Data prediction for cases of incorrect data in multi-node electrocardiogram monitoring

The development of a mesh topology in multi-node electrocardiogram (ECG) monitoring based on the ZigBee protocol still has limitations. When more than one active ECG node sends a data stream, there will be incorrect data or damage due to a failure of synchronization. The incorrect data will affect signal interpretation. Therefore, a mechanism is needed to correct or predict the damaged data. In this study, the method of expectation-maximization (EM) and regression imputation (RI) was proposed to overcome these problems. Real data from previous studies are the main modalities used in this study. The ECG signal data that has been predicted is then compared with the actual ECG data stored in the main controller memory. Root mean square error (RMSE) is calculated to measure system performance. The simulation was performed on 13 ECG waves, each of them has 1000 samples. The simulation results show that the EM method has a lower predictive error value than the RI method. The average RMSE for the EM and RI methods is 4.77 and 6.63, respectively. The proposed method is expected to be used in the case of multi-node ECG monitoring, especially in the ZigBee application to minimize errors.

Category Trees - Classifiers that Branch on Category

This paper presents a batch classifier that splits a dataset into tree branches depending on the category type. It has been improved from the earlier version and fixed a mistake in the earlier paper. Two important changes have been made. The first is to represent each category with a separate classifier. Each classifier then classifies its own subset of data rows, using batch input values to create the centroid and also represent the category itself. If the classifier contains data from more than one category however, it needs to create new classifiers for the incorrect data. The second change therefore is to allow the classifier to branch to new layers when there is a split in the data, and create new classifiers there for the data rows that are incorrectly classified. Each layer can therefore branch like a tree - not for distinguishing features, but for distinguishing categories. The paper then suggests a further innovation, which is to represent some data columns with fixed value ranges, or bands. When considering features, it is shown that some of the data can be classified directly through fixed value ranges, while the rest must be classified using a classifier technique and the idea allows the paper to discuss a biological analogy with neurons and neuron links. Tests show that the method can successfully classify a diverse set of benchmark datasets to better than the state-of-the-art.

Day-Ahead Forecasting of Losses in the Distribution Network

Utility companies in the Nordics have to nominate how much electricity is expected to be lost in their power grid the next day. We present a commercially deployed machine learning system that automates this day-ahead nomination of the expected grid loss. It meets several practical constraints and issues related to, among other things, delayed, missing and incorrect data and a small data set. The system incorporates a total of 24 different models that performs forecasts for three sub-grids. Each day one model is selected for making the hourly day-ahead forecasts for each sub-grid. The deployed system reduced the mean average percentage error (MAPE) with 40% from 12.17 to 7.26 per hour from mid-July to mid-October, 2019. It is robust, flexible and reduces manual work. Recently, the system was deployed to forecast and nominate grid losses for two new grids belonging to a new customer. As the presented system is modular and adaptive, the integration was quick and needed minimal work. We have shared the grid loss data-set on Kaggle.

Notice of Retraction: Adaptive Decentralized Tracking Control for Nonlinear Large-Scale Systems

This article has been retracted at the request of the first and corresponding author, Dr. Karthik Chandran. The author has alerted the Editor-in-Chief of IJUFKS the reasons for the retraction: The proposed system was modelled with the incorrect data set. The system response has become incorrect because of this incorrect data set. Two percent of the information (mathematical assumptions) was taken from one paper without proper citation of the source.

Using a Digital Implant Catalog Improves Data Quality and Reduces Administrative Burden in the Dutch Breast Implant Registry

Background Correct registration of implant characteristics is essential to monitor the safety of implants within implant registries. Currently, in the nationwide Dutch Breast Implant Registry (DBIR) these characteristics are being registered manually by plastic surgeons, resulting in administrative burden and potentially incorrect data entry. Objectives This study evaluated the accuracy of manually registered implant data, possible consequences of incorrect data, and the potential of a Digital Implant Catalog (DIC) on increasing data quality and reducing the administrative burden. Methods Manually entered implant characteristics (fill, shape, coating, texture) of newly inserted breast implants in DBIR, from 2015 to 2019, were compared with the corresponding implant characteristics in the DIC. Reference numbers were used to match characteristics between the two databases. The DIC was based on manufacturers’ product catalogs and was set as the gold standard. Results 57,361 DBIR records could be matched with the DIC. Accuracy of implant characteristics varied from 70.6 to 98.0 percent, depending on the implant characteristic. The largest discrepancy was observed for ‘texture’, the smallest for ‘coating’. All manually registered implant characteristics resulted in different conclusions about implant performance when compared to the DIC (P<0.01). Implementation of the DIC reduced the administrative burden from 14 to 7 variables (50 percent). Conclusions Implementation of a Digital Implant Catalog increases data quality in DBIR and reduces the administrative burden. However, correct registration of reference numbers in the registry by plastic surgeons remains key for adequate matching. Furthermore, all implant manufacturers should be involved and regular updates of the DIC are required.

DAM: A Theoretical Framework for SensorSecurity in IoT Applications

As IoT applications are pervasively deployed across multiple domains, the potential impact of their security vulnerabilities are also accentuated. Sensor nodes represent a critical security vulnerability in the IoT ecosystem as they are exposed to the environment and accessible to hackers. When compromised or manipulated, sensor nodes can transmit incorrect data which can have a damaging impact on the overall operation and effectiveness of the system. Researchers have addressed the security vulnerabilities in sensor nodes with several mechanisms being proposed to address them. This paper presents DAM (Detect, Avoid, Mitigate), a theoretical framework to evaluate the security threats and solutions for sensor security in IoT applications and deployments. The framework leads to the classification of sensor security threats and categorization of available solutions which can be used to either detect vulnerabilities and attacks, recover from them or completely avoid them. The proposed framework will be useful for evaluating sensor security in real-world IoT deployments in terms of potential threats and designing possible solution

Kesalahan Peserta Didik Menyelesaikan Soal Cerita Pada Materi Matriks Berdasarkan Kriteria Watson

This study aimed to describe the errors made by students in working on math story problems on the subject of the matrix based on Watson's error criteria. This type of research was descriptive research with quantitative methods. This study's data collection techniques used the method of tests, interviews, and documentation, while the subjects in this study were 35 people. The test was carried out only once with many questions of 4 story questions. Then the data obtained were analyzed by data triangulation techniques. This study indicated that most errors made by students were incorrect data errors (inappropriate data / ID) with an error percentage of 20.39%. In comparison, the minor errors made by students were omitted data (OD) errors with an error percentage of 2.63%. For the omitted conclusion (OC) error, response level conflict (RLC), indirect manipulation (UM), skill hierarchy problem (SHP), and in addition to the seven errors (others/ O) with an error percentage range of 10%≤ P<25% and for inappropriate procedure errors (IP) with an error percentage range of P <10%. The causes of errors were lack of accuracy in reading and solving problems, errors in performing calculations, errors in using formulas, and lack of understanding of the material.

An Australian collector's authority file, 1973–2020

Biodiversity databases contain omissions and errors, including those resulting from data entry mistakes and from the use of outdated or incorrect data sources. Some of these omissions and errors can be minimised by the use of authority files, such as expert-compiled taxonomic name databases. However, there are few publicly available authority files for collecting events, and the "where", "when" and "by whom" of specimen data are typically entered into biodiversity databases separately and directly, item by item from specimen labels. Here I describe a publicly available compilation of 3829 of my own collecting events over a 48-year period in Australia. Each record contains a unique combination of date, georeferenced location and location notes.

Human sample authentication in biomedical research: comparison of two platforms

Samples used in biomedical research are often collected over years, in some cases from subjects that may have died and thus cannot be retrieved in any way. The value of these samples is priceless. Sample misidentification or mix-up are unfortunately common problems in biomedical research and can eventually result in the publication of incorrect data. Here we have compared the Fluidigm SNPtrace and the Agena iPLEX Sample ID panels for the authentication of human genomic DNA samples. We have tested 14 pure samples and simulated their cross-contamination at different percentages (2%, 5%, 10%, 25% and 50%). For both panels, we report call rate, allele intensity/probability score, performance in distinguishing pure samples and contaminated samples at different percentages, and sex typing. We show that both panels are reliable and efficient methods for sample authentication and we highlight their advantages and disadvantages. We believe that the data provided here is useful for sample authentication especially in biorepositories and core facility settings.

Analisis Kesalahan Siswa SD Dalam Menyelesaikan Permasalahan Luas Gabungan Bangun Datar Berdasarkan Watson’s Error Category

Geometry is a branch of mathematics and is one of the subject matter in mathematics in elementary schools. Measurement of area is one of the fundamental topics in mathematics. In fact, with regard to broad measurement skills, most of the students have difficulty in describing the problem. the mistakes that students make in answering a problem or problem need to be identified, the information obtained about errors in answering math problems can be used in improving mathematics teaching and learning activities. The purpose of this study was to identify errors made in solving the broad problem of combining data shapes based on the Watson error category. This type of research is descriptive qualitative research. The subjects used were 6 4th grade students of MI Nurul Huda with three different ability criteria. The selection is based on the advice of the math teacher and the daily test scores of the previous material. The results of the study show that the errors made by the research are missing conclusion errors, incorrect data errors, incorrect procedures, missing data error, and skill hierarchy problem