Soil Quality Variation under Different Land Use Types in Haramosh Valley, Gilgit, Pakistan

Soil quality is a fundamental component of environmental quality and impact of land use is also a keydetrimental factor in today’s rapid urbanization era. The study aims to evaluate the effects of different land-use type on selected soil quality indicators. Sixty soil samples were collected from various land use types, i.e, pasture, forest and agriculture from a depth of 0-15cm. Analysis of variance (ANOVA) showed that the land use type significantly affected the soil’s physical and chemical properties. The moisture content was significantly higher (p<0.001) in the pasture (41.7%) than the forest (26.2%) and lowest in agricultural land (14.4%). The soil pH was significantly higher or slightly alkaline for agriculture (7.8), while for pasture (6.5) and forest (6.1), it was found to be slightly acidic. Electric conductivity (EC) and bulk density (BD) did not vary significantly with land use type, but the EC followed the decreasing order: forest (203.7μS/cm) < pasture (235μS/cm) < agriculture (328.7μS/cm). The soil organic matter (SOM) and soil organic carbon (SOC) significantly (p<0.05) differed with land use type and found in the order: forest (3.0%, 1.3 %) > pasture land (2.9%, 1.2%) > arable land (2.5%, 1.1%). NO3-N, available P and exchangeable K did not vary significantly across land use types. However, mean values were higher for agriculture (10.2mg/kg, 4.5mg/kg, 66mg/kg) than forest (10mg/kg,3.5mg/kg, 60mg/kg) and pasture (9.8mg/kg, 4.3, 60.2mg/kg). Alpine soils are good ecological indicators because of vulnerability to environmental change, therefore, regular monitoring of soil properties along with carbon stocks is essential to maintain soil health, enhance agricultural productivity and sustain agroecosystems

Substantial and sustained reduction in under-5 mortality, diarrhea, and pneumonia in Oshikhandass, Pakistan : Evidence from two longitudinal cohort studies 15 years apart

Funding Information: Study 1 was funded through the Applied Diarrheal Disease Research Program at Harvard Institute for International Development with a grant from USAID (Project 936–5952, Cooperative Agreement # DPE-5952-A-00-5073-00), and the Aga Khan Health Service, Northern Areas and Chitral, Pakistan. Study 2 was funded by the Pakistan US S&T Cooperative Agreement between the Pakistan Higher Education Commission (HEC) (No.4–421/PAK-US/HEC/2010/955, grant to the Karakoram International University) and US National Academies of Science (Grant Number PGA-P211012 from NAS to the Fogarty International Center). The funding bodies had no role in the design of the study, data collection, analysis, interpretation, or writing of the manuscript. Publisher Copyright: © 2020 The Author(s).Peer reviewedPublisher PD

Synthesis of Fe3O4@mZrO2-Re (Re = Y/La/Ce) by Using Uniform Design, Surface Response Methodology, and Orthogonal Design & Its Application for As3+ and As5+ Removal

In this study, iron oxide (Fe3O4) was coated with ZrO2, and doped with three rare earth elements((Y/La/Ce), and a multi-staged rare earth doped zirconia adsorbent was prepared by using uniform design U14, Response Surface methodology, and orthogonal design, to remove As3+ and As5+ from the aqueous solution. Based on the results of TEM, EDS, XRD, FTIR, and N2-adsorption desorption test, the best molar ratio of Fe3O4:TMAOH:Zirconium butoxide:Y:La:Ce was selected as 1:12:11:1:0.02:0.08. The specific surface area and porosity was 263 m2/g, and 0.156 cm3/g, respectively. The isothermal curves and fitting equation parameters show that Langmuir model, and Redlich Peterson model fitted well. As per calculations of the Langmuir model, the highest adsorption capacities for As3+ and As5+ ions were recorded as 68.33 mg/g, 84.23 mg/g, respectively. The fitting curves and equations of the kinetic models favors the quasi second order kinetic model. Material regeneration was very effective, and even in the last cycle the regeneration capacities of both As3+ and As5+ were 75.15%, and 77.59%, respectively. Adsorption and regeneration results suggest that adsorbent has easy synthesis method, and reusable, so it can be used as a potential adsorbent for the removal of arsenic from aqueous solution

A New Deep Hybrid Boosted and Ensemble Learning-Based Brain Tumor Analysis Using MRI

Brain tumor analysis is essential to the timely diagnosis and effective treatment of patients. Tumor analysis is challenging because of tumor morphology factors like size, location, texture, and heteromorphic appearance in medical images. In this regard, a novel two-phase deep learning-based framework is proposed to detect and categorize brain tumors in magnetic resonance images (MRIs). In the first phase, a novel deep-boosted features space and ensemble classifiers (DBFS-EC) scheme is proposed to effectively detect tumor MRI images from healthy individuals. The deep-boosted feature space is achieved through customized and well-performing deep convolutional neural networks (CNNs), and consequently, fed into the ensemble of machine learning (ML) classifiers. While in the second phase, a new hybrid features fusion-based brain-tumor classification approach is proposed, comprised of both static and dynamic features with an ML classifier to categorize different tumor types. The dynamic features are extracted from the proposed brain region-edge net (BRAIN-RENet) CNN, which is able to learn the heteromorphic and inconsistent behavior of various tumors. In contrast, the static features are extracted by using a histogram of gradients (HOG) feature descriptor. The effectiveness of the proposed two-phase brain tumor analysis framework is validated on two standard benchmark datasets, which were collected from Kaggle and Figshare and contain different types of tumors, including glioma, meningioma, pituitary, and normal images. Experimental results suggest that the proposed DBFS-EC detection scheme outperforms the standard and achieved accuracy (99.56%), precision (0.9991), recall (0.9899), F1-Score (0.9945), MCC (0.9892), and AUC-PR (0.9990). The classification scheme, based on the fusion of feature spaces of proposed BRAIN-RENet and HOG, outperform state-of-the-art methods significantly in terms of recall (0.9913), precision (0.9906), accuracy (99.20%), and F1-Score (0.9909) in the CE-MRI dataset