Type 1 diabetes is a chronic disease marked by high blood glucose levels, called hyperglycemia. Diagnosis of diabetes typically requires one or more blood tests. The aim of this paper is to discuss a non-invasive method of type 1 diabetes detection, based on physical activity measurement. We solved a binary classification problem using a variety of computational intelligence methods, including non-linear classification algorithms, which were applied and comparatively assessed. Prediction of disease presence among children and adolescents was evaluated using performance measures, such as accuracy, sensitivity, specificity, precision, the goodness index, and AUC. The most satisfying results were obtained when using the random forest method. The primary parameters in disease detection were weekly step count and the weekly number of vigorous activity minutes. The dependance between the weekly number of steps and the type 1 diabetes presence was established after an insightful analysis of data using classification and clustering algorithms. The findings have shown promising results that type 1 diabetes can be diagnosed using physical activity measurement. This is essential regarding the non-invasiveness and flexibility of the detection method, which can be tested at any time anywhere. The proposed technique can be implemented on a mobile device.

Background Next Generation Sequencing (NGS) techniques dominate today’s landscape of genetics and genomics research. Though Illumina still dominates worldwide sequencing, Oxford Nanopore is one of the leading technologies currently being used by biologists, medics and geneticists across various applications. Oxford Nanopore is automated and relatively simple for conducting experiments, but generates gigabytes of raw data, to be processed by often ambiguous set of alternative bioinformatics command-line tools, and genomics frameworks which require a knowledge of bioinformatics to run. Results We established an inter-collegiate collaboration across experimentalists and bioinformaticians in order to provide a novel bioinformatics tool, free for academics. This tool allows people without extensive bioinformatics knowledge to simply process their raw genome sequencing data. Currently, due to ICT resources’ maintenance reasons, our server is only capable of handling small genomes (up to 15 Mb). In this paper, we introduce our tool, NanoForms: an intuitive and integrated web server for the processing and analysis of raw prokaryotic genome data, coming from Oxford Nanopore. NanoForms is freely available for academics at the following locations: http://nanoforms.tech (webserver) and https://github.com/czmilanna/nanoforms (GitHub source repository).

No abstract

The article presents the working principles and construction of a brushless DC motor. The BLDC motor control methods were discussed, including the division into methods using sensors for determining the rotor position and sensorless methods with an example of implementing BLDC motor control using microcontrollers. High efficiency and high durability as well as falling prices of electronic components and controllers cooperating with BLDC motors in recent years have caused a growing interest in this type of motors. The design of BLDC motors does not require the use of brushes, which in traditional brush motors supply voltage through the commutator to the rotor windings. Brushless motors are characterized by high efficiency, durability and reliability. The use of sensors in BLDC motor control systems increases the number of motor and commutator connections, thereby reducing the reliability of the drive system. For this reason, direct position and speed measurements are often abandoned for the use of sensorless control methods. DC brushless motors have various application areas, and due to their reliability and the ability to control speed, they are used, among others, in electric vehicles, medicine and industry.

No abstract