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The article aims to assess the governance of the social safety net programme in Bangladesh. The data were collected from the beneficiaries and stakeholders of the employment generation programme for the poorest scheme of the government of Bangladesh. A total of 150 respondents were selected randomly, and data and information were collected using a structured questionnaire for interviews and a guideline for focus group discussions. The analysis shows that beneficiaries and stakeholders of the programme are mostly satisfied with the quality and timeliness of work, beneficiaries selection and benefits distribution process, but many respondents expressed their dissatisfaction with monitoring and wage rates. Furthermore, the research reveals several factors that contribute to establishing poor governance, such as weak monitoring, absenteeism, proxy work and inappropriate project selection. The finding suggests that it is necessary to make local-level institutions fully operational, determine district-wise wage rates and enhance the monitoring process to achieve the programme objectives.

Flower classification is a challenging task due to the wide range of flower species, which have a similar shape, appearance or surrounding objects such as leaves and grass. In this study, the authors propose a novel two-step deep learning classifier to distinguish flowers of a wide range of species. First, the flower region is automatically segmented to allow localisation of the minimum bounding box around it. The proposed flower segmentation approach is modelled as a binary classifier in a fully convolutional network framework. Second, they build a robust convolutional neural network classifier to distinguish the different flower types. They propose novel steps during the training stage to ensure robust, accurate and real-time classification. They evaluate their method on three well known flower datasets. Their classification results exceed 97% on all datasets, which are better than the state-of-the-art in this domain.

Background Ultrasound imaging is able to quantify carotid arterial wall structure for the assessment of cerebral and cardiovascular disease risks. We describe a protocol and quality assurance process to enable carotid imaging at large scale that has been developed for the UK Biobank Imaging Enhancement Study of 100,000 individuals.Design An imaging protocol was developed to allow measurement of carotid intima-media thickness from the far wall of both common carotid arteries. Six quality assurance criteria were defined and a web-based interface (Intelligent Ultrasound) was developed to facilitate rapid assessment of images against each criterion.Results and conclusions Excellent inter and intra-observer agreements were obtained for image quality evaluations on a test dataset from 100 individuals. The image quality criteria then were applied in the UK Biobank Imaging Enhancement Study. Data from 2560 participants were evaluated. Feedback of results to the imaging team led to improvement in quality assurance, with quality assurance failures falling from 16.2% in the first two-month period examined to 6.4% in the last. Eighty per cent had all carotid intima-media thickness images graded as of acceptable quality, with at least one image acceptable for 98% of participants. Carotid intima-media thickness measures showed expected associations with increasing age and gender. Carotid imaging can be performed consistently, with semi-automated quality assurance of all scans, in a limited timeframe within a large scale multimodality imaging assessment. Routine feedback of quality control metrics to operators can improve the quality of the data collection.