Assessment of International GNSS Service Global Ionosphere Map products over China region based on measurements from the Crustal Movement Observation Network of China

The global ionosphere maps(GIM)provided by the International GNSS Service(IGS)are extensively utilized for ionospheric morphology monitoring,scientific research,and practical application.Assessing the credibility of GIM products in data-sparse regions is of paramount importance.In this study,measurements from the Crustal Movement Observation Network of China(CMONOC)are leveraged to evaluate the suitability of IGS-GIM products over China region in 2013-2014.The indices of mean error(ME),root mean square error(RMSE),and normalized RMSE(NRMSE)are then utilized to quantify the accuracy of IGS-GIM products.Results revealed distinct local time and latitudinal dependencies in IGS-GIM errors,with substantially high errors at nighttime(NRMSE:39%)and above 40°latitude(NRMSE:49%).Seasonal differences also emerged,with larger equinoctial deviations(NRMSE:33.5%)compared with summer(20%).A preliminary analysis implied that the irregular assimilation of sparse IGS observations,compounded by China’s distinct geomagnetic topology,may manifest as error variations.These results suggest that modeling based solely on IGS-GIM observations engenders inadequate representations across China and that a thorough examination would proffer the necessary foundation for advancing regional total electron content(TEC)constructions.

Comparison of Adaptive Simulation Observation Experiments of the Heavy Rainfall in South China and Sichuan Basin

This study examines the effectiveness of adaptive observation experiments using the ensemble transformation sensitivity(ETS) method to improve precipitation forecasts during heavy rainfall events in South China and the Sichuan Basin. High-resolution numerical models are employed to simulate adaptive observations. By identifying the sensitive areas of key weather system positions 42 hours before heavy rainfall events, the adaptive observations improve the prediction of jet streams, strong winds, and shear lines, which are essential for accurate heavy rainfall forecasting. This improvement is reflected in both the precipitation structure and location accuracy within the verification region. In South China, targeted observations enhance rainfall predictions by improving water vapor transport. In the Sichuan Basin, adaptive observations refine water vapor transport and adjust vortex dynamics. This research highlights the importance of accurately predicting shear lines and jet streams for forecasting heavy rainfall in these areas. Overall, this study found that adaptive observation enhances the precipitation forecast skills of the structure and location for heavy rainfall in South China and the Sichuan Basin, emphasizing their potential utility in operational numerical weather prediction.

Comparative Study of Satellite Precipitation and Synoptic Observations in the Republic of Guinea

This research work involves a comparative study of satellite rainfall and synoptic observations in the Republic of Guinea over a 30-year period.The methodology used consists,firstly,in assessing rainfall trends over the study period in Guinea’s four natural regions,using the temporal averages of the three stations located in each region.Secondly,we calculated the correlations between synoptic and satellite observation data,in order to determine the links between them on the basis of data analysis.The results for synoptic stations on average seasonal rainfall cycles and satellite products show that in Lower Guinea,the CRU(Climatic Research Unit)and GPCC(Global Precipitation Climatology Center)data are good estimates of observations.In the Fouta Djallon region,they also estimate observations well,but at two synoptic stations,with the exception of Mamou,they underestimate them.In Upper Guinea,during the monsoon period,satellites give a good estimate of rainfall in this area.In the forest region,these products show highly variable behavior,sometimes underestimating and sometimes overestimating observations,depending on the stations in the zone.

Examining the Quality Metrics of a Communication Network with Distributed Software-Defined Networking Architecture

Software-Defined Networking(SDN),with segregated data and control planes,provides faster data routing,stability,and enhanced quality metrics,such as throughput(Th),maximum available bandwidth(Bd(max)),data transfer(DTransfer),and reduction in end-to-end delay(D(E-E)).This paper explores the critical work of deploying SDN in large-scale Data Center Networks(DCNs)to enhance its Quality of Service(QoS)parameters,using logically distributed control configurations.There is a noticeable increase in Delay(E-E)when adopting SDN with a unified(single)control structure in big DCNs to handle Hypertext Transfer Protocol(HTTP)requests causing a reduction in network quality parameters(Bd(max),Th,DTransfer,D(E-E),etc.).This article examines the network performance in terms of quality matrices(bandwidth,throughput,data transfer,etc.),by establishing a large-scale SDN-based virtual network in the Mininet environment.The SDN network is simulated in three stages:(1)An SDN network with unitary controller-POX to manage the data traffic flow of the network without the server load management algorithm.(2)An SDN network with only one controller to manage the data traffic flow of the network with a server load management algorithm.(3)Deployment of SDN in proposed control arrangement(logically distributed controlled framework)with multiple controllers managing data traffic flow under the proposed Intelligent Sensing Server Load Management(ISSLM)algorithm.As a result of this approach,the network quality parameters in large-scale networks are enhanced.

Unveiling the Predictive Capabilities of Machine Learning in Air Quality Data Analysis: A Comparative Evaluation of Different Regression Models

Air quality is a critical concern for public health and environmental regulation. The Air Quality Index (AQI), a widely adopted index by the US Environmental Protection Agency (EPA), serves as a crucial metric for reporting site-specific air pollution levels. Accurately predicting air quality, as measured by the AQI, is essential for effective air pollution management. In this study, we aim to identify the most reliable regression model among linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression, and K-nearest neighbors (KNN). We conducted four different regression analyses using a machine learning approach to determine the model with the best performance. By employing the confusion matrix and error percentages, we selected the best-performing model, which yielded prediction error rates of 22%, 23%, 20%, and 27%, respectively, for LDA, QDA, logistic regression, and KNN models. The logistic regression model outperformed the other three statistical models in predicting AQI. Understanding these models' performance can help address an existing gap in air quality research and contribute to the integration of regression techniques in AQI studies, ultimately benefiting stakeholders like environmental regulators, healthcare professionals, urban planners, and researchers.

The Construction and Implementation Path of the Audit System for Quality Control in the Big Data Era

In the era of big data,the construction and implementation of a quality control audit system are particularly crucial.This article delves into the impact of big data technology on quality control auditing,establishes a quality control auditing system in the big data era,and elucidates the pathway to realizing this system.Through the application of big data technology to quality control audits,there is an enhancement in audit efficiency,the attainment of more accurate risk assessment,and the provision of robust support for the sustainable development of enterprises.

Design and Application of an S-band Fast Radio Bursts Search Pipeline for the Nanshan 26 m Radio Telescope

Fast radio bursts(FRBs)are among the most studied radio transients in astrophysics,but their origin and radiation mechanism are still unknown.It is a challenge to search for FRB events in a huge amount of observational data with high speed and high accuracy.With the rapid advancement of the FRB research process,FRB searching has changed from archive data mining to either long-term monitoring of the repeating FRBs or all-sky surveys with specialized equipments.Therefore,establishing a highly efficient and high quality FRB search pipeline is the primary task in FRB research.Deep learning techniques provide new ideas for FRB search processing.We have detected radio bursts from FRB 20201124A in the L-band observational data of the Nanshan 26 m radio telescope(NSRT-26m)using the constructed deep learning based search pipeline named dispersed dynamic spectra search(DDSS).Afterwards,we further retrained the deep learning model and applied the DDSS framework to S-band observations.In this paper,we present the FRB observation system and search pipeline using the S-band receiver.We carried out search experiments,and successfully detected the radio bursts from the magnetar SGR J1935+2145and FRB 20220912A.The experimental results show that the search pipeline can complete the search efficiently and output the search results with high accuracy.

Block Incremental Dense Tucker Decomposition with Application to Spatial and Temporal Analysis of Air Quality Data

How can we efficiently store and mine dynamically generated dense tensors for modeling the behavior of multidimensional dynamic data?Much of the multidimensional dynamic data in the real world is generated in the form of time-growing tensors.For example,air quality tensor data consists of multiple sensory values gathered from wide locations for a long time.Such data,accumulated over time,is redundant and consumes a lot ofmemory in its raw form.We need a way to efficiently store dynamically generated tensor data that increase over time and to model their behavior on demand between arbitrary time blocks.To this end,we propose a Block IncrementalDense Tucker Decomposition(BID-Tucker)method for efficient storage and on-demand modeling ofmultidimensional spatiotemporal data.Assuming that tensors come in unit blocks where only the time domain changes,our proposed BID-Tucker first slices the blocks into matrices and decomposes them via singular value decomposition(SVD).The SVDs of the time×space sliced matrices are stored instead of the raw tensor blocks to save space.When modeling from data is required at particular time blocks,the SVDs of corresponding time blocks are retrieved and incremented to be used for Tucker decomposition.The factor matrices and core tensor of the decomposed results can then be used for further data analysis.We compared our proposed BID-Tucker with D-Tucker,which our method extends,and vanilla Tucker decomposition.We show that our BID-Tucker is faster than both D-Tucker and vanilla Tucker decomposition and uses less memory for storage with a comparable reconstruction error.We applied our proposed BID-Tucker to model the spatial and temporal trends of air quality data collected in South Korea from 2018 to 2022.We were able to model the spatial and temporal air quality trends.We were also able to verify unusual events,such as chronic ozone alerts and large fire events.

Data reliability of the emerging citizen science in the Greater Bay Area of China

The potential of citizen science projects in research has been increasingly acknowledged,but the substantial engagement of these projects is restricted by the quality of citizen science data.Based on the largest emerging citizen science project in the country-Birdreport Online Database(BOD),we examined the biases of birdwatching data from the Greater Bay Area of China.The results show that the sampling effort is disparate among land cover types due to contributors’ preference towards urban and suburban areas,indicating the environment suitable for species existence could be underrepresented in the BOD data.We tested the contributors’ skill of species identification via a questionnaire targeting the citizen birders in the Greater Bay Area.The questionnaire show that most citizen birdwatchers could correctly identify the common species widely distributed in Southern China and the less common species with conspicuous morphological characteristics,while failed to identify the species from Alaudidae;Caprimulgidae,Emberizidae,Phylloscopidae,Scolopacidae and Scotocercidae.With a study example,we demonstrate that spatially clustered bird watching visits can cause underestimation of species richness in insufficiently sampled areas;and the result of species richness mapping is sensitive to the contributors’ skill of identifying bird species.Our results address how avian research can be influenced by the reliability of citizen science data in a region of generally high accessibility,and highlight the necessity of pre-analysis scrutiny on data reliability regarding to research aims at all spatial and temporal scales.To improve the data quality,we suggest to equip the data collection frame of BOD with a flexible filter for bird abundance,and questionnaires that collect information related to contributors’ bird identification skill.Statistic modelling approaches are encouraged to apply for correcting the bias of sampling effort.

Digital Continuity Guarantee Approach of Electronic Record Based on Data Quality Theory

Since the British National Archive put forward the concept of the digital continuity in 2007,several developed countries have worked out their digital continuity action plan.However,the technologies of the digital continuity guarantee are still lacked.At first,this paper analyzes the requirements of digital continuity guarantee for electronic record based on data quality theory,then points out the necessity of data quality guarantee for electronic record.Moreover,we convert the digital continuity guarantee of electronic record to ensure the consistency,completeness and timeliness of electronic record,and construct the first technology framework of the digital continuity guarantee for electronic record.Finally,the temporal functional dependencies technology is utilized to build the first integration method to insure the consistency,completeness and timeliness of electronic record.

EXPERIMENTAL APPLICATION OF A THINNING SCHEME FOR THE ASSIMILATION OF SURFACE OBSERVATION DATA IN GRAPES-3DVAR

To reduce the spatial correlation of representation error in observations and computational complexity, we propose a thinning scheme that can extract typical observations within a certain range. This scheme is applied to the Global/Regional Assimilation and Prediction System(GRAPES) with three-dimensional variation(3 DVAR) to study the effect of the thinning radius on the assimilation results. The assimilation experiments indicate that when the ratio of the model resolution to the observational resolution is 1:3, the simulated results for precipitation are relatively good and have a relatively high equitable threat score(ETS). Moreover, the analysis errors in the temperature and the specific humidity are the smallest, the dependence of the norm gradient vector of the objective function on the number of iterations is slow, gentle, and close to 0, and the minimization results in improved conditions.

Comparison of remote sensing data with in-situ wind observation during the development of the South China Sea monsoon

Wind measurements derived from QuikSCAT data were compared with those measured by anemometer on Yongxing Island in the South China Sea (SCS) for the period from April 2008 to November 2009. The comparison confirms that QuikSCAT estimates of wind speed and direction are generally accurate, except for the extremes of high wind speeds (>13.8m/s) and very low wind speeds (<1.5m/s) where direction is poorly predicted. In-situ observations show that the summer monsoon in the northern SCS starts between May 6 and June 1. From March 13, 2010 to August 31, 2010, comparisons of sea surface temperature (SST) and rainfall from AMSR-E with data from a buoy located at Xisha Islands, as well as wind measurements derived from ASCAT and observations from an automatic weather station show that QuikSCAT, ASCAT and AMSR-E data are good enough for research. It is feasible to optimize the usage of remote-sensing data if validated with in-situ measurements. Remarkable changes were observed in wind, barometric pressure, humidity, outgoing longwave radiation (OLR), air temperature, rainfall and SST during the monsoon onset. The eastward shift of western Pacific subtropical high and the southward movement of continental cold front preceded the monsoon onset in SCS. The starting dates of SCS summer monsoon indicated that the southwest monsoon starts in the Indochinese Peninsula and forms an eastward zonal belt, and then the belt bifurcates in the SCS, with one part moving northeastward into the tropical western North Pacific, and another southward into western Kalimantan. This largely determined the pattern of the SCS summer monsoon. Wavelet analysis of zonal wind and OLR at Xisha showed that intra-seasonal variability played an important role in the summer. This work improves the accuracy of the amplitude of intra-seasonal and synoptic variation obtained from remote-sensed data.

Novel method for the evaluation of data quality based on fuzzy control

One of the goals of data collection is preparing for decision-making, so high quality requirement must be satisfied. Rational evaluation of data quality is an effective way to identify data problem in time, and the quality of data after this evaluation is satisfactory with the requirement of decision maker. A fuzzy neural network based research method of data quality evaluation is proposed. First, the criteria for the evaluation of data quality are selected to construct the fuzzy sets of evaluating grades, and then by using the learning ability of NN, the objective evaluation of membership is carried out, which can be used for the effective evaluation of data quality. This research has been used in the platform of ＇data report of national compulsory education outlay guarantee＇ from the Chinese Ministry of Education. This method can be used for the effective evaluation of data quality worldwide, and the data quality situation can be found out more completely, objectively, and in better time by using the method.

Effect of the Fu Zheng Method Applied by Hong-Feng CHEN on the Quality of Life in TNBC Patients and Empirical Research Based on Data Mining

Objective To preliminarily evaluate the efficacy of the Fu Zheng method(supporting resistance against pathogenic factors)applied by Professor Hong-Feng CHEN in improving the quality of life in patients with triple-negative breast cancer(TNBC)to collect and organize the traditional Chinese medicine(TCM)formulas applied by Professor Hong-Feng CHEN in the treatment of TNBC in order to explore the patterns in prescription.Methods The Functional Assessment of Cancer Therapy-Breast scale(FACT-B V4.0)were collected before and after treatment;the database and data mining platform for prescribed TCM formulas were constructed using Microsoft SQL Server 2005,and the patterns in drug pairing and combination were summarized by correlation analysis of data mining.Results The formulas improved the mean scores of the patients’physical well-being,social/family well-being,emotional well-being,physical function well-being and additional concerns(P<0.01);the drug combinations and pairs frequently used by Professor Hong-Feng CHEN were summarized.Conclusion The TCM formulas applied by Professor Hong-Feng CHEN can alleviate adverse physiological reactions,improve psychological conditions and improve function in patients.The formulas take spleen invigoration and stomach nourishment as well as blood circulation promotion and stagnation dissipation as the therapeutic principles,with simplicity in prescription and focus on care and protection the foundation of acquired constitution.

The quality examination of observative data at Geomagnetic observatories

The basic task of geomagnetic observatory is .to produce accurate, relaible,continuous and complete observative data. The aim of examination is to judge the quality status of data. According to the operative principle of geomagnetic instruments and its operative status that should be achieved, geomagnetic activity and spread characteristics in time domain and location domain, authers proposed a complete set of data quality examination. The paper discusses respectively physical basement, examination method and the result about scalevalues, base-line values, monthly mean values, daily mean values, maximum and minimum values in daily range, magnetic storm and K index. The practice has proved that this set of examination is feasible and useful to raise and to guarantee the quality of observative data.

Analyses on normal background characteristics about deformation observation data on the basis of wavelet transform method

Wavelet transform method is applied to measure time-frequency distribution characteristics of digital deformation data and noise. Based on the characteristics of primary modulus and stochastic white noise discrimination factor of wavelet decomposition, we analyze the variation rule of normal background and noise data from Shandong digital deformation observation data. The research results indicate that: a) 1/4 daily wave, semi-diurnal tide wave, daily wave and half lunar wave and so on quasi-periodic signal exist in the detail decomposing signal of wavelet when scale are equal to 2, 3 and 4; b) The amplitude of detail decomposing signal is the biggest when scale is equal to 3; c) The detail decomposing signal contains mainly noise corresponding to scale 1 and 5, respectively; d) We may trace the abnormal precursory which is related to earthquake by analyzing non-earthquake wavelet decomposing signal whose scale is specified from digital deformation observation data.

Study on Rear-end Real-time Data Quality Control Method of Regional Automatic Weather Station

[Objective] The aim was to study the rear-end real-time data quality control method of regional automatic weather station. [Method] The basic content and steps of rear-end real-time data quality control of regional automatic weather station were introduced. Each element was treated with systematic quality control procedure. The existence of rear-end real time data of regional meteorological station in Guangxi was expounded. Combining with relevant elements and linear changes, improvement based on traditional quality control method was made. By dint of evaluation and relevant check of element, the quality of temperature and pressure was controlled. [Result] The method was optimized based on traditional quality control method, and it narrowed the effectiveness of real-time data quality control. The quality check of hourly precipitation applied relevant check of hourly minimum temperature, vertical consistency check of radar data, which can effectively improve the accuracy and credibility of hourly precipitation quality control. [Conclusion] The method was on trial for one year in the quality control of real-time data in the regional automatic meteorological station in Guangxi and had gained good outcome.

An Analysis of Observed Ocean Currents Data and Numerical Simulation in the Offshore Area of Jiaonan

Based on the diurnal consecutively observed data in the offshore area of Jiaonan in 2005, the paper tries to make a preliminary analysis of the specificity of ocean currents, tidal current property and residual current property in the area in observing dates. Then on the basis of observed data analysis and by employing the split-step method, the paper conducts a numerical simulation of the tidal current field, which can show the M2 tidal constituent tidal wave system, current ellipse distribution, maximum current velocity distribution and time-dependent current field. The calculated results agree well with the observed data, which can on the one hand reflect the basic specificities of temporal and spatial distribution of the M2 tidal constituent current field to some extent, and, on the other hand, offer more information about the hydrodynamic condition. So the paper would provide a scientific basis for the making of sea environment protection plans in the offshore area of Jiaonan under certain conditions.

A Short Review of the Literature on Automatic Data Quality

Several organizations have migrated to the cloud for better quality in business engagements and security. Data quality is crucial in present-day activities. Information is generated and collected from data representing real-time facts and activities. Poor data quality affects the organizational decision-making policy and customer satisfaction, and influences the organization’s scheme of execution negatively. Data quality also has a massive influence on the accuracy, complexity and efficiency of the machine and deep learning tasks. There are several methods and tools to evaluate data quality to ensure smooth incorporation in model development. The bulk of data quality tools permit the assessment of sources of data only at a certain point in time, and the arrangement and automation are consequently an obligation of the user. In ensuring automatic data quality, several steps are involved in gathering data from different sources and monitoring data quality, and any problems with the data quality must be adequately addressed. There was a gap in the literature as no attempts have been made previously to collate all the advances in different dimensions of automatic data quality. This limited narrative review of existing literature sought to address this gap by correlating different steps and advancements related to the automatic data quality systems. The six crucial data quality dimensions in organizations were discussed, and big data were compared and classified. This review highlights existing data quality models and strategies that can contribute to the development of automatic data quality systems.

Assessment of Knowledge and Practices of Community Health Nurses on Data Quality in the Ho Municipality of Ghana

Background: High data quality provides correct and up-to-date information which is critical to ensure, not only for the maintenance of health care at an optimal level, but also for the provision of high-quality clinical care, continuing health care, clinical and health service research, and planning and management of health systems. For the attainment of achievable improvements in the health sector, good data is core. Aim/Objective: To assess the level of knowledge and practices of Community Health Nurses on data quality in the Ho municipality, Ghana. Methods: A descriptive cross-sectional study was employed for the study, using a standard Likert scale questionnaire. A census was used to collect 77 Community Health Nurses’ information. The statistical software, Epi-Data 3.1 was used to enter the data and exported to STATA 12.0 for the analyses. Chi-square and logistic analyses were performed to establish associations between categorical variables and a p-value of less than 0.05 at 95% significance interval was considered statistically significant. Results: Out of the 77 Community Health Nurses studied, 49 (63.64%) had good knowledge on data accuracy, 51 (66.23%) out of the 77 Community Health Nurses studied had poor knowledge on data completeness, and 64 (83.12%) had poor knowledge on data timeliness out of the 77 studied. Also, 16 (20.78%) and 33 (42.86%) of the 77 Community Health Nurses responded there was no designated staff for data quality review and no feedback from the health directorate respectively. Out of the 16 health facilities studied for data quality practices, half (8, 50.00%) had missing values on copies of their previous months’ report forms. More so, 10 (62.50%) had no reminders (monthly data submission itineraries) at the facility level. Conclusion: Overall, the general level of knowledge of Community Health Nurses on data quality was poor and their practices for improving data quality at the facility level were woefully inadequate. Therefore, Community Health Nurses need to be given on-job training and proper education on data quality and its dimensions. Also, the health directorate should intensify its continuous supportive supervisory visits at all facilities and feedback should be given to the Community Health Nurses on the data submitted.