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.

Building Support Vector Machines in the Context of Regularised Least Squares

This paper formulates a linear kernel support vector machine (SVM) as a regularized least-squares (RLS) problem. By defining a set of indicator variables of the errors, the solution to the RLS problem is represented as an equation that relates the error vector to the indicator variables. Through partitioning the training set, the SVM weights and bias are expressed analytically using the support vectors. It is also shown how this approach naturally extends to Sums with nonlinear kernels whilst avoiding the need to make use of Lagrange multipliers and duality theory. A fast iterative solution algorithm based on Cholesky decomposition with permutation of the support vectors is suggested as a solution method. The properties of our SVM formulation are analyzed and compared with standard SVMs using a simple example that can be illustrated graphically. The correctness and behavior of our solution (merely derived in the primal context of RLS) is demonstrated using a set of public benchmarking problems for both linear and nonlinear SVMs.

Kelvin-Helmholtz instability in solar chromospheric jets: theory and observation

Using data obtained by the high-resolution CRisp Imaging SpectroPolarimeter instrument on the Swedish 1 m Solar Telescope, we investigate the dynamics and stability of quiet-Sun chromospheric jets observed at the disk center. Small-scale features, such as rapid redshifted and blueshifted excursions, appearing as high-peed jets in the wings of the Hα line, are characterized by short lifetimes and rapid fading without any descending behavior. To study the theoretical aspects of their stability without considering their formation mechanism, we model chromospheric jets as twisted magnetic flux tubes moving along their axis, and use the ideal linear incompressible magnetohydrodynamic approximation to derive the governing dispersion equation. Analytical solutions of the dispersion equation indicate that this type of jet is unstable to Kelvin–Helmholtz instability (KHI), with a very short (few seconds) instability growth time at high upflow speeds. The generated vortices and unresolved turbulent flows associated with the KHI could be observed as a broadening of chromospheric spectral lines. Analysis of the Hα line profiles shows that the detected structures have enhanced line widths with respect to the background. We also investigate the stability of a larger-scale Hα jet that was ejected along the line of sight. Vortex-like features, rapidly developing around the jet’s boundary, are considered as evidence of the KHI. The analysis of the energy equation in the partially ionized plasma shows that ion–neutral collisions may lead to fast heating of the KH vortices over timescales comparable to the lifetime of chromospheric jets.

Directed Selection using Reinforcement Learning for the Examination Timetabling Problem

Traditional heuristic approaches to the Examination Timetabling Problem normally utilize a stochastic method during Optimization for the selection of the next examination to be considered for timetabling within the neighbourhood search process. This paper presents a technique whereby the stochastic method has been augmented with information from a weighted list gathered during the initial adaptive construction phase, with the purpose of intelligently directing examination selection. In addition, a Reinforcement Learning technique has been adapted to identify the most effective portions of the weighted list in terms of facilitating the greatest potential for overall solution improvement. The technique is tested against the 2007 International Timetabling Competition datasets with solutions generated within a time frame specified by the competition organizers. The results generated are better than those of the competition winner in seven of the twelve examinations, while being competitive for the remaining five examinations. This paper also shows experimentally how using reinforcement learning has improved upon our previous technique.

Ultrafast charge-transfer in organic photovoltaic interfaces: geometrical and functionalization effects

Understanding the microscopic mechanisms of electronic excitation in organic photovoltaic cells is a challenging problem in the design of efficient devices capable of performing sunlight harvesting. Here we develop and apply an ab initio approach based on time-dependent density functional theory and Ehrenfest dynamics to investigate photoinduced charge transfer in small organic molecules. Our calculations include mixed quantum–classical dynamics with ions moving classically and electrons quantum mechanically, where no experimental external parameter other than the material geometry is required. We show that the behavior of photocarriers in zinc phthalocyanine (ZnPc) and C60 systems, an effective prototype system for organic solar cells, is sensitive to the atomic orientation of the donor and the acceptor units as well as the functionalization of covalent molecules at the interface. In particular, configurations with the ZnPc molecules facing on C60 facilitate charge transfer between substrate and molecules that occurs within 200 fs. In contrast, configurations where ZnPc is tilted above C60 present extremely low carrier injection efficiency even at longer times as an effect of the larger interfacial potential level offset and higher energetic barrier between the donor and acceptor molecules. An enhancement of charge injection into C60 at shorter times is observed as binding groups connect ZnPc and C60 in a dyad system. Our results demonstrate a promising way of designing and controlling photoinduced charge transfer on the atomic level in organic devices that would lead to efficient carrier separation and maximize device performance.