Order Modeling Approach Research Articles

Predicting pattern formation in embryonic stem cells using a minimalist, agent-based probabilistic model

The mechanisms of pattern formation during embryonic development remain poorly understood. Embryonic stem cells in culture self-organise to form spatial patterns of gene expression upon geometrical confinement indicating that patterning is an emergent phenomenon that results from the many interactions between the cells. Here, we applied an agent-based modelling approach in order to identify plausible biological rules acting at the meso-scale within stem cell collectives that may explain spontaneous patterning. We tested different models involving differential motile behaviours with or without biases due to neighbour interactions. We introduced a new metric, termed stem cell aggregate pattern distance (SCAPD) to probabilistically assess the fitness of our models with empirical data. The best of our models improves fitness by 70% and 77% over the random models for a discoidal or an ellipsoidal stem cell confinement respectively. Collectively, our findings show that a parsimonious mechanism that involves differential motility is sufficient to explain the spontaneous patterning of the cells upon confinement. Our work also defines a region of the parameter space that is compatible with patterning. We hope that our approach will be applicable to many biological systems and will contribute towards facilitating progress by reducing the need for extensive and costly experiments.

Syk Inhibitors: New Computational Insights into Their Intraerythrocytic Action in Plasmodium falciparum Malaria

Resistance to antimalarial drugs has spread rapidly over the past few decades. The WHO recommends artemisinin-based combination therapies for the treatment of uncomplicated malaria, but unfortunately these approaches are losing their efficacy in large areas of Southeast Asia. In 2016, artemisinin resistance was confirmed in 5 countries of the Greater Mekong subregion. We focused our study on Syk inhibitors as antimalarial drugs. The Syk protein is present in human erythrocytes, and the membrane of protein band 3 is its major target following activation by oxidant stress. Tyr phosphorylation of band 3 occurs during P. falciparum growth, leading to the release of microparticles containing hemicromes and structural weakening of the host cell membrane, simplifying merozoite reinfection. Syk inhibitors block these events by interacting with the Syk protein’s catalytic site. We performed in vitro proteomics and in silico studies and compared the results. In vitro studies were based on treatment of the parasite’s cellular cultures with different concentrations of Syk inhibitors, while proteomics studies were focused on the Tyr phosphorylation of band 3 by Syk protein with the same concentrations of drugs. In silico studies were based on different molecular modeling approaches in order to analyze and optimize the ligand–protein interactions and obtain the highest efficacy in vitro. In the presence of Syk inhibitors, we observed a marked decrease of band 3 Tyr phosphorylation according to the increase of the drug’s concentration. Our studies could be useful for the structural optimization of these compounds and for the design of novel Syk inhibitors in the future.

Modal parameter identification of time-varying systems via wavelet-based frequency response function

Modal parameter identification of systems, structures and machines with variable physical parameters is an important task for their damage diagnosis, maintenance and repair, and life cycle management, especially when time-varying vibration modes are involved. The paper proposes a new combined, two-step, i.e., estimated wavelet-based frequency response function (FRF) and least-squares iterative algorithm, modeling approach in order to determine time-varying vibration modes based on Gaussian white noise input excitation. The time-varying wavelet-based FRF is estimated based on output noise estimation model $$H_1$$ ; the second step identifies time-varying modal parameters based on estimated wavelet-based FRF with the use of least-squares iterative algorithm. Estimated wavelet-based FRF can reveal the dynamic characteristics of the system correctly similar to the theoretical FRF by single degree of freedom (SDOF). The combined method is demonstrated using system identification analysis based on the simulated stiffness-varying and mass-varying multiple-degree-of-freedom (MDOF) system subjected to random Gaussian white noise excitation. The results show that the proposed combined method accurately identifies modal parameter of the time-invariant analyzed structure, and correctly captures modal parameter of the time-varying analyzed structure. The modal frequency and shape agree with theoretical results well. The analysis results also indicate that the proposed combined method is sensitive to the position of input excitation. The estimation accuracy wavelet-based FRF can be improved by selecting the average number of times and wavelet parameters.

A Study of Whole Joint Model Calibration Using Quasi-Static Modal Analysis

Abstract Small length and time scales resulting from high-fidelity frictional contact elements make long duration, low frequency simulations intractable. Alternative reduced order modeling approaches for structural dynamics models have been developed over the last several decades to approximate joint physics based on empirical or mathematical models within a whole joint model representation. The challenge with nonlinear constitutive elements based on empirical models is that the parameters must be calibrated to either experimental or simulation data. This research proposes a model calibration technique that identifies the joint parameters of a four-parameter Iwan element based on the nonlinear natural frequencies and damping ratios computed with quasi-static modal analysis (QSMA). The QSMA algorithm is applied to the full-order finite element model (FEM) to obtain reference data, and a genetic algorithm optimizes the joint parameters within a reduced order model (ROM) by minimizing the difference between the nonlinear modal characteristics for the modes of interest. The calibration method is demonstrated on a C-Beam bolted assembly and the resulting reduced order model is validated by comparing simulations of broadband, forced transient response. The resulting calibrated model captures the nonlinear, multimodal response at a significantly reduced computational cost and can be utilized for producing efficient models that do not have supporting experimental data for calibration.

Probing Sensitivity of Discharge Characteristics to Model Selection Using Uncertainty Quantification in an Aprotic Li-Oxygen Battery

Currently, there are several models in the literature, such as kinetic, microstructural, and mass transport, that describe a Li-oxygen battery’s discharge behavior. Many of these models are calibrated and tested at low current densities and are not easily transferable to higher current densities (local current density exceeding 1µA/cm2). Even at low current densities, there is no quantitative method for a researcher to choose a reaction kinetic model such as classical Butler-Volmer and its derivatives, or modified Marcus-Hush-Chidsey, a resistance model for lithium peroxide such as electron transport via tunneling or linear resistivity, a surface coverage model (lithium peroxide growth) such as partial coverage or full coverage, and mass transport model (a review of these models is discussed in Ref. [1]). Also, it is time-consuming to test different models at high current density (≥1C) due to a lack of well-tested models and well-calibrated model parameters. For this presentation, we will develop an analytical model, which acts as a surrogate model for a full finite element model to predict discharge time and discharge voltage. Next, we use an uncertainty quantifying technique called reduced-order stochastic optimization [2, 3] to determine the uncertainty in model parameters for rate kinetics, lithium peroxide resistivity, and parasitic resistance. Finally, a finite element simulation is performed to determine the error introduced by the surrogate model (from various assumptions and simplifications) and its influence on the uncertainty in the model parameters.Our preliminary polarization analysis (overvoltage vs. current density) shows that the resistance models, coupled with Butler-Volmer kinetics, cannot describe the voltage polarization observed in the experiments at high current densities. In order to quantify and rank models describing the lithium peroxide deposition, first, an analytical model is developed to account for discharge product distribution, concentration polarization, and voltage polarization as a function of time, based on the previous work on aqueous electrolytes, presented in Ref. [4]. The analytical model considers reaction kinetics, oxygen mass transport, voltage polarization from lithium peroxide deposition as a conformal coating, and the change in porosity, lithium peroxide thickness, and the active surface is assumed to be uniform throughout the cathode.In order to rank the kinetic and resistance models, the uncertainty in the model parameters is assumed using a probability distribution function, and the resulting statistics are gathered using the Stochastic Reduced Order Model (SROM) approach. SROM is a computationally lite approach that uses significantly less number of samples compared to Monte Carlo to represent the input distribution. Since the number of samples required for Monte Carlo increases with model complexity, which significantly increases the computational time and resources required for full finite element simulations. The SROM approach can reduce the computational requirement by at least ten times without introducing algorithm based error into the analysis.Although the analytical model incorporates the important aspects to simulate a battery, it neglects the complexity of spatial distribution and temporal variation of the discharge product and the complicated relationship between microstructure and mass transport of oxygen and lithium ions. These neglected interactions introduce a model error in the final parameter estimation and need to be accounted for in order to rank various models. The finite element model (FEM) is used to quantify the error in the surrogate model because of its low fidelity. The FEM model is implemented in COMSOL and considers porous electrode theory for the cathode, concentrated theory for the electrolyte, a resistance model for the discharge products, a kinetic model, Fickian diffusion for the oxygen transport, and an oxygen dissolution model [5].

The prediction of crack propagation in coarse grain RR1000 using a unified modelling approach

The polycrystalline nickel-base superalloy RR1000 is used as turbine rotor material in Rolls-Royce aero engines and has to withstand a wide variety of load and temperature changes during operation. In order to maximize the potential of the material and to improve component design, it is of great interest to understand, and subsequently be able to accurately model the crack propagation caused by thermo-mechanical fatigue conditions. In this work, experimental data is analysed and used to inform unified modelling approaches in order to predict the crack propagation behaviour of RR1000 under a variety of stress-controlled thermo-mechanical fatigue conditions.

An Evolve-Then-Correct Reduced Order Model for Hidden Fluid Dynamics

In this paper, we put forth an evolve-then-correct reduced order modeling approach that combines intrusive and nonintrusive models to take hidden physical processes into account. Specifically, we split the underlying dynamics into known and unknown components. In the known part, we first utilize an intrusive Galerkin method projected on a set of basis functions obtained by proper orthogonal decomposition. We then present two variants of correction formula based on the assumption that the observed data are a manifestation of all relevant processes. The first method uses a standard least-squares regression with a quadratic approximation and requires solving a rank-deficient linear system, while the second approach employs a recurrent neural network emulator to account for the correction term. We further enhance our approach by using an orthonormality conforming basis interpolation approach on a Grassmannian manifold to address off-design conditions. The proposed framework is illustrated here with the application of two-dimensional co-rotating vortex simulations under modeling uncertainty. The results demonstrate highly accurate predictions underlining the effectiveness of the evolve-then-correct approach toward real-time simulations, where the full process model is not known a priori.

A long short-term memory embedding for hybrid uplifted reduced order models

In this paper, we introduce an uplifted reduced order modeling (UROM) approach through the integration of standard projection based methods with long short-term memory (LSTM) embedding. Our approach has three modeling layers or components. In the first layer, we utilize an intrusive projection approach to model the dynamics represented by the largest modes. The second layer consists of an LSTM model to account for residuals beyond this truncation. This closure layer refers to the process of including the residual effect of the discarded modes into the dynamics of the largest scales. However, the feasibility of generating a low rank approximation tails off for higher Kolmogorov n-width systems due to the underlying nonlinear processes. The third uplifting layer, called super-resolution, addresses this limited representation issue by expanding the span into a larger number of modes utilizing the versatility of LSTM. Therefore, our model integrates a physics-based projection model with a memory embedded LSTM closure and an LSTM based super-resolution model. In several applications, we exploit the use of Grassmann manifold to construct UROM for unseen conditions. We perform numerical experiments by using the Burgers and Navier–Stokes equations with quadratic nonlinearity. Our results show the robustness of the proposed approach in building reduced order models for parameterized systems and confirm the improved trade-off between accuracy and efficiency.

Environmental Drivers and Distribution Patterns of Carnivoran Assemblages (Mammalia: Carnivora) in the Americas: Past to Present

Understanding species distributions and the variation of assemblage structure in time and space are fundamental goals of biogeography and ecology. Here, we use an ecological niche modeling and macroecological approach in order to assess whether constraints patterns in carnivoran richness and composition structures in replicated assemblages through time and space should reflect environmental filtering through ecological niche constraints from the Last Inter-glacial (LIG), Last Glacial Maximum (LGM) to the present (C) time. Our results suggest a diverse distribution of carnivoran co-occurrence patterns at the continental scale as a result of spatial climatic variation as an important driver constrained by the ecological niches of the species. This influence was an important factor restructuring assemblages (more directly on richness than composition patterns) not only at the continental level, but also from regional and local scales and this influence was geographically different throughout the space in the continent. These climatic restrictions and disruption of the niche during the environmental changes at the LIG-LGM-C transition show a considerable shift in assemblage richness and composition across the Americas, which suggests an environmental filtering mainly during the LGM, explaining between 30 and 75% of these variations through space and time, with more accentuated changes in North than South America. LGM was likely to be critical in species functional adaptation and distribution and therefore on assemblage structuring and rearranging from continental to local scales through time in the continent. Still, extinction processes are the result of many interacting factors, where climate is just one part of the picture.

An Efficient Radial Basis Function Meshfree Local Petrov–Galerkin Method for Modeling the Unidirectional Fully Developed Fluid Flow

In hydrodynamics the applications range of incompressible flows is very wide. In this study, a robust, high order modeling approach is introduced, based on the MLPG meshfree method -based radial basis functions (RBF- MLPG) method, for solving the incompressible flow field. In other words a MLPG meshfree method based on an interpolation function is presented to solve the 2‐D unsteady incompressible fully developed fluid flows. This meshfree method is based on the quartic (4^th order) spline. The method is then compared against the Finite Element Method on a test case of unidirectional fully developed incompressible fluid. The performance of this weight function proved that the quartic (4th order) spline gains the highest accuracy, convergence and efficiency. Finally, it can be concluded that the presented method is formidable for simulating fluid dynamics.

Breaking the Kolmogorov Barrier in Model Reduction of Fluid Flows

Turbulence modeling has been always a challenge, given the degree of underlying spatial and temporal complexity. In this paper, we propose the use of a partitioned reduced order modeling (ROM) approach for efficient and effective approximation of turbulent flows. A piecewise linear subspace is tailored to capture the fine flow details in addition to the larger scales. We test the partitioned ROM for a decaying two-dimensional (2D) turbulent flow, known as 2D Kraichnan turbulence. The flow is initiated using an array of random vortices, corresponding to an arbitrary energy spectrum. We show that partitioning produces more accurate and stable results than standard ROM based on a global application of modal decomposition techniques. We also demonstrate the predictive capability of partitioned ROM through an energy spectrum analysis, where the recovered energy spectrum significantly converges to the full order model’s statistics with increased partitioning. Although the proposed approach incurs increased memory requirements to store the local basis functions for each partition, we emphasize that it permits the construction of more compact ROMs (i.e., of smaller dimension) with comparable accuracy, which in turn significantly reduces the online computational burden. Therefore, we consider that partitioning acts as a converter which reduces the cost of online deployment at the expense of offline and memory costs. Finally, we investigate the application of closure modeling to account for the effects of modal truncation on ROM dynamics. We illustrate that closure techniques can help to stabilize the results in the inertial range, but over-stabilization might take place in the dissipative range.

Reduced order modeling of hysteretic structural response and applications to seismic risk assessment

A reduced order modeling approach is presented to alleviate the computational burden associated with using high-fidelity finite element models (FEMs) to describe hysteretic structural response for earthquake engineering time-history analysis. The reduced order model (ROM) is developed using data from the original high-fidelity FEM. Static condensation is first used to obtain the condensed stiffness matrix and the linear equations of motion for the dynamic degrees of freedom (DoFs). The restoring forces prescribed by the linear stiffness matrix are then substituted with hysteretic ones by replacing the linear springs connecting each of the DoFs with hysteretic ones. Different hysteretic models are considered, including peak-oriented, Masing and Bouc-Wen type of hysteresis, whereas more complex relationships obtained by combining multiple simpler hysteretic models are also discussed. The hysteretic spring parameters are calibrated by comparing the reduced order model time-history response to the time-history response of the initial FEM for a range of different excitations. The characteristics for each of the considered springs are separately selected and an efficient solution of the associated calibration problem is facilitated through a sequential, hierarchical approach. The excitations utilized for the reduced order model calibration are carefully selected so that nonlinear characteristics of the FEM are appropriately excited to support the tuning of all the important hysteretic spring features. The accuracy and the computational savings of the calibrated reduced order model are evaluated for risk assessment applications, separately examining different levels of intensity. Comparison extends to three structures, with the high-fidelity FEMs developed in OpenSees.

A nonintrusive adaptive reduced order modeling approach for a molten salt reactor system

We use a novel nonintrusive adaptive Reduced Order Modeling method to build a reduced model for a molten salt reactor system. Our approach is based on Proper Orthogonal Decomposition combined with locally adaptive sparse grids. Our reduced model captures the effect of 27 model parameters on keff of the system and the spatial distribution of the neutron flux and salt temperature. The reduced model was tested on 1000 random points. The maximum error in multiplication factor was found to be less than 50 pcm and the maximum L2 error in the flux and temperature were less than 1%. Using 472 snapshots, the reduced model was able to simulate any point within the defined range faster than the high-fidelity model by a factor of 5×106. We then employ the reduced model for uncertainty and sensitivity analysis of the selected parameters on keff and the maximum temperature of the system.

Numerical Study of Perturbators Influence on Heat Transfer and Investigation of Collector Performance for a Micro-Combined Heat and Power System Application

The objective of this article is to analyze complex micro-channels with wire-net and S-shaped perturbators and implement a reduced order modeling (ROM) approach to assess the entire heat exchanger performance and validate through experiments. Shifting the critical Reynolds number to lower values using perturbators decreases the pressure losses and enhance the thermal efficiency. There is an optimum mass flow (for both perturbators) where the thermal efficiency reaches maximum. The thermal efficiency of the wire-net perturbator is relatively high compared to S-shaped perturbators. The S-shaped perturbators induces strong wall-normal velocity fluctuations and enhances the heat transfer. Furthermore, the turbulence production term provides a deeper insight into flow attachment and detachment near the wire net intersections. The computational fluid dynamics approach (conjugate heat transfer models and ROM) was introduced to reduce the computational grid size and predict the collector performance. The secondary collector performance is determined by considering the microchannels as porous mediums. Apparently, the primary collector performance is determined by considering both secondary collectors and microchannels as porous mediums. The cylindrical secondary collectors contribute nearly 40–50% of the pressure drop. Experimental validation showed that the ROM predicts the heat exchanger performance with a good (<4.4%) accuracy.

Development of operation multi-objective model of dam reservoir under conditions of temperature variation and loading using NSGA-II and DANN models: a case study of Karaj/Amir Kabir dam

For determining optimal operation policies, it is of vital importance to the structural stability of dams, besides meeting the downstream water demands during the operation period. Since water level variations in upstream dam cause changes in loadings of the dam body, ignoring factors, which are effective in the structural stability of dams, might result in cracks and instability of dams in the long term. Therefore, in the present study, a multi-objective model was developed by integrating the reservoir structure simulation model and optimization approach in order to meet water supply demands and maintain the dam structural stability. Linear static analysis of Amir Kabir dam was performed using ABAQUS 6.14.3 software to extract the structural parameters, indicating the dam structural stability. For this purpose, a dynamic artificial neural network was also used as an interfaced model to relate the results of the simulator model to those of the proposed approach. The best solution was extracted from the optimal trade-off curve, which was developed using non-dominated sorting genetic algorithm (NSGA-II) and multi-criteria decision-making methods. Results showed that the allocation to the downstream water demands was applied in the best possible way based on the stability of the dam. A comparison between the existing and optimal conditions indicated that the value of reliability and vulnerability coefficients was improved in optimal conditions in comparison with the existing conditions. According to the results, there was a 7% rise in the fuzzy stability index in optimal conditions compared to the existing conditions that indicate better performance in the optimal model. Therefore, as to consider the downstream water demands, summer was the most deficient and spring was the least deficient with the water supply of 30% and 72%, respectively. During summer, the average optimal allocation and the average demand were 20.51 MCM and 67.50 MCM, respectively. However, this season showed the best performance in preserving the dam stability with having the lowest parameters of the dam structural stability compared to other seasons. Finally, the support vector regression method was used to develop an optimal operational policy based on optimal allocation values obtained from the proposed model structure. The optimal developed policy can be used to determine optimal allocation based on hydrological parameters of the dam and its water demands. Meanwhile, it can maintain dam stability without implementing the proposed structure.

Preface

The conference proceedings contain the selected papers presented at the 5th International Conference “Ecosystem dynamics in the Holocene”. This conference is a multidisciplinary scientific meeting, which is organized every 3 years in different scientific centers across Russia since 2004. It was held in Moscow, 11-15 November 2019, and dedicated to the 100th anniversary of L.G. Dinesman, the famous Russian biologist and palaeoecologist. The conference was organized by the Institute of Geography of the Russian Academy of Science (RAS), the Geological Institute of RAS, A.N. Severtsov Institute of Ecology and Evolution of RAS and the Environmental Evolution Commission of the International Geographical Union.The conference was focused on the long-term studies of ecosystem dynamics, climate changes and human activity in different geographical regions during the Holocene (the last 11700 years) as well as on new methods and approaches for reconstructions of the past environmental changes. It provided unique opportunities for scientists to introduce novel ideas and studies in the following topics:• Dynamics of marine, fresh water and terrestrial ecosystems in the Holocene;• Holocene landscape and climate changes;• Natural and anthropogenic factors in the Holocene ecosystem dynamics;• Environment condition and human occupation in the Holocene;• Ecological modeling and forecasting; and• Methods of ecosystem studies.More than 100 oral and poster presentations were made at the sessions of the conference. The present special issue of IOP Conference Series: Earth and Environmental Science contains the results of 27 specially selected reports focused of the most important problems of modern palaeogeography, palaeoecology and palaeoclimatology.The papers demonstrate that the expertise of the modern state of natural ecosystems and projections of their future changes is quite impossible without comprehensive understanding of the past environment and climate dynamics and ecosystem functioning including its adaptation to human impact. The papers relate to different scientific areas and introduce new results of the experimental and theoretical studies that are based on detailed palaeoecological information (pollen, plant macrofossil, diatoms, testate amoebae, etc), radiocarbon dates, evidence about beginning of human activities and archeological findings. The special attention in the Special Issue is given to the studies of the land surface - atmosphere interaction using various experimental and modeling approaches in order to provide a better understanding of the response of terrestrial vegetation to climate variability in past epochs.The Organizing Committee expresses deep gratitude to all organizers and participants of the conference for their active participation, interest and discussion on the Holocene ecosystem dynamics, climate changes and human activity in different regions which is a significant contribution to the development of palaeoecology. The Organizing Committee thanks the Russian Foundation for Basic Research (project No. 19-05-20072) and the Environment Evolution Commission of the International Geographical Union for provided financial support.EditorsDr.Sc. Elena Novenko, the chair of the Organizing CommitteeProf. Dr. Alexander Olchev

Data Driven Prognosis of Fracture Dynamics Using Tensor Train and Gaussian Process Regression

Accurate prediction of the mechanical failure of structural components plays an important role in the design of engineering structures. However, the fracture process is challenging to model numerically due to the existence of an elastic singularity at the crack tip. The phase field model (PFM) is one promising approach for modeling brittle fracture using an auxiliary field variable to regularize discontinuities associated with sharp cracks. Unfortunately, it is generally computationally expensive due to the need to solve a fourth-order partial differential equation. In order to reduce the computational burden for parameterized problems, different reduced order modeling approaches such as proper orthogonal decomposition (POD) and discrete empirical interpolation method (DEIM), have been proposed. However, these frameworks are model-based and intrusive approaches and need in-depth efforts in modifying existing complex simulation codes. In this work, a tensor train (TT) approach is proposed in combination with Gaussian process regression (GPR) for modeling and predicting the dynamics of fracture in composite materials. As opposed to POD and DEIM, the TT-GPR approach is a fully data-driven and non-intrusive approach. In this work, we study the fracture of a brittle elastic 2D rectangular slab with a pre-existing crack under Mode I (crack opening) loading conditions. The high dimensional training data for the TT model was generated using PFM with the finite difference method. The predictions by the TT-GPR model are compared with the results from the finite difference method. The TT-GPR is robust enough to predict the catastrophic evolution of the crack growth trend with an accuracy of up to 95% with computational load reduced by two orders of magnitude.

Reduced Order Model Approach to Inverse Scattering

We study an inverse scattering problem for a generic hyperbolic system of equations with an unknown coefficient called the reflectivity. The solution of the system models waves (sound, electromagne...

The Effectiveness of Dog Population Management: A Systematic Review.

Simple SummaryDomestic dogs are abundant worldwide—as owned pets, unowned strays, and feral animals. High numbers of free-roaming dogs can be a concern because of the risks they pose to public health, animal welfare, and wildlife. Using a systematic review process, we investigated what the research published to date can tell us about the effectiveness of different dog population management methods. We found that management methods for dog populations have been researched in multiple countries worldwide, using a wide range of indicators to assess method effectiveness. We outline the results and suggest improvements to help guide future dog population management policy.The worldwide population of domestic dogs is estimated at approximately 700 million, with around 75% classified as “free-roaming”. Where free-roaming dogs exist in high densities, there are significant implications for public health, animal welfare, and wildlife. Approaches to manage dog populations include culling, fertility control, and sheltering. Understanding the effectiveness of each of these interventions is important in guiding future dog population management. We present the results of a systematic review of published studies investigating dog population management, to assess: (1) where and when studies were carried out; (2) what population management methods were used; and (3) what was the effect of the method. We evaluated the reporting quality of the published studies for strength of evidence assessment. The systematic review resulted in a corpus of 39 papers from 15 countries, reporting a wide disparity of approaches and measures of effect. We synthesised the management methods and reported effects. Fertility control was most investigated and had the greatest reported effect on dog population size. Reporting quality was low for power calculations (11%), sample size calculations (11%), and the use of control populations (17%). We provide recommendations for future studies to use common metrics and improve reporting quality, study design, and modelling approaches in order to allow better assessment of the true impact of dog population management.

Predictive multiscale modeling for Unidirectional Carbon Fiber Reinforced Polymers

This paper presents a predictive multiscale modeling scheme for Unidirectional (UD) Carbon Fiber Reinforced Polymers (CFRP). A bottom-up modeling procedure is discussed for predicting the performance of UD structures. UD material responses are computed from high fidelity Representative Volume Elements (RVEs). A data-driven Reduced Order Modeling approach compresses RVEs into Reduced Order Models so material responses can be computed in a concurrent fashion along with the structural level simulation. The approach presented in this paper is validated against experimental data and is believed to provide design guidance for future fiber reinforced polymers development.