Modeling and simulation of high pressure composite cylinders for hydrogen storage

Composite hydrogen cylinders provide an efficient means of storage of hydrogen gas for modern hydrogen vehicles. Safety issues arising from the high gas pressure, temperature variation, fire attack, and fracture failures are the main concerns. The focus of this study is to develop a Finite Element (FE) model to predict the performance of composite cylinders subjected to extreme mechanical/thermal loadings and provide guidance for design optimization --Abstract, page iv

Manifold Regularized Correlation Object Tracking

In this paper, we propose a manifold regularized correlation tracking method with augmented samples. To make better use of the unlabeled data and the manifold structure of the sample space, a manifold regularization-based correlation filter is introduced, which aims to assign similar labels to neighbor samples. Meanwhile, the regression model is learned by exploiting the block-circulant structure of matrices resulting from the augmented translated samples over multiple base samples cropped from both target and nontarget regions. Thus, the final classifier in our method is trained with positive, negative, and unlabeled base samples, which is a semisupervised learning framework. A block optimization strategy is further introduced to learn a manifold regularization-based correlation filter for efficient online tracking. Experiments on two public tracking data sets demonstrate the superior performance of our tracker compared with the state-of-the-art tracking approaches

Generalized Pooling for Robust Object Tracking

Feature pooling in a majority of sparse coding-based tracking algorithms computes final feature vectors only by low-order statistics or extreme responses of sparse codes. The high-order statistics and the correlations between responses to different dictionary items are neglected. We present a more generalized feature pooling method for visual tracking by utilizing the probabilistic function to model the statistical distribution of sparse codes. Since immediate matching between two distributions usually requires high computational costs, we introduce the Fisher vector to derive a more compact and discriminative representation for sparse codes of the visual target. We encode target patches by local coordinate coding, utilize Gaussian mixture model to compute Fisher vectors, and finally train semi-supervised linear kernel classifiers for visual tracking. In order to handle the drifting problem during the tracking process, these classifiers are updated online with current tracking results. The experimental results on two challenging tracking benchmarks demonstrate that the proposed approach achieves a better performance than the state-of-the-art tracking algorithms

Robust Object Tracking by Nonlinear Learning

We propose a method that obtains a discriminative visual dictionary and a nonlinear classifier for visual tracking tasks in a sparse coding manner based on the globally linear approximation for a nonlinear learning theory. Traditional discriminative tracking methods based on sparse representation learn a dictionary in an unsupervised way and then train a classifier, which may not generate both descriptive and discriminative models for targets by treating dictionary learning and classifier learning separately. In contrast, the proposed tracking approach can construct a dictionary that fully reflects the intrinsic manifold structure of visual data and introduces more discriminative ability in a unified learning framework. Finally, an iterative optimization approach, which computes the optimal dictionary, the associated sparse coding, and a classifier, is introduced. Experiments on two benchmarks show that our tracker achieves a better performance compared with some popular tracking algorithms.This work was supported in part by the National Natural Science Foundation of China under Grant 61472036, Grant 61272359, Grant 61672099, and Grant 81627803, in part by the National Key Research and Development Program of China under Grant 2017YFC0112000, in part by the Australian Research Council’s Discovery Projects Funding Scheme under Grant DP150104645, in part by the Fok Ying-Tong Education Foundation for Young Teachers, and in part by the Joint Building Program through the Beijing Municipal Education Commission

Multi-phase-combined CECT radiomics models for Fuhrman grade prediction of clear cell renal cell carcinoma

ObjectiveThis study aimed to evaluate the effectiveness of multi-phase-combined contrast-enhanced CT (CECT) radiomics methods for noninvasive Fuhrman grade prediction of clear cell renal cell carcinoma (ccRCC).MethodsA total of 187 patients with four-phase CECT images were retrospectively enrolled and then were categorized into training cohort (n=126) and testing cohort (n=61). All patients were confirmed as ccRCC by histopathological reports. A total of 110 3D classical radiomics features were extracted from each phase of CECT for individual ccRCC lesion, and contrast-enhanced variation features were also calculated as derived radiomics features. These features were concatenated together, and redundant features were removed by Pearson correlation analysis. The discriminative features were selected by minimum redundancy maximum relevance method (mRMR) and then input into a C-support vector classifier to build multi-phase-combined CECT radiomics models. The prediction performance was evaluated by the area under the curve (AUC) of receiver operating characteristic (ROC).ResultsThe multi-phase-combined CECT radiomics model showed the best prediction performance (AUC=0.777) than the single-phase CECT radiomics model (AUC=0.711) in the testing cohort (p value=0.039).ConclusionThe multi-phase-combined CECT radiomics model is a potential effective way to noninvasively predict Fuhrman grade of ccRCC. The concatenation of first-order features and texture features extracted from corticomedullary phase and nephrographic phase are discriminative feature representations

Transposable elements cause the loss of self-incompatibility in citrus

Self-incompatibility (SI) is a widespread prezygotic mechanism for flowering plants to avoid inbreeding depression and promote genetic diversity. Citrus has an S-RNase-based SI system, which was frequently lost during evolution. We previously identified a single nucleotide mutation in Sm-RNase, which is responsible for the loss of SI in mandarin and its hybrids. However, little is known about other mechanisms responsible for conversion of SI to self-compatibility (SC) and we identify a completely different mechanism widely utilized by citrus. Here, we found a 786-bp miniature inverted-repeat transposable element (MITE) insertion in the promoter region of the FhiS2-RNase in Fortunella hindsii Swingle (a model plant for citrus gene function), which does not contain the Sm-RNase allele but are still SC. We demonstrate that this MITE plays a pivotal role in the loss of SI in citrus, providing evidence that this MITE insertion prevents expression of the S-RNase; moreover, transgenic experiments show that deletion of this 786-bp MITE insertion recovers the expression of FhiS2-RNase and restores SI. This study identifies the first evidence for a role for MITEs at the S-locus affecting the SI phenotype. A family-wide survey of the S-locus revealed that MITE insertions occur frequently adjacent to S-RNase alleles in different citrus genera, but only certain MITEs appear to be responsible for the loss of SI. Our study provides evidence that insertion of MITEs into a promoter region can alter a breeding strategy and suggests that this phenomenon may be broadly responsible for SC in species with the S-RNase system

A real-world study of anlotinib combined with GS regimen as first-line treatment for advanced pancreatic cancer

BackgroundAnlotinib may boost the efficacy of pancreatic cancer (PC) treatment if timely added to the GS regimen (Gemcitabine, Tegafur-gimeracil-oteracil potassium); however, no data has been published. This study evaluated the safety and efficacy of anlotinib in combination with the GS regimen(hereafter referred to as the A+GS regimen) in the first-line treatment of patients with unresectable or metastatic PC.MethodsPatients with unresectable or metastatic PC treated at Yueyang Central Hospital and Yueyang People’s Hospital between October 2018 and June 2022 were enrolled in this retrospective real-world investigation. Treatment efficacy was evaluated based on the overall survival (OS), progression-free survival (PFS), disease control rate (DCR), and objective response rate (ORR), while the treatment safety was assessed by the frequency of major adverse events (AEs).ResultsSeventy-one patients were included in this study, 41 in the GS group and 30 in the A+GS group. The A+GS group had a longer mPFS than the GS group (12.0 months (95% CI, 6.0–18.0) and 6.0 months (95% CI, 3.0–8.1)), respectively (P = 0.005). mOS was longer in the GS+A group) when compared with the GS group (17.0 months (95%CI, 14.0–20.0) and 10.0 months (95% CI, 7.5–12.5)), respectively (P = 0.018). The GS+A group had higher ORR (50.0% vs 26.8%, P = 0.045) and DCR (83.3% vs 58.5%, P = 0.026). Furthermore, there were no grade 4-5 AEs and no treatment-related deaths, and no discernible increase in AEs in the GS+A group when compared with the GS group.ConclusionThe A+GS regimen therapy holds great promise in managing treatment-naive advanced PC, except that future prospective studies with larger sample sizes and multiple centers are required to determine its efficacy and safety

Synchronization of fractional-order chaotic systems with multiple delays by a new approach

summary:In this paper, we propose a new approach of designing a controller and an update rule of unknown parameters for synchronizing fractional-order system with multiple delays and prove the correctness of the approach according to the fractional Lyapunov stable theorem. Based on the proposed approach, synchronizing fractional delayed chaotic system with and without unknown parameters is realized. Numerical simulations are carried out to confirm the effectiveness of the approach