Reminding Forgetful Organic Neuromorphic Device Networks

Organic neuromorphic device networks can accelerate neural network algorithms and directly integrate with microfluidic systems or living tissues. Proposed devices based on the bio-compatible conductive polymer PEDOT:PSS have shown high switching speeds and low energy demand. However, as electrochemical systems, they are prone to self-discharge through parasitic electrochemical reactions. Therefore, the network's synapses forget their trained conductance states over time. This work integrates single-device high-resolution charge transport models to simulate neuromorphic device networks and analyze the impact of self-discharge on network performance. Simulation of a single-layer nine-pixel image classification network reveals no significant impact of self-discharge on training efficiency. And, even though the network's weights drift significantly during self-discharge, its predictions remain 100\% accurate for over ten hours. On the other hand, a multi-layer network for the approximation of the circle function is shown to degrade significantly over twenty minutes with a final mean-squared-error loss of 0.4. We propose to counter the effect by periodically reminding the network based on a map between a synapse's current state, the time since the last reminder, and the weight drift. We show that this method with a map obtained through validated simulations can reduce the effective loss to below 0.1 even with worst-case assumptions. Finally, while the training of this network is affected by self-discharge, a good classification is still obtained. Electrochemical organic neuromorphic devices have not been integrated into larger device networks. This work predicts their behavior under nonideal conditions, mitigates the worst-case effects of parasitic self-discharge, and opens the path toward implementing fast and efficient neural networks on organic neuromorphic hardware

Quasiparticle operators with non-Abelian braiding statistics

We study the gauge invariant fermions in the fermion coset representation of $SU(N)_k$ Wess-Zumino-Witten models which create, by construction, the physical excitations (quasiparticles) of the theory. We show that they provide an explicit holomorphic factorization of $SU(N)_k$ Wess-Zumino-Witten primaries and satisfy non-Abelian braiding relations.Comment: 13 pages, no figures, final version to appear in Physics Letters

Wet-Spun PEDOT/CNT Composite Hollow Fibers as Flexible Electrodes for H2O2 Production

The electrochemical synthesis of hydrogen peroxide (H2O2) using the oxygen reduction reaction (ORR) requires highly catalytic active, selective, and stable electrode materials to realize a green and efficient process. The present publication shows for the first time the application of a facile one-step bottom-up wet-spinning approach for the continuous fabrication of stable and flexible tubular poly(3,4-ethylene dioxythiophene) (PEDOT : PSS) and PEDOT : PSS/carbon nanotube (CNT) hollow fibers. Additionally, electrochemical experiments reveal the catalytic activity of acid-treated PEDOT : PSS and its composites in the ORR forming hydrogen peroxide for the first time. Under optimized conditions, the composite electrodes with 40 wt % CNT loading could achieve a high production rate of 0.01 mg/min/cm2 and a current efficiency of up to 54 %. In addition to the high production rate, the composite hollow fiber has proven its long-term stability with 95 % current retention after 20 h of hydrogen peroxide production. © 2021 The Authors. ChemElectroChem published by Wiley-VCH Gmb

Porous PEDOT:PSS Particles and their Application as Tunable Cell Culture Substrate

Due to its biocompatibility, electrical conductivity, and tissue-like elasticity, poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) constitutes a highly promising material regarding the fabrication of smart cell culture substrates. However, until now, high-throughput synthesis of pure PEDOT:PSS geometries was restricted to flat sheets and fibers. In this publication, the first microfluidic process for the synthesis of spherical, highly porous, pure PEDOT:PSS particles of adjustable material properties is presented. The particles are synthesized by the generation of PEDOT:PSS emulsion droplets within a 1-octanol continuous phase and their subsequent coagulation and partial crystallization in an isopropanol (IPA)/sulfuric acid (SA) bath. The process allows to tailor central particle characteristics such as crystallinity, particle diameter, pore size as well as electrochemical and mechanical properties by simply adjusting the IPA:SA ratio during droplet coagulation. To demonstrate the applicability of PEDOT:PSS particles as potential cell culture substrate, cultivations of L929 mouse fibroblast cells and MRC-5 human fibroblast cells are conducted. Both cell lines present exponential growth on PEDOT:PSS particles and reach confluency with cell viabilities above 95 vol.% on culture day 9. Single cell analysis could moreover reveal that mechanotransduction and cell infiltration can be controlled by the adjustment of particle crystallinity

Hydraulic Structures at a Crossroads Towards the SDGs

Hydraulic structures engineering is one of the most important fields of civil and environmental engineering with challenges arising from new and complex environmental issues, the refurbishment of aging infrastructure, and the need to increase resilience to climate change. The IAHR Technical Committee on Hydraulic Structures purpose is to champion the subject area of hydraulic structures in an era of increasing specialisation in the hydraulic profession. There are important new developments in the planning, design, construction, and life cycle maintenance of hydraulic struc tures that need to be addressed by both researchers and practitioner

Duality in deformed coset fermionic models

We study the $SU(2)_k/U(1)$-parafermion model perturbed by its first thermal operator. By formulating the theory in terms of a (perturbed) fermionic coset model we show that the model is equivalent to interacting WZW fields modulo free fields. In this scheme, the order and disorder operators of the $Z_k$ parafermion theory are constructed as gauge invariant composites. We find that the theory presents a duality symmetry that interchanges the roles of the spin and dual spin operators. For two particular values of the coupling constant we find that the theory recovers conformal invariance and the gauge symmetry is enlarged. We also find a novel self-dual point.Comment: 13 pages, LaTex. Minor corrections. One reference added. Version to appear in Nuc. Phys.

We Try to Create the World That We Want : Intentional Communities Forging Livable Lives in St. Louis

This paper analyzes ethnographic research conducted in five intentional communities in the St. Louis region. Intentional communities have long been formed and entered into by people seeking to create more ideal, more livable lives. Our research focused on the demographic and socioeconomic characteristics of the members of the five communities, the motivations of members for joining, and the benefits and shortcomings they experience. In reporting these findings we summarize common themes that help us to better understand why people join intentional communities, how those communities work, and the values and goals that underpin conceptions of quality of life there. We also draw from our data a set of recommendations related to policy obstacles and opportunities that are present in municipalities like St. Louis that facilitate or obstruct the formation of intentional communities and their endeavors to create more livable lives

Sea level rise will change estuarine tidal energy: A review

Climate change induced sea level rise (SLR) is likely to impact estuarine hydrodynamics and associated processes, including tidal energy. In this study, a hierarchy of factors influencing the future of estuarine tidal energy resources is proposed based on their relevance to SLR. These include primary factors (e.g., tidal prism, tidal range, tidal current, tidal asymmetry), secondary factors (e.g., sediment transport), and tertiary factors (e.g., shifts in estuarine shape/landform). The existing uncertainty regarding SLR impacts on tidal energy resource is high, given the spatial variability of estuaries. SLR may cause tidal ranges or currents to strengthen or weaken, depending on estuarine shape and boundary conditions (e.g., presence or absence of levees and adjacent low-lying areas). To date, local site studies have not resulted in an overarching assessment of SLR effects on tidal energy resources and comparative studies encompassing different regions and estuary types are recommended in order to address the existing knowledge gaps and provide insights for policymakers and stakeholders. SLR implications to estuarine tidal energy resources may be particularly important as SLR-induced changes can alter the available resource within a renewable energy development's operational lifetime (-20-30 years for tidal stream devices and-120 years for tidal barrages). In this regard, broader environmental impacts, as well as technoeconomic assessments, are difficult to predict and long-term management decisions associated with harnessing the potential of tidal energy schemes within estuaries should be made with caution

Energy-Flexible Job-Shop Scheduling Using Deep Reinforcement Learning

Considering its high energy demand, the manufacturing industry has grand potential for demand response studies to increase the use of clean energy while reducing its own electricity cost. Production scheduling, driven by smart demand response services, plays a major role in adjusting the manufacturing sector to the volatile energy market. As a state-of-the-art method for scheduling problems, reinforcement learning has not yet been applied to the job-shop scheduling problem with demand response objectives. To address this gap, we conceptualize and implement deep reinforcement learning as a single-agent approach, combining energy cost and makespan minimization objectives. We consider makespan as an ancillary objective in order not to entirely abandon the timely completion of production operations while assigning different weights to both objectives and analyzing the resulting trade-offs between them. Our main contribution is the integration of the energy cost-related objective. We present two innovative reward functions, which consider the dynamic energy prices to select a job for the machine or allow the machine idle. The reinforcement learning agent finds optimal schedules determined by cumulative energy costs for benchmark scheduling cases from the literature