Neurocognitive impairments are prevalent in persons seeking treatment for alcohol use disorders (AUDs). These impairments and their physical, social, psychological and occupational consequences vary in severity across persons, much like those resulting from traumatic brain injury; however, due to their slower course of onset, alcohol-related cognitive impairments are often overlooked both within and outside of the treatment setting. Evidence suggests that cognitive impairments can impede treatment goals through their effects on treatment processes. Although some recovery of alcohol-related cognitive impairments often occurs after cessation of drinking (time-dependent recovery), the rate and extent of recovery is variable across cognitive domains and individuals. Following a long hiatus in scientific interest, a new generation of research aims to facilitate treatment process and improve AUD treatment outcomes by directly promoting cognitive recovery (experience-dependent recovery). This review updates knowledge about the nature and course of cognitive and brain impairments associated with AUD, including cognitive effects of adolescent AUD. We summarize current evidence for indirect and moderating relationships of cognitive impairment to treatment outcome, and discuss how advances in conceptual frameworks of brain-behavior relationships are fueling the development of novel AUD interventions that include techniques for cognitive remediation. Emerging evidence suggests that such interventions can be effective in promoting cognitive recovery in persons with AUD and other substance use disorders, and potentially increasing the efficacy of AUD treatments. Finally, translational approaches based on cognitive science, neurophysiology, and neuroscience research are considered as promising future directions for effective treatment development that includes cognitive rehabilitation.

Model Driven Engineering is a promizing approach that could lead to the emergence of a new paradigm for software evolution, namely Model Driven Software Evolution. Models, Metamodels and Transformations are the cornerstones of this approach. Combining these concepts leads to very complex structures which revealed to be very difficult to understand especially when different technological spaces are considered such as XMLWare (the technology based on XML), Grammarware and BNF, Modelware and UML, Dataware and SQL, etc. The concepts of model, metamodel and transformation are usually ill-defined in industrial standards like the MDA or XML. This paper provides a conceptual framework, called a megamodel, that aims at modelling large-scale software evolution processes. Such processes are modeled as graphs of systems linked with well-defined set of relations such as RepresentationOf (µ), ConformsTo (χ) and IsTransformedIn (τ ).

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It is worth understanding state machines better because various kinds of systems can be formalized as state machines and therefore understanding state machines has something to do with comprehension of systems. Understanding state machines can be interpreted as knowing properties they enjoy and comprehension of systems is interpreted as knowing whether they satisfy requirements. We (mainly the second author) have developed a tool called SMGA that basically takes a finite sequence of states from a state machine and generates a graphical animation of the finite sequence or the state machine. Observing such a graphical animation helps us guess properties of the state machine. We should confirm whether the state machine enjoys the guessed properties because such guessed properties may not be true properties of the state machine. Model checking is one possible technique to do so. If the state machine has a fixed small number of reachable states, model checking is enough. Otherwise, however, it is not. If that is the case, we should use some other techniques to make sure that the system enjoys the guessed properties. Interactive theorem proving is one such technique. The paper reports on a case study in which a mutual exclusion protocol called Qlock is used as an example to exemplify the abovementioned idea or methodology.