A Cognitive Model of an Epistemic Community: Mapping the Dynamics of Shallow Lake Ecosystems

We used fuzzy cognitive mapping (FCM) to develop a generic shallow lake ecosystem model by augmenting the individual cognitive maps drawn by 8 scientists working in the area of shallow lake ecology. We calculated graph theoretical indices of the individual cognitive maps and the collective cognitive map produced by augmentation. The graph theoretical indices revealed internal cycles showing non-linear dynamics in the shallow lake ecosystem. The ecological processes were organized democratically without a top-down hierarchical structure. The steady state condition of the generic model was a characteristic turbid shallow lake ecosystem since there were no dynamic environmental changes that could cause shifts between a turbid and a clearwater state, and the generic model indicated that only a dynamic disturbance regime could maintain the clearwater state. The model developed herein captured the empirical behavior of shallow lakes, and contained the basic model of the Alternative Stable States Theory. In addition, our model expanded the basic model by quantifying the relative effects of connections and by extending it. In our expanded model we ran 4 simulations: harvesting submerged plants, nutrient reduction, fish removal without nutrient reduction, and biomanipulation. Only biomanipulation, which included fish removal and nutrient reduction, had the potential to shift the turbid state into clearwater state. The structure and relationships in the generic model as well as the outcomes of the management simulations were supported by actual field studies in shallow lake ecosystems. Thus, fuzzy cognitive mapping methodology enabled us to understand the complex structure of shallow lake ecosystems as a whole and obtain a valid generic model based on tacit knowledge of experts in the field.Comment: 24 pages, 5 Figure

Representing preferences by choquet integral: Guidelines to specify the capacity type

© 2020 by the authors; licensee Growing Science, Canada.This study considers representing decision maker preferences by Choquet integral in existence of interactions among criteria. Parameters of the Choquet integral are capacities which assign weights not only to criteria but also to each subset of criteria. This property provides Choquet integral with the ability of modeling some types of interactions. Different capacity types with different degrees of complexity have been defined in the literature. After making a review on the dependence (interaction) and independence concepts used in the multiple criteria decision making literature, we study and represent structures of interactions that can be handled by different capacity types through intuitive graphical demonstrations. Afterwards, we provide guidelines for specifying the appropriate capacity type in practical applications. Such guidance has not been provided in the literature for the practitioners to the best of our knowledge

Mixture design: A review of recent applications in the food industry

Design of experiments (DOE) is a systematic approach to applying statistical methods to the experimental process. The main purpose of this study is to provide useful insights into mixture design as a special type of DOE and to present a review of current mixture design applications in the food industry. The theoretical principles of mixture design and its application in the food industry, based on an extensive review of the literature, are described. Mixture design types, such as simplex-lattice, simplex-centroid, D-optimal and crossed mixture, are compared in terms of their characteristics and advantages. Multi-response optimization and the application of some heuristics and softwares are discussed. This review focuses on an overview of the more specialized and novel food applications in the recent literature.</span

Assisting continuous biomanufacturing through advanced control in downstream purification

Aiming to significantly improve their processes and secure market share, monoclonal antibody (mAb) manufacturers seek innovative solutions that will yield improved production profiles. In that space, continuous manufacturing has been gaining increasing interest, promising more stable processes with lower operating costs. However, challenges in the operation and control of such processes arise mainly from the lack of appropriate process analytics tools that will provide the required measurements to guarantee product quality. Here we demonstrate a Process Systems Engineering approach for the design a novel control scheme for a semi-continuous purification process. The controllers are designed employing multi-parametric Model Predictive Control (mp-MPC) strategies and the successfully manage to: (a) follow the system periodicity, (b) respond to measured disturbances and (c) result in satisfactory yield and product purity. The proposed strategy is also compared to experimentally optimized profiles, yielding a satisfactory agreement