Low cycle fatigue,fatigue crack propagation and substructures in a series of polycrystalline Cu-Al alloys

Low Cycle Fatigue (LCF) on smooth hour glass specimens and Fatigue Crack Propagation (FCP) studies on Single Edge Notch (SEN) specimens were carried out at room temperature on four Cu-Al polycrystalline alloys to investigate the effects of Stacking Fault Energy (SFE) and mechanical property variations on fatigue characteristics. Significant improvements in fatigue properties were observed for alloys of low SFE. A microhardness technique was used to delineate the fatigue plastic zone ahead of stopped cracks at several stress intensity ranges for all the alloys. Planar slip was associated with a less than a second power dependence of plastic zone size on the stress intensity range. Transmission Electron Microscopy (TEM) was used to observe the substructures that developed both in LCF at different strain ranges and also ahead of fatigue cracks at different stress intensity ranges. Fractography was carried out to study the micromechanisms of crack propagation using a two stage replication technique. The experimental results were in good agreement with a theoretical model for FCP developed previously by the authors which incorporates mechanical and microstructural variables.     

Classification of heart rate data using artificial neural network and fuzzy equivalence relation

The electrocardiogram is a representative signal containing information about the condition of the heart. The shape and size of the P-QRS-T wave, the time intervals between its various peaks, etc. may contain useful information about the nature of disease afflicting the heart. However, these subtle details cannot be directly monitored by the human observer. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the signal parameters, extracted and analysed using computers, are highly useful in diagnostics. This paper deals with the classification of certain diseases using artificial neural network (ANN) and fuzzy equivalence relations. The heart rate variability is used as the base signal from which certain parameters are extracted and presented to the ANN for classification. The same data is also used for fuzzy equivalence classifier. The feedforward architecture ANN classifier is seen to be correct in about 85% of the test cases, and the fuzzy classifier yields correct classification in over 90% of the cases.     

Truncated O-Glycan-Bearing MUC16 Enhances Pancreatic Cancer Cells Aggressiveness via α4β1 Integrin Complexes and FAK Signaling

Elevated levels of Mucin-16 (MUC16) in conjunction with a high expression of truncated O-glycans is implicated in playing crucial roles in the malignancy of pancreatic ductal adenocarcinoma (PDAC). However, the mechanisms by which such aberrant glycoforms present on MUC16 itself promote an increased disease burden in PDAC are yet to be elucidated. This study demonstrates that the CRISPR/Cas9-mediated genetic deletion of MUC16 in PDAC cells decreases tumor cell migration. We found that MUC16 enhances tumor malignancy by activating the integrin-linked kinase and focal adhesion kinase (ILK/FAK)-signaling axis. These findings are especially noteworthy in truncated O-glycan (Tn and STn antigen)-expressing PDAC cells. Activation of these oncogenic-signaling pathways resulted in part from interactions between MUC16 and integrin complexes (α4β1), which showed a stronger association with aberrant glycoforms of MUC16. Using a monoclonal antibody to functionally hinder MUC16 significantly reduced the migratory cascades in our model. Together, these findings suggest that truncated O-glycan containing MUC16 exacerbates malignancy in PDAC by activating FAK signaling through specific interactions with α4 and β1 integrin complexes on cancer cell membranes. Targeting these aberrant glycoforms of MUC16 can aid in the development of a novel platform to study and treat metastatic pancreatic cancer.     

Design and development of a machine vision system using artificial neural network-based algorithm for automated coal characterization

Coal is heterogeneous in nature,and thus the characterization of coal is essential before its use for a specific purpose.Thus,the current study aims to develop ...     

PriorVAE: encoding spatial priors with variational autoencoders for small-area estimation

Gaussian processes (GPs), implemented through multivariate Gaussian distributions for a finite collection of data, are the most popular approach in small-area spatial statistical modelling. In this context, they are used to encode correlation structures over space and can generalize well in interpolation tasks. Despite their flexibility, off-the-shelf GPs present serious computational challenges which limit their scalability and practical usefulness in applied settings. Here, we propose a novel, deep generative modelling approach to tackle this challenge, termed PriorVAE: for a particular spatial setting, we approximate a class of GP priors through prior sampling and subsequent fitting of a variational autoencoder (VAE). Given a trained VAE, the resultant decoder allows spatial inference to become incredibly efficient due to the low dimensional, independently distributed latent Gaussian space representation of the VAE. Once trained, inference using the VAE decoder replaces the GP within a Bayesian sampling framework. This approach provides tractable and easy-to-implement means of approximately encoding spatial priors and facilitates efficient statistical inference. We demonstrate the utility of our VAE two-stage approach on Bayesian, small-area estimation tasks.     

A new strain hardening model for rate-dependent crystal plasticity

This paper presents a crystal plasticity based finite element analysis employing the new microstructure-based strain hardening model recently presented by Saimoto and Van Houtte (2011) [7] to simulate formability and texture evolution in the commercial aluminum alloy 5754. Simulations are performed to compare the predictive capability of the new hardening model against the common work hardening models using a rate-dependent plasticity formulation. The parameters in the numerical models are calibrated using the X-ray data published by Iadicola et al. (2008) [9] for the aluminum sheet alloy 5754. The predictions of the model for balanced biaxial tension and in-plane plane-strain tests are compared against experimental observations presented in Iadicola et al. (2008) [9]. It is concluded that the new model provides the best predictions of the large strain behavior of Aluminum sheet alloy 5754 subjected to various strain paths.     

Effect of chromium on the mechanical properties of powder-processed Fe–0.45 wt.% P alloys

The present investigation shows the role of chromium in Fe–P binary and Fe–P–Cr ternary alloys. The compositions are characterized in terms of microstructure, porosity content, hardness and tensile properties. The alloys were made using a hot powder forging technique. In this process mild steel encapsulated powders were hot forged into slabs. Then the slabs were hot rolled and annealed to relieve the residual stresses. Densifications as high as 98.9% of theoretical density have been realized. Microstructures of these alloys consist of single-phase ferrite only. Both Fe–0.45P and Fe–0.45P–3Cr alloys showed very high strength. As forged and hot rolled Fe–0.45P alloy showed low elongation. It was observed that, the addition of Cr to Fe–P based alloys caused an increase in strength associated with the reduction in ductility. Alloys developed in the present investigation were capable of hot working to very thin gage of sheets and wires.     

Highly Efficient Quasi 2D Blue Perovskite Electroluminescence Leveraging a Dual Ligand Composition

Perovskite light-emitting diodes (PeLEDs) are advancing because of their superior external quantum efficiencies (EQEs) and color purity. Still, additional work is needed for blue PeLEDs to achieve the same benchmarks as the other visible colors. This study demonstrates an extremely efficient blue PeLED with a 488 nm peak emission, a maximum luminance of 8600 cd m⁻², and a maximum EQE of 12.2% by incorporating the double-sided ethane-1,2-diammonium bromide (EDBr₂) ligand salt along with the long-chain ligand methylphenylammonium chloride (MeCl). The EDBr₂ successfully improves the interaction between 2D perovskite layers by reducing the weak van der Waals interaction and creating a Dion–Jacobson (DJ) structure. Whereas the pristine sample (without EDBr₂) is inhibited by small stacking number (n) 2D phases with nonradiative recombination regions that diminish the PeLED performance, adding EDBr₂ successfully enables better energy transfer from small n phases to larger n phases. As evidenced by photoluminescence (PL), scanning electron microscopy (SEM), and atomic force microscopy (AFM) characterization, EDBr₂ improves the morphology by reduction of pinholes and passivation of defects, subsequently improving the efficiencies and operational lifetimes of quasi-2D blue PeLEDs.