Social class, education, and smoking cessation: Long-term follow-up of patients treated at a smoking cessation unit.

Our objective was to examine social class and educational differences in long-term smoking cessation success among a cohort of smokers attending a specialized smoking clinic. We studied sustained abstinence after cessation among 1,516 smokers (895 men and 621 women) treated for smoking cessation between 1995 and 2001 at a university teaching hospital in the metropolitan area of Barcelona, Spain. We calculated 1-year and long-term (up to 8-year) abstinence probabilities by means of Kaplan-Meier curves and the hazard ratio of relapse by means of Cox regression, after adjusting for other predictors of relapse. Overall abstinence probability was .277 (95% CI = .254-.301). Men and women in social classes IV-V had significant hazard ratios of relapse after long-term follow-up (men: 1.36, 95% CI = 1.07-1.72; women: 1.60, 95% CI = 1.24-2.06), as compared with patients in social classes I-II. The same independent effect was observed for education: Men and women with primary or less than primary studies had higher hazard ratios of relapse (men: 1.75, 95% CI = 1.35-2.25; women: 1.92, 95% CI = 1.51-2.46), as compared with patients with a university degree. Similar estimates were obtained after adjustment for stage of change, Fagerstr?m score for nicotine dependence, and type of treatment. Patients of lower socioeconomic status are at higher risk of relapse, and this association is independent of other well-known predictors of relapse. Social differences have to be taken into account in the clinical setting when tailoring specific actions to treat smoking dependence.     

Data-driven and tool-supported elicitation of quality requirements in agile companies

Software Quality Journal - Quality requirements (QRs) are a key artifact needed to ensure the quality and success of a software system. Despite their importance, QRs rarely get the same degree of...     

Non-Linear Rewrite Closure and Weak Normalization

A rewrite closure is an extension of a term rewrite system with new rules, usually deduced by transitivity. Rewrite closures have the nice property that all rewrite derivations can be transformed into derivations of a simple form. This property has been useful for proving decidability results in term rewriting. Unfortunately, when the term rewrite system is not linear, the construction of a rewrite closure is quite challenging. In this paper, we construct a rewrite closure for term rewrite systems that satisfy two properties: the right-hand side term in each rewrite rule contains no repeated variable (right-linear) and contains no variable occurring at depth greater than one (right-shallow). The left-hand side term is unrestricted, and in particular, it may be non-linear. As a consequence of the rewrite closure construction, we are able to prove decidability of the weak normalization problem for right-linear right-shallow term rewrite systems. Proving this result also requires tree automata theory. We use the fact that right-shallow right-linear term rewrite systems are regularity preserving. Moreover, their set of normal forms can be represented with a tree automaton with disequality constraints, and emptiness of this kind of automata, as well as its generalization to reduction automata, is decidable. A preliminary version of this work was presented at LICS 2009 (Creus 2009).     

Biophysical and Computational Approaches to Study Ternary Complexes: A ‘Cooperative Relationship’ to Rationalize Targeted Protein Degradation

Degraders have illustrated that compound-induced proximity to E3 ubiquitin ligases can prompt the ubiquitination and degradation of disease-relevant proteins. Hence, this pharmacology is becoming a promising alternative and complement to available therapeutic interventions (e. g., inhibitors). Degraders rely on protein binding instead of inhibition and, hence, they hold the promise to broaden the druggable proteome. Biophysical and structural biology approaches have been the cornerstone of understanding and rationalizing degrader-induced ternary complex formation. Computational models have now started to harness the experimental data from these approaches with the aim to identify and rationally help design new degraders. This review outlines the current experimental and computational strategies used to study ternary complex formation and degradation and highlights the importance of effective crosstalk between these approaches in the advancement of the targeted protein degradation (TPD) field. As our understanding of the molecular features that govern drug-induced interactions grows, faster optimizations and superior therapeutic innovations for TPD and other proximity-inducing modalities are sure to follow.     

Expanding the propositional logic of a t-norm with truth-constants: completeness results for rational semantics

In this paper we consider the expansions of logics of a left-continuous t-norm with truth-constants from a subalgebra of the rational unit interval. From known results on standard semantics, we study completeness for these propositional logics with respect to chains defined over the rational unit interval with a special attention to the completeness with respect to the canonical chain, i.e. the algebra over $[0,1] \cap {{\mathbb{Q}}}In this paper we consider the expansions of logics of a left-continuous t-norm with truth-constants from a subalgebra of the rational unit interval. From known results on standard semantics, we study completeness for these propositional logics with respect to chains defined over the rational unit interval with a special attention to the completeness with respect to the canonical chain, i.e. the algebra over [0,1] ?\mathbbQ[0,1] \cap {{\mathbb{Q}}} where each truth-constant is interpreted in its corresponding rational truth-value. Finally, we study rational completeness results when we restrict ourselves to deductions between the so-called evaluated formulae.     

A multiagent network for peer norm enforcement

In a multiagent system where norms are used to regulate the actions agents ought to execute, some agents may decide not to abide by the norms if this can benefit them. Norm enforcement mechanisms are designed to counteract these benefits and thus the motives for not abiding by the norms. In this work we propose a distributed mechanism through which agents in the multiagent system that do not abide by the norms can be ostracised by their peers. An ostracised agent cannot interact anymore and looses all benefits from future interactions. We describe a model for multiagent systems structured as networks of agents, and a behavioural model for the agents in such systems. Furthermore, we provide analytical results which show that there exists an upper bound to the number of potential norm violations when all the agents exhibit certain behaviours. We also provide experimental results showing that both stricter enforcement behaviours and larger percentage of agents exhibiting these behaviours reduce the number of norm violations, and that the network topology influences the number of norm violations. These experiments have been executed under varying scenarios with different values for the number of agents, percentage of enforcers, percentage of violators, network topology, and agent behaviours. Finally, we give examples of applications where the enforcement techniques we provide could be used.     

Digital inverse model control using Generalised holds with extensions to the adaptive case

In this paper, a digital implementation of an inverse-model based control scheme is proposed using Generalised Sampling and Hold Functions. The implementation of the controller using this kind of holds allows overcoming the difficulties related to the presence of unstable zeros in the continuous-time model and the usual appearance of unstable discretisation zeros in the discrete model when a ZOH is applied. The Generalised Sampling and Hold Functions allows obtaining a discrete model of the plant with all its zeros stable which allows realizing an exact inverse model of the plant in comparison to the use of a classical ZOH which only allows, in general, an approximate inversion of the plant. The proposed approach is then extended to the adaptive case where the stability and tracking properties of the general scheme are fully proved. Simulation examples showing the scope and application of the method are also presented.     

Negotiation decision functions for autonomous agents

We present a formal model of negotiation between autonomous agents. The purpose of the negotiation is to reach an agreement about the provision of a service by one agent for another. The model defines a range of strategies and tactics that agents can employ to generate initial offers, evaluate proposals and offer counter proposals. The model is based on computationally tractable assumptions, demonstrated in the domain of business process management and empirically evaluated.     

A Negotiation Meta Strategy Combining Trade-off and Concession Moves

In this paper we present a meta strategy that combines two negotiation tactics. The first one based on concessions, and the second one, a trade-off tactic. The goal of this work is to demonstrate by experimental analysis that the combination of different negotiation tactics allows agents to improve the negotiation process and as a result, to obtain more satisfactory agreements. The scenario proposed is based on two agents, a buyer and a seller, which negotiate over four issues. The paper presents the results and analysis of the meta strategy’s behaviour.