A Simulation-Based Optimization Model for Flood Management on a Watershed Scale

Using structural and nonstructural measures for flood damage reduction is a long-standing problem in water resources planning and management. In the present study, an algorithm is presented for the optimal design of structural and nonstructural flood mitigation measures based on simulation-based optimization approach. For this purpose, the MIKE-11 simulation model, a one-dimensional hydrodynamic model, was used to calculate the potential damages of different flood scenarios under the various combinations of structural and nonstructural measures and this model was coupled with the NSGA-II multi-objective optimization model to provide the optimal Pareto solutions between two conflict objectives of minimizing the investment costs of flood mitigation measures and the potential damages of the floodplain. The proposed model was then applied to a small watershed in central part of Iran as a case study and the optimal trade-off solutions were calculated for different flood scenarios. Using these trade-offs, for each level of funding, decision makers can assign the optimal design of flood mitigation measures considering decision criteria.     

Prediction of geological hazardous zones in front of a tunnel face using TSP-203 and artificial neural networks

This research aims at improving the methods of prediction of hazardous geotechnical structures in the front of a tunnel face. We propose and showcase our methodology using a case study on a water supply system in Cheshmeh Roozieh, Iran. Geotechnical investigations had previously reported three measurements of the newly established method of TSP-203 (Tunnel Seismic Prediction) along 684 m of the 3200 m long tunnel up to a depth of 600 m. We use the results of TSP-203 in a trained artificial neural network (ANN) to estimate the unknown nonlinear relationships between TSP-203 results and those obtained by the methods of Rock Mass Rating classification (RMR – treated here as real values). Our results show that an appropriately trained neural network can reliably predict the weak geological zones in front of a tunnel face accurately.     

Turbo Coded Modulation for Unequal Error Protection

A major concern in designing communication systems is to maintain quality of service for a wide range of channel conditions. This is an important issue particularly for the applications where precise characterization of the channel is impossible. For such applications, the source data can be classified into several classes and Unequal Error Protection (UEP) can be used to effectively protect the more important classes even in poor receiving conditions. This paper is focused on the study, design, and performance evaluation of unequal error protecting turbo coded modulation schemes. We first propose several schemes for unequal error protecting using turbo coded modulation. All these schemes provide high performance gains for more important classes that can hardly be achieved using conventional coded modulation schemes. We then study unequal error protecting turbo coded modulation schemes by deriving channel capacity and cutoff rates for different protection levels. We show that for more important classes more room is available for improvement.     

Assessment of thermal,morphological, and mechanical properties of poly(methyl methacrylate)/glass flake composites

In this work, poly(methyl methacrylate)/(glass flake) (GF) composites were prepared with different compositions via melt mixing. The effect of the filler ingredient on thermal behavior, morphology, and mechanical and optical properties was investigated by using various techniques, namely differential scanning calorimetry, ultraviolet‐visible spectra, mechanical testing, and scanning electron microscopy. For evaluating the level of dispersion of particles, energy dispersive X‐ray analysis was performed. Differential scanning calorimetry analysis showed that the glass transition temperature of the samples slightly increased by increasing GF content. Scanning electron microscopy images showed that sized flakes were uniformly dispersed within poly(methyl methacrylate). Energy dispersive X‐ray analysis images of samples with different inclusions of GFs showed that the appearance of white dense spots represents the GF particles. It was found that the presence of 0.5 wt% of GF in composites gave more transparency than the other compositions. Furthermore, this composition indicated maximum tensile strength and elongation‐at‐break values in comparison with the other compositions. J. VINYL ADDIT. TECHNOL., 23:62–69, 2017. © 2015 Society of Plastics Engineers     

Comparative Application of Model Predictive Control and Particle Swarm Optimization in Optimum Operation of a Large-Scale Water Transfer System

Water Resources Management - This research evaluates the application and performance of two methods of Model Predictive Control (MPC) and Particle Swarm Optimization (PSO) in real time control and...     

Extended adsorption transport models for permeation of copper ions through nanocomposite chitosan/polyvinyl alcohol thin affinity membranes

Transport of copper ions through nanocomposite chitosan/polyvinyl alcohol thin adsorptive membranes has been mathematical y investigated in the current study. Unsteady-state diffusive transport model was coupled with the Freundlich isotherm to predict the concentration of the ions in dialysis permeation operation. Pristine model was not successful in predicting the experimental data based upon its low coefficients of determination (0.1﹤R2﹤0.65). Well-behaved polynomial and exponential functions were used to describe time-dependency of the inlet-concentration in the first extension of the model with a little improvement in the model adjustment (0.4﹤R2﹤0.69). Similar time-dependent functions were employed for tracking the ion diffusivity and then applied in combination with the optimized functions of inlet-concentration in the second extension of the model. A sensible enhancement was obtained in the adjustment of the second extended models as a result of this combination (0.73﹤R2﹤0.93). APRE, AAPRE, RSME, RMSE, STD and R-square statistical analyses were per-formed to verify the agreement of the models with the experimental results. Concentration distribution versus time and location (inside the membrane) was obtained as 3D plots with the help of the optimized models. Modeling results emphasized on the transiency of diffusivity and feed-side concentration in dialysis permeation through chitosan membranes.     

CH4-Selective Mixed-Matrix Membranes Containing Functionalized Silica for Natural Gas Purification

Mixed-matrix membranes were prepared by incorporating functionalized silica nanoparticles (SNPs) into the poly(ether-block-amide). The gas permeation properties of membranes were investigated for the separation of N₂ and CO₂ from CH₄. Results revealed that chemical modification of SNPs and incorporation of the carboxylic groups on its surface had a strong interaction with the polymer matrix and improved the distribution of the nanofiller in the membrane matrix. According to the gas permeation experiments at various SNPs loadings and feed pressures, different trends were observed for the permeability and selectivity. Incorporation of the modified-SNPs nanofiller into the membrane enhanced the CH₄ permeability, as well as the CH₄/N₂ and CO₂/N₂ selectivities.     

Parameter estimation for nonlinear systems with multirate measurements and random delays

We present an approach for parameter estimation with multirate measurements, with the slow measurements having variable time delays due to laboratory analysis, and also being functions of all the states during the sample collection. We formulate a particle filter-based approach under the framework of the expectation maximization algorithm to develop the estimates. The effectiveness and applicability of the proposed method are demonstrated though a simulation example, a hybrid tank experiment and an industrial case study; in each case, the slow and fast measurements are for the same variable. We show that this approach results in improved parameter estimation when the information from the delayed measurements is fused with the fast measurement information.     

Co-Stimulation of Oxytocin and Arginine-Vasopressin Receptors Affect Hypothalamic Neurospheroid Size

Oxytocin (OXT) is a neuropeptide involved in a plethora of behavioral and physiological processes. However, there is a prominent lack of 3D cell culture models that investigate the effects of OXT on a cellular/molecular level. In this study, we established a hypothalamic neuronal spheroid model to investigate the cellular response in a more realistic 3D setting. Our data indicate that the formation of spheroids itself does not alter the basic characteristics of the cell line and that markers of cellular morphology and connectivity are stably expressed. We found that both OXT and arginine vasopressin (AVP) treatment increase spheroid size (surface area and volume), as well as individual nucleus size, which serves as an indicator for cellular proliferation. The cellular response to both OXT and AVP seems mainly to be mediated by the AVP receptor 1a (V1aR); however, the OXT receptor (OXTR) contributes significantly to the observed proliferative effect. When we blocked the OXTR pharmacologically or knocked down the OXTR by siRNA, the OXT- or AVP-induced cellular proliferation decreased. In summary, we established a 3D cell culture model of the neuronal response to OXT and AVP and found that spheroids react to the treatment via their respective receptors but also via cross-talk between the two receptor types.     

Glucosamine conjugated iron oxide and graphene oxide nanohybrid for smart drug delivery

Smart drug delivery systems have attracted a lot of attention as one of the new treatment methods for cancer. In this study, a smart drug delivery system carrying anticancer drugs was obtained by the intelligent synthesis of glucosamine (GA)-functionalized graphene oxide (GO)-based iron oxide nanoparticles (Fe₃O₄@GO-GA) using Hummers and chemical co-precipitation processes. Nanohybrids have a high surface area (280.26 m²/g) and superparamagnetic behaviour (Ms = 26.017 emu/g), indicating a significant loading capacity (373.78 mg/mg) and efficiency (96.3%) for pharmaceutical loading. An adsorption study of conventional daunorubicin (DNR) on this carrier showed that the drug release is more prone to occur under acidic conditions (pH = 5.5), at moderately high temperatures (T = 40°C), and in the absence of smart carriers. The toxicity of the smart nanohybrids was examined using the sulphorhodamine B (SRB) assay in Michigan Cancer Foundation-7 (MCF-7) cell lines. The rate of death of cells exposed to smart drug-containing systems in comparison to the systems without GA shows that GA reduces the toxicity of Fe₃O₄@GO.