Recent trends in foreign direct investment in retail sector
This paper attempts to examine those issues that affect the economy with the entrance of Foreign Direct Investment in to Retail sector in India. Foreign direct investment (FDI) plays an extraordinary and growing role in global business. FDI would serve the purpose of much needed capital and bring a boom in the Retail sector. Retail industry is organized and unorganized in combination. The international players currently in India include McDonald's, Pizza Hut, Dominos, Levis, Lee, Nike, Adidas, TGIF, Benetton, Swarovski, Sony, Sharp, Kodak, and the Medicine Shoppe. Global players are entering India indirectly, via the licensee/franchisee route, since Foreign Direct Investment (FDI) is not allowed in the sector. Indian central government is attempting to transform unorganized retailing to organized retailing by introducing 100% investment in multi-branded retail. Government is facing diverse reactions from different sections of society .There are some advantages such as Increase in GDP, reduction of prices, improved choices of products, and increase in employment opportunities besides having some disadvantages such as adverse effect like destroying employment opportunities & winding up of small and medium scale industries. The big Indian retail players looking to expand their operations include Shopper's Stop, Pantaloon, Lifestyle, Subhiksha, Food World, Vivek's, Nilgiris, Ebony, Crosswords, Café Coffee Day, Wills Lifestyle, Raymond, However, in this era of globalization reforms in the retail sector are necessary to with stand competition.Titan, and Bata .Indian retail chains would get integrated with global supply chains since FDI will bring in technology, quality standards and marketing.
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A modified RSA cryptosystem based on ‘n’ prime numbers
To secure data or information by a modified RSA cryptosystem based on ‘n’ prime. This is a new technique to provide maximum security for data over the network. It is involved encryption, decryption, and key generation. Prime number used in a modified RSA cryptosystem to provide security over the networks. In this technique we used ‘n’ prime number which is not easily breakable. ‘n’ prime numbers are not easily decompose. This technique provides more efficiency and reliability over the networks. In this paper we are used a modified RSA cryptosystem algorithm to handle ‘n’ prime numbers and provides security.
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Estimation of Seasonal and Annual Precipitation Using Interpolation Multivariate Methods to identify areas prone to wind erosion (case study: Khorasan Razavi province)
There are two basic conditions necessary for the occurrence of wind erosion in widespread locations. Firstly it must occur regions that have constant winds and the second condition is that the soil must be dry so that it can be easily transported by wind and its average annual precipitation must be less than 250 mm. This study aimed to estimate seasonal and annual rainfall of Khorasan Razavi province with a 20-year period using three geostatistical methods: kriging, IDW and Cokriging. Results showed that between the three geostatical methods that were used in this study, Cokriging showed higher accuracy than the other two methods (based on RMSE and ME less), so the final maps were prepared using his method. Based on the analysis of statistical data in the SPSS software and the variogram , the most appropriate data model for the seasonal and annual precipitation maps that was selected was the ArcGIS9.3 software. According to the maps, the more north you go the more precipitation occurs; which shows the correlation between height and precipitation. Thus the correlation coefficient between height and seasonal and annual precipitation is stronger, and there will be higher accuracy of prediction through Cokriging. These factors are also effective in wind erosion when the amount of annual rainfall and distribution is in different seasons. According to the results obtained, areas other than the north and northwest, particularly in the eastern provinces are prone to wind erosion.
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LRS Bianchi type-I Magnetized Dark Energy Models in a Scalar – Tensor Theory of Gravitation
We have studied LRS Bianchi type-I magnetized cosmological models in the presence of scalar tensor theory proposed by Saez Ballester [1]. We assume that the dark energy (DE) is minimally interacting, has dynamical energy- density, anisotropic equation of state parameter (EoS). Exact solutions of Einstein’s field equations are obtained by assuming a special law of variation for the mean Hubble parameter, which yields a constant value of the deceleration parameter. The geometrical and physical aspects for the models are also studied.
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Automatic Line Scratch Detection and Removal in Digitized Film Sequence
Video restoration or old film restoration is challenging but sometimes necessary process. Line scratches in old videos appearas thin bright or dark lines which are roughly vertical and straight. We propose, Frame – By – Frame line scratch detection algorithm to detect scratches from old films. For false detection temporal algorithm is used. Some assumptions and hypothesis from old scratch removal strategies are eliminated from Frame-By-Frame line scratch detection algorithm so that variety of line scratches can be detected. Contrario methodology and local statistical estimation is used in combinatorial way for robustness. Using these technologies over detection and confusion creating areas are greatly reduced. Vertical structure in video can cause false detection but temporal filtering algorithm eliminates false detection by considering and analyzing unity of underlying scenes. We contribute for removal of scratches using pixel filling technique. This concept of detecting and eliminating line scratch from video contribute fine methods for video restoration.
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Assessing Adequacy of Probability Distribution for Development of IDF relationship for South-Eastern region in Bangladesh
The objective of this research is to assess the adequacy of probability distribution for development of Rainfall IDF relationship at South-Eastern region of Bangladesh. Two common frequency analysis techniques Gumbel and Log Pearson Type III (LPTIII) distribution were used to develop the IDF relationship from rainfall data of this region. Yearly extreme rainfall data for last 41 years (1974-2014) from Bangladesh Meteorological Department (BMD) was used in this study. Indian Meteorological Department (IMD) empirical reduction formula was used to estimate the short duration rainfall intensity from yearly maximum rainfall data. The effects obtained using Gumbel method are slightly higher than LPT III distribution method. The chi-square goodness of fit test was used to determine the best fit probability distribution. The parameters of the IDF equations and coefficient of correlation for different return periods (2, 5, 10, 25, 50 and 100 years) were calculated by using nonlinear regression method. The results obtained offered that in all the cases the correlation coefficient is very high representing the goodness of fit of the formulae to estimate IDF curves in the region of interest. It was found that intensity of rainfall decreases with increase in rainfall duration. Further, a rainfall of any given duration will have a larger intensity if its return period is large. In other words, for a rainfall of given duration, rainfalls of higher intensity in that duration are rarer than rainfalls of smaller intensity.
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Intelligent Mobile Agents for Heterogeneous Devices in Cloud Computing
Cloud computing enables highly scalable services to be easily consumed over the Internet on an as-needed basis. A software agent offers a new computing paradigm in which a program, can suspend its execution on a host computer and can transfer to another agent enabled computer on network so that it could run there. Such mobile agents are used when there is limited capacity in computers. But at times, the systems could not be able to run even the mobile agents. This paper proposes an approach to execute mobile agents on any sort of systems even with limited capacity of Cloud. A platform that supports various mobile agents depending upon the capacity of the system will be developed. The mobile clients are differentiated into two types depending upon their capacity and the corresponding mobile agent is chosen for the traversal.
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Analyzing Role of Subliminal Perception on Effectiveness of Non-Personal Advertising
Marketing and advertising experts seek for novel methods and intelligent means to attract consumers and customers. Subliminal messages can play crucial role in attracting consumers in the branding world. The important task in advertising is to find novel techniques for convincing the consumers to use the products and services. The first impression of the word “advertising” is the publicities on posters and via public media which are in fact audio and visual media. However, advertising industry affects the behaviors of consumers unconsciously with the aid of the tools they use via promoting the emotions followed by changes in their purchase decisions. Utilization of subliminal stimuli is among the new methods deployed by marketing specialists for sales of products aimed at persuading the customer and changing their minds in purchase process. The present study proposes role of subliminal perception in effectiveness of non-personal advertisements. The model is researcher-made and derived from literature review. Having reviewed historical background of the respective research and codifying the objective relationships between variables within a theoretical framework, subliminal perception was tested using three indexes namely, personal factors, stimulus-related factors, and attention to stimulus. Personal factors were analyzed using three components (perceptual consciousness, acceptance, and experience) and stimulus-related factors were taken into account using four components (color, freshness, size, and situation). The data were collected via codification of a structured questionnaire with 5-choice Likert scale consisting of 4 general and 33 specialized questions. The questionnaire was distributed among the sample members after checking its reliability and validity levels. Kolmogorov–Smirnov test was used to verify normality of data distribution. The results corroborated normality of data distribution. Statistical parametric test was also applied to analyze and test the research hypotheses. The data were analyzed using LISREL software. Based on the results of structural equations method and confirmatory factor analysis, personal factors influenced effectiveness of non-personal advertisements with a value of 0.52%. The respective values for stimulus-related factors and attention to stimulus were respectively equal to 0.45% and 0.67%. Furthermore, the independent variable of subliminal perception influenced effectiveness of non-personal advertising with a value of 0.33%.
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Introduce a model to determination the optimum amounts of supply indexes by use of Analytical Hierarchy process (AHP) in supply chain management
In this article, the main aim is determination the optimum amounts of major supply indexes of producers to the optimum supply of their consumption raw materials in supply chain management. The considered indexes in this article are: supply cost, supply time and supply quality status. In this article tried that with consider to amounts of each supply indexes in terms of producers and also maximum capacity of each suppliers in terms of each supply indexes, be calculated the optimum amounts of each supply index. In this model with the use of bipolar scale space technique, convert the qualitative values to numerical values and the use of Entropy technique to determination the weighted matrix and the use of Analytical Hierarchy process (AHP) to determination the supply optimum amounts for each supply indexes.
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Energy consumption and modeling of output energy with MLP Neural Network for dry wheat production in Iran
The aim of this study was to examine energy use pattern and predict the output energy for dry wheat production in Silakhor plain from Lorestan province of Iran. The data used in this study were collected from farmers by using a face to face survey. The results revealed that chemical fertilizer with seed and diesel fuel have consumed 57.93% and 36.58% of total energy, respectively. In this study, several direct and indirect factors have been identified to create an artificial neural networks (ANN) model to predict output energy for dry wheat production. The final model can predict output energy based on human power, machinery, diesel fuel, chemical fertilizer with seed and transportation. The results of ANNs analyze showed that the (5-10-10-1)-MLP, namely, a network having ten neurons in the first and second hidden layer was the best-suited model estimating the output energy. For this topology, MSE and R2 were 0.029 and 90%, respectively. The sensitivity analysis of input parameters on output showed that chemical fertilizer with seed and human power had the highest and lowest sensitivity on output energy with 0.21and 0.03, respectively.
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