Resource Scheduling, Object-Oriented Programming, Constraint Logic Programming, Metaprogramming, Application Oriented Language Integrating Object and Constraint Technologies for Stating and Solving Resource Scheduling Problems

Resource scheduling is a difficult and time consuming problem, especially when human resources are involved. The major reason of this difficulty emerges from the complexity and the dynamically changing nature of the regulations that restrict the solutions of problem. Constraint Logic Programming (CLP) is currently considered as the most powerful computational paradigm for stating and solving such problems. However, the statement of complex regulations is still extremely hard and results in a large semantic gap between the implied formalization and the problem regulations, as perceived by the end-user. Ideally, the end-user should be able to represent and modify directly the regulations, but since a high level formalism is not available, the interference of an expert programmer is always required. In this paper, we suggest an Object-Oriented meta-representation for the abstract, natural specification of regulations. With the target of obtaining an efficient scheduling system, we describe the relationship between our formalism and the CLP one, laying the foundation for automatic transformation between the two representations. 1

Peri-implantitis: a complex condition with non-linear characteristics

Aim To cluster peri-implantitis patients and explore non-linear patterns in peri-implant bone levels. Materials and Methods Clinical and radiographic variables were retrieved from 94 implant-treated patients (340 implants, mean 7.1 ± 4.1 years in function). Kernel probability density estimations on patient mean peri-implant bone levels were used to identify patient clusters. Inter-relationships of all variables were evaluated by principal component analysis; a k-nearest neighbours method was performed for supervised prediction of implant bone levels at the patient level. Self-similar patterns of mean bone level per implant from different jaw bone sites were examined and their associated fractal dimensions were estimated. Results Two clusters of implant-treated patients were identified, one at patient mean bone levels of 1.7 mm and another at 4.0 mm. Five of thirteen available variables (number of teeth, age, gender, periodontitis severity, years of implant service), were predictive for peri-implant bone levels. A high jaw bone fractal dimension was associated with less severe peri-implantitis. Conclusions Non-linearity of peri-implantitis was evidenced by finding different peri-implant bone levels between two main clusters of implant-treated patients and among six different jaw bone sites. The patient mean peri-implant bone levels were predicted from five variables and confirmed complexity for peri-implantitis

Prediction of individual implant bone levels and the existence of implant “phenotypes”

Objectives: To cluster implants placed in patients of a private practice and identify possible implant “phenotypes” and predictors of individual implant mean bone levels (IIMBL). Materials and methods: Clinical and radiographical variables were collected from 72 implant-treated patients with 237 implants and a mean 7.4 ± 3.5 years of function. We clustered implants using the k-means method guided by multidimensional unfolding. For predicting IIMBL, we used principal component analysis (PCA) as a variable reduction method for an ensemble selection (ES) and a support vector machines models (SVMs). Network analysis investigated variable interactions. Results: We identified a cluster of implants susceptible to peri-implantitis (96% of the implants in the cluster were affected by peri-implantitis) and two overlapping clusters of implants resistant to peri-implantitis. The cluster susceptible to peri-implantitis showed a mean IIMBL of 5.2 mm and included implants placed mainly in the lower front jaw and in mouths having a mean of eight teeth. PCA extracted the parameters such as number of teeth, full-mouth plaque scores, implant surface, periodontitis severity, age and diabetes as significant in explaining the data variability. ES and SVMs showed good results in predicting IIMBL (root-mean-squared error of 0.133 and 0.149, 10-fold cross-validation error of 0.147 and 0.150, respectively). Network analysis revealed limited interdependencies of variables among peri-implantitis-affected and non-affected implants and supported the hypothesis of the existence of distinct implant “phenotypes.”. Conclusion: Two implant “phenotypes” were identified, one with susceptibility and another with resistance to peri-implantitis. Prediction of IIMBL could be achieved by using six variables

Parental stress, coping, resilience, general health and social support in families with a child with MPS III.

In S. Cagnoni, J. Gottlieb, E. Hart, M. Middendorf, and G.R. Raidl, editors, Applications of Evolutionary Computing, Springer Verlag, 2002.info:eu-repo/semantics/publishe