of 412 completed surveys shows that MGA uses a modified t-test to determine the significant difference between regression coefficients.FindingsThe results of the moderation effect confirm that Covid-19 has a substantial effect on AIS adoption and AIQ. All hypotheses about how AIS implementation affects AIQ in post-Covid-19 era were approved. The study finds that adopting AIS is critical for enterprises to maintain successful operations and efficiency.Practical implicationsThis model may assist businesses in comprehending the relationship between AIS and AIQ, how it affects it in the current technological adoption phase under extremely unpredictable economic conditions and what characteristics support its widespread usage. This research offered the theoretical underpinning for examining AIS’s ability to improve AIQ among Jordanian family firms. The results can also be applied to other Middle Eastern countries and AIQ, how it affects this relationship during this period of technological adoption under extraordinarily unpredictable economic conditions and what factors support its widespread use. Practitioners and scholars must recognise how AIS can develop organisational values and impacts as the AIS environment grows. This study recommends a holistic AIS-implementation methodology and evaluates organisational implications to address this empirical challenge.Originality/valueThis study investigates whether adopting AIS results in significantly higher AIQ in the post-Covid-19 era. To the best of the author’s knowledge, there have been a few attempts to investigate how AIS influences data quality. Still, there is little evidence of how this relationship has evolved in the aftermath of the Covid-19 tragedy. As a result, much work remains to be done, particularly in analytical decision-making culture, which is critical to the development of the growing economies of the Middle East and Jordan. To address this knowledge gap, the Covid-19 and AIQ criteria were incorporated into the model utilised in this study. This research adds to existing knowledge and may be valuable in areas with low AIS framework frequency.

Multiple Data Imputation Methods Advance Risk Analysis and Treatability of Co-occurring Inorganic Chemicals in Groundwater.

Accurately assessing and managing risks associated with inorganic pollutants in groundwater is imperative. Historic water quality databases are often sparse due to rationale or financial budgets for sample collection and analysis, posing challenges in evaluating exposure or water treatment effectiveness. We utilized and compared two advanced multiple data imputation techniques, AMELIA and MICE algorithms, to fill gaps in sparse groundwater quality data sets. AMELIA outperformed MICE in handling missing values, as MICE tended to overestimate certain values, resulting in more outliers. Field data sets revealed that 75% to 80% of samples exhibited no co-occurring regulated pollutants surpassing MCL values, whereas imputed values showed only 15% to 55% of the samples posed no health risks. Imputed data unveiled a significant increase, ranging from 2 to 5 times, in the number of sampling locations predicted to potentially exceed health-based limits and identified samples where 2 to 6 co-occurring chemicals may occur and surpass health-based levels. Linking imputed data to sampling locations can pinpoint potential hotspots of elevated chemical levels and guide optimal resource allocation for additional field sampling and chemical analysis. With this approach, further analysis of complete data sets allows state agencies authorized to conduct groundwater monitoring, often with limited financial resources, to prioritize sampling locations and chemicals to be tested. Given existing data and time constraints, it is crucial to identify the most strategic use of the available resources to address data gaps effectively. This work establishes a framework to enhance the beneficial impact of funding groundwater data collection by reducing uncertainty in prioritizing future sampling locations and chemical analyses.

Demonstrating almost half of cotton fiber quality variation is attributed to climate change using a hybrid machine learning-enabled approach

Understanding the effects of climate change on cotton fiber quality will reduce the risks to production caused by global warming. Machine learning algorithms are effective for forecasting climate impacts on crops. However, the impact of climate change on cotton fiber quality is unclear. Hence, a hybrid machine learning-enabled approach, the Bayesian model average (BMA) method with multiple machine learning algorithms (linear regressor, SVR, RFR, GBDT, LightGBM, and XGBoost) and bootstrap resampling, was developed to explore the impact and screen the important climatic factors affecting various traits of fiber quality. On the basis of fiber quality data from 1033 test stations across Xinjiang, China, from 2016 to 2022, the explained variance for climate change in the hybrid machine learning model was as follows: 44.72 %–50.55 % for white cotton grade, 44.06 %–53.95 % for length, 51.72 %–56.81 % for micronaire, 32.70 %–49.50 % for length uniformity, and 45.66 %–53.09 % for strength in the 1000 bootstrapping samples. In addition, recursive feature elimination with cross-validation (RFECV) was used to select the optimal feature set and calculate the contribution of each feature. The variability in micronaire in the hybrid model was affected primarily by climate factors, such as the daily minimum temperature, rainfall, and wind speed, whereas the other quality traits were affected mainly by radiation-related climatic indicators. The climate during the harvest stage in October had a significant effect on cotton quality, explaining 33.0 % of the variance in white cotton grade, 32.1 % in length, and 48.3 % in fiber strength. Conversely, the climate during the boll opening and early harvest stages in September had a greater influence on micronaire and length uniformity, accounting for 21.4 % and 37.2 % of the variance, respectively. This study highlights that climate change explains nearly 50 % of the variation in fiber quality, with the influence being notably more considerable during the later stages of the cotton growth period. These findings clarify the uncertainty in the impact of climate change on cotton fiber quality considering the uncertainty of the single machine model and model errors. Equally important, this information can be valuable for farmers and growers seeking to improve fiber quality under climate change.

Automated Water Quality Assessment Using Big Data Analytics

Water is one of the world's most precious resources, essential to life. Industrial waste, agricultural runoff, and urban discharge degrade water, rendering it unfit for consumption. Water quality monitoring and evaluation are more important than ever. Big Data analytics is used to examine water quality utilizing enormous datasets of pH, hardness, solids concentration, chloramine, sulfate, conductivity, organic carbon, trihalomethanes, and turbidity. This work classifies water potability, which is vital for human consumption, using strong machine learning on massive datasets. Classifiers were Random Forest, Gradient Boosting, and Support Vector Machine on 3,276 water bodies. The Random Forest classifier obtained the highest accuracy at 66.77% after significant data preparation and training, followed by Gradient Boosting at 66.01% and SVM at 62.80%. This shows that Big Data analytics and machine learning algorithms can interpret complex water quality data for public health and natural resource management. The Random Forest classifier and SVM in this study accurately calculate water potability. Prediction algorithms consider water cleanliness data and may aid public safety and water resource monitoring.

Electronic Health Record Data Quality and Performance Assessments: Scoping Review.

Electronic health records (EHRs) have an enormous potential to advance medical research and practice through easily accessible and interpretable EHR-derived databases. Attainability of this potential is limited by issues with data quality (DQ) and performance assessment. This review aims to streamline the current best practices on EHR DQ and performance assessments as a replicable standard for researchers in the field. PubMed was systematically searched for original research articles assessing EHR DQ and performance from inception until May 7, 2023. Our search yielded 26 original research articles. Most articles had 1 or more significant limitations, including incomplete or inconsistent reporting (n=6, 30%), poor replicability (n=5, 25%), and limited generalizability of results (n=5, 25%). Completeness (n=21, 81%), conformance (n=18, 69%), and plausibility (n=16, 62%) were the most cited indicators of DQ, while correctness or accuracy (n=14, 54%) was most cited for data performance, with context-specific supplementation by recency (n=7, 27%), fairness (n=6, 23%), stability (n=4, 15%), and shareability (n=2, 8%) assessments. Artificial intelligence-based techniques, including natural language data extraction, data imputation, and fairness algorithms, were demonstrated to play a rising role in improving both dataset quality and performance. This review highlights the need for incentivizing DQ and performance assessments and their standardization. The results suggest the usefulness of artificial intelligence-based techniques for enhancing DQ and performance to unlock the full potential of EHRs to improve medical research and practice.

Harmonizing data across the accelerating colorectal cancer screening and follow-up through implementation science (ACCSIS) program to enhance data quality and promote data sharing

BackgroundThe purpose of the Accelerating Colorectal Cancer Screening and Follow-up through Implementation Science (ACCSIS) Program, a Cancer Moonshot℠ Initiative, is to support research to build the evidence base on multilevel interventions that increase rates of colorectal cancer screening, follow-up, and referral to care to address disparities in colorectal cancer screening. The National Cancer Institute funded eight Research Projects to implement multilevel interventions to improve colorectal cancer screening among communities who traditionally have been medically underserved. To analyze the impact of ACCSIS across Research Projects, the consortium developed a set of common data elements. The purpose of this paper is to describe the process of developing the common data elements to facilitate analysis of ACCSIS data as well as support and inform implementation science research studies.MethodsThe ACCSIS Data, Design, and Analysis Work Group was tasked with designing common data elements through a review of existing data collection instruments, examination of data elements proposed by the ACCSIS Research Projects, and deliberations on the data required to compare across the Research Projects. ACCSIS Consortium members drafted, revised, and finalized a common data elements document consisting of variables to collect and surveys to administer to evaluate ACCSIS implementation activities and outcomes in a standardized manner across the Research Projects.ResultsThe ACCSIS Consortium decided to collect the following categories of common data elements: characteristics across multiple levels of the multicomponent interventions, implementation climate, and determinants; interventions and strategies; implementation outcome constructs and definitions; colorectal cancer screening episodes (screening, diagnostic testing follow-up, cancer detection, and cancer treatment); and cost measures. To assess implementation climate, the consortium prioritized constructs from five domains of the Consolidated Framework for Implementation Research.ConclusionsThe ACCSIS common data elements offer a set of harmonization data for future implementation efforts. The consortium is conducting a systematic assessment using both quantitative and qualitative approaches to assess data quality and approaches to improve and sustain data collection. Lessons learned from these ongoing activities will offer additional insights to tailor the ACCSIS common data elements and support efforts to increase colorectal cancer screening for populations experiencing disparities.

Wearable device data-driven athlete injury detection and rehabilitation monitoring algorithm

In recent years, sports wearable technology has completely changed the way athletes prepare, compete, and recover. Wearable technology has a lot to offer in the rehabilitation process, which is essential to an athlete’s return to their best performance. Wearable devices for athlete injury detection pose potential challenges like data quality, security, and privacy, impacting accuracy, reliability, and effectiveness. To solve these problems, an innovative injury detection and rehabilitation monitoring (IDRM) system was proposed for athletes. By employing an adjustable recurrent neural network (ARNN) to detect anomalies in injury risks such as abnormal joint movements in athletes. In this study, biomechanics data was collected from sports athletes through wearable devices, and the wearable system provided feedback to the user. A redefined convolutional neural network (RCNN) was utilized to monitor the rehabilitation process. This system tracks athlete’s rehabilitation progress and ensures that progress monitors were performed correctly, and the system, feasibility was evaluated on 10 healthy subjects performing 4 different rehabilitation exercises. Each exercise was performed four times monitoring and validation. The data was preprocessed using a Gaussian filter to remove noise from the obtained data. Then the features are extracted using independent component analysis (ICA) for dimensionality reduction from preprocessed data. The proposed method is implemented using Python software. In comparative analysis, the performance of ARNN showed high performance, with an F1-measure of 91.6%, accuracy of 93.5%, recall of 92.8%, and precision of 91.4%. With a 95% accuracy rate, 98% F1 measure, 94% precision, and 93% recall, the RCNN model functioned effectively. The result showed the proposed method achieved better performance in athlete injury detection and accurately recognizing all rehabilitation monitoring. This study provides a complete approach to athlete health management by highlighting the integration of rehabilitation monitoring and injury detection into an overall structure.

Conflict Resolution in the Nigerian Oil and Gas Construction Industry

Purpose: The high incidence of conflicts within this sector has significant ramifications, such as project delays, exceeding budgeted costs, and project abandonment (Oyewunmi et al., 2018). Cultural and linguistic disparities, inadequate communication, and competing interests among stakeholders are some of the causes of these conflicts. These conflicts are made worse by the intricate contractual arrangements, weak conflict resolution procedures, and weak regulatory frameworks that characterize the sector. Even though Nigeria's economy greatly benefits from the oil and gas sector, the frequency of conflicts impedes the sector's expansion and stability. Materials and Methodology: The methodology adopted is by secondary data which entails the examination of pre-existing data from many sources, including academic journals, industry reports, and government papers. The quality of the secondary data used in these studies depended on the reliability and validity of the sources, the methodology used in data collection, and the rigor of the analysis. Findings: The ecological consequences of oil production are a significant cause for conflict in the Nigerian Oil and Gas Construction sector. It is critical for all parties involved to collaborate to reduce this impact and ensure the industry's long-term viability. Regional interests frequently influence the oil sector, particularly in areas with abundant oil resources. These interests may occasionally result in confrontations with other parties. Implications to Theory, Practice and Policy: The research recommends that fairness and equity should be the watchword in distribution of the gains and pains of the economy so that no regions of the country will feel cheated. There should be transparent allocation of proceeds. The active participation of young individuals in the Niger Delta region in the formulation of policies that will directly impact their welfare is crucial and should be adopted.

Pioneering Museum Data Transformation: Unifying Legacy Systems and Enhancing Data Integrity

In the digital era, museums confront the challenge of modernising legacy data systems to align with current standards. Part of the RECODE (Rethinking Collections Data Ecosystems) Programme (Dupont et al. 2022) examines the complex transition from disparate departmental object models to a unified system, reflecting the broader museum community's struggle with data standardisation. The case study centres on consolidating five distinct data models accumulated over two decades, each tailored to different departmental needs under various constraints. The data mapping process identifies and aligns similar fields across models, facilitating the integration of analogous data points and exposing redundancies and unique practices embedded over years. Major challenges include data complexity, diversity, and quality issues (cleaning, standardizing, and deduplicating), compounded by the massive scale of the data: currently, the Natural History Museum London (NHM) has 262,831,590 records across 8,175 fields, and a total volume of 12 TB of data, which define both the ongoing data modelling work and our future data migration. A key innovation in our process is the immediate visibility of data issues that became apparent upon migrating to Amazon Web Services (AWS). AWS serves as the staging environment for our transition to the new NHM Collections Management System (CMS) and offers an unprecedented platform for directly addressing these challenges. It enables us to query all our data—across all departments and fields—in just seconds. This capability provides a comprehensive view that was previously unattainable. The core of this presentation is sharing "lessons learned" from navigating the intricacies of an ongoing CMS transition within a museum. The endeavor to untangle and unify diverse data models is a common challenge (IEEE Big Data Governance and Metadata Management Industry Connections Activity 2020,Wu et al. 2022, Wu et al. 2021,Little et al. 2022,Woodburn et al. 2022) highlighting the importance of community engagement and knowledge exchange. Our findings underscore the necessity of cross-departmental collaboration and the benefits of a data-driven data modelling approach. By sharing our journey and the developed comprehensive object model (Collier and Woodburn 2022), we aim to contribute valuable insights to museums undergoing similar transformations. The RECODE Programme sheds light on the practical aspects of CMS modernisation through the analytics and knowledge derived from over two decades of collections data and data use.

Clinical Efficacy and Complications of Spinal Surgery Unilateral Biportal Endoscopy for Cervical Spondylosis: A Systematic Review and Meta-Analysis

This systematic review and meta-analysis evaluated the clinical efficacy and complications of unilateral biportal endoscopy (UBE) for treating cervical spondylosis (CS), providing evidence-based guidance for optimal treatment decisions. Relevant studies on UBE for CS were identified through comprehensive searches of PubMed, Embase, Web of Science, and the Cochrane Library up to April 22, 2024. Studies included patients diagnosed with CS who underwent UBE surgery and reported outcomes such as VAS scores, NDI scores, and postoperative complications. Data extraction and quality assessment were performed independently by two reviewers. Statistical analyses were conducted using Stata 15, employing random-effects models due to significant heterogeneity (I2 > 50%). Nine publications, comprising 353 patients and 400 operation levels, met the inclusion criteria. The mean follow-up duration was 17.01 months, and the operative time for a single level was 71.29 minutes. The average VAS score for neck pain decreased from 6.08 preoperatively to 1.52 postoperatively, the average VAS score for arm pain decreased from 7.13 to 1.35, and the average NDI decreased from 34.61 to 6.83 at the final follow-up. The mean satisfaction rate (good or excellent using the Macnab criteria) was 91%. The overall complication rate was 5%, with the most frequent complication being a dural tear (1%), followed by nerve root injury or transient hypoesthesia (0.3%). UBE demonstrates strong efficacy and safety in treating CS, with significant improvements in pain and disability scores, shorter operative times, and lower complication rates.

Cloud-Native ETL: Integrating Databricks and Azure Data Factory for Scalable Data Processing in Enterprise Environments

This article examines the implementation of cloud-native ETL solutions leveraging Databricks and Azure Data Factory (ADF) for scalable data processing in enterprise environments. The article presents a comprehensive analysis of the architectural design, integration strategies, and performance optimization techniques for combining Databricks' powerful data transformation capabilities with ADF's robust workflow orchestration. Through a series of case studies and empirical evaluations, we demonstrate how this integrated approach addresses the challenges of big data processing, including scalability, flexibility, and cost-effectiveness. Our findings reveal significant improvements in processing efficiency and resource utilization, with observed reductions in ETL pipeline execution times by up to 40% and overall cloud infrastructure costs by 25%. The article also highlights best practices for data governance, security, and quality management within this framework. These insights provide valuable guidance for data engineers and IT professionals seeking to modernize their data processing infrastructure and harness the full potential of cloud-native ETL solutions.

Local fuzzy rough attribute reduction for large-scale mixed data with limited missing labels based on local fuzzy self information

The advent of the era of big data is accompanied by the generation of large-scale data of various types. Extracting the potential value and rules from such data has always been a challenge. Due to various external and internal factors, it is commonplace for large-scale data to exhibit the phenomenon of missing limited labels. In addressing a large-scale mixed information system with limited label missing (LSMDISLML), local neighborhood rough set model (LNRS-model) is typically employed. However, the identical neighborhood radius is often used by such model when confronted with numerical attributes, which could potentially attenuate the classification capability of the data. Local fuzzy rough set model (LFRS-model) can overcome this point. This paper studies local fuzzy rough attribute reduction for large-scale mixed data with limited missing labels based on LFRS-model via local fuzzy self information and overlap degree function. First, leveraging the statistical distribution of data as a foundation, fuzzy relations on the entire sample set are established, which has the advantage of being able to use different fuzzy similarity radii to calculate similarity, thereby adapting to different data distributions. Subsequently, the samples with missing labels are discarded as they constitute a small proportion of the entire sample set and have little impact on overall performance of dataset. The limited computing resources and storage space are focused on the sample set with complete labels (denoted as target set). Thereafter, based on the target set, local fuzzy λ-upper and lower approximations are defined, and LFRS-model is constructed. This model not only reduces processing time and sources of error in large-scale data but also improves data quality and enhances the reliability of the experimental results. Then, local fuzzy λ-self information is introduced and used to design a local fuzzy rough attribute reduction algorithm in a LSMDISLML. Furthermore, a overlap degree function is introduced to evaluate and reorder the attributes based on their importance, prioritizing the elimination of redundant attributes with high overlap and low importance from the preordered attribute set. This strategy effectively improves the efficiency of obtaining the optimal subset. Finally, a series of experiments are carried out. The experiment results demonstrate that the designed algorithm exhibits excellent performance in classification tasks and outlier detection tasks, surpassing existing four algorithms.

Assessing the Performance of Primary School Information Management Systems (PReM) in Data Provision: A Focus on Two Selected Public Primary Schools at Temeke Municipal Council, Dar es Salaam, Tanzania

The study aimed to assess the performance of Primary School Information Management Systems (PReM) in data provision at Dar es Salaam city. Specifically, the study aimed to determine the school-based requirements to deploy PReM for students - data collection, examine the impacts of PReM utilization, and explore stakeholders feeling on the use of PReM in education aspects from Mtoni Kijichi and Bwawani Primary School at Temeke Municipality, This study was guided by Information Systems Theory and Data Quality Theory. The study adopted a cross-sectional design collecting quantitative data through surveys and questionnaires. Interview and documentary review were utilized to collect the qualitative data. The study adopted non-probability and purposive sampling techniques for selecting 399 respondents (School pupils, parents, academic teachers, Ward Executive Officers, and Examination Administration and Certification Directorate staff) from the total population within the study area. Descriptive statistics (frequency), thematic and multiple linear regression models were used for analysis. Research findings from specific objective one portray that hardware with high capability has been purchased, stakeholder involvement before launching any information management system is important, and the implementation of the system must comply with regulations and pilot conducting before full implementation. Moreover, on the impacts of PReM utilization towards accuracy data provision, multiple linear regression results divulge that the independent variables (reduced workload, time savings, improved monitoring, data-driven interventions, enhanced learning outcomes, data accuracy as well as security and privacy) lead the positive relationship with PReM performance. Furthermore, respondents had positive feelings concerning the use of PReM in education aspects. The study conclude that availability of adequate hardware (Computers, tablets, and servers), reliable internet connectivity, robust network infrastructure, and compatibility with existing school management systems are the main school-based requirements for PReM deployment in student data collection. The study recommended that National Examinations Council of Tanzania (NECTA) in collaboration with MoEST had to ensure improved PReM infrastructure and training programs for smooth integration and better school management and performance.

HANZE v2.1: an improved database of flood impacts in Europe from 1870 to 2020

Abstract. Assessing long-term trends in flood losses and attributing them to climatic and socioeconomic changes requires comprehensive and systematic collection of historical information. Here, we present flood impact data for Europe that are part of the HANZE (Historical Analysis of Natural HaZards) database. The dataset covers riverine, pluvial, coastal, and compound floods that have occurred in 42 European countries between 1870 and 2020. The data were obtained by extensive data collection from more than 800 sources ranging from news reports through government databases to scientific papers. The dataset includes 2521 events characterized by at least one impact statistic: area inundated, fatalities, persons affected. or economic loss. Economic losses are presented both in the original currencies and price levels and with the inflation and exchange rate adjusted to the 2020 value of the euro. The spatial footprint of affected areas is consistently recorded using more than 1400 subnational units corresponding, with minor exceptions, to the European Union's Nomenclature of Territorial Units for Statistics (NUTS) level 3. Daily start and end dates, information on causes of the events, notes on data quality issues or associated non-flood impacts, and full bibliography of each record supplement the dataset. Apart from the possibility of downloading the data (https://doi.org/10.5281/zenodo.8410025; Paprotny, 2023a), the database can be viewed, filtered, and visualized online at https://naturalhazards.eu (last access: 4 November 2024). The dataset is designed to be complementary to HANZE-Exposure, a high-resolution model of historical exposure changes (such as population and asset values) and be easily usable in statistical and spatial analyses, including multi-hazard studies.

RlaNet: A Residual Convolution Nested Long-Short-Term Memory Model with an Attention Mechanism for Wind Turbine Fault Diagnosis

This paper proposes a new fault diagnosis model for wind power systems called residual convolution nested long short-term memory network with an attention mechanism (rlaNet). The method first preprocesses the SCADA data through feature engineering, uses the Hermite interpolation method to handle missing data, and uses the mutual information-based dimensionality reduction technique to improve data quality and eliminate redundant information. rlaNet combines residual networks and nested long short-term memory networks to replace traditional convolutional neural networks and standard long short-term memory architectures, thereby improving feature extraction and ensuring the abstractness and depth of the extracted features. In addition, the model emphasizes the weighted learning of spatiotemporal features in the input data, enhances the focus on key features, and improves training efficiency. Experimental results show that rlaNet achieves an accuracy of more than 90% in wind turbine fault diagnosis, showing good robustness. Furthermore, noise simulation experiments verify the model’s resistance to interference, providing a reliable solution for wind turbine fault diagnosis under complex operating conditions.

Retraction Note: Quantum optical techniques for quality data transmission process in cognitive networks

Retraction Note: Quantum optical techniques for quality data transmission process in cognitive networks

Data management practice of health extension workers and associated factors in Central Gondar Zone, northwest Ethiopia.

Generating quality data for decision-making at all levels of a health system is a global imperative. The assessment of the Ethiopian National Health Information System revealed that health information system resources, data management, dissemination, and their use were rated as "not adequate" among the six major components of the health system. Health extension workers are the frontline health workforce where baseline health data are generated in theEthiopian health system. However, the data collected, compiled, and reported by health extension workers are unreliable and of low quality. Despite huge problems in data management practices, there is a lack of sound evidence on how to overcome these health data management challenges, particularly among health extension workers. Thus, this study aimed to assess data management practices and their associated factors among health extension workers in theCentral Gondar Zone. An institution-based cross-sectional study was conducted among 383 health extension workers. A simple random sampling method was used to select districts, all health extension workers were surveyed in the selected districts, and a structured self-administered questionnaire was used for data collection. The data was entered using EpiData version 4.6 and analyzed using STATA, version 16. Bivariable and multivariable binary logistic regression analyses were executed. An odds ratio with a 95% confidence interval and a p-value of <0.05 was calculated to determine the strength of theassociation and to evaluate statistical significance, respectively. Of the 383 health extension workers enrolled, all responded to the questionnaire with a response rate of 100%. Furthermore, 54.7% ofthe respondents had good data management practices. In the multivariable logistic regression analysis, being a married woman, having good data management knowledge, having agood attitude toward data management, having 1-5 years of working experience, and havingasalary ranging from 5,358 to 8,013 Ethiopian Birr were the factors significantly associated with good data management practices among health extension workers. The overall data management practice was poor with only five health extension workers out of ten having good data management practices.

Multi-classification prediction of PM2.5 concentration based on improved adaptive boosting rotation forest

Developing efficient, stable, and user-friendly methods and technologies for predicting air quality has contributed to environmental research and management. Most traditional machine learning (ML) models often struggle to efficiently process extensive air quality data and grapple with the challenge of imbalanced data distributions. To this end, we introduced a novel multi-strategy collaborative approach that incorporates weighted feature selection, an adaptive enhanced rotation forest algorithm, and Bayesian Optimization for parameter tuning. Moreover, to improve the transparency in black box ML models, the novel Shapley Additive Explanations (SHAP) method was applied to interpret the outputs and analyze the importance of individual variables. Through experiments on real air quality datasets, we have verified the accuracy of our proposed method in classifying air quality levels. Notably, our model demonstrated an average test set accuracy improvement of approximately 10 % in cities like Beijing, Tianjin, Shijiazhuang, and Baoding after hyperparameter optimization using Bayesian methods. Furthermore, compared to alternative algorithms, our model showed an improvement of 1–2 % in evaluation metrics. By introducing novel methodologies and tools, our research contributes significantly to the advancement of air quality classification technology and holds profound implications for informing environmental policy decisions.