EVALUATION AND OPTIMIZATION OF LEPIDIUM SATIVUM SEED MUCILAGE AS BINDER IN TABLET FORMULATION

Objective: The study elaborates isolation of mucilage from Lepidium sativum seeds and explores it as a tablet binder. Methods: The mucilage from seed was extracted by precipitation of soaked and blended seed in acetone. The mucilage was evaluated for its binding properties in tablets prepared by wet granulation and direct compression method. The prepared tablets were evaluated for hardness, thickness, friability, disintegrating time and in-vitro drug release and compared with established binder like starch, PVP K-30, HPMC, MCC. Results: The results of isolated mucilage from Lepidium sativum seeds as a binder were very promising. The results indicated that mucilage is required in concentration as low as 2% for wet granulation and 4% for direct compression to give equivalent binding effect. Conclusion: Lepidium sativum seed mucilage [LSM] shows promising potential for its application as a binder in the tablet formulation. Low concentration of LSM as binder would also help to reduce cost of formulation

Studies on thermoluminescence parameters of erbium doped Y2O3 nanophosphors

The thermoluminescence (TL) glow curve of Y2O3:Er3+ with different radiation time has been studied. The experimental glow curve shows the presence of two peaks at linear heating rate 10°C/min. The activation Energy and frequency factor are determined by thermoluminescence glow curve

Thermoluminescence of Cu doped ZnS nanoparticles

ZnS nanoparticles doped with Cu were synthesized by wet chemical route method at 4°C temperature. We have investigated the Thermoluminescence (TL) properties of the undoped ZnS and Cu doped ZnS with increase in Cu concentration from 0.5 millimole to 1.5 millimole and also study for different weight of Sodium Hexa Meta Phosphate (SHMP) capping agent from 2gm to 10gm. The XRD studies indicate that most of the samples are cubic in nature. The broadening of peaks tends to increase with increasing weight of capping agent showing decrease in particle size. The crystalline size computed using Scherer formula is found 2nm to 3nm. Absorption spectra show blue shift with different weight of capping agent. TL glow curve shows a single peak at 543 K temperature. Variation in TL intensity as a function of copper concentration is studied and 1mM is found to be the optimum concentration for TL. The trap parameters namely, activation energy (E), order of kinetics (b) and frequency factor(s) of ZnS: Cu sample have been determined using Chen’s method. The effect of heating rates and UV radiation dose for different time on TL glow curve has also been studied

Occupational Stress and Mental Health: A Longitudinal Study in High-Stress Professions

This long-term study looks at the complicated link between job stress and mental health in people who have high-stress jobs. The study takes a broad method to figure out how movement changes over time because it knows that work demands have a big effect on people's health and happiness. By carefully choosing high-stress fields like law enforcement, emergency services, and healthcare, the study aims to find the link between low-stress factors in these settings and long-lasting effects on mental health. Get both numeric and personal information This method not only finds similar sources of stress, like problems at work, disagreements with others, and difficult emotions like sadness, but it also looks at how people deal with these problems. The data should help us understand how complicatedly work-related stress and mental health are connected, and they might also shed light on possible ways to avoid stress and help people who are experiencing it. The talk will look at what works in high-stress jobs and make suggestions for changes to the workplace and programs to help with mental health. Even though the study has some flaws, it hopes to serve as a starting point for more research that aims to create healthier workplaces in high-stress fields

Extraction of built-up areas from Landsat-8 OLI data based on spectral-textural information and feature selection using support vector machine method

Information of built-up area is essential for various applications, such as sustainable development or urban planning. Built-up area extraction using optical data is challenging due to spectral confusion between built-up and other classes (bare land or river sand, etc.). Here an automated approach has been proposed to generate built-up maps using spectral-textural features and feature selection techniques. Eight Grey-Level Co-Occurrence Matrix based texture features are extracted using Landsat-8 Operational Land Imager bands and combined with multispectral data. The most informative features are selected from combined spectral-textural dataset using feature selection techniques. Further, Support Vector Machine (SVM) classifiers are trained on labelled samples using optimal features and results are compared with Back Propagation-Neural Network (BP-NN) and k-Nearest Neighbour (k-NN). The results show that inclusion of textural features and applying feature selection methods increases the highest overall accuracy of Linear-SVM, RBF-SVM, BP-NN, and k-NN by 9.20%, 9.09%, 8.42%, and 7.39%, respectively

EXTRACTION OF BUILT-UP AREAS USING CONVOLUTIONAL NEURAL NETWORKS AND TRANSFER LEARNING FROM SENTINEL-2 SATELLITE IMAGES

With rapid globalization, the extent of built-up areas is continuously increasing. Extraction of features for classifying built-up areas that are more robust and abstract is a leading research topic from past many years. Although, various studies have been carried out where spatial information along with spectral features has been utilized to enhance the accuracy of classification. Still, these feature extraction techniques require a large number of user-specific parameters and generally application specific. On the other hand, recently introduced Deep Learning (DL) techniques requires less number of parameters to represent more abstract aspects of the data without any manual effort. Since, it is difficult to acquire high-resolution datasets for applications that require large scale monitoring of areas. Therefore, in this study Sentinel-2 image has been used for built-up areas extraction. In this work, pre-trained Convolutional Neural Networks (ConvNets) i.e. Inception v3 and VGGNet are employed for transfer learning. Since these networks are trained on generic images of ImageNet dataset which are having very different characteristics from satellite images. Therefore, weights of networks are fine-tuned using data derived from Sentinel-2 images. To compare the accuracies with existing shallow networks, two state of art classifiers i.e. Gaussian Support Vector Machine (SVM) and Back-Propagation Neural Network (BP-NN) are also implemented. Both SVM and BP-NN gives 84.31 % and 82.86 % overall accuracies respectively. Inception-v3 and VGGNet gives 89.43 % of overall accuracy using fine-tuned VGGNet and 92.10 % when using Inception-v3. The results indicate high accuracy of proposed fine-tuned ConvNets on a 4-channel Sentinel-2 dataset for built-up area extraction

Phosphors MMgAl₁₀O₁₇: Eu,Dy (M=Ba,Sr,Ca) irradiated by Cs¹³⁷ for thermoluminescence dosimetry

695-697The thermoluminescence (TL) of Eu, Dy activated MMgAl₁₀O₁₇ [M=Ba,Sr,Ca] phosphors has been reported in this paper. These phosphors are prepared by combustion synthesis. TL glow intensity of these phosphors is higher as compared to conventional CaSO₄:Dy TL-phosphor. MMgAl₁₀O₁₇ [M=Ba,Sr,Ca] phosphors may be the possible candidate for thermoluminescence dosimetry of ionizing radiations