Generalizable Metamaterials Design Techniques Inspire Efficient Mycelial Materials Inverse Design

Fungal mycelial materials can mimic numerous nonrenewable materials; they are even capable of outperforming certain materials at their own applications. Fungi's versatility makes mock leather, bricks, wood, foam, meats, and many other products possible. That said, there is currently a critical need to develop efficient mycelial materials design techniques. In mycelial materials, and the wider field of biomaterials, design is primarily limited to costly forward techniques. New mycelial materials could be developed faster and cheaper with robust inverse design techniques, which are not currently used within the field. However, computational inverse design techniques will not be tractable unless clear and concrete design parameters are defined for fungi, derived from genotype and bulk phenotype characteristics. Through mycelial materials case studies and a comprehensive review of metamaterials design techniques, we identify three critical needs that must be addressed to implement computational inverse design in mycelial materials. These critical needs are the following: 1) heuristic search/optimization algorithms, 2) efficient mathematical modeling, and 3) dimensionality reduction techniques. Metamaterials researchers already use many of these computational techniques that can be adapted for mycelial materials inverse design. Then, we suggest mycelium-specific parameters as well as how to measure and use them. Ultimately, based on a review of metamaterials research and the current state of mycelial materials design, we synthesize a generalizable inverse design paradigm that can be applied to mycelial materials or related design fields.

Towards a sustainable environment and carbon neutrality: Optimal sizing of standalone, green, reliable, and affordable water-power cogeneration systems

Today, the limited sources of freshwater supply are a significant concern. Exploiting alternative sources, especially seawater, has been the focus, but purifying it is energy-intensive. Integrating desalination with renewable energy is a proposed solution, but it comes with high costs and environmental risks during construction. Hence, this study presents a framework to enhance the modeling, optimization, and evaluation of green water-power cogeneration systems to achieve the sustainability goals of cities and societies. An improved division algorithm (DA) determines the optimal component sizes based on criteria like minimal energy demand, reduced environmental and resource damage, low total life cycle cost (TLCC), and high reliability. Optimization considers varying loss of power supply probability (LPSP) levels (0 %, 2 %, 5 %, and 10 %). The environmental assessment utilizes a life cycle assessment (LCA) approach with IMPACT 2002+ and cumulative energy demand (CED) calculations. The study models the green cogeneration systems based on weather conditions, water demand, and power requirements of Al Lulu Island, Abu Dhabi, UAE. The system comprises photovoltaic panels, wind turbines, tidal generators, and backup systems (fuel cells). Results reveal that TLCC ranges from $186,263 to $486,876 for the highest LPSP. The solar-tidal-based configuration offers the lowest TLCC ($186,263) while substituting solar with wind energy increases TLCC by 160 %. The wind-tidal-based configuration has the lowest specific environmental impact (1020 mPt/yr) and cumulative energy demand (39.06 GJ/yr) for the highest LPSP. In contrast, the solar-tidal-wind-based configuration inflicts the most damage, with 62.63 GJ/yr and 1794 mPt/yr for the highest LPSP. The finding indicates that the DA is faster (100 iterations) than the genetic algorithm (1000 iterations), particle swarm optimization (400 iterations), and artificial bee swarm optimization (300 iterations). The study underscores the solar-tidal-based configuration as the optimal choice across multiple criteria, offering a promising solution for freshwater supply and environmental sustainability on Al Lulu Island.

Parameter design of biped robot motion system based on multi-objective optimization

In recent years humanoid robots have been widely used in toy, performance, education and other service industries, but most biped robots walk slowly and have poor stability. The reason is that the driver parameters of the robot cannot properly match the walking gait algorithm, and the insufficient performance of the robot driver leads to the poor motion capability of the robot. In this paper, the optimization design process of biped robot parameters is studied and expounded, and its motion capability is improved by optimizing the driving parameters of the robot. Firstly, the contradiction between walking speed, stability and driver performance of biped robot is analysed. The performance evaluation functions of the three are further established, and the optimal parameter design to a certain extent is realized based on the multi-objective optimization method. Finally, combining with the physical simulation engine, the design parameters are simulated and checked, and the robot design process is completed through the guidance of simulation results.