Impact of non-normal random effects on inference by multiple imputation: A simulation assessment

Multivariate extensions of well-known linear mixed-effects models have been increasingly utilized in inference by multiple imputation in the analysis of multilevel incomplete data. The normality assumption for the underlying error terms and random effects plays a crucial role in simulating the posterior predictive distribution from which the multiple imputations are drawn. The plausibility of this normality assumption on the subject-specific random effects is assessed. Specifically, the performance of multiple imputation created under a multivariate linear mixed-effects model is investigated on a diverse set of incomplete data sets simulated under varying distributional characteristics. Under moderate amounts of missing data, the simulation study confirms that the underlying model leads to a well-calibrated procedure with negligible biases and actual coverage rates close to nominal rates in estimates of the regression coefficients. Estimation quality of the random-effect variance and association measures, however, are negatively affected from both the misspecification of the random-effect distribution and number of incompletely-observed variables. Some of the adverse impacts include lower coverage rates and increased biases.     

TN approximation on the critical size of time-dependent,one-speed and one-dimensional neutron transport problem with anisotropic scattering

The criticality problem is studied based on one-speed time-dependent neutron transport theory, for a uniform and finite slab, using the Marshak boundary condition. The time-dependent neutron transport equation is reduced to a stationary equation. The variation of the critical thickness of the time-dependent system is investigated by using the linear anisotropic scattering kernel together with the combination of forward and backward scattering. Numerical calculations for various combinations of the scattering parameters and selected values of the time decay constant and the reflection coefficient are performed by using the Chebyshev polynomials approximation method. The results are compared with those previously obtained by other methods which are available in the literature.     

Multilayer Graphene Broadband Terahertz Modulators with Flexible Substrate

Fabrication of terahertz modulators as simple devices with high modulation depth across a broad bandwidth is still very challenging. In this study, four different chemical vapor deposition grown multilayer graphene (MLG) modulators based on MLG/ionic liquid/gold sandwich structures have been investigated. Flexible substrates (PVC and PE) were chosen as host materials, and devices were fabricated with three different thicknesses. The resultant MLG devices can be operated at low voltages between 0 and 3.4 V providing nearly complete modulation between 0.2 and 1.5 THz with low insertion losses. Even with such low gate voltages, the devices have been doped significantly inducing 7–11-fold improvement in their sheet conductivities depending on device thickness. In addition, sheet conductivity has been improved more than three times as the graphene layer number increased from 30 to 100. With the demonstration of promising device performances, the proposed modulators can be potential candidates for applications in terahertz and related optoelectronic technologies.     

Heat pipes thermoelectric solar collectors for energy applications

This study investigates the load characteristics of heat pipe thermoelectric solar collector (HPTSC) in practice. Heat pipe thermoelectric solar collector converts the heat generated by the Sun directly into electrical energy and produces hot water as well. The maximum power in HPTSC is obtained when the internal resistance of the thermoelectric module is equal to the load resistance. It has been observed to be possible to produce both hot water and electricity by improving available solar collectors or producing new generation HPTSC. While it is possible to generate an electrical power of 160 W from a HPTSC of one square meter using the thermoelectric method, the power produced with an average photovoltaic panel with the same area is only 132 W. Accordingly, HPTSC is a superior alternative not only to available solar collectors, but also to available PV panels. HPTSC, involving three different technologies, is environmentally friendly and certainly a product that allows for more efficient use of solar energy.     

Diversity and Software Development

Scott E. Page's diversity framework provides useful insights regarding software engineering research, practice, and education. This editorial discusses the concepts and implications of diversity in this context.     

Effect of reuterin produced by Lactobacillus reuteri on the surface of sausages to inhibit the growth of Listeria monocytogenes and Salmonella spp

Reuterin is a bacteriocin produced by some strains of Lactobacillus reuteri. The strain used in this study was isolated from raw milk from a dairy farm nearby Ankara. Beef sausage is a long years produced bratwurst style meat product in Turkey, as well as in some other countries in the Mediterranean region. Sausages are produced by raw meat; sometimes lactic starter cultures are added or spontaneous fermentation is employed. The production and storage conditions of the product promotes the growth of Listeria monocytogenes and Salmonella spp. Although nitrate is added as an antimicrobial substance against many pathogens, sometimes however nitrate application is not preventive enough on the surface because of the natural film around the sausages. Since most of the contaminations take place at post production steps, pathogenic growth is more effective on the surface of the sausages in refrigerated conditions. In this study, reuterin was applied to the surface of the sausages in order to prevent the growth of these two pathogens along with nitrate used as an additive in the product. Reuterin has inhibited the growth of L. monocytogenes considerably but not of Salmonella spp. on the surface of the sausages.     

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针对大位移井眼轨迹控制技术难点,文章分析了西江24－3油田4口大位移井的井眼轨迹控制技术,探讨了大位移井井眼轨迹控制技术的机理,并对大位移井井眼轨迹的控制方案进行了优化。结果表明,西江24－3油田大位移井的井眼轨迹控制技术实际上是可调和不可调滑动导向钻具组合连续导向和旋转导向工具连续导向钻井技术的综合。旋转导向工具进行连续导向钻井具有随时可控性,轨迹控制质量高,但使用成本很高。在充分掌握滑动导向钻具组合导向力变化规律的基础上,在一定范围内,这种钻具组合仍然具有很好的应用效果。技术和经济方面的因素决定了小位移时采用滑动导向钻具组合连续导向钻井技术,而在较大位移时采用旋转导向工具的控制方法是最优的轨迹控制方案。     

Packet Reservation Window Multiple Access for Microcellular Voice/Data Transmission

We propose a new packet reservation multiple access (PRMA) scheme for the joint transmission of voice and data traffics in a microcellular medium. The collision resolution protocol within the system is based on a modification of the window random access algorithm, which has superior properties compared to the conventional slotted Aloha. The proposed algorithm, which we call packet reservation window multiple access (PRWMA), works in distinct modes for voice and data without prioritization, and the user performs slightly different operations depending on the information type. Simulation results show that PRWMA outperforms PRMA by a significant margin in terms of voice user capacity.     

The kernel common vector method: a novel nonlinear subspace classifier for pattern recognition.

The common vector (CV) method is a linear subspace classifier method which allows one to discriminate between classes of data sets, such as those arising in image and word recognition. This method utilizes subspaces that represent classes during classification. Each subspace is modeled such that common features of all samples in the corresponding class are extracted. To accomplish this goal, the method eliminates features that are in the direction of the eigenvectors corresponding to the nonzero eigenvalues of the covariance matrix of each class. In this paper, we introduce a variation of the CV method, which will be referred to as the modified CV (MCV) method. Then, a novel approach is proposed to apply the MCV method in a nonlinearly mapped higher dimensional feature space. In this approach, all samples are mapped into a higher dimensional feature space using a kernel mapping function, and then, the MCV method is applied in the mapped space. Under certain conditions, each class gives rise to a unique CV, and the method guarantees a 100% recognition rate with respect to the training set data. Moreover, experiments with several test cases also show that the generalization performance of the proposed kernel method is comparable to the generalization performances of other linear subspace classifier methods as well as the kernel-based nonlinear subspace method. While both the MCV method and its kernel counterpart did not outperform the support vector machine (SVM) classifier in most of the reported experiments, the application of our proposed methods is simpler than that of the multiclass SVM classifier. In addition, it is not necessary to adjust any parameters in our approach.     

Intelligent association selection of embedded agents in intelligent inhabited environments

Recent advances in technology and manufacturing have resulted in more powerful and smaller processors to be embedded in the various artefacts within smart environments. Most of these artefacts are network enabled and thanks to pervasive networking such artefacts can communicate and collaborate together to support our daily lives. Furthermore, these artefacts can also be equipped with embedded agents to provide intelligent reasoning, planning and learning capabilities. However, the multitude of interconnected devices and artefacts can result in major network and processing delays as well as creating inherent complexities in programming and configuring smart environments to personalise themselves to suit the individual needs. Hence, a major challenge to the design and use of smart environments involves finding the best set of device associations and interconnections that are most suitable to the environment and user needs. In this paper, we will present a novel intelligent method for reducing the number of associations and interconnections between the various devices and artefacts within smart environments to minimise the network and processing overheads while reducing the cognitive load associated with configuring and programming smart environments.