In an r-uniform hypergraph on n vertices, a tight Hamilton cycle consists of n edges such that there exists a cyclic ordering of the vertices where the edges correspond to consecutive segments of r vertices. We provide a first deterministic polynomial-time algorithm, which finds a.a.s. tight Hamilton cycles in random r-uniform hypergraphs with edge probability at least C log3 n/n.

Our result partially answers a question of Dudek and Frieze, who proved that tight Hamilton cycles exist already for p = ω(1/n) for r = 3 and p = (e + o(1))/n for $r \ge 4$ using a second moment argument. Moreover our algorithm is superior to previous results of Allen, Böttcher, Kohayakawa and Person, and Nenadov and Škorić, in various ways: the algorithm of Allen et al. is a randomized polynomial-time algorithm working for edge probabilities $p \ge {n^{ - 1 + \varepsilon}}$, while the algorithm of Nenadov and Škorić is a randomized quasipolynomial-time algorithm working for edge probabilities $p \ge C\mathop {\log }\nolimits^8 n/n$.

The main objective of this paper is to address the following question: are the containment measures imposed by most of the world governments effective and sufficient to stop the epidemic of COVID-19 beyond the lock-down period? In this paper, we propose a mathematical model which allows us to investigate and analyse this problem. We show by means of the reproductive number, ${\cal R}_0$ that the containment measures appear to have slowed the growth of the outbreak. Nevertheless, these measures remain only effective as long as a very large fraction of population, p, greater than the critical value $1-1/{\cal R}_0$ remains confined. Using French current data, we give some simulation experiments with five scenarios including: (i) the validation of model with p estimated to 93%, (ii) the study of the effectiveness of containment measures, (iii) the study of the effectiveness of the large-scale testing, (iv) the study of the social distancing and wearing masks measures and (v) the study taking into account the combination of the large-scale test of detection of infected individuals and the social distancing with linear progressive easing of restrictions. The latter scenario was shown to be effective at overcoming the outbreak if the transmission rate decreases to 75% and the number of tests of detection is multiplied by three. We also noticed that if the measures studied in our five scenarios are taken separately then the second wave might occur at least as far as the parameter values remain unchanged.

The coronavirus disease (COVID-19), while mild in most cases, has nevertheless caused significant mortality. The measures adopted in most countries to contain it have led to colossal social and economic disruptions, which will impact the medium- and long-term health outcomes for many communities. In this paper, we deliberate on the reality and facts surrounding the disease. For comparison, we present data from past pandemics, some of which claimed more lives than COVID-19. Mortality data on road traffic crashes and other non-communicable diseases, which cause more deaths each year than COVID-19 has so far, is also provided. The indirect, serious health and social effects are briefly discussed. We also deliberate on how misinformation, confusion stemming from contrasting expert statements, and lack of international coordination may have influenced the public perception of the illness and increased fear and uncertainty. With pandemics and similar problems likely to re-occur, we call for evidence-based decisions, the restoration of responsible journalism and communication built on a solid scientific foundation.

A multivariate quantile regression model with a factor structure is proposed to study data with multivariate responses with covariates. The factor structure is allowed to vary with the quantile levels, which is more flexible than the classical factor models. Assuming the number of factors is small, and the number of responses and the input variables are growing with the sample size, the model is estimated with the nuclear norm regularization. The incurred optimization problem can only be efficiently solved in an approximate manner by off-the-shelf optimization methods. Such a scenario is often seen when the empirical loss is nonsmooth or the numerical procedure involves expensive subroutines, for example, singular value decomposition. To show that the approximate estimator is still statistically accurate, we establish a nonasymptotic bound on the Frobenius risk and prediction risk. For implementation, a numerical procedure that provably marginalizes the approximation error is proposed. The merits of our model and the proposed numerical procedures are demonstrated through the Monte Carlo simulation and an application to finance involving a large pool of asset returns.

‘Recurrence’ of coronavirus disease 2019 (COVID-19) has triggered numerous discussions of scholars at home and abroad. A total of 44 recurrent cases of COVID-19 and 32 control cases admitted from 11 February to 29 March 2020 to Guanggu Campus of Tongji Hospital affiliated to Tongji Medical College Huazhong University of Science and Technology were enrolled in this study. All the 44 recurrent cases were classified as mild to moderate when the patients were admitted for the second time. The gender and mean age in both cases (recurrent and control) were similar. At least one concomitant disease was observed in 52.27% recurrent cases and 34.38% control cases. The most prevalent comorbidity among them was hypertension. Fever and cough being the most prevalent clinical symptoms in both cases. On comparing both the cases, recurrent cases had markedly elevated concentrations of alanine aminotransferase (ALT) (P = 0.020) and aspartate aminotransferase (AST) (P = 0.007). Moreover, subgroup analysis showed mild to moderate abnormal concentrations of ALT and AST in recurrent cases. The elevated concentrations of ALT and AST may be recognised as predictive markers for the risk of ‘recurrence’ of COVID-19, which may provide insights into the prevention and control of COVID-19 in the future.

This study presents the main motivation to investigate the COVID-19 pandemic, a major threat to the whole world from the day when it first emerged in China city of Wuhan. Predictions on the number of cases of COVID-19 are crucial in order to prevent and control the outbreak. In this research study, an artificial neural network with rectifying linear unit-based technique is implemented to predict the number of deaths, recovered and confirmed cases of COVID-19 in Pakistan by using previous data of 137 days of COVID-19 cases from the day 25 February 2020 when the first two cases were confirmed, until 10 July 2020. The collected data were divided into training and test data which were used to test the efficiency of the proposed technique. Furthermore, future predictions have been made by the proposed technique for the next 7 days while training the model on whole available data.

Coronavirus disease 2019 (COVID-19) has had a tremendous impact in China and abroad since its onset in December 2019 and poses a major threat to human health. Healthcare workers (HCWs) are at the forefront of the response to outbreaks. This study reviewed literature data and found that HCWs were at high risk of infection during the COVID-19 pandemic, especially at the early stage of the epidemic, and many factors greatly affected their occupational safety. Although SARS-CoV-2 transmission was controlled in China, the Chinese experience can help protect HCWs from COVID-19 and other respiratory diseases.

Due to the outbreak of the deadly coronavirus disease in 2019 (COVID-19), Wuhan was on lockdown for more than 60 days by the state government. This study investigated the perceptions and attitudes of the public on quarantine as a practical approach to halting the spread of COVID-19. An online survey was conducted via WeChat between 10 January 2020 and 10 March 2020 on the general population in Hubei province at the height of the COVID-19 outbreak. In total, 549 respondents participated in the survey. Results revealed that the public displayed significantly strong support towards quarantine throughout the outbreak period, apart from locking people up and using imprisonment legal sanctions against those who failed to comply with the stringent regulations. The support exerted by the public stemmed from the execution of authorised officers to protect the public interest and provision of psychosocial support for those affected. In situations where quarantine could not be imposed, public health policy-makers and government officials should implement an extensive system of psychosocial support to safeguard, instruct and inform frontline public health workers. The public should also be enlisted in an open conversation concerning the ethical utility of restrictive values during the COVID-19 outbreak.

Commonly used tests to assess evidence for the absence of autocorrelation in a univariate time series or serial cross-correlation between time series rely on procedures whose validity holds for i.i.d. data. When the series are not i.i.d., the size of correlogram and cumulative Ljung–Box tests can be significantly distorted. This paper adapts standard correlogram and portmanteau tests to accommodate hidden dependence and nonstationarities involving heteroskedasticity, thereby uncoupling these tests from limiting assumptions that reduce their applicability in empirical work. To enhance the Ljung–Box test for non-i.i.d. data, a new cumulative test is introduced. Asymptotic size of these tests is unaffected by hidden dependence and heteroskedasticity in the series. Related extensions are provided for testing cross-correlation at various lags in bivariate time series. Tests for the i.i.d. property of a time series are also developed. An extensive Monte Carlo study confirms good performance in both size and power for the new tests. Applications to real data reveal that standard tests frequently produce spurious evidence of serial correlation.

A practical bit condition monitoring system is a necessary component of autonomous drilling. Tricone bits are widely used in blasthole drilling in mining. Bits experience a variety of wear mechanisms during the operation and rolling element failure is the dominant catastrophic failure mode of tricone bits. Bit lifetime and performance significantly vary based on the working condition and the critical components of the bit i.e. rolling elements, are invisible to the direct condition monitoring systems. At McGill University, extensive research work is conducted to develop an indirect bit condition monitoring and failure prediction approach relying on the vibration signals and the technology is currently patent pending. This article presents real-world experimental evidence to show the unreliability of conservative bit changing strategy based on the bit operation life or drop in the rate of penetration (ROP) and ineffectiveness of direct wear monitoring techniques to cover the dominant failure mode.

In March 2020, China had periodically controlled the coronavirus disease-19 (COVID-19) epidemic. We reported the results of health screening for COVID-19 among returned staff of a hospital and conducted a summary analysis to provide valuable experience for curbing the COVID-19 epidemic and rebound. In total, 4729 returned staff from Zhongnan Hospital of Wuhan University, Wuhan, China were examined for COVID-19, and the basic information, radiology and laboratory test results were obtained and systematically analysed. Among the 4729 employees, medical staff (62.93%) and rear-service personnel (30.73%) were the majority. The results of the first physical examination showed that 4557 (96.36%) were normal, 172 (3.64%) had abnormal radiological or laboratory test results. After reexamination and evaluation, four were at high risk (asymptomatic infections) and were scheduled to transfer to a designated hospital, and three were at low risk (infectivity could not be determined) and were scheduled for home isolation observation. Close contacts were tracked and managed by the Center for Disease Control and Prevention (CDC) in China. Asymptomatic infections are a major risk factor for returning to work. Extensive health screening combined with multiple detection methods helps to identify asymptomatic infections early, which is an important guarantee in the process of returning to work.

A variety of machines are currently being used for mechanical excavation in mining and civil industries. A series of research works have been conducted at McGill University in the past decade to study the effects of microwave (MW) irradiation on rock mechanical properties. The idea is to enhance the excavation performance by improving the rate of penetration and decreasing the wear rate on the cutting tools. These two effects would eventually translate into economic benefits for mine operators. The effectiveness of MW on weakening rocks is proven, however the most efficient method to employ MW in mines is still under investigation. This article presents some experimental results on the effects of cooling- rate on rock strength. Brazilian Tensile Strength (BTS) of microwave treated samples were compared in natural air-cooled and water rapid-cooled conditions.

The influence of nutrient loading and other anthropogenic stressors is thought to be greater in low inflow, microtidal estuaries, where there is limited water exchange. This 11-month study compared spatial changes in macrofaunal communities adjacent to regions that varied in land cover in Oso Bay, Texas, an estuarine secondary bay with inflow dominated by hypersaline discharge, in addition to discharge from multiple municipal wastewater treatment plants. Macrofauna communities changed in composition with distance away from a wastewater treatment plant in Oso Bay, with the western region of the bay containing different communities than the head and the inlet of the bay. Ostracods were numerically dominant close to the wastewater discharge point. Macrobenthic community composition is most highly correlated with silicate concentrations in the water column. Silicate is negatively correlated with salinity and dissolved oxygen, and positively correlated with nutrients within the bay. Results are relevant for environmental management purposes by demonstrating that point-source discharges can still have ecological effects in hydrologically altered estuaries.

There are numerous associations between psychological characteristics and political values, but it is unclear whether messages tailored to these psychological characteristics can influence political decisions. Two studies (N = 398, N = 395) tested whether psychological-based argument tailoring could influence participants’ decision-making. We constructed arguments based on the 2016 Brexit referendum; Remain supporters were presented with four arguments supporting the Leave campaign, tailored to reflect the participant’s strongest (/weakest) moral foundation (Loyalty or Fairness) or personality trait (Conscientiousness or Openness). We tested whether individuals scoring high on a trait would find the tailored arguments more persuasive than individuals scoring low on the same trait. We found clear evidence for targeting, particularly for Loyalty, but either no evidence or weak evidence, in the case of Conscientiousness, for tailoring. Overall, the results suggest that targeting political messages could be effective, but provide either no, or weak evidence that tailoring these messages influences political decision-making.

Earlier work by the authors suggested that the formation of molten eutectic regions in Mg-Ca binary alloys caused a discrepancy in ignition temperature when different heating rates are used. This effect was observed for alloys where Ca content is greater than 1 wt%. In this work, the effect of two heating rates (25 °C/min and 45 °C/min) on the ignition resistance of Mg-3Ca is evaluated in terms of oxide growth using X-ray Photoelectron Spectroscopy. It is found that the molten eutectic regions develop a thin oxide scale of ~100 nm rich in Ca at either heating rate. The results prove that under the high heating rate, solid intermetallics are oxidized forming CaO nodules at the metal/oxide interface that eventually contribute to the formation of a thick and non-protective oxide scale in the liquid state.

In this paper, enlightened by the asymptotic expansion methodology developed by Li [(2013). Maximum-likelihood estimation for diffusion processes via closed-form density expansions. Annals of Statistics 41: 1350–1380] and Li and Chen [(2016). Estimating jump-diffusions using closed-form likelihood expansions. Journal of Econometrics 195(1): 51–70], we propose a Taylor-type approximation for the transition densities of the stochastic differential equations (SDEs) driven by the gamma processes, a special type of Lévy processes. After representing the transition density as a conditional expectation of Dirac delta function acting on the solution of the related SDE, the key technical method for calculating the expectation of multiple stochastic integrals conditional on the gamma process is presented. To numerically test the efficiency of our method, we examine the pure jump Ornstein–Uhlenbeck model and its extensions to two jump-diffusion models. For each model, the maximum relative error between our approximated transition density and the benchmark density obtained by the inverse Fourier transform of the characteristic function is sufficiently small, which shows the efficiency of our approximated method.

The Philippines confirmed local transmission of COVID-19 on 7 March 2020. We described the characteristics and epidemiological time-to-event distributions for laboratory-confirmed cases in the Philippines recorded up to 29 April 2020 and followed until 22 May 2020. The median age of 8212 cases was 46 years (IQR 32–61), with 46.2% being female and 68.8% living in the National Capital Region. Health care workers represented 24.7% of all detected infections. Mean length of hospitalisation for those who were discharged or died were 16.00 days (95% CI 15.48–16.54) and 7.27 days (95% CI 6.59–8.24). Mean duration of illness was 26.66 days (95% CI 26.06–27.28) and 12.61 days (95% CI 11.88–13.37) for those who recovered or died. Mean serial interval was 6.90 days (95% CI 5.81–8.41). Epidemic doubling time prior to the enhanced community quarantine (ECQ; 11 February and 19 March) was 4.86 days (95% CI 4.67–5.07) and the reproductive number was 2.41 (95% CI 2.33–2.48). During the ECQ (20 March to 9 April), doubling time was 12.97 days (95% CI 12.57–13.39) and the reproductive number was 0.89 (95% CI 0.78–1.02).