Background and objective Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning approaches to use situational features associated with having or not having urges to smoke during a quit attempt in order to accurately classify high-urge states. Methods To test our machine learning approaches, specifically, Bayes, discriminant analysis and decision tree learning methods, we used a dataset collected from over 300 participants who had initiated a quit attempt. The three classification approaches are evaluated observing sensitivity, specificity, accuracy and precision. Results The outcome of the analysis showed that algorithms based on feature selection make it possible to obtain high classification rates with only a few features selected from the entire dataset. The classification tree method outperformed the naive Bayes and discriminant analysis methods, with an accuracy of the classifications up to 86%. These numbers suggest that machine learning may be a suitable approach to deal with smoking cessation matters, and to predict smoking urges, outlining a potential use for mobile health applications. Conclusions In conclusion, machine learning classifiers can help identify smoking situations, and the search for the best features and classifier parameters significantly improves the algorithms’ performance. In addition, this study also supports the usefulness of new technologies in improving the effect of smoking cessation interventions, the management of time and patients by therapists, and thus the optimization of available health care resources. Future studies should focus on providing more adaptive and personalized support to people who really need it, in a minimum amount of time by developing novel expert systems capable of delivering real-time interventions.

INTRODUCTION Working in a healthy work environment is the ultimate goal of every employee. Dentistry is a stressful career, and the reasons for dissatisfaction are numerous. AIM The aim of this study was to determine the factors of work satisfaction in dental professionals of the Bosnia and Herzegovina Federation. MATERIALS AND METHODS A total of 134 dental professionals selected randomly from the Registry of Dental Chamber of Bosnia and Herzegovina Federation were included in the study. All of them filled out the Demographic Questionnaire and Job Satisfaction Scale (JSS). RESULTS An increase in the influence of work on the quality of life as well as an increase in its frequency results in leaving the job and significantly reducing the overall job satisfaction. General dental practitioners are significantly more satisfied as compared with specialists. Significant predictors of the job satisfaction are employment status, type of the practice, and availability of dental assistants. General dental practitioners with a dental assistant employed at a private practice are more likely to be satisfied with their jobs. CONCLUSIONS Employment status, practice type and availability of dental assistants are significant predictors of job satisfaction. General dental practitioners working in a private practice with a dental assistant are most likely to be satisfied.

Abstract We confront the indications of lepton flavor universality (LFU) violation observed in semi-tauonic B meson decays with new physics (NP) searches using high p T tau leptons at the LHC. Using effective field theory arguments we correlate possible non-standard contributions to semi-tauonic charged currents with the τ + τ − signature at high energy hadron colliders. Several representative standard model extensions put forward to explain the anomaly are examined in detail: (i) weak triplet of color-neutral vector resonances, (ii) second Higgs doublet and (iii) scalar or (iv) vector leptoquark. We find that, in general, τ + τ − searches pose a serious challenge to NP explanations of the LFU anomaly. Recasting existing 8 TeV and 13 TeV LHC analyses, stringent limits are set on all considered simplified models. Future projections of the τ + τ − constraints as well as caveats in interpreting them within more elaborate models are also discussed.

A bstractWe discuss how to perform consistent extractions of anomalous triple gauge couplings (aTGC) from electroweak boson pair production at the LHC in the Standard Model Effective Field Theory (SMEFT). After recasting recent ATLAS and CMS searches in pp → W Z(W W ) → ℓ′νℓ+ℓ−(νℓ) channels, we find that: (a) working consistently at order Λ−2 in the SMEFT expansion the existing aTGC bounds from Higgs and LEP-2 data are not improved, (b) the strong limits quoted by the experimental collaborations are due to the partial Λ−4 corrections (dimension-6 squared contributions). Using helicity selection rule arguments we are able to explain the suppression in some of the interference terms, and discuss conditions on New Physics (NP) models that can benefit from such LHC analyses. Furthermore, standard analyses assume implicitly a quite large NP scale, an assumption that can be relaxed by imposing cuts on the underlying scale of the process (s^$$ \sqrt{\widehat{s}} $$). In practice, we find almost no correlation between s^$$ \sqrt{\widehat{s}} $$ and the experimentally accessible quantities, which complicates the SMEFT interpretation. Nevertheless, we provide a method to set (conservative) aTGC bounds in this situation, and recast the present searches accordingly. Finally, we introduce a simple NP model for aTGC to compare the bounds obtained directly in the model with those from the SMEFT analysis.