A Customer-driven Decision Making Framework for Drinking Water Systems

According to the 2013 ASCE infrastructure report card, the USA potable water system needs an investment of $384.2 billion over the next 20 years to meet current and future needs. The American infrastructure, which has a GPA of D+ and rated as poor, needs an investment of$3.6 trillion for all infrastructure assets by 2020. One of the key proposed solutions was to prioritize the maintenance of infrastructure considering the Level of Service (LOS). Most of research works on water distribution network (WDN) focused on the condition or performance of water systems creating a gap between municipal goals and public expectations. It is evident that there is a lack of research in the area of LOS and its link with WDN condition. There is a vital need in municipalities to link renewal plans with the Level of Service. Therefore, the main objectives of the present research are to: 1) identify and study the factors that impact the LOS, 2) establish an assessment model for LOS in the WDN, and 3) map the LOS to WDN condition. Building upon recent work on the LOS of drinking water supply systems, the present research identifies LOS factors based on the review of water supply system (WSS) performance indicators from literature and experts in the domain. It consequently develops a framework that is dependent on two main models: (1) Best-Worst Method (BWM) model that determines the LOS of a WSS considering the relative weight of importance of the identified LOS factors and (2) Artificial Neural Network (ANN) model that maps the WDN condition to LOS. Using the water network data set of the city of Montreal, the framework is tested and the impact of pipe material and environmentalconditions on breakage rate is studied. This research proves that breakage rate varies significantly for different pipe materials and neighborhood areas with different environmental conditions. Questionnaire responses from the industry experts show that supply pressure and continuity, quality of supplied water, and customer complaints are the main factors that govern the quality of service. They also show that water quality is the most important factor to the LOS among the other significant factors. The relationship between WDN condition and LOS is determined considering the metrics of water quality, customer complaints, as well as pressure and continuity of water supply. An Artificial Neural Networks (ANN) model is developed in which the above metrics are considered the input variables and the LOS total score resulting from the developed BWM model is the output variable. The model is cross-validated using the embedded validation in the used software resulting in an R2 value of 0.871, which reflects a good representation of the relationship between the inputs and the outputs. Municipal management teams will be able to connect the technical world of condition assessment of WDN to the customer world by adopting a customer-oriented decision making process. This enables them to understand the customer perception of the provided service, optimize the budget allocation process and forecast the LOS based on the network condition. It also opens perspectives to key issues for future research work to diagnose the customer perception of municipal infrastructure performance. i

Spatio-temporal Modeling and Analysis of Brain Development

The incidence of preterm birth is increasing and has emerged as a leading cause of neurodevelopmental impairment in childhood. In early development, defined here as the period before and around birth, the brain undergoes significant morphological, functional and appearance changes. The scope and rate of change is arguably greater than at any other time in life, but quantitative markers of this period of development are limited. Improved understanding of cerebral changes during this critical period is important for mapping normal growth, and for investigating mechanisms of injury associated with risk factors for maldevelopment such as premature birth. The objective of this thesis is the development of methods for spatio-temporal modeling and quantitative measures of brain development that can assist understanding the patterns of normal growth and can guide interventions designed to reduce the burden of preterm brain injury. An approach for constructing high-definition spatio-temporal atlases of the developing brain is introduced. A novelty in the proposed approach is the use of a time-varying kernel width, to overcome the variations in the distribution of subjects at different ages. This leads to an atlas that retains a consistent level of detail at every time-point. The resulting 4D fetal and neonatal average atlases have greater anatomic definition than currently available 4D atlases, an important factor in improving registrations between the atlas and individual subjects with clear anatomical structures and atlas-based automatic segmentation. The fetal atlas provides a natural benchmark for assessing preterm born neonates and gives some insight into differences between the groups. Also, a novel framework for longitudinal registration which can accommodate large intra-subject anatomical variations is introduced. The framework exploits previously developed spatio-temporal atlases, which can aid the longitudinal registration process as it provides prior information about the missing anatomical evolution between two scans taken over large time-interval. Finally, a voxel-wise analysis framework is proposed which complements the analysis of changes in brain morphology by the study of spatio-temporal signal intensity changes in multi-modal MRI, which can offer a useful marker of neurodevelopmental changes

Evaluation of the role of gracilis free flap in reconstruction of orofacial defects

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Synthesis, Spectral Characterization, Thermal and In vitro Antimicrobial Studies for Novel Co(II) and Ni(II) Complexes of (N,N'-(1,2-Phenylene)Bis(2-Aminobenzamide)

Novel metal complexes of Co(II) and Ni(II) have been  prepared from reaction of their different salts with previously prepared ligand (L) namely (N,N'-(1,2-phenylene)bis(2-aminobenzamide). Synthesis ligand and its metal (II) complexes (1-5) were reported and characterized with the help of analytical and physiochemical analysis as elemental, IR spectra, thermal (TG/DTG), UV-Vis, magnetic susceptibility and molar conductance in DMF, On the view of the previous data and measurements, the structure and composition species behave as mononuclear and octahedral geometry has been proposed for all the complexes except for complex (1) adopted tetrahedral structure. Furthermore, the in vitro antibacterial Staphylococcus aureus (ATCC 25923) as Gram-positive strain, Escherichia coli (ATCC 25922) as Gram-negative strain and antifungal Candida albicans (ATCC 10231) have been studied for all samples using disc diffusion method against Ampicillin and Fluconazole as positive controls, respectively. The results show that complexation facilitates the activity of most studied metal complexes than the free ligand

A population of wheat multiple synthetic derivatives: an effective platform to explore, harness and utilize genetic diversity of Aegilops tauschii for wheat improvement

Introducing genes from wild relatives is the best option to increase genetic diversity and discover new alleles necessary for wheat improvement. A population harboring genomic fragments from the diploid wheat progenitor Aegilops tauschii Coss. in the background of bread wheat (Triticum aestivum L.) was developed by crossing and backcrossing 43 synthetic wheat lines with the common wheat cultivar Norin 61. We named this population multiple synthetic derivatives (MSD). To validate the suitability of this population for wheat breeding and genetic studies, we randomly selected 400 MSD lines and genotyped them by using Diversity Array Technology sequencing markers. We scored black glume as a qualitative trait and heading time in two environments in Sudan as a quantitative trait. Our results showed high genetic diversity and less recombination which is expected from the nature of the population. Genome-wide association (GWA) analysis showed one QTL at the short arm of chromosome 1D different from those alleles reported previously indicating that black glume in the MSD population is controlled by new allele at the same locus. For heading time, from the two environments, GWA analysis revealed three QTLs on the short arms of chromosomes 2A, 2B and 2D and two on the long arms of chromosomes 5A and 5D. Using the MSD population, which represents the diversity of 43 Ae. tauschii accessions representing most of its natural habitat, QTLs or genes and desired phenotypes (such as drought, heat and salinity tolerance) could be identified and selected for utilization in wheat breeding

Assessment of Passive Retrofitting Scenarios in Heritage Residential Buildings in Hot, Dry Climates

peer reviewedRetrofitting heritage buildings for energy efficiency is not always easy where cultural values are highly concerned, which requires an integrated approach. This paper aims to assess the potential of applying passive retrofitting scenarios to enhance indoor thermal comfort of heritage buildings in North Africa, as a hot climate, a little attention has been paid to retrofit built heritage in that climate. A mixed-mode ventilation residential building in Cairo, Egypt, was selected as a case study. The study combines field measurements and observations with energy simulations. A simulation model was created and calibrated on the basis of monitored data in the reference building, and the thermal comfort range was evaluated. Sets of passive retrofitting scenarios were proposed. The results (based on the ASHRAE-55-2020 adaptive comfort model at 90% acceptability limits) showed that the annual thermal comfort in the reference building is very low, i.e., 31.4%. The application of hybrid passive retrofitting scenarios significantly impacts indoor thermal comfort in the reference building, where annual comfort hours of up to 66% can be achieved. The originality of this work lies in identifying the most effective energy measures to improve indoor thermal comfort that are optimal from a conservation point of view. The findings contribute to set a comprehensive retrofitting tool that avoids potential risks for the conservation of residential heritage buildings in hot climates

Prosopis juliflora leave extracts induce cell death of MCF-7, HepG2, and LS-174T cancer cell lines

Prosopis juliflora (P. juliflora) is a widespread phreatophytic tree, which belongs to the Fabaceae family. The goal of the present study is to investigate the potential anti-cancer effect of P. juliflora leave extracts and to identify its chemical composition. For this purpose, MCF-7 (breast), HepG2 (liver), and LS-174T (colorectal) cancer cell lines were cultivated and incubated with various concentrations of P. juliflora leave extracts, and its impact on cell viability, proliferation, and cell cycle stages was investigated. P. juliflora leave extracts induced concentration-dependent cytotoxicity against all tested cancer cell lines. The calculated IC50 was 18.17, 33.1 and 41.9 μg/ml for MCF-7, HePG2 and LS-174T, respectively. Detailed analysis revealed that the cytotoxic action of P. juliflora extracts was mainly via necrosis but not apoptosis. Moreover, DNA content flow cytometry analysis showed cell-specific anti-proliferative action and cell cycle stages arrest. In order to identify the anti-cancer constituents of P. juliflora, the ethyl extracts were analyzed by liquid chromatography-mass spectrometry. The major constituents identified in the ethyl extracts of P. juliflora leaves were hydroxymethyl-pyridine, nicotinamide, adenine, and poly-(methyl methacrylate) (PMMA). In conclusion, P. juliflora ethyl acetate extracts have a potential anti-cancer effect against breast adenocarcinoma, hepatocellular carcinoma, and colorectal adenocarcinoma, and is enriched with anti-cancer constituents

SEGMA: an automatic SEGMentation Approach for human brain MRI using sliding window and random forests

Quantitative volumes from brain magnetic resonance imaging (MRI) acquired across the life course may be useful for investigating long term effects of risk and resilience factors for brain development and healthy aging, and for understanding early life determinants of adult brain structure. Therefore, there is an increasing need for automated segmentation tools that can be applied to images acquired at different life stages. We developed an automatic segmentation method for human brain MRI, where a sliding window approach and a multi-class random forest classifier were applied to high-dimensional feature vectors for accurate segmentation. The method performed well on brain MRI data acquired from 179 individuals, analyzed in three age groups: newborns (38–42 weeks gestational age), children and adolescents (4–17 years) and adults (35–71 years). As the method can learn from partially labeled datasets, it can be used to segment large-scale datasets efficiently. It could also be applied to different populations and imaging modalities across the life course