Importance of participation rate in sampling of data in population based studies, with special reference to bone mass in Sweden

OBJECTIVE: To study the effects of participation rate in sampling on "normative" bone mass data. DESIGN: This was a comparison between two randomly selected samples from the same population. The participation rates in the two samples were 61.9% and 83.6%. Measurements were made of bone mass at different skeletal sites and of muscle strength, as well as an assessment of physical activity. SETTING: Malm?, Sweden. SUBJECTS: There were 230 subjects (117 men, 113 women), aged 21 to 42 years. RESULTS: Many subjects participated in both studies (163). Those who took part only in the study with the higher participation rate (67) almost invariably had higher values for bone mass density at the sites measured (up to 7.6% for men) than participants in the study with the lower participation rate. No differences in muscle strength were recorded. CONCLUSION: A high degree of compliance is important to achieve a reliable result in determining normal values in population based studies.     

Guiding students to the right questions: adaptive navigation support in an E‐Learning system for Java programming

Rapid growth of the volume of interactive questions available to the students of modern E‐Learning courses placed the problem of personalized guidance on the agenda of E‐Learning researchers. Without proper guidance, students frequently select too simple or too complicated problems and ended either bored or discouraged. This paper explores a specific personalized guidance technology known as adaptive navigation support. We developed JavaGuide, a system, which guides students to appropriate questions in a Java programming course, and investigated the effect of personalized guidance a three‐semester long classroom study. The results of this study confirm the educational and motivational effects of adaptive navigation support.     

Comparison of forecasting methods with an application to predicting excess equity premium

This paper reviews various forecast methods including combination using theoretically optimal weights and those under model selection approaches. In addition, we suggest two modified simple averaging forecast combination methods—a mean corrected and a mean and scale corrected method. We conclude that due to the fact that real data is usually subject to structural breaks, rolling forecasting scheme has a better performance than fixed window and continuously updating scheme. In addition, methods that use less information appear to perform better than methods using all the sample information about the covariance structure of the available forecasts. The mean and scale corrected simple average approach yield smaller mean squared forecast error than the three widely used regression approaches suggested by Granger and Ramanathan [11].     

A neural-network-based exponential H∞ synchronisation for chaotic secure communication via improved genetic algorithm

In this study, a novel approach via improved genetic algorithm (IGA)-based fuzzy observer is proposed to realise exponential optimal H^∞ synchronisation and secure communication in multiple time-delay chaotic (MTDC) systems. First, an original message is inserted into the MTDC system. Then, a neural-network (NN) model is employed to approximate the MTDC system. Next, a linear differential inclusion (LDI) state-space representation is established for the dynamics of the NN model. Based on this LDI state-space representation, this study proposes a delay-dependent exponential stability criterion derived in terms of Lyapunov's direct method, thus ensuring that the trajectories of the slave system approach those of the master system. Subsequently, the stability condition of this criterion is reformulated into a linear matrix inequality (LMI). Due to GA's random global optimisation search capabilities, the lower and upper bounds of the search space can be set so that the GA will seek better fuzzy observer feedback gains, accelerating feedback gain-based synchronisation via the LMI-based approach. IGA, which exhibits better performance than traditional GA, is used to synthesise a fuzzy observer to not only realise the exponential synchronisation, but also achieve optimal H^∞ performance by minimizing the disturbance attenuation level and recovering the transmitted message. Finally, a numerical example with simulations is given in order to demonstrate the effectiveness of our approach.     

Novel in-line WAT processing of 55 nm CIS product and effect of process waiting time on void growth at interface of SiCN with copper capping layer

The WAT (wafer acceptance test) is the last examination that is performed before a wafer or a chip fab out to ensure the quality and stability of chip performance. In 55 nm CIS (CMOS Image Sensor) technology, a highly smooth wafer surface is critical for the BSI (backside illumination) process. The traditional WAT process cannot be used; rather the in-line WAT must be performed during the process for forming copper interconnect. However, increasing the processing time increases the period of exposure of the copper interconnect to air, which is called the Q-time, affecting the reliability of copper interconnect. Nitrogen-doped silicon carbide (also called NDC or SiCN) has been used to fabricate copper diffusion barrier films. PECVD SiCN dielectric has a promisingly low dielectric constant for use as a copper diffusion barrier. Copper diffusion barrier films comprise one or more layers of silicon carbide. Covering a copper layer with a single thin NDC pre-layer significantly increases the maximum allowable Q-time for wafer probing. However, after the Q-time, a void forms between NDC layer and the NDC pre-layer. This work proposes a new two-step NDC process and the optimization of the thickness of the NDC pre-layer. The process has the advantages of providing a high stability for parametric test and a long allowable Q-time. These advantages are achieved by changing the thickness of the NDC pre-layer. This new approach has been analyzed using TEM and by performing parametric tests, and the feasibility has been confirmed experimentally. No void is formed between the NDC layers and a high test stability is achieved when the thickness of the NDC pre-layer is 120 Å.     

GPU accelerated tensor contractions in the plaquette renormalization scheme

We use the graphical processing unit (GPU) to accelerate the tensor contractions, which is the most time consuming operations in the variational method based on the plaquette renormalized states. Using a frustrated Heisenberg J₁–J₂ model on a square lattice as an example, we implement the algorithm based on the compute unified device architecture (CUDA). For a single plaquette contraction with the bond dimensions C = 3 of each rank of the tensor, results are obtained 25 times faster on GPU than on a current CPU core. This makes it possible to simulate systems with the size 8 × 8 and larger, which are extremely time consuming on a single CPU. This technology successfully relieves the computing time dependence with C, while in the CPU serial computation, the total required time scales both with C and the system size.     

Robust H infinity fuzzy control for a class of uncertain discrete fuzzy bilinear systems.

The main theme of this paper is to present robust fuzzy controllers for a class of discrete fuzzy bilinear systems. First, the parallel distributed compensation method is utilized to design a fuzzy controller, which ensures the robust asymptotic stability of the closed-loop system and guarantees an H(infinity) norm-bound constraint on disturbance attenuation for all admissible uncertainties. Second, based on the Schur complement and some variable transformations, the stability conditions of the overall fuzzy control system are formulated by linear matrix inequalities. Finally, the validity and applicability of the proposed schemes are demonstrated by a numerical simulation and the Van de Vusse example.     

Exploring the Interaction between Java's Implicitly Thrown Exceptions and Instruction Scheduling

The frequent occurrence of implicitly thrown exceptions poses one of the challenges present in a Java compiler. Not only do these implicitly thrown exceptions directly affect the performance by requiring explicit checks, they also indirectly impact the performance by restricting code movement in order to satisfy the precise exception model in Java. In particular, instruction scheduling is one transformation that is restricted by implicitly thrown exceptions due to the heavy reliance on reordering instructions to exploit maximum hardware performance. The goal of this study is two-fold: first, investigate the degree to which implicitly thrown exceptions in Java hinder instruction scheduling, and second, find new techniques for allowing more efficient execution of Java programs containing implicitly thrown exceptions. Experimental results show that with aggressive scheduling techniques, such as superblock scheduling, the negative performance impact can be greatly reduced.     

Exploiting Network Locality for CC-NUMA Multiprocessors

The Journal of Supercomputing - Rapid advances in interconnection networks in multiprocessors are closing the gap between computation and communication. Given this trend, how can we utilize fast...