Ionic conductivity in poly(vinyl butyral) based polymeric electrolytes: Effect of solvents and salts

Li⁺ conductive solid polymeric electrolytes (SPEs) have been prepared by complexing Li salts LiX (X = I, SCN, CLO4, CF3SO3) with poly(vinyl butyral) (PVB) an amorphous polymer with a low T_g value and well known for its outstanding laminating properties. These self-supporting SPEs have been prepared by the solution cast technique using cyclohexanone, tetrahydrofuran, and n-butyl alcohol as solvents. The XRD and DSC investigations correlated to measured ionic conductivities have revealed that in addition to the amorphous nature of the resulting complex, its T_g value, the concentration and the values of the lattice energies of the complexing salts, the solvent used in making the SPEs also govern the value of ionic conductivity. Highest ionic conductivity values attained in SPEs made with n-butyl alcohol are explained in terms of the interception of the hydrogen bonding due to solvation.     

Using keyphrases as search result surrogates on small screen devices

This paper investigates user interpretation of search result displays on small screen devices. Such devices present interesting design challenges given their limited display capabilities, particularly in relation to screen size. Our aim is to provide users with succinct yet useful representations of search results that allow rapid and accurate decisions to be made about the utility of result documents, yet minimize user actions (such as scrolling), the use of device resources, and the volume of data to be downloaded. Our hypothesis is that keyphrases that are automatically extracted from documents can support this aim. We report on a user study that compared how accurately users categorized result documents on small screens when the document surrogates consisted of either keyphrases only, or document titles. We found no significant performance differences between the two conditions. In addition to these encouraging results, keyphrases have the benefit that they can be extracted and presented when no other document metadata can be identified.     

Automatic segmentation of optic disc in retinal fundus images using semi-supervised deep learning

Diseases of the eye require manual segmentation and examination of the optic disc by ophthalmologists. Though, image segmentation using deep learning techniques is achieving remarkable results, it leverages on large-scale labeled datasets. But, in the field of medical imaging, it is challenging to acquire large labeled datasets. Hence, this article proposes a novel deep learning model to automatically segment the optic disc in retinal fundus images by using the concepts of semi-supervised learning and transfer learning. Initially, a convolutional autoencoder (CAE) is trained to automatically learn features from a large number of unlabeled fundus images available from the Kaggle’s diabetic retinopathy (DR) dataset. The autoencoder (AE) learns the features from the unlabeled images by reconstructing the input images and becomes a pre-trained network (model). After this, the pre-trained autoencoder network is converted into a segmentation network. Later, using transfer learning, the segmentation network is trained with retinal fundus images along with their corresponding optic disc ground truth images from the DRISHTI GS1 and RIM-ONE datasets. The trained segmentation network is then tested on retinal fundus images from the test set of DRISHTI GS1 and RIM-ONE datasets. The experimental results show that the proposed method performs on par with the state-of-the-art methods achieving a 0.967 and 0.902 dice score coefficient on the test set of the DRISHTI GS1 and RIM-ONE datasets respectively. The proposed method also shows that transfer learning and semi-supervised learning overcomes the barrier imposed by the large labeled dataset. The proposed segmentation model can be used in automatic retinal image processing systems for diagnosing diseases of the eye.

     

Ultrasensitive label-free electrical detection of charged biomolecules using a metal–semiconductor–metal Schottky silicon nanowire biristor

Journal of Computational Electronics - An open-base metal–semiconductor–metal Schottky silicon nanowire (MSM-SiNW) biristor device is applied for ultrasensitive, label-free, real-time...