Deep Learning on Abnormal Chromosome Segments: An Intelligent Copy Number Variants Detection System Design

Gene testing emerged as a business in the last two decades, and the testing cost has been reduced from 100 million to 1000 dollars for the development of technologies. Preimplantation genetic screening (PGS) is a popular genetic profiling of embryos prior to implantation in gene testing. Copy number variants (CNVs) detection is a key task in PGS which still needs the manual operation and evaluation. At the same time, deep learning technology earns a booming development and wide application in recent years for its strong computing and learning capability. This research redesigns the PGS workflow with the intelligent CNVs detection system, and proposes the corresponding system framework. Deep learning is selected as the proper technology in the system design for CNVs detection, which also fit the task of denoising. The evaluation is conducted on simulation dataset with high accuracy and low time cost, which may achieve the requirements of clinical application and reduce the workload of bioinformatics experts. Moreover, the redesigned process and proposed framework may enlighten the intelligent system design for gene testing in following work, and provide a guidance of deep learning application in AI healthcar

Multi-System Coupling DMi Hybrid Vehicle Modeling and Its Performance Analysis Based on Simulation

Key subsystems, such as driving resistance, component performance, and energy management strategy, determine the power performance and energy consumption of hybrid electric vehicles. Qin Plus performs excellently in fuel consumption due to its low driving resistance, high thermal efficiency of the engine, and multi-factor multi-mode energy management strategy. This article aims to explain the outstanding energy consumption of DMi vehicles by analyzing the driving resistance, component parameters of Qin Plus and introducing the drive modes selection and vehicle energy management strategy through multi-system modeling and simulation. The ultra-low fuel consumption of 3.8 L is obtained under the NEDC driving cycle and evaluated by the corresponding experiment

Energy distribution and chaotic pressure pulsation analysis of vortex ropes in Francis-99

Francis turbines, essential for stability in diverse operating conditions and variable-speed scenarios, encounter efficiency-compromising vortex rope formations in the draft tube, leading to substantial pressure fluctuations. This research delves into the analysis of energy loss and pressure fluctuations associated with these vortex ropes. Employing the local entropy generation rate (LEGR) method and chaos theory, we scrutinize the behaviour of vortex ropes and their resultant pressure fluctuations. Notably, vortex ropes exhibit maximum LEGR near the runner cone, with secondary vortices escalating instability downstream. In the elbow section, the collision of vortex ropes with the outer elbow amplifies LEGR, primarily driven by fluctuating velocities (approximately 90%). Leveraging the GWO-VMD algorithm, non-stationary signals are decomposed, unveiling a significant 1.6 Hz vortex rope frequency under partial load (PL) conditions and isolating external noise frequencies, such as the prominent 300 Hz. Following decomposition, chaos theory tools, including phase space reconstruction and phase trajectory graphs, unveil the chaotic nature of PL conditions attributed to spiral vortex ropes, resulting in profound pressure fluctuations. This study enhances our understanding of such systems and provides methodologies for improved noise reduction and optimization of turbine performance

Investigate the full characteristic of a centrifugal pump-as-turbine (PAT) in turbine and reverse pump modes

Large centrifugal pumps can generate electricity in reverse, reducing the power supply pressure of the power grid. However, S region may occurs during the operation and regulation of the pump as a steam turbine (PAT). The S region can cause unstable unit startup, not only causing hydraulic system oscillation, but also accompanied by strong vibration and noise. Understanding the characteristic curve of the S region is of great significance for improving unit stability and efficiency. Previously, the main research object in S area was small generator units. This article conducted unsteady numerical simulation and experimental research on a large centrifugal pump. Analysed the internal flow rate, blade load, entropy generation, and pressure pulsation under three special operating conditions: the optimal operating condition, runaway operating condition, and braking operating condition in the S region. In addition, a comparative analysis was conducted on three pairs of operating conditions: S region turbine mode and reverse pump mode, with opposite flow direction, similar flow rate, and the same speed. Research has found that in the S region of large centrifugal pumps, the flow chaos in the turbine mode mainly occurs in the impeller section, and the maximum pressure occurs in the stationary blade section. The flow chaos under reverse pump operation mainly occurs in the stationary blade section, and the maximum pressure occurs in the impeller section