An improved multi-strategy beluga whale optimization for global optimization problems.

This paper presents an improved beluga whale optimization (IBWO) algorithm, which is mainly used to solve global optimization problems and engineering problems. This improvement is proposed to solve the imbalance between exploration and exploitation and to solve the problem of insufficient convergence accuracy and speed of beluga whale optimization (BWO). In IBWO, we use a new group action strategy (GAS), which replaces the exploration phase in BWO. It was inspired by the group hunting behavior of beluga whales in nature. The GAS keeps individual belugas whales together, allowing them to hide together from the threat posed by their natural enemy, the tiger shark. It also enables the exchange of location information between individual belugas whales to enhance the balance between local and global lookups. On this basis, the dynamic pinhole imaging strategy (DPIS) and quadratic interpolation strategy (QIS) are added to improve the global optimization ability and search rate of IBWO and maintain diversity. In a comparison experiment, the performance of the optimization algorithm (IBWO) was tested by using CEC2017 and CEC2020 benchmark functions of different dimensions. Performance was analyzed by observing experimental data, convergence curves, and box graphs, and the results were tested using the Wilcoxon rank sum test. The results show that IBWO has good optimization performance and robustness. Finally, the applicability of IBWO to practical engineering problems is verified by five engineering problems.

A group theoretic approach to model comparison with simplicial representations.

The complexity of biological systems, and the increasingly large amount of associated experimental data, necessitates that we develop mathematical models to further our understanding of these systems. Because biological systems are generally not well understood, most mathematical models of these systems are based on experimental data, resulting in a seemingly heterogeneous collection of models that ostensibly represent the same system. To understand the system we therefore need to understand how the different models are related to each other, with a view to obtaining a unified mathematical description. This goal is complicated by the fact that a number of distinct mathematical formalisms may be employed to represent the same system, making direct comparison of the models very difficult. A methodology for comparing mathematical models based on their underlying conceptual structure is therefore required. In previous work we developed an appropriate framework for model comparison where we represent models, specifically the conceptual structure of the models, as labelled simplicial complexes and compare them with the two general methodologies of comparison by distance and comparison by equivalence. In this article we continue the development of our model comparison methodology in two directions. First, we present a rigorous and automatable methodology for the core process of comparison by equivalence, namely determining the vertices in a simplicial representation, corresponding to model components, that are conceptually related and the identification of these vertices via simplicial operations. Our methodology is based on considerations of vertex symmetry in the simplicial representation, for which we develop the required mathematical theory of group actions on simplicial complexes. This methodology greatly simplifies and expedites the process of determining model equivalence. Second, we provide an alternative mathematical framework for our model-comparison methodology by representing models as groups, which allows for the direct application of group-theoretic techniques within our model-comparison methodology.

An Integrative Perspective on Interpersonal Coordination in Interactive Team Sports.

Interpersonal coordination is a key factor in team performance. In interactive team sports, the limited predictability of a constantly changing context makes coordination challenging. Approaches that highlight the support provided by environmental information and theories of shared mental models provide potential explanations of how interpersonal coordination can nonetheless be established. In this article, we first outline the main assumptions of these approaches and consider criticisms that have been raised with regard to each. The aim of this article is to define a theoretical perspective that integrates the coordination mechanisms of the two approaches. In doing so, we borrow from a theoretical outline of group action. According to this outline, group action based on a priori shared mental models is an example of how interpersonal coordination is established from the top down. Interpersonal coordination in reaction to the perception of affordances represents the bottom-up component of group action. Both components are inextricably involved in the coordination of interactive sports teams. We further elaborate on the theoretical outline to integrate a third, constructivist approach. Integrating this third approach helps to explain interpersonal coordination in game situations for which no shared mental models are established and game situations that remain ambiguous in terms of perceived affordances. The article describes how hierarchical, sequential, and complex dimensions of action organization are important aspects of this constructivist perspective and how mental models may be involved. A basketball example is used to illustrate how top-down, bottom-up and constructivist processes may be simultaneously involved in enabling interpersonal coordination. Finally, we present the implications for research and practice.

Regression Models on Riemannian Symmetric Spaces.

The aim of this paper is to develop a general regression framework for the analysis of manifold-valued response in a Riemannian symmetric space (RSS) and its association with multiple covariates of interest, such as age or gender, in Euclidean space. Such RSS-valued data arises frequently in medical imaging, surface modeling, and computer vision, among many others. We develop an intrinsic regression model solely based on an intrinsic conditional moment assumption, avoiding specifying any parametric distribution in RSS. We propose various link functions to map from the Euclidean space of multiple covariates to the RSS of responses. We develop a two-stage procedure to calculate the parameter estimates and determine their asymptotic distributions. We construct the Wald and geodesic test statistics to test hypotheses of unknown parameters. We systematically investigate the geometric invariant property of these estimates and test statistics. Simulation studies and a real data analysis are used to evaluate the finite sample properties of our methods.