Get in-depth understanding of each part of visual pathway yields insights to conquer the challenges that classic computer vision is facing. Here, we first report the bioinspired striate cortex with binocular and orientation selective receptive field based on the crossbar array of self-powered memristors which is solution-processed monolithic all-perovskite system with each cross-point containing one CsFAPbI3 solar cell directly stacking on the CsPbBr2I memristor. The plasticity of self-powered memristor can be modulated by optical stimuli following triplet-STDP rules. Furthermore, plasticity of 3 × 3 flexible crossbar array of self-powered memristors has been successfully modulated based on generalized BCM learning rule for optical-encoded pattern recognition. Finally, we implemented artificial striate cortex with binocularity and orientation selectivity based on two simulated 9 × 9 self-powered memristors networks. The emulation of striate cortex with binocular and orientation selectivity will facilitate the brisk edge and corner detection for machine vision in the future applications.

In the past decade, the volume of “omics” data generated by the different high-throughput technologies has expanded exponentially. The managing, storing, and analyzing of this big data have been a great challenge for the researchers, especially when moving towards the goal of generating testable data-driven hypotheses, which has been the promise of the high-throughput experimental techniques. Different bioinformatics approaches have been developed to streamline the downstream analyzes by providing independent information to interpret and provide biological inference. Text mining (also known as literature mining) is one of the commonly used approaches for automated generation of biological knowledge from the huge number of published articles. In this review paper, we discuss the recent advancement in approaches that integrate results from omics data and information generated from text mining approaches to uncover novel biomedical information.

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Electrodeposition of Ni-Co alloy foils on titanium substrate was performed in an acid chloride- sulphate bath. The influences of electrodeposition parameters such as current density, temperature, pH value, cobalt sulphate and saccharin concentration on composition and current efficiency were investigated in detail. The morphology and the microstructure of deposits were analyzed by SEM and XRD, respectively. The results indicated that the optimum parameters were current density 3-4 A/dm2, pH 2-3, temperature 40-50?C, cobalt sulphate 20 g/l and saccharin 2-3 g/l. Chemical analysis of the deposits by EDS revealed anomalous Ni-Co codeposition occured in this system. The SEM showed that hydroxide particles were not present on the surface and that fine-grain, smooth and compact Ni-Co alloy deposits were obtained. The crystallographic structures of Ni-Co alloy foils were the fcc Ni solid solution. The Ni-Co alloy foils with Co content 17.3-37.2 wt% and thickness of 20-45 μm were bright with low residual stress and super toughness