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The vibration prediction model of diesel engine is generally established by finite element method (FEM). In FEM, the components are firstly represented by mesh models, which will be connected to develop a combined model through joint surfaces. The processing method of joint surfaces is key to the accuracy of the combined model, for joint surfaces play an important role in transmitting force and vibration. For this purpose, an equivalent model of transversely isotropic virtual material is proposed to connect the mesh models of the components in this paper. In this model, the different connection characteristics of both normal and tangential are taken into consideration. In addition, the calculation models of the virtual material layer parameters, such as elastic modulus, shear modulus, Poisson’s ratio, thickness, density, and damping coefficient, are established in theory. At the same time, the modal test is introduced to acquire the natural frequency and damping ratio for identifying the unknown parameters of the virtual material layer, by using the BP neural network algorithm. After that, the complete finite element model of a diesel engine assembly can be established, and the vibration prediction is made in time domain, where cylinder gas pressures, piston slap forces, main bearing forces, and valve train forces are taken into consideration comprehensively. Furthermore, the experiment of the engine vibration response is carried out as well. The vibration acceleration level of engine feet and the vibration severity of engine block are adopted to evaluate the structure vibration response of the diesel engine. The results show that there is a good consistency between the prediction results and the experiment results in frequency domain. The consistency of the vibration acceleration level in 1/3 octave is up to 8 kHz, and that of the vibration severity is up to 1 kHz. And the results verify the accuracy of the vibration prediction model of the diesel engine, which is based on transversely isotropic virtual material modeling method of joint surfaces.

In this paper, we study the problem of unsupervised object segmentation from single images. We do not introduce a new algorithm, but systematically investigate the effectiveness of existing unsupervised models on challenging real-world images. We firstly introduce four complexity factors to quantitatively measure the distributions of object- and scene-level biases in appearance and geometry for datasets with human annotations. With the aid of these factors, we empirically find that, not surprisingly, existing unsupervised models catastrophically fail to segment generic objects in real-world images, although they can easily achieve excellent performance on numerous simple synthetic datasets, due to the vast gap in objectness biases between synthetic and real images. By conducting extensive experiments on multiple groups of ablated real-world datasets, we ultimately find that the key factors underlying the colossal failure of existing unsupervised models on real-world images are the challenging distributions of object- and scene-level biases in appearance and geometry. Because of this, the inductive biases introduced in existing unsupervised models can hardly capture the diverse object distributions. Our research results suggest that future work should exploit more explicit objectness biases in the network design., NeurIPS 2022. Code and data are available at project page: https://vlar-group.github.io/UnsupObjSeg.html

Di Huang,1 Xue Chen,1 Xuan Chen,1 Yan Qu,1 Yuanyuan Wang,2 Yafei Yang,3 Yufeng Cheng1 1Department of Radiation Oncology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, People’s Republic of China; 2Department of Oncology, People’s Hospital of Linyi County, Dezhou, Shandong 251500, People’s Republic of China; 3Department of Hepatobiliary Surgery, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, People’s Republic of ChinaCorrespondence: Yufeng Cheng Department of Radiation OncologyQilu Hospital, Cheeloo College of Medicine, Shandong University, 107 West Wenhua Road, Jinan, Shandong 250012, People’s Republic of China, Email qiluchengyufeng@126.comBackground: The WD40 protein family member WD repeat domain 5 (WDR5) plays significant roles in the tumorigenesis and development of multiple organ tumours. However, the correlation between WDR5 expression and oesophageal squamous cell carcinoma (ESCC) has not been elucidated.Methods: WDR5 mRNA expression data were acquired from The Cancer Genome Atlas (TCGA) database, and the expression and prognostic potential of WDR5 were assessed by immunohistochemistry (IHC) and Western blot. The cell counting kit-8 (CCK-8), colony formation assay and cell cycle evaluation were performed to verify the WDR5 function in vitro. The xenograft model was used to verify WDR5 function in vivo.Results: The mRNA and protein expression levels of WDR5 were significantly upregulated in ESCC tissues compared with expression in adjacent normal tissues. Kaplan–Meier analysis showed that high WDR5 expression in ESCC patients was associated with poor overall survival (P=0.004). Multivariate analysis revealed that WDR5 overexpression emerged as an independent predictor of poor overall survival (P=0.013) in ESCC. The in vitro and in vivo experiments revealed that downregulation of WDR5 expression blocked cell proliferation of ESCC. Mechanistically, we found that WDR5 may influence ESCC proliferation by targeting the PI3K/AKT/mTOR signalling pathway.Conclusion: Our data demonstrate that overexpression of WDR5 was associated with poor prognosis in patients with ESCC and that WDR5 may act as a potential novel prognostic biomarker for ESCC.Keywords: WDR5, oesophageal squamous cell carcinoma, prognosis, proliferation

Wastewater treatment plants (WWTPs) could emit a large amount of bioaerosols containing pathogenic bacteria. Assessing the health risks of exposure to these bioaerosols by using quantitative microbial risk assessment (QMRA) is important to protect workers in WWTPs. However, the relative impacts of the stochastic input variables on the health risks determined in QMRA remain vague. Hence, this study performed a Monte Carlo simulation-based QMRA case study for workers exposing to S. aureus or E. coli bioaerosols and a sensitivity analysis in two WWTPs with various aeration modes. Results showed that when workers equipped without personal protective equipment (PPE) were exposed to S. aureus or E. coli bioaerosol in the two WWTPs, the annual probability of infection considerably exceeded the U.S. EPA benchmark (≤10E-4 pppy), and the disease burden did not satisfy the WHO benchmark (≤10E-6 DALYs pppy) (except exposure to E. coli bioaerosol for disease health risk burden). Nevertheless, the use of PPE effectively reduced the annual infection health risk to an acceptable level and converted the disease health risk burden to a highly acceptable level. Referring to the sensitivity analysis, the contribution of mechanical aeration modes to the variability of the health risks was absolutely dominated in the WWTPs. On the aeration mode that showed high exposure concentration, the three input exposure parameters (exposure time, aerosol ingestion rate, and breathing rate) had a great impact on health risks. The health risks were also prone to being highly influenced by the various choices of the dose–response model and related parameters. Current research systematically delivered new data and a novel perspective on the sensitivity analysis of QMRA. Then, management decisions could be executed by authorities on the basis of the results of this sensitivity analysis to reduce related occupational health risks of workers in WWTPs., Graphical abstract Unlabelled Image, Highlights • Even without PPE, DB of workers exposed to E. coli bioaerosol was still acceptable • The use of PPE effectively reduced the health risks to an acceptable level>With high exposure concentration, input exposure parameters highly impact health risk • Mechanical aeration modes’ contribution to health risks’ variability was dominated • Health risks were highly influenced by the various choices of the dose–response model

The combination of the high-level data model called ISO 10303-238 with the open programmable multi-axis controller (PMAC) presents a vision for the open, intelligent and integrated computer numerical control (CNC) systems whose demands have been growing with the rapid development of modern manufacturing. Evolved from design philosophy, this paper proposes a novel open, intelligent and integrated CNC system based on ISO 10303-238 and PMAC. In the system, ISO 10303-238 is chosen as the numerical control (NC) data in order to make the CNC system interoperable. And the open master-slave hardware structure on the basis of industrial process computer (IPC) + PMAC with double central processing units (CPUs) is designed in order to make the CNC system flexible. Also, the open and modular software structure is designed in order to make the CNC system intelligent. In addition, the development of the prototype system is given. At the end, it has been verified by case study that the proposed CNC system is feasible and effective.

As a new technology in the field of agricultural plant protection, spraying by unmanned helicopter with large load has the advantages of high spraying efficiency, wide application range, low operation cost and less environmental pollution. The characteristics of flow and deposition in spraying process of uavs with large load were studied, which can provide theoretical basis for optimizing spraying parameters of uavs, and further improve spraying quality of uavs.

For the control of large angle maneuvers of a spacecraft, variable gain backstepping control is proposed. The controller can make the states of the system converge to the commanded position along the input vector field orientation. The controller stabilizes the system with the amplitude of the commanded torques decreased. Considering the uncertainty of the disturbance torques and the estimation error of the inertia matrix, the control design is improved to provide strong self-adaptability and robustness of the system. Simulation is conducted, and the results show that the design has good tracking performance and convergence, consistent with the theoretical analysis.

The purpose of this paper is to propose a novel equivalent circuit model of an ultrasonic motor with measurable parameters. The novel model consists of three submodels: the stator vibrator submodel, the contact zone submodel and the rotor submodel. The forced-vibration electrical model of the stator vibrator is built, based on the existing free-vibration circuit model. The contact zone electric model is obtained with the help of the simplified model of the friction transmission. The rotors equivalent circuit model, whose principle is the rotors kinematical equation, is consisted. All the parameters, in each submodel, are readily available due to their intuitionistic physical meanings. As a result, the proposed method can be demonstrated by the experiment.

This paper provides a performance comparison between the extended and unscented Kalman filters for the localization of a spacecraft. The unscented filter uses a well chosen set of sample points to approximate the probability distribution more accurately than the first-order linearization of the extended Kalman filter. Firstly, a brief description and comparison of the two filters is given. Then, a dynamic model of the spacecraft accounting for several perturbing effects is described. The measurement process is based on distance and angle of the satellite with respect to the inertial reference system. Finally, Position and attitude of the satellite are estimated through the above mentioned estimation algorithms implemented in MATLAB. The simulation results show that the unscented Kalman filter is a superior alternative to the extended Kalman filter for the nonlinear satellite motion estimation problems.

A novel spacecraft attitude and angular rate estimation algorithm is proposed using particle filter (PF) with the modified Rodrigues parameters (MRPs) representing the attitude, under both gyroless and gyro-disable modes. Belonging to the class of Monte Carlo sequential methods, the filter uses the unscented Kalman filter (UKF) to generate importance proposal distribution. It can not only avoid the limitation of the UKF which can only apply to Gaussian distribution, but also avoid the limitation of the standard PF which can not include the new measurements. A special procedure is developed to account for the curse of the dimensionality related to the PF in existence of augmented state vector. MRPs are used for attitude representations. The singularity problem associated with the MRPs is addressed as well using switching method. Simulation results demonstrate that the estimation algorithm has faster convergence rate and higher accuracy compared with the recently presented UKF, and it shows a reduction of about 10% in computational load compared with that using the quaternion estimation algorithm.

The planar circular restricted three-body problem (PCRTBP) model and calculation method of invariant manifolds of a periodic orbit about a libration point are presented, intersection conditions for transfer orbits designed for different periodic orbits (original orbit and desired orbit) based on invariant manifolds of PCRTBP are researched and given: first one is that the Jacobi constants of a pair of periodic orbit must be equal; second one is that choice of the departure moment of original orbit and the entry moment of object orbit is made in order to the manifold conjugated by unstable manifold of original orbit formed at the departure moment and stable manifold of object orbit formed at the entry moment is most optimal in energy. Finally, as an example, the transfer orbit between the Lagrange points of the Earth-Moon System is designed by making a choice equal Jacobi constants and departure/entry moments on the state space plot for a pair of periodic orbit.