Highly enhanced energy storage properties of H2O2-hydroxylated rare earth ferrites (LaFeO3 and GdFeO3) nanofillers in poly(vinylidene fluoride)-based nanocomposite films

Journal of Materials Science: Materials in Electronics - Pure PVDF has higher breakdown strength but low dielectric and ferroelectric properties. Thus, we synthesized the LaFeO3 and GdFeO3...     

Effect of interface coupling between polarization and magnetization in La0.7Pb0.3MnO3 (LPMO)/P(VDF-TrFE) flexible nanocomposite films

Journal of Materials Science - La0.7Pb0.3MnO3(LPMO) nanoparticles (NPs) were prepared by sol–gel auto-combustion method. These were embedded in P(VDF-TrFE) to form (0–3) nanocomposite...     

B-GWO based multi-UAV deployment and power allocation in NOMA assisted wireless networks

Wireless Networks - Unmanned Aerial Vehicles (UAVs) deployed as flying base stations is a promising technology for enhancing the quality of service (QoS) and quick recovery from unexpected damages...     

Band gap tuning of ferroelectric PbTiO3 by Mo doping

Journal of Materials Science: Materials in Electronics - Tuning the band gap of ferroelectric materials to visible region without reducing the polarization can provide an ideal photovoltaic...     

ResearchGate and Google Scholar: how much do they differ in publications,citations and different metrics and why?

ResearchGate has emerged as a popular professional network for scientists and researchers in a very short span. Similar to Google Scholar, the ResearchGate indexing uses an automatic crawling algorithm that extracts bibliographic data, citations, and other information about scholarly articles from various sources. However, it has been observed that the two platforms often show different publication and citation data for the same institutions, journals, and authors. While several previous studies analysed different aspects of ResearchGate and Google Scholar, the quantum of differences in publications, citations, and metrics between the two and the probable reasons for the same are not explored much. This article, therefore, attempts to bridge this research gap by analysing and measuring the differences in publications, citations, and different metrics of the two platforms for a large data set of highly cited authors. The results indicate that there are significantly high differences in publications and citations for the same authors captured by the two platforms, with Google Scholar having higher counts for a vast majority of the cases. The different metrics computed by the two platforms also differ in their values, showing different degrees of correlation. The coverage policy, indexing errors, author attribution mechanism, and strategy to deal with predatory publishing are found to be the main probable reasons for the differences in the two platforms.

     

Glycaemic response of pseudocereal-based gluten-free food products: a review

Rapid digestion and absorption of carbohydrates have become a health issue (high glycaemic index, GI), which can be a matter of greater concern when consumed in large quantities. Depending upon the influence of carbohydrates on the blood sugar levels, GI classifies carbohydrates (on a scale of 100) as low (<55), medium (55–70) and high (>70) GI foods. Among the pseudocereals, chia seed possesses relatively lower GI (28.53), as compared to buckwheat (52.35), amaranth (47.65) and quinoa (61.50). Consumers now prefer foods with a high GI over the ones with low GI to prevent various metabolic alterations. Celiac disease is a lifelong disorder prevalent worldwide and can only be controlled by following a strict lifelong gluten-free diet. Therefore, pseudocereals could be a potential alternate for low GI food and developing gluten-free food products, including bread, cookies, noodles and pasta. This review synthesises the recently published literatures on pseudocereals as a lowering GI and healthy food option. This review also gives insights into developing pseudocereals as a potential and novel ingredient for gluten-free food applications and the latest research conducted worldwide.     

Learning to hash: a comprehensive survey of deep learning-based hashing methods

Explosive growth of big data demands efficient and fast algorithms for nearest neighbor search. Deep learning-based hashing methods have proved their efficacy to learn advanced hash functions that suit the desired goal of nearest neighbor search in large image-based data-sets. In this work, we present a comprehensive review of different deep learning-based supervised hashing methods particularly for image data-sets suggested by various researchers till date to generate advanced hash functions. We categorize prior works into a five-tier taxonomy based on: (i) the design of network architecture, (ii) training strategy based on nature of data-set, (iii) the type of loss function, (iv) the similarity measure and, (v) the nature of quantization. Further, different data-sets used in prior works are reported and compared based on various challenges in the characteristics of images that are part of the data-sets. Lastly, different future directions such as incremental hashing, cross-modality hashing and guidelines to improve design of hash functions are discussed. Based on our comparative review, it has been observed that generative adversarial networks-based hashing models outperform other methods. This is due to the fact that they leverage more data in the form of both real world and synthetically generated data. Furthermore, it has been perceived that triplet-loss-based loss functions learn better discriminative representations by pushing similar patterns together and dis-similar patterns away from each other. This study and its observations shall be useful for the researchers and practitioners working in this emerging research field.

     

Towards a super-resolution based approach for improved face recognition in low resolution environment

Multimedia Tools and Applications - The video surveillance activity generates a vast amount of data, which can be processed to detect miscreants. The task of identifying and recognizing an object...