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1. Merging dynamical and structural indicators to measure resilience in multispecies systems

   https://doi.org/10.1111/1365-2656.13421  

   Medeiros, Lucas P.; Song, Chuliang; Saavedra, Serguei (February 2021, Journal of Animal Ecology)

   Abstract Resilience is broadly understood as the ability of an ecological system to resist and recover from perturbations acting on species abundances and on the system's structure. However, one of the main problems in assessing resilience is to understand the extent to which measures of recovery and resistance provide complementary information about a system. While recovery from abundance perturbations has a strong tradition under the analysis of dynamical stability, it is unclear whether this same formalism can be used to measure resistance to structural perturbations (e.g. perturbations to model parameters).Here, we provide a framework grounded on dynamical and structural stability in Lotka–Volterra systems to link recovery from small perturbations on species abundances (i.e. dynamical indicators) with resistance to parameter perturbations of any magnitude (i.e. structural indicators). We use theoretical and experimental multispecies systems to show that the faster the recovery from abundance perturbations, the higher the resistance to parameter perturbations.We first use theoretical systems to show that the return rate along the slowest direction after a small random abundance perturbation (what we call full recovery) is negatively correlated with the largest random parameter perturbation that a system can withstand before losing any species (what we call full resistance). We also show that the return rate along the second fastest direction after a small random abundance perturbation (what we call partial recovery) is negatively correlated with the largest random parameter perturbation that a system can withstand before at most one species survives (what we call partial resistance). Then, we use a dataset of experimental microbial systems to confirm our theoretical expectations and to demonstrate that full and partial components of resilience are complementary.Our findings reveal that we can obtain the same level of information about resilience by measuring either a dynamical (i.e. recovery) or a structural (i.e. resistance) indicator. Irrespective of the chosen indicator (dynamical or structural), our results show that we can obtain additional information by separating the indicator into its full and partial components. We believe these results can motivate new theoretical approaches and empirical analyses to increase our understanding about risk in ecological systems. 

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2. Resilient Network Dynamics for Food, Energy, and Water Systems

   https://doi.org/10.1109/RNDM59149.2023.10293043  

   Modarresi, Alex; Symons, John (September 2023, IEEE)

   Developing resilience in food, energy, and water (FEW) systems is a critical priority. The structural topology of the components of complex agricultural systems interact in ways that can only be grasped via models and simulations. We extend previous work on graph models that represent complex inter-level topology. We show some results of simulating system dynamic model as a formally tractable way of understanding resilience in these systems. 

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3. Exit time as a measure of ecological resilience

   https://doi.org/10.1126/science.aay4895  

   Arani, Babak M. S.; Carpenter, Stephen R.; Lahti, Leo; van Nes, Egbert H.; Scheffer, Marten (June 2021, Science)

   Ecological resilience is the magnitude of the largest perturbation from which a system can still recover to its original state. However, a transition into another state may often be invoked by a series of minor synergistic perturbations rather than a single big one. We show how resilience can be estimated in terms of average life expectancy, accounting for this natural regime of variability. We use time series to fit a model that captures the stochastic as well as the deterministic components. The model is then used to estimate the mean exit time from the basin of attraction. This approach offers a fresh angle to anticipating the chance of a critical transition at a time when high-resolution time series are becoming increasingly available. 

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4. Computer-Aided Design-Based Topology Optimization System With Dynamic Feature Shape and Modeling History Evolution

   https://doi.org/10.1115/1.4045301  

   Liu, Jikai; To, Albert C. (July 2020, Journal of Mechanical Design)

   null (Ed.)

   Abstract Conventional topology optimization presentations generally highlight the numerical and optimization details established on the specially customized discrete geometric modeling system, which is incompatible with the existing computer-aided design (CAD)/computer-aided engineering (CAE) systems. Therefore, tedious preprocessing and postprocessing are required to improve the editability and manufacturability, which are both time consuming and labor intensive. Hence, to address this challenging issue, a novel CAD-based topology optimization system is developed in this work. The following points are highlighted: (i) interoperability issue between CAD and topology optimization was addressed by using macro files to communicate the feature and modeling history information; then, (ii) structural shape and topology optimization is performed based on a B-spline-based approach, which inherits the original spline information from the upstream CAD model and of course, can return spline-based geometric information for optimized CAD model generation, and the last but the most important point to mention is that, (iii) modeling history was incorporated into the optimization process and dynamic modeling history change is enabled based on the optimality criteria. This final point is significant because history-based CAD modeling is still a main-stream approach, especially given the excellent postmodeling editability and design intent capture. 

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5. Characterizations and Optimization for Resilient Manufacturing Systems With Considerations of Process Uncertainties

   https://doi.org/10.1115/1.4055425  

   Ma, Qiyang; Che, Yiming; Cheng, Changqing; Wang, Zimo (February 2023, Journal of Computing and Information Science in Engineering)

   Abstract The recent COVID-19 pandemic reveals the vulnerability of global supply chains: the unforeseen supply crunches and unpredictable variability in customer demands lead to catastrophic disruption to production planning and management, causing wild swings in productivity for most manufacturing systems. Therefore, a smart and resilient manufacturing system (S&RMS) is promised to withstand such unexpected perturbations and adjust promptly to mitigate their impacts on the system’s stability. However, modeling the system’s resilience to the impacts of disruptive events has not been fully addressed. We investigate a generalized polynomial chaos (gPC) expansion-based discrete-event dynamic system (DEDS) model to capture uncertainties and irregularly disruptive events for manufacturing systems. The analytic approach allows a real-time optimization for production planning to mitigate the impacts of intermittent disruptive events (e.g., supply shortages) and enhance the system’s resilience. The case study on a hybrid bearing manufacturing workshop suggests that the proposed approach allows a timely intervention in production planning to significantly reduce the downtime (around one-fifth of the downtime compared to the one without controls) while guaranteeing maximum productivity under the system perturbations and uncertainties. 

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