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Workers’ compensation insurance, which provides no-fault coverage for work-related injuries, is mandatory in nearly all states. We use administrative data from a unique market without a coverage mandate to estimate the demand for workers’ compensation insurance, leveraging regulatory premium updates for identification. We find that a 1 percent increase in premiums leads to approximately a 0.3 percent decline in coverage. Drawing upon these estimates and data on costs, we examine potential justifications for government intervention to increase coverage. This analysis suggests that several forms of market failure—such as adverse selection, market power, and externalities—may not justify a mandate in this setting. (JEL G22, G52, J28, K13, K31) 

Abstract Neuromorphic computing mimics the organizational principles of the brain in its quest to replicate the brain’s intellectual abilities. An impressive ability of the brain is its adaptive intelligence, which allows the brain to regulate its functions “on the fly” to cope with myriad and ever-changing situations. In particular, the brain displays three adaptive and advanced intelligence abilities of context-awareness, cross frequency coupling, and feature binding. To mimic these adaptive cognitive abilities, we design and simulate a novel, hardware-based adaptive oscillatory neuron using a lattice of magnetic skyrmions. Charge current fed to the neuron reconfigures the skyrmion lattice, thereby modulating the neuron’s state, its dynamics and its transfer function “on the fly.” This adaptive neuron is used to demonstrate the three cognitive abilities, of which context-awareness and cross-frequency coupling have not been previously realized in hardware neurons. Additionally, the neuron is used to construct an adaptive artificial neural network (ANN) and perform context-aware diagnosis of breast cancer. Simulations show that the adaptive ANN diagnoses cancer with higher accuracy while learning faster and using a more compact and energy-efficient network than a nonadaptive ANN. The work further describes how hardware-based adaptive neurons can mitigate several critical challenges facing contemporary ANNs. Modern ANNs require large amounts of training data, energy, and chip area, and are highly task-specific; conversely, hardware-based ANNs built with adaptive neurons show faster learning, compact architectures, energy-efficiency, fault-tolerance, and can lead to the realization of broader artificial intelligence. 

Abstract Topological solitons are exciting candidates for the physical implementation of next-generation computing systems. As these solitons are nanoscale and can be controlled with minimal energy consumption, they are ideal to fulfill emerging needs for computing in the era of big data processing and storage. Magnetic domain walls (DWs) and magnetic skyrmions are two types of topological solitons that are particularly exciting for next-generation computing systems in light of their non-volatility, scalability, rich physical interactions, and ability to exhibit non-linear behaviors. Here we summarize the development of computing systems based on magnetic topological solitons, highlighting logical and neuromorphic computing with magnetic DWs and skyrmions. 

Ion selective electrode (ISE) sensors have been broadly applied for real-time in situ monitoring of ion concentrations in water environments. However, ISE sensors suffer from critical problems, such as ionophore leaching, water-penetration, poor electrochemical stability, and resulting short life spans. In this study, a template-guided membrane matrix immobilization strategy was pursued as a novel ISE sensor fabrication methodology to enhance its sensing characteristics and longevity. Specifically, nano-porous anodized aluminum oxide (AAO) was used as the template for an NH 4 + -specific ISE sensor. A nano-porous nickel mesh eventually replaced the template and formed a compact, high-surface juncture with the NH 4 + ion-selective membrane matrix. The resulting template-guided nano-mesh ISE (TN-ISE) sensor displayed enhanced electrochemical stability ( i.e. , capacitance increased by 50%, reading drift reduced by 75%) when compared to a regular single-wall carbon nanotube (SW-CNT) ISE sensor used as the standard. The interface between the nano-mesh electrode and the ion selective membrane matrix was compact enough to prevent water influx at the electrode interface. This minimized ionophore leaching and increased the mechanical integrity of the TN-ISE sensor. The practical advantages of the novel sensor were validated via long-term (360 hours) tests in real wastewater, returning a small average error of 1.28% over this time. The results demonstrate the feasibility of the template-guided nano-mesh design and fabrication strategy toward ISEs for long-term continuous monitoring of water or wastewater quality. 

Neuromorphic computing is a promising candidate for beyond-von Neumann computer architectures, featuring low power consumption and high parallelism. Lateral inhibition and winner-take-all (WTA) features play a crucial role in neuronal competition of the nervous system as well as neuromorphic hardwares. The domain wall - magnetic tunnel junction (DWMTJ) neuron is an emerging spintronic artificial neuron device exhibiting intrinsic lateral inhibition. In this paper we show that lateral inhibition parameters modulate the neuron firing statistics in a DW-MTJ neuron array, thus emulating soft-winner-take-all (WTA) and firing group selection.