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Phononic band gap crystals are made of periodic inclusions embedded in a base material, which can forbid the propagation of elastic and acoustic waves within certain range of frequencies. In the past two decades, the systematic design of phononic band gap crystals has attracted increasing attention due to their wide practical applications such as sound insulation, waveguides, or acoustic wave filtering. This paper proposes a new topology optimization algorithm based on bi-directional evolutionary structural optimization (BESO) method and finite element analysis for the design of phononic band gap crystals. The study on the maximizing gap size between two adjacent bands has been systematically conducted for out-of-plane waves, in-plane waves and the coupled in-plane and out-of-plane waves. Numerical results demonstrate that the proposed optimization algorithm is effective and efficient for the design of phononic band gap crystals and various topological patterns of optimized phononic structures are presented. Several new patterns for phononic band gap crystals have been successfully obtained.

Following the trend Natural populations are responding to global climate change by shifting their geographical distribution and the timing of their growth and reproduction, but for many species, such responses are likely to be inadequate to counter the speed and magnitude of climate change. Can evolutionary change help their cause? Ary Hoffmann and Carla Sgrò review the evidence for evolutionary adaptation in response to recent climate change and consider the implications for population and ecosystem management. Evolutionary adaptation can be rapid and potentially help species counter stressful conditions or realize ecological opportunities arising from climate change. The challenges are to understand when evolution will occur and to identify potential evolutionary winners as well as losers, such as species lacking adaptive capacity living near physiological limits. Evolutionary processes also need to be incorporated into management programmes designed to minimize biodiversity loss under rapid climate change. These challenges can be met through realistic models of evolutionary change linked to experimental data across a range of taxa.

Since there is a competition between subcarriers because FBMC (Filter Bank Multicarrier) modulation technology does not need subcarriers to be orthogonal to each other, we consider the evolutionary game method to optimize subcarrier allocation. Because the adjacent subcarriers do not need to be orthogonal to each other in FBMC, there is conflict and competition, thus the evolutionary game theory is used to optimize the subcarrier allocation problem. We innovatively introduced the channel state matrix to show the quality of subcarriers. Considering the height of secondary user and base station’s antenna, the total data transmission rate limit, total power consumption constraint and power consumption constraint on a single subcarrier, a nonlinear fractional programming problem is established where maximum energy efficiency is the objective function, total data transmission rate limit, total power consumption constraint and power consumption constraint on a single subcarrier are constraint conditions. The utility function for each secondary user is established when the evolutionary game operator is designed. When the utility function becomes optimal, the evolutionary game reaches Nash equilibrium, and the strategy combination is considered to be the energy efficient resource allocation scheme. Through experimental simulation, EESA-EG proposed in this paper gives the most reasonable subcarrier allocation scheme, allocates more subcarriers for the subcarriers with better channel state and the energy efficiency in EESA-EG is optimal.

The evolutionary-aided design process is a method to find solutions to design and optimisation problems. Evolutionary algorithms (EAs) are applied to search for optimal solutions from a solution space that evolves over several generations. EAs have found applications in many areas of robotics. This paper covers the efforts to determine body morphology of robots through evolution and body morphology with the controller of robots or similar creatures through co-evolution. The works are reviewed from the perspective of how different algorithms are applied and includes a brief explanation of how they are implemented.

The evolutionary origin of cooperation among unrelated individuals remains a key unsolved issue across several disciplines. Prominent among the several mechanisms proposed to explain how cooperation can emerge is the existence of a population structure that determines the interactions among individuals. Many models have explored analytically and by simulation the effects of such a structure, particularly in the framework of the Prisoner's Dilemma, but the results of these models largely depend on details such as the type of spatial structure or the evolutionary dynamics. Therefore, experimental work suitably designed to address this question is needed to probe these issues. We have designed an experiment to test the emergence of cooperation when humans play Prisoner's Dilemma on a network whose size is comparable to that of simulations. We find that the cooperation level declines to an asymptotic state with low but nonzero cooperation. Regarding players' behavior, we observe that the population is heterogeneous, consisting of a high percentage of defectors, a smaller one of cooperators, and a large group that shares features of the conditional cooperators of public goods games. We propose an agent-based model based on the coexistence of these different strategies that is in good agreement with all the experimental observations. In our large experimental setup, cooperation was not promoted by the existence of a lattice beyond a residual level (around 20%) typical of public goods experiments. Our findings also indicate that both heterogeneity and a \"moody\" conditional cooperation strategy, in which the probability of cooperating also depends on the player's previous action, are required to understand the outcome of the experiment. These results could impact the way game theory on graphs is used to model human interactions in structured groups.

Rational design and controlled fabrication of efficient and cost-effective electrodes for the oxygen evolution reaction （OER） are critical for addressing the unpre- cedented energy crisis. Nickel-iron layered double hydroxides （NiFe-LDHs） with specific interlayer anions （i.e. phosphate, phosphite, and hypophosphite） were fabricated by a co-predpitation method and investigated as oxygen evolution electrocatalysts. Intercalation of the phosphorus oxoanion enhanced the OER activity in an alkaline solution; the optimal performance （i.e., a low onset potential of 215 mV, a small Tafel slope of 37.7 mV/dec, and stable electrochemical behavior） was achieved with the hypophosphite-intercalated NiFe-LDH catalyst, demonstrating dramatic enhancement over the traditional carbonate-intercalated NiFe-LDH in terms of activity and durability. This enhanced performance is attributed to the interaction between the intercalated phosphorous oxoanions and the edge-sharing MO6 （M = Ni, Fe） layers, which modifies the surface electronic structure of the Ni sites. This concept should be inspiring for the design of more effective LDH-based oxygen evolution electrocatalvsts.

In recent years, the works on design theory (and particularly the works of the design theory SIG of the design society) have contributed to reconstruct the science of design, comparable in its structure, foundations and impact to decision theory, optimization or game theory in their time. These works have reconstructed historical roots and the evolution of design theory, conceptualized the field at a high level of generality and uncovered theoretical foundations, in particular the logic of generativity, the “design-oriented” structures of knowledge, and the logic of design spaces. These results give the academic field of engineering design an ecology of scientific objects and models, which allows for expanding the scope of engineering education and design courses. They have contributed to a paradigm shift in the organization of R&D departments, supporting the development of new methods and processes in innovation departments, and to establishing new models for development projects. Emerging from the field of engineering design, design theory development has now a growing impact in many disciplines and academic communities. The research community may play a significant role in addressing contemporary challenges if it brings the insights and applicability of design theory to open new ways of thinking in the developing and developed world.

Hydraulic jumps generally occur subsequent to structures such as ogee spillways, control gates, and weirs. The jump roller length is considered one of the main hydraulic jump parameters. In this study, the roller length of a hydraulic jump on a rough channel bed is predicted using a novel, evolutionary, generalized structure design of a group method of data handling (GS-GMDH)-type neural network. The topology of GMDH is designed with a genetic algorithm . Initially, the three most important non-dimensional parameters affecting hydraulic jump roller length, including the Froude number upstream of a hydraulic jump Fr, the ratio of sequent depths h2/h1, and the relative roughness ks/h1 were used to generate four different GS-GMDH models, and the most accurate model is identified. The best new GS-GMDH model prediction statistics, including RMSE, MARE, and correlation coefficient are 1.816, 0.081, and 0.966, respectively, while the scatter index and BIAS values are 0.084 and 1.45, respectively. A partial derivative sensitivity analysis of the input parameters for the new model is also performed. The new model predictions are then compared with predictions of a number of other models. The superior performance of the new GS-GMDH over these existing models is illustrated.

Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels.

This paper presents an approach to shape and topology optimization of fluid-structure interaction (FSI) problems at steady state. The overall approach builds on an immersed boundary method that couples a Lagrangian formulation of the structure to an Eulerian fluid model, discretized on a deforming mesh. The geometry of the fluid-structure boundary is manipulated by varying the nodal parameters of a discretized level set field. This approach allows for topological changes of the fluid-structure interface, but free-floating volumes of solid material can emerge in the course of the optimization process. The free-floating volumes are tracked and modeled as fluid in the FSI analysis. To sense the isolated solid volumes, an indicator field described by linear, isotropic diffusion is computed prior to analyzing the FSI response of a design. The fluid is modeled with the incompressible Navier-Stokes equations, and the structure is assumed linear elastic. The FSI model is discretized by an extended finite element method, and the fluid-structure coupling conditions are enforced weakly. The resulting nonlinear system of equations is solved monolithically with Newton’s method. The design sensitivities are computed by the adjoint method and the optimization problem is solved by a gradient-based algorithm. The characteristics of this optimization framework are studied with two-dimensional problems at steady state. Numerical results indicate that the proposed treatment of free-floating volumes introduces a discontinuity in the design evolution, yet the method is still successful in converging to meaningful designs.