Radiological modalities in the diagnosis and screening of liver cirrhosis and its complications

Liver cirrhosis is the pathologic outcome of many chronic liver diseases, in which repeated injury to the liver results in fibrosis, scarring, and ultimately functional impairment.Includes bibliographical reference

Convective dehydration kinetics of noodles prepared from taro (Colocasia esculenta), rice (Oryza sativa) and pigeonpea (Cajanus cajan) flours

Drying characteristics of noodles prepared from 50% taro and remaining equal proportions of rice and pigeonpea flours were investigated in a convective type dryer for a temperature range of 50°C to 80°C at a constant air velocity of     1.5 m s-1.  Results indicated that drying took place in falling rate period.  The sample dried at 50°C was found better in color as compared to samples at 60°C, 70°C and 80°C.  The rehydration weight of noodles decreased with the increase in drying temperature.  Moisture transfer from noodles was described by applying Fick’s diffusion model and the effective moisture diffusivity was calculated.  Effective moisture diffusivity increased with increase in temperature.  An Arrhenius relation with activation energy of 38.53 kJ mol-1 expressed the effect of temperature on moisture diffusivity.  Mathematical models were fitted to the experimental data and the performance of these models were evaluated by comparing the coefficient of determination (R2), Root mean square error (RMSE), reduced chi-square (χ2), percent mean relative deviation modulus (E%) between observed and predicted moisture ratio.  Verma model gave the best results for describing the drying behaviour of noodles.   Keywords: noodle, taro, dehydration kinetics, temperature, moistur

REDD+ STRATEGY FOR FOREST CARBON SEQUESTRATION IN INDIA

Deforestation and forest degradation due to land use, land cover change (LULCC) have become one of the prime contributors to global greenhouse gas (GHG) emissions, after fossil fuel combustion. Greenhouse gas emission from forestry is occurring in the atmosphere as a result of forest biomass combustion, forest fires and decomposition of deadwood materials. This is how increasing carbon dioxide in the atmosphere is adding to the global warming and climate change. Many worldwide recognized studies have measured that forest ecosystems have the capacity to absorb more than 1/3rd of total carbon dioxide from the atmosphere which is the minimum requirement for keeping the atmospheric temperature under 2 °C by 2030. One of the commonly accepted methods for reducing carbon is carbon sequestration through forests. India has committed to capture 2.5 to 3 billion tonnes of CO2 by enhancing forest and tree cover through 2030. To achieve this target, India has adopted REDD+ (Reducing Emissions from Deforestation and Forest Degradation) strategy which aims to mitigate climate change by enhancing forest carbon sequestration through incentivizing forest conservation. Furthermore, this strategy strives to address the drivers of forest degradation and deforestation and also provides a roadmap for forest carbon stocks enhancement and sustainable forest management through REDD+ actions. This study investigates REDD+ contribution against global warming and climate change in India through forest carbon sequestration

Imaging and spectroscopy of solid-state quantum emitters

Efficient generation of single photons can revolutionize the field of quantum communication and linear optical quantum computing. Solid-state semiconductor quantum dots present a promising platform to realize this long term vision. In particular, self-assembled InAs quantum dots exhibit near unity radiative efficiency. Enhancement of collection efficiency of photon from self-assembled InAs quantum dots is a key necessity to realize quantum technologies. In this respect, GaAs nanowire single photon source and planar membrane devices have shown considerable promise. Photoluminescence spectroscopy has been undertaken to study the photon collection from these devices and the behaviour such as lifetime of quantum dots when embedded in these nanophotonic structures. The ease of implementing electrical contacts onto the planar membrane devices is another significant feature which allows complete control over the number of electrons in the quantum dot. The key concept behind the enhancement of photon emission from planar membrane devices is the radiation from an electric dipole emitter whose angular radiation pattern is modified by choice of materials and design of sample. Using Fourier microscopy also known as back focal plane imaging, a match between the design of the angular radiation profile and the obtained experimental data is made. This is an excellent way to figure out if the light being emitted by the quantum emitter is being collected into the optical system. In addition, the transfer-matrix model used for design of angular radiation profile also yields different efficiency depending upon the orientation of the emission dipole in the quantum emitter. Thus, in order to design samples for higher photon collection efficiency, the knowledge of the orientation of the emission dipole is important. In order to extract the full three-dimensional orientation of the quantum emitter, defocused imaging of the dipole radiation is performed. A relatively new material system for quantum photonics is based on novel two-dimensional semiconductors such as WSe2. To understand the nature of emission from these solid-state emitters, Fourier microscopy and defocused imaging have been used to obtain the angular distribution of radiation and orientation of emission dipoles in these emitters, respectively

Rainfall Analysis and Forecasting Using Deep Learning Technique

Rainfall forecasting is very challenging due to its uncertain nature and dynamic climate change. It's always been a challenging task for meteorologists. In various papers for rainfall prediction, different Data Mining and Machine Learning (ML) techniques have been used. These techniques show better predictive accuracy. A deep learning approach has been used in this study to analyze the rainfall data of the Karnataka Subdivision. Three deep learning methods have been used for prediction such as Artificial Neural Network (ANN) - Feed Forward Neural Network, Simple Recurrent Neural Network (RNN), and the Long Short-Term Memory (LSTM) optimized RNN Technique. In this paper, a comparative study of these three techniques for monthly rainfall prediction has been given and the prediction performance of these three techniques has been evaluated using the Mean Absolute Percentage Error (MAPE%) and a Root Mean Squared Error (RMSE%). The results show that the LSTM Model shows better performance as compared to ANN and RNN for Prediction. The LSTM model shows better performance with mini-mum Mean Absolute Percentage Error (MAPE%) and Root Mean Squared Error (RMSE%)

A STUDY ON THE STANDARDIZATION PARAMETERS OF MADHUCA LONGIFOLIA

Objective: There is an increase demand of herbal remedies due to their effective and safer way of treating various disorders. In today's scenario, the herbal medicines are much efficient for the treatment of various disorders as they have minimal side effects in comparison to the allopathic medicines. Madhuca longifolia, commonly called Mahua/Mahwa, belongs to the family Sapotaceae. It grows up to a height of about 20 m. The objectives of thisstudy are to investigate various pharmacognostic, phytochemical analysis, and pharmacological properties of M. longifolia.Methods: The powdered drug was used for estimating the loss on drying, ash values, fluorescence studies, chemical tests, and extractive values. Macroscopic and microscopic studies were also performed.Results: The leaf microscopy revealed the presence of upper and lower epidermis, palisade tissue, and well-developed vascular bundle. The fluorescence characteristics of leaf powder were studied both in visible light and ultraviolet (UV) light (254 nm and 365 nm) after treatment with various reagents. Mahua is composed of glycosides, sapogenins, steroids, saponins, flavonoids, and triterpenoids. It was reported that the total ash value was 5.56±0.2% w/w. The acid-insoluble and water-insoluble ash values were 0.62±0.025% w/w and 0.47±0.025% w/w, respectively. Water soluble, ethanol, methanol, petroleum ether, and chloroform extractive values were 25.9±0.51% w/w, 28.1±1.38% w/w, 1.73±0.20% w/w,0.83±0.20% w/w, and 25.5±2.29% w/w, respectively.Conclusions: The main pharmacological activities of M. longifolia are anthelmintic, antiulcer, antitumor, antimicrobial, antidiabetic, anti-inflammatory,antigoitrogenic, and hepatoprotective. The present investigation provides the information on its pharmacognostic, phytochemical analysis, and pharmacological properties

A STUDY ON THE STANDARDIZATION PARAMETERS OF CASSIA ANGUSTIFOLIA

Objective: Now-a-days, the herbal medicines are much efficient for the treatment of various disorders as they have minimal side effects in comparison to the allopathic medicines. Cassia angustifolia, commonly called Senna belongs to the family Leguminosae and is a well-known laxative throughout the world. Senna is mostly found in Tirunelveli, Madurai, and Ramnathpuram districts of Tamil Nadu. Carbohydrates, tannins, alkaloids, flavonoids, and amino acid are the important chemical constituents of C. angustifolia. The objectives of the present study are to investigate various pharmacognostic, phytochemical analysis, and pharmacological properties of C. angustifolia.Methods: The powdered drug was used for estimating the loss on drying, ash values, fluorescence studies, chemical tests, and extractive values. Macroscopic and microscopic studies were also performed.Results: The transverse section (T.S). of leaf showed isobilateral structure along with paracytic stomata, nonlignified unicellular trichomes with warty walls, and fibrovascular bundle. The fluorescence characteristics of leaf powder were studied both in visible light and ultraviolet (UV) light (254 nm and 365 nm) after treatment with various reagents. Senna is composed of carbohydrates, tannins, alkaloids, flavonoids, and amino acid. It was reported that the total ash value was 11.23±0.25 w/w. The acid insoluble ash value was 1.4±0.1% w/w. Water soluble, ethanol, methanol, petroleum ether, and chloroform extractive values were 16.6±0.26% w/w, 3.7±1.75% w/w, 0.83±0.05% w/w, 1.6±0.1% w/w, and 3.2±0.25% w/w, respectively.Conclusion: The main pharmacological activities of Bauhinia variegata are anthelmintic, antiulcer, antitumor, antimicrobial, antidiabetic, anti-inflammatory, antigoitrogenic, and hepatoprotective. The present investigation provides the information on its pharmacognostic, phytochemical analysis, and pharmacological properties