Emerging challenges and health system capacity: the case of non- communicable diseases in Pakistan; a review

Background: Pakistan is facing double burden of disease and the contribution of mortality by non-communicable diseases has over numbered the communicable diseases. The focus of health system of Pakistan is inclined more towards communicable disease and maternal & child health. Therefore, there is a need to review health policy, health sector budgeting and health setup in order to meet the needs of healthcare in context of non-communicable disease. Objective: To review the health system capacity to manage the emerging challenge of non-communicable diseases in Pakistan. Methodology: A thorough literature search on PubMed and Google Scholar was done. Reports from W.H.O, other national and international organizations and government & non-government policy papers were also reviewed. We used following search terms; Non Communicable Disease, Health system capacity, Pakistan. Results: Several health system issues emerged through the review of the health system capacity for NCDs. These included lack of political commitment, services more focused on communicable disease and MNCH, inadequate human resources, lack of inter-sectoral approach, insufficient funding opportunities and fragmented health system. These issues can be addressed through government support for combating burden of NCDs, provision of services for NCD at PHC level, human resource training regarding NCDs and integrated care system. Finance should be allocated for NCDs and existing HMIS should also be used for utilizing information regarding NCDs. Conclusion: Health systems framework to NCD means in summary re-examining the planning and organization of the entire health system, from service provision to financing, from information generation to ensuring adequate supply of pharmaceuticals/technologies or human resources, from improving facility management to performance monitoring

A Quantum Key Distribution Network Through Single Mode Optical Fiber

Quantum key distribution (QKD) has been developed within the last decade that is provably secure against arbitrary computing power, and even against quantum computer attacks. Now there is a strong need of research to exploit this technology in the existing communication networks. In this paper we have presented various experimental results pertaining to QKD like Raw key rate and Quantum bit error rate (QBER). We found these results over 25 km single mode optical fiber. The experimental setup implemented the enhanced version of BB84 QKD protocol. Based upon the results obtained, we have presented a network design which can be implemented for the realization of large scale QKD networks. Furthermore, several new ideas are presented and discussed to integrate the QKD technique in the classical communication networks.Comment: This paper has been submitted to the 2006 International Symposium on Collaborative Technologies and Systems (CTS 2006)May 14-17, 2006, Las Vegas, Nevada, US

Antecedents of Dividend Policy: Empirical Evidence from Banking Sector of Pakistan

This paper explores the determinants of dividend policy of commercial banks operating in Pakistan. Dividend decision of any bank primarily depends upon its profitability, retained earnings, cash flows, corporate taxes and leverage. This study is an attempt to find out key determinants and their impact on cash payout and total payout ratios. It also aims to test the implication of dividend theories on Pakistani banks using data for a period of 8 years ranging from 2006 to 2013. Balanced panel data regression with fixed effects model has been used in this study. All independent variables - PAT, SLACK, EPS, CTA and TD[1] reported significant results. We found significant role of profitability theory, packing order theory, free cash flow theory and agency cost theory in determining dividend policies whereas, tax effect and financial slack has no effect in banking sector of Pakistan. [1] Profitability, retained earnings, earnings per share, cash flows, and leverag

Geotechnical characteristics of effluent contaminated cohesive soils

In developing countries like Pakistan, raw industrial effluents are usually disposed-off directly into open lands or in water bodies resulting in soil contamination. Leachate formation due to rainfalls in openly dumped solid waste also adds to soil contamination. In this study, engineering behavior of soils contaminated by two industrial effluents, one from paper industry (acidic) and another from textile industry (basic), has been investigated. Laboratory testing revealed significant effects of effluent contamination on engineering behavior of tested soils. Liquid limit, plasticity index, optimum moisture content and compression index of tested soils were found to increase with effluent contaminant, indicating a deterioration in the engineering behavior of soils. Whereas maximum dry density, undrained shear strength and coefficient of consolidation of the contaminated soils showed a decreasing trend. The dilapidation in engineering characteristics of soils due to the addition of industrial effluents could pose serious threats to existing and future foundations in terms of loss of bearing capacity and increase in settlement. Keywords: soil contamination, industrial waste, engineering behavior, effluent waste, leachate. First published online: 28 Nov 201

Adaptive Input-image Normalization for Solving the Mode Collapse Problem in GAN-based X-ray Images

Biomedical image datasets can be imbalanced due to the rarity of targeted diseases. Generative Adversarial Networks play a key role in addressing this imbalance by enabling the generation of synthetic images to augment datasets. It is important to generate synthetic images that incorporate a diverse range of features to accurately represent the distribution of features present in the training imagery. Furthermore, the absence of diverse features in synthetic images can degrade the performance of machine learning classifiers. The mode collapse problem impacts Generative Adversarial Networks' capacity to generate diversified images. Mode collapse comes in two varieties: intra-class and inter-class. In this paper, both varieties of the mode collapse problem are investigated, and their subsequent impact on the diversity of synthetic X-ray images is evaluated. This work contributes an empirical demonstration of the benefits of integrating the adaptive input-image normalization with the Deep Convolutional GAN and Auxiliary Classifier GAN to alleviate the mode collapse problems. Synthetically generated images are utilized for data augmentation and training a Vision Transformer model. The classification performance of the model is evaluated using accuracy, recall, and precision scores. Results demonstrate that the DCGAN and the ACGAN with adaptive input-image normalization outperform the DCGAN and ACGAN with un-normalized X-ray images as evidenced by the superior diversity scores and classification scores.Comment: Submitted to the Elsevier Journa