65 research outputs found
Work-Life Balance: A Study on Female Teachers of Private Education Institutions of Bangladesh
As society goes ahead in Bangladesh, there is an increasing expectation that women can not only play their roles by nurturing and raising families to ensure confident and competent future generations for the betterment of society but can also generate income by joining as paid workforce in the working place. Growing cost of living and change of mindset are the key reasons for which increasing number of educated women are now working outside their house. Even in recent time, the tendency is enhancing gradually. As a result, the traditional family is being replaced by the dual career family. Thus, socio-demographic changes are occurring similar to those in developing and developed societies. This dual responsibility is putting mounting pressure on women to achieve and maintain work life balance. In this article, a survey is conducted on 62 education institutions of Bangladesh taking sample of 320 teachers to know the real status of work-life balance institutions. The study finds that the work-life balance situation is moderate which can be improved by ensuring flexible working hours (roistered days off and family friendly starting and finishing times), transport facility, residential facility, child care center, flexible work arrangements/ job sharing , reduced working hours & workload and child schooling for the female teachers. Keywords: Work load, Job sharing, Child care, Flexible working hour, Family support.
Work-Life Balance: A Study on Female Teachers of Private Education Institutions of Bangladesh.
As society goes ahead in Bangladesh, there is an increasing expectation that women can not only play their roles by nurturing and raising families to ensure confident and competent future generations for the betterment of society but can also generate income   by joining as paid workforce in the working place. Growing cost of living as well as change of mindset is the key reasons for which increasing number of educated women are now working outside their house. Even in recent time, the tendency is increasing gradually. As a result, the traditional family is being replaced by the dualcareer family, thus socio-demographic changes are occurring similar to those in developing and developed societies. This dual responsibility is putting increasing pressure on women to achieve and maintain work life balance. In this article, a survey is conducted on 62 education institutions of Bangladesh taking sample of 320 teachers to know the real status of work-life balance institutions. The study finds that the work-life balance situation is moderate which can be improved by ensuring flexible working hours (roistered days off and family friendly starting and finishing times), transport facility, residential facility, child care center, flexible work arrangements/ job sharing , reduced working hours & workload and child schooling for the female teachers. Keywords: Work life balance, disturbance, energy, time, mood, support, Banglades
Generative Adversarial Networks for Visible to Infrared Video Conversion
Deep learning models are data driven. For example, the most popular convolutional neural network (CNN) model used for image classification or object detection requires large labeled databases for training to achieve competitive performances. This requirement is not difficult to be satisfied in the visible domain since there are lots of labeled video and image databases available nowadays. However, given the less popularity of infrared (IR) camera, the availability of labeled infrared videos or image databases is limited. Therefore, training deep learning models in infrared domain is still challenging. In this chapter, we applied the pix2pix generative adversarial network (Pix2Pix GAN) and cycle-consistent GAN (Cycle GAN) models to convert visible videos to infrared videos. The Pix2Pix GAN model requires visible-infrared image pairs for training while the Cycle GAN relaxes this constraint and requires only unpaired images from both domains. We applied the two models to an open-source database where visible and infrared videos provided by the signal multimedia and telecommunications laboratory at the Federal University of Rio de Janeiro. We evaluated conversion results by performance metrics including Inception Score (IS), Frechet Inception Distance (FID) and Kernel Inception Distance (KID). Our experiments suggest that cycle-consistent GAN is more effective than pix2pix GAN for generating IR images from optical images
Generative Adversarial Networks for Visible to Infrared Video Conversion
Deep learning models are data driven. For example, the most popular convolutional neural network (CNN) model used for image classification or object detection requires large labeled databases for training to achieve competitive performances. This requirement is not difficult to be satisfied in the visible domain since there are lots of labeled video and image databases available nowadays. However, given the less popularity of infrared (IR) camera, the availability of labeled infrared videos or image databases is limited. Therefore, training deep learning models in infrared domain is still challenging. In this chapter, we applied the pix2pix generative adversarial network (Pix2Pix GAN) and cycle-consistent GAN (Cycle GAN) models to convert visible videos to infrared videos. The Pix2Pix GAN model requires visible-infrared image pairs for training while the Cycle GAN relaxes this constraint and requires only unpaired images from both domains. We applied the two models to an open-source database where visible and infrared videos provided by the signal multimedia and telecommunications laboratory at the Federal University of Rio de Janeiro. We evaluated conversion results by performance metrics including Inception Score (IS), Frechet Inception Distance (FID) and Kernel Inception Distance (KID). Our experiments suggest that cycle-consistent GAN is more effective than pix2pix GAN for generating IR images from optical images
Quality of Education in Bangladesh: A Survey on Private Business Schools
A survey was conducted to study the customers’ (students’) evaluation of private higher education sector in Bangladesh with special reference to the quality of business education. The sample was taken from business schools on a random basis from seven private universities of Bangladesh. The respondents (students) were asked to evaluate the quality of business education at private universities in light of sixty six variables and they ranked the attributes in a seven-point Likert’s summated scale. The result of this study shows that faculty credentials, intake (student) selection system, assessment system, campus facilities, research environment, leadership of university, market orientation, and corporate attachment are associated with quality of business education. Finally, the study suggests that the policymakers and administrators should address the identified factors for ensuring quality in their business schools. Keywords: Intake, Assessment, Leadership, Faculty, Market orientation
Flood Detection Using Multi-Modal and Multi-Temporal Images: A Comparative Study
Natural disasters such as flooding can severely affect human life and property. To provide rescue through an emergency response team, we need an accurate flooding assessment of the affected area after the event. Traditionally, it requires a lot of human resources to obtain an accurate estimation of a flooded area. In this paper, we compared several traditional machine-learning approaches for flood detection including multi-layer perceptron (MLP), support vector machine (SVM), deep convolutional neural network (DCNN) with recent domain adaptation-based approaches, based on a multi-modal and multi-temporal image dataset. Specifically, we used SPOT-5 and RADAR images from the flood event that occurred in November 2000 in Gloucester, UK. Experimental results show that the domain adaptation-based approach, semi-supervised domain adaptation (SSDA) with 20 labeled data samples, achieved slightly better values of the area under the precision-recall (PR) curve (AUC) of 0.9173 and F1 score of 0.8846 than those by traditional machine approaches. However, SSDA required much less labor for ground-truth labeling and should be recommended in practice
Converting Optical Videos to Infrared Videos Using Attention GAN and Its Impact on Target Detection and Classification Performance
To apply powerful deep-learning-based algorithms for object detection and classification in infrared videos, it is necessary to have more training data in order to build high-performance models. However, in many surveillance applications, one can have a lot more optical videos than infrared videos. This lack of IR video datasets can be mitigated if optical-to-infrared video conversion is possible. In this paper, we present a new approach for converting optical videos to infrared videos using deep learning. The basic idea is to focus on target areas using attention generative adversarial network (attention GAN), which will preserve the fidelity of target areas. The approach does not require paired images. The performance of the proposed attention GAN has been demonstrated using objective and subjective evaluations. Most importantly, the impact of attention GAN has been demonstrated in improved target detection and classification performance using real-infrared videos
Ship Deck Segmentation in Engineering Document Using Generative Adversarial Networks
Generative adversarial networks (GANs) have become very popular in recent years. GANs have proved to be successful in different computer vision tasks including image-translation, image super-resolution etc. In this paper, we have used GAN models for ship deck segmentation. We have used 2D scanned raster images of ship decks provided by US Navy Military Sealift Command (MSC) to extract necessary information including ship walls, objects etc. Our segmentation results will be helpful to get vector and 3D image of a ship that can be later used for maintenance of the ship. We applied the trained models to engineering documents provided by MSC and obtained very promising results, demonstrating that GANs can be potentially good candidates for this research area
Extracellular vesicles-mediated intercellular communication: roles in the tumor microenvironment and anti-cancer drug resistance.
The tumor microenvironment represents a complex network, in which tumor cells not only communicate with each other but also with stromal and immune cells. Current research has demonstrated the vital role of the tumor microenvironment in supporting tumor phenotype via a sophisticated system of intercellular communication through direct cell-to-cell contact or by classical paracrine signaling loops of cytokines or growth factors. Recently, extracellular vesicles have emerged as an important mechanism of cellular interchange of bioactive molecules. Extracellular vesicles isolated from tumor and stromal cells have been implicated in various steps of tumor progression, such as proliferation, angiogenesis, metastasis, and drug resistance. Inhibition of extracellular vesicles secretion, and thus of the transfer of oncogenic molecules, holds promise for preventing tumor growth and drug resistance. This review focuses on the role of extracellular vesicles in modulating the tumor microenvironment by addressing different aspects of the bidirectional interactions among tumor and tumor-associated cells. The contribution of extracellular vesicles to drug resistance will also be discussed as well as therapeutic strategies targeting extracellular vesicles production for the treatment of cancer
Association of genes with phenotype in autism spectrum disorder.
Autism spectrum disorder (ASD) is a genetic heterogeneous neurodevelopmental disorder that is characterized by impairments in social interaction and speech development and is accompanied by stereotypical behaviors such as body rocking, hand flapping, spinning objects, sniffing and restricted behaviors. The considerable significance of the genetics associated with autism has led to the identification of many risk genes for ASD used for the probing of ASD specificity and shared cognitive features over the past few decades. Identification of ASD risk genes helps to unravel various genetic variants and signaling pathways which are involved in ASD. This review highlights the role of ASD risk genes in gene transcription and translation regulation processes, as well as neuronal activity modulation, synaptic plasticity, disrupted key biological signaling pathways, and the novel candidate genes that play a significant role in the pathophysiology of ASD. The current emphasis on autism spectrum disorders has generated new opportunities in the field of neuroscience, and further advancements in the identification of different biomarkers, risk genes, and genetic pathways can help in the early diagnosis and development of new clinical and pharmacological treatments for ASD
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