Un estudio empírico de los parámetros que influyen en la compra de los consumidores que utilizan el comercio electrónico en India

Electronic Commerce has experienced rapid growth during the last few years. It is a business technique by which consumers buy and sell goods and services online. It has not only helped in increasing speed of service delivery but also helped in reducing cost factors. Leading e- commerce Companies such as Snapdeal, Jabong, and Flipkartare aiding in growth and development of this concept. As per IMAI, domestic digital commerce market is expected to register much higher growth in coming years because of better internet penetration, increase in trust level and pricing advantage. Hence, it became important for stakeholders to know more about the e-attitude of the consumers and triggered an idea to conduct study on e-commerce shopping in India. Using the Multiple Regression Model, Means Comparison Analysis and Demographic Details, the study gave decent business in sights into the Indian consumer’s cognitive decision behavior when choosing products onlineEl comercio electrónico ha experimentado un crecimiento rápido durante estos últimos años. Es una técnica de negocio por la que muchos clientes compran, y venden bienes y servicios online. Esto no solo ha ayudado a aumentar la velocidad del servicio de reparto, sino que también a reducir los factores de coste. Empresas que lideran en este tipo de comercio, como Snapdeal, Jabong, y Flipkartare, ayudan en el crecimiento y desarrollo de este concepto. Según el IMAI, se espera que el mercado de comercio digital doméstico registre un mayor crecimiento en los siguientes años por su mejor entrada en el internet, su aumento en el nivel de confianza, y ventaja en los precios. Por tanto, el hecho de conocer mejor la actitud electrónica de los clientes se convirtió en algo muy importante para los inversores, y provocó que se dirigiera un estudio en las compras en el comercio electrónico en India. Con el uso del Modelo de Regresión Múltiple, el Análisis de Comparación de Medios, y los Detalles Demográficos, este estudio proporcionó conocimientos decentes sobre el negocio en el comportamiento, en cuanto a la decisión cognitiva del cliente indio, a la hora de elegir productos online

Ultralight vector dark matter search using data from the KAGRA O3GK run

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for U(1)B−L gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the U(1)B−L gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM

ConvGRUText: A Deep Learning Method for Fake Text Detection on Online Social Media

Fake texts have become prevalent in current digital era. These are utilized by businesses and individuals to tarnish brand reputation of their competitors and enhance own revenues, and market shares. Due to rise and acceptance of social media, fake text detection is receiving impetus by academicians and practitioners. Fake news is made of text, images and/or URLs. We focus on identifying fake news using textual information. Many writers/bots are hired by organizations for writing fake text to invoke negative sentiments among population. The key objective is to evaluate effectiveness of fake text detection while progressing from statistical to deep learning models. In this work, we propose hybrid model using deep neural network based techniques for text classification. The text mining process was applied then multiple classification methods were implemented to categorize text as fake or real. Multiple experiments on three real-world datasets were executed to show viability of our approach

An empirical study of key parameters that impact purchase decisions of consumers who use e-commerce websites for online shopping in India

Electronic Commerce has experienced rapid growth during the last few years. It is a business technique by which consumers buy and sell goods and services online. It has not only helped in increasing speed of service delivery but also helped in reducing cost factors. Leading e-commerce Companies such as Snapdeal, Jabong, and Flipkartare aiding in growth and development of this concept. As per IMAI, domestic digital commerce market is expected to register much higher growth in coming years because of better internet penetration, increase in trust level and pricing advantage. Hence, it became important for stakeholders to know more about the e-attitude of the consumers and triggered an idea to conduct study on e-commerce shopping in India.Using the Multiple Regression Model, Means Comparison Analysis and Demographic Details, the study gave decent business in sights into the Indian consumer’s cognitive decision behavior when choosing products online

Impact of endoscopic ultrasound-guided fine needle aspiration of small lymph nodes

Background: There is very limited literature on results of fine needle aspiration (FNA) of small (defined as ≤1 cm at long and short axis) lymph nodes, particularly in the setting of pyrexia of unknown origin (PUO). Methods: The study was conducted from July 2014 to December 2015 at a tertiary care center. A total of 34 endoscopic ultrasound (EUS)-guided FNAs in 33 patients were done for lymph nodes ≤1 cm at long and short axis and these were included in the analysis. Results: The study cohort comprised 33 patients; 23 males and 10 females, mean age of 58 ± 12 years. Indication of FNA was to look for malignancy (n = 15), PUO (n = 16), unexplained weight loss (n = 1), and presence of lymphadenopathy in prospective liver donor (n = 1). The FNA was taken from mediastinal nodes (n = 20, 14 subcarinal) and abdominal (n = 14, 8 at porta). The mean size of lymph nodes was 87 ± 11 mm at large axis and 68 ± 17 mm at short axis. A total of 3 (8.8%) FNAs were nondiagnostic (inadequate material). The cytopathologic diagnosis was malignancy in 8 (23.5%), granulomatous change in 8 (23.5%), and reactive lymphadenopathy in 15 (44.1%). Thus, EUS-guided FNA of these small nodes changed the management decisions in 44% of cases (one patient had tubercular lymphadenopathy at two sites). The 22-gauge EUS FNA needle was used in majority of patients (n = 26). There was no significant difference between pathologic (malignant and granulomatous) and reactive lymph nodes regarding size at long or short axis, ratio of long and short axis, hypoechogenicity, and sharply defined borders. Conclusion: EUS-guided FNA of small lymph nodes showed pathological enlargement in 44% of cases