skater: an R package for SNP-based kinship analysis, testing, and evaluation

Motivation: SNP-based kinship analysis with genome-wide relationship estimation and IBD segment analysis methods produces results that often require further downstream process- ing and manipulation. A dedicated software package that consistently and intuitively imple- ments this analysis functionality is needed. Results: Here we present the skater R package for SNP-based kinship analysis, testing, and evaluation with R. The skater package contains a suite of well-documented tools for importing, parsing, and analyzing pedigree data, performing relationship degree inference, benchmarking relationship degree classification, and summarizing IBD segment data. Availability: The skater package is implemented as an R package and is released under the MIT license at https://github.com/signaturescience/skater. Documentation is available at https://signaturescience.github.io/skater.

Tools from the Indian space programme for observing and forecasting extreme weather events - Retrospect and prospect

Extreme weather events (EWEs) cause hardship, economic loss and have severe socio-economic consequences. It is necessary to develop forecast capability for such events so as to minimise losses and take appropriate measures for combating. Methods relying on only ground based weather observation networks are not adequate. Observations from space platforms offer regular and frequent measurements over a wide area. Observations from geostationary platforms provide information on atmospheric parameters and processes, while low earth orbiting platforms provide global observations at higher spatial resolutions. This paper describes the evolution of space programme in India, consisting of the space segment, data processing and products, and science and applications for observing and monitoring weather systems. Emphasis is on developing end-to-end capacity in weather monitoring. Forecasting of EWEs is illustrated with a few case studies. With the integration of space technology inputs, it is expected that forecast skill and lead time of the forecast will improve. Some of the future Indian space missions planned to enhance the capacity are also described. A multidisciplinary approach comprising the use of space technology, ground based measurement network and high speed computing power, can form a backbone for observing and forecasting EWEs.

Recognition of Child Congenital Heart Disease Using Cardiac Cycle Segment of Electrocardiogram

The results of previous studies showed that ECG could detect CHD in children with a detection rate of 76.43%. Although this result is better than the traditional CHD screening method, the sensitivity still needs to be improved if it is to be popularized clinically. Based on the previous ECG recording data, this study selects the more representative cardiac cycle segments to identify CHD, in order to achieve better screening effect. Firstly, better cardiac cycle segment data were extracted from ECG records of each patient. The final data set contains 72626 patients and each patient has a 9-lead ECG segment with duration of about one second. Then we trained a RoR network to identify the underlying patients with CHD using 62626 samples in the dataset. When tested on an independent set of 10000 patients, the network model yielded values for the sensitivity, specificity, and accuracy of 0.93, 86.3%, 85.7%, and 85.7% respectively. It can be seen that extracting more effective cardiac cycle fragments can significantly improve the sensitivity of CHD screening on the basis of ensuring better specificity, so as to find more potential patients with congenital heart disease.

Analysis of Data Point Cloud Preprocessing and Feature Angle Detection Algorithm

Background: The two main stages are utilized for feature extraction, from which the first stage consists of a penalty weight to the neighbor graph’s edges. The edge penalty weights are minimized by the neighbor sub-graph extraction to produce the set of feature patterns. For noisy data, the second stage is helpful. Methodology: In order to realize the measurement of the geometric dimensions of the ship block, this paper uses the theory of computer vision and reverse engineering to obtain the data of the segmented-hull with the method of digitizing the physical parts based on the vision, and processes the data by using the relevant knowledge of reverse engineering. Result: The results show that the efficiency of the edge extraction algorithm based on mathematical morphology is 30% higher than that of the mesh generation method. An adaptive corner detection algorithm based on the edge can adaptively determine the size of the support area and accurately detect the corner position. Conclusion: According to the characteristics of the point cloud of ship hull segment data, an adaptive corner detection algorithm based on the edge is adopted to verify its feasibility.

A Hierarchical View Pooling Network for Multichannel Surface Electromyography-Based Gesture Recognition

Hand gesture recognition based on surface electromyography (sEMG) plays an important role in the field of biomedical and rehabilitation engineering. Recently, there is a remarkable progress in gesture recognition using high-density surface electromyography (HD-sEMG) recorded by sensor arrays. On the other hand, robust gesture recognition using multichannel sEMG recorded by sparsely placed sensors remains a major challenge. In the context of multiview deep learning, this paper presents a hierarchical view pooling network (HVPN) framework, which improves multichannel sEMG-based gesture recognition by learning not only view-specific deep features but also view-shared deep features from hierarchically pooled multiview feature spaces. Extensive intrasubject and intersubject evaluations were conducted on the large-scale noninvasive adaptive prosthetics (NinaPro) database to comprehensively evaluate our proposed HVPN framework. Results showed that when using 200 ms sliding windows to segment data, the proposed HVPN framework could achieve the intrasubject gesture recognition accuracy of 88.4%, 85.8%, 68.2%, 72.9%, and 90.3% and the intersubject gesture recognition accuracy of 84.9%, 82.0%, 65.6%, 70.2%, and 88.9% on the first five subdatabases of NinaPro, respectively, which outperformed the state-of-the-art methods.

Determination of airports’ atmospheric mixing height boundaries using operational data

Purpose This study aims to determine the distance and duration to reach airports mixing height of 3,000 feet limit. Airport operations significantly contribute to the aircraft landing and take-off (LTO) cycle. Eurocontrol’s SO6 data sets comprise several abutted segment data to analyse the duration and distance for specific flights. Design/methodology/approach Two consequential methods have been used to calculate the distance and destination from the SO6 databases. First, SQL filtering and pivot tables were formed for the required data. Second, over 583,000 data lines for a year of Boeing 747–400 aircraft routes were calculated and filtered for the monthly assessments. Findings LTO cycles’ durations have deviated −24% to 76% from the ICAO assumptions. Distance facts determined for specific airports as 2.57 to 3.66 nm for take-off and 5.02 to 23.25 nm for the landing. The average duration of the aircraft’s in mentioned airport take-off are 66 to 74 s and 40 to 50 s; averages have been calculated as 70 to 44 s. Landing durations have been calculated for four different airports as 173 to 476 s. Practical implications This study provides a re-evaluation chance for the current assumptions and helps for better assessments. Each airport and aircraft combinations have their duration and distance figures. Originality/value This study has calculated the first LTO distances in the literature for the aerodrome. This method applies to all airports, airline fleets and aircraft if the segmented SO6 data are available.

Application of the Scoring Approach to Monitoring Function of Central Bank Credit Registry

The Central Bank Credit Registry was established in Ukraine in 2018. The two key functions which are fulfilled by Credit Register are monitoring and credit information sharing. This paper is devoted to applying a scoring approach for monitoring function realization in segments of individuals. The logic of using scoring tools to monitoring is based on an objective to create an effective form which reflects the dynamic of the above-mentioned segment. Data mining procedures for Credit Registry were realized and most significant characteristics were chosen. Correlation analysis for characteristics was applied. Different approaches to construct scoring for monitoring functions were analyzed. Namely, logistic regression, Machine Learning, method grounded on tree created by the XGBoost algorithm. Last method demonstrated the best efficiency for scoring construction and can be developed for implementation. The views expressed are those of the authors and do not necessarily reflect those of the National Bank of Ukraine. JEL classіfіcatіon: G21

skater: An R package for SNP-based Kinship Analysis, Testing, and Evaluation

Motivation: SNP-based kinship analysis with genome-wide relationship estimation and IBD segment analysis methods produces results that often require further downstream processing and manipulation. A dedicated software package that consistently and intuitively implements this analysis functionality is needed. Results: Here we present the skater R package for SNP-based kinship analysis, testing, and evaluation with R. The skater package contains a suite of well-documented tools for importing, parsing, and analyzing pedigree data, performing relationship degree inference, benchmarking relationship degree classification, and summarizing IBD segment data. Availability: The skater package is implemented as an R package and is released under the MIT license at https://github.com/signaturescience/skater. Documentation is available at https://signaturescience.github.io/skater.