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Ecological momentary assessment (EMA) has been broadly used to collect real-time longitudinal data in behavioral research. Several analytic methods have been applied to EMA data to understand the changes of motivation, behavior, and emotions on a daily or within-day basis. One challenge when utilizing those methods on intensive datasets in the behavioral field is to understand when and why the methods are appropriate to investigate particular research questions. In this manuscript, we compared two widely used methods (generalized estimating equations and generalized linear mixed models) in behavioral research with three other less frequently used methods (Markov models, generalized linear mixed-effects Markov models, and differential equations) in behavioral research but widely used in other fields. The purpose of this manuscript is to illustrate the application of five distinct analytic methods to one dataset of intensive longitudinal data on drinking behavior, highlighting the utility of each method.

We consider nonparametric estimation of probability measures for parameters in problems where only aggregate (population level) data are available. We summarize an existing computational method for the estimation problem which has been developed over the past several decades [24, 5, 12, 28, 16]. Theoretical results are presented which establish the existence and consistency of very general (ordinary, generalized and other) least squares estimates and estimators for the measure estimation problem with specific application to random PDEs.

Food webs map feeding interactions among species, providing a valuable tool for understanding and predicting community dynamics. Using species' body sizes is a promising avenue for parameterizing food-web models, but such approaches have not yet been able to fully recover observed community dynamics. Such discrepancies suggest that traits other than body size also play important roles. For example, differences in species' use of microhabitat or non-consumptive effects of intraguild predators may affect dynamics in ways not captured by body size. In Laubmeier et al. (2018), we developed a dynamic food-web model incorporating microhabitat and non-consumptive predator effects in addition to body size, and used simulations to suggest an optimal sampling design of a mesocosm experiment to test the model. Here, we perform the mesocosm experiment to generate empirical timeseries of insect herbivore and predator abundance dynamics. We minimize least squares error between the model and time-series to determine parameter values of four alternative models, which differ in terms of including vs excluding microhabitat use and non-consumptive predator-predator effects. We use both statistical and expert-knowledge criteria to compare the models and find including both microhabitat use and non-consumptive predatorpredator effects best explains observed aphid and predator population dynamics, followed by the model including microhabitat alone. This ranking suggests that microhabitat plays a larger role in driving population dynamics than non-consumptive predator-predator effects, although both are clearly important. Our results illustrate the importance of additional traits alongside body size in driving trophic interactions. They also point to the need to consider trophic interactions and population dynamics in a wider community context, where non-trophic impacts can dramatically modify the interplay between multiple predators and prey. Overall, we demonstrate the potential for utilizing traits beyond body size to improve traitbased models and the value of iterative cycling between theory, data and experiment to hone current insights into how traits affect food-web dynamics. © 2022 Wootton et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. CC BY 4.0Correspondence Address: Wootton, K.L.; Swedish University of Agricultural Sciences, Sweden; email: kate.l.wootton@gmail.comData Availability Statement: All data are available from the Swedish national data service https://snd.gu.se/en. Title: Population dynamics in greenhouse experiments of aphids and their predators, based on body size and habitat use.Funding: This research was supported by the Swedish University of Agricultural Sciences, Faculty of Natural Resources and Agricultural Sciences (KLW, RB, TR) and the Swedish University of Agricultural Sciences, August T. Larsson guest researchers programme (awarded to RB) URL:https://www.slu.se/en/faculties/nj/

In this note we present a critical review of the some of the positive features as well as some of the shortcomings of the generalized sensitivity functions (GSF) of Thomaseth–Cobelli in comparison to traditional sensitivity functions (TSF). We do this from a computational perspective of ordinary least squares estimation or inverse problems using two illustrative examples: the Verhulst–Pearl logistic growth model and a recently developed agricultural production network model. Because GSF provide information on the relevance of data measurements for the identification of certain parameters in a typical parameter estimation problems, we argue that they provide the basis for new tools for investigators in design of inverse problem studies.

An approach to modeling the impact of disturbances in an agricultural production network is presented. A stochastic model and its approximate deterministic model for averages over sample paths of the stochastic system are developed. Simulations, sensitivity and generalized sensitivity analyses are given. Finally, it is shown how diseases may be introduced into the network and corresponding simulations are discussed.

Inverse problems are considered for the estimation of temporally and spatially varying coefficients in the Fokker–Planck or forward Kolmogorov equation that often arise in size/age structured population models. These are difficult problems even for simulation studies since they share certain numerical difficulties with transport-dominated diffusion-convection problems of fluid dynamics. These difficulties stem from the fact that when the convection is large compared to the diffusion, both traditional finite difference or finite element methods produce erroneous oscillatory solutions. In this paper, computational techniques are presented that combine a variation of the moving finite element method with spline approximations for the parameter estimation problems. Computational details of our numerical algorithm along with theoretical convergence results are presented. Several numerical examples illustrating the effectiveness of the method are also given.

We consider computational aspects of two problems governed by partial differential equations systems: (i) the computation of approximate Riccati feedback gains for LQR control problems,(ii) the estimation of parameters using methods based on approximate gradients computed via a costate algorithm. Sufficient conditions for convergence of algorithms for these problems are discussed; in both cases, some type of adjoint or costate system convergence is required in addition to the expected system convergence. Examples are discussed that illustrate computational difficulties which may arise if only convergence of the approximate system holds (i.e., in the event costate convergence does not hold).

In this 10-month funded effort, we report progress on the development of methods in a number of specific areas: stochastic and deterministic models for complex networks and development of inverse problem methodologies (generalized sensitivity functions). These efforts are part of our continuing fundamental research program in modeling, estimation and control methodology) theoretical, statistical and computational) for systems in the presence of major model observation uncertainties.

We proposed experiments to investigate electromagnetic acoustic waves interaction as part of a technology for pulsed microwave interrogation of materials. The technology involves using reflections of electromagnetic pulses from moving acoustic wavefronts to detect the presence of and determine composition (dielectric and geometric) of hidden objects. Applications of interest to DoD involve non-invasive interrogation of tissue, look-down surveillance and camouflage penetration (e.g., tanks under trees, and subsurface mines and bunkers) and environmental monitoring.