Modeling and dynamic analysis of spiral bevel gear coupled system of intermediate and tail gearboxes in a helicopter.

The coupled dynamic model of the intermediate and tail gearboxes’ spiral bevel gear-oblique tail shaft-laminated membrane coupling was established by employing the hybrid modeling method of finite element and lumped mass. Among them, the dynamic equation of the shaft was constructed by Timoshenko beam; spiral bevel gears were derived theoretically by the lumped-mass method, where the effects of time-varying meshing stiffness, transmission error, external imbalance excitation and the like were considered simultaneously; laminated membrane coupling was simplified to a lumped parameter model, in which the stiffness was obtained by the finite element simulation and experiment. On this basis, the laminated membrane coupling and effects of several important parameters, including the unbalance value, tail rotor excitation, oblique tail shaft’s length and transmission error amplitude, on the system’s dynamic characteristics were discussed. The results showed that the influences of laminated membrane coupling and transmission error amplitude on the coupled system’s vibration response were prominent, which should be taken into consideration in the dynamic model. Due to the bending-torsional coupled effect, the lateral vibration caused by gear eccentricity would enlarge the oblique tail shaft’s torsional vibration; similarly, the tail rotor’s torsional excitation also varies the lateral vibration of the oblique tail shaft. The coupled effect between the eccentricity of gear pairs mainly hit the torsional vibration. Also, as the oblique tail shaft’s length increased, the torsional vibration of the oblique tail shaft tended to diminish while the axis orbit became larger. The research provides theoretical support for the design of the helicopter tail transmission system

Comparison of retinal thickness measurements of normal eyes between topcon algorithm and a graph based algorithm

To assess the agreement between Topcon built-in algorithm and our developed graph based algorithm, the retinal thickness of 9-sectors on an Early Treatment of Diabetic Retinopathy Study(ETDRS) chart measurements for normal subjects was compared. A total of fifty eyes were enrolled in this study. The overall and sectoral thickness on ETDRS chart were calculated using Topcon built-in algorithm and our developed three-dimensional graph based algorithm. Correlation analysis and agreement analysis were performed between the commercial algorithm and our algorithm. A high degree of correlation was found between the results obtained from the two methods was from 0.856 to 0.960. It’s showed that our developed graph based algorithm can provide excellent performance similar to Topcon algorithm

An automated framework of inner segment/outer segment defect detection for retinal SD-OCT images

The integrity of inner segment/outer segment (IS/OS) has high correlation with lower visual acuity in patients suffering from blunt trauma. An automated 3D IS/OS defect detection method based on the SD-OCT images was proposed. First, 11 surfaces were automatically segmented using the multiscale 3D graph-search approach. Second, the sub-volumes between surface 7 and 8 containing IS/OS region around the fovea (diameter of mm) were extracted and flattened based on the segmented retinal pigment epithelium layer. Third, 5 kinds of texture based features were extracted for each voxel. A KNN classifier was trained and each voxel was classified as disrupted or nondisrupted and the responding defect volume was calculated. The proposed method was trained and tested on 9 eyes from 9 trauma subjects using the leave-one-out cross validation method. The preliminary results demonstrated the feasibility and efficiency of the proposed method

Experimental and numerical investigation of rubber damping ring and its application in multi-span shafting

A new approach for establishing the mechanical model of the rubber damping ring was studied numerically and experimentally. Firstly, parameters of Mooney–Rivlin and Prony series models of the rubber material were identified based on ISIGHT integrating with ANSYS and MATLAB, in which the rubber damping ring’s hysteresis loop was obtained by vibration experiment and ANSYS simulation, respectively; meanwhile, the dynamic stiffness and damping were calculated simultaneously by a parameter separation and identification method. Subsequently, the accuracy of the constitutive model parameters was verified experimentally. In the light of this, based on the experimental design and the approximate model method of the joint simulation platform, a mechanical model of dynamic stiffness and damping of the rubber damping ring was established. Finally, the rubber damping ring’s mathematical model was employed to perform a vibration reduction analysis in a multi-span shafting, where the numerical and experimental investigation was conducted, respectively. The results show that the theoretical and experimental error of vibration reduction rate is less than 17%, which verifies the accuracy of the mechanical model of the rubber damping ring

Deep learning-based image segmentation model using an MRI-based convolutional neural network for physiological evaluation of the heart

Background and Objective: Cardiovascular disease is a high-fatality health issue. Accurate measurement of cardiovascular function depends on precise segmentation of physiological structure and accurate evaluation of functional parameters. Structural segmentation of heart images and calculation of the volume of different ventricular activity cycles form the basis for quantitative analysis of physiological function and can provide the necessary support for clinical physiological diagnosis, as well as the analysis of various cardiac diseases. Therefore, it is important to develop an efficient heart segmentation algorithm.Methods: A total of 275 nuclear magnetic resonance imaging (MRI) heart scans were collected, analyzed, and preprocessed from Huaqiao University Affiliated Strait Hospital, and the data were used in our improved deep learning model, which was designed based on the U-net network. The training set included 80% of the images, and the remaining 20% was the test set. Based on five time phases from end-diastole (ED) to end-systole (ES), the segmentation findings showed that it is possible to achieve improved segmentation accuracy and computational complexity by segmenting the left ventricle (LV), right ventricle (RV), and myocardium (myo).Results: We improved the Dice index of the LV to 0.965 and 0.921, and the Hausdorff index decreased to 5.4 and 6.9 in the ED and ES phases, respectively; RV Dice increased to 0.938 and 0.860, and the Hausdorff index decreased to 11.7 and 12.6 in the ED and ES, respectively; myo Dice increased to 0.889 and 0.901, and the Hausdorff index decreased to 8.3 and 9.2 in the ED and ES, respectively.Conclusion: The model obtained in the final experiment provided more accurate segmentation of the left and right ventricles, as well as the myocardium, from cardiac MRI. The data from this model facilitate the prediction of cardiovascular disease in real-time, thereby providing potential clinical utility

Targeted metabolomics analysis of nucleosides and the identification of biomarkers for colorectal adenomas and colorectal cancer

The morbidity and mortality of colorectal cancer (CRC) have been increasing in recent years, and early detection of CRC can improve the survival rate of patients. RNA methylation plays crucial roles in many biological processes and has been implicated in the initiation of various diseases, including cancer. Serum contains a variety of biomolecules and is an important clinical sample for biomarker discovery. In this study, we developed a targeted metabolomics method for the quantitative analysis of nucleosides in human serum samples by using liquid chromatography with tandem mass spectrometry (LC-MS/MS). We successfully quantified the concentrations of nucleosides in serum samples from 51 healthy controls, 37 patients with colorectal adenomas, and 55 patients with CRC. The results showed that the concentrations of N6-methyladenosine (m6A), N1-methyladenosine (m1A), and 3-methyluridine (m3U) were increased in patients with CRC, whereas the concentrations of N2-methylguanosine (m2G), 2′-O-methyluridine (Um), and 2′-O-methylguanosine (Gm) were decreased in patients with CRC, compared with the healthy controls and patients with colorectal adenomas. Moreover, the levels of 2′-O-methyluridine and 2′-O-methylguanosine were lower in patients with colorectal adenomas than those in healthy controls. Interestingly, the levels of Um and Gm gradually decreased in the following order: healthy controls to colorectal adenoma patients to CRC patients. These results revealed that the aberrations of these nucleosides were tightly correlated to colorectal adenomas and CRC. In addition, the present work will stimulate future investigations about the regulatory roles of these nucleosides in the initiation and development of CRC

Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification

Failure to recognize samples from the classes unseen during training is a major limit of artificial intelligence (AI) in real-world implementation of retinal anomaly classification. To resolve this obstacle, we propose an uncertainty-inspired open-set (UIOS) model which was trained with fundus images of 9 common retinal conditions. Besides the probability of each category, UIOS also calculates an uncertainty score to express its confidence. Our UIOS model with thresholding strategy achieved an F1 score of 99.55%, 97.01% and 91.91% for the internal testing set, external testing set and non-typical testing set, respectively, compared to the F1 score of 92.20%, 80.69% and 64.74% by the standard AI model. Furthermore, UIOS correctly predicted high uncertainty scores, which prompted the need for a manual check, in the datasets of rare retinal diseases, low-quality fundus images, and non-fundus images. This work provides a robust method for real-world screening of retinal anomalies

Methodology and applications of city level CO2 emission accounts in China

China is the world's largest energy consumer and CO2 emitter. Cities contribute 85% of the total CO2 emissions in China and thus are considered as the key areas for implementing policies designed for climate change adaption and CO2 emission mitigation. However, the emission inventory construction of Chinese cities has not been well researched, mainly owing to the lack of systematic statistics and poor data quality. Focusing on this research gap, we developed a set of methods for constructing CO2 emissions inventories for Chinese cities based on energy balance table. The newly constructed emission inventory is compiled in terms of the definition provided by the IPCC territorial emission accounting approach and covers 47 socioeconomic sectors, 17 fossil fuels and 9 primary industry products, which is corresponding with the national and provincial inventory. In the study, we applied the methods to compile CO2 emissions inventories for 24 common Chinese cities and examined uncertainties of the inventories. Understanding the emissions sources in Chinese cities is the basis for many climate policy and goal research in the future

Tudor-SN protein expression in colorectal cancer and its association with clinical characteristics

Tudor-SN protein (SND1) is known to be up-regulated in some types of human malignancies and functions as an oncogene. The objective of our study was to investigate the expression and prognostic value of SND1 in human colorectal cancer (CRC)

Clustering Hidden Markov Models with Variational Bayesian Hierarchical em

data-language=&quot;eng&quot;&nbsp;data-ev-field=&quot;abstract&quot;&gt;The&nbsp;An-Shi&nbsp;Rebellion&nbsp;is an important stage in both history and literature. Though&nbsp;the&nbsp;study&nbsp;of&nbsp;its impact&nbsp;on&nbsp;Du&nbsp;Fu&#39;s works has reached a consensus, there is little&nbsp;study&nbsp;on&nbsp;its impact&nbsp;on&nbsp;his&nbsp;psychology, let alone in-depth and comprehensive results. In this paper, we make a systematic and in-depth exploration&nbsp;of&nbsp;the&nbsp;changes&nbsp;of&nbsp;Du&nbsp;Fu&#39;s&nbsp;psychological&nbsp;characteristics before and after&nbsp;the&nbsp;An-Shi&nbsp;Rebellion&nbsp;by analyzing his prose with&nbsp;the&nbsp;big&nbsp;data&nbsp;approaches.&nbsp;The&nbsp;results show that&nbsp;Du&nbsp;Fu&#39;s&nbsp;psychology&nbsp;changed&nbsp;significantly before and after&nbsp;the&nbsp;An-Shi&nbsp;Rebellion. He became disappointed with society, devoted more&nbsp;of&nbsp;his affection to&nbsp;the&nbsp;people, and strengthened his desire to save&nbsp;the&nbsp;country and&nbsp;the&nbsp;people. Moreover,&nbsp;the&nbsp;change&nbsp;did not happen overnight, but over a long time period. This work provides a new case for&nbsp;the&nbsp;application&nbsp;of&nbsp;big&nbsp;data&nbsp;technology in&nbsp;psychology&nbsp;research.&nbsp;The&nbsp;findings could help people to understand&nbsp;Du&nbsp;Fu more scientifically and deeply, and to better promote cultural heritage.</p