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Socio-demographic risk factors for severe malnutrition in children aged under five among various birth cohorts in Bangladesh
Get PDFTackling malnutrition is a major global health priority for a developing country like Bangladesh. This paper explores the epidemiological differences between only one form and multiple forms of severe malnutrition among children in Bangladesh aged under-five years. Also, it identifies important factors that are associated with both these forms. Data were extracted from the Bangladesh Demographic Health Surveys of 2007, 2011 and 2014. The outcome measures included only one form and multiple forms of severe malnutrition in children aged under-five years. A Chi-square test was performed to find the association of outcome variables with selected socio-demographic factors and logistic regression models helped identify the risk factors. A total of 19,874 children aged under-five years were included in the current analysis. The overall proportion with one form of severe child malnutrition was approximately 11.53% and with multiple forms was 7.60%. The analysis showed that age, mothers' education, fathers' occupation, mothers' currently working, watching television, source of water, solid waste used in cooking, intimate partner violence (IPV), wealth index, place of residence and birth cohort were significant factors for both one and multiple forms of severe child malnutrition. Those children that had a non-educated mother and came from a poor socioeconomic class were associated with a high risk of severe malnutrition. Where a father had an occupation service, any children were at less risk of multiple forms of severe malnutrition. The proportion of severe malnutrition in children aged under-five years with one form and multiple forms was shown to be high in Bangladesh with the risk identified using selected variables. Prevention of malnutrition in Bangladesh should, therefore, be seen as a significant public health issue and given top priority
The art of teaching the piano
No full textIn this piano workshop, Emilie Capulet discusses the current research underpinning effective instrumental tuition and demonstrates creative approaches to teaching the piano
A point-of-care device for sensitive protein quantification
Get PDFIn this paper we present the design of a new point-of-care device for protein quantification. The proposed design is based on a novel aptamer-mediated methodology and real time polymerase chain reaction (RT-PCR), a robust and ultrasensitive method for DNA amplification, which we employ for very sensitive quantification of proteins. In addition, we have also developed an algorithm for the processing of raw fluorescence data from the portable RT-PCR device. The algorithm leads to better linearity than a proprietary software from a commercially available RT-PCR machine. The modular nature of the system allows for easy assembly and adjustment towards a variety of biomarkers for applications in disease diagnosis and personalised medicine
Blended Learning to Fly
Get PDFProfound change hit Higher Education globally in Spring 2020, leaving campuses around the world closed to face-to-face teaching. Once universities reopen premises more fully, we may see a mix of more socially-distanced campuses and a wider use of online and blended learning. Could our experiences in blended synchronous learning indicate ways forward for teaching practices
Lignin modified PVDF membrane with antifouling properties for oil filtration
Get PDFLignin is a sustainable chemical that can be extracted from a wide range of lignocellulosic biowaste. It was blended into polymeric membranes to improve membrane morphology for filtration. Lignin dissolved in NaOH solution can be coated on different substrates to improve the surface hydrophilicity. In this work, the polyvinylidene fluoride (PVDF) membrane was coated with lignin to improve the filtration of oily water. Lignin was dissolved in NaOH solution with varied alkaline concentration (0.25–1.50 wt%) and lignin concentration (0.25–1.00 wt%). The PVDF membrane degraded in the highly alkaline solution, but the increasing lignin content reduced the membrane pore size for the effective rejection of oil emulsion. The PVDF membrane modified with 0.75 wt% of lignin in 0.5 wt% of NaOH solution attained a permeate flux about 70 L⋅m-2⋅h-1, but a slightly lower permeate flux of 55 L⋅m-2⋅h-1 was recorded after immersed in alkaline solution 12 h. The lignin modified membrane rejected up to 99.30% of oil, while the neat PVDF membrane only rejected 83.30% of oil. The lignin modified membrane showed slightly lower but stable flux than the neat PVDF membrane due to the reduction of membrane fouling
Dynamic group formation with intelligent tutor collaborative learning: a novel approach for next generation collaboration
Get PDFGroup Formation (GF) strongly influences the collaborative learning process in Computer-Supported Collaborative Learning (CSCL). Various factors affect GF that include personal characteristics, social, cultural, psychological, and cognitive diversity. Although different group formation methods aim to solve the group compatibility problem, an optimal solution for dynamic group formation is still not addressed. In addition, the research lacks to supplement collaborative group formation with a collaborative platform. In this study, the next level of collaboration in CSCL and Intelligent Tutoring System (ITS) platforms is achieved. First, initial groups are formed based on students learning styles, and knowledge level, i.e. for knowledge level, an activity-based dynamic group formation technique is proposed. In this activity, swapping of students takes place on each permutation based on their knowledge level. Second, the formed heterogeneous balanced groups are used to augment the collaborative learning system. For this purpose, a hybrid framework of Intelligent Tutor Collaborative Learning (ITSCL) is used that provides a unique and real-time collaborative learning platform. Third, an experiment is conducted to evaluate the significance of the proposed study. Inferential and descriptive statistics of Paired T-Tests are applied for comprehensive analysis of recorded observations. The statistical results show that the proposed ITSCL framework positively impacts student learning and results in higher learning gains
Cyber supply chain security a cost benefit analysis using net present value
Get PDFCyber supply chain (CSC) security cost effectiveness should be the first and foremost decision to consider when integrating various networks in supplier inbound and outbound chains. CSC systems integrate different organizational network systems nodes such as SMEs and third-party vendors for business processes, information flows, and delivery channels. Adversaries are deploying various attacks such as RAT and Island-hopping attacks to penetrate, infiltrate, manipulate and change delivery channels. However, most businesses fail to invest adequately in security and do not consider analyzing the long term benefits of that to monitor and audit third party networks. Thus, making cost benefit analysis the most overriding factor. The paper explores the cost-benefit analysis of investing in cyber supply chain security to improve security. The contribution of the paper is threefold. First, we consider the various existing cybersecurity investments and the supply chain environment to determine their impact. Secondly, we use the NPV method to appraise the return on investment over a period of time. The approach considers other methods such as the Payback Period and Internal Rate of Return to analyze the investment appraisal decisions. Finally, we propose investment options that ensure CSC security performance investment appraisal, ROI, and business continuity. Our results show that NVP can be used for cost-benefit analysis and to appraise CSC system security to ensure business continuity planning and impact assessment
Challenges of world tourism cities: London, Singapore and Dubai
Get PDFThis chapter looks at the challenges faced by world tourism cities, destinations that attract large number of visitors due to their specific characteristics. These cities perform multiple functions, such as centres for business, as well as cultural excellence, and are home to many world-class tourist attractions. They are also important players in the world economy, and offer easy access through better connectivity. These traits, together with others that will be discussed later in the chapter, contribute to the important role played by world tourism cities in the global visitor economy. In addition, these cities play an important role in the visitor economy of a destination, with the success of the tourism industry in a country often reliant on their success. However, despite their advantages, world cities face numerous challenges that result from the complex economic, social and political functions they exhibit, as well as the diversity of people they attract (e.g. residents, immigrants or visitors).
In a very competitive world, where many new as well as traditional destinations try to attract ever more visitors, policy makers in cities need to better understand the challenges they face so they can implement specific measures when planning and managing tourism in these destinations. Without such measures, world tourism cities could face numerous negative impacts that may affect their sustainability and competitiveness on the global market, such as increased traffic congestion, pollution and conflicts between visitors and hosts, to name but a few. This section therefore looks at a number of world tourism cities (e.g. London, Singapore, and Dubai) and briefly highlights their particularities together with the challenges they face
RFC: A feature selection algorithm for software defect prediction
Get PDFSoftware defect prediction (SDP) is used to perform the statistical analysis of historical defect data to find out the distribution rule of historical defects, so as to effectively predictdefects in the new software. However, there are redundant and irrelevant features in the software defect datasets affecting the performance of defect predictors. In order to identify and remove the redundant and irrelevant features in software defectdatasets, we propose Relief F-based clustering (RFC), a cluster-based feature selection algorithm. Then, the correlation between features is calculated based on the symmetric uncertainty. According to the correlation degree, RFC partitions features into kclusters based on the k-medoids algorithm, and finally selects the representative features from each cluster to form the final feature subset. In the experiments, we compare the proposed RFC with classical feature selection algorithms on nine National Aeronautics and Space Administration (NASA) software defectprediction datasets in terms of area under curve (AUC) and F-value. The experimental results show that RFC can effectively improve the performance of SDP