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Water Distribution Network earlier will have challenge in fulfilling the growing need of water supply. Forecasting and monitoring the flow, pressure, demand, and quality of water in water distribution network are key requirement for sustainable and reliable water supply. Lekhnath city, due to the recent merger with the biggest metropolitan city of Nepal, Pokhara, has created the rapid growth of population resulting to haphazard pipeline extension in this area. For hydraulic analysis of Lekhnath Small Town Water Supply Distribution Network, one of the networks i.e., Shishuwakhudi distribution network was faced with problem analysis. This hydraulic analysis is a part of on-going doctoral research and Youth-led Initiative ‘drinkPani’ on ‘Water Supply and Quality Monitoring using Emerging Technologies’ where capacity building of Youth and Women through data-driven engagements (Water-informatics) is one of the core targets in the overall project-work package. The preliminary analysis of water quality was also derived from Young Water Volunteers (YWVs) under the research work on Youth-led Participatory Sensing (YPS) Model to enhance drinking water security of the study area. The Shishuwakhudi Water Distribution Network (WDN) supplies water to 1138 household at present and consisting of 41 junction nodes and 11.2km distribution pipeline distributing water from a reservoir. With field survey, households at each node of the network were determined. Demand driven analysis using EPANET was done to determine the pressure deficit at various nodes. Demand pattern based on design guideline was adopted for analysis. At present scenario, four nodes at end of the network showed negative pressure at peak hours (5 am- 7am). At other times, the pressure was above 10m head. Demand was forecasted for 10 years from now by projecting the household population based on growth rate of census 2011. When projected to future scenario, the existing pipeline showed only 17% reliability with a minimum pressure requirement of 10 m head as per norms of urban water supply project in Nepal. Genetic algorithm was used for optimization of the Shishuwakhudi Network with total cost of replacing pipes as an objective function with pressure constraint of minimum 10m head. With 500 generation of run, the optimal diameter for each pipe was obtained that gave optimal cost of NRs 4,21,50,917 (USD 359,690.64). This optimization helps in selecting the proper pipeline network for future expansion to fulfil the growing need of water demand.

Climate change and its impacts on the environment have become more than ever a worldwide challenging issue. Hence, decision makers are seeking reliable climate-impact models to take long term appropriate actions against this phenomena. In this study, ten regional climate models (RCMs) obtained from the European Coordinated Downscaling Experiment (EURO CORDEX) platform are evaluated on the Chiese river catchment located in the northeast of Italy. The models’ ensembles are assessed in terms of the uncertainty and error calculated through different statistical and error indices. The uncertainties are investigated in terms of signal (increase, decrease or neutral changes in the variables) and value uncertainties. Together with the spatial analysis of the data over the catchment, the weighted averaged values are used for the models’ evaluations and data projections. Using weighted catchment variables, climate change impacts are assessed on 10 different hydro-climatological variables showing the changes in the temperature, precipitation, rainfall events’ features and the hydrological variables of the Chiese catchment between historical (1991–2000) and future (2071–2080) decades under RCP (Representative Concentration Path for increasing greenhouse gas emissions) scenario 4.5. The results show that, even though the multi-model ensemble mean (MMEM) could cover the outputs’ uncertainty of the models, it increases the error of the outputs. On the other hand, the RCM with the least error could cause high signal and value uncertainties for the results. Hence, different multi-model subsets of ensembles (MMEM-s) of ten RCMs are obtained through a proposed algorithm for different impact models’ calculation and projection, making tradeoffs between two important shortcomings of model outputs, which are error and uncertainty. The single model (SM) and multi-model (MM) outputs imply that catchment warming is obvious in all cases and, therefore, evapotranspiration will be intensified in the future where there are about 1.28 C° and 6% value uncertainties for monthly temperature increase and the decadal relative balance of evapotranspiration. While rainfall events feature higher intensity and shorter duration in the SM, there are no significant differences for the mentioned features in the MM, showing high signal uncertainties in this regard. The unchanged catchment rainfall events’ depth can be observed in two SM and MM approaches implying good signal certainty for the depth feature trend; there is still high uncertainty about the depth values. As a result of climate change, the percolation component change is negligible, with low signal and value uncertainties, while decadal evapotranspiration and discharge uncertainties show the same signal and value. While extreme events and their anomalous outcomes direct the uncertainties in rainfall events' features values towards zero, they remain critical for yearly maximum catchment discharge in 2071–2080 as the highest value uncertainty is observed for this variable.Keywords: climate change; regional climate model; specific region; ensemble evaluation; spatial analysis; impact model; error; uncertainty; hydrological variables

Climate change increasingly is affecting every aspect of human life on the earth. Many regional climate models (RCMs) have so far been developed to carefully assess this important phenomenon on specific regions. In this study, ten RCMs captured from the European Coordinated Downscaling Experiment (EURO CORDEX) platform are evaluated on the river Chiese catchment located in the northeast of Italy. The models’ ensembles are assessed in terms of the uncertainty and error calculated through different statistical and error indices. The uncertainties are investigated in terms of signal (increase, decrease, or neutral changes in the variables) and value uncertainties. Together with the spatial analysis of the data over the catchment, the weighted averaged values are used for the models’ evaluations and data projections. Using weighted catchment variables, climate change impacts are assessed on 10 different hydro-climatological variables showing the changes in the temperature, precipitation, rainfall events’ features, and the hydrological variables of the Chiese catchment between historical (1991–2000) and future (2071–2080) decades under RCP (Representative Concentration Path for increasing greenhouse gas emissions) scenario 4.5. The results show that, even though the multi-model ensemble mean (MMEM) could cover the outputs’ uncertainty of the models, it increases the error of the outputs. On the other hand, the RCM with the least error could cause high signal and value uncertainties for the results. Hence, different multi-model subsets of ensembles (MMEM-s) of 10 RCMs are obtained through a proposed algorithm for different impact models’ calculations and projections, making tradeoffs between two important shortcomings of model outputs, which are error and uncertainty. The single model (SM) and multi-model (MM) outputs imply that catchment warming is obvious in all cases and, therefore, evapotranspiration will be intensified in the future where there are about 1.28% and 6% value uncertainties for monthly temperature increase and the decadal relative balance of evapotranspiration, respectively. While rainfall events feature higher intensity and shorter duration in the SM, there are no significant differences for the mentioned features in the MM, showing high signal uncertainties in this regard. The unchanged catchment rainfall events’ depth can be observed in two SM and MM approaches, implying good signal certainty for the depth feature trend; there is still high uncertainty about the depth values. As a result of climate change, the percolation component change is negligible, with low signal and value uncertainties, while decadal evapotranspiration and discharge uncertainties show the same signal and value. While extreme events and their anomalous outcomes direct the uncertainties in rainfall events’ features’ values towards zero, they remain critical for yearly maximum catchment discharge in 2071–2080 as the highest value uncertainty is observed for this variable.

Climate change is increasingly affecting every aspect of human life on Earth. Many regional climate models (RCMs) have so far been developed to carefully assess this important phenomenon on specific regions. Hence, the functional evaluation of RCMs for simulating catchment climatic characteristics has been the target of many studies in the literature. To accomplish this task, many studies apply interpolation techniques (re-gridding, remapping and rescaling) for matching the resolutions of observations with the ones related to RCMs or vice versa. Moreover, they calculate arithmetic mean value of climatic data over a catchment for representing hydro-climatic variables (precipitation and temperature) of a catchment. This study proposes a novel approach that does not require any interpolation techniques for matching the resolutions, resulting in the improvement of RCM evaluations. In addition, the weighted average of data over a catchment is a comparison variable for the evaluation of RCMs, considering the different distribution of data´s geographical locations within a catchment. The weights for every data point are calculated based on the Thiessen polygon area of the corresponding point divided into the total area of catchment. To see the application of the method,10 RCMs captured from the European Coordinated Downscaling Experiment (EURO CORDEX) platform are evaluated by the proposed method on the river Chiese catchment located in the northeast of Italy, identifying the models with the appropriate performance for precipitation and temperature simulation of the catchment.

This paper presents some developments in the optimization effectiveness for the dynamic design of water distribution networks (WDNs), tackled employing multi-objective genetic algorithms. Unlike the traditional single-phase design, the dynamic multi-phase design operates on planning WDN upgrades on short time intervals, also called phases or stages, while fitting them into a long-term planning horizon, thus requiring bespoke research efforts for the improvement of the optimization effectiveness. A modified version of dynamic NSGA-II optimization is introduced here, including: no penalty on the objective functions for infeasible solutions, adoption of engineering judgments in the construction of optimization individuals, restricting the number of parallel pipes at each site. This results in the improvement of convergence speed and solution quality in two case studies with different complexities.

Water Distribution Networks (WDNs) along with other utilities are among the most important assets in every industrialized society. The rehabilitation and upgrade of WDNs refer to a very complex and multi-criteria problem which needs a comprehensive decision support system providing optimal renewal plans for the asset managers. There is a multitude of real-world problems faced with such activities. In this regard, this study aims to propose a novel method to cope with three practical challenges in WDNs rehabilitation activities including 1-budget limitation, 2- hydraulic deficiency and 3- correlation of WDNs with other infrastructures bringing the risk of cascading failures to a multiplex network. This results in a multi-objective optimization problem with three objectives which are cost, hydraulic and decoupled reliabilities. The problem is solved dynamically with the contribution of a nature inspired optimization algorithm, the Non-Dominated Sorting Genetic Algorithm (NSGA-II). The method is applied to a deteriorated water pipe network, and the results are compared with the ones obtained only by the consideration of two objectives, costs and hydraulic reliability showing how much the objectives are conflicted with each other.

OBJECTIVE: Schizophrenia, commonly develops in adolescents and young adults and Paranoid is the most common type. Newly progress in molecular biology and imaging techniques has enabled new insight into schizophrenia research. Recent research reveals the key roles of D3, D4 and D5 dopamine receptor in presenting types of schizophrenia, particularly the paranoid. The purpose of this study was to the evaluation of D5 along with D3 dopamine receptor expression in schizophrenia patients. METHOD: 96 venous blood from 51 drug-free, 15 naive and 30 healthy were prepared and their expression level of mRNA for D3 and D5 dopamine receptor were investigated based on β-actin expression as housekeeping gene. RESULTS: D3 expressed about 33.3%, 73.3% and 27.4% in healthy, naive and drug-free patients respectively, but D5 just expressed in 86.6% of healthy samples and none of the patients were expressed D5. CONCLUSION: Expression frequency of D3 and D5 dopamine receptor reveals significant differences between naïve and drug-free patient against healthy individuals. D3 and D5, both have the possible potential for almost more accurate diagnosis usage, and the severity of the disease could be related to the D5 dopamine receptor.

This study introduces a decision support system for upgrading aged water distribution mains. The network hydraulics is simulated and evaluated by a hybrid reliability measure, and the desig...