Volume 8 | Issue 8 |

 Abstract

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Etymologically the expression history is derived from Latin Historia �narrative of past events,account, tale, story �from Greek historia �a learning or knowledge, account, historical account ; record or narrative� from historein � be witness or expert, give testimony, recount, find out, search, inquire� and history �knowing expert, witness.� It also refers to old French estorie, estorie �story ; chronicle, history.1

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Global Historical and cultural Aspects Defination and Scope of expression History

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The purpose of the study is to explore the role of self-service technology (SST) service quality in enhancing customer-bank relationships. Specifically, based on the commitment-trust theory of relationship marketing, the study aims to examine the effect of SST service quality on customer trust and customer commitment in the context of mobile banking in Thailand. To test research hypotheses, the study employed a quantitative survey of 226 respondents. Structural equation modeling was utilized for data analysis. Results indicated that SST service quality embracing convenience, functionality, perceived control, and security within the mobile banking context has a large direct effect on both customer trust and customer commitment, two key constructs of relationship marketing success. The current study has extended the understanding of technology-driven service quality to relationship marketing body of knowledge. For managerial implications, it is imperative for bankers to provide higher quality services of mobile banking by prioritizing service quality factors to develop ongoing relationships with their customers.

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Self-service technology, service quality, customer relationships, mobile banking, customer trust, customer commitment.

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Sthanika Chikitsa plays an important role in the management of many Yonivyapadas. In Ayurveda, given equal importance to Abhyantara as well as Sthanika Chikitsa. In the context of yonivyapada Chikitsa , it is mentioned that the local treatment should be done and must be followed by shodhana therapy (panchkarma).1 Sthanika Chikitsa in Streeroga includes yoni pichu, yoni dhavana, yoni dhoopan, yoni varti, yoni pooran, yoni lepana, Yoni Parishek, kshara karma and agni karma. In the present study all these local therapies are explained in brief and in simpler way.

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Ayurveda, Sthanika Chikitsa, local therapies in Streeroga, yonivyapada.

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Production of bulky silk is the need of the hour as the same is required for weft in the production of silk sarees. So far raw silk produced in charkha reeling device was used for the purpose. But due to non-availability of skilled labourers and improper working atmosphere in charkha reeling sector, alternative methods of production of bulky raw silk were thought off. In this context, introducing PVA yarn in the process of silk reeling has been developed by CSTRI by modifying the multiend reeling machine for the production of bulky void raw silk. The bivoltine cocoons along with Indian PVA yarn is used for the production of bulky silk. Using the Box and Behnken design of experiments the process variables viz., Number of cocoons, croissure length, reeling basin water temperature and reel speed have been standardized with 27 combinations of experiments. The study indicates that process variables significantly influence the reeling performance and quality of bulky void raw silk produced viz., reelability, non-broken filament length, single cocoon filament denier, raw silk percentage, average denier, cross section view of bulky void raw silk. Based on the response optimization in statistical analysis, it was observed that 20 bivoltine cocoons reeled per end along with Indian PVA yarn with 6 mm croissure length, 50�C basin temperature and reel speed of 100 m/min is ideal for the production of bulky void raw silk on modified CSTRI multiend reeling machine. The bulky void raw silk is quite useful as weft in the production of silk sarees with unique surface characteristics.

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Bulky void raw silk, Indian PVA yarn, Croissure length, Basin temperature and Reel speed

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The present study deals with the cluster, discriminant and principal component analysis. The data for the present study was collected. Data on different morphological and seedling characters, namely tree height (m), tree diameter (cm), crown spread E-W (m), crown spread N-S (m), fruit weight (g), leaf length(cm), internodal length(cm), collar diameter (mm), number of branches per plant and leaf petiole (cm) were considered from five different districts comprising of ten seed sources. Two seed sources were considered from each district viz; Narag and Neripul from Sirmour district, Waknaghat and Sadhupul from Solan district, Sundernagar and Rewalsar from Mandi district, Mohal and Banjar from Kullu district, Basantpur and Sunni from Shimla district. Different morphological and seedling characteristics of ten seed sources were evaluated. Cluster analysis was performed and the seed sources were grouped into three clusters. Discriminant analysis was carried out to categorize the seed sources into high and low yielders. Tree diameter, Tree height and crown spread E-W are the most important characters that discriminate the two groups. Six seed sources from Mandi, Kullu and Shimla district were high yielder whereas other four seed sources were low yielder. Three principal components (PCS) were extracted out of ten which explained 34.675, 23.002, and 11.587 per cent of the total variation respectively amounting to 69.26 per cent of total variation.

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Principle component analysis, cluster analysis, discriminant analysis, Punica granatum L, Wild Pomegranate

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In this paper software-defined network (SDN) approach used to provide a set of deliberate active allocators for virtual machines (VMs) in the cloud data center (DC). Each VM request has four parameters: CPU, RAM, disk, or memory. Virtual machine allocation is one of the challenges in cloud computing environments, especially for private cloud design. In this environment, each virtual machine is assigned to a physical host based on the resources available on the host. Specifically, in the case of different performance indicators and system requirements, quantifying the performance of scheduling and allocation strategies in the cloud infrastructure for different applications and service models is a very challenging and difficult solution.

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Cloud Computing, Network, Storage, VM, IT resources.

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Nowadays, there are numerous causes for visual impairment. Eye diseases which are evident in the retina can be spotted through a systematic and structured ophthalmologic examination. Subsequent rise of the patients with these diseases due to population growth, aging made the number of Ophthalmologists a controlling factor. Glaucoma is a type of deep-rooted disease which leads to vision loss thus making it irreparable. Therefore, this disease has to be detected at an early or intermediate stage for timely treatment. Various models have been developed based on image processing or deep learning to detect Glaucoma but the systems have either been computationally complex or of insufficient accuracy. In the proposed system, structured learning which makes use of the edge information of the fundus image is used for OD detection. Finally, the convolutional neural network (CNN) is used to check if it is glaucoma affected eye or not. Thus detecting Glaucoma at early stage with system producing the increased accuracy, specificity and sensitivity.

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Convolutional Neural Network, Hough Transform, Optic Cup, Optic disk, Principal Component Analysis, Watershed Segmentation

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BIM

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Insurance industry provides enormous opportunities to the domestic as well as foreign investors. India has a great potential in this sector. This industry contributes a huge amount to country�s GDP and economic development. This remarkable development in the insurance sector attracted foreign investors in India also. This present study considered two important factors insurance density and insurance penetration. For analyzing the data last 15 years� (2004-2018) insurance density and penetration of life, non-life and industry sector have been considered and different statistical tools have been used to judge the relationship between industry insurance sector with life and non-life insurance sector, significance difference between life and non-life insurance sector, breakpoint during this study period, normality of data, heteroscedasticity and auto-correlation between the 2 models. For this researchers used Multiple Linear Regression Model, ANOVA, Bai-Perron and Residual test. This study also identified the future scope of research and constructive conclusion a feasible recommendation for growth of the industry.

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Economic Development, GDP, Insurance Density, Insurance Penetration, Investors

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