Coronavirus disease-2019 (COVID-19) poses a significant threat to the population and urban sustainability worldwide. The surge mitigation is complicated and associates many factors, including the pandemic status, policy, socioeconomics and resident behaviours. Modelling and analytics with spatial-temporal big urban data are required to assist the mitigation of the pandemic. This study proposes a novel perspective to analyse the spatial-temporal potential exposure risk of residents by capturing human behaviours based on spatial-temporal car park availability data. Near real-time data from 1,904 residential car parks in Singapore, a classical megacity, are collected to analyse car mobility and its spatial-temporal heat map. The implementation of the circuit breaker, a COVID-19 measure, in Singapore has reduced the mobility and heat (daily frequency of mobility) significantly at about 30.0%. It contributes to a 44.3%–55.4% reduction in the transportation-related air emissions under two scenarios of travelling distance reductions. Urban sustainability impacts in both environment and economy are discussed. The spatial-temporal potential exposure risk mapping with space-time interactions is further investigated via an extended Bayesian spatial-temporal regression model. The maximal reduction rate of the defined potential exposure risk lowers to 37.6% by comparison with its peak value. The big data analytics of changes in car mobility behaviour and the resultant potential exposure risks can provide insights to assist in (a) designing a flexible circuit breaker exit strategy, (b) precise management via identifying and tracing hotspots on the mobility heat map, and (c) making timely decisions by fitting curves dynamically in different phases of COVID-19 mitigation. The proposed method has the potential to be used by decision-makers worldwide with available data to make flexible regulations and planning.

The remaining useful lifetime (RUL) estimated from the in-situ degradation data has shown to be useful for online predictive maintenance. In the literature, the RUL is often estimated by assuming a soft-failure threshold for the degradation data. In practice, however, systems may not be subject to the degradation-induced soft failures. Instead, the systems are deemed to be fail when they cannot perform the intended function, and such failures are known as hard failures. Because there are no fixed thresholds for hard failures, the corresponding RUL estimation is not an easy task, which causes difficulties in finding the optimal maintenance schedule. In this study, a Weibull proportional hazards model is proposed to jointly model the degradation data and the failure time data. The degradation data are treated as the time-varying covariates so that the degradation does not directly lead to system failures, but increases the hazard rate of hard failures. A random-effects Wiener process is proposed to model the degradation data by considering the system heterogeneities. Based on the developed proportional hazards model, closed-form distribution of the RUL is derived upon each inspection and the optimal maintenance schedule is then obtained by minimizing the system maintenance cost. The proposed maintenance strategy is successfully applied to predictive maintenance of lead-acid batteries.

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Diazotrophs can produce bioavailable nitrogen from inert N2 gas by bioelectrochemical nitrogen fixation (e-BNF), which is emerging as an energy-saving and highly selective strategy for agriculture and industry. However, current e-BNF technology is impeded by requirements for NH4+-assimilation inhibitors to facilitate intracellular ammonia secretion and precious metal catalysts to generate H2 as the energy-carrying intermediate. Herein, we initially demonstrate inhibitor- and catalyst-less extracellular NH4+ production by the diazotroph Pseudomonas stutzeri A1501 using an electrode as the sole electron donor. Multiple lines of evidence revealed that P. stutzeri produced 2.32±0.25 mg/L of extracellular NH4+ at a poised potential of -0.3 V (vs. standard hydrogen electrode (SHE)) without the addition of inhibitors or expensive catalysts. The electron uptake mechanism was attributed to the endogenous electron shuttle phenazine-1-carboxylic acid, which was excreted by P. stutzeri and mediated electron transfer from electrodes into cells to directly drive N2 fixation. The faradaic efficiency was 20%±3% which was 2-4 times that of previous e-BNF using the H2-mediated pathway. This study reports a diazotroph capable of producing secretable NH4+ via extracellular electron uptake, which has important implications for optimizing the performance of e-BNF systems and exploring the novel nitrogen-fixing mode of syntrophic microbial communities in the natural environment. IMPORTANCE Ammonia greatly affects the global ecology, agriculture and the food industry. Diazotrophs with an enhanced capacity of extracellular NH4+ excretion have been proven to be more beneficial to the growth of microalgae and plants, whereas most previously reported diazotrophs produce intracellular organic nitrogen in the absence of chemical suppression and genetic manipulation. Here, we demonstrate that Pseudomonas stutzeri A1501 is capable of extracellular NH4+ production without chemical suppression or genetic manipulation when the extracellular electrode is used as the sole electron donor. We also reveal the electron uptake pathway from the extracellular electron-donating partner to P. stutzeri A1501 via redox electron shuttle phenazines. Since both P. stutzeri A1501 and potential electron-donating partners (such as electroactive microbes and natural semiconductor minerals) are abundant in diverse soils and sediments, P. stutzeri A1501 has broader implications on the improvement of nitrogen fertilization in the natural environment.

The Weibull and lognormal distributions are the most common distributions used in reliability applications. However, use of these two distributions for aggregate data is not tractable because the sum of i.i.d. Weibull or lognormal random variables does not have closed-form probability density function (PDF) or cumulative density function (CDF). The gamma and IG distributions, although not as widely-used, are also popular models for lifetime data. The purpose of this section is to show that even if the underlying component lifetimes follow the Weibull/lognormal distribution, the gamma and IG distributions are robust in terms of the estimated quantiles, as long as there is not a substantial amount of extrapolation to the tails.The procedure is as follows. We first generate the aggregate data of size n and m i = 3 from the Weibull distribution wb(k, 1), where k is the shape parameter of the Weibull distribution. Then we fit the data by the gamma distribution and IG distribution and afterwards we can obtain the ML estimates of τ 0.1 , τ 0.5 and τ 0.9 , the 0.1, 0.5 and 0.9 quantiles of the random component lifetime. After 10, 000 replications, we can obtain biases of τ0.1 , τ0.5 and τ0.9 . Also, based on the interval estimation methods proposed in the paper, we can obtain the coverage probabilities under model-misspecification when the nominal value is set