Azizur Rahman

Jurisdictional, socioeconomic and gender inequalities in child health and development: analysis of a national census of 5-year-olds in Australia

Objectives Early child development may have important consequences for inequalities in health and well-being. This paper explores population level patterns of child development across Australian jurisdictions, considering socioeconomic and demographic characteristics. Design Census of child development across Australia. Setting and participants Teachers complete a developmental checklist, the Australian Early Development Index (AEDI), for all children in their first year of full-time schooling. Between May and July 2009, the AEDI was collected by 14 628 teachers in primary schools (government and non-government) across Australia, providing information on 261 147 children (approximately 97.5% of the estimated 5-year-old population). Outcome measures Level of developmental vulnerability in Australian children for five developmental domains: physical well-being, social competence, emotional maturity, language and cognitive skills and communication skills and general knowledge. Results The results show demographic and socioeconomic inequalities in child development as well as within and between jurisdiction inequalities. The magnitude of the overall level of inequality in child development and the impact of covariates varies considerably both between and within jurisdiction by sex. For example, the difference in overall developmental vulnerability between the best-performing and worst-performing jurisdiction is 12.5% for males and 7.1% for females. Levels of absolute social inequality within jurisdictions range from 8.2% for females to 12.7% for males. Conclusions The different mix of universal and targeted services provided within jurisdictions from pregnancy to age 5 may contribute to inequality across the country. These results illustrate the potential utility of a developmental census to shed light on the impact of differences in universal and targeted services to support child development by school entry.

Acute Malnutrition in Bangladeshi Children Levels and Determinants

The main purpose of the study was to identify the levels and determinants of acute malnutrition or wasting in Bangladeshi children. A 2-stage stratified random sampling design was used to collect the Bangladesh Demographic and Health Survey data during November 1999 to March 2000, in which 5333 living children aged 0 to 59 months and their mothers were weighed and measured to obtain their anthropometric data. The prevalence of wasting was assessed by the z scores approach, using the anthropometric criterion of weight-for-height and following the WHO guidelines and cutoff points. Results reveal that the prevalence of severe and moderate wasting were more common among children, and the overall prevalence of acute malnutrition was about 10%, indicating that it is one of the major public health problems in the country. Multivariate analysis showed that mothers BMI and media exposure, childs age and birth size, and respiratory sickness in childhood were significantly associated with both severe and moderate wasting.

Simulating the characteristics of populations at the small area level

These days spatial microsimulation modelling plays a vital role in policy analysis for small areas. Most developed countries are using these tools in ways to make knowledgeable decisions on major policy issues at local levels. However, building an appropriate model is very difficult for many reasons. For example, the creation of reliable spatial microdata is still challenging. In addition there has not been much research on testing statistical significance of the model outputs yet, and deriving estimates of how reliable these outputs may be. This paper deals with the spatial microsimulation model building procedure for simulating synthetic spatial microdata, and then estimating small area housing stress in Australia. Geographic maps for small area housing stress estimates are illustrated. The research also demonstrates a new system to test the statistical significance of the model estimates.

Quantitative Analysis of Amartya Sen's Theory: An ICT4D Perspective

In this article, the authors attempted to evaluate the contribution of Information Communication Technology ICT for development ICT4D project in a context of developing country: Bangladesh. Though ICT4D is a general term referring to the application of ICT within the fields of development of a country, there are many cases where the potential benefits of ICT linked with the individual, group/community and organizational level. Considering two case studies, the authors have attempted to understand how ICT can be linked with the lives of community in rural areas of Bangladesh. They have adopted Amartya Sens five freedoms as conceptual framework for this study. Through a quantitative perspective the authors argue that ICT projects can lead to development in general and five freedoms at particular.

Malware analysis and detection using data mining and machine learning classification

Exfiltration of sensitive data by malicious software or malware is a serious cyber threat around the world that has catastrophic effect on businesses, research organizations, national intelligence, as well as individuals. Thousands of cyber criminals attempt every day to attack computer systems by employing malicious software with an intention to breach crucial data, damage or manipulate data, or to make illegal financial transfers. Protection of this data is therefore, a critical concern in the research community. This manuscript aims to propose a comprehensive framework to classify and detect malicious software to protect sensitive data against malicious threats using data mining and machine learning classification techniques. In this work, we employ a robust and efficient approach for malware classification and detection by analyzing both signature-based and anomaly-based features. Experimental results confirm the superiority of the proposed approach over other similar methods.

A Distributed and Elastic Application Processing Model for Mobile Cloud Computing

The latest developments in mobile computing technology have increased the computing capabilities of smart mobile devices (SMDs). However, SMDs are still constrained by low bandwidth, processing potential, storage capacity, and battery lifetime. To overcome these problems, the rich resources and powerful computational cloud is tapped for enabling intensive applications on SMDs. In Mobile Cloud Computing (MCC), application processing services of computational clouds are leveraged for alleviating resource limitations in SMDs. The particular deficiency of distributed architecture and runtime partitioning of the elastic mobile application are the challenging aspects of current offloading models. To address these issues of traditional models for computational offloading in MCC, this paper proposes a novel distributed and elastic applications processing (DEAP) model for intensive applications in MCC. We present an analytical model to evaluate the proposed DEAP model, and test a prototype application in the real MCC environment to demonstrate the usefulness of DEAP model. Computational offloading using the DEAP model minimizes resources utilization on SMD in the distributed processing of intensive mobile applications. Evaluation indicates a reduction of 74.6% in the overhead of runtime application partitioning and a 66.6% reduction in the CPU utilization for the execution of the application on SMD.

Small Area Housing Stress Estimation in Australia: Calculating Confidence Intervals for a Spatial Microsimulation Model

ABSTRACT This study provides small area housing stress estimates by tenure type in Australia with a way of calculating confidence intervals for a spatial microsimulation model. Findings reveal that prevalence of housing stress for private-renter, buyer, public-renter and owner households are 59.6%, 33.2%, 6.9%, and 0.3%, respectively. Almost two-thirds of these households are located in statistical local areas (SLAs) in eight capital cities, and a large number of them are in Sydney and Melbourne. Estimates for private renters and buyers are significantly high in some capitals and southeast coastal regions. About 95.7% of SLAs show accurate estimates with narrow confidence intervals.

Measuring Modified Mass Energy Equivalence in Nutritional Epidemiology: A Proposal to Adapt the Biophysical Modelling Approach

The calculation of net dietary energy is in great triumph on the helm of designing an apt dieting for both the therapeutic and normal diet. There are some procedures in this connection in nutritional science which is relatively time consuming, laboratory tests induced and often the misleading data contributors in view of assuring balanced dieting. The dietician is often at bay to approve an exact dieting to sustain health and nutritional soundness adhering to the existing dietary energy measuring methods because the frequently using methods are not informing the net dietary energy level required at all in correct amount for the sample at a population in a community. The aim of the current study is to make a dot over these ongoing panics exploring an easy and accurate way in prescribing a confounding free diet. The study can divulge an open secret in measuring net dietary energy which is mandatory for dieting practices worldwide to resist the possible health horrors in nutritional epidemiology. The study finding is the Modified Mass Energy Equivalence [equation (xi)] can be an outstanding biophysical model in measuring net dietary energy as a dieting tool in health pedagogy of health science.

Nexus Between Cultural Dissonance, Management Accounting Systems, and Managerial Effectiveness: Evidence from an Asian Developing Country

This article examines the links between corporate cultural dissonance, the management accounting system (MAS) information adequacy gap, and managerial effectiveness of the financial sector in Bangladesh. Data were collected from a random sample of 146 bank managers and were analyzed using correlation matrices. The findings suggest that the level of managerial effectiveness can be improved by decentralizing the management accounting practices through maintaining minimal authoritative power distance, improving the system for gathering and sharing information, and enhancing transparency in information flow. These findings, which have important implications for the effective performance management of banks, are highlighted in the article.

Developing an Automated Machine Learning Approach to Test Discontinuity in DNA for Detecting Tuberculosis

Abstract: Discontinuity in long DNA sequences creates harmful diseases like Tuberculosis (TB). Given the 21th centurys exponential growth of big-data environments, knowing the precise breaks position of DNA sequences is essential for many reasons including advanced medical intervention. This study designs an automated framework to assess the breaks positions in long DNA sequences which are responsible for TB and then empirically tests it by analyzing a big DNA dataset from the National Center for Biotechnology Information (NCBI) database. The method consists of a range of data cleansing and deep neural network tools for big data situation. Findings reveal that the proposed approach is better than other methods in detecting DNA sequence breaks for TB via resolving a sample size issue of the training dataset and recursively divide the whole dataset into certain length to detect the breaks. It also provides a faster predictive analysis with more accurate and reliable outcomes.