Implementation of Bioenergy Systems towards Achieving United Nations’ Sustainable Development Goals in Rural Bangladesh

This research presents a conceptual model to illustrate how people living in rural areas can harness bioenergy to create beneficial ‘community-driven’ income-generating activities. The research is contextualised within the rural developing areas of Bangladesh where people live in abject poverty and energy deficiency. The research methodology applied in this study aims to determine the basic requirements for implementing community-based anaerobic digestion (AD) facilities and illustrate how an AD facility positively impacts upon the lives of rural communities directly after its installation. The survey results demonstrate that implementing a biogas plant can save 1 h and 43 min of worktime per day for a rural family where women are generally expected to for cook (by the long-term tradition). In addition to the positive impacts on health and climate change through adoption of clean energy generation, this time saving could be utilised to improve women0s and children’s education. The research concludes that, by providing easy access to clean bioenergy, AD can change people’s quality of life, yielding major social, economic and environmental transformations; key benefits include: extending the working day; empowering women; reducing indoor air pollution; and improving people’s health and welfare. Each of these tangible benefits can positively contribute towards achievement of the UN’s Sustainable Development Goals. This work demonstrates the potential to increase the implementation of AD systems in other developing world countries that have similar geographic and socioeconomic conditions

Dispersion Curve Engineering of TiO2/Silver Hybrid Substrates for Enhanced Surface Plasmon Resonance Detection

As surface plasmon resonance (SPR)-based biosensors are well translated into biological, chemical, environmental, and clinical fields, it is critical to further realize stable and sustainable systems, avoiding oxidation susceptibility of metal films—in particular, silver substrates. We report an enhanced SPR detection performance by incorporating a TiO2 layer on top of a thin silver film. A uniform TiO2 film fabricated by electron beam evaporation at room temperature is an effective alternative in bypassing oxidation of a silver film. Based on our finding that the sensor sensitivity is strongly correlated with the slope of dispersion curves, SPR sensing results obtained by parylene film deposition shows that TiO2/silver hybrid substrates provide notable sensitivity improvement compared to a conventional bare silver film, which confirms the possibility of engineering the dispersion characteristic according to the incidence wavelength. The reported SPR structures with TiO2 films enhance the sensitivity significantly in water and air environments and its overall qualitative trend in sensitivity improvement is consistent with numerical simulations. Thus, we expect that our approach can extend the applicability of TiO2-mediated SPR biosensors to highly sensitive detection for biomolecular binding events of low concentrations, while serving a practical and reliable biosensing platform

Deep churn prediction method for telecommunication industry

Being able to predict the churn rate is the key to success for the telecommunication industry. It is also important for the telecommunication industry to obtain a high profit. Thus, the challenge is to predict the churn percentage of customers with higher accuracy without comprising the profit. In this study, various types of learning strategies are investigated to address this challenge and build a churn predication model. Ensemble learning techniques (Adaboost, random forest (RF), extreme randomized tree (ERT), xgboost (XGB), gradient boosting (GBM), and bagging and stacking), traditional classification techniques (logistic regression (LR), decision tree (DT), and k-nearest neighbor (kNN), and artificial neural network (ANN)), and the deep learning convolutional neural network (CNN) technique have been tested to select the best model for building a customer churn prediction model. The evaluation of the proposed models was conducted using two pubic datasets: Southeast Asian telecom industry, and American telecom market. On both of the datasets, CNN and ANN returned better results than the other techniques. The accuracy obtained on the first dataset using CNN was 99% and using ANN was 98%, and on the second dataset it was 98% and 99%, respectively

An assessment of mortgage loan default propensity in Ghana

Purpose Credit market development requires appropriate credit assessment and default policies. This paper aims to examine the impact of household characteristics on mortgage default, using survey data collected from Ghanaian financial institutions. Design/methodology/approach Data were gathered using semi-structured questionnaires from customers of five universal banks in Ghana. A logistic regression was used to model the determinants of credit default propensity. Findings Contrary to established knowledge, the study shows that females are more likely to default on credit than their male counterparts. This is even more likely if the female is older, unmarried, divorced and financially illiterate and has lower educational attainments. These factors are associated with lower earning capacity, which increases default tendencies. The findings confirm that price instability (typified by excessive movements in inflation and exchange rates in addition to low national savings rate) are adversely linked to credit defaults. Borrower’s perception of constraints to credit access (such as collateral requirements, interest rate and loan size) influence credit default. Banks should be encouraged to invest in the financial literacy skills development of their customers to mitigate credit default tendencies. Social implications The study is of practical value to credit officers and the development of the credit market in Ghana. A novel model is presented for assessing credit applications and developing credit default policies. Originality/value The research findings have not only expanded the frontiers of literature but also empirically examined the determinants of credit default propensity, which provides a basis for developing and improving credit default policy in the credit market

An Assessment of Students Job Preference Using a Discrete Choice Experiment: a postgraduate case study

Purpose: Using a discrete choice experiment (DCE), this study aims to better understand the job preference of postgraduate students studying at the Kwame Nkrumah University of Science and Technology-Institute of Distance Learning (KNUST-IDL), Ghana and also rank the attributes of a job they deem important. Methodology: The research adopted a positivist epistemological design contextualised within an deductive approach and case study strategy. Primary survey data was collected from a stratified random sample of 128 postgraduate students with multi-sectorial career prospects. Sample students were subjected to a DCE in which their stated preferences were collected using closed ended questionnaires with twenty-eight pairs of hypothetical job profiles. Respondents’ preferences from the DCE data were then modelled using the conditional logit. Findings: The research reveals that: salary in the range GHC 2,800.00 to GHC 3,400.00 ($1=GHS 5.3); supportive management; very challenging jobs; and jobs located in the city were the top attributes that were significant and had the most impact in increasing the utility of selecting a particular job. Interestingly, jobs with no extra hours workload was not significant hence, had a negative impact upon student preferences. Originality: This novel research is the first to utilise a DCE to better elicit preference and trade-offs of postgraduate students in a developing country towards varying job characteristics that have an impact on their future employment decisions. Knowledge advancements made provide invaluable insight to employers and policy makers on the key criteria that should be implemented in order to retain the best candidate

Quasi-Gray labelling for Grassmannian constellations

Abstract-This paper presents a technique for assigning binary labels to the points in an arbitrary Grassmannian constellation in a manner that approximates the Gray labelling. The idea behind this technique is to match the Grassmannian constellation of interest to the points in an auxiliary constellation that can be readily Gray labelled. In order to demonstrate the efficacy of the proposed technique, the labelled constellations are utilized in a BICM-encoded non-coherent MIMO communication system with iterative detection and decoding. Numerical simulations indicate that this labelling technique results in a non-coherent communication system that provides better bit error rate performance than systems that utilize the same constellation but employ labels that are generated either randomly or via a quasi-set-partitioning technique

Measurement of Upper Limb Range of Motion Using Wearable Sensors: A Systematic Review.

Background: Wearable sensors are portable measurement tools that are becoming increasingly popular for the measurement of joint angle in the upper limb. With many brands emerging on the market, each with variations in hardware and protocols, evidence to inform selection and application is needed. Therefore, the objectives of this review were related to the use of wearable sensors to calculate upper limb joint angle. We aimed to describe (i) the characteristics of commercial and custom wearable sensors, (ii) the populations for whom researchers have adopted wearable sensors, and (iii) their established psychometric properties. Methods: A systematic review of literature was undertaken using the following data bases: MEDLINE, EMBASE, CINAHL, Web of Science, SPORTDiscus, IEEE, and Scopus. Studies were eligible if they met the following criteria: (i) involved humans and/or robotic devices, (ii) involved the application or simulation of wearable sensors on the upper limb, and (iii) calculated a joint angle. Results: Of 2191 records identified, 66 met the inclusion criteria. Eight studies compared wearable sensors to a robotic device and 22 studies compared to a motion analysis system. Commercial (n = 13) and custom (n = 7) wearable sensors were identified, each with variations in placement, calibration methods, and fusion algorithms, which were demonstrated to influence accuracy. Conclusion: Wearable sensors have potential as viable instruments for measurement of joint angle in the upper limb during active movement. Currently, customised application (i.e. calibration and angle calculation methods) is required to achieve sufficient accuracy (error < 5°). Additional research and standardisation is required to guide clinical application

Testing and Validating Customer Relationship Management Implementation Constructs in Egyptian Tourism Organizations

To date, Critical Success Factors (CSFs) for implementing Customer Relationship Management (CRM) have not been systematically investigated. Existing studies have derived their CSFs from different perspectives. However, it lacks scientifically developed and tested constructs that represent an integrative CRM philosophy. Through a detailed analysis of the literature, as well as adding new factors, this research identifies eight constructs for integrated CRM implementation in developing economies. The proposed CSFs are tested and validated through a sample of 162 Egyptian tourism organizations that utilize CRM systems, using Amos 19. The overall results from the empirical assessment were positive, reflecting the appropriateness of the proposed CSFs. This study is one of very few studies to provide an integrative perspective of CSFs for implementing CRM in the tourism sector and developing economies; it adds to the extremely limited number of empirical studies that have been conducted to investigate CRM implementation in developing countries. Copyright © Taylor & Francis Group, LLC