About: Lorestan University is a education organization based out in Khorramabad, Iran. It is known for research contribution in the topics: Population & Nanocomposite. The organization has 1952 authors who have published 3329 publications receiving 38154 citations. The organization is also known as: LU.
Papers published on a yearly basis
TL;DR: The findings show substantial progress in the reduction of lower respiratory infection burden, but this progress has not been equal across locations, has been driven by decreases in several primary risk factors, and might require more effort among elderly adults.
Abstract: Summary Background Lower respiratory infections are a leading cause of morbidity and mortality around the world The Global Burden of Diseases, Injuries, and Risk Factors (GBD) Study 2016, provides an up-to-date analysis of the burden of lower respiratory infections in 195 countries This study assesses cases, deaths, and aetiologies spanning the past 26 years and shows how the burden of lower respiratory infection has changed in people of all ages Methods We used three separate modelling strategies for lower respiratory infections in GBD 2016: a Bayesian hierarchical ensemble modelling platform (Cause of Death Ensemble model), which uses vital registration, verbal autopsy data, and surveillance system data to predict mortality due to lower respiratory infections; a compartmental meta-regression tool (DisMod-MR), which uses scientific literature, population representative surveys, and health-care data to predict incidence, prevalence, and mortality; and modelling of counterfactual estimates of the population attributable fraction of lower respiratory infection episodes due to Streptococcus pneumoniae, Haemophilus influenzae type b, influenza, and respiratory syncytial virus We calculated each modelled estimate for each age, sex, year, and location We modelled the exposure level in a population for a given risk factor using DisMod-MR and a spatio-temporal Gaussian process regression, and assessed the effectiveness of targeted interventions for each risk factor in children younger than 5 years We also did a decomposition analysis of the change in LRI deaths from 2000–16 using the risk factors associated with LRI in GBD 2016 Findings In 2016, lower respiratory infections caused 652 572 deaths (95% uncertainty interval [UI] 586 475–720 612) in children younger than 5 years (under-5s), 1 080 958 deaths (943 749–1 170 638) in adults older than 70 years, and 2 377 697 deaths (2 145 584–2 512 809) in people of all ages, worldwide Streptococcus pneumoniae was the leading cause of lower respiratory infection morbidity and mortality globally, contributing to more deaths than all other aetiologies combined in 2016 (1 189 937 deaths, 95% UI 690 445–1 770 660) Childhood wasting remains the leading risk factor for lower respiratory infection mortality among children younger than 5 years, responsible for 61·4% of lower respiratory infection deaths in 2016 (95% UI 45·7–69·6) Interventions to improve wasting, household air pollution, ambient particulate matter pollution, and expanded antibiotic use could avert one under-5 death due to lower respiratory infection for every 4000 children treated in the countries with the highest lower respiratory infection burden Interpretation Our findings show substantial progress in the reduction of lower respiratory infection burden, but this progress has not been equal across locations, has been driven by decreases in several primary risk factors, and might require more effort among elderly adults By highlighting regions and populations with the highest burden, and the risk factors that could have the greatest effect, funders, policy makers, and programme implementers can more effectively reduce lower respiratory infections among the world's most susceptible populations Funding Bill & Melinda Gates Foundation
TL;DR: In this article, a standard methodology has been applied to delineate groundwater resource potential zonation based on integrated analytical hierarchy process (AHP), geographic information system (GIS), and remote sensing (RS) techniques in Kurdistan plain, Iran.
Abstract: Multi-criteria decision analysis (MCDA) as an advantageous tool has been applied by various researchers to improve their management ability. Management of groundwater resource, especially under data-scarce and arid areas, encountered a lot of problems and issues which drives the planers to use of MCDA. In this research, a standard methodology has been applied to delineate groundwater resource potential zonation based on integrated analytical hierarchy process (AHP), geographic information system (GIS), and remote sensing (RS) techniques in Kurdistan plain, Iran. At first, the effective thematic layers on the groundwater potential such as rainfall, lithology, drainage density, lineament density, and slope percent were derived from the spatial geodatabase. Then, the assigned weights of thematic layers based on expert knowledge were normalized by eigenvector technique of AHP. To prepare the groundwater potential index, the weighted linear combination (WLC) method was applied in GIS. Finally, the receiver operating characteristic (ROC) curve was drawn for groundwater potential map, and the area under curve (AUC) was computed. Results indicated that the rainfall and slope percent factors have taken the highest and lowest weights, respectively. Validation of results showed that the AHP method (AUC = 73.66 %) performed fairly good predication accuracy. Such findings revealed that in the regions suffering from data scarcity through the MCDM methodology, the planners would be able to having accurate knowledge on groundwater resources based on geospatial data analysis. Therefore, the developing scenario for future planning of groundwater exploration can be achieved in an efficient manner.
TL;DR: In this paper, the application of random forest (RF) and maximum entropy (ME) models for groundwater potential mapping is investigated at Mehran Region, Iran and the results of the GPMs were quantitatively validated using observed groundwater dataset and the receiver operating characteristic (ROC) method.
Abstract: Groundwater is considered as the most important natural resources in arid and semi-arid regions. In this study, the application of random forest (RF) and maximum entropy (ME) models for groundwater potential mapping is investigated at Mehran Region, Iran. Although the RF and ME models have been applied widely to environmental and ecological modeling, their applicability to other kinds of predictive modeling such as groundwater potential mapping has not yet been investigated. About 163 groundwater data with high potential yield values of ≥ 11 m 3 /h were obtained from Iranian Department of Water Resources Management (IDWRM). Further, these selected wells were randomly divided into a dataset 70% (114 wells) for training and the remaining 30% (49 wells) was applied for validation purposes. In total, ten groundwater conditioning factors that affect the storage of groundwater occurrences (e.g. altitude, slope percent, slope aspect, plan curvature, drainage density, distance from rivers, topographic wetness index (TWI), landuse, lithology, and soil texture) were used as input to the models. Subsequently, the RF and ME models were applied to generate the groundwater potential maps (GPMs). Moreover, a sensitivity analysis was used to identify the impact of variable uncertainties on the produced GPMs. Finally, the results of the GPMs were quantitatively validated using observed groundwater dataset and the receiver operating characteristic (ROC) method. Area under ROC curve (AUC) was used to compare the performance of RF with ME. The uncertainty on the preparation of conditioning factors was taken in account to enhance the model. The validation results showed that the AUC for success rate of RF and ME models was 86.5 and 91%, respectively. In contrast, the AUC for prediction rate of RF and ME methods was obtained 83.1 and 87.7%, respectively. Therefore, RF and ME were found to be effective models for groundwater potential mapping.
TL;DR: This review continues to update this series of biennial reviews on developments in the field of on‐line/in‐line concentration methods in capillaries and microchips, covering the period July 2014–June 2016, covering all methods from field amplified sample stacking and large volume sample stacking, through to isotachophoresis, dynamic pH junction, and sweeping.
Abstract: CE has been alive for over two decades now, yet its sensitivity is still regarded as being inferior to that of more traditional methods of separation such as HPLC. As such, it is unsurprising that overcoming this issue still generates much scientific interest. This review continues to update this series of reviews, first published in Electrophoresis in 2007, with updates published in 2009 and 2011 and covers material published through to June 2012. It includes developments in the field of stacking, covering all methods from field amplified sample stacking and large volume sample stacking, through to isotachophoresis, dynamic pH junction and sweeping. Attention is also given to online or inline extraction methods that have been used for electrophoresis.
TL;DR: This study investigates the analytical hierarchy process (AHP), frequency ratio (FR), and certainty factor (CF) models for groundwater potential mapping using geographical information system (GIS) at Varamin Plain, Tehran province, Iran and finds that the FR model performs better than AHP and CF models.
Abstract: The main goal of this study was to investigate the analytical hierarchy process (AHP), frequency ratio (FR), and certainty factor (CF) models for groundwater potential mapping using geographical information system (GIS) at Varamin Plain, Tehran province, Iran In the first step, the groundwater conditioning factors such as altitude, slope angle, slope aspect, topographic witness index, rainfall, drainage density, water table level, aquifer thickness, lithology, and distance from rivers were prepared The groundwater yield dataset was prepared using earlier reports, and extensive field surveys In total, 71 groundwater data with high potential yield values of ≥40 m3/h were collected and mapped in GIS Out these, 50 (70 %) cases were randomly selected for models training, and the remaining 21 (30 %) cases were used for the validation purposes Subsequently, groundwater potential maps were produced using AHP, FR, and CF models in ArcGIS 102 Finally, the receiver operating characteristic (ROC) curves for all the groundwater potential models were constructed and the areas under the curves (AUC) were computed From the analysis, it is seen that the FR model (AUC = 7755 %) performs better than AHP (AUC = 7347 %) and CF (AUC = 6508 %) models The results of groundwater potential map can be helpful for future planning in groundwater resource management and land use planning
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|Mohammad Mehdi Aslani||21||109||1560|
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