Jennifer Smith

Range-Separated DFT Functionals are Necessary to Model Thio-Michael Additions.

The textbook mechanism for the addition of a thiol to an olefin is the Michael-type addition, which involves a nucleophilic attack of a thiolate anion on an alkene to form a carbanion intermediate. Previous computational models of these reactions have proposed alternative mechanisms, as no minimum corresponding to the carbanion intermediate was present on the potential energy surface. We show that many popular pure and hybrid DFT functionals, such as PBE and B3LYP, erroneously predict that the carbanion is not an intermediate, favoring a noncovalent charge-transfer complex stabilized spuriously by delocalization error. Range-separated DFT functionals correct this problem and predict stable carbanion structures and energies. In particular, calculations using the ωB97X-D functional are in close agreement with CCSD(T) data for the structures and energies of a series of thio-carbanions. Range-separated functionals will make it possible to model the reaction mechanisms of Michael-type additions that occur in biochemistry, such as the covalent modification of a cysteine side chain by drugs containing an electrophilic double bond.

Assessing offshore emergency evacuation behavior in a virtual environment using a Bayesian Network approach

In the performance influencing factor (PIF) hierarchy, person-based influencing factors reside in the top level along with machine-based, team-based, organization-based and situation/stressor-based factors. Though person-based PIFs like morale, motivation, and attitude (MMA) play an important role in shaping performance, it is nearly impossible to assess such PIFs directly. However, it is possible to measure behavioral indicators (e.g. compliance, use of information) that can provide insight regarding the state of the unobservable person-based PIFs. One common approach to measuring these indicators is to carry out a self-reported questionnaire survey. Significant work has been done to make such questionnaires reliable, but the potential validity problem associated with any questionnaire is that the data are subjective and thus may bear a limited relationship to reality. This paper describes the use of a virtual environment to measure behavioral indicators, which in turn can be used as proxies to assess otherwise unobservable PIFs like MMA. A Bayesian Network (BN) model is first developed to define the relationship between person-based PIFs and measurable behavioral indicators. The paper then shows how these indicators can be measured using evidence collected from a virtual environment of an offshore petroleum installation. A study that focused on emergency evacuation scenarios was done with 36 participants. The participants were first assessed using a multiple choice test. They were then assessed based on their observed performance during simulated offshore emergency evacuation conditions. A comparison of the two assessments demonstrates the potential benefits and challenges of using virtual environments to assess behavioral indicators, and thus the person-based PIFs.

Automated computational screening of the thiol reactivity of substituted alkenes

AbstractElectrophilic olefins can react with the S–H moiety of cysteine side chains. The formation of a covalent adduct through this mechanism can result in the inhibition of an enzyme. The reactivity of an olefin towards cysteine depends on its functional groups. In this study, 325 reactions of thiol-Michael-type additions to olefins were modeled using density functional theory. All combinations of ethenes with hydrogen, methyl ester, amide, and cyano substituents were included. An automated workflow was developed to perform the construction, conformation search, minimization, and calculation of molecular properties for the reactant, carbanion intermediate, and thioether products for a model reaction of the addition of methanethiol to the electrophile. Known cysteine-reactive electrophiles present in the database were predicted to react exergonically with methanethiol through a carbanion with a stability in the 30–40xa0kcalxa0mol−1 range. 13 other compounds in our database that are also present in the PubChem database have similar properties. Natural bond orbital parameters were computed and regression analysis was used to determine the relationship between properties of the olefin electronic structure and the product and intermediate stability. The stability of the intermediates is very sensitive to electronic effects on the carbon where the anionic charge is centered. The stability of the products is more sensitive to steric factors.n

Experimental Investigation of the Retention of Emergency Egress Competence Acquired in a Virtual Environment

Retention of egress skills is critical during high-stress emergencies on offshore oil and gas platforms. This paper uses a virtual offshore platform to investigate the long-term retention of emergency egress competence. A two-phased empirical experiment was designed to first teach basic egress skills and subsequently assess skill retention. The first phase of the experiment used a simulation based mastery learning (SBML) pedagogical approach to teach 36 naive subjects the necessary spatial and procedural skills to evacuate safely from an offshore platform. In the second phase of the experiment, the same participants were tested after 6 to 9 months on their ability to respond to a series of egress test scenarios. These results indicated that emergency egress skills are susceptible to skill decay. Recommendations to improve the retention of offshore egress skills are discussed.

Human performance data collected in a virtual environment

This data article describes the experimental data used in the research article “Incorporating individual differences in human reliability analysis: an extension to the virtual experimental technique” (Musharraf et al., 2017) [1]. The article provides human performance data for 36 individuals collected using a virtual environment. Each participant was assigned to one of two groups for training: 1) G1: high level training and 2) G2: low level training. Participants’ performance was tested in 4 different virtual scenarios with different levels of visibility and complexity. Several performance metrics of the participants were recorded during each scenario. The metrics include: time to muster, time spent running, interaction with fire doors and watertight doors, interaction with hazards, and reporting at different muster locations.