Resuspension experiments were performed in a single-family residence. Resuspension by human activity was found to elevate the mass concentration of indoor particulate matter with an aerodynamic diameter less than 10 microm (PM10) an average of 2.5 times as high as the background level. As summarized from 14 experiments, the average estimated PM10 resuspension rate by a person walking on a carpeted floor was (1.4 +/- 0.6) x 10(-4) hr(-1). The estimated residence time for PM in the indoor air following resuspension was less than 2 hr for PM10 and less than 3 hr for 2-microm tracer particles. However, experimental results show that the 2-microm tracer particles stayed in the combined indoor air and surface compartments much longer (>>19 days). Using a two-compartment model to simulate a regular deposition and resuspension cycle by normal human activity (e.g., walking and sitting on furniture), we estimated residence time for 2-microm conservative particulate pollutants to be more than 7 decades without vacuum cleaning, and months if vacuum cleaning was done once per week. This finding supports the observed long residence time of persistent organic pollutants in indoor environments. This study introduces a method to evaluate the particle resuspension rate from semicontinuous concentration data of particulate matter (PM). It reveals that resuspension and subsequent exfiltration does not strongly affect the overall residence time of PM pollutants when compared with surface cleaning. However, resuspension substantially increases PM concentration, and thus increases short-term inhalation exposure to indoor PM pollutants.

Driven by the need to reduce the installation cost and maintenance cost of structural health monitoring (SHM) systems, wireless sensor networks (WSNs) are becoming increasingly popular. Perfect time synchronization amongst the wireless sensors is a key factor enabling the use of low-cost, low-power WSNs for structural health monitoring applications based on output-only modal analysis of structures. In this paper we present a theoretical framework for analysis of the impact created by time delays in the measured system response on the reconstruction of mode shapes using the popular frequency domain decomposition (FDD) technique. This methodology directly estimates the change in mode shape values based on sensor synchronicity. We confirm the proposed theoretical model by experimental validation in modal identification experiments performed on an aluminum beam. The experimental validation was performed using a wireless intelligent sensor and actuator network (WISAN) which allows for close time synchronization between sensors (0.6-10 μs in the tested configuration) and guarantees lossless data delivery under normal conditions. The experimental results closely match theoretical predictions and show that even very small delays in output response impact the mode shapes.

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Abstract. Pseudo-transient continuation is a Newton-like iterative method for computing steady-state solutions of differential equations in cases where the initial data is far from a steady state. The iteration mimics a temporal integration scheme, with the time step being increased as steady state is approached. The iteration is an inexact Newton iteration in the terminal phase.In this paper we show how steady-state solutions to certain ordinary and differential algebraic equations with nonsmooth dynamics can be computed with the method of pseudo-transient continuation. An example of such a case is a discretized partial differential equation with a Lipschitz continuous, but non-differentiable, constitutive relation as part of the nonlinearity. In this case we can approximate a generalized derivative with a difference quotient.The existing theory for pseudo-transient continuation requires Lipschitz continuity of the Jacobian. Newton-like methods for nonsmooth equations have been globalized by trust-region methods, smooth approximations, and splitting methods in the past, but these approaches require problem-specific components in an algorithm. The method in this paper addresses the nonsmoothness directly.Key words. Pseudo-transient Continuation, Nonlinear equations, Semismooth functions, Clarke differential AMS subject classifications. 65H10, 65H20, 65L05, 1. Introduction. In this paper we show how pseudo-transient continuation (Ψtc ) can be used to solve a class of nonsmooth nonlinear equations. Ψtc is a predictor-corrector method for efficient integration of a time-dependent differential equation to steady state. The objective of the method is not temporal accuracy, but rather to resolve the transient behavior of the solution until the iteration is close to steady state, and then to increase the "time step" and transition to a fast Newton-like method.In this paper we extend the theoretical convergence results of [7,18] to problems with certain nonsmooth nonlinearities and, thereby, partially explain the results reported in [8,10]. We also show how generalized derivatives can be approximated by finite differences, and how those approximate derivatives can be used effectively both in locally convergent iterations, such as those which arise in temporal integration, and in the context of Ψtc . This aspect of the work is motivated by several papers on simulation of unsaturated flow, [10,14,15,24,30,31], in which Lipschitz continuous spline approximations to the non-Lipschitz continuous van Geneuchten and Mualem [25,33] constitutive laws are used. These nonsmooth functions are then differentiated with finite differences as if they were smooth. The results in this paper explain the success reported in those papers. Another aspect of the paper is an extension of the local results in [9,21,27,28].Ψtc methods are particularly appropriate for the types of nonsmooth nonlinearities which we discuss in this paper. Traditional methods for globalizing iterative methods for nonlinear equa-

Efficient and powerful methods are needed to overcome the inherent difficulties in the numerical solution of many simulation-based engineering design problems. Typically, expensive simulation codes are included as black-box function generators; therefore, gradient information that is required by mathematical optimization methods is entirely unavailable. Furthermore, the simulation code may contain iterative or heuristic methods, low-order approximations of tabular data, or other numerical methods which contribute noise to the objective function. This further rules out the application of Newton-type or other gradient-based methods that use traditional finite difference approximations. In addition, if the optimization formulation includes integer variables the complexity grows even further. In this paper we consider three different modeling approaches for a mixed-integer nonlinear optimization problem taken from a set of water resources benchmarking problems. Within this context, we compare the performance of a genetic algorithm, the implicit filtering algorithm, and a branch-and-bound approach that uses sequential surrogate functions. We show that the surrogate approach can greatly improve computational efficiency while locating a comparable, sometimes better, design point than the other approaches.