Are agricultural markets location-optimal? A case study of Gaya District (Bihar)

The thesis of efficiency and optimality of Indian agricultural system has several facets that have called for attention of a number of scholars. Some have proved allocative optimality of resource utilization, the others have proved optimality of distribution of gains from agriculture, while still others have come up with the cases of marketing optimality. However, there is hardly any work that studies location optimality of market centers in any region of India. In this paper we examine if the empirically observed market locations are optimal and as a case study take up the agricultural markets located in Gaya district of Bihar. We have used the location-allocation model for optimality analysis. Our findings reveal that existing locations and arrivals of merchandise at the agricultural markets of Gaya are very close to what might have been if they had been located on the principle of optimality. There are minor deviations, of course. However, as the existing markets have developed in an open region, unlike our cost-optimal locations searched out in a closed region, a discount must be made in favour of the existing locations, and we do not have enough reasons and evidence to conclude that the existing markets are sub-optimally located. We conclude, therefore, that market forces automatically establish location optimality and assert that the existing agricultural markets in Gaya district are location-optimal

Methods of enhancing conductivity of a polymer-ceramic composite electrolyte

Methods for enhancing conductivity of polymer-ceramic composite electrolytes are provided which include forming a polymer-ceramic composite electrolyte film by a melt casting technique and uniaxially stretching the film from about 5 to 15% in length. The polymer-ceramic composite electrolyte is also preferably annealed after stretching such that it has a room temperature conductivity of from 10.sup.-4 S cm.sup.-1 to 10.sup.-3 S cm.sup.-1. The polymer-ceramic composite electrolyte formed by the methods of the present invention may be used in lithium rechargeable batteries

Polarized non-abelian representations of slim near-polar spaces

In (Bull Belg Math Soc Simon Stevin 4:299-316, 1997), Shult introduced a class of parapolar spaces, the so-called near-polar spaces. We introduce here the notion of a polarized non-abelian representation of a slim near-polar space, that is, a near-polar space in which every line is incident with precisely three points. For such a polarized non-abelian representation, we study the structure of the corresponding representation group, enabling us to generalize several of the results obtained in Sahoo and Sastry (J Algebraic Comb 29:195-213, 2009) for non-abelian representations of slim dense near hexagons. We show that with every polarized non-abelian representation of a slim near-polar space, there is an associated polarized projective embedding

Efficacy of Social Skills Training for the Persons with Chronic Schizophrenia

There are various quantitative studies have been conducted both nationally as well as internationally that revealed the effectiveness of social skills training in schizophrenia. However, very few qualitative studies have been conducted to measure the relevance of social skills training in schizophrenia. The present study investigated the effectiveness of six months social skills training program with 5 inpatients chronic schizophrenia, conducted for one and half an hour in a week. Employing phenomenological approach, psychosocial assessment was done on the basis of interviews, observations, role-plays, and work assignments, which was analyzed using Stevick-Colaizzi-Keen Method of phenomenology. The social skills training resulted in decreasing social anxiety and enhancing social functioning as maintaining personal hygiene, significant gain in adherence to medications, making request, expressing feeling, and sorting out problematic issues that sustained up to 18 months following intervention. It has been effective in changing the patient’s behaviors and boosted their capacity to confront problematic situations, but weaker effects were found for auditory hallucination in one of the patients

Vertex connectivity of the power graph of a finite cyclic group

Let $n=p_1^{n_1}p_2^{n_2}\ldots p_r^{n_r}$, where $r,n_1,\ldots, n_r$ are positive integers and $p_1,p_2,\ldots,p_r$ are distinct prime numbers with $p_1<p_2<\cdots <p_r$. For the cyclic group $C_n$ of order $n$, let $\mathcal{P}(C_n)$ be the power graph of $C_n$ and $\kappa(\mathcal{P}(C_n))$ be the vertex connectivity of $\mathcal{P}(C_n)$. It is known that $\kappa(\mathcal{P}(C_n))=p_1^{n_1} -1$ if $r=1$. For $r\geq 2$, we determine the exact value of $\kappa(\mathcal{P}(C_n))$ when $2\phi(p_1\ldots p_{r-1})\geq p_1\ldots p_{r-1}$, and give an upper bound for $\kappa(\mathcal{P}(C_n))$ when $2\phi(p_1\ldots p_{r-1}) < p_1\ldots p_{r-1}$, which is sharp for many values of $n$ but equality need not hold always

Determination of soil pH by using digital image processing technique

Soil is recognized as one of the most valuable natural resource whose soil pH property used to describe the degree of acidity or basicity which affect nutrient availability and ultimately plant growth. Fifty soil samples were collected and their pH was determined by using digital image processing technique. Soil colour is visual perceptual property corresponding in humans to the categories i.e red, green, blue and others. Soil colours are the parts of visual perceptual property where digital values of red, green and blue (RGB) provide a clue for spectral signature capture of different pH in soil. For the capturing images, digital camera was used. Transformation of the multispectral image was carried out through TNT Mips spatial software. On the basis of RGB grey values, pixels properties and their digital correlations, results showed that there was a clear cut gap in grey values of colours in the images 1, 2, 3, 4,10,11,14 and 16. Ranges of soil pH and pH index values were 7.30-7.50 and 0.0070-0.0261, respectively in deep brown colour. Similarly, soil pH range varies from 6.80-7.04 and 5.58-6.58 in light yellowish and greenish colour respectively while their corresponding pH index values were 0.0071-0.0451 and 0.0084-0.0239. Thus soil pH range varies from 7.30-7.50, 6.80-7.04 and 5.58-6.58 in deep brown colour, light yellowish colour and greenish colour respectively