THE IMPACT OF MOBILE FINANCIAL SERVICES ON THE USAGE DIMENSION OF FINANCIAL INCLUSION: AN EMPIRICAL STUDY FROM BANGLADESH

A plethora of studies have investigated how Mobile Financial Services (MFS) induces financial inclusion around the world. However, research in the context of Bangladesh was rather limited. Hence, the primary objective of this paper was to investigate whether there was a statistically significant relationship between MFS and financial inclusion, measured by two time series variables – the number of MFS agents and number of registered MFS users per 100,000 of population, from 2017 to 2020. For analyzing the relationship between these two variables, multiple statistical methods were employed – including Vector Auto Regression, Cointegration and Granger Causality. The analysis revealed that both time series variables had an increasing trend with time. More importantly, the analysis specified that there was no statistically significant relationship between MFS, measured by the number of agents and the ‘usage’ dimension of financial inclusion, measured by number of registered MFS users in the context of Bangladesh. Moreover, the study was unable to find any significant changes in the trends of these variables that could be attributed to the COVID-19 pandemic in Bangladesh

Impact of ICT usage on indigenous peoples’ quality of life: Evidence from an Asian developing country

Indigenous communities across the world have been suffering disadvantages in several domains, e.g. erosion of land rights, language and other cultural aspects, while at the same time being discriminated against when prepared to integrate into the dominant cultures. It has been argued in the literature that information communication technologies (ICTs) have the potential of contributing to addressing some of these disadvantages – both in terms of rebuilding what has been eroded and facilitating integration into non-Indigenous societies. In trying to understand how ICTs can be useful for these processes, it is important to do so from a conceptual framework that encompasses the multi-dimensionality of the issues faced by Indigenous communities. The conceptual frameworks frequently used in the ICT literature tend to focus on adoption, use and diffusion of technologies rather than how the use of ICTs affects the livelihoods of the users, which is the focus of this paper. The conceptual framework is informed by the capability approach (CA), in particular by the five freedoms identified in the seminal work of Amartya Sen (2001), “Development as Freedom” (DaF). Data were collected from a purposive sample in an Indigenous community in Bangladesh, using a qualitative method to map how ICTs had affected the lives of these community members The findings suggest that the participants perceived that ICTs had made positive contributions, particularly the benefits they gained from learning how to use computers in the domains that are relevant from the perspective of the five freedoms espoused in DaF. The findings reported in this paper are useful for policy formulation in Bangladesh. As the study is contextualised in a transitional economy setting and can therefore not be generalised, but we believe that the conceptual framework has much to offer future research designed to understand how ICTs can improve the livelihoods of Indigenous individuals and communities

Automatic Dispersion, Defect, Curing, and Thermal Characteristics Determination of Polymer Composites using Micro-Scale Infrared Thermography and Machine Learning Algorithm

Infrared thermography is a non-destructive technique that can be exploited in many fields including polymer composite investigation. Based on emissivity and thermal diffusivity variation, components, defects, and curing state of the composite can be identified. However, manual processing of thermal images that may contain significant artifacts, is prone to erroneous component and property determination. In this study, thermal images of different graphite/graphene-based polymer composites fabricated by hand, planetary, and batch mixing techniques were analyzed through an automatic machine learning model. Filler size, shape, and location can be identified in polymer composites and thus, the dispersion of different samples was quantified with a resolution of ~ 20 µm despite having artifacts in the thermal image. Thermal diffusivity comparison of three mixing techniques was performed for 40% graphite in the elastomer. Batch mixing demonstrated superior dispersion than planetary and hand mixing as the dispersion index (DI) for batch mixing was 0.07 while planetary and hand mixing showed 0.0865 and 0.163 respectively. Curing was investigated for a polymer with different fillers (PDMS took 500s while PDMS-Graphene and PDMS Graphite Powder took 800s to cure), and a thermal characteristic curve was generated to compare the composite quality. Therefore, the above-mentioned methods with machine learning algorithms can be a great tool to analyze composite both quantitatively and qualitatively

Relationship Between Stock Price and Financial Distress: A Study on Banks of Bangladesh

The purpose of this study is to empirically test relationship between financial distress and stock price. Financial distress is calculated using Altman Z-score. Data for the study consists of financial information of 29 banks listed in Dhaka Stock Exchange (DSE). Variables in this study are Z-score (independent) and Stock price (dependent). This study analyzes and describes the data associated with these variables and linear regression has been done between them to ascertain the level of and direction of their relationship. The trends of z-score for the study period (2015-2019) have been tested. This study finds there is no significant relationship between the variables. In other words, Z-score is unable to explain the variability in the stock price. Keywords: Stock price, Financial Distress, Altman Z-Score, Banking Industry DOI: 10.7176/RJFA/12-11-03 Publication date:June 30th 202

Graphene-conductive polymer-based electrochemical sensor for dopamine detection

The central nervous system\u27s (CNS) dopaminergic system dysfunction has been linked to neurological illnesses like schizophrenia and Parkinson\u27s disease. As a result, sensitive and selective detection of dopamine is critical for the early diagnosis of illnesses associated with aberrant dopamine levels. In this research, we have investigated the performance of electrochemical screen-printed sensors for different concentrations of dopamine detection using graphene-based conductive PEDOT: PSS(G-PEDOT: PSS) and Polyaniline(GPANI) inks on the working electrode and compared the sensitivity. SEM characterization technique has been performed to visualize the microstructures of the proposed inks. We have investigated cyclic voltammetry (CV) electrochemical techniques with ferri/ferrocyanide redox couple to assess the efficiency of the designed electrodes in detecting dopamine. GPANI ink has shown to have better LOD and stability to detect dopamine with screen-printed electrodes. Further, we have also studied electrochemical analysis for the selective detection of dopamine without the interference of Ascorbic Acid (AA)

Hydrogel and Graphene Embedded Piezoresistive Microcantilever Sensor for Solvent and Gas Flow Detection

Piezoresistive microcantilever sensor is widely used in sensing applications including liquid and gas flow detection. Microcantilevers can function as an embedded system if they are coated with polymers or nanomaterials to improve sensing performance. In this paper, we investigated the performance of piezoresistive microcantilevers (PMC) with and without additional coating. We studied the sensitivity of the PMC sensor after coating it with a three-dimensional porous hydrogel and piezoresistive graphene oxide layer. Hydrogel embedded piezoresistive microcantilever (EPM) showed better results than PMC during solvent sensing application. The resistance change for hydrogel embedded PMC was higher compared to bare PMC by 430% (3.2% to 17%) while detecting isopropyl alcohol (IPA), by approximately 1.5 orders of magnitude (0.19% to 5.7%) while detecting the presence of deionized water. Graphene Oxide coated PMC showed a wider detection range by 30 milliliter/min and 24% better sensitivity than bare PMC during the gas detection experiment. Additionally, we compared the experiment result with COMSOL simulation to develop a model for our embedded PMC sensing. Simulation shows significantly higher deflection of the EPM compared to the bare PMC (66.67% higher while detecting IPA, consistent with the trend observed during the experiment). The facile drop casting-based embedded microcantilever fabrication technique can lead to improved performance in different sensing applications. Our future work will focus on detecting biomolecules by using our constructed embedded systems