Branch-and-Price-and-Cut for the Active-Passive Vehicle-Routing Problem

This paper presents a branch-And-price-And-cut algorithm for the exact solution of the active-passive vehicle-routing problem (APVRP). The APVRP covers a range of logistics applications where pickup-And-delivery requests necessitate a joint operation of active vehicles (e.g., trucks) and passive vehicles (e.g., loading devices such as containers or swap bodies). The objective is to minimize aweighted sum of the total distance traveled, the total completion time of the routes, and the number of unserved requests. To this end, the problem supports a flexible coupling and decoupling of active and passive vehicles at customer locations. Accordingly, the operations of the vehicles have to be synchronized carefully in the planning. The contribution of the paper is twofold: First, we present an exact branch-And-price-And-cut algorithm for this class of routing problems with synchronization constraints. To our knowledge, this algorithm is the first such approach that considers explicitly the temporal interdependencies between active and passive vehicles. The algorithm is based on a nontrivial network representation that models the logical relationships between the different transport tasks necessary to fulfill a request as well as the synchronization of the movements of active and passive vehicles. Second, we contribute to the development of branch-And-price methods in general, in that we solve, for the first time, an ng-path relaxation of a pricing problem with linear vertex costs by means of a bidirectional labeling algorithm. Computational experiments show that the proposed algorithm delivers improved bounds and solutions for a number of APVRP benchmark instances. It is able to solve instances with up to 76 tasks, four active, and eight passive vehicles to optimality within two hours of CPU time

Comparison of the fracture resistance of endodontically treated teeth restored with prefabricated posts and composite resin cores with different post lenghts

OBJECTIVE: This study evaluated the fracture strengths of endodontically treated teeth restored with prefabricated posts with different post lengths. MATERIAL AND METHODS: Thirty freshly extracted canines were endodontically treated. They were randomly divided into groups of 10 teeth and prepared according to 3 experimental protocols, as follows; Group 1/3 PP: teeth restored with prefabricated post and composite resin core (Z250) with post length of 5.0mm; Group 1/2 PP and Group 2/3 PP: teeth restored with prefabricated post and composite resin core (Z250) with different combinations of post length of 7.5mm and 10mm, respectively. All teeth were restored with full metal crowns. The fracture resistance (N) was measured in a universal testing machine (crosshead speed 0.5mm/min) at 45 degrees to the tooth long axis until failure. Data were analyzed by one-way analysis of variance (alpha=.05). RESULTS: The one-way analysis of variance demonstrated no significant difference among the different post lengths (P>;.05) (Groups 1/3 PP = 405.4 N, 1/2 PP = 395.6 N, 2/3 PP = 393.8 N). Failures occurred mainly due to core fracture. CONCLUSIONS: The results of this study showed that an increased post length in teeth restored with prefabricated posts did not significantly increase the fracture resistance of endodontically treated teeth

Decoding Brain Activity Associated with Literal and Metaphoric Sentence Comprehension Using Distributional Semantic Models

Recent years have seen a growing interest within the natural language processing (NLP)community in evaluating the ability of semantic models to capture human meaning representation in the brain. Existing research has mainly focused on applying semantic models to de-code brain activity patterns associated with the meaning of individual words, and, more recently, this approach has been extended to sentences and larger text fragments. Our work is the first to investigate metaphor process-ing in the brain in this context. We evaluate a range of semantic models (word embeddings, compositional, and visual models) in their ability to decode brain activity associated with reading of both literal and metaphoric sentences. Our results suggest that compositional models and word embeddings are able to capture differences in the processing of literal and metaphoric sentences, providing sup-port for the idea that the literal meaning is not fully accessible during familiar metaphor comprehension