Alcohol and cannabis use among adolescents in Flemish secondary school in Brussels: effects of type of education

<p>Abstract</p> <p>Background</p> <p>Research regarding socio-economic differences in alcohol and drug use in adolescence yields mixed results. This study hypothesizes that (1) when using education type as a proxy of one's social status, clear differences will exist between students from different types of education, regardless of students' familial socio-economic background; (2) and that the effects of education type differ according to their cultural background.</p> <p>Methods</p> <p>Data from the Brussels youth monitor were used, a school survey administered among 1,488 adolescents from the 3rd to 6th year of Flemish secondary education. Data were analyzed using multilevel logistic regression models.</p> <p>Results</p> <p>Controlling for their familial background, the results show that native students in lower educational tracks use alcohol and cannabis more often than students in upper educational tracks. Such a relationship was not found for students from another ethnic background.</p> <p>Conclusion</p> <p>Results from this study indicate that research into health risks should take into account both adolescents' familial background and individual social position as different components of youngsters' socio-economic background.</p

A reservoir balancing constraint with applications to bike-sharing

\u3cp\u3eA global CP constraint is presented which improves the propagation of reservoir constraints on cumulative resources in schedules with optional tasks. The global constraint is incorporated in a CP approach to solve a Single-Commodity Pickup and Delivery Problem: the Bicycle Rebalancing Problem with Time-Windows and heterogeneous fleet. This problem was recently introduced at the 2015 ACP Summer School on Constraint Programming competition. The resulting CP approach outperforms a Branch-and-Bound approach derived from two closely related problems. In addition, the CP approach presented in this paper resulted in a first place position in the competition.\u3c/p\u3

JGraphT Release v1.1.0

JGraphT is a Java library for graph data structures and algorithm

JGraphT Release v1.3.0

JGraphT is a Java library for graph data structures and algorithm

L'identité des enfants issus des viols commis pendant le génocide des tutis au Rwanda

info:eu-repo/semantics/publishe

Improved call graph comparison using simulated annealing

\u3cp\u3eThe amount of suspicious binary executables submitted to Anti-Virus (AV) companies are in the order of tens of thousands per day. Current hash-based signature methods are easy to deceive and are inefficient for identifying known malware that have undergone minor changes. Examining malware executables using their call graphs view is a suitable approach for overcoming the weaknesses of hash-based signatures. Unfortunately, many operations on graphs are of high computational complexity. One of these is the Graph Edit Distance (GED) between pairs of graphs, which seems a natural choice for static comparison of malware. We demonstrate how Simulated Annealing can be used to approximate the graph edit distance of call graphs, while outperforming previous approaches both in execution time and solution quality. Additionally, we experiment with opcode mnemonic vectors to reduce the problem size and examine how Simulated Annealing is affected.\u3c/p\u3

Hybrid optimization methods for time-dependent sequencing problems

\u3cp\u3eIn this paper, we introduce novel optimization methods for sequencing problems in which the setup times between a pair of tasks depend on the relative position of the tasks in the ordering. Our proposed methods rely on a hybrid approach where a constraint programming model is enhanced with two distinct relaxations: One discrete relaxation based on multivalued decision diagrams, and one continuous relaxation based on linear programming. Both relaxations are used to generate bounds and enhance constraint propagation. Experiments conducted on three variants of the time-dependent traveling salesman problem indicate that our techniques substantially outperform general-purpose methods, such as mixed-integer linear programming and constraint programming models.\u3c/p\u3

A combinatorial Benders decomposition for the lock scheduling problem

\u3cp\u3eThe Lock Scheduling Problem (LSP) is a combinatorial optimization problem that represents a real challenge for many harbours and waterway operators. The LSP consists of three strongly interconnected subproblems: scheduling lockages, assigning ships to chambers, and positioning the ships inside the chambers. These should be interpreted respectively as a scheduling, an assignment, and a packing problem. By combining the first two problems into a master problem and using the packing problem as a subproblem, a decomposition is achieved that can be solved efficiently by a Combinatorial Benders approach. The master problem is solved first, thereby sequencing the ships into a number of lockages. Next, for each lockage, a packing subproblem is checked for feasibility, possibly returning a number of combinatorial inequalities (cuts) to the master problem. The result is an exact approach to the LSP. Experiments are conducted on a set of instances that were generated in correspondence with real world data. The results indicate that the decomposition approach significantly outperforms other exact approaches presented in the literature, in terms of solution quality and computation time.\u3c/p\u3

The fuel replenishment problem: a split-delivery multi-compartment vehicle routing problem with multiple trips

\u3cp\u3eThe Fuel Replenishment Problem (FRP) is a multi-compartment, multi-trip, split-delivery VRP in which tanker trucks transport different types of petrol, separated over multiple vehicle compartments, from an oil depot to petrol stations. Large customer demands often necessitate multiple deliveries. Throughout a single working day, a tanker truck returns several times to the oil depot to resupply. A solution to the FRP involves computing a delivery schedule of minimum duration, thereby determining for each vehicle (1) the allocation of oil products to vehicle compartments, (2) the delivery routes, and (3) the delivery patterns. To solve FRP efficiently, an Adaptive Large Neighborhood Search (ALNS) heuristic is constructed. The heuristic is evaluated on data from a Chinese petroleum transportation company and compared against exact results from a MILP model and lower bounds from a column generation approach. In addition, we perform sensitivity analysis on different problem features, including the number of vehicles, products, vehicle compartments and their capacities. Computational results show that the ALNS heuristic is capable of solving instances with up to 60 customers and 3 different products in less than 25 minutes with an average optimality gap of around 10%. On smaller instances, the heuristic finds optimal solutions in significantly less time than the exact MILP formulation.\u3c/p\u3