Level-resolved quantum statistical theory of electron capture into many-electron compound resonances in highly charged ions

The strong mixing of many-electron basis states in excited atoms and ions with open f shells results in very large numbers of complex, chaotic eigenstates that cannot be computed to any degree of accuracy. Describing the processes which involve such states requires the use of a statistical theory. Electron capture into these “compound resonances” leads to electron-ion recombination rates that are orders of magnitude greater than those of direct, radiative recombination and cannot be described by standard theories of dielectronic recombination. Previous statistical theories considered this as a two-electron capture process which populates a pair of single-particle orbitals, followed by “spreading” of the two-electron states into chaotically mixed eigenstates. This method is similar to a configuration-average approach because it neglects potentially important effects of spectator electrons and conservation of total angular momentum. In this work we develop a statistical theory which considers electron capture into “doorway” states with definite angular momentum obtained by the configuration interaction method. We apply this approach to electron recombination with W²⁰⁺, considering 2×10⁶ doorway states. Despite strong effects from the spectator electrons, we find that the results of the earlier theories largely hold. Finally, we extract the fluorescence yield (the probability of photoemission and hence recombination) by comparison with experiment.

Towards a knowledge-based approach for effective decision-making in railway safety

Purpose – This paper aims to contribute towards understanding how safety knowledge can be elicited from railway experts for the purposes of supporting effective decision-making. Design/methodology/approach – A consortium of safety experts from across the British railway industry is formed. Collaborative modelling of the knowledge domain is used as an approach to the elicitation of safety knowledge from experts. From this, a series of knowledge models is derived to inform decision-making. This is achieved by using Bayesian networks as a knowledge modelling scheme, underpinning a Safety Prognosis tool to serve meaningful prognostics information and visualise such information to predict safety violations. Findings – Collaborative modelling of safety-critical knowledge is a valid approach to knowledge elicitation and its sharing across the railway industry. This approach overcomes some of the key limitations of existing approaches to knowledge elicitation. Such models become an effective tool for prediction of safety cases by using railway data. This is demonstrated using passenger–train interaction safety data. Practical implications – This study contributes to practice in two main directions: by documenting an effective approach to knowledge elicitation and knowledge sharing, while also helping the transport industry to understand safety. Social implications – By supporting the railway industry in their efforts to understand safety, this research has the potential to benefit railway passengers, staff and communities in general, which is a priority for the transport sector. Originality/value – This research applies a knowledge elicitation approach to understanding safety based on collaborative modelling, which is a novel approach in the context of transport.

Behavior change theory, content and delivery of interventions to enhance adherence in chronic respiratory disease: A systematic review

Background: We sought to describe the theory used to design treatment adherence interventions, the content delivered, and the mode of delivery of these interventions in chronic respiratory disease. Methods: We included randomized controlled trials of adherence interventions (compared to another intervention or control) in adults with chronic respiratory disease (8 databases searched; inception until March 2015). Two reviewers screened and extracted data: post-intervention adherence (measured objectively); behavior change theory, content (grouped into psychological, education and self-management/supportive, telemonitoring, shared decision-making); and delivery. “Effective” studies were those with p < 0.05 for adherence rate between groups. We conducted a narrative synthesis and assessed risk of bias. Results: 12,488 articles screened; 46 included studies (n = 42,91% in OSA or asthma) testing 58 interventions (n = 27, 47% were effective). Nineteen (33%) interventions (15 studies) used 12 different behavior change theories. Use of theory (n = 11,41%) was more common amongst effective interventions. Interventions were mainly educational, self-management or supportive interventions (n = 27,47%). They were commonly delivered by a doctor (n = 20,23%), in face-to-face (n = 48,70%), one-to-one (n = 45,78%) outpatient settings (n = 46,79%) across 2–5 sessions (n = 26,45%) for 1–3 months (n = 26,45%). Doctors delivered a lower proportion (n = 7,18% vs n = 13,28%) and pharmacists (n = 6,15% vs n = 1,2%) a higher proportion of effective than ineffective interventions. Risk of bias was high in >1 domain (n = 43, 93%) in most studies. Conclusions: Behavior change theory was more commonly used to design effective interventions. Few adherence interventions have been developed using theory, representing a gap between intervention design recommendations and research practice.