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This paper deals with the comparison of empirical computational models used to achieve the geometrical parameters of pumps impellers with models based on Computational Fluid Dynamics (CFD) simulations and optimization algorithms. Also, the actuality of the empirical models application in modern pump manufacturing industry is analyzed. Further, an empirical model for calculating the pump impeller geometrical parameters was presented. Applying this model, the calculation of the centrifugal pump impeller parameters for CH 6,3/20 1,1-2 canned motor pump was performed. The procedure for parameterization and generation of the geometric model based on parameters obtained from this model through ANSYS DesignModeler was also presented. CFD simulation based on ANSYS CFX was used to obtain the operating characteristic of the obtained centrifugal pump impeller. The authors carried out the comparison of the results of the designed model with the original one and with the optimized impeller obtained using optimization algorithms and CFD calculations.

This paper explores the optimal simulation settings required by the optimization process of centrifugal canned motor pump parts using the CH 6, 3/32-2, 2-2 pump as an example. The optimization of centrifugal pump parts is particularly important in order to increase pump characteristics: pump efficiency, net positive suction head, as well as other optimization criteria. The optimal conditions to obtain a geometric model and optimal mesh properties, initial and boundary conditions, solver settings were investigated. The mathematical models used in the simulation were also presented. In conclusion, there have been revealed optimal simulation settings.

Humans have a complex way of expressing their intuitive intentions in real gestures. That is why many gesture detection and recognition techniques have been studied and developed. There are many methods of human hand signal reading, such as those using electroencephalography, electrocorticography, and electromyography, as well as methods for gesture recognition. In this paper, we present a method based on real-time surface electroencephalography hand-based gesture recognition using a multilayer neural network. For this purpose, the sEMG signals have been amplified, filtered and sampled; then, the data have been segmented, feature extracted and classified for each gesture. To validate the method, 100 signals for three gestures with 64 samples each signal have been recorded from 2 users with OYMotion sensors and 100 signals for three gestures from 4 users with the MyWare sensors. These signals were used for feature extraction and classification using an artificial neuronal network. The model converges after 10 sessions, achieving 98% accuracy. As a result, an algorithm was developed that aimed to recognize two specific gestures (handling a bottle and pointing with the index finger) in real time with 95% accuracy. [ABSTRACT FROM AUTHOR]

In this study, we propose a new toothed gear for mechanical transmissions built from a satellite wheel with two toothed conical crowns, one of which conjugates with a fixed central conical wheel mounted in the transmission housing and the other with a movable conical wheel installed on the flange of the driven shaft. The satellite wheel is mounted on the inclined portion of the crankshaft and performs spherospatial motion around a fixed point. When the crankshaft rotates, the teeth of the wheels engage with spherospatial interaction in two lateral gearings of the satellite wheel, yielding kinematic ratios dependent on the correlation of the number of teeth. The teeth of the satellite wheel are used with circular arc profiles, and the teeth of the central wheel have flank profiles with variable curvatures increasing continuously from the root to the tip, so that, in meshing, the teeth form multipair contacts with convex–concave geometry with a small difference in flank curvatures. The flank profile geometry and pairs of teeth simultaneously engage depending on the configurational parameters of the gearing and can use up to 100% of pairs of simultaneously conjugated teeth. [ABSTRACT FROM AUTHOR]

Data-driven computational analysis is becoming increasingly important in biomedical research, as the amount of data being generated continues to grow. However, the lack of practices of sharing research outputs, such as data, source code and methods, affects transparency and reproducibility of studies, which are critical to the advancement of science. Many published studies are not reproducible due to insufficient documentation, code, and data being shared. We conducted a comprehensive analysis of 453 manuscripts published between 2016–2021 and found that 50.1% of them fail to share the analytical code. Even among those that did disclose their code, a vast majority failed to offer additional research outputs, such as data. Furthermore, only one in ten articles organized their code in a structured and reproducible manner. We discovered a significant association between the presence of code availability statements and increased code availability. Additionally, a greater proportion of studies conducting secondary analyses were inclined to share their code compared to those conducting primary analyses. In light of our findings, we propose raising awareness of code sharing practices and taking immediate steps to enhance code availability to improve reproducibility in biomedical research. By increasing transparency and reproducibility, we can promote scientific rigor, encourage collaboration, and accelerate scientific discoveries. We must prioritize open science practices, including sharing code, data, and other research products, to ensure that biomedical research can be replicated and built upon by others in the scientific community. [ABSTRACT FROM AUTHOR]