74 research outputs found
Political Economy of Local Level Budgeting in Bangladesh: A Critical Analysis
Accompanying the estimation of revenue and expenditure, the local level budget manifests a grassroots area's development plan for a defined period. In Bangladesh, the Union Parishad (UP), the lowest tier of local government, is obliged to prepare its annual budget by ensuring people's participation through various mechanisms and committees following the Local Government (Union Parishad) Act, 2009. With this background, the study explores the UPs' budgeting procedure to identify the influential actors and effectiveness of people's participation from political economy perspectives. Following the qualitative case study approach, this study was conducted on the four Union Parishads in Sylhet, Sunamganj, Cumilla and Narsingdi districts. It follows the in-depth interview and Focus Group Discussion techniques to collect primary data from the UP chairman, members, Upazila Nirbahi Officers (UNO), and other members of various UP committees. The finding shows that mechanisms like Ward Committee (WC), Ward Shava (WS), Standing Committee (SC), Planning Committee (PC) and Social Mapping for ensuring people's participation are not working accordingly. In most cases, these committees are confined to the papers. In practice, the budgeting procedure is dominated by the politically and economically empowered groups, e.g. UP chairman and his allies, ruling party members, local elites and bureaucrats, which is hindering the socio-economic development at the grass-root level in Bangladesh
Zoom-in-Net: Deep Mining Lesions for Diabetic Retinopathy Detection
We propose a convolution neural network based algorithm for simultaneously
diagnosing diabetic retinopathy and highlighting suspicious regions. Our
contributions are two folds: 1) a network termed Zoom-in-Net which mimics the
zoom-in process of a clinician to examine the retinal images. Trained with only
image-level supervisions, Zoomin-Net can generate attention maps which
highlight suspicious regions, and predicts the disease level accurately based
on both the whole image and its high resolution suspicious patches. 2) Only
four bounding boxes generated from the automatically learned attention maps are
enough to cover 80% of the lesions labeled by an experienced ophthalmologist,
which shows good localization ability of the attention maps. By clustering
features at high response locations on the attention maps, we discover
meaningful clusters which contain potential lesions in diabetic retinopathy.
Experiments show that our algorithm outperform the state-of-the-art methods on
two datasets, EyePACS and Messidor.Comment: accepted by MICCAI 201
Numerical Study of a Three-Bed Adsorption Chiller Employing an Advanced Mass Recovery Process
The performance improvement of a three-bed (equal bed) adsorption chiller employing an advanced mass recovery process has been numerically studied in this paper. The mass recovery scheme is used to improve the cooling effect and a CFC-free-based sorption chiller driven by the low-grade waste heat or any renewable energy source can be developed for the next generation of refrigeration. Silica gel/water is taken as adsorbent/adsorbate pair for the present chiller. The three-bed adsorption chiller comprises with three adsorber/desorber heat exchanger, one evaporator and one condenser. In the present numerical solution, the heat source temperature variation is taken from 500C to 900C along with coolant inlet temperature at 300C and the chilled water inlet temperature at 140C. In the new strategy, if any one bed (3rd bed) is connect with the evaporator during pre-heating or pre-cooling time then it will give better performance than that of existing system. In this strategy, mass recovery process also occurs in all bed. A cycle simulation computer program is constructed to analyze the influence of operating conditions (hot and cooling water temperature) on COP (coefficient of performance), and CC (cooling capacity)
FORMULATION AND DEVELOPMENT OF MICROSPHERES FOR THE TREATMENT OF FAMILIAL ADENOMATOUS POLYPOSIS
Objective: Familial adenomatous polyposis (FAP) also known as familial polyposis coli, is a hereditary disease characterized by progressive appearance of numerous polyps mainly in the large intestine. Polyps are initially benign but can easily become cancerous and as such it is a life threatening condition. Celecoxib, a cyclooxygenase-2 (COX-2) inhibitor is thought to induce cell death, and thus prevent or delay the growth of polyps. So in the present study celecoxib loaded microspheres were prepared using control release Hydroxy propyl methyl cellulose (HPMC K4M) and pH dependent polymer eudragit L 100-55 in different ratios (1:1 to 1:4) respectively. The main objective of the study is to identify the polymer concentration required to prevent the drug release in stomach region and promotes in intestinal region.Methods: Emulsification solvent evaporation method was selected for the preparation and all the optimized formulations were evaluated for drug-polymer interactions, percentage yield, micrometric properties, entrapment efficiency, particle size analysis, differential scanning calorimetry and in vitro dissolution study.Results: Drug and polymer interactions were evaluated by using FTIR and DSC. The FTIR spectrum and DSC thermograms stated that drug and polymer are compatible to each other. The micrometric properties of drug loaded microspheres were carried out and they were found to be as the angle of repose (18.26 °-40.69 °), bulk density (0.2846-0.3875), tapped density (0.4111-0.5428), Carr's index (9.66-14.77), Hausner's ratio (1.112-1.2642) which were within the limits. In vitro dissolution, drug release was found to be from 4.5 to 6.5 h for the prepared four formulations (F1–F4). From the kinetic data modeling the order of drug release was found to be zero order and korsmeyer-peppas with n value above 0.5 for all the formulations indicating non-fickian diffusion.Conclusion: All the result demonstrated that celecoxib microspheres can be effectively used in the treatment of familial adenomatous polyposi
Transforming strategies in the digital era: The role of social media in customer value analysis and crisis management for tourism firms
Social media is gaining popularity nowadays and is increasingly being used by many small and large organisations. Organisations are adopting new social platforms and technologies to achieve their key and effective management strategies. However, there are still opportunities to explore the role of new technologies in developing useful strategies. In current research, the utilisation of technological tools especially social media was examined to improve the customer value analysis in the organisations. Besides, the research of social media use for crisis management is also increasing and the relevant strategies are still being-investigated. To overcome this gap, this research aims to evaluate the impact of the use of social media on customer value analysis and crisis management. To attain this, a detailed questionnaire was adapted from several different studies. Data were collected from a diverse targeted sample of tourism-related firms from all over Malaysia, including hotels, resorts, travel agencies and transportation companies. The model was tested using Smart PLS software and the results were generalised. Overall, this research will add a noteworthy contribution to the literature by highlighting the significance of social media and recognising its urgency during crisis for businesses. It will also help in answering questions regarding the role of social media usage towards customer value analysis and crisis management of organisations in the Malaysian tourism sector. Moreover, the practitioners will use the findings to make strategies for crisis management and build customer value chain
Combining Fine- and Coarse-Grained Classifiers for Diabetic Retinopathy Detection
Visual artefacts of early diabetic retinopathy in retinal fundus images are
usually small in size, inconspicuous, and scattered all over retina. Detecting
diabetic retinopathy requires physicians to look at the whole image and fixate
on some specific regions to locate potential biomarkers of the disease.
Therefore, getting inspiration from ophthalmologist, we propose to combine
coarse-grained classifiers that detect discriminating features from the whole
images, with a recent breed of fine-grained classifiers that discover and pay
particular attention to pathologically significant regions. To evaluate the
performance of this proposed ensemble, we used publicly available EyePACS and
Messidor datasets. Extensive experimentation for binary, ternary and quaternary
classification shows that this ensemble largely outperforms individual image
classifiers as well as most of the published works in most training setups for
diabetic retinopathy detection. Furthermore, the performance of fine-grained
classifiers is found notably superior than coarse-grained image classifiers
encouraging the development of task-oriented fine-grained classifiers modelled
after specialist ophthalmologists.Comment: Pages 12, Figures
An Explainable AI-Based Computer Aided Detection System for Diabetic Retinopathy Using Retinal Fundus Images
Diabetic patients have a high risk of developing diabetic retinopathy (DR), which is one of the major causes of blindness. With early detection and the right treatment patients may be spared from losing their vision. We propose a computer-aided detection system, which uses retinal fundus images as input and it detects all types of lesions that define diabetic retinopathy. The aim of our system is to assist eye specialists by automatically detecting the healthy retinas and referring the images of the unhealthy ones. For the latter cases, the system offers an interactive tool where the doctor can examine the local lesions that our system marks as suspicious. The final decision remains in the hands of the ophthalmologists. Our approach consists of a multi-class detector, that is able to locate and recognize all candidate DR-defining lesions. If the system detects at least one lesion, then the image is marked as unhealthy. The lesion detector is built on the faster R-CNN ResNet 101 architecture, which we train by transfer learning. We evaluate our approach on three benchmark data sets, namely Messidor-2, IDRiD, and E-Ophtha by measuring the sensitivity (SE) and specificity (SP) based on the binary classification of healthy and unhealthy images. The results that we obtain for Messidor-2 and IDRiD are (SE: 0.965, SP: 0.843), and (SE: 0.83, SP: 0.94), respectively. For the E-Ophtha data set we follow the literature and perform two experiments, one where we detect only lesions of the type micro aneurysms (SE: 0.939, SP: 0.82) and the other when we detect only exudates (SE: 0.851, SP: 0.971). Besides the high effectiveness that we achieve, the other important contribution of our work is the interactive tool, which we offer to the medical experts, highlighting all suspicious lesions detected by the proposed system.<br/
Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification
Diabetic Retinopathy (DR) is one of the microvascular complications of
Diabetes Mellitus, which remains as one of the leading causes of blindness
worldwide. Computational models based on Convolutional Neural Networks
represent the state of the art for the automatic detection of DR using eye
fundus images. Most of the current work address this problem as a binary
classification task. However, including the grade estimation and quantification
of predictions uncertainty can potentially increase the robustness of the
model. In this paper, a hybrid Deep Learning-Gaussian process method for DR
diagnosis and uncertainty quantification is presented. This method combines the
representational power of deep learning, with the ability to generalize from
small datasets of Gaussian process models. The results show that uncertainty
quantification in the predictions improves the interpretability of the method
as a diagnostic support tool. The source code to replicate the experiments is
publicly available at https://github.com/stoledoc/DLGP-DR-Diagnosis
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The Impact of Community Masking on COVID-19: A Cluster Randomized Trial in Bangladesh
Background: Mask usage remains low across many parts of the world during the COVID- 19 pandemic, and strategies to increase mask-wearing remain untested. Our objectives were to identify strategies that can persistently increase mask-wearing and assess the impact of increasing mask-wearing on symptomatic SARS-CoV-2 infections. Methods: We conducted a cluster-randomized trial of community-level mask promotion in rural Bangladesh from November 2020 to April 2021 (N=600 villages, N=342,126 adults). We cross-randomized mask promotion strategies at the village and household level, including cloth vs. surgical masks. All intervention arms received free masks, information on the importance of masking, role modeling by community leaders, and in-person reminders for 8 weeks. The control group did not receive any interventions. Neither participants nor field staff were blinded to intervention assignment. Outcomes included symptomatic SARS-CoV-2 seroprevalence (primary) and prevalence of proper mask-wearing, physical distancing, and symptoms consistent with COVID-19 (secondary). Mask-wearing and physical distancing were assessed through direct observation at least weekly at mosques, markets, the main entrance roads to villages, and tea stalls. At 5 and 9 weeks follow-up, we surveyed all reachable participants about COVID-related symptoms. Blood samples collected at 10-12 weeks of follow-up for symptomatic individuals were analyzed for SARS-CoV-2 IgG antibodies. Results: There were 178,288 individuals in the intervention group and 163,838 individuals in the control group. The intervention increased proper mask-wearing from 13.3% in control villages (N=806,547 observations) to 42.3% in treatment villages (N=797,715 observations) (adjusted percentage point difference = 0.29 [0.27, 0.31]). This tripling of mask usage was sustained during the intervention period and two weeks after. Physical distancing increased from 24.1% in control villages to 29.2% in treatment villages (adjusted percentage point difference = 0.05 [0.04, 0.06]). After 5 months, the impact of the intervention faded, but mask-wearing remained 10 percentage points higher in the intervention group. The proportion of individuals with COVID-like symptoms was 7.62% (N=13,273) in the intervention arm and 8.62% (N=13,893) in the control arm. Blood samples were collected from N=10,952 consenting, symptomatic individuals. Adjusting for baseline covariates, the intervention reduced symptomatic seroprevalence by 9.3% (adjusted prevalence ratio (aPR) = 0.91 [0.82, 1.00]; control prevalence 0.76%; treatment prevalence 0.68%). In villages randomized to surgical masks (n = 200), the relative reduction was 11.2% overall (aPR = 0.89 [0.78, 1.00]) and 34.7% among individuals 60+ (aPR = 0.65 [0.46, 0.85]). No adverse events were reported. Conclusions: Our intervention demonstrates a scalable and effective method to promote mask adoption and reduce symptomatic SARS-CoV-2 infections. Trial registration: ClinicalTrials.gov Identifier: NCT04630054 Funding: GiveWell.or
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