Sedation by intranasal dexmedetomidine at 3 mcg·kg(-1) is associated with acceptable success rate in children undergoing echocardiography with no adverse events in this cohort.

This paper investigates the irregular shape packing problem. We represent the problem as an ordered list of pieces to be packed where the order is decoded by a placement heuristic. A placement heuristic from the literature is presented and modified with a more powerful nofit polygon generator and new evaluation criteria. We implement a beam search algorithm to search over the packing order. Using this approach many parallel partial solutions can be generated and compared. Computational results for benchmark problems show that the algorithm generates highly competitive solutions in significantly less time than the best results currently in the literature.

Multi-tenant cloud, which features utility-like computing resources to tenants in a "pay-as-you-go" style, has been commercially popular for years. As one of the sole purposes of such a cloud is maximizing resource usages to increase its revenue, it usually uses virtualization to consolidate VMs from different and even mutually-malicious tenants atop a powerful physical machine. This, however, also enables a malicious tenant to steal securitycritical information such as crypto keys from victims, due to the shared physical resources such as caches.In this paper, we show that stealing crypto keys in a virtualized cloud may be a real threat by evaluating a cache-based side-channel attack against an encryption process. To mitigate such attacks while not notably degrading performance, we propose an approach that leverages dynamic cache coloring: when an application is doing security-sensitive operations, the VMM is notified to swap the associated data to a safe and isolated cache line. This approach may eliminate cache-based side-channel for security-critical operations, yet ensure efficient resource sharing during normal operations. We demonstrate the applicability by illustrating a preliminary implementation based on Xen and its performance overhead.

This paper considers variants of the one-dimensional bin packing (and stock cutting) problem in which both the ordering and orientation of items in a container influences the validity and quality of a solution. Two new real-world problems of this type are introduced, the first that involves the creation of wooden trapezoidal-shaped trusses for use in the roofing industry, the second that requires the cutting and scoring of rectangular pieces of cardboard in the construction of boxes. To tackle these problems, two variants of a local search-based approximation algorithm are proposed, the first that attempts to determine item ordering and orientation via simple heuristics, the second that employs more accurate but costly branch-and-bound procedures. We investigate the inevitable trade-off between speed and accuracy that occurs with these variants and highlight the circumstances under which each scheme is advantageous.

The paper examines a new problem in the irregular packing literature that has existed in industry for decades; two-dimensional irregular (convex) bin packing with guillotine constraints. Due to the cutting process of certain materials, cuts are restricted to extend from one edge of the stock-sheet to another, called guillotine cutting. This constraint is common place in glass cutting and is an important constraints in two-dimensional cutting and packing problems. In the literature, various exact and approximate algorithms exist for finding the two dimensional cutting patterns that satisfy the guillotine cutting constraint. However, to the best of our knowledge, all of the algorithms are designed for solving rectangular cutting where cuts are orthogonal with the edges of the stock-sheet. In order to satisfy the guillotine cutting constraint using these approaches, when the pieces are non-rectangular, practitioners implement a two stage approach. First, pieces are enclosed within rectangle shapes and then the rectangles are packed. Clearly, imposing this condition is likely to lead to additional waste. This paper aims to generate guillotine-cutting layouts of irregular shapes using a number of strategies. The investigation compares two two-stage approaches; one approximates pieces by rectangles, the other approximates pairs of pieces by rectangles using phi-functions for optimal clustering. Both these approaches use state of the art rectangle bin packing with guillotine constraints. Further, we design and implement a one-stage approach using a self-adapted forest search algorithm. Experimental results show the one-stage strategy to produce good solutions in less time over the two-stage approach.