Accuracy Of Data Collection Research Articles

E-Gov. Application Integration Design In Indonesia

Decentralization gives authority to agencies / institutions / organizations in each province to plan the creation and development of applications tailored to their needs. Data related to the development is stored in each application of the organization which is periodically reported to the respective leader of government. The development data is very necessary for decision making in the next period development planning. Transparency and accountability are the absolute requirements for reporting development data, for that it is necessary to increase the speed and accuracy of data collection, so that progress in regional development can be monitored continuously and decision making can be carried out more quickly. Therefore, it is necessary to integrate applications from all applications found in agencies / institutions / organizations in each province.
 Application integration may use various technology such as : SOAP, REST, SSO, etc. This research is aimed at studying alternative E-Gov application integration architecture that could be implemented in several government agencies / institutions / organizations. Qualitative methods are used in this study, namely by studying some related literatures to obtain alternative architectures and then create new architecture and design. With the existence of an alternative integration architectures, it is expected to facilitate the design of application integration in more detail.

P33 Antimicrobial prescribing point prevalence study at a paediatric tertiary/quaternary centre

AimsIncreasing antibiotic resistant organisms combined with frequent, inappropriate use of antibiotics is giving rise to infections which may no longer be able to be treated1. The aim of this prevalence study was to audit antimicrobial prescribing at a Hospital against Trust antimicrobial policies to determine whether the rising trend in antimicrobial prescribing is appropriate.MethodsThe data was collected in a point prevalence manner; prescriptions that were active at the time of auditing were included and those which were discontinued or prescribed and not yet administered were excluded. A data collection template was designed and distributed to ward pharmacists with education on how to complete. The following parameters were audited; allergy status, antibiotic name, route, indication, duration, review date as well as the ward and speciality. Ward pharmacists assessed whether the prescription was in line with Trust guidelines/ID/Micro recommendations. Data was collected into a central database, as well time taken to audit.The audit standards were90% of patients prescribed an antimicrobial for an indication in line with Trust policy or ID/Micro90% of patients prescribed an antimicrobial for a duration in line with Trust policy or ID/Micro90% of patients have an allergy status documentedResults272 inpatient charts were reviewed. 153 of these patients (56%) were prescribed an antimicrobial.398 antibiotic prescriptions were included for audit. 38% of prescriptions were for medical/surgical prophylaxis. Prophylactic prescriptions were not included for further analysis. 85% of prescriptions had an indication documented either on the electronic chart (JAC) or written in the paper medical notes. 98% of prescriptions were as per policy or in line with recommendations from ID/Micro. 61% of prescriptions had a review date documented. 100% of patients had an allergy status documented. Average duration of antibiotic prescription was 8 days, range 1–50 days, median 5. 80% of prescriptions were IV. 70% of antimicrobial prescribing takes place in the ICU/cancer/transplant setting. Respiratory tract infections were the most common indication for antimicrobial prescribing, 35%. Amikacin was the most commonly prescribed antibiotic (15%), followed by piperacillin/tazobactam (14%). The audit cost in terms of pharmacist time was £763, at a total of 33 hours.ConclusionsPolicy compliant prescribing was very high at 98%; this figure is surprisingly high and poses questions as to the accuracy of data collection and whether bias was present. As a Trust we are now interested and will focus on improving intravenous to oral switches and reviewing and documenting patients’ antimicrobial therapy regularly. As a tertiary/quaternary centre we treat complicated immunocompromised patients; we are unlikely to lower the burden of infection. Approximately 75% of antimicrobial prescribing was in the ICU and cancer and transplant setting, however we must optimise the use of antimicrobials and demonstrate good antimicrobial stewardship. This data will act as a baseline for a subsequent audits which will be carried out using the newly implemented EPIC® patient management system.ReferenceGOV.UK. 2019. Antimicrobial Resistance (AMR). [ONLINE]Available at: https://www.gov.uk/government/collections/antimicrobial-resistance-amr-information-and-resources [Accessed 2 July 2019].

The Ground Control Station Design For Can-Sized Satellite (CanSAT) System

Nowadays, Natural disaster tragedy is now one of the world's biggest concerns. Can-sized satellite, MedSAT: Location-Aware CanSAT for On-Site Emergency Medical Supplies develop a platform for finding direction and accurately locating an emergency patient and providing emergency medical supplies such as bandages, antiseptic wipes, sterile gauze pads of various sizes, insulin, pills, syringe and antivenom, as well as real-time visual feed for medical diagnosis during and after landing. This project focuses on the design of MedSAT and provides a real-time system to capture MedSAT’s real-time data during descent. The objective of the real-time system is to improve the accuracy and location speed of MedSAT data collection which can provide readings of altitude, latitude and longitude to help MedSAT navigate to the patient location. Hardware design (flight controller, GPS module and telemetry kit), software design (Mission Planner) and real-time system (RTS) are the main components of this platform. In addition, the ground station was developed to communicate with users via wireless telemetry communication using MAVLink protocol. Based on the overall findings, MedSAT and ground station's compact and lightweight design was developed in search and rescue operations for emergency location.

Research on Error and Compensation of Volume Measurement System for Nondestructive Testing

In order to improve the measurement accuracy of the three-dimensional non-destructive testing device for slice volume, the experimental error caused by the hydraulic loss of the experimental container is analyzed and compensated. Through the analysis of the overflow characteristics, the weir flow model of the overflow is established, the profile curve of the overflow and the overflow round tube are optimized to reduce local losses, and verified by experiment. The results show that the measurement accuracy of the liquid volume is improved by about 54.85%, and this optimization scheme is effective. By optimizing the experimental device, the error is compensated to ensure the accuracy of data collection.

Rapid counting and spectral sorting of live coral larvae using large-particle flow cytometry

Research with coral embryos and larvae often requires laborious manual counting and sorting of individual specimens, usually via microscopy. Because many coral species spawn only once per year during a narrow temporal window, sample processing is a time-limiting step for research on the early life-history stages of corals. Flow cytometry, an automated technique for measuring and sorting particles, cells, and cell-clusters, is a potential solution to this bottleneck. Yet most flow cytometers do not accommodate live organisms of the size of most coral embryos (> 250 µm), and sample processing is often destructive. Here we tested the ability of a large-particle flow cytometer with a gentle pneumatic sorting mechanism to process and spectrally sort live and preserved Montipora capitata coral embryos and larvae. Average survival rates of mechanically-sorted larvae were over 90% and were comparable to those achieved by careful hand-sorting. Preserved eggs and embryos remained intact throughout the sorting process and were successfully sorted based on real-time size and fluorescence detection. In-line bright-field microscopy images were captured for each sample object as it passed through the flow-cell, enabling the identification of early-stage embryos (2-cell to morula stage). Samples were counted and sorted at an average rate of 4 s larva−1 and as high as 0.2 s larva−1 for high-density samples. Results presented here suggest that large-particle flow cytometry has the potential to significantly increase efficiency and accuracy of data collection and sample processing during time-limited coral spawning events, facilitating larger-scale and higher-replication studies with an expanded number of species.

Comfort and health evaluation of live mutton sheep during the transportation based on wearable multi-sensor system

The uncertainty of transport environment threatens the comfort and health of live mutton sheep and even affects the quality of meat after slaughter. Using modern information technology to solve the deficiencies of live animal centralized transport is of great significance for the stable development of animal husbandry. In this paper, the wearable multi-sensor system was specially designed, and the system test experiment and transport monitoring experiment were carried out. According to the continuous and real-time data of environmental and physiological parameters obtained from the transport monitoring experiment, the optimization extraction, data collection analysis, correlation analysis and simulation analysis of the internal environment in the carriage were carried out, and the prediction model between environmental and physiological parameters based on generalized regression neural network (GRNN) and the prediction model of comfort and health evaluation based on back propagation neural network (BPNN) were established. The results show that: (1) The wearable multi-sensor system had high accuracy and stability of data collection, and the power consumption and communication performance can meet the monitoring requirements. (2) The observation values of internal environmental parameters increased gradually with the accumulation of transport time, and finally reached the state of dynamic balance. The heart rate fluctuated greatly and was higher than the normal range. The blood oxygen saturation showed a gradual decrease trend, but the overall was in the normal range. The body temperature gradually increased, and was affected by discomfort. (3) The correlation analysis showed that there was a significant correlation between environmental and physiological parameters. (4) The area with larger value of environmental parameters was close to the middle and front of the carriage, which was mainly caused by the high density and poor air circulation. (5) The prediction model between environmental and physiological parameters based on GRNN and the prediction model of comfort and health evaluation based on BPNN had high prediction accuracy, and the combination of the two models can map the impact of environmental factors on the change of vital signs to the relationship between comfort and health. Therefore, in the case of unknown vital signs information, only obtaining environmental information can also predict the health levels, which can provide decision-making basis for relevant practitioners.

Adoption of Photoelectric Sensor in Automobile Electronic Detection

The continuous improvement of auto-related electronic detection technology has become a new direction of the development of the automobile industry. First, the ZigBee technology is compared with other wireless communication technologies, and the advantages of ZigBee technology are analyzed. Then it is used in automobile electronic detection jointly with photoelectric sensor. The wireless sensor network (WSN) composed of photoelectric sensor is introduced into automobile electronic detection platform, and a set of information acquisition system for automobile electronic detection based on ZigBee technology is designed. In the hardware field, CC2520 chip is selected as the main data transmission chip according to the actual needs of the system. The ZigBee node with CC2520 and photoelectric sensor as the core architecture is mainly discussed. At the same time, the anti-interference design of the node is added, so that the node has the ability to collect information such as tire pressure, speed, acceleration, temperature, and humidity. In the software field, IAR Embedded Workbench is used to debug the program of ZigBee node based on CC2520+ photoelectric sensor, so as to establish coordination points and optimize network performance. During the experiment, it is necessary to build a WSN based on photoelectric sensor and ZigBee technology. ZigBee nodes have fast response speed, accurate data return, and improved transmission speed. The WSN based on photoelectric sensor +ZigBee technology can maintain good communication quality and low packet loss rate. After applying it to speed information collection, the results further verify the accuracy of data collection of WSN established in this study.

Short-term Traffic Flow Prediction Based on Deep Learning

The accuracy of short-term traffic flow prediction is affected by two factors: one is the accuracy of data collection, and another is model selection. During data collection, aiming at the high cost and low precision of the traditional use of fixed equipment to collect traffic flow data, this paper proposes an algorithm model combining convolutional neural network and support vector regression, and designs an input matrix considering the influence degree of the road segment. The example is proved that the proposed algorithm model is better than the ARIMA and SVR model, and it is an effective traffic flow prediction method.

Efficient Data Collection Method in Sensor Networks

Wireless sensor networks are widely used in many fields, such as medical and health care, military monitoring, target tracking, and people’s life, because of their advantages of convenient deployment, low cost, and good concealment. However, due to the low battery capacity of sensor nodes and environmental changes, the energy consumption of nodes is serious and the accuracy of data collection is low. In the data collection method of multiple random paths, due to the uneven geographical distribution between nodes and the influence of the environment, it is easy to cause the communication between nodes to be blocked and the construction of random paths to fail. This paper proposes an efficient data collection algorithm for this problem. The algorithm is improved on the basis of the random node selection algorithm. This method can effectively avoid the failure of random path node selection and improve the node selection of random path in wireless sensor networks. Then, the sensor network in the dynamic environment is analyzed based on the static environment. An efficient data collection algorithm based on the position prediction of extreme learning machines is proposed. This method uses extreme learning machine methods to perform trajectory prediction for nodes in a dynamic environment.

Differential privacy in the 2020 US census: what will it do? Quantifying the accuracy/privacy tradeoff

Background: The 2020 US Census will use a novel approach to disclosure avoidance to protect respondents’ data, called TopDown. This TopDown algorithm was applied to the 2018 end-to-end (E2E) test of the decennial census. The computer code used for this test as well as accompanying exposition has recently been released publicly by the Census Bureau. Methods: We used the available code and data to better understand the error introduced by the E2E disclosure avoidance system when Census Bureau applied it to 1940 census data and we developed an empirical measure of privacy loss to compare the error and privacy of the new approach to that of a (non-differentially private) simple-random-sampling approach to protecting privacy. Results: We found that the empirical privacy loss of TopDown is substantially smaller than the theoretical guarantee for all privacy loss budgets we examined. When run on the 1940 census data, TopDown with a privacy budget of 1.0 was similar in error and privacy loss to that of a simple random sample of 50% of the US population. When run with a privacy budget of 4.0, it was similar in error and privacy loss of a 90% sample. Conclusions: This work fits into the beginning of a discussion on how to best balance privacy and accuracy in decennial census data collection, and there is a need for continued discussion.

Evaluating the influence of data collector training for predictive risk of death models: an observational study

BackgroundSeverity-of-illness scoring systems are widely used for quality assurance and research. Although validated by trained data collectors, there is little data on the accuracy of real-world data collection practices.ObjectiveTo evaluate...

Differential privacy in the 2020 US census: what will it do? Quantifying the accuracy/privacy tradeoff

Background: The 2020 US Census will use a novel approach to disclosure avoidance to protect respondents’ data, called TopDown. This TopDown algorithm was applied to the 2018 end-to-end (E2E) test of the decennial census. The computer code used for this test as well as accompanying exposition has recently been released publicly by the Census Bureau. Methods: We used the available code and data to better understand the error introduced by the E2E disclosure avoidance system when Census Bureau applied it to 1940 census data and we developed an empirical measure of privacy loss to compare the error and privacy of the new approach to that of a (non-differentially private) simple-random-sampling approach to protecting privacy. Results: We found that the empirical privacy loss of TopDown is substantially smaller than the theoretical guarantee for all privacy loss budgets we examined. When run on the 1940 census data, TopDown with a privacy budget of 1.0 was similar in error and privacy loss to that of a simple random sample of 50% of the US population. When run with a privacy budget of 4.0, it was similar in error and privacy loss of a 90% sample. Conclusions: This work fits into the beginning of a discussion on how to best balance privacy and accuracy in decennial census data collection, and there is a need for continued discussion.

Participatory Land Administration in Indonesia: Quality and Usability Assessment

This paper presents the results from a quality and usability analysis of participatory land registration (PaLaR) in Indonesia’s rural areas, focusing on data quality, cost, and time. PaLaR was designed as a systematic community-centered land titling project collecting requisite spatial and legal data. PaLaR was piloted in two communities situated in Tanggamus and Grobogan districts in Indonesia. The research compared spatial data accuracy between two approaches, PaLaR and the normal systematic land registration approach (PTSL) with respect to point accuracy and polygon area. Supplementary observations and interviews were undertaken in order to evaluate the effectiveness of the spatial and legal data collection, as well as logical consistency of the data collected by the community committee, using a mobile application. Although the two pilots showed a lower spatial accuracy than the normal method (PTSL), PaLaR better suited local circumstances and still delivered complete spatial and legal data in a more effective means. The accuracy and efficiency of spatial data collection could be improved through the use of more accurate GNSS antennas and a seamless connection to the national land databases. The PaLaR method is dependent on, amongst other aspects, inclusive and flexible community awareness programs, as well as the committed participation of the community and local offices.

Combining spatially balanced sampling, route optimisation and remote sensing to assess biodiversity response to reclamation practices on semi-arid well pads

ABSTRACT Biodiversity decline is widely considered a critical global environmental threat. In the western United States, land surface disturbance associated with oil and gas development is considered a top driver of habitat fragmentation and biodiversity decline. Land reclamation and ecosystem restoration activities help mitigate biodiversity loss, though monitoring practices to track these efforts are inconsistent, often lack measures of biodiversity response and are labour-intensive. Digital image analysis has been shown to reduce labour requirements and can provide robust, statistically valid reports on vegetation cover. We compare handheld image analysis to unmanned aerial system (UAS) image analysis to measure vegetation and ground cover on reclaimed oil and gas well pads. We utilise a spatially balanced sample design called balanced acceptance sampling along with a travelling salesperson algorithm to optimise walking and flight paths to obtain imagery in our study design. Images are then analysed with a free software program, ‘SamplePoint’, to classify vegetation on reclaimed well pads into functional groups. We conclude image acquisition is significantly faster with the UAS than with the handheld approach. We found that UAS image analysis and handheld analysis produced similar results in assessing vegetation and ground cover and we discuss pros and cons of each method. Key policy insights Rapid monitoring techniques which are statistically sound and provide robust datasets should help enhance knowledge of land reclamation practices in oil and gas fields. Unmanned aerial systems can cover well pads significantly faster than a human walking with a camera, and images gathered by each have similar results when vegetation is analyzed at the functional group level. Although slower, hand-held images may provide finer detail than UAS images flown 7.6 m above ground level, which may make hand-held images more useful for classifying vegetation to species-specific levels. Utilizing GPS technology along with spatially balanced sampling, route optimization, and digitial images increases speed of data collection and spatial accuracy of data compared to traditional line point intercept techniques.

Autoregressive integrated moving average model–based secure data aggregation for wireless sensor networks

Nodes in a wireless sensor network are normally constrained by hardware and environmental conditions and face challenges of reduced computing capabilities and system security vulnerabilities. This fact calls for special requirements for network protocol design, security assessment models, and energy-efficient algorithms. Data aggregation is an effective energy conservation technique, which removes redundant information from the data aggregated from neighbor sensor nodes. How to further improve the effectiveness of data aggregation plays an important role in improving data collection accuracy and reducing the overall network energy consumption. Unfortunately, sensor nodes are normally deployed in an open environment and thus are subject to various attacks conducted by adversaries. Consequently, data aggregation brings new challenges to wireless sensor network security. In this article, we propose a novel secure data aggregation solution based on autoregressive integrated moving average model, a time series analysis technique, to prevent private data from being learned by adversaries. We leverage the autoregressive integrated moving average model to predict the data volume in sensor nodes, and update and synchronize the model as needed. The experimental results demonstrate that our model provides accurate predictions and that, compared with competing methods, our solution achieves better security, lower computation and communication costs, and better flexibility.

Data Manager in a Bone Marrow Transplant Team in Brazil: Training, Certifications, International Connections and Dissemination

Introduction Regular analysis of demographic, clinical and outcome data is indispensable for ensuring and maintaining quality in a hematopoietic stem cell transplant (HSCT) center. For this, data needs to be extracted accurately. Organization accreditations such as FACT (Foundation for the Accreditation of Cellular Therapy) require submission of data to the US (CIBMTR) or the European (EBMT) Registries correctly. Due to discrepancies found between medical charts data and the data submitted to CIBMTR in the first FACT accreditation cycle (2012), our HSCT center decided to hire a dedicated data manager. Objective We describe here the training and performance of the data manager in the process of collecting and transferring HSCT data to national and international registries. Methods The professional hired as data manager received internal and external training. Externally, she attended national and international congresses and visited the CIBMTR (2016). Internally, she first received training by the clinical team about the forms to be filled in. Together with the HSCT multi-professional team, they evaluated the data collection process and defined which data should appear in the medical records. Appropriate outcomes were defined for data analysis based on literature searches, the FACT requirements and the need of the clinical staff, and the data manager was trained in data analysis using R. The clinical team together with data manager developed templates appropriate for the clinical follow-up of patients, such as the Dentistry to collect data on mucositis. The quality nurse developed a regular internal auditing system. Results One of the results was the reduction of three hours in filling out the forms (TED 2400). From the structuring, organization and updating of the database, it was possible to obtain relevant indicators on survival and cumulative incidence of acute and chronic graft versus host disease, transplant-related mortality and disease relapse. These are used by the HSCT team in clinical rounds and in critical analyses of the program performance. In re-accreditations (2015 and 2018) after hiring and training, no non-compliance was identified in item B9, related to the insertion and submission of transplant data. In 2017, the clinical team designed, with the data manager, an ethically-approved multicenter study until 2027, about on transplant-related outcomes, currently with 19 centers in Brazil that sending data to CIBMTR. Conclusion Training of the data manager at our HSCT center (a profession not official in Brazil) allowed the development of strategies and tools that resulted in greater accuracy in data collection and analysis, and FACT-compliant.

Human accuracy in mobile data collection

The collection of participant data ‘in the wild’ is widely employed by Human-Computer Interaction researchers. A variety of methods, including experience sampling, mobile crowdsourcing, and citizen science, rely on repeated participant contributions for data collection. Given this strong reliance on participant data, ensuring that the data is complete, reliable, timely, and accurate is key. Although previous work has made significant progress on ensuring that a sufficient amount of data is collected, the accuracy of human contributions has remained underexposed. In this article we argue for an emerging need for an increased focus on this aspect of human-labelled data. The articles published in this special issue demonstrate how a focus on the accuracy of the collected data has implications on all aspects of a study – ranging from study design to the analysis and reporting of results. We put forward a five-point research agenda in which we outline future opportunities in assessing and improving human accuracy in mobile data collection.

On-line Fault Diagnosis Method for Electronic Transformer Based on Wavelet Theory

Electronic transformer (ET) is wildly used in digital smart substation. However, the actual running time of ETs is generally short, and most of them have higher failure rate than that of traditional transformers. Since the accuracy and stability of ETs data collection affect the safe operation of smart grid, it is necessary to study the fault diagnosis method of ETs. According to the characteristics of ETs, this paper proposes an on-line fault diagnosis method of ET based on wavelet transform theory. After noise reduction the output signal waveform of the transformer, the high frequency decomposition coefficient is obtained by wavelet transform, so as to diagnose and locate the three kinds of faults: drift deviation fault, variable ratio deviation fault, and fixed deviation fault

Compressed Data Collection Method for Wireless Sensor Networks Based on Optimized Dictionary Updating Learning

Wireless sensor networks (WSNs) is composed of a large number of tiny sensors. These energy-constrained sensors are deployed in a variety of environments to collect data such as temperature, humidity, and light intensity. Therefore, how to suppress the impact of environmental noise on the collection accuracy and extend the lifetime of WSNs is one of the prominent issues. This article proposes an optimized dictionary updating learning-based compressed data collection algorithm (ODUL-CDC) to suppress the impact of environmental noise on the accuracy of WSNs data collection and extend the life cycle of WSNs. The proposed algorithm uses the dictionary learning method to obtain a sparse dictionary by learning from the training data. The collection error caused by environmental noise is positively correlated with the degree of self-coherence of the sparse dictionary. Therefore, the self-coherence penalty term is introduced during the dictionary updating process, which can reduce the over-fitting of the training data in the dictionary learning process. Moreover, the self-coherence penalty term endows the learned sparse dictionary with a low-self-coherence structure. Experimental and simulation results show that, as compared with discrete cosine transform(DCT), K-SVD and IDL learning-based data collection methods, the proposed algorithm exhibits the highest increase in recovery accuracy of 3.2% in the signal-to-noise ratio (SNR) range of 30-50 dB, the sampling ratio range of 25%-40% and the sparsity range from 3 to 30. Furthermore, the energy consumption is significantly less than that of the compared methods, which helps improve the network lifetime.

An evaluation of estimation data collection to trial‐by trial data collection during discrete trial teaching

There are many data collection procedures used during discrete trial teaching including first‐trial data collection, probe data, trial‐by‐trial data collection, and estimation data. Continuous, or trial‐by‐trial data collection, consists of the interventionist collecting data on learner behavior on each trial. Estimation data consists of the interventionist estimating learner performance after a teaching session using a rating scale. The purpose of the present study was to compare trial‐by‐trial data collection to estimation data collection during discrete trial teaching to teach children expressive labels. The data collection procedures were examined in terms of accuracy of data collection, efficiency of teaching (i.e., number of trials delivered per session), and rate of child acquisition of targets. Results of the adapted alternating treatment design replicated across three participants and multiple targets found estimation data collection to be as accurate as trial‐by‐trial data collection in determining mastery of targets. Estimation data collected by the interventionist was also found to be accurate when compared to the actual trial‐by‐trial data collected after the study concluded.