RESUMO
The COVID-19 pandemic has caused escalating levels of business, economic and societal uncertainty and created extensive disruptions around the world. Policymakers have responded with a variety of measures to combat this unprecedented crisis. This paper investigates the stock market reactions to the national policy responses. We focus on the two influential policy actions: the nationwide lockdown order aiming to slow down the spread of the Coronavirus, and the interest rate cut policy aiming to minimize the negative economic impact of the pandemic. The Difference-In-Difference method is employed to conduct a cross-country analysis. We find that both policy actions have a significant and positive impact on the stock market performance. We also document a larger stock market reaction to the announcement of an interest rate cut policy than that of a lockdown order.
RESUMO
Electrospun nanofiber (NF)-based triboelectric nanogenerators (TENGs) have attracted significant attention in recent years due to their high specific surface area, flexibility, and facile fabrication. However, these TENGs' triboelectric (TE) layers composed of electrospun NFs fail easily due to the poor mechanical properties and fluffy characteristics of the NFs. Herein, electropositive and electronegative TE layers based on ethylcellulose-coated nylon-11 (EC/nylon-11) NFs and polytetrafluoroethylene-coated poly(vinylidene fluoride) (PTFE/PVDF) NFs are prepared via electrospinning and postcoating processes. The obtained EC/nylon-11 and PTFE/PVDF NFs are fluffy-free and exhibit 12.26 and 20.33-fold enhancements of Young's modulus compared with those of pure nylon-11 and PVDF NFs, respectively. The optimized TENG exhibits not only superior performance, including an open-circuit voltage (VOC) of 212 V, a short-circuit current (ISC) of 18.5 µA, and a maximum power density of 1.76 W/m2 but also excellent electrical durability for over 100,000 cycles. The TENG's capability is further demonstrated by continuously driving electronics for over 5 min and by being integrated into a self-powered sensor array of electric skin to detect different in vitro stimuli. This work provides an effective approach to obtaining mechanically robust and electrically stable NF-based high-performance TENGs, which may have potential applications in durable, wearable, and self-powered nanoelectronics.
RESUMO
Users with cognitive impairments use an assistive technology email system, CogLink, for socialisation and help in their activities of daily living. As users interact with the AT email client, the logged stream of events is monitored and analysed to aid decision-making. When caregivers receive monitor notifications, they know that the user has had a significant change in her emailing behaviour. Consequently, caregivers select adaptations to the email client that can challenge the user to gain still more emailing skills. The monitor in this emailing system analyzes user data to recognise when significant changes in user behaviour warrants caregiver attention. Moreover, the monitor can distinguish newly learned skills from new, but transient behaviours. This article summarises the CogLink assistive technology monitoring techniques, introduces the learning likelihood algorithm, which distinguishes transient from learned behaviour, and provides lessons learnt from a decade of monitoring CogLink users.