In situ observation of phosphorous and non-phosphorous antiwear films using a mini traction machine with spacer layer image mapping

Since 1940s the principal source of an antiwear additive in crankcase applications has been due to a family of additives known as zinc dialkydithiophosphate (ZDDP). In this study, we have applied a novel technique, the mini traction machine with spacer layer image mapping (MTM SLIM), to study film formation characteristics of monoblend oils containing only basestock and ZDDP and also fully forrmulated oils containing a boron antiwear additive. The purpose of this study was initially to understand the build up of ZDDP film and then to establish whether boron compounds alone could provide significant antiwear films under mixed rolling and sliding contact.     

The effects of socioeconomic status, race, and parenting on language development in early childhood.

The authors examined the associations between socioeconomic status (SES), race, maternal sensitivity, and maternal negative-intrusive behaviors and language development in a sample selected to reduce the typical confound between race and SES (n = 146). Mother–child interactions were observed at 12 and 24 months (coded by randomly assigned African American and European American coders); language abilities were assessed at 18, 24, 30, and 36 months. For receptive language, race was associated with ability level, and maternal sensitivity and negative-intrusive parenting were related to rate of growth. For expressive communication, race, SES, and maternal sensitivity were associated with rate of growth; race moderated the association between negative-intrusive parenting and rate of growth such that the relation was weaker for African American than for European American children. The results highlight the importance of sensitive parenting and suggest that the association between negative-intrusive parenting and language development may depend upon family context. Future work is needed concerning the race differences found, including examining associations with other demographic factors and variations in language input experienced by children, using culturally and racially validated indices of language development. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Probabilistic learning on manifolds (PLoM) with partition

The probabilistic learning on manifolds (PLoM) introduced in 2016 has solved difficult supervised problems for the “small data” limit where the number N of points in the training set is small. Many extensions have since been proposed, making it possible to deal with increasingly complex cases. However, the performance limit has been observed and explained for applications for which N is very small and for which the dimension of the diffusion-map basis is close to N. For these cases, we propose a novel extension based on the introduction of a partition in independent random vectors. We take advantage of this development to present improvements of the PLoM such as a simplified algorithm for constructing the diffusion-map basis and a new mathematical result for quantifying the concentration of the probability measure in terms of a probability upper bound. The analysis of the efficiency of this extension is presented through two applications.     

Some Grubbs-Type Statistics for the Detection of Several Outliers

Several widely used tests for outlying observations are reviewed. Problems of repeated application and “masking” are described. Suggested as appropriate to over-come these problems are two new statistics: L_k which is based on the k largest (observed) values and E_k which is based on the k largest (in absolute value) residuals. Tables of approximate critical values for these statistics are given for 0.01, .025,0.05, and 0.10 levels of significance and for sample size n = 3 (1) 20 (5) 50.     

Localized Heat Therapy Improves Mitochondrial Respiratory Capacity but Not Fatty Acid Oxidation

AIM: Mild heat stress can improve mitochondrial respiratory capacity in skeletal muscle. However, long-term heat interventions are scarce, and the effects of heat therapy need to be understood in the context of the adaptations which follow the more complex combination of stimuli from exercise training. The purpose of this work was to compare the effects of 6 weeks of localized heat therapy on human skeletal muscle mitochondria to single-leg interval training. METHODS: Thirty-five subjects were assigned to receive sham therapy, short-wave diathermy heat therapy, or single-leg interval exercise training, localized to the quadriceps muscles of the right leg. All interventions took place 3 times per week. Muscle biopsies were performed at baseline, and after 3 and 6 weeks of intervention. Mitochondrial respiratory capacity was assessed on permeabilized muscle fibers via high-resolution respirometry. RESULTS: The primary finding of this work was that heat therapy and exercise training significantly improved mitochondrial respiratory capacity by 24.8 ± 6.2% and 27.9 ± 8.7%, respectively (p < 0.05). Fatty acid oxidation and citrate synthase activity were also increased following exercise training by 29.5 ± 6.8% and 19.0 ± 7.4%, respectively (p < 0.05). However, contrary to our hypothesis, heat therapy did not increase fatty acid oxidation or citrate synthase activity. CONCLUSION: Six weeks of muscle-localized heat therapy significantly improves mitochondrial respiratory capacity, comparable to exercise training. However, unlike exercise, heat does not improve fatty acid oxidation capacity.     

Inductive transfer for learning Bayesian networks

In several domains it is common to have data from different, but closely related problems. For instance, in manufacturing, many products follow the same industrial process but with different conditions; or in industrial diagnosis, where there is equipment with similar specifications. In these cases it is common to have plenty of data for some scenarios but very little for others. In order to learn accurate models for rare cases, it is desirable to use data and knowledge from similar cases; a technique known as transfer learning. In this paper we propose an inductive transfer learning method for Bayesian networks, that considers both structure and parameter learning. For structure learning we use conditional independence tests, by combining measures from the target task with those obtained from one or more auxiliary tasks, using a novel weighted sum of the conditional independence measures. For parameter learning, we propose two variants of the linear pool for probability aggregation, combining the probability estimates from the target task with those from the auxiliary tasks. To validate our approach, we used three Bayesian networks models that are commonly used for evaluating learning techniques, and generated variants of each model by changing the structure as well as the parameters. We then learned one of the variants with a small dataset and combined it with information from the other variants. The experimental results show a significant improvement in terms of structure and parameters when we transfer knowledge from similar tasks. We also evaluated the method with real-world data from a manufacturing process considering several products, obtaining an improvement in terms of log-likelihood between the data and the model when we do transfer learning from related products.     

Performance comparison of the CRAY X-MP/24 with SDD and the CRAY-2

The CRAY-2 is considered to be one of the most powerful supercomputers. Its state-of-the-art technology features a faster clock and more memory than any other supercomputer available today. In this report the single processor performance of the CRAY-2 is compared with the older, more mature CRAY X-MP. Benchmark results are included for both the slow and the fast memory DRAM MOS CRAY-2. Our comparison is based on a kernel benchmark set aimed at evaluating the performance of these two machines on some standard tasks in scientific computing. Particular emphasis is placed on evaluating the impact of the availability of large real memory on the CRAY-2 versus fast secondary memory on the CRAY X-MP with SSD. Our benchmark includes large linear equation solvers and FFT routines, which test the capabilities of the different approaches to providing large memory. We find that in spite of its higher processor speed the CRAY-2 does not perform as well as the CRAY X-MP on the Fortran kernel benchmark. We also find that for large-scale applications, which have regular and predictable memory access patterns, a high-speed secondary memory device such as the SSD can provide performance equal to the large real memory of the CRAY-2.The author is an employee of SCA Division of Boeing Computer Services.     

Towards van der Waals Epitaxial Growth of GaAs on Si using a Graphene Buffer Layer

Van der Waals growth of GaAs on silicon using a two‐dimensional layered material, graphene, as a lattice mismatch/thermal expansion coefficient mismatch relieving buffer layer is presented. Two‐dimensional growth of GaAs thin films on graphene is a potential route towards heteroepitaxial integration of GaAs on silicon in the developing field of silicon photonics. Hetero‐layered GaAs is deposited by molecular beam epitaxy on graphene/silicon at growth temperatures ranging from 350 °C to 600 °C under a constant arsenic flux. Samples are characterized by plan‐view scanning electron microscopy, atomic force microscopy, Raman microscopy, and X‐ray diffraction. The low energy of the graphene surface and the GaAs/graphene interface is overcome through an optimized growth technique to obtain an atomically smooth low­ temperature GaAs nucleation layer. However, the low adsorption and migration energies of gallium and arsenic atoms on graphene result in cluster‐growth mode during crystallization of GaAs films at an elevated temperature. In this paper, we present the first example of an ultrasmooth morphology for GaAs films with a strong (111) oriented fiber‐texture on graphene/silicon using quasi van der Waals epitaxy, making it a remarkable step towards an eventual demonstration of the epitaxial growth of GaAs by this approach for heterogeneous integration.     

An invariant subspace‐based approach to the random eigenvalue problem of systems with clustered spectrum

The repeated or closely spaced eigenvalues and corresponding eigenvectors of a matrix are usually very sensitive to a perturbation of the matrix, which makes capturing the behavior of these eigenpairs very difficult. Similar difficulty is encountered in solving the random eigenvalue problem when a matrix with random elements has a set of clustered eigenvalues in its mean. In addition, the methods to solve the random eigenvalue problem often differ in characterizing the problem, which leads to different interpretations of the solution. Thus, the solutions obtained from different methods become mathematically incomparable. These two issues, the difficulty of solving and the non‐unique characterization, are addressed here. A different approach is used where instead of tracking a few individual eigenpairs, the corresponding invariant subspace is tracked. The spectral stochastic finite element method is used for analysis, where the polynomial chaos expansion is used to represent the random eigenvalues and eigenvectors. However, the main concept of tracking the invariant subspace remains mostly independent of any such representation. The approach is successfully implemented in response prediction of a system with repeated natural frequencies. It is found that tracking only an invariant subspace could be sufficient to build a modal‐based reduced‐order model of the system. Copyright © 2012 John Wiley & Sons, Ltd.