Effective reduction of blood pressure to accepted goals is the key to reduce the risk of CV events and stroke. Dual inhibition of neprilysin and the angiotensin receptor with sacubitril/valsartan may represent an attractive and serendipitous therapeutic approach for a range of CV diseases, including hypertension and HF, in which vasoconstriction, volume overload and neuro-hormonal activation play a part in pathophysiology. Sacubitril/Valsartan appears to be more efficacious in reducing blood pressure than currently available ACEi and ARBs with a similar safety and tolerability profile. Besides, pleiotropic benefits like HbA reduction, better eGFR progression and a greater decrease in blood pressure and serum creatinine levels make this drug a novel addition to the current hypertension armamentarium.

<p><strong>Abstract.</strong> Spatio-temporal crop phenological information helps in understanding trends in food supply, planning of seed/fertilizer inputs, etc. in a region. Rice is one of the major food sources for many regions of the world especially in monsoon Asia and accounts for more than 11<span class="thinspace"></span>% of the global cropland. Accurate, on-time and early information on spatial distribution of rice would be useful for stakeholders (cultivators, fertilizer/pesticide manufacturers and agriculture extension agencies) to effectively plan supply of inputs, market activities. Also, government agencies can plan and formulate policies regarding food security. Conventional methods involves manual surveying for developing spatio-temporal crop datasets while remote sensing satellite observations provide cost effective alternatives with better spatial extent and temporal frequency. Remote sensing is one of the effective technologies to map the areal extent of the crops using optical as well as microwave/Synthetic Aperture RADAR (SAR) sensors. Cloud cover is the major problem faced in using the optical datasets during monsoon (June to Sept. locally called <i>Kharif</i> season). Hence, Sentinel-1 C-band (center frequency: 5.405<span class="thinspace"></span>GHz) RADAR sensor launched by European Space Agency (ESA) which has an Interferometric Wide-swath mode (IW) with dual polarization (VV and VH) has been used for rice area mapping. Limited studies have attempted to establish operational early season rice area mapping to facilitate local governance, agri-input management and crop growers. The key contribution of this work is towards operational near real time and early season rice area mapping using multi-temporal SAR data on GEE platform. The study has been carried out in four districts viz., Guntur, Krishna, East Godavari andWest Godavari from Andhra Pradesh (AP), India during the period of <i>Kharif</i> 2017. The study region is also called as coastal AP where rice transplanting during the <i>Kharif</i> season is carried out during mid Jun. till Aug. and harvesting during Oct. to mid Dec. months. The training data for various classes viz, Rice, NonRice-Agriculture, Waterbodies, Settlements, Forest and Aquaculture have been obtained from GEE, Global Land Cover (GLC) layers developed by ESA and field observations. We have evaluated the performance of Random Forest (RF) classifier by varying the number of trees and incrementally adding the SAR images for model training. Initially the model has been trained considering two images available from mid June 2017. Further, various models have been trained by adding one consecutive image till end of August 2017 and classification performance has been evaluated on validation dataset. The classified output has been further masked with agriculture non-agriculture layer derived from global land-cover layer obtained from ESA. Analysis shows that incremental addition of temporal observations improves the performance of the classifier. The overall classification accuracy ranges between 78.11 to 87.00<span class="thinspace"></span>%. We have found that RF classifier with 30 trees trained on six images available from mid June till end August performed better with classification accuracy of 87.00<span class="thinspace"></span>%. However, accuracy assessment performed using independent stratified random sampling approach showed the classification accuracy of 84.45<span class="thinspace"></span>%. An attempt is being made to follow the proposed approach for current (i.e. 2018) season and provide incremental rice area estimates in near real-time.</p>

This paper aims at understanding the effect of demonetization announced in November 2016 on agribusiness subsystems, its effect on consumer purchasing criteria, and the problems that surfaced in rural areas to move towards digitalization. This research was exploratory in nature and so adopted an open-ended approach through interviews of 200 farmers selected from 20 villages of two districts; one economically progressive and the other economically backward, in central region of the state of Gujarat in India. Besides, 50 landless farm laborers, 12 input dealers, 20 wholesalers, 20 retailers, 4 each of processors, exporters, and logistic service providers operating in study area were also interviewed. The focus group discussions were also conducted. Secondary data were gathered from various government sources. Exploratory factor analysis was conducted to examine the consumer purchasing criteria. Garrett’s ranking technique was used to find the problems of moving to digital transaction. The production activity slowed down due to immediate cash crunch. Agriculture also suffered a setback. Socio-economic factors contributed strongly to the consumer purchasing criteria post-demonetization. The rural economy worked on local credit basis during the demonetization period. Delayed sowing was observed due to non-availability of inputs during initial days of announcement of demonetization. Farm labor suffered losses. Prices of perishables crashed. More bank accounts opened in rural area as dairy payments were made mandatory through electronic mode only. Infrastructural bottlenecks, complex e-banking language and lack of technical knowhow, etc., posed problems before the rural economy to go digital. Strong policy interventions were suggested to make digitalization more popular in rural areas. Removal of infrastructural bottlenecks was suggested to absorb the shock effects of announcements like demonetization.

Background: The dairy industry is an important sector in the global food industry, offering various essential products like milk, cheese, butter, yogurt, and ice cream. These products are widely consumed for their nutritional benefits, taste, and versatility in cooking. Diced cheese has become a popular culinary ingredient due to its convenience and versatility. It is appreciated for its ease of use and the various ways it can be used in cooking. With pre-cut small pieces, diced cheese eliminates the need for slicing or grating, saving valuable time in the kitchen. Overall, diced cheese is a time-saving and convenient option for home cooks and chefs alike. Methods: This study aimed to understand consumption patterns for diced cheese in the cities of Anand and Vadodara, Gujarat, India. The research design involved descriptive research using non-probability convenience sampling. Data were collected from 120 consumers through a semi-structured schedule and analyzed using Microsoft Excel and SPSS software. Findings: The findings revealed that the majority of consumers were male, primarily belonging to the age group of 21-30 years. Nuclear families were the most common family type in both cities. The results also highlighted that a significant percentage of the population was unaware of diced cheese, with social media being the primary source of information. A portion of the respondents had not purchased diced cheese, suggesting a potential growth opportunity for the product in the market. Taste, price, and availability were identified as important factors influencing purchase decisions.