Mathematical modeling of epidemic spreading has been widely adopted to estimate the threats of epidemic diseases (i.e., the COVID-19 pandemic) as well as to evaluate epidemic control interventions. The indoor place is considered to be a significant epidemic spreading risk origin, but existing widely-used epidemic spreading models are usually limited for indoor places since the dynamic physical distance changes between people are ignored, and the empirical features of the essential and non-essential travel are not differentiated. In this paper, we introduce a pedestrian-based epidemic spreading model that is capable of modeling indoor transmission risks of diseases during people’s social activities. Taking advantage of the before-and-after mobility data from the University of Maryland COVID-19 Impact Analysis Platform, it’s found that people tend to spend more time in grocery stores once their travel frequencies are restricted to a low level. In other words, an increase in dwell time could balance the decrease in travel frequencies and satisfy people’s demands. Based on the pedestrian-based model and the empirical evidence, combined non-pharmaceutical interventions from different operational levels are evaluated. Numerical simulations show that restrictions on people’s travel frequency and open hours of indoor places may not be universally effective in reducing average infection risks for each pedestrian who visit the place. Entry limitations can be a widely effective alternative, whereas the decision-maker needs to balance the decrease in risky contacts and the increase in queue length outside the place that may impede people from fulfilling their travel needs. The results show that a good coordination among the decision-makers can contribute to the improvement of the performance of combined non-pharmaceutical interventions, and it also benefits the short-term and long-term interventions in the future.

The high-dimensional propensity score (hdPS) algorithm was proposed for automation of confounding adjustment in problems involving large healthcare databases. It has been evaluated in comparative effectiveness research (CER) with point treatments to handle baseline confounding through matching or covariance adjustment on the hdPS. In observational studies with time-varying interventions, such hdPS approaches are often inadequate to handle time-dependent confounding and selection bias. Inverse probability weighting (IPW) estimation to fit marginal structural models can adequately handle these biases under the fundamental assumption of no unmeasured confounders. Upholding of this assumption relies on the selection of an adequate set of covariates for bias adjustment. We describe the application and performance of the hdPS algorithm to improve covariate selection in CER with time-varying interventions based on IPW estimation and explore stabilization of the resulting estimates using Super Learning. The evaluation is based on both the analysis of electronic health records data in a real-world CER study of adults with type 2 diabetes and a simulation study. This report (i) establishes the feasibility of IPW estimation with the hdPS algorithm based on large electronic health records databases, (ii) demonstrates little impact on inferences when supplementing the set of expert-selected covariates using the hdPS algorithm in a setting with extensive background knowledge, (iii) supports the application of the hdPS algorithm in discovery settings with little background knowledge or limited data availability, and (iv) motivates the application of Super Learning to stabilize effect estimates based on the hdPS algorithm.

The leader self‐sacrifice literature has largely drawn on the norm of reciprocity to examine the positive impacts of leader self‐sacrifice on employee attitudes and behaviours, but little attention has been paid to the potential negative impacts of leader self‐sacrifice on leaders’ own work outcomes. Grounded in social exchange theory and ego depletion theory, our research focuses on why leader self‐sacrifice brings about both beneficial and detrimental effects and considers how leader competence shapes these effects. Two field studies were conducted to test our hypotheses. Study 1 explored the underlying mechanisms through which leader self‐sacrifice influences team/leader work engagement. Study 2 replicated and extended the findings of Study 1 by further testing the moderating role of leader competence. Our results indicated that team affective commitment to leaders acts as a key mediator of the positive relationship between leader self‐sacrifice and team work engagement, and that leader depletion serves as a crucial mechanism underlying the negative relationship between leader self‐sacrifice and leader work engagement. Additionally, leader competence affects how team members view self‐sacrificing leaders and the extent to which self‐sacrificing leaders consume their energy. For competent leaders, the positive influence of leader self‐sacrifice on team work engagement (via team affective commitment to leaders) is stronger, and the negative influence of leader self‐sacrifice on leader work engagement (via leader depletion) is weaker. Practitioner points To avoid self‐sacrificing leaders being seriously depleted, organizations should design activities or training programmes to help leaders replenish self‐control resources and, if possible, increase their self‐control capacity. To encourage team members’ investment in their work, leaders may need to establish psychological attachment and bonds with members to motivate them to repay sacrificial behaviours by fully engaging in the work. Organizations can provide systematic training to enhance leader competence to magnify the beneficial impacts and buffer the detrimental impacts of leader self‐sacrifice.

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SUMMARYTraffic flow prediction is an essential part of intelligent transportation systems (ITS). Most of the previous traffic flow prediction work treated traffic flow as a time series process only, ignoring the spatial relationship from the upstream flows or the correlation with other traffic attributes like speed and density. In this paper, we utilize a linear conditional Gaussian (LCG) Bayesian network (BN) model to consider both spatial and temporal dimensions of traffic as well as speed information for short-term traffic flow prediction. The LCG BN allows both continuous and discrete variables, which enables the consideration of categorical variables in traffic flow prediction. A microscopic traffic simulation dataset is used to test the performance of the proposed model compared to other popular approaches under different predicting time intervals. In addition, the authors investigate the importance of spatial data and speed data in flow prediction by comparing models with different levels of information. The results indicate that the prediction accuracy will increase significantly when both spatial data and speed data are included.

Electronic health records (EHR) data provide a cost and time-effective opportunity to conduct cohort studies of the effects of multiple time-point interventions in the diverse patient population found in real-world clinical settings. Because the computational cost of analyzing EHR data at daily (or more granular) scale can be quite high, a pragmatic approach has been to partition the follow-up into coarser intervals of pre-specified length. Current guidelines suggest employing a 'small' interval, but the feasibility and practical impact of this recommendation has not been evaluated and no formal methodology to inform this choice has been developed. We start filling these gaps by leveraging large-scale EHR data from a diabetes study to develop and illustrate a fast and scalable targeted learning approach that allows to follow the current recommendation and study its practical impact on inference. More specifically, we map daily EHR data into four analytic datasets using 90, 30, 15 and 5-day intervals. We apply a semi-parametric and doubly robust estimation approach, the longitudinal TMLE, to estimate the causal effects of four dynamic treatment rules with each dataset, and compare the resulting inferences. To overcome the computational challenges presented by the size of these data, we propose a novel TMLE implementation, the 'long-format TMLE', and rely on the latest advances in scalable data-adaptive machine-learning software, xgboost and h2o, for estimation of the TMLE nuisance parameters.

A two-step solution sequential deposition has been successfully applied to narrow-bandgap (below 1.60 eV) perovskite solar cells (PSCs), while it has not been widely used in wide-bandgap PSCs and monolithic tandem solar cells (TSCs). Here, a lead halide complex is formed by introducing a formamidinium iodide (FAI) and rubidium acetate (RbAc) into the first step. The results show that the lead halide complex alters the crystallization kinetics and promotes a prominent orientation (100) growth of the perovskite film, resulting in an efficiency of 27.64% for monolithic perovskite/silicon TSCs. Additionally, an enlarged-scaled tandem device with an efficiency of 22.81% (active area of 11.879 cm 2 ) is also achieved, which is one of the most efficient devices with an area greater than 10 cm 2 . This work provides an effective strategy to fabricate perovskite/silicon TSCs with high performance, reproducibility, and large area via the two-step solution method, which will accelerate the industrialization of perovskite/silicon tandem technique.

While almost three-fourths of adults with SMI taking antipsychotic medications received a lab order for diabetes screening, only 55% received screening within a 12-month period. Young adults and smokers were less likely to be screened, despite their disproportionate metabolic risk. Future studies should assess the barriers and facilitators with regard to diabetes screening in this vulnerable population at the patient, provider, and system levels.