Once Good, Always Good? Testing Nudge’s Spillovers on Pro Environmental Behavior

It is a common assumption to believe that encouraging pro environmental behavior (PEB) in one domain would lead to increased PEB in other domains (best-case scenario) or just be restricted to the initial targeted domain (worst-case scenario). Evidence from a rapidly growing literature on moral licensing suggests that interventions targeting behavioral change could lead to an even worse scenario, with individuals starting to underperform in one domain, as a compensation for their good performance in other domains. We propose to study the dynamic of PEBs when individuals are exposed to a specific nudge (priming) via an original experiment designed to capture actual behavior. We found that priming could increase PEB, but does not thwart moral licensing. Primed individuals end up doing worse than non-primed individual under a moral licensing condition. A more comprehensive view of the mechanisms underlying behavioral change is essential to support sustainable policies.

Risk assessment of banknotes as a fomite of SARS-CoV-2 in cash payment transactions

BackgroundThe COVID 19 pandemic has triggered concerns and assumptions globally about transmission of the SARS-CoV-2 virus via cash transactions.ObjectivesAssess the risk of contracting COVID-19 through exposure to SARS-CoV-2 via cash acting as a fomite in payment transactions.MethodsA quantitative microbial risk assessment was conducted for a worst-case scenario assuming an infectious person at the onset of symptoms, when virion concentrations in coughed droplets are at their highest. This person then contaminates a banknote by coughing on it and immediately hands it over to another person, who might then be infected by transferring the virions with a finger from the contaminated banknote to a facial mucous membrane. The scenario considered transfer efficiency of virions on the banknote to fingertips when droplets were still wet and after having dried up and subsequently being touched by finger printing or rubbing the object.ResultsAccounting for the likelihood of the worst-case scenario to occur by considering 1) a local prevalence of 100 COVID-19 cases/100,000 persons, 2) a maximum of about 1/5th of infected persons transmit high virus loads and 3) the numbers of cash transactions/person/day, the risk of contracting COVID-19 via person-to-person cash transactions was estimated to be much lower than once per 39,000 days (107 years) for a single person. In the general populace, there will be a maximum of 2.6 expected cases/100,000 persons/day. The risk for a cashier at an average point of sale was estimated to be much less than once per 430 working days (21 months).DiscussionThe worst-case scenario is a rare event, therefore, for a single person, the risk of contracting COVID-19 via person-to-person cash transactions is very low. At a point of sale, the risk to the cashier proportionally increases but it is still low.

On the Estimation of the Initial Stock in the Problem of Resource Extraction

We consider a differential game of non-renewable resource extraction, in which the players do not know the precise value of the resource stock and, thus, have to make an estimate. We define the value of information about the initial stock and give recommendations for the choice of the estimate depending on the parameters of the problem. Further, we consider the situation where the players only know the bounds for the stock of the resource and solve the problem of computing the optimal estimate, such that it minimizes the players’ losses in the worst-case scenario. The analysis allows us to give a simple rule for the choice of the optimal estimate of the resource stock.

A preliminary scenario analysis of the impacts of teleworking on energy consumption and greenhouse gas (GHG) emissions

This paper uses scenario analysis to investigate the broader impact of teleworking in four scenarios including the COVID-19 pandemic, worst-, moderate-, and best-case scenarios on building-level energy use, energy consumption in transportation, and information and communication technology (ICT) usage by using the databases of the Government of Canada. The COVID-19 scenario relies on the available data for the pandemic period. The worst-case scenario is when telework has an adverse effect on energy use while the moderate-and best-case scenarios are when the minimum and maximum savings are achieved by telework. The data includes commuting distances, electricity and natural gas consumption for offices and residential buildings, and ICT usage. Then, the associated GHG emissions are calculated for transportation, residential and office buildings, and ICT and the analysis are carried out by applying a potential fraction of saving to the associated GHG emissions of each domain and scenario. This paper demonstrates the potential energy savings of teleworking significantly depends on teleworker behavior to a degree that in the worst-case scenario no potential saving is observed while the savings are significant in the best-case scenario. Therefore, the impact of telework is highly uncertain and complicated and current statistics are insufficient for accurate estimates.

Does an Explicit Value Clarification Method with Computer-Tailored Advice Increase the Effectiveness of a Web-based Decision Aid to Assist Smokers in Choosing Evidence-Based Cessation Assistance? Findings From a Randomized Controlled Trial. (Preprint)

BACKGROUND Smoking continues to be a driver of mortality. Various forms of evidence-based cessation assistance exist, but their use is limited. The decision between them may also induce decisional conflict. Offering decision aids (DAs) may be beneficial, but insights into their effective elements are lacking. OBJECTIVE To test the added value of an effective element (ie, an 'explicit value clarification method' [VCM] paired with computer-tailored advice) of a web-based DA focusing on cessation assistance. The computer-tailored advice indicated the most fitting cessation assistance. The primary outcome measure was 7-day point prevalence abstinence 6 months post baseline (t=3). Secondary outcome measures were 7-day point prevalence abstinence 1 month post baseline (t=2), evidence-based cessation assistance use (t=2 and t=3), and decisional conflict (immediately post DA, t=1). METHODS A randomized controlled trial (RCT) was conducted. The intervention group received a DA with an explicit VCM with computer-tailored advice, the control group received the same DA without these elements. Participants were mainly recruited online (eg, social media). All data was self-reported. Logistic and linear regression analyses (crude and adjusted for covariates) were performed to assess the outcomes. To test the robustness, analyses were conducted following 2 (decisional conflict) and 3 (smoking cessation outcomes) different scenarios: (1) Complete cases, (2) worst-case scenario (dropout respondents are considered to smoke, smoking outcomes only), and (3) multiple imputations. According to an a priori sample size calculation (α=.05; β=.20), 796 participants were needed. RESULTS 2375 participants were randomized (n = 1164 intervention), 599 participants completed the DAs (n = 275 intervention), 276 (n = 143 intervention), 97 (n = 54 intervention), and 103 (n = 56 intervention) participants completed t=1, t=2 and t=3, respectively. Effects in favor of the intervention group on the primary outcome were only observed in the worst-case scenario (P = .02 [crude]; P = .04 [adjusted]). Effects on the secondary outcomes were only observed regarding smoking abstinence after 1 month (P = .02 in the crude and adjusted model), cessation assistance uptake after 1 months (only in the crude model, P = .04) and after 6 months (P = .01 [crude]; P = .02 [adjusted]), but also only in the worst-case scenario. Non-usage attrition was 34.19% higher in the intervention group than in the control group (P < .001). CONCLUSIONS We cannot confidently recommend the inclusion of explicit VCMs and computer-tailored advice at this point. In fact, they might result in higher attrition rates during DA completion, thereby limiting their potential. However, because a lack of statistical power may influenced our findings regarding the outcomes, we recommend replicating this study, taking our lessons learned into account. For example, we found indications that a stronger emphasis on usage times is justified in relation to digital DAs. CLINICALTRIAL Netherlands Trial Register NL8270; https://www.trialregister.nl/trial/8270 INTERNATIONAL REGISTERED REPORT RR2-10.2196/21772

Differential Evolution with Estimation of Distribution for Worst-Case Scenario Optimization

Worst-case scenario optimization deals with the minimization of the maximum output in all scenarios of a problem, and it is usually formulated as a min-max problem. Employing nested evolutionary algorithms to solve the problem requires numerous function evaluations. This work proposes a differential evolution with an estimation of distribution algorithm. The algorithm has a nested form, where a differential evolution is applied for both the design and scenario space optimization. To reduce the computational cost, we estimate the distribution of the best worst solution for the best solutions found so far. The probabilistic model is used to sample part of the initial population of the scenario space differential evolution, using a priori knowledge of the previous generations. The method is compared with a state-of-the-art algorithm on both benchmark problems and an engineering application, and the related results are reported.

Resilient Predictive Control Coupled with a Worst-Case Scenario Approach for a Distributed-Generation-Rich Power Distribution Grid

In a context of accelerating deployment of distributed generation in power distribution grid, this work proposes an answer to an important and urgent need for better management tools in order to ‘intelligently’ operate these grids and maintain quality of service. To this aim, a model-based predictive control (MPC) strategy is proposed, allowing efficient re-routing of power flows using flexible assets, while respecting operational constraints as well as the voltage constraints prescribed by ENEDIS, the French distribution grid operator. The flexible assets used in the case study—a low-voltage power distribution grid in southern France—are a biogas plant and a water tower. Non-parametric machine-learning-based models, i.e., Gaussian process regression (GPR) models, are developed for intraday forecasting of global horizontal irradiance (GHI), grid load, and water demand, to better anticipate emerging constraints. The forecasts’ quality decreases as the forecast horizon grows longer, but quickly stabilizes around a constant error value. Then, the impact of forecasting errors on the performance of the control strategy is evaluated, revealing a resilient behaviour where little degradation is observed in terms of performance and computation cost. To enhance the strategy’s resilience and minimise voltage overflow, a worst-case scenario approach is proposed for the next time step and its contribution is examined. This is the main contribution of the paper. The purpose of the min–max problem added upstream of the main optimisation problem is to both anticipate and minimise the voltage overshooting resulting from forecasting errors. In this min–max problem, the feasible space defined by the confidence intervals of the forecasts is searched, in order to determine the worst-case scenario in terms of constraint violation, over the next time step. Then, such information is incorporated into the decision-making process of the main optimisation problem. Results show that these incidents are indeed reduced thanks to the min–max problem, both in terms of frequency of their occurrence and the total surface area of overshooting.

Optimal loading dose of meropenem before continuous infusion in critically ill patients: a simulation study

The aim of this study was to investigate optimal loading doses prior to continuous infusion of meropenem in critically ill patients. A previously published and successfully evaluated pharmacokinetic model of critically ill patients was used for stochastic simulations of virtual patients. Maintenance doses administered as continuous infusion of 1.5–6 g/24 h with preceding loading doses (administered as 30 min infusion) of 0.15–2 g were investigated. In addition to the examination of the influence of individual covariates, a best-case and worst-case scenario were simulated. Dosing regimens were considered adequate if the 5th percentile of the concentration–time profile did not drop at any time below four times the S/I breakpoint (= 2 mg/L) of Pseudomonas aeruginosa according to the EUCAST definition. Low albumin concentrations, high body weight and high creatinine clearances increased the required loading dose. A maximum loading dose of 0.33 g resulted in sufficient plasma concentrations when only one covariate showed extreme values. If all three covariates showed extreme values (= worst-case scenario), a loading dose of 0.5 g was necessary. Higher loading doses did not lead to further improvements of target attainment. We recommend the administration of a loading dose of 0.5 g meropenem over 30 min immediately followed by continuous infusion.