船舶进气畸变模拟板结构设计与参数分析

Significant aerodynamic engine instability can occur during the operation of marine gas turbines as airflow enters the compressor through a 90°turning and causes inlet distortion.This study adopts the method of simulating board equivalence to provide the target distortion flow field for ship compressors.The characteristics of the flow field behind the simulated board are obtained through experiments and numerical simulations,through which the relationship between the height of the simulated board and the total pressure distortion is elucidated.Subsequently,the study summarizes the prediction formula to achieve a distortion prediction of 0.8%–7.8%.In addition,this work analyzes the effects of drilling methods and diameters on flow nonuniformity by drilling holes into the simulation board.The results indicate that drilling holes on the board can weaken the nonuniformity of the flow field within a certain range and change the distribution pattern of total pressure in the cross-section.Furthermore,the total pressure distortion no longer changes significantly when the number of holes is too large.The proposed double simulation board structure is capable of obtaining the following two types of distorted flow fields:symmetrical dual lowpressure zones and low-pressure zones with high distortion intensity at the compressor inlet.The distortion equivalent simulation method proposed in this work can obtain various types of distortion spectra,thereby meeting the distortion parameter requirements for the antidistortion testing of marine engines.

Detection and classification of breast lesions using multiple information on contrast-enhanced mammography by a multiprocess deep-learning system: A multicenter study

Objective: Accurate detection and classification of breast lesions in early stage is crucial to timely formulate effective treatments for patients. We aim to develop a fully automatic system to detect and classify breast lesions using multiple contrast-enhanced mammography(CEM) images.Methods: In this study, a total of 1,903 females who underwent CEM examination from three hospitals were enrolled as the training set, internal testing set, pooled external testing set and prospective testing set. Here we developed a CEM-based multiprocess detection and classification system(MDCS) to perform the task of detection and classification of breast lesions. In this system, we introduced an innovative auxiliary feature fusion(AFF)algorithm that could intelligently incorporates multiple types of information from CEM images. The average freeresponse receiver operating characteristic score(AFROC-Score) was presented to validate system’s detection performance, and the performance of classification was evaluated by area under the receiver operating characteristic curve(AUC). Furthermore, we assessed the diagnostic value of MDCS through visual analysis of disputed cases,comparing its performance and efficiency with that of radiologists and exploring whether it could augment radiologists’ performance.Results: On the pooled external and prospective testing sets, MDCS always maintained a high standalone performance, with AFROC-Scores of 0.953 and 0.963 for detection task, and AUCs for classification were 0.909[95% confidence interval(95% CI): 0.822-0.996] and 0.912(95% CI: 0.840-0.985), respectively. It also achieved higher sensitivity than all senior radiologists and higher specificity than all junior radiologists on pooled external and prospective testing sets. Moreover, MDCS performed superior diagnostic efficiency with an average reading time of 5 seconds, compared to the radiologists’ average reading time of 3.2 min. The average performance of all radiologists was also improved to varying degrees with MDCS assistance.Conclusions: MDCS demonstrated excellent performance in the detection and classification of breast lesions,and greatly enhanced the overall performance of radiologists.

A Time-Varying Parameter Estimation Method for Physiological Models Based on Physical Information Neural Networks

In the establishment of differential equations,the determination of time-varying parameters is a difficult problem,especially for equations related to life activities.Thus,we propose a new framework named BioE-PINN based on a physical information neural network that successfully obtains the time-varying parameters of differential equations.In the proposed framework,the learnable factors and scale parameters are used to implement adaptive activation functions,and hard constraints and loss function weights are skillfully added to the neural network output to speed up the training convergence and improve the accuracy of physical information neural networks.In this paper,taking the electrophysiological differential equation as an example,the characteristic parameters of ion channel and pump kinetics are determined using BioE-PINN.The results demonstrate that the numerical solution of the differential equation is calculated by the parameters predicted by BioE-PINN,the RootMean Square Error(RMSE)is between 0.01 and 0.3,and the Pearson coefficient is above 0.87,which verifies the effectiveness and accuracy of BioE-PINN.Moreover,realmeasuredmembrane potential data in animals and plants are employed to determine the parameters of the electrophysiological equations,with RMSE 0.02-0.2 and Pearson coefficient above 0.85.In conclusion,this framework can be applied not only for differential equation parameter determination of physiological processes but also the prediction of time-varying parameters of equations in other fields.

Resistance to powdery mildew in the pea cultivar Xucai 1 is conferred by the gene er1

Powdery mildew, caused by Erysiphe pisi D.C., is a major constraint to pea production worldwide. The pea cultivar Xucai 1 has shown high resistance to E. pisi under greenhouse and field conditions. The objectives of this study were to identify and characterize genes conferring resistance to powdery mildew in Xucai 1. Three crosses, Qizhen 76 × Xucai 1,Bawan 6 × Xucai 1, and Xucai 1 × Bawan 6, were made to generate populations for genetic analysis. The resistance to E. pisi and segregation ratios in the F_1, F_2, and F_(2:3)populations suggested a single recessive gene conferring the resistance of Xucai 1. Bulked segregant analysis was used to map the resistance gene using two F2 populations. The resistance gene was close to markers AD60 and c5 DNAmet on linkage group VI with genetic distances of9.9 c M and 15.4 c M in the Xucai 1 × Bawan 6 F_2 population and 8.7 c M and 8.1 c M in the Qizhen 76 × Xucai 1 F_2 population, respectively, suggesting that the resistance gene was an er1 allele. This hypothesis was confirmed by comparison of the c DNA sequences of the Ps MLO1 gene between the parents and the Ps MLO1 wild type. Three distinct types of transcripts in Xucai 1, characterized by a 129-bp deletion and 155- and 220-bp insertions,were detected, consistent with the structure of the er1-2 allele. We concluded that resistance in Xucai 1 was conferred by er1-2 and that its linked markers will be useful in pea breeding programs.

燃气轮机排气蜗壳流动损失的优化

The exhaust volute is a device that can change the exhaust direction of the ship’s gas turbine to reduce the flow loss of the high-temperature and high-speed turbine exhaust gas in the box-type exhaust volute,thereby improving its power output performance.This paper first investigates the internal flow field characteristics of the exhaust volute via numerical simulation and reveals the main source of the internal resistance loss of the volute.On the premise of not affecting the installation size of the volute and matching it with other components in the cabin,the design scheme of volute bottom shunt and volute chamfer are then optimized in accordance with the flow characteristics inside the volute.Numerical simulation results show that the partial flow structure at the bottom of the volute can effectively improve the low-velocity region and the vortex flow at the bottom of the volute,and the chamfered angle scheme can control the regular expansion and compression of the airflow.When the volute adopts the appropriate chamfer angle and the bottom split-flow structure,the total pressure loss can be reduced by 19.6%,and the static pressure recovery coefficient can be increased by 42.05%.

Smart heightened-containment biological laboratory:Technologies,modules,and aims

With the rapid development of intelligent technology,the smart heightened-containment biological laboratory(sHCBL)has moved from concept to reality.Experimental activities and laboratory construction,operation,and management will undoubtedly lead to disruptive changes.Conventional laboratories are increasingly being replaced by smart laboratories;however,the key technologies involved in this transition remain at an exploratory stage.It is necessary for HCBLs to absorb the advanced ideas of smart laboratories to guarantee the establishment of biosafety and biosecurity in a more automated way.This study examines in detail sHCBL module structures,the functions of each module,laboratory operation processes,and the advanced nature of smart laboratories.It may provide a theoretical foundation for the future transformation and smart construction of sHCBLs.

Similar aerosol emission rates and viral loads in upper respiratory tracts for COVID-19 patients with Delta and Omicron variant infection

Dear Editor,In 2021,the Delta variant of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)caused the coronavirus disease 2019(COVID-19)pandemic to spread in the UK,Nepal,southeast Asia and elsewhere,which seems to be approximately 60%more transmissible than the already highly infectious Alpha variant in late 2020(Callaway,2021).In addition to the high efficiency of the invading infection and the acquired immune escape ability,changes in the aerodynamic characteristics of SARS-CoV-2 aerosols may be another important reason for the Delta variant spread.Previously,it was identified that SARS-CoV-2 can spread through close contact and airborne routes(Guo et al.,2020;Morawska and Milton,2020).

Magnetic field-induced electronic phase transition in the Dirac semimetal state of black phosphorus under pressure

Different instabilities have been confirmed to exist in the three-dimensional(3D) electron gas when it is confined to the lowest Landau level in the extreme quantum limit. The recently discovered 3D topological semimetals offer a good platform to explore these phenomena due to the small sizes of their Fermi pockets, which means the quantum limit can be achieved at relatively low magnetic fields. In this work, we report the high-magnetic-field transport properties of the Dirac semimetal state in pressurized black phosphorus. Under applied hydrostatic pressure, the band structure of black phosphorus goes through an insulator-semimetal transition. In the high pressure topological semimetal phase, anomalous behaviors are observed on both magnetoresistance and Hall resistivity beyond the relatively low quantum limit field, which is demonstrated to indicate the emergence of an exotic electronic state hosting a density wave ordering. Our findings bring the first insight into the electronic interactions in black phosphorus under intense field.