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The Wilson–Cowan model has been widely applied for the simulation of electroencephalography (EEG) waves associated with neural activities in the brain. The Runge–Kutta (RK) method is commonly used to numerically solve the Wilson–Cowan equations. In this paper, we focus on enhancing the accuracy of the numerical method by proposing a strategy to construct a class of fourth-order RK methods using a generalized iterated Crank–Nicolson procedure, where the RK coefficients depend on a free parameter c2. When c2 is set to 0.5, our method becomes a special case of the classical fourth-order RK method. We apply the proposed methods to solve the Wilson–Cowan equations with two and three neuron populations, modeling EEG epileptic dynamics. Our simulations demonstrate that when c2 is set to 0.4, the proposed RK4-04 method yields smaller errors compared to those obtained using the classical fourth-order RK method. This is particularly visible when the spectral radius of the connection matrix or the excitation-inhibition coupling coefficient is relatively large. 

Energy expenditure for quadrotor control has a likelihood of being costly given parameter-dependent controllers that are less than optimal. The cost can grow proportionally when applied to multiple quadrotors for tracking and collaborative navigation tasks. This research aims to establish a basic approach to tuning PID (Proportional-Integral-Derivative) parameters for a simulated quadrotor drone. A PID controller for autonomy provides a straightforward method for correcting robotic movement based on its current state. However, applying a PID system to a flight controller poses challenges with an inherently under-actuated system, which includes the likelihood of large overshoots and lengthy adjustment times. To address this, we utilize PSO (Particle Swarm Optimization) for optimizing PID parameters in a simulated quadrotor. The PSO is employed to find optimal PID values for thrust, yaw, and translational movement on x- and y-positions by identifying converging values across randomly created particles. We conducted a set of experiments and compared it to the default PID controller. The experiments demonstrate converging properties for particles that achieve minimal fitness scores, particularly in reducing overshoot. The results indicate that the optimized PID controller outperforms the default PID controller without optimization. Using optimized PID controllers can decrease the amount of positional error during flight and when adjusting position with collaborative navigation and collision avoidance algorithms. 

The Distributed Deterministic Spiral Algorithm (DDSA) has shown great foraging efficiency in robot swarms. However, when the number of robots in the swarm increases, scalability becomes a significant bottleneck due to increased collisions among robots, making it challenging to deploy them in the search space (e.g., 20 robots). To address this issue, we propose an adaptive Multiple-Distributed Bidirectional Spiral Algorithm (MDBSA) that enhances scalability. Our proposed algorithm partitions the squared search arena into multiple identical squared regions and assigns robots to regions dynamically based on the number of regions. In each region, a bidirectional spiral search path is planned, and when a robot completes its search, it is assigned to either an unassigned region or a region with one robot. The two robots will then travel along the path from the starting and ending points of the spiral path. We evaluated the performance of robot swarms using the MDBSA algorithm in the ARGoS robot simulator. Our experimental results show that the proposed MDBSA algorithm outperforms DDSA. When robots deliver collected resources to regions instead of the center, it reduces collisions and significantly improves the scalability of the robot swarm. Our findings suggest that a multiple-distributed search strategy is an efficient solution for foraging robot swarms. 

Abstract Rigorous electrokinetic results are key to understanding the reaction mechanisms in the electrochemical CO reduction reaction (CORR), however, most reported results are compromised by the CO mass transport limitation. In this work, we determined mass transport-free CORR kinetics by employing a gas-diffusion type electrode and identified dependence of catalyst surface speciation on the electrolyte pH using in-situ surface enhanced vibrational spectroscopies. Based on the measured Tafel slopes and reaction orders, we demonstrate that the formation rates of C 2+ products are most likely limited by the dimerization of CO adsorbate. CH 4 production is limited by the CO hydrogenation step via a proton coupled electron transfer and a chemical hydrogenation step of CO by adsorbed hydrogen atom in weakly (7 < pH < 11) and strongly (pH > 11) alkaline electrolytes, respectively. Further, CH 4 and C 2+ products are likely formed on distinct types of active sites. 

Abstract Harnessing renewable electricity to drive the electrochemical reduction of CO₂is being intensely studied for sustainable fuel production and as a means for energy storage. Copper is the only monometallic electrocatalyst capable of converting CO₂to value-added products, e.g., hydrocarbons and oxygenates, but suffers from poor selectivity and mediocre activity. Multiple oxidative treatments have shown improvements in the performance of copper catalysts. However, the fundamental underpinning for such enhancement remains controversial. Here, we combine reactivity, in-situ surface-enhanced Raman spectroscopy, and computational investigations to demonstrate that the presence of surface hydroxyl species by co-electrolysis of CO₂with low concentrations of O₂can dramatically enhance the activity of copper catalyzed CO₂electroreduction. Our results indicate that co-electrolysis of CO₂with an oxidant is a promising strategy to introduce catalytically active species in electrocatalysis.