3,211 research outputs found

    Measuring Multidimensional Parameters of Poverty Using Alkire and Foster Methodology in Qasimabad: A Case Study

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    This paper measures the multidimensional poverty using Alkire and Foster methodology for ten regions of Qasimabad on the primary data. No one indicator alone gives us clear picture of poverty as poverty is multidimensional in nature. We have taken three dimensions having equal weights, education, health and living standard. These dimensions are further divided in ten indicators, two for each, education and health, and six for living standards. Results suggest that region Gul Baig Chandio has the highest multidimensional poverty whereas Muslim Society has the lowest multidimensional poverty among the selected regions of Qasimabad. Results further suggest that the indicators which contribute more to multidimensional poverty are life expectancy, year of schooling, Assets, Improved sanitation, child mortality, flooring and child school attendance. Analyzing the data we came to know that the Percentage of people who are MPI poor in Qasimabad is 45(Incidence of poverty), whereas their average deprivations are 43.27% .Furthermore, Multidimensional poverty Index (MPI) is 19.47% in Qasimabad. Keywords: Multidimensional poverty, incidence of poverty, Average deprivatio

    AMCTD: Adaptive Mobility of Courier nodes in Threshold-optimized DBR Protocol for Underwater Wireless Sensor Networks

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    In dense underwater sensor networks (UWSN), the major confronts are high error probability, incessant variation in topology of sensor nodes, and much energy consumption for data transmission. However, there are some remarkable applications of UWSN such as management of seabed and oil reservoirs, exploration of deep sea situation and prevention of aqueous disasters. In order to accomplish these applications, ignorance of the limitations of acoustic communications such as high delay and low bandwidth is not feasible. In this paper, we propose Adaptive mobility of Courier nodes in Threshold-optimized Depth-based routing (AMCTD), exploring the proficient amendments in depth threshold and implementing the optimal weight function to achieve longer network lifetime. We segregate our scheme in 3 major phases of weight updating, depth threshold variation and adaptive mobility of courier nodes. During data forwarding, we provide the framework for alterations in threshold to cope with the sparse condition of network. We ultimately perform detailed simulations to scrutinize the performance of our proposed scheme and its comparison with other two notable routing protocols in term of network lifetime and other essential parameters. The simulations results verify that our scheme performs better than the other techniques and near to optimal in the field of UWSN.Comment: 8th International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA'13), Compiegne, Franc

    Birth preparedness among antenatal clients

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    Objective: To evaluate birth preparedness and complication readiness among antenatal care clients. Design: A descriptive cross- sectional study. Setting: Antenatal care clinic at Kenyatta National Hospital, Nairobi, Kenya. Subjects: Three hundred and ninety four women attending antenatal care at Kenyatta National hospital were interviewed using a pre-tested questionnaire between May 2006 and August 2006. Clients who were above 32 weeks gestation and had attended the clinic more than twice were recruited. Systematic sampling was used to select the study participants with every third client being interviewed. Main outcome measures: Health education on birth preparedness, knowledge of danger signs, preparations for delivery and emergencies. Results: Over 60% of the respondents were counselled by health workers on various elements of birth preparedness. Eighty seven point three per cent of the respondents were aware of their expected date of delivery, 84.3% had set aside funds for transport to hospital during labour while 62.9% had funds for emergencies. Sixty seven per cent of the respondents knew at least one danger sign in pregnancy while only 6.9% knew of three or more danger signs. One hundred and nine per cent of the respondents did not have a clear plan of what to do in case of an obstetric emergency. Level of education positively influenced birth preparedness. Conclusions: Education and counselling on different aspects of birth preparedness was not provided to all clients. Respondents knowledge of danger signs in pregnancy was low. Many respondents did not know about birth preparedness and had no plans for emergencies. East African Medical Journla Vol. 85 (6) 2008: pp. 275-28

    Knowledge about hepatitis B and C among patients attending family medicine clinics in Karachi

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    Knowledge about hepatitis B and C was assessed in a cross-sectional study of 300 adults aged 18 or older attending family medicine clinics at The Aga Khan University Hospital, Karachi. Most knew that hepatitis B and C are viral diseases that primarily affect the liver, but knowledge about risk factors for disease transmission was poor. Approximately 70% knew that hepatitis B is vaccine preventable; 60% had the misconception that hepatitis C is also vaccine preventable. The majority incorrectly believed that people with hepatitis B or C should follow the diet \u27parhaiz\u27. Generally women knew more than men about the diseases. This study suggests that health education about these infections should be provided to the public. Family physicians can play an important role in educating people about the prevention of these diseases

    SpACNN-LDVAE: Spatial Attention Convolutional Latent Dirichlet Variational Autoencoder for Hyperspectral Pixel Unmixing

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    The Hyperspectral Unxming problem is to find the pure spectral signal of the underlying materials (endmembers) and their proportions (abundances). The proposed method builds upon the recently proposed method, Latent Dirichlet Variational Autoencoder (LDVAE). It assumes that abundances can be encoded as Dirichlet Distributions while mixed pixels and endmembers are represented by Multivariate Normal Distributions. However, LDVAE does not leverage spatial information present in an HSI; we propose an Isotropic CNN encoder with spatial attention to solve the hyperspectral unmixing problem. We evaluated our model on Samson, Hydice Urban, Cuprite, and OnTech-HSI-Syn-21 datasets. Our model also leverages the transfer learning paradigm for Cuprite Dataset, where we train the model on synthetic data and evaluate it on real-world data. We are able to observe the improvement in the results for the endmember extraction and abundance estimation by incorporating the spatial information. Code can be found at https://github.com/faisalqureshi/cnn-ldva
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