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A potentiostat is an analytical device and a crucial component in electrochemical instruments used for studying chemical reaction mechanisms, with potential applications in early diagnosis of disease or critical health conditions. Conventional potentiostats are typically benchtop devices designed for laboratory use, whereas a wearable potentiostat can be interfaced with biochemical sensors for disease diagnostics at home. This work presents a low-power potentiostat designed to connect with a sensor array consisting of eight to ten working electrodes. The potentiostat is capable of running Electrochemical Impedance Spectroscopy and Cyclic Voltammetry. The system is powered by lithium-ion batteries and uses Bluetooth for data transmission to the user. A single ARM M4 microcontroller, integrated with a Bluetooth low-energy radio module, controls the entire system. The accuracy, reliability, and power efficiency of the potentiostat were evaluated and compared against existing commercial benchtop potentiostats. Additionally, we have outlined future steps to enhance circuit miniaturization and power efficiency, aiming to develop fully integrated wearable sensing devices comparable in size to a wristwatch.

Multi-agent systems in biology, society, and engineering are capable of making decisions through the dynamic interaction of their elements. Nonlinearity of the interactions is key for the speed, robustness, and flexibility of multi-agent decision-making. In this work we introduce modulatory, that is, multiplicative, in contrast to additive, interactions in a nonlinear opinion dynamics model of fast-and-flexible decision-making. The original model is nonlinear because network interactions, although additive, are saturated. Modulatory interactions introduce an extra source of nonlinearity that greatly enriches the model decision-making behavior in a mathematically tractable way. Modulatory interactions are widespread in both biological and social decision-making networks; our model provides new tools to understand the role of these interactions in networked decision-making and to engineer them in artificial systems., Comment: 6 pages, 4 figures, submitted to American Control Conference 2025

Where and under what conditions the transfer of energy between electromagnetic fields and particles takes place in the solar wind remains an open question. We investigate the conditions that promote the growth of kinetic instabilities predicted by linear theory, to infer how turbulence and temperature-anisotropy-driven instabilities are interrelated. Using a large dataset from Solar Orbiter, we introduce the radial rate of strain, a novel measure computed from single-spacecraft data, that we interpret as a proxy for the double-adiabatic strain rate. The solar wind exhibits high absolute values of the radial rate of strain at locations with large temperature anisotropy. We measure the kurtosis and skewness of the radial rate of strain from the statistical moments to show that it is non-Gaussian for unstable intervals and increasingly intermittent at smaller scales with a power-law scaling. We conclude that the velocity field fluctuations in the solar wind contribute to the presence of temperature anisotropy sufficient to create potentially unstable conditions., Comment: Accepted for publication in the Journal of Plasma Physics

We propose a nonlinear opinion dynamics model for an agent making decisions about a continuous distribution of options in the presence of distributed input. Inspired by perceptual decision-making, we develop the theory for options distributed on the circle, representing, e.g., the set of possible heading directions in planar robotic navigation problems. Interactions among options are encoded through a spatially invariant kernel. We design the kernel to ensure that decision-making is robust, in the sense that only a finite subset of options can be favored over the continuum. We prove the spatial invariance of the model linearization and use this result to prove an opinion-forming bifurcation in the model with zero input. We then use space and time frequency domain analysis of the model linearization to infer the ultra-sensitive input-output behavior of the nonlinear dynamics with input. We illustrate our model's versatility with a robotic navigation example in crowded spaces.

We present Excitable Nonlinear Opinion Dynamics (E-NOD), which describe opinion-forming and decision-making behavior with superior "agility" in responding and adapting to fast and unpredictable changes in context, environment, or information about available options. E-NOD is derived by introducing a single extra term to the previously presented Nonlinear Opinion Dynamics (NOD), which have been shown to enable fast and flexible multi-agent behavior. This extra term is inspired by the fast-positive, slow-negative mixed-feedback structure of excitable systems. The agile behaviors resulting from the excitable nature of decision-making driven by E-NOD are analyzed in a general setting and illustrated through an application to robot navigation around human movers., Comment: 6 pages, 5 figures

Plasma in the terrestrial magnetosheath is characterised by very weak particle-particle collisions, so kinetic microinstabilities are thought to be responsible for regulating the thermodynamics of the plasma. By exciting electromagnetic waves, these instabilities redistribute free energy in velocity space, moulding the velocity distribution function (VDF) into a lower energy state. In the high-beta magnetosheath, relatively small perturbations to the VDF can easily excite instabilities compared to in the low-beta inner heliosphere. Since magnetic fields cannot do work on the particles, electric fields mediate energy exchange between the electromagnetic field and the bulk fluid properties of the plasma. We investigate signatures of non-ideal energy conversion associated with turbulent fluctuations in the context of electron and ion temperature anisotropy-beta instabilities, utilising over 24 hours of data spread over 163 distinct intervals of in situ magnetosheath observations from Magnetospheric Multiscale (MMS). We find that average energy conversion into fluid flow is enhanced along instability boundaries, suggesting that turbulence is playing a role in how free energy is redistributed in the plasma. The work enables a quantification of the energetics which are associated with the role of kinetic microinstabilities in regulating collisionless plasma thermodynamics. This work provides insight into the open question of how specific plasma processes couple into the turbulent dynamics and ultimately lead to energy dissipation and particle energisation in collisionless plasmas., Comment: 10 pages, 3 figures, 1 table. Submitted for peer review at Astrophysical Journal Letters (ApJL), July 2024

We propose and analyze the suitability of a spiking controller to engineer the locomotion of a soft robotic crawler. Inspired by the FitzHugh-Nagumo model of neural excitability, we design a bistable controller with an electrical flipflop circuit representation capable of generating spikes on-demand when coupled to the passive crawler mechanics. A proprioceptive sensory signal from the crawler mechanics turns bistability of the controller into a rhythmic spiking. The output voltage, in turn, activates the crawler's actuators to generate movement through peristaltic waves. We show through geometric analysis that this control strategy achieves endogenous crawling. The electro-mechanical sensorimotor interconnection provides embodied negative feedback regulation, facilitating locomotion. Dimensional analysis provides insights on the characteristic scales in the crawler's mechanical and electrical dynamics, and how they determine the crawling gait. Adaptive control of the electrical scales to optimally match the mechanical scales can be envisioned to achieve further efficiency, as in homeostatic regulation of neuronal circuits. Our approach can scale up to multiple sensorimotor loops inspired by biological central pattern generators., Comment: 5 pages, MTNS 2024 extended abstract

Neuronal systems maintain stable functions despite large variability in their physiological components. Ion channel expression, in particular, is highly variable in neurons exhibiting similar electrophysiological phenotypes, which poses questions regarding how specific ion channel subsets reliably shape neuron intrinsic properties. Here, we use detailed conductance-based modeling to explore the origin of stable neuronal function from variable channel composition. Using dimensionality reduction, we uncover two principal dimensions in the channel conductance space that capture most of the variance of the observed variability. Those two dimensions correspond to two physiologically relevant sources of variability that can be explained by feedback mechanisms underlying regulation of neuronal activity, providing quantitative insights into how channel composition links to neuronal electrophysiological activity. These insights allowed us to understand and design a model-independent, reliable neuromodulation rule for variable neuronal populations.

This paper presents an enhanced version of the Learner Performance-based Behavior (LPB), a novel metaheuristic algorithm inspired by the process of accepting high-school students into various departments at the university. The performance of the LPB is not according to the required level. This paper aims to improve the performance of a single objective LPB by embedding ten chaotic maps within LPB to propose Chaotic LPB (CLPB). The proposed algorithm helps in reducing the Processing Time (PT), getting closer to the global optima, and bypassing the local optima with the best convergence speed. Another improvement that has been made in CLPB is that the best individuals of a sub-population are forced into the interior crossover to improve the quality of solutions. CLPB is evaluated against multiple well-known test functions such as classical (TF1_TF19) and (CEC_C06 2019). Additionally, the results have been compared to the standard LPB and several well-known metaheuristic algorithms such as Dragon Fly Algorithm (DA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). Finally, the numerical results show that CLPB has been improved with chaotic maps. Furthermore, it is verified that CLPB has a great ability to deal with large optimization problems compared to LPB, GA, DA, and PSO. Overall, Gauss and Tent maps both have a great impact on improving CLPB., Comment: 6 pages

We illustrate the potential of neuromorphic control on the simple mechanical model of a pendulum, with both event-based actuation and sensing. The controller and the pendulum are regarded as event-based systems that occasionally interact to coordinate their respective rhythms. Control occurs through a proper timing of the interacting events. We illustrate the mixed nature of the control design: the design of a rhythmic automaton, able to generate the right sequence of events, and the design of a feedback regulator, able to tune the timing of events., Comment: 6 pages, 10 figures. Accepted by IEEE Control Systems Letters

We design a computational approach to find equilibria in a class of Nash games possessing a hierarchical structure. By using tools from mixed-integer programming and the characterization of variational equilibria in terms of the Karush--Kuhn--Tucker conditions, we propose a mixed-integer game formulation for solving such a challenging instance. Besides providing an equivalent reformulation, we build upon our previous work and design a proximal Gauss--Seidel method with global convergence guarantees for the case in which the game enjoys a potential structure. In addition to proving the convergence of the resulting scheme, we show its performance on a numerical instance of a ride-hail market problem., Comment: submitted as a conference proceeding

An important problem in many areas of science is that of recovering interaction networks from simultaneous time-series of many interacting dynamical processes. A common approach is to use the elements of the correlation matrix or its inverse as proxies of the interaction strengths, but the reconstructed networks are necessarily undirected. Transfer entropy methods have been proposed to reconstruct directed networks but the reconstructed network lacks information about interaction strengths. We propose a network reconstruction method that inherits the best of the two approaches by reconstructing a directed weighted network from noisy data under the assumption that the network is sparse and the dynamics are governed by a linear (or weakly-nonlinear) stochastic dynamical system. The two steps of our method are i) constructing an (infinite) family of candidate networks by solving the covariance matrix Lyapunov equation for the state matrix and ii) using L1-regularization to select a sparse solution. We further show how to use prior information on the (non)existence of a few directed edges to drastically improve the quality of the reconstruction.

The theory of mixed-feedback systems provides an effective framework for the design of robust and tunable oscillations in nonlinear systems characterized by interleaved fast positive and slow negative feedback loops. The goal of this paper is to extend the mixed-feedback oscillation design framework to networks. To this aim, we introduce a network model of coupled mixed-feedback systems, ask under which conditions it exhibits a collective oscillatory rhythm, and if, and how, this rhythm can be shaped by network design. In the proposed network model, node dynamics are nonlinear and defined by a tractable realization of the mixed-feedback structure. Coupling between nodes is also nonlinear and defined by a tractable abstraction of synaptic coupling between neurons. We derive constructive conditions under which the spectral properties of the network adjacency matrix fully and explicitly determine both the emergence of a stable network rhythm and its detailed rhythmic profile, i.e., the pattern of relative oscillation amplitudes and phase differences. Our theoretical developments are grounded on ideas from dominant systems and bifurcation theory. They provide a new framework for the analysis and design of nonlinear network rhythms., Comment: 20 pages, 6 figures

We propose a threshold decision-making framework for controlling the physical dynamics of an agent switching between two spatial tasks. Our framework couples a nonlinear opinion dynamics model that represents the evolution of an agent's preference for a particular task with the physical dynamics of the agent. We prove the bifurcation that governs the behavior of the coupled dynamics. We show by means of the bifurcation behavior how the coupled dynamics are adaptive to the physical constraints of the agent. We also show how the bifurcation can be modulated to allow the agent to switch tasks based on thresholds adaptive to environmental conditions. We illustrate the benefits of the approach through a decentralized multi-robot task allocation application for trash collection.

The Parker Solar Probe (PSP) and Solar Orbiter (SolO) missions opened a new observational window in the inner heliosphere, which is finally accessible to direct measurements. On September 05, 2022, a coronal mass ejection (CME)-driven interplanetary (IP) shock has been observed as close as 0.07 au by PSP. The CME then reached SolO, which was well radially-aligned at 0.7 au, thus providing us with the opportunity to study the shock properties at so different heliocentric distances. We characterize the shock, investigate its typical parameters and compare its small-scale features at both locations. Using the PSP observations, we investigate how magnetic switchbacks and ion cyclotron waves are processed upon shock crossing. We find that switchbacks preserve their V--B correlation while compressed upon the shock passage, and that the signature of ion cyclotron waves disappears downstream of the shock. By contrast, the SolO observations reveal a very structured shock transition, with a population of shock-accelerated protons of up to about 2 MeV, showing irregularities in the shock downstream, which we correlate with solar wind structures propagating across the shock. At SolO, we also report the presence of low-energy ($\sim$ 100 eV) electrons scattering due to upstream shocklets. This study elucidates how the local features of IP shocks and their environments can be very different as they propagate through the heliosphere., Comment: In review in ApJ

We study stochastic Nash equilibrium problems subject to heterogeneous uncertainty on the cost functions of the individual agents. In our setting, we assume no prior knowledge of the underlying probability distributions of the uncertain variables. To account for this lack of knowledge, we consider an ambiguity set around the empirical probability distribution under the Wasserstein metric. We then show that, under mild assumptions, finite-sample guarantees on the probability that any resulting distributionally robust Nash equilibrium is also robust with respect to the true probability distributions with high confidence can be obtained. Furthermore, by recasting the game as a distributionally robust variational inequality, we establish asymptotic convergence of the set of data-driven distributionally robust equilibria to the solution set of the original game. Finally, we recast the distributionally robust Nash game as a finite-dimensional Nash equilibrium problem. We illustrate the proposed distributionally robust reformulation via numerical experiments of stochastic Nash-Cournot games.

We propose and analyze a nonlinear dynamic model of continuous-time multi-dimensional belief formation over signed social networks. Our model accounts for the effects of a structured belief system, self-appraisal, internal biases, and various sources of cognitive dissonance posited by recent theories in social psychology. We prove that agents become opinionated as a consequence of a bifurcation. We analyze how the balance of social network effects in the model controls the nature of the bifurcation and, therefore, the belief-forming limit-set solutions. Our analysis provides constructive conditions on how multi-stable network belief equilibria and belief oscillations emerging at a belief-forming bifurcation depend on the communication network graph and belief system network graph. Our model and analysis provide new theoretical insights on the dynamics of social systems and a new principled framework for designing decentralized decision-making on engineered networks in the presence of structured relationships among alternatives., Comment: 16 pages, 7 figures

We consider multi-agent coalitional games with uncertainty in the coalitional values. We provide a novel methodology to study the stability of the grand coalition in the case where each coalition constructs ambiguity sets for the (possibly) unknown probability distribution of the uncertainty. As a less conservative solution concept compared to worst-case approaches for coalitional stability, we consider a stochastic version of the so-called core set, i.e., the expected value core. Unfortunately, without exact knowledge of the probability distribution, the evaluation of the expected value core is an extremely challenging task. Hence, we propose the concept of distributionaly robust (DR) core. Leveraging tools from data-driven DR optimization under the Wasserstein distance, we provide finite-sample guarantees that any allocation which lies in the DR core is also stable with respect to the true probability distribution. Furthermore, we show that as the number of samples grows unbounded, the DR core converges almost surely to the true expected value core. We dedicate the last section to the computational tractability of finding an allocation in the DR core., Comment: Accepted for publication at the IEEE Conference on Decision and Control 2023