Geomorphology Based Semi-Distributed Approach for Modelling Rainfall-Runoff Process

This study presents a geomorphology based semi-distributed methodology for prediction of runoff of a catchment. In this proposed methodology, the catchment area is divided into a number of sub-catchments using the Thiessen polygon method. The rainfall records of particular rain-gauge station are considered as uniformly distributed over the entire sub-watershed. Four different weighting factors are proposed to obtain the sub-catchment’s contribution towards runoff. The weighting factors are calculated based on the geomorphological parameters of the catchment. The geomorphological parameters of the sub-watersheds are obtained from SRTM digital elevation data. The weighted contributions from all the sub-watersheds at current and previous time steps and the previous time step discharge are used to develop an Artificial Neural Network (ANN) for predicting the discharge at the basin outlet. A lump model considering average rainfall of the catchment is also developed using ANN for evaluating the performance of the proposed distributed model. For the lump model, average rainfall is calculated using Thiessen polygon method. The historic rainfall and runoff data recorded at the Dikrong basin, a sub-catchment of the river Brahmaputra is used to evaluate the efficiency of the developed methodology. The evaluation results show that the presented model is superior to the lump model and has the potential for field application. A comparative study is also carried out to obtain the most influential combination of geomorphological parameters in predicting the catchment’s runoff.     

Optimal design of unit hydrographs using probability distribution and genetic algorithms

A nonlinear optimization model is developed to transmute a unit hydrograph into a probability distribution function (PDF). The objective function is to minimize the sum of the square of the deviation between predicted and actual direct runoff hydrograph of a watershed. The predicted runoff hydrograph is estimated by using a PDF. In a unit hydrograph, the depth of rainfall excess must be unity and the ordinates must be positive. Incorporation of a PDF ensures that the depth of rainfall excess for the unit hydrograph is unity, and the ordinates are also positive. Unit hydrograph ordinates are in terms of intensity of rainfall excess on a discharge per unit catchment area basis, the unit area thus representing the unit rainfall excess. The proposed method does not have any constraint. The nonlinear optimization formulation is solved using binary-coded genetic algorithms. The number of variables to be estimated by optimization is the same as the number of probability distribution parameters; gamma and log-normal probability distributions are used. The existing nonlinear programming model for obtaining optimal unit hydrograph has also been solved using genetic algorithms, where the constrained nonlinear optimization problem is converted to an unconstrained problem using penalty parameter approach. The results obtained are compared with those obtained by the earlier LP model and are fairly similar.     

Evaluation of Optimal River Training Work Using GA Based Linked Simulation-Optimization Approach

Use of structural measures for controlling a river to minimize its devastating effect and to utilize it for the benefit of mankind is a common practice all over the world. Because of high investment, such measures require prior investigation through model study. As lab based physical model study is very expensive and time consuming, mathematical modeling is generally used for investigating different alternatives of river training works. In this study, a new approach is proposed for deciding appropriate river training measure in a particular reach of a river or channel. In this methodology, an optimization model is linked with the hydrodynamic model for obtaining cost effective combination of groynes which will maintain a user defined flow speed in a pre-decided portion of a river reach. The optimization model is developed using binary coded Genetic Algorithm (GA) and the flow simulation model uses the Beam and Warming scheme for solving the two dimensional (2D) hydrodynamic equations of unsteady flow. The performance of the model is tested by applying the methodology in a rectangular channel for attaining different target speed values at a pre-defined portion of the channel and logical results have been obtained for all the tested scenarios.     

Optimal Design of a Stable Trapezoidal Channel Section Using Hybrid Optimization Techniques

A cost effective channel section for a specified flow rate, roughness coefficients, longitudinal slope, and various cost parameters can be determined using an optimization technique. However, the derived optimal channel section may not be feasible for construction because of in situ conditions. The local soil conditions may not support the optimal side slope of the channel and if constructed, the slope may fail. It is therefore necessary to also incorporate the criteria for side slope stability in designing an optimal open channel section. In this paper, a new methodology has been developed to design a stable and optimal channel section using hybrid optimization techniques. A genetic algorithm based optimization model is developed initially to determine the factor of safety of a channel slope for given soil parameters. This optimization model is then externally linked with a separate sequential quadratic programming based optimization model to evaluate the parameters of the stable and optimal channel section. Solution for various example problems incorporating different soil parameters are illustrated to demonstrate the applicability of the developed methodology.     

Atomic-Resolution Structures and Mode of Action of Clinically Relevant Antimicrobial Peptides

Global rise of infections and deaths caused by drug-resistant bacterial pathogens are among the unmet medical needs. In an age of drying pipeline of novel antibiotics to treat bacterial infections, antimicrobial peptides (AMPs) are proven to be valid therapeutics modalities. Direct in vivo applications of many AMPs could be challenging; however, works are demonstrating encouraging results for some of them. In this review article, we discussed 3-D structures of potent AMPs e.g., polymyxin, thanatin, MSI, protegrin, OMPTA in complex with bacterial targets and their mode of actions. Studies on human peptide LL37 and de novo-designed peptides are also discussed. We have focused on AMPs which are effective against drug-resistant Gram-negative bacteria. Since treatment options for the infections caused by super bugs of Gram-negative bacteria are now extremely limited. We also summarize some of the pertinent challenges in the field of clinical trials of AMPs.     

Optimal Design of Open Channel Section Incorporating Critical Flow Condition

The flow at critical condition of an open channel is unstable. At critical condition, a small change in specific energy will cause abrupt fluctuation in water depth of the channel. This is because the specific energy curve is almost vertical at critical state. Therefore, if the design depth of the channel is near or equal to critical depth of the channel, the shape of the channel must be altered to avoid a large fluctuation in water depth. In the present study, a nonlinear optimization model is presented for designing an optimal channel section incorporating the critical flow condition of the channel. The optimization model derives the optimal channel section at a desirable difference from the critical condition of the channel so that a small change in the specific energy of the channel will not cause an abrupt change in flow depth. The objective of the optimization model is to minimize the total construction costs of the channel. Manning’s equation is used to specify the uniform flow condition in the channel. The developed optimization model is solved by sequential quadratic programming using MATLAB. Applicability of the model is demonstrated for a trapezoidal channel section with composite roughness. However, it also can be extended to other shapes of channel.     

Sensitivity importance‐based robust optimization of structures with incomplete probabilistic information

Robust design optimization (RDO) is usually performed by minimizing the nominal value of a performance function and its dispersion considering equal importance to each individual gradient of the performance function. However, it is well known that all gradients are not equally important. An efficient sensitivity importance‐based RDO technique is proposed in the present study for optimum design of structures characterized by bounded uncertain input parameters. The basic idea of the proposed RDO formulation is to improve the robustness of a performance function by using a new gradient index that utilizes the importance factors proportional to the importance of the gradients of the performance function. The same concept is also extended to the constraints. To enhance the robustness of the constraints, the constraint functions are also modified by using the importance factor proportional to the importance of the associated gradient of the constraint. Because all the variables are not equally important to capture the presence of uncertainty, an improved robust solution is obtained by the proposed approach compared with the conventional RDO approach. The present formulation is illustrated with the help of three informative examples. The results are compared with the conventional RDO results to study the effectiveness of the proposed RDO approach. Copyright © 2012 John Wiley & Sons, Ltd.     

Designed Di‐Heme Binding Helical Transmembrane Protein

De novo designing of functional membrane proteins is fundamental in terms of understanding the structure, folding, and stability of membrane proteins. In this work, we report the design and characterization of a transmembrane protein, termed HETPRO (HEme‐binding Transmembrane PROtein), that binds two molecules of heme in a membrane and catalyzes oxidation/reduction reactions. The primary structure of HETPRO has been optimized in a guided fashion, from an antimicrobial peptide, for transmembrane orientation, defined 3D structure, and functions. HETPRO assembles into a tetrameric form, from an apo dimeric helical structure, in complex with cofactor in detergent micelles. The NMR structure of the apo HETPRO in micelles reveals an antiparallel helical dimer that inserts into the nonpolar core of detergent micelles. The well‐defined structure of HETPRO and its ability to bind to heme moieties could be utilized to develop a functional membrane protein mimic for electron transport and photosystems.