Optimal placement,scheduling, and routing to maximize lifetime in sensor networks

A wireless sensor network is a network consisting of distributed autonomouselectronic devices called sensors. Sensors have limited energy and capabilityfor sensing, data processing, and communicating, but they can collectivelybehave to provide an effective network that monitors an area and transmitinformation to gateway nodes or sinks, either directly or through other sensornodes. In most applications the network must operate for long periods of time,so the available energy resources of the sensors must be managed efficiently. Inthis paper, we first develop a mixed integer linear programming model tomaximize network lifetime by optimally determining locations of sensors andsinks, activity schedules of deployed sensors, and data flow routes from sensorsto sinks over a finite planning horizon subject to coverage, flow conservation,energy consumption, and budget constraints. Unfortunately, it is difficult tosolve this model exactly even for small instances. Therefore, we propose twoapproximate solution methods: a Lagrangean heuristic and a two-stage heuristicin which sensors are deployed and an activity schedule is found in the firststage, whereas sinks are located and sensor-to-sink data flow routes aredetermined in the second stage. Computational experiments performed on varioustest instances indicate that the Lagrangean heuristic is both efficient andaccurate and also outperforms the two-stage heuristic.     

Modeling and optimizing a vendor managed replenishment system using machine learning and genetic algorithms

Using a supply chain network, we demonstrate the feasibility, viability, and robustness of applying machine learning and genetic algorithms to respectively model, understand, and optimize such data intensive environments. Deployment of these algorithms, which learn from and optimize data, can obviate the need to perform more complex, expensive, and time consuming design of experiments (DOE), which usually disrupt system operations. We apply and compare the behavior and performance of the proposed machine learning algorithms to that obtained via DOE in a simulated Vendor Managed Replenishment system, developed for an actual firm. The results show that the models resulting from the proposed algorithms had strong explanatory and predictive power, comparable to that of DOE. The optimal system settings and profit were also similar to that obtained from DOE. The virtues of using machine learning and evolutionary algorithms to model and optimize data rich environments thus seem promising because they are automatic, involving little human intervention and expertise. We believe and are exploring how they can be made adaptive to improve parameter estimates with increasing data, as well as seamlessly detecting system (and therefore model) changes, thus being capable of recursively updating and reoptimizing a modified or new model.     

Effect of diffusion on gain saturation in gas lasers

A theoretical model is developed to account for the effect of diffusion on gain saturation in gas lasers. It is shown that the effect can be characterized by the functionK(k ₂ a, k ₂ b), which depends on pressure, temperature, radius of the beama, and the radius of the cylindrical cavityb. It is concluded that diffusion can be very effective whenk ₂ a is small andb is several times larger thana.     

A framework for optimization under limited information

In many real world problems, optimisation decisions have to be made with limited information. The decision maker may have no a priori or posteriori data about the often nonconvex objective function except from on a limited number of data points. The scarcity of data may be due to high cost of observation or fast-changing nature of the underlying system. This paper presents a “black-box” optimisation framework that takes into account the information collection, estimation, and optimisation aspects in a holistic and structured manner. Explicitly quantifying the observations at each optimisation step using the entropy measure from information theory, the often nonconvex-objective function to be optimised is modelled and estimated by adopting a Bayesian approach and using Gaussian processes as a state-of-the-art regression method. The resulting iterative scheme allows the decision maker to address the problem by expressing preferences for each aspect quantitatively and concurrently.     

WDS versus silicon drift detector EDS: a case report for the comparison of quantitative chemical analyses of natural silicate minerals

Electron probe microanalysis (EPMA) is an essential analytical approach to determine elemental concentrations of various solid specimens quantitatively in mineralogical, petrological and materials research. Either wavelength dispersive X-ray (WDS) or energy dispersive X-ray (EDS) spectrometric techniques can collect the characteristic X-rays generated from each element in the specimen by an incident electron beam in order to define chemical constituents. Although WDS has been the preferred technique because of its higher spectral resolution and ability to detect trace elements, new generation EDS systems with silicon drift detectors (SDD), equipped with thin windows and integrated digital processing electronics, are claimed to approach the WDS throughput. In this study, we compared the analytical capability of a SDD EDS system with respect to WDS equipped systems on natural silicate minerals. For this purpose, natural rock samples, in which the silicate minerals present had already been analysed by various WDS systems, were chosen to compare these results with the ones acquired with a SDD EDS system. SDD EDS yielded satisfactory results for major elements (Na, Mg, Al, Si, K, Ca, Ti, Mn and Fe) compared with the results of the same minerals obtained by various WDS systems.     

Investigation of 2-aminoethanethiol as corrosion inhibitor for steel using response surface methodology (RSM)

The inhibition efficiency of 2-aminoethanethiol (2-AEE) has been investigated against steel corrosion in 0.1 M HCl solution. The effect of temperature, pH, and concentration were studied with the help of potentiodynamic measurement, electrochemical impedance spectroscopy, scanning electron microscopy, and atomic absorption spectroscopy techniques. The potential of zero charge (E_pzc) studies showed that the adsorption occurs via ?SH group; the metal surface is positively charged in corrosive test solution. The adsorptive interaction is evaluated, and best correlation was obtained with Langmuir isotherm. 2-AEE was shown to have significant inhibition efficiency against steel corrosion. The response surface methodology was employed to explain the relation between pH, inhibitor concentration, and the efficiency. The regression analysis was realized for development of an equation between independent variables and the output. The success of fitting model was tested with basic statistical arguments, residual and variance analysis, T and F tests, and R ² value. The statistical evaluations showed that the obtained polynomial equation can be successfully used for optimization of applications involving the use of 2-AEE as inhibitor.     

Effect of anthraquinone on brightness value and crystalline structure of pulp during soda processes

The dependence of crystalline structure and optical properties of pulp on anthraquinone (AQ) added to the soda process at different cooking times was determined in this study. Wheat (Triticum aestevum L.) straw was used as the raw material for pulp. Soda and soda-AQ processes were selected for pulping at 80 min and 120 min. The soda-AQ process improved the yield and viscosity of pulp delignification ratio for pulping in comparison with the soda process. Crystallinity of pulp samples decreased by adding anthraquinone to the soda process because of stabilized less ordered cellulose and amorphous hemicelluloses in pulp. It was determined that crystallinity of pulp samples decreased with longer cooking time, from 80 min to 120 min, in both soda and soda-AQ processes. Monoclinic structure was dominant in pulp samples; however, the triclinic structure ratio increased in both soda and soda-AQ processes compared to raw material. It was found that brightness and lightness values in pulp samples decreased when using anthraquinone depending on the changes of the crystalline structure.