CO2 laser surgery in osteomyelitis

Spirituality is a critical component of the holistic mind-body-spirit model embraced by Hospice. Hospice chaplains, as part of the caregiving team, must understand their abilities and limitations in providing spiritual guidance to others who may differ in religious and spiritual beliefs.     

Improvement of the optical properties of electrodeposited CuInSe2 thin films by thermal and chemical treatments

Cu, In and Se have been codeposited in thin films by potentiostatic one-step electrodeposition. The as-deposited material has shown direct optical transitions attributable to the CuInSe₂ semiconductor, but also additional absorption corresponding to another semimetallic phase. The secondary phases are selenium and copper selenide compounds which have been determined by composition measurements. In order to eliminate the semimetallic phases and to improve the semiconductor behaviour of the electrodeposited material, thermal and chemical treatments have been performed. After heat-treatment of the samples at 400°C in flowing argon, elemental selenium loss has been detected together with an enhancement of the allowed direct optical transition. The subsequent chemical etching of the layers in a KCN solution has showed to be successful in eliminating the copper selenide phases which were responsible of the remaining sub-bandgap absorption.     

Multiband properties of a fractal tree antenna generated byelectrochemical deposition

Experimental results on a fractal tree-like antenna are reported. The fractal structure is generated by electrochemical deposition. The antenna presents a multiband behaviour with a denser band distribution than the previously reported Sierpinski fractal antenna. The matched frequencies are related to the length distribution over the fractal shape     

Learning Sets of Antecedent-restricted Functional and Multivalued Dependencies with Queries

Identifying dependencies that hold in relational databases is essential to produce good databases designs. In particular, functional and multivalued dependencies are used to obtain relation schemes that satisfy the 4th normal form, a property that is considered satisfactory for most applications. It is known that the class of sets of functional dependencies is learnable in the exact model of learning with queries. Also a subclass of multivalued dependencies, the class of consequent-restricted multivalued dependencies, has been shown to be learnable in this model. In this paper, we present an algorithm that learns a class that contains sets of both functional and multivalued dependencies under some restrictions imposed on the antecedents of dependencies. We also show, as a by-product, an algorithm that learns a non-trivial subclass of 2-quasi Horn formulas, closely related to the class just mentioned.     

Information fusion for automotive applications – An overview

This article focusses on the fusion of information from various automotive sensors like radar, video, and lidar for enhanced safety and traffic efficiency. Fusion is not restricted to data from sensors onboard the same vehicle but vehicular communication systems allow to propagate and fuse information with sensor data from other vehicles or from the road infrastructure as well. This enables vehicles to perceive information from regions that are hardly accessible otherwise and represents the basis for cooperative driving maneuvers. While the Bayesian framework builds the basis for information fusion, automobile environments are characterized by their a priori unknown topology, i.e., the number, type, and structure of the perceived objects is highly variable. Multi-object detection and tracking methods are a first step to cope with this challenge. Obviously, the existence or non-existence of an object is of paramount importance for safe driving. Such decisions are highly influenced by the association step that assigns sensor measurements to object tracks. Methods that involve multiple sequences of binary assignments are compared with soft-assignment strategies. Finally, fusion based on finite set statistics that (theoretically) avoid an explicit association are discussed.     

A genetic solution based on lexicographical goal programming for a multiobjective job shop with uncertainty

In this work we consider a multiobjective job shop problem with uncertain durations and crisp due dates. Ill-known durations are modelled as fuzzy numbers. We take a fuzzy goal programming approach to propose a generic multiobjective model based on lexicographical minimisation of expected values. To solve the resulting problem, we propose a genetic algorithm searching in the space of possibly active schedules. Experimental results are presented for several problem instances, solved by the GA according to the proposed model, considering three objectives: makespan, tardiness and idleness. The results illustrate the potential of the proposed multiobjective model and genetic algorithm.     

Learning a subclass of k-quasi-Horn formulas with membership queries

Boolean formulas have been widely studied in the field of learning theory. We focus on the model of learning with queries, and study a restriction of the class of k-quasi-Horn formulas, that is, conjunctive normal form formulas where the number of unnegated literals per clause is at most k. This class is known to be as hard to learn as the general class CNF of conjunctive normal form formulas. By imposing some constraints, we define a fragment of this logic that can be learned using only membership queries. Also we prove that none of these constraints makes by itself the class learnable.