Effects of Hf, B, Cr and Zr alloying on mechanical properties and oxidation resistance of Nb-Si based ultrahigh temperature alloy

Multi-component Nb-Si based ultrahigh temperature alloys were prepared by vacuum non-consumable arc melting. The effects of Hf, B, Zr and Cr alloying on the phase selection, phase stability, both non-equilibrium and equilibrium microstructure, room-temperature fracture toughness, hardness and oxidation resistance at 1250 oC of the alloys have been investigated and estimated systematically. The results show that the addition of B or Cr promotes the formation of hypereutectic structures. The alloying with both Hf and B suppresses the formation of β(Nb,X)5Si3 and promotes the formation of α(Nb,X)5Si3 and γ(Nb,X)5Si3, while the alloying with Cr has no effect on the crystal structures of 5-3 silicides. The room-temperature fracture toughness of the alloys is always degraded by the addition of Cr but almost not influenced by the combined additions of Hf and B. The hardness of 5-3 silicides exhibits a tendency of γ \u3e α \u3e β. The macrohardness of the alloys increases with Cr addition, and it obviously reduces in the presence of Hf after 1450 oC/50 h heat-treatment. The best oxidation-resistant performance has been obtained for the alloy with both B and Cr additions. However, in the presence of B and/or Cr, the oxidation resistance of the alloys has been degraded by further addition of Hf. Both sizes and amounts of primary γ-(Nb, X)5Si3 increase with Zr contents in the alloy. Both adhesion and compactness of the scales are improved effectively by increase in Zr content. The mass gain and thickness of the scale decrease with increase in Zr contents, indicating that Zr addition can improve the oxidation resistance of the alloys significantly. Please click Additional Files below to see the full abstract

Multi-Modal Human Authentication Using Silhouettes, Gait and RGB

Whole-body-based human authentication is a promising approach for remote biometrics scenarios. Current literature focuses on either body recognition based on RGB images or gait recognition based on body shapes and walking patterns; both have their advantages and drawbacks. In this work, we propose Dual-Modal Ensemble (DME), which combines both RGB and silhouette data to achieve more robust performances for indoor and outdoor whole-body based recognition. Within DME, we propose GaitPattern, which is inspired by the double helical gait pattern used in traditional gait analysis. The GaitPattern contributes to robust identification performance over a large range of viewing angles. Extensive experimental results on the CASIA-B dataset demonstrate that the proposed method outperforms state-of-the-art recognition systems. We also provide experimental results using the newly collected BRIAR dataset

Development and test of a mini-Data Acquisition system for the High-Luminosity LHC upgrade of the ATLAS Monitored Drift Tube detector

New front-end electronics including ASICs and FPGA boards are under development for the ATLAS Monitored Drift Tube (MDT) detector to handle the large data rates and harsh environment expected at high-luminosity LHC runs. A mobile Data Acquisition (miniDAQ) system is designed to perform integration tests of these front-end electronics. In addition, it will be used for surface commissioning of 96 small-radius MDT (sMDT) chambers and for integration and commissioning of new front-end electronics on the present ATLAS MDT chambers. Details of the miniDAQ hardware and firmware are described in this article. The miniDAQ system is also used to read out new front-end electronics on an sMDT prototype chamber using cosmic muons and results obtained are shown.Comment: 10 pages, 12 figure

IDET: Iterative Difference-Enhanced Transformers for High-Quality Change Detection

Change detection (CD) aims to detect change regions within an image pair captured at different times, playing a significant role for diverse real-world applications. Nevertheless, most of existing works focus on designing advanced network architectures to map the feature difference to the final change map while ignoring the influence of the quality of the feature difference. In this paper, we study the CD from a new perspective, i.e., how to optimize the feature difference to highlight changes and suppress unchanged regions, and propose a novel module denoted as iterative difference-enhanced transformers (IDET). IDET contains three transformers: two transformers for extracting the long-range information of the two images and one transformer for enhancing the feature difference. In contrast to the previous transformers, the third transformer takes the outputs of the first two transformers to guide the enhancement of the feature difference iteratively. To achieve more effective refinement, we further propose the multi-scale IDET-based change detection that uses multi-scale representations of the images for multiple feature difference refinements and proposes a coarse-to-fine fusion strategy to combine all refinements. Our final CD method outperforms seven state-of-the-art methods on six large-scale datasets under diverse application scenarios, which demonstrates the importance of feature difference enhancements and the effectiveness of IDET.Comment: conferenc

A study on the impact of pre-trained model on Just-In-Time defect prediction

Previous researchers conducting Just-In-Time (JIT) defect prediction tasks have primarily focused on the performance of individual pre-trained models, without exploring the relationship between different pre-trained models as backbones. In this study, we build six models: RoBERTaJIT, CodeBERTJIT, BARTJIT, PLBARTJIT, GPT2JIT, and CodeGPTJIT, each with a distinct pre-trained model as its backbone. We systematically explore the differences and connections between these models. Specifically, we investigate the performance of the models when using Commit code and Commit message as inputs, as well as the relationship between training efficiency and model distribution among these six models. Additionally, we conduct an ablation experiment to explore the sensitivity of each model to inputs. Furthermore, we investigate how the models perform in zero-shot and few-shot scenarios. Our findings indicate that each model based on different backbones shows improvements, and when the backbone's pre-training model is similar, the training resources that need to be consumed are much more closer. We also observe that Commit code plays a significant role in defect detection, and different pre-trained models demonstrate better defect detection ability with a balanced dataset under few-shot scenarios. These results provide new insights for optimizing JIT defect prediction tasks using pre-trained models and highlight the factors that require more attention when constructing such models. Additionally, CodeGPTJIT and GPT2JIT achieved better performance than DeepJIT and CC2Vec on the two datasets respectively under 2000 training samples. These findings emphasize the effectiveness of transformer-based pre-trained models in JIT defect prediction tasks, especially in scenarios with limited training data

The splice variant Ehm2/1 in breast cancer MCF-7 cells interacted with β--catenin and increased its localization to plasma membrane

Ehm2, which belongs to the FERM superfamily, is a metastasis-associated protein. However, its function in cancer metastasis and the associated molecular mechanism is not definitely clear. Alternative splicing is an important biological step during mRNA processing and has been reported to be related with many diseases including cancers. Ehm2 has two transcript variants. Transcript variant 1(Ehm2/1) encodes isoform 1 of 518 amino acids, while transcript variant 2(Ehm2/2) encodes isoform 2 of 913 amino acids. In this study, we found that Ehm2/1 was the main transcript variant in the MCF-7 breast cancer cell line. Forced expression of Ehm2/1 upregulated the total protein amount but had no effect on the mRNA levels of β-catenin. The increased β-catenin was found to be dominantly located at the cell membrane. Meanwhile, knockdown of Ehm2/1 in MCF-7 cells decreased the total protein amount but not the mRNA levels of β-catenin. Further results showed that Ehm2/1 interacted with β-catenin and colocalized with it at the cell membrane. E-cadherin, a partner of β-catenin in cadherin-catenin complexes, was also upregulated by the overexpression of Ehm2/1 and also colocalized with it at the cell membrane. Meanwhile, overexpression of Ehm2/1 inhibited the migration ability of MCF-7 cells. These results suggested that Ehm2/1 may render β-catenin at the cell membrane by interacting with β-catenin and E-cadherin

On sensitivity analysis of parameters for fractional differential equations with Caputo derivatives

In this paper, we discuss the effect of parameter variations on the performance of fractional differential equations and give the concept of fractional sensitivity functions and fractional sensitivity equations. Meanwhile, by employing Laplace transform and the inverse Laplace transform, some main results on fractional differential equations are proposed. Finally, two simple examples with numerical simulations are provided to show the validity and feasibility of the proposed theorem

On sensitivity analysis of parameters for fractional differential equations with Caputo derivatives

In this paper, we discuss the effect of parameter variations on the performance of fractional differential equations and give the concept of fractional sensitivity functions and fractional sensitivity equations. Meanwhile, by employing Laplace transform and the inverse Laplace transform, some main results on fractional differential equations are proposed. Finally, two simple examples with numerical simulations are provided to show the validity and feasibility of the proposed theorem

Effect of junctional adhesion molecule-2 expression on cell growth, invasion and migration in human colorectal cancer

The junctional adhesion molecule (JAMs) family belongs to the immunoglobulin subfamily involved in the formation of tight junctions (TJ) in both endothelial and epithelial cells. Aberrant expression of JAM-2 is associated with cancer progression but little work has been carried out in discovering how this affects changes in cell behaviour. The present study aimed to examine the expression of JAM-2 in human colon cancer specimens and cell lines and its role in the development of colon cancer. JAM-2 expression in human colon cancer specimens (normal, n=75; cancer, n=94) and cell lines was analysed using quantitative real-time PCR and conventional RT-PCR. Colon cancer cells were stably transfected with a mammalian expression vector to overexpress JAM-2-Flag. The effect on growth, adhesion and migration following overexpression of JAM-2 was then investigated using in vitro models. TJ function was assessed using a trans-epithelial resistance assay (TER, with an EVOM voltammeter). JAM-2 was lowly expressed in colon cancer cells such as RKO, HT115. JAM-2 overexpression in RKO cells (RKO-JAM-2) and HT115 cells (HT115-JAM-2) showed retarded adhesion (P<0.05). An in vivo tumour model showed that RKO-JAM-2 had significantly reduced growth (P<0.05), invasion (P<0.05) and migration (P<0.05) as well as in HT115-JAM-2, except on proliferation and migration. Expression of JAM-2 resulted in a significant increase in TER and decrease in permeability of polarized monolayers (P<0.05). Further analysis of JAM-2 transcript levels against clinical aspects demonstrated that the decreasing JAM-2 expression correlated to disease progression, metastasis and poor survival. Taken together, JAM-2 may function as a putative tumour suppressor in the progression and metastasis of colorectal cance