Effects of Human Capital on Farm and Non-Farm Productivity and Occupational Stratification in Rural Pakistan

This paper investigates the effects of human capital on productivity using micro panel data of rural households in the NorthWest Frontier Province, Pakistan, where a substantial job stratification is observed in terms of income and education. To clarify the mechanism underlying this stratification, the human capital effects are estimated for wages(individual level) and for self-employed activities(household level), and for farm and non-farm sectors. Estimation results show a clear contrast between farm and non-farm sectors - wages and productivity in non-farm activities rise with education at an increasing rate, whereas those in agriculture respond only to the primary education.human capital, returns to education, non-farm employment, self-employment

Impact of Micro Hydropower Projects on Household Income, Expenditure and Diversification of Livelihood Strategies in Azad Jammu and Kashmir

The study examines the impact of Micro Hydropower (MHP) projects on households’ income, consumption and diversification of livelihood strategies in District Hattian Bala, Azad Jammu and Kashmir. A multinomial logistic model is used to investigate the possible role of MHP and other control variables on households’ adoption of livelihood strategies. The Results show that MHP-micro hydropower has a positive significant effect on household’s adoption of non-farm and diversified livelihood strategies. These findings suggest that MHP projects in Northern areas of Pakistan could help in improving household’s income and consumption through adoption of high income livelihood strategies. Keywords: Micro Hydropower (MHP), Livelihood Strategies, Income and Expenditures, Poverty Alleviation, Multinomial Logistic Mode

Natural Disasters, Relief Aid, and Household Vulnerability in Pakistan: Evidence from a Pilot Survey in Khyber Pakhtunkhwa

Based on a pilot survey, we analyze the damages caused by floods in Pakistan, 2010, the istribution of aid, and the extent of recovery at he household level. With regard to the nature of damages, we show that flood damages had both between-village and within-village variation, and damages to houses, land (crops), livestock, and other business assets were not highly correlated. In the distribution of aid from outside, we again find substantial between-village and within-village variation - the aid distribution across villages appeared well-targeted toward the severely affected villages, while aid within villages was targeted toward households with larger house damages, but not toward households with larger damages to land, crop, or other assets. The positive aid response to house damages and the negative aid response to the initial wealth level were found but the marginal response of aid to these characteristics was not large. With regard to the recovery from flood damages, we find that aid recipients did not show higher or lower recovery than non-recipients, especially for house damages, which could be due to mixing of a recovery-promoting effect of aid and a selection effect of aid toward households that have more difficulty in recovery. We also show that households who had initially fewer assets and hit by larger flood damages had more difficulty in recovery.natural disaster, relief distribution, resilience, Pakistan

Visual-Inertial first responder localisation in large-scale indoor training environments.

Accurately and reliably determining the position and heading of first responders undertaking training exercises can provide valuable insights into their situational awareness and give a larger context to the decisions made. Measuring first responder movement, however, requires an accurate and portable localisation system. Training exercises of- ten take place in large-scale indoor environments with limited power infrastructure to support localisation. Indoor positioning technologies that use radio or sound waves for localisation require an extensive network of transmitters or receivers to be installed within the environment to ensure reliable coverage. These technologies also need power sources to operate, making their use impractical for this application. Inertial sensors are infrastructure independent, low cost, and low power positioning devices which are attached to the person or object being tracked, but their localisation accuracy deteriorates over long-term tracking due to intrinsic biases and sensor noise. This thesis investigates how inertial sensor tracking can be improved by providing correction from a visual sensor that uses passive infrastructure (fiducial markers) to calculate accurate position and heading values. Even though using a visual sensor increase the accuracy of the localisation system, combining them with inertial sensors is not trivial, especially when mounted on different parts of the human body and going through different motion dynamics. Additionally, visual sensors have higher energy consumption, requiring more batteries to be carried by the first responder. This thesis presents a novel sensor fusion approach by loosely coupling visual and inertial sensors to create a positioning system that accurately localises walking humans in largescale indoor environments. Experimental evaluation of the devised localisation system indicates sub-metre accuracy for a 250m long indoor trajectory. The thesis also proposes two methods to improve the energy efficiency of the localisation system. The first is a distance-based error correction approach which uses distance estimation from the foot-mounted inertial sensor to reduce the number of corrections required from the visual sensor. Results indicate a 70% decrease in energy consumption while maintaining submetre localisation accuracy. The second method is a motion type adaptive error correction approach, which uses the human walking motion type (forward, backward, or sideways) as an input to further optimise the energy efficiency of the localisation system by modulating the operation of the visual sensor. Results of this approach indicate a 25% reduction in the number of corrections required to keep submetre localisation accuracy. Overall, this thesis advances the state of the art by providing a sensor fusion solution for long-term submetre accurate localisation and methods to reduce the energy consumption, making it more practical for use in first responder training exercises

A Vector Error Correction Model (VECM) Approach in explaining the relationship between Fixed Investment and Economic Growth in Rural China

A rural economy can be affected by fixed investment in a rural area positively or negatively Investment in fixed assets is one of the core measures of capital spending in rural China and the rural economy is a prominent part of china s national economy It is important to study the dynamic relationship between fixed investment and economic growth in rural China Based on time-series data from 1990 to 2016 this paper employed a Vector Error Correction Model VECM approach to lead the stationarity test Cointegration test stability test and granger causality test The result indicated that in the long term Fixed Investment fluctuation promotes GDP growth in rural China while GDP fluctuation is not the source of fixed investment increase in rural Chin

Flow-Aware Elephant Flow Detection for Software-Defined Networks

Software-defined networking (SDN) separates the network control plane from the packet forwarding plane, which provides comprehensive network-state visibility for better network management and resilience. Traffic classification, particularly for elephant flow detection, can lead to improved flow control and resource provisioning in SDN networks. Existing elephant flow detection techniques use pre-set thresholds that cannot scale with the changes in the traffic concept and distribution. This paper proposes a flow-aware elephant flow detection applied to SDN. The proposed technique employs two classifiers, each respectively on SDN switches and controller, to achieve accurate elephant flow detection efficiently. Moreover, this technique allows sharing the elephant flow classification tasks between the controller and switches. Hence, most mice flows can be filtered in the switches, thus avoiding the need to send large numbers of classification requests and signaling messages to the controller. Experimental findings reveal that the proposed technique outperforms contemporary methods in terms of the running time, accuracy, F-measure, and recall

Toxoplasma gondii specific IgG avidity assay: Role and implication in the confirmatory diagnosis of acute toxoplasmosis in seropositive pregnant women

This study was undertaken to apply Toxoplasma gondii specific IgG avidity test in seropositive pregnant women to differentiate acute and past infection. T. gondii specific IgG avidity test was conducted in 39 seropositive pregnant women and their pregnancy outcomes were observed later on. Out of 39 T. gondii seropositive pregnant women 33 (84%) were only IgG positive and 6 (15.4%) were both IgG-IgM positive. All the IgG positive cases (100%) and 2(33.3%) IgG-IgM positive cases had high avidity antibodies and they gave birth to healthy babies. Rest of the 4 (66.7%) IgG-IgM positive women had low avidity and 50% of them had abortion and 50% gave birth to unhealthy babies. This reveals that the seropositive mothers having high IgG avidity had past infection and no risk of congenital transmission. Seropositive mothers having low IgG avidity had acute infection and so congenital transmission occurred. Presence of T. gondii specific IgG and IgM antibody does not indicate acute infection always. IgG-IgM positive pregnant women should be further evaluated by IgG avidity assay to confirm acute infection.

An Ensemble of Transfer, Semi-supervised and Supervised Learning Methods for Pathological Heart Sound Classification

In this work, we propose an ensemble of classifiers to distinguish between various degrees of abnormalities of the heart using Phonocardiogram (PCG) signals acquired using digital stethoscopes in a clinical setting, for the INTERSPEECH 2018 Computational Paralinguistics (ComParE) Heart Beats SubChallenge. Our primary classification framework constitutes a convolutional neural network with 1D-CNN time-convolution (tConv) layers, which uses features transferred from a model trained on the 2016 Physionet Heart Sound Database. We also employ a Representation Learning (RL) approach to generate features in an unsupervised manner using Deep Recurrent Autoencoders and use Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) classifiers. Finally, we utilize an SVM classifier on a high-dimensional segment-level feature extracted using various functionals on short-term acoustic features, i.e., Low-Level Descriptors (LLD). An ensemble of the three different approaches provides a relative improvement of 11.13% compared to our best single sub-system in terms of the Unweighted Average Recall (UAR) performance metric on the evaluation dataset.Comment: 5 pages, 5 figures, Interspeech 2018 accepted manuscrip

Assessment of Urban Sprawl in Sargodha City using Remotely Sense Data

The current study focuses on tracking urban sprawl in one of the rapidly growing cities of Pakistan i.e., Sargodha. The secondary cities have the capacity to persuade people from rural areas to relocate. In this regard, the current study is unique in that it will give a comprehensive analysis of urban sprawl of Sargodha City. The remotely sensed data is used for this purpose. The study is primarily based on the collecting of both primary and secondary data. For the last 30 years, from 1987 to 2017, primary data was gathered from the United States Geological Survey (USGS) and the Global Land Cover Facility (GLCF). ERDAS Imagine 2013 is used to classify land use using remotely sensed data. The Kappa Coefficient was used to compute the accuracy assessment of classified maps. Maps are used to depict a comparative analysis of urban sprawl in the city. In addition, regression analysis and simple statistical computations are also utilised to assess dynamic changes in urban sprawl. From 1987 to 2017, a 10-year interval was used to measure change in built-up land using an equation. According to the findings of the study, Sargodha City has experienced considerable changes in land use patterns. This current study is beneficial for policymakers to design the city in a well-planned manner. The appropriate design of cities can pave the way for higher living standards in rapidly growing cities of Pakistan