Graphical constraints: a graphical user interface for constraint problems

A constraint satisfaction problem is a classical artificial intelligence paradigm characterized by a set of variables (each variable with an associated domain of possible values), and a set of constraints that specify relations among subsets of these variables. Solutions are assignments of values to all variables that satisfy all the constraints. Many real world problems may be modelled by means of constraints. The range of problems that can use this representation is very diverse and embraces areas like resource allocation, scheduling, timetabling or vehicle routing. Constraint programming is a form of declarative programming in the sense that instead of specifying a sequence of steps to execute, it relies on properties of the solutions to be found, which are explicitly defined by constraints. The idea of constraint programming is to solve problems by stating constraints which must be satisfied by the solutions. Constraint programming is based on specialized constraint solvers that take advantage of constraints to search for solutions. The success and popularity of complex problem solving tools can be greatly enhanced by the availability of friendly user interfaces. User interfaces cover two fundamental areas: receiving information from the user and communicating it to the system; and getting information from the system and deliver it to the user. Despite its potential impact, adequate user interfaces are uncommon in constraint programming in general. The main goal of this project is to develop a graphical user interface that allows to, intuitively, represent constraint satisfaction problems. The idea is to visually represent the variables of the problem, their domains and the problem constraints and enable the user to interact with an adequate constraint solver to process the constraints and compute the solutions. Moreover, the graphical interface should be capable of configure the solver’s parameters and present solutions in an appealing interactive way. As a proof of concept, the developed application – GraphicalConstraints – focus on continuous constraint programming, which deals with real valued variables and numerical constraints (equations and inequalities). RealPaver, a state-of-the-art solver in continuous domains, was used in the application. The graphical interface supports all stages of constraint processing, from the design of the constraint network to the presentation of the end feasible space solutions as 2D or 3D boxes.

In-depth search focused on furans, lactones, volatile phenols, and acetals as potential age markers of Madeira wines by comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry combined with solid phase microextraction

The establishment of potential age markers of Madeira wine is of paramount significance as it may contribute to detect frauds and to ensure the authenticity of wine. Considering the chemical groups of furans, lactones, volatile phenols, and acetals, 103 volatile compounds were tentatively identified; among these, 71 have been reported for the first time in Madeira wines. The chemical groups that could be used as potential age markers were predominantly acetals, namely, diethoxymethane, 1,1-diethoxyethane, 1,1-diethoxy-2-methyl-propane, 1-(1-ethoxyethoxy)-pentane, trans-dioxane and 2-propyl-1,3-dioxolane, and from the other chemical groups, 5-methylfurfural and cis-oak-lactone, independently of the variety and the type of wine. GC × GC-ToFMS system offers a more useful approach to identify these compounds compared to previous studies using GC−qMS, due to the orthogonal systems, that reduce coelution, increase peak capacity and mass selectivity, contributing to the establishment of new potential Madeira wine age markers. Remarkable results were also obtained in terms of compound identification based on the organized structure of the peaks of structurally related compounds in the GC × GC peak apex plots. This information represents a valuable approach for future studies, as the ordered-structure principle can considerably help the establishment of the composition of samples. This new approach provides data that can be extended to determine age markers of other types of wines.