An Economic Analysis of Cabbage Marketing in Rajasthan

This study analyzes the marketing cost, margin and price spread of cabbage crops in Rajasthan state using multistage random sampling design. The study covered 120 cabbage cultivators from Nagaur and Sikar districts of Rajasthan State. For marketing aspects, five functionaries from each category of cabbage marketing were randomly selected from kuchaman city and Sikar markets. The total marketed surplus of cabbage was observed at 1610.56 quintals. Out of this, a major share of 44.18 percent was sold through wholesalers- cum commissions agents, followed by wholesalers (28.01 per cent), retailers (20.62 per cent) and village traders (7.19 per cent) to cabbage growers. Among the different marketing costs borne by the grower, transportation cost ranked first and commission charges was highest for wholesalers cum commission agents. Among the various costs borne by the retailer, the maximum share was observed for spoilage. The total margin was higher at a retailer’s level than at the wholesale level, representing 11.41 per cent and 2.73 per cent of the consumer price, respectively. Marketing cost incurred by various functionaries was to Rs.210.18 per quintal of cabbage, which represented 25.49 per cent of the consumer price. The producer's share was 60.36 per cent of the price paid by cabbage consumers. It was proposed to sell cabbage to direct consumer, malls, catering etc. to have higher share in consumer rupees

Recent progress with the top to bottom approach to vectorization in GeantV

SIMD acceleration can potentially boost by factors the application throughput. Achieving efficient SIMD vectorization for scalar code with complex data flow and branching logic, goes however way beyond breaking some loop dependencies and relying on the compiler. Since the refactoring effort scales with the number of lines of code, it is important to understand what kind of performance gains can be expected in such complex cases. We started to investigate a couple of years ago a top to bottom vectorization approach to particle transport simulation. Percolating vector data to algorithms was mandatory since not all the components can internally vectorize. Vectorizing low-level algorithms is certainly necessary, but not sufficient to achieve relevant SIMD gains. In addition, the overheads for maintaining the concurrent vector data flow and copy data have to be minimized. In the context of a vectorization R&D for simulation we developed a framework to allow different categories of scalar and vectorized components to co-exist, dealing with data flow management and real-time heuristic optimizations. The paper describes our approach on coordinating SIMD vectorization at framework level, making a detailed quantitative analysis of the SIMD gain versus overheads, with a breakdown by components in terms of geometry, physics and magnetic field propagation. We also present the more general context of this R&D work and goals for 2018

Recent progress with the top to bottom approach to vectorization in GeantV

SIMD acceleration can potentially boost by factors the application throughput. Achieving efficient SIMD vectorization for scalar code with complex data flow and branching logic, goes however way beyond breaking some loop dependencies and relying on the compiler. Since the refactoring effort scales with the number of lines of code, it is important to understand what kind of performance gains can be expected in such complex cases. We started to investigate a couple of years ago a top to bottom vectorization approach to particle transport simulation. Percolating vector data to algorithms was mandatory since not all the components can internally vectorize. Vectorizing low-level algorithms is certainly necessary, but not sufficient to achieve relevant SIMD gains. In addition, the overheads for maintaining the concurrent vector data flow and copy data have to be minimized. In the context of a vectorization R&D for simulation we developed a framework to allow different categories of scalar and vectorized components to co-exist, dealing with data flow management and real-time heuristic optimizations. The paper describes our approach on coordinating SIMD vectorization at framework level, making a detailed quantitative analysis of the SIMD gain versus overheads, with a breakdown by components in terms of geometry, physics and magnetic field propagation. We also present the more general context of this R&D work and goals for 2018

J-PARC E27 Experiment to Search for a K−pp Bound State

We have carried out an experimental search for the simplest kaonic nucleus, $K^{ - }pp$, by using the $d(\pi ^{ + },K^{ + })$ reaction at $p_{\pi ^{ + }}$ = 1.69 GeV/$c$. The differential cross section of this reaction with covering a wide missing-mass range from the $\Lambda$ production threshold to the $\Lambda (1405)/\Sigma (1385)$ region has been measured for the first time. The inclusive missing-mass shape of the $\Lambda$ and $\Sigma$ production region was understood with a simple quasi-free picture except for an enhancement at 2.13 GeV/$c^{2}$ due to a $\Sigma N$ cusp. An obtained peak attributed to $Y^{*}$ production was significantly shifted to the low mass side compared with the simulation by $- 22.4$ MeV/$c^{2}$