Synthesis and characterization of Mn-ZnFe2O4 and Mn-ZnFe2O4/rGO nanocomposites from waste batteries for photocatalytic, electrochemical and thermal studies M Mylarappa, V Venkata Lakshmi, K R Vishnu Mahesh et al. Abstract. This study has been conducted on synthesis of (La 1-x Gd x )Ba 2 Cu 3 O 7-δ superconductors with the substitution of Gadolinium (Gd) (x = 0.25, 0.5, 0.75, 1) and sintering temperature (700-960 o C) variation using a wet-mixing method. Characterization is done by XRD, RAMAN and SEM. XRD characterization results of all samples have shown sharp peaks which indicate that the sample had crystallized well. Search match results showed an impurity phase such as BaCO 3 , CuO and BaCuO 2 . Rietveld analyses for all samples gave decreasing lattice parameters (a-axis from 3.9069 to 3.8936 Å, b-axis from 3.9231 to 3.9055 Å and c-axis from 11.8489 to 11.6597 Å) with the addition of Gd contents (from 0.25 to 1.00). Such addition also caused a decrease of lattice parameters ( a-axis from 4,901 to 3.8975 Å, b-axis from 3.9658 to 3.8986 Å and c-axis from 11.7254 to 11.6758 Å) with the addition of sintering temperature (from 700 to 960 o C). Characterization of FTIR seen the bending vibration absorption by CO 3 2-, absorbs mode apical oxygen of La (Gd) -O-Cu(2) and Cu(1)-O(1)-Cu(2). The addition of sintering temperatures also increases the intensity of the superconducting phase, reduce the intensity of an impurity phase (based on search results match) and increase the particle size (based on SEM characterization and Scherrer calculation).

The aim of this work was to observe homogeneity of human tooth surface using classification technique by laser-induced breakdown spectroscopy (LIBS) coupled with principle component analysis (PCA) algorithm. The human tooth was irradiated by 110 mJ Nd-YaG laser (1064 nm) under Helium gas with flow rate of 50 ml/s to produce plasma. Photon emission of the plasma was captured by ocean optic spectrometer HR 2500+ and displayed spectra of intensity as a function of wavelength. The spectra data were analysed by different strategies in PCA algorithm for classifying human tooth surface. The spectra data were split into three ranges that were a full spectral window, FW (200-850 nm), long special spectral window, LSW (380 – 660 nm) and short special spectral window, SSW (550 – 600 nm). These selected suitable input variables using spectral windows can reduce the influence of over fitting phenomena on classification results. Prior to PCA analysing, data were treated by different strategies of pre-processing namely linear baseline correction, area normalisation, and no pre-processing. The results showed that the short special spectral window (SSW) using pre-processing of area normalization could either clustering and distinguishing parts of human tooth surface clearly. Conclusion dentin surface has highest homogeneity of all.

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In order to study and practice the method of meteorological atmospheric pressure gauge calibration, the design, assembly and calibration of an experimental wireless atmospheric pressure gauge based on the BMP280 digital pressure sensor and the System on Chip ESP-12S has been carried out. Using the Vaisalla PTB330 digital barometer secondary pressure standard, the instrument is calibrated in the pressure chamber in the pressure range 850-1050 hPa with a maximum tolerance limit of ± 0.15 hPa at a 95% confidence level. Based on the test results of the correction parameters and U95, it shows that the reliability of the sensor interface system and the internal correction application method used in the calibration process provide calibration results that meet the requirements of the WMO standard. The precision test on repeatability conditions based on ISO5725: 1994 is also used as a measure of tool precision. Through this calibration report, the performance and accuracy of the BMP280 sensor in relation to measurements on meteorological objects, especially atmospheric pressure can be known and studied further.

Image classification need two main components, i.e., features and classifier. The feature commonly used for classification of images with different scale is invariant moment; its value is invariant against the spatial transformation dealing with translation, scale and rotation. The classifier that is widely used for classification is LVQ NN. It is shallow network containing only two layers, the initial value of its weight is more fixed so that its output is more stable and its algorithm is relatively simple thus both training and testing process are run fast. Based on these facts, therefore, this research proposed a combination method of invariant moment and LVQ NN (IM-LVQ). The ability of the proposed method would be compared with two other methods. Firstly, the combination method of invariant moment and Euclidean distance (IM-ED). Secondly, the combination of invariant moment and principal component analysis (IM-PCA). The performance of the three methods was evaluated quantitatively with several metrics, viz.: Confusion Matrix, Accuracy, Precision, True Positive Rate, False Positive Rate, ROC graph and training time. The evaluation of the metrics was based upon the changing (reduction) of the scale/size of training image. The results showed that IM-LVQ method outperformed the other two methods in aforementioned metrics.