The influence of gravity on respiratory kinematics during phonation measured by dynamic magnetic resonance imaging

Respiratory kinematics are important for the regulation of voice production. Dynamic MRI is an excellent tool to study respiratory motion providing high-resolution cross-sectional images. Unfortunately, in clinical MRI systems images can only be acquired in a horizontal subject position, which does not take into account gravitational effects on the respiratory apparatus. To study the effect of body posture on respiratory kinematics during phonation, 8 singers were examined both in an open-configuration MRI with a rotatable gantry and a conventional horizontal MRI system. During dynamic MRI the subjects sang sustained tones at different pitches in both supine and upright body positions. Sagittal images of the respiratory system were obtained at 1–3 images per second, from which 6 anatomically defined distances were extracted to characterize its movements in the anterior, medium and posterior section of the diaphragm as well as the rip cage (diameter at the height of the 3rd and 5th rip) and the anterior–posterior position of the diaphragm cupola. Regardless of body position, singers maintained their general principles of respiratory kinematics with combined diaphragm and thorax muscle activation for breath support. This was achieved by expanding their chest an additional 20% during inspiration when singing in the supine position but not for sole breathing. The diaphragm was cranially displaced in supine position for both singing and breathing and its motion range increased. These results facilitate a more realistic extrapolation of research data obtained in a supine position.

Well Control Optimization of Waterflooding Oilfield Based on Deep Neural Network

A well control optimization method is a key technology to adjust the flow direction of waterflooding and improve the effect of oilfield development. The existing well control optimization method is mainly based on optimization algorithms and numerical simulators. In the face of larger models, longer optimization periods, or reservoir models with a large number of optimized wells, there are many optimization variables, which will cause algorithm convergence difficulties and optimization costs. The application effect is not good because of the problems of time length, few comparison schemes, and only fixed control frequency. This paper proposes a new method of a well control optimization method based on a multi-input deep neural network. This method takes the production history data of the reservoir as the main input and the saturation field as the auxiliary input and establishes a multi-input deep neural network for learning, forming a production dynamic prediction model instead of conventional numerical simulators. Based on the production dynamic prediction model, a series of model generation, production prediction, comparison, and optimization are carried out to find the best production plan of the reservoir. The calculation results of the examples show that (1) compared with the single-input production dynamic prediction model, the production dynamic prediction model based on multiple inputs has better prediction accuracy, and the results are close to the calculation results of the conventional numerical simulator; (2) the well control optimization method based on the multiple-input deep neural network has a fast optimization speed, with many comparison schemes and good optimization effect.

Steel industry of the world and of Russia in pandemic environment

The crisis of the world economy, caused by coronavirus pandemic early 2020, resulted in a recession in demand for steel products, decrease of ferrous metals production. Dynamic of steel production by world regions and separate countries in 2019 and by 4 months of 2020 is presented. Results of IQ-2020 comparing with the analogue period of 2019, published by WSA, showed more than 10% production drop in such countries like Italy, Spain, Belgium, Taiwan, Venezuela and other, less than 10% production drop in the USA, Russia, Japan, India, Germany, Vietnam. Total decrease of production in IQ-2020 in Top-20 countries accounted for about 1%. In Russia, as per Rosstat data, production of steel and rolled products in the IQ-2020 was less by 1.1% comparing with the volume of IQ-2019. Dynamic of prices within the period from April of 2019 till April of 2020 presented for iron ore raw materials, steel billets, rebars, HRC and CRC at the world market. The decrease of steel rolled products import in the USA in January-April of 2020 by 5,8 million tons was noted, which is 28,2 % lower than the volume of January-April 2019. EC countries in IQ- 2020 decreased export of steel rolled products by 11% down to 4.51 million tons, comparing with IQ-2019, import decreased by 20.6% down to 5.7 million tons. China in January-April of 2020 comparing with January-April of 2019 decreased export of steel by 11.7% down to 20.6 million tons, and increased import by 7.4% up to 4.2 million tons. It was noted, that demand increase at the domestic market of China and tariff-wall, imposed by the USA, EC countries and other countries contribute to the decrease of Chinese export. In 2020 further decrease of steel products demand is expected by 6.4%. In EC countries the metal products consumption due to estimation will decrease by 15.8%, in developing countries (without China), as expected, by the results of 2020 the indices will deteriorate by 11.6%. Consumption of steel products in China in 2020 will increase by 1%. In CIS countries and Russia the decrease of steel consumption in 2020 will be about 10%.

Improved capacitance model considering bottom-hole flowing pressure and interference between oil wells

With the continuous production of oil wells, the reservoir properties, such as permeability and porosity, are changing accordingly, and the reservoir heterogeneity is also enhanced. This development is vulnerable to the problem of the one-way advance of injected water and low efficiency of water flooding. The interwell connectivity between injection and production wells controls the flow capacity of the subsurface fluid. Therefore, the analysis of interwell connectivity helps to identify the flow direction of injected water, which is of great significance for guiding the profile control and water plugging in the later stage of the oilfield. In this study, based on the principle of mass conservation, a capacitance model considering the bottom-hole flowing pressure was established and solved by using the production dynamic data of injection–production wells. Then, the validity of the capacitance model was verified by numerical simulation, and the influences of well spacing, compression coefficient, frequent switching wells, injection speed, and bottom-hole flowing pressure on interwell connectivity were eliminated. Finally, a practical mine technique for inversion of connectivity between wells using dynamic data was developed. The advantage of this model is that the production dynamic data used in the modeling process are easy to obtain. It overcomes the shortcomings of previous models and has a wider range of applications. It can provide a theoretical basis for the formulation of profile control and water-plugging schemes in the high-water-cut period.

Comparing Static and Dynamic Weighted Software Coupling Metrics

Coupling metrics that count the number of inter-module connections in a software system are an established way to measure internal software quality with respect to modularity. In addition to static metrics, which are obtained from the source or compiled code of a program, dynamic metrics use runtime data gathered, e.g., by monitoring a system in production. Dynamic metrics have been used to improve the accuracy of static metrics for object-oriented software. We study weighted dynamic coupling that takes into account how often a connection (e.g., a method call) is executed during a system’s run. We investigate the correlation between dynamic weighted metrics and their static counterparts. To compare the different metrics, we use data collected from four different experiments, each monitoring production use of a commercial software system over a period of four weeks. We observe an unexpected level of correlation between the static and the weighted dynamic case as well as revealing differences between class- and package-level analyses.