Health curriculum and school quality: AKU-IED’s

This paper is based on the experiences of the Health Action Schools project at AKU-IED and looks at issues surrounding the definition, choice and implementation of planned content of health education for primary schools in Pakistan. The paper argues that health education is a vital component to achieving quality because it links home with school; ‘needs now’ with ‘needs later’. Yet it proves exceptionally difficult to plan and deliver such content effectively because curriculum planning bodies are geared to work with separate subjects rather than across the curriculum, with classroom content rather than wider learning experiences in and from school, and with textbooks and examinations rather than the physical and human environment of the school community. The paper asserts that there is confusion about the definition and purpose of health education and that a wide gap exists between what is planned centrally and what is actually delivered in a school. The paper also assets the need to rethink approaches aimed at improving content, methodology, materials and evaluation strategies and raises issues of wide relevance to the planning of health education and other themes such as environmental education and inclusive education

Data analytics to evaluate the impact of infectious disease on economy: Case study of COVID-19 pandemic

SARS-CoV-2 (COVID-19) is a new strain of coronavirus that is regarded as a respiratory disease and is transmittable among humans. At present, the disease has caused a pandemic, and COVID-19 cases are ballooning out of control. The impact of such turbulent situations can be controlled by tracking the patterns of infected and death cases through accurate prediction and by taking precautions accordingly. We collected worldwide COVID-19 case information and successfully predicted infected victims and possible death cases around the world and in the United States. In addition, we analyzed some leading stock market shares and successfully forecast their trends. We also scrutinized the share market price by proper reasoning and considered the state of affairs of COVID-19, including geographical dispersity. We publicly release our developed dashboard that presents statistical data of COVID-19 cases, shows predicted results, and reveals the impact of COVID19 on leading companies and different countries\u27 job markets

A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computing

Federated Learning (FL) has gained widespread popularity in recent years due to the fast booming of advanced machine learning and artificial intelligence along with emerging security and privacy threats. FL enables efficient model generation from local data storage of the edge devices without revealing the sensitive data to any entities. While this paradigm partly mitigates the privacy issues of users' sensitive data, the performance of the FL process can be threatened and reached a bottleneck due to the growing cyber threats and privacy violation techniques. To expedite the proliferation of FL process, the integration of blockchain for FL environments has drawn prolific attention from the people of academia and industry. Blockchain has the potential to prevent security and privacy threats with its decentralization, immutability, consensus, and transparency characteristic. However, if the blockchain mechanism requires costly computational resources, then the resource-constrained FL clients cannot be involved in the training. Considering that, this survey focuses on reviewing the challenges, solutions, and future directions for the successful deployment of blockchain in resource-constrained FL environments. We comprehensively review variant blockchain mechanisms that are suitable for FL process and discuss their trade-offs for a limited resource budget. Further, we extensively analyze the cyber threats that could be observed in a resource-constrained FL environment, and how blockchain can play a key role to block those cyber attacks. To this end, we highlight some potential solutions towards the coupling of blockchain and federated learning that can offer high levels of reliability, data privacy, and distributed computing performance

Learning Front-end Filter-bank Parameters using Convolutional Neural Networks for Abnormal Heart Sound Detection

Automatic heart sound abnormality detection can play a vital role in the early diagnosis of heart diseases, particularly in low-resource settings. The state-of-the-art algorithms for this task utilize a set of Finite Impulse Response (FIR) band-pass filters as a front-end followed by a Convolutional Neural Network (CNN) model. In this work, we propound a novel CNN architecture that integrates the front-end bandpass filters within the network using time-convolution (tConv) layers, which enables the FIR filter-bank parameters to become learnable. Different initialization strategies for the learnable filters, including random parameters and a set of predefined FIR filter-bank coefficients, are examined. Using the proposed tConv layers, we add constraints to the learnable FIR filters to ensure linear and zero phase responses. Experimental evaluations are performed on a balanced 4-fold cross-validation task prepared using the PhysioNet/CinC 2016 dataset. Results demonstrate that the proposed models yield superior performance compared to the state-of-the-art system, while the linear phase FIR filterbank method provides an absolute improvement of 9.54% over the baseline in terms of an overall accuracy metric.Comment: 4 pages, 6 figures, IEEE International Engineering in Medicine and Biology Conference (EMBC

Secondary Education During Lockdown Situation Due to Covid-19 Pandemic in Bangladesh: Teachers’ Response on Online Classes

Online classes started in many secondary schools one month after the compulsory closure of all education institutions in Bangladesh. This study explores the current scenario as well as challenges of adaptation of online classes in secondary education in terms of teachers’ experience. The study followed sequential exploratory mixed-method approach. Five secondary teachers were interviewed and 54 secondary teachers from 17 districts in Bangladesh were surveyed over telephone, Google forms and by email. The quantitative data was analyzed with Microsoft Excel and the thematic analysis approach had been followed for the qualitative one. The findings revealed that a good number of teachers have started teaching online by using social media platforms despite of not having any training or experience. Very few respondent teachers were found to take online examination; instead they are trying to assess the students from the feedback of given home works and home assignments. Teachers are facing numerous challenges like deficit of digital equipment, lack of expertise, unfamiliarity with the LMS, proficiency in assessment technique etc. The paper concludes with few recommendations such as providing proper devices to the teachers and students to participate in online class; facilitate rigorous training to enhance technology-based skills and capacities of the teachers so as to get the expected outcome. Keywords: Secondary education, Education in pandemic, Online class, Teachers’ response. DOI: 10.7176/JEP/11-20-11 Publication date:July 31st 202

An Ensemble of Transfer, Semi-supervised and Supervised Learning Methods for Pathological Heart Sound Classification

In this work, we propose an ensemble of classifiers to distinguish between various degrees of abnormalities of the heart using Phonocardiogram (PCG) signals acquired using digital stethoscopes in a clinical setting, for the INTERSPEECH 2018 Computational Paralinguistics (ComParE) Heart Beats SubChallenge. Our primary classification framework constitutes a convolutional neural network with 1D-CNN time-convolution (tConv) layers, which uses features transferred from a model trained on the 2016 Physionet Heart Sound Database. We also employ a Representation Learning (RL) approach to generate features in an unsupervised manner using Deep Recurrent Autoencoders and use Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) classifiers. Finally, we utilize an SVM classifier on a high-dimensional segment-level feature extracted using various functionals on short-term acoustic features, i.e., Low-Level Descriptors (LLD). An ensemble of the three different approaches provides a relative improvement of 11.13% compared to our best single sub-system in terms of the Unweighted Average Recall (UAR) performance metric on the evaluation dataset.Comment: 5 pages, 5 figures, Interspeech 2018 accepted manuscrip

Efficacy of nebulised L-adrenaline with 3% hypertonic saline versus normal saline in bronchiolitis

Background: Bronchiolitis is one of the most common respiratory diseases requiring hospitalization. Nebulized epineph­rine and salbutamol therapy has been used in different centres with varying results. Objective: The objective of the study was to compare the efficacy of nebulised adrenaline diluted with 3% hypertonic saline with nebulised adrenaline diluted with normal saline in bronchiolitis. Methods: Fifty three infants and young children with bronchiolitis, age ranging from 2 months to 2 years, presenting in the emergency department of Manikganj Sadar Hospital were enrolled in the study. After initial evaluation, patients were randomized to receive either nebulized adrenaline I .5 ml ( 1.5 mg) diluted with 2 ml of3% hypertonic saline (group I) ornebulised adrenaline 1.5 ml (1.5 mg) diluted with 2 ml of normal saline (group II). Patients were evaluated again 30 minutes after nebulization. Results: Twenty eight patients in the group I (hypertonic saline) and twenty five in groupII (normal saline) were included in the study. After nebulization, mean respiratory rate decreased from 63.7 to 48.1 (p<.01), mean clinical severity score decreased from 8.5 to 3.5 (p<.01) and mean oxygen satw·ation increased 94.7% to 96.9% (p<.01) in group I. In group II, mean respiratory rate decreased from 62.4 to 47.4 (p<.01), mean clinical severity score decreased from 7.2 to 4.1 (p<.01) and mean oxygen saturation increased from 94. 7% to 96. 7% (p<.01). Mean respiratory rate decreased by 16 in group I versus 14.8 (p>.05) in group 11, mean clinical severity score decreased by 4.6 in group versus 3 (p<.05) in group, and mean oxygen saturation increased by 2.2% and 1.9% in group and group respectively. Difference in reduction in clinical severity score was statistically significant , though the changes in respiratory rate and oxygen saturation were not statistically significant. Conclusion: The study concluded that both nebulised adrenaline diluted with 3% hypertonic saline and nebulised adrenaline with normal saline are effective in improving respiratory rate, clinical severity score and oxygen saturation in infants with bronchiolitis; and nebulised adrenaline with hypertonic saline is more effective than nebulised adrenaline with normal saline in improving clinical severity score in bronchiolitis

Anticancer Efficacy of a Difluorodiarylidenyl Piperidone (HO-3867) in Human Ovarian Cancer Cells and Tumor Xenografts

The purpose of this study was to evaluate the anticancer potency and mechanism of a novel difluorodiarylidenyl piperidone (H-4073) and its N-hydroxypyrroline modification (HO-3867) in human ovarian cancer. Studies were done using established human ovarian cancer cell lines (A2870, A2780cDDP, OV-4, SKOV3, PA-1, and OVCAR3) as well as in a murine xenograft tumor (A2780) model. Both compounds were comparably and significantly cytotoxic to A2780 cells. However, HO-3867 showed a preferential toxicity toward ovarian cancer cells while sparing healthy cells. HO-3867 induced G2-M cell cycle arrest in A2780 cells by modulating cell cycle regulatory molecules p53, p21, p27, cyclin-dependent kinase 2, and cyclin, and promoted apoptosis by caspase-8 and caspase-3 activation. It also caused an increase in the expression of functional Fas/CD95 and decreases in signal transducers and activators of transcription 3 (STAT3; Tyr705) and JAK1 phosphorylation. There was a significant reduction in STAT3 downstream target protein levels including Bcl-xL, Bcl-2, survivin, and vascular endothelial growth factor, suggesting that HO-3867 exposure disrupted the JAK/STAT3 signaling pathway. In addition, HO-3867 significantly inhibited the growth of the ovarian xenografted tumors in a dosage-dependent manner without any apparent toxicity. Western blot analysis of the xenograft tumor tissues showed that HO-3867 inhibited pSTAT3 (Tyr705 and Ser727) and JAK1 and increased apoptotic markers cleaved caspase-3 and poly ADP ribose polymerase. HO-3867 exhibited significant cytotoxicity toward ovarian cancer cells by inhibition of the JAK/STAT3 signaling pathway. The study suggested that HO-3867 may be useful as a safe and effective anticancer agent for ovarian cancer therapy