Initial Geometrical Templates with Parameter Sets for Active Contour on Skin Cancer Boundary Segmentation

For active-contour-based surgery systems, the success of skin cancer boundary segmentation depends on the initialization point of the snake model, which is a task originally performed by skillful experts, and on the parameters set for the algorithms of active contour. This paper proposes initial geometrical templates and parameter sets for the active contour on skin cancer boundary segmentation. To establish initial geometrical templates and parameter sets for the active contour, first, template candidates, which are geometrically designed by users in advance, are simply calculated based on similarity with a skin cancer boundary, and the candidate with the least difference is selected as an initial template. Initially, all candidate templates are performed before the test with some selected skin cancer samples by randomly varying needed parameters to determine parameter sets for each template. The parameter set is therefore implicitly selected as the suitable set with the selected initial template. Experiments with 227 skin cancer samples were performed based on our proposed initial templates and parameter sets, and the results show 99.46% accuracy, 97.43% sensitivity, and 99.87% specificity approximately in which accuracy, sensitivity, and specificity were improved by 0.26%, 0.36%, and 0.26%, respectively, compared with the conventional method.

Intervention Time in Target-Oriented Chaos Control

The timing of interventions plays a central role in managing and exploiting biological populations. However, few studies in the literature have addressed its effect on population stability. The Seno equation is a discrete-time equation that describes the dynamics of single-species populations harvested according to the proportional feedback method at any moment between two consecutive censuses. Here we study a discrete-time equation that generalizes the Seno equation by considering the management and exploitation of populations through the target-oriented chaos control method. We investigate the combined effect of timing, targeting, and control on population stability, focusing on global stability. We prove that high enough control values create a positive equilibrium that attracts all positive solutions. We also prove that it is possible to determine parameter values to stabilize the controlled populations at any preset population size. Finally, we investigate the parameter combinations for which the management and exploitation are optimized in different scenarios.

From theory to experiment and back again — Challenges in quantifying a trait-based theory of predator-prey dynamics

Food webs map feeding interactions among species, providing a valuable tool for understanding and predicting community dynamics. Trait-based approaches to food webs are increasingly popular, using e.g. species’ body sizes to parameterize dynamic models. Although partly successful, models based on body size often cannot fully recover observed dynamics, suggesting that size alone is not enough. For example, differences in species’ use of microhabitat or non-consumptive effects of other predators may affect dynamics in ways not captured by body size. Here, we report on the results of a pre-registered study (Laubmeier et al. 2018) where we developed a dynamic food-web model incorporating body size, microhabitat use, and non-consumptive predator effects and used simulations to optimize the experimental design. Now, after performing the mesocosm experiment to generate empirical time-series of insect herbivore and predator abundance dynamics, we use the inverse method to determine parameter values of the dynamic model. We compare four alternative models with and without microhabitat use and non-consumptive predator effects. The four models achieve similar fits to observed data on herbivore population dynamics, but build on different estimates for the same parameters. Thus, each model predicts substantially different effects of each predator on hypothetical new prey species. These findings highlight the imperative of understanding the mechanisms behind species interactions, and the relationships mediating the effects of traits on trophic interactions. In particular, we believe that increased understanding of the estimates of optimal predator-prey body-size ratios and maximum feeding rates will improve future predictions. In conclusion, our study demonstrates how iterative cycling between theory, data and experiment may be needed to hone current insights into how traits affect food-web dynamics.

Computing knots by quadratic and cubic polynomial curves

A new method is presented to determine parameter values (knot) for data points for curve and surface generation. With four adjacent data points, a quadratic polynomial curve can be determined uniquely if the four points form a convex polygon. When the four data points do not form a convex polygon, a cubic polynomial curve with one degree of freedom is used to interpolate the four points, so that the interpolant has better shape, approximating the polygon formed by the four data points. The degree of freedom is determined by minimizing the cubic coefficient of the cubic polynomial curve. The advantages of the new method are, firstly, the knots computed have quadratic polynomial precision, i.e., if the data points are sampled from a quadratic polynomial curve, and the knots are used to construct a quadratic polynomial, it reproduces the original quadratic curve. Secondly, the new method is affine invariant, which is significant, as most parameterization methods do not have this property. Thirdly, it computes knots using a local method. Experiments show that curves constructed using knots computed by the new method have better interpolation precision than for existing methods.

Determination of COVID-19 parameters for an agent-based model: Easing or tightening control strategies

Different agent-based models have been developed to estimate the spread progression of coronavirus disease 2019 (COVID-19) and to evaluate different control strategies to control outbreak of the infectious disease. While there are several estimation methods for the disease-specific parameters of COVID-19, they have been used for aggregate level models such as SIR and not for agent-based models. We propose a mathematical structure to determine parameter values of agent-based models considering the mutual effects of parameters. Then, we assess the extent to which different control strategies can intervene the transmission of COVID-19. Accordingly, we consider scenarios of easing social distancing restrictions, opening businesses, speed of enforcing control strategies and quarantining family members of isolated cases on the disease progression. We find the social distancing compliance level in the Sydney greater metropolitan area to be around 85%. Then we elaborate on consequences of easing the compliance level in the disease suppression. We also show that tight social distancing levels should be considered when the restrictions on businesses and activity participations are easing.

Bayesian Inference in Snow Avalanche Simulation with r.avaflow

Simulation tools for gravitational mass flows (e.g., avalanches, debris flows) are commonly used for research and applications in hazard assessment or mitigation planning. As a basis for a transparent and reproducible decision making process, associated uncertainties need to be identified in order to quantify and eventually communicate the associated variabilities of the results. Main sources of variabilities in the simulation results are associated with parameter variations arising from observation and model uncertainties. These are connected to the measurement inaccuracies or poor process understanding and the numerical model implementation. Probabilistic approaches provide various theoretical concepts to treat these uncertainties, but their direct application is not straightforward. To provide a comprehensive tool, introducing conditional runout probabilities for the decision making process we (i) introduce a mathematical framework based on well-established Bayesian concepts, (ii) develop a work flow that couples this framework to the existing simulation tool r.avaflow, and (iii) apply the work flow to two case studies, highlighting its application potential and limitations. The presented approach allows for back, forward and predictive calculations. Back calculations are used to determine parameter distributions, identifying and mapping the model, implementation and data uncertainties. These parameter distributions serve as a base for forward and predictive calculations, embedded in the probabilistic framework. The result variability is quantified in terms of conditional probabilities with respect to the observed data and the associated simulation and data uncertainties. To communicate the result variability the conditional probabilities are visualized, allowing to identify areas with large or small result variability. The conditional probabilities are particularly interesting for predictive avalanche simulations at locations with no prior information where visualization explicitly shows the result variabilities based on parameter distributions derived through back calculations from locations with well-documented observations.

Search for Ranking Approaches of Expanded Clay Concrete Quality Criteria

The article is devoted to ranking of quality criteria for multiparameter, multicriteria production technology of lightweight concrete depending on their significancy using fractal formalism. The proposed approach allows to determine the operation area of stability of the physicomechanical properties of expanded clay concrete with preset technology parameters depending on the size of their self-similarity area and to control the technological process relative to the determine parameter - watertightness. For concrete thin-walled hydraulic structures watertightness is one of the main indicators of quality, ensuring the durability of structures. In the experiment conducted on 15 points optimal plan, 3 factors of the composition varied: sulfate-resistant portland cement, silica fume, water reducing admixture. Possibility of ranking the indicators of W/C and physicomechanical properties of expanded clay concrete depending on the variation of concrete mixture components is shown. Stability of physical and mechanical properties of expanded clay concrete is necessary to ensure its durability and accordance with design requirements. Expanded clay concrete similar to those described in this article can be used in reinforced concrete shipbuilding to reduce weight of structures.

Parameter identification through mode isolation for reaction–diffusion systems on arbitrary geometries

We present a computational framework for isolating spatial patterns arising in the steady states of reaction–diffusion systems. Such systems have been used to model many natural phenomena in areas such as developmental and cancer biology, cell motility and material science. In many of these applications, often one is interested in identifying parameters which will lead to a particular pattern for a given reaction–diffusion model. To attempt to answer this, we compute eigenpairs of the Laplacian on a variety of domains and use linear stability analysis to determine parameter values for the system that will lead to spatially inhomogeneous steady states whose patterns correspond to particular eigenfunctions. This method has previously been used on domains and surfaces where the eigenvalues and eigenfunctions are found analytically in closed form. Our contribution to this methodology is that we numerically compute eigenpairs on arbitrary domains and surfaces. Here we present examples and demonstrate that mode isolation is straightforward especially for low eigenvalues. Additionally, we show that in some cases the inhomogeneous steady state can be a linear combination of eigenfunctions. Finally, we show an example suggesting that pattern formation is robust on similar surfaces in cases that the surface either has or does not have a boundary.

Determination of Support Vector Boundaries in Generalized Maximum Entropy for Multilevel Models

Generalized Maximum Entropy (GME) approach is one of the alternative estimation methods for Regression Analysis. GME approach is superior to other classical approaches in terms of parameter estimation accuracy when some or none of the assumptions of classical approaches are violated. However, determining bounds of parameter support vectors is one of the open parts of this approach when researchers have no prior information about the parameters. If support vectors cannot be determined correctly, parameters estimations will not be obtained correctly. There are some theoretical studies about GME for different datasets in the literature, but there are fewer studies about how to determine parameter support vectors. To obtain robust parameter estimations in GME, we introduced a new iterative procedure for determining parameter support vectors bounds for multilevel dataset. In this study, the new iterative procedure was applied for multi-level random intercept model and the new procedure was tested both simulation study and the real life data. The Classical and the new procedures of GME estimations were compared to Generalized Least Square Estimations in terms of Root Mean Square Error (RMSE) statistics. As a result, the estimations of the new approach provided lower RMSE values than classical methods.