Impact of foreign trade agreement (FTA) on trade patterns of goods between Pakistan and China

Thesis(Master) --KDI School:Master of Public Policy,2013masterpublishedEhsan, Sana

Comparison of Biodiversity and Abundance of Earthworms in Maize Croplands, Irrigated with Sewage and Canal Water of District Faisalabad, Pakistan

The diversity and richness of earthworms that were irrigated with canal (Chak 200 RB Lathianwala) and sewage (Chokera) water in the district of Faisalabad were determined from August to November 2020. For collection of specimens, hand picking and digging method was used and then preserved in formalin. AAS (Atomic absorption spectrophotometer) was used to check the concentration of heavy metals in both water and soil samples. Total 10 species were identified from Chak 200 RB Lathianwala and showed Shannon-Weiner Diversity Index (H\u27) =2.13, Evenness = 0.93 and Dominance = 0.07. The significant difference in abundance were determined (t = 7.7115, p = 0.0001 at p \u3c 0.05). Water and soil samples of sewage water had an acidic pH (5.960, 6.213), while water and soil samples of canal water had a basic pH (7.748, 7.867). The levels of total dissolved solids, electrical conductance, dissolved oxygen, and alkalinity of sewage water were lower than those of canal water except for total suspended solids, turbidity, and metals, viz. Cr, Ca, Pb, Co, Cd, Zn, and Mg. In sewage water irrigated soil, their concentrations were also higher than canal water irrigated soil, except for Mg. A significantly positive correlation coefficient was observed among temperature and abundance at both localities. Results showed that acidic pH and higher concentrations of pollutants alter the diversity and abundance of earthworms

Machine learning model demonstrates stunting at birth and systemic inflammatory biomarkers as predictors of subsequent infant growth - A four-year prospective study

Background: Stunting affects up to one-third of the children in low-to-middle income countries (LMICs) and has been correlated with decline in cognitive capacity and vaccine immunogenicity. Early identification of infants at risk is critical for early intervention and prevention of morbidity. The aim of this study was to investigate patterns of growth in infants up through 48 months of age to assess whether the growth of infants with stunting eventually improved as well as the potential predictors of growth.Methods: Height-for-age z-scores (HAZ) of children from Matiari (rural site, Pakistan) at birth, 18 months, and 48 months were obtained. Results of serum-based biomarkers collected at 6 and 9 months were recorded. A descriptive analysis of the population was followed by assessment of growth predictors via traditional machine learning random forest models.Results: Of the 107 children who were followed up till 48 months of age, 51% were stunted (HAZ \u3c - 2) at birth which increased to 54% by 48 months of age. Stunting status for the majority of children at 48 months was found to be the same as at 18 months. Most children with large gains started off stunted or severely stunted, while all of those with notably large losses were not stunted at birth. Random forest models identified HAZ at birth as the most important feature in predicting HAZ at 18 months. Of the biomarkers, AGP (Alpha- 1-acid Glycoprotein), CRP (C-Reactive Protein), and IL1 (interleukin-1) were identified as strong subsequent growth predictors across both the classification and regressor models.Conclusion: We demonstrated that children most children with stunting at birth remained stunted at 48 months of age. Value was added for predicting growth outcomes with the use of traditional machine learning random forest models. HAZ at birth was found to be a strong predictor of subsequent growth in infants up through 48 months of age. Biomarkers of systemic inflammation, AGP, CRP, IL1, were also strong predictors of growth outcomes. These findings provide support for continued focus on interventions prenatally, at birth, and early infancy in children at risk for stunting who live in resource-constrained regions of the world

Sensing and Artificial Intelligent Maternal-Infant Health Care Systems: A Review

Currently, information and communication technology (ICT) allows health institutions to reach disadvantaged groups in rural areas using sensing and artificial intelligence (AI) technologies. Applications of these technologies are even more essential for maternal and infant health, since maternal and infant health is vital for a healthy society. Over the last few years, researchers have delved into sensing and artificially intelligent healthcare systems for maternal and infant health. Sensors are exploited to gauge health parameters, and machine learning techniques are investigated to predict the health conditions of patients to assist medical practitioners. Since these healthcare systems deal with large amounts of data, significant development is also noted in the computing platforms. The relevant literature reports the potential impact of ICT-enabled systems for improving maternal and infant health. This article reviews wearable sensors and AI algorithms based on existing systems designed to predict the risk factors during and after pregnancy for both mothers and infants. This review covers sensors and AI algorithms used in these systems and analyzes each approach with its features, outcomes, and novel aspects in chronological order. It also includes discussion on datasets used and extends challenges as well as future work directions for researchers