Composition-dependent xBa(Zr0.2Ti0.8)O3-(1-x)(Ba0.7Ca0.3)TiO3 bulk ceramics for high energy storage applications

This work reports the composition dependent microstructure, dielectric, ferroelectric and energy storage properties, and the phase transitions sequence of lead free xBa(Zr0.2Ti0.8)O3-(1-x)(Ba0.7Ca0.3)TiO3 [xBZT-(1-x)BCT] ceramics, with x = 0.4, 0.5 and 0.6, prepared by solid state reaction method. The XRD and Raman scattering results confirm the coexistence of rhombohedral and tetragonal phases at room temperature (RT). The temperature dependence of Raman scattering spectra, dielectric permittivity and polarization points a first phase transition from ferroelectric rhombohedral phase to ferroelectric tetragonal phase at a temperature (TR-T) of 40 0C and a second phase transition from ferroelectric tetragonal phase - paraelectric pseudocubic phase at a temperature (TT-C) of 110 0C. The dielectric analysis suggests that the phase transition at TT-C is of diffusive type and the BZT-BCT ceramics are a relaxor type ferroelectric materials. The composition induced variation in the temperature dependence of dielectric losses was correlated with full width half maxima (FWHM) of A1, E(LO) Raman mode. The saturation polarization (Ps) ≈ 8.3 μC/cm2 and coercive fields ≈ 2.9 kV/cm were found to be optimum at composition x = 0.6 and is attributed to grain size effect. It is also shown that BZT-BCT ceramics exhibit a fatigue free response up to 105 cycles. The effect of a.c. electric field amplitude and temperature on energy storage density and storage efficiency is also discussed. The presence of high TT-C (110 0C), a high dielectric constant (εr ≈ 12285) with low dielectric loss (0.03), good polarization (Ps) ≈ 8.3 μC/cm2) and large recoverable energy density (W = 121 mJ/cm3) with an energy storage efficiency (η) of 70 % at an electric field of 25 kV/cm in 0.6BZT 0.4BCT ceramics make them suitable candidates for energy storage capacitor applications.This work was supported by (i) DST-SERB, Govt. of India through grant ECR/2017/000068 and (ii) UGC through Grant Nos. F.4-5(59-FRP/ 2014(BSR)). The authors AR Jayakrishnan acknowledges Central University of Tamil Nadu, India for his Ph.D fellowship. K.V.A. acknowledges the DST for the Inspire fellowship IF170601. J.P.B.S. is grateful for the financial support through the FCT Grant SFRH/BPD/92896/2013.The authors thank to Vivek Sudarsanan from the Central University of Kerala for XRD measurements.info:eu-repo/semantics/publishedVersio

Manually Annotated High Resolution Satellite Image Dataset of Mumbai for Semantic Segmentation

The dataset was created from high-resolution, true-color satellite imagery of Pleiades-1A acquired on March 15, 2017. Pleiades is an Airbus product that provides imagery with a 0.5m resolution at different spectral combinations. A total of 110 patches of size 600×600 pixels were selected by visually eyeballing random locations in the city that contain a wide variety of urban characteristics such as vegetation, slums, built-up, roads, etc. The patches were manually annotated with polygons using Intel’s Computer Vision Annotation Tool (CVAT). Six unique classes were used to categorize the images, namely (1) vegetation; (2) built-up; (3) informal settlements; (4) impervious surfaces (roads/highways, streets, parking lots, road-like areas around buildings, etc.); (5) barren; and (6) water. In addition to these six major classes, the dataset also contains another class termed ‘Unlabelled’, which makes up only 0.08% of the dataset. It primarily consists of airplanes and a few other obscure spots and structures. Each 600×600 pixels patch was further divided into 120×120 pixels sized tiles with 50% horizontal and vertical overlapping, making a total of 8910 tiles. This helped generate more training data that would result in better classification. Out of the total 8910 annotated patches, 80% of patches (total: 7128) are present in the training set, 10% as the validation set (total: 891), and the remaining 10% for testing (total: 891)

Evaluating the levels of salivary alkaline and acid phosphatase activities as biochemical markers for periodontal disease: A case series

Background: The purpose of this study was to determine the salivary levels of alkaline phosphatase (ALP) and acid phosphatase (ACP) activities in patients with periodontal disease and to evaluate the use of these enzymes as biochemical markers for periodontal tissue damage. Materials and Methods: In this prospective analytical study, we examined the activities of salivary ALP and ACP in patients with periodontal disease, before and after periodontal treatment. The experimental groups consisted of 20 gingivitis patients and 20 periodontitis patients and the control group had healthy subjects (20 samples). The stimulated saliva of the patient was collected in a sterile test tube and analyzed using Hitachi′s Diagnostic Automatic Analyser. Periodontal disease was determined based on clinical parameters such as gingival index, probing depth and clinical attachment loss. Patients with periodontal disease were under conventional periodontal treatment. The statistical analysis applied was Student′s t-test. Probabilities less than 0.05 (P < 0.05) were considered significant. Results: The obtained results showed statistically significant increased activities of ALP and ACP in saliva from patients with periodontal disease in relation to control group. A significant reduction in the enzyme levels was seen after conventional periodontal therapy. Conclusions: Based on these results, salivary ALP and ACP can be considered to be the biomarkers for evaluating periodontal tissue damage

A remarkable role of growth factors in resolving oral and specific periodontal pathologies: A strategic review

The knowledge and the understanding of the role of growth factors, their mechanisms of action, and molecular signaling pathways, which have been reviewed in this article, suggest the potential for many novel therapeutic targets, not only for applying growth factors but also for the potential use of growth factor inhibitors or agents that target specific parts of the intracellular signaling pathways in controlling oral pathologies. There remains an enormous challenge to convert some of the knowledge from basic studies of bone cell physiology and inflammatory cells to therapeutically useful techniques for the future

Manually Annotated High Resolution Satellite Image Dataset of Mumbai for Semantic Segmentation

The dataset was created from high-resolution, true-color satellite imagery of Pleiades-1A acquired on March 15, 2017. Pleiades is an Airbus product that provides imagery with a 0.5m resolution at different spectral combinations. A total of 110 patches of size 600×600 pixels were selected by visually eyeballing random locations in the city that contain a wide variety of urban characteristics such as vegetation, slums, built-up, roads, etc. The patches were manually annotated with polygons using Intel’s Computer Vision Annotation Tool (CVAT). Six unique classes were used to categorize the images, namely (1) vegetation; (2) built-up; (3) informal settlements; (4) impervious surfaces (roads/highways, streets, parking lots, road-like areas around buildings, etc.); (5) barren; and (6) water. In addition to these six major classes, the dataset also contains another class termed ‘Unlabelled’, which makes up only 0.08% of the dataset. It primarily consists of airplanes and a few other obscure spots and structures. Each 600×600 pixels patch was further divided into 120×120 pixels sized tiles with 50% horizontal and vertical overlapping, making a total of 8910 tiles. This helped generate more training data that would result in better classification. Out of the total 8910 annotated patches, 80% of patches (total: 7128) are present in the training set, 10% as the validation set (total: 891), and the remaining 10% for testing (total: 891).THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV