International river basin organizations: variation,options and insights

Permanent international river basin organizations (IRBOs) come in many shapes and sizes, ranging from those which are mainly facilitative in nature to those empowered to act on their own. Although differences in IRBO types may have important impacts on transboundary water resources management, systematic analysis of variation in their structure and responsibilities is scant. This paper synthesizes and applies a typology to determine the structural composition, abundance, spatial distribution, scale and in-basin configurations of the different forms of IRBOs. The results provide a set of options for future IRBOs, and serve to ground-truth and nuance theoretical divisions between different types of organizations in transboundary basins.     

Use of sesame stalk biomass for the removal of Ni(II) and Zn(II) from aqueous solutions

In this study an agricultural residue, sesame stalk, was evaluated for the removal of Ni(II) and Zn(II) metal ions from aqueous solutions. Biosorption studies were carried out at different pH, biosorbent dosage, initial metal ion concentrations, contact time, and solution temperature to determine the optimum conditions. The experimental data were modeled by Langmuir, Freundlich, Dubinin-Radushkevich (D-R) and Temkin isotherm models. Langmuir model resulted in the best fit of the biosorption data. The pseudo-first-order and pseudo-second-order kinetic models were used to describe the kinetic data and to evaluate rate constants. The best correlation was provided by the second-order kinetic model. The thermodynamic parameters such as ΔG°, ΔH° and ΔS° were calculated for predicting the nature of adsorption. The experimental results showed that sesame stalk can be used as an effective and low-cost biosorbent precursor for the removal of heavy metal ions from aqueous solutions.     

A stochastic neighborhood search approach for airport gate assignment problem

An appropriate and efficient gate assignment is of great importance in airports since it plays a major role in the revenue obtained from the airport operations. In this study, we have focused mainly on maximum gate employment, or in other words minimize the total duration of un-gated flights. Here, we propose a method that combines the benefits of heuristic approaches with some stochastic approach instead of using a purely probabilistic approach to top-down solution of the problem. The heuristic approaches are usually used in order to provide a fast solution of the problem and later stochastic searches are used in order to ameliorate the previous results of the heuristic approach whenever possible. The proposed method generates an assignment order for the whole planes that corresponds to assignment priority. The ordering process is followed by the allocation step. Since, in practice, each airport has its own physical architecture, there have been arisen many constraints mainly concerning airplane types and parking lots in this step. Sequentially handling the plane ordering and allocation phases provides us great modularity in handling the constraints. The effectiveness of the proposed methodology has been tried to be illustrated firstly on fictively generated flight schedule data and secondly on the real world data obtained from a real world application developed for ?stanbul Atatürk Airport.     

Model Inadequacy and Residuals Control Charts for Autocorrelated Processes

As a result of time series parameter estimation based on previous data, the probability content of residuals control charts may vary when standard control limits are used. In this paper, we consider the AR(1) process with the autoregressive parameter being estimated from a sample of observations. The performance of the exponentially weighted moving average (EWMA) control chart for residuals is investigated. Modified control limits that account for the uncertainty in the parameter estimate are provided. Comparisons through simulation signify the importance of the modified control limits. Copyright © 2004 John Wiley & Sons, Ltd.     

Estimation of Length Limits for Integral Bridges Built on Clay

In this paper, the maximum length limits for integral bridges built on clay are determined as a function of the ability of steel H-piles supporting the abutments to sustain thermal-induced cyclic displacements and the flexural capacity of the abutment. First, H-pile sections that can accommodate large plastic deformations are determined considering their local buckling instability. Then, a low-cycle fatigue damage model is used to determine the maximum cyclic deformations that such piles can sustain. Next, nonlinear static pushover analyses of two typical integral bridges are conducted to study the effect of various geometric, structural, and geotechnical parameters on the performance of integral bridges subjected to uniform temperature variations. Using the pushover analyses results, design guidelines are developed to enhance and determine the maximum length limits for integral bridges built on clay. It is recommended that the maximum length of concrete integral bridges be limited to 210 m (689 ft) in cold climates and 260 m (853 ft) in moderate climates and that of steel integral bridges be limited to 120 m (394 ft) in cold climates and 180 m (590 ft) in moderate climates.     

ASRSM: A Sequential Experimental Design for Response Surface Optimization

Most preset response surface methodology (RSM) designs offer ease of implementation and good performance over a wide range of process and design optimization applications. These designs often lack the ability to adapt the design on the basis of the characteristics of application and experimental space so as to reduce the number of experiments necessary. Hence, they are not cost‐effective for applications where the cost of experimentation is high or when the experimentation resources are limited. In this paper, we present an adaptive sequential response surface methodology (ASRSM) for industrial experiments with high experimentation cost, limited experimental resources, and high design optimization performance requirement. The proposed approach is a sequential adaptive experimentation approach that combines concepts from nonlinear optimization, design of experiments, and response surface optimization. The ASRSM uses the information gained from the previous experiments to design the subsequent experiment by simultaneously reducing the region of interest and identifying factor combinations for new experiments. Its major advantage is the experimentation efficiency such that for a given response target, it identifies the input factor combination (or containing region) in less number of experiments than the classical single‐shot RSM designs. Through extensive simulated experiments and real‐world case studies, we show that the proposed ASRSM method outperforms the popular central composite design method and compares favorably with optimal designs. Copyright © 2012 John Wiley & Sons, Ltd.