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COVID-19 has affected every sector of our society, among which human mobility is taking a dramatic change due to quarantine and social distancing. We investigate the impact of the pandemic and subsequent mobility changes on road traffic safety. Using traffic accident data from the city of Los Angeles and New York City, we find that the impact is not merely a blunt reduction in traffic and accidents; rather, (1) the proportion of accidents unexpectedly increases for “Hispanic” and “Male” groups; (2) the “hot spots” of accidents have shifted in both time and space and are likely moved from higher-income areas (e.g., Hollywood and Lower Manhattan) to lower-income areas (e.g., southern LA and southern Brooklyn); (3) the severity level of accidents decreases with the number of accidents regardless of transportation modes. Understanding those variations of traffic accidents not only sheds a light on the heterogeneous impact of COVID-19 across demographic and geographic factors, but also helps policymakers and planners design more effective safety policies and interventions during critical conditions such as the pandemic.

Background: The outbreak of the COVID-19 pandemic has caused great panic among the public, with many people suffering from adverse stress reactions. To control the spread of the pandemic, governments in many countries have imposed lockdown policies. In this unique pandemic context, people can obtain information about pandemic dynamics on the internet. However, searching for health-related information on the internet frequently increases the possibility of individuals being troubled by the information that they find, and consequently, experiencing symptoms of cyberchondria. Objective: We aimed to examine the relationships between people’s perceived severity of the COVID-19 pandemic and their depression, anxiety, and stress to explore the role of cyberchondria, which, in these relationship mechanisms, is closely related to using the internet. In addition, we also examined the moderating role of lockdown experiences. Methods: In February 2020, a total of 486 participants were recruited through a web-based platform from areas in China with a large number of infections. We used questionnaires to measure participants’ perceived severity of the COVID-19 pandemic, to measure the severity of their cyberchondria, depression, anxiety, and stress symptoms, and to assess their lockdown experiences. Confirmatory factor analysis, exploratory factor analysis, common method bias, descriptive statistical analysis, and correlation analysis were performed, and moderated mediation models were examined. Results: There was a positive association between perceived severity of the COVID-19 pandemic and depression (β=0.36, t=8.51, P<.001), anxiety (β=0.41, t=9.84, P<.001), and stress (β=0.46, t=11.45, P<.001), which were mediated by cyberchondria (β=0.36, t=8.59, P<.001). The direct effects of perceived severity of the COVID-19 pandemic on anxiety (β=0.07, t=2.01, P=.045) and stress (β=0.09, t=2.75, P=.006) and the indirect effects of cyberchondria on depression (β=0.10, t=2.59, P=.009) and anxiety (β=0.10, t=2.50, P=.01) were moderated by lockdown experience. Conclusions: The higher the perceived severity of the COVID-19 pandemic, the more serious individuals’ symptoms of depression, anxiety, and stress. In addition, the associations were partially mediated by cyberchondria. Individuals with higher perceived severity of the COVID-19 pandemic were more likely to develop cyberchondria, which aggravated individuals’ depression, anxiety, and stress symptoms. Negative lockdown experiences exacerbated the COVID-19 pandemic’s impact on mental health.

The extant literature on interpersonal and inter-organisational trust reveals there are many factors that can influence an organisations’ services to integrate and exchange. While these studies have enhanced our understanding of organisational collaboration, we propose an asymmetric perspective that concentrates on factors that eventually lead to the loss of inter-organisational trust in the context of the (National Health Services) NHS and local government by seeking to join-up health and care services. This paper explores trust and asymmetry factors that undermine collaborative spirits towards successful service integration among health and care players. Based on interviews with 42 subjects in the NHS England Better Care Fund (BCF) programme, we present a model that distinguishes between asymmetric factors and affected health and care service integration. Our findings contribute to a scholarly understanding of asymmetry in the public sector and the role of trust in overcoming divisions and facilitating joint-up services among health and care organisations.

The on-line feedback control of sliding friction of metallic tribopairs lubricated by adsorbed sodium dodecyl sulfate (SDS) films was demonstrated on a customized tribosystem, in which the external electric field applied on the tribopair was modulated in feedback according to the electrical contact resistance signal. When a positive voltage was applied, the adsorption of SDS anions on the surface of tribopair was enhanced so that the boundary film was stable. When the contact resistance increased to a pre-set threshold (e.g., 6~10 Ω), which indicated the formation of a relatively complete boundary film, the external voltage was switched off for saving energy. For an aqueous solution with 160 mM SDS as the lubricant, the coefficient of friction (COF) was decreased by 24% for the 316 L plate/304 steel ball under 804 MPa by modulating the applied potential of +3.5 V. For the propylene carbonate lubricant with 5 mM SDS, the COF was decreased by 39% for the Cu plate/304 steel ball under 499 MPa and 54% for the Cu plate/bearing steel ball under 520 MPa by modulating the applied potential of +20 V. This novel approach could be effective to keep good boundary lubrication of machine components under variable work conditions by on-line sensing and actuation.

Small- and medium-sized enterprises (SMEs) play an important role in the European economy. A critical challenge faced by SME leaders, as a consequence of the continuing digital technology revolution, is how to optimally align business strategy with digital technology to fully leverage the potential offered by these technologies in pursuit of longevity and growth. There is a paucity of empirical research examining how e-leadership in SMEs drives successful alignment between business strategy and digital technology fostering longevity and growth. To address this gap, in this paper we develop an empirically derived e-leadership model. Initially we develop a theoretical model of e-leadership drawing on strategic alignment theory. This provides a theoretical foundation on how SMEs can harness digital technology in support of their business strategy enabling sustainable growth. An in-depth empirical study was undertaken interviewing 42 successful European SME leaders to validate, advance and substantiate our theoretically driven model. The outcome of the two stage process – inductive development of a theoretically driven e-leadership model and deductive advancement to develop a complete model through in-depth interviews with successful European SME leaders – is an e-leadership model with specific constructs fostering effective strategic alignment. The resulting diagnostic model enables SME decision makers to exercise effective e-leadership by creating productive alignment between business strategy and digital technology improving longevity and growth prospects.

Clinical pathways have been adopted for various diseases in clinical departments for quality improvement as a result of standardization of medical activities in treatment process. Using knowledge-based decision support on the basis of clinical pathways is a promising strategy to improve medical quality effectively. However, the clinical pathway knowledge has not been fully integrated into treatment process and thus cannot provide comprehensive support to the actual work practice. Therefore this paper proposes a knowledge-based clinical pathway management method which contributes to make use of clinical knowledge to support and optimize medical practice. We have developed a knowledge-based clinical pathway management system to demonstrate how the clinical pathway knowledge comprehensively supports the treatment process. The experiences from the use of this system show that the treatment quality can be effectively improved by the extracted and classified clinical pathway knowledge, seamless integration of patient-specific clinical pathway recommendations with medical tasks and the evaluating pathway deviations for optimization.

Accurately predicting network-level traffic conditions has been identified as a critical need for smart and advanced transportation services. In recent decades, machine learning and artificial intelligence have been widely applied for traffic state, including traffic volume prediction. This paper proposes a novel deep learning model, Graph Convolutional Neural Network with Data-driven Graph Filter (GCNN-DDGF), for network-wide multi-step traffic volume prediction. More specifically, the proposed GCNN-DDGF model can automatically capture hidden spatiotemporal correlations between traffic detectors, and its sequence-to-sequence recurrent neural network architecture is able to further utilize temporal dependency from historical traffic flow data for multi-step prediction. The proposed model was tested in a network-wide hourly traffic volume dataset between 1 January 2018 and 30 June 2019 from 150 sensors in the Los Angeles area. Detailed experimental results illustrate that the proposed model outperforms the other five widely used deep learning and machine learning models in terms of computational efficiency and prediction accuracy. For instance, the GCNN-DDGF model improves MAE, MAPE, and RMSE by 25.33%, 20.45%, and 29.20% compared to the state-of-the-art models, such as Diffusion Convolution Recurrent Neural Network (DCRNN), which is widely accepted as a popular and effective deep learning model.

In this work, a series of experiments on tribofilm formation of sulfur- and phosphorus-free organic molybdenum additive (SPFM) on bearing steel surfaces have been performed in a ZrO 2 ball-on-steel plate tester lubricated by gas-to-liquid (GTL), di-2-ethylhexylsebacate (Ester), or polyalphaolefin (PAO) synthetic oils under different temperature and external voltage conditions. The synergy effect of SPFM with 2,5-dimercapto-1,3,4-thiadiazole derivative (DMTD) and zinc dithiophosphate (ZDDP) additives has also been investigated. The results reveal that SPFM additive plays a major role in reduction of friction and wear by formation of MoO x tribofilm on steel surface lubricated with either GTL or PAO oil samples, which can be enhanced by an externally applied voltage. S element provided by DMTD additive can react with Mo element in SPFM to form MoS 2 , which also contributes friction reduction. Comparing with GTL and PAO base oils, the Ester oil is less effective in the tribofilm formation because of its low solubility for the additives. Higher temperatures (at 60 °C, 100 °C or 140 °C) and an initial running-in are beneficial to the tribofilm formation. A three-step mechanism, hydrolysis of SPFM, adsorption of the MoO 4 2− anions, and tribochemical reactions at the rubbing surface, is proposed to explain the observed voltage-assisted tribofilm formation results. Graphical Abstract