Abstract

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Adoption factors of Financial Technology (Fintech) services have been the subject of investigation in a growing body of extant literature. Macro-level as well as user-specific factors that contribute to the adoption of customer-facing fintech services have been studied. Emerging market studies mostly considered targeted demographic and socio-economic segments, limiting their ability to reflect a wide spectrum of relevant factors. We conducted a nationwide representative survey of 1282 individuals in Bangladesh. A total of 16 administrative districts from all 8 administrative divisions were included. Addressing sample imbalance with Synthetic Minority Oversampling Technique (SMOTE), we deployed Recursive Feature Elimination (RFE) to reduce number of customer features down to the most important. Using Library of Large Linear Classification (LIBLINEAR) for multivariate Logistic Regression, we identified significant features that predict customer-facing fintech adoption among individual respondents. We found that customers were less likely to adopt fintech services if they had higher reported levels of concern with security, information secrecy, limited government control, and high levels of reported service intuitiveness obstacles. Our evidence suggests these concern factors constitute the prominent factor behind fintech adoption, as opposed to demographic variables, for example. Our findings hold insights for fintech services providers and policy makers.

We developed a laser long-path absorption lidar system using a gas correlation method for measuring atmospheric methane. This technique uses a broad-band laser and a gas correlation cell. We developed a potassium titanyl arsenate (KTA) optical parametric oscillator at 3.416 µm for the laser source. The optical round-trip path length is 20 m. The experiment was carried out using this system. It was estimated that the error in the density of methane is 4.4 ppm for measurement within 1 s.

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Molecular dynamics simulation was performed for three-dimensional (3D) cubic mass-spring model crystals. Anharmonic potential up to the fourth order was taken into account, and central forces between the nearest neighbor (nn) and the next nearest neighbor (nnn) atoms were considered. The ratio of the potential between the nnn atoms to the potential between the nn atoms was varied. An input pulse displacement was given to central atomic planes in the crystal or to the end atomic plane of the crystal, and induced displacements and velocities of all atoms were computed. As the nnn interaction was enhanced, the soliton velocity increased and the soliton energy decreased. The results were compared with those of 1D and 2D crystals obtained previously. The increase of soliton velocity due to the enhanced nnn interaction was largest for 1D crystals, and the decrease of soliton energy was smallest for 2D crystals. Discussions and remarks were presented for these results.

Nowadays, traffic congestion is becoming a severe problem for almost every urban area. It badly hampers the economic growth of a country because it has negative effects on productivity and business. Increasing populations and urbanization are the main reasons for traffic congestion in most cities. However, traffic prediction, forecasting, and modeling can help provide appropriate routes and times for traveling and can significantly impact traffic jam reduction. Currently, there is a lot of research being done on traffic flow analysis in all developed countries, and they are planning their future accordingly. The objective of this review paper is to provide a comprehensive and systematic review of the traffic prediction literature, containing 98 papers published from 2010 to 2020. The papers are extracted from four well-known publishers and databases: Scopus, ScienceDirect, IEEE Xplore, and ACM. This article concentrates on the research approaches, directions, and gaps in traffic flow prediction. It also talks about current trends in predicting traffic flow and what might be taken into account in the future.

Fintech has emerged with transformative potential to impact financial growth and stability in any nation. While extant literature has steadily grown in scope and depth on fintech adoption factors and their impact on financial inclusion, a cross-country perspective is still largely missing. In this study, we conducted a qualitative research to examine the current situation of Fintech ecosystems in 18 developing nations selected based on World Bank GNI per capita categorization. We have compared these nations according to parameters taken from the literature which include population, median age, GNI per capita, literacy rate, mobile phone connections, number of internet users, unbanked population, investment in fintech, number of fintech companies, and regulation. A cornerstone measurement throughout our analysis was country ranking from Global Findexable Ranking 2021. The findings suggest that nations with high populations, median age, GNI per capita, literacy rate, mobile phone connections, internet usage, number of companies, investment, and low unbanked population enjoyed higher fintech ranking. Countries with highly regulated fintech industries like India, China, and Indonesia have been at the top of the ranking. A notable exception here is Vietnam, which has actually slipped down the rankings from the previous year. The paper provides a number of policy recommendations based on the exploratory findings for fintech ecosystems in developing nations in general and the ecosystem in Bangladesh in particular.