Shear modulus estimation using parallelized partial volumetric reconstruction

Magnetic resonance elastography can be limited by the computationally intensive nonlinear inversion schemes that are sometimes employed to estimate shear modulus from externally induced internal tissue displacements. Consequently, we have developed a parallelized partial volume reconstruction approach to overcome this limitation. In this paper, we report results from experiments conducted on breast phantoms and human volunteers to validate the proposed technique. More specifically, we demonstrate that computational cost is linearly related to the number of subzones used during image recovery and that both subzone parallelization and partial volume domain reduction decrease execution time accordingly. Importantly, elastograms computed based on the parallelized partial volume technique are not degraded in terms of either image quality or accuracy relative to their full volume counterparts provided that the estimation domain is sufficiently large to negate the effects of boundary conditions. The clinical results presented in this paper are clearly preliminary; however, the parallelized partial volume reconstruction approach performs sufficiently well to warrant more in-depth clinical evaluation.     

Properties and durability of alkali-activated ladle slag

This paper examines the durability of alkali activated ladle slag mortars. Ladle slag, currently an under-utilized crystalline metallurgical by-product, is a promising raw material for alkali activation. However, there is no information about the durability of this material. The aim of this study is to investigate the durability of the alkali activated ladle slag mortar and the effect of aggregate content on the properties. The mechanical strength tests were carried out at 7, 28, 56 and 90 days. In addition, the samples were subjected to drying shrinkage, freeze–thaw, and water absorption tests. Furthermore, the reaction products conversion related to the strength properties were also analysed through thermal analysis. Overall, significant strength and durability properties was attained and significantly influenced by the mix design.     

Optimal Design with Probabilistic Objective and Constraints

Significant challenges are associated with solving optimal structural design problems involving the failure probability in the objective and constraint functions. In this paper, we develop gradient-based optimization algorithms for estimating the solution of three classes of such problems in the case of continuous design variables. Our approach is based on a sequence of approximating design problems, which is constructed and then solved by a semiinfinite optimization algorithm. The construction consists of two steps: First, the failure probability terms in the objective function are replaced by auxiliary variables resulting in a simplified objective function. The auxiliary variables are determined automatically by the optimization algorithm. Second, the failure probability constraints are replaced by a parametrized first-order approximation. The parameter values are determined in an adaptive manner based on separate estimations of the failure probability. Any computational reliability method, including first-order reliability and second-order reliability methods and Monte Carlo simulation, can be used for this purpose. After repeatedly solving the approximating problem, an approximate solution of the original design problem is found, which satisfies the failure probability constraints at a precision level corresponding to the selected reliability method. The approach is illustrated by a series of examples involving optimal design and maintenance planning of a reinforced concrete bridge girder.     

Morphological development during injection molding of self‐reinforcing composites. I: Experimental results

The correlation between structure development during injection molding and the modulus of injection molded PET/LCP blends were studied. Process parameters such as injection speed and mold and melt temperatures were varied to determine the effect of these parameters on the tensile modulus and structure development of the blends. The skin/core structure in the cross section of injection molded samples was observed with both optical and scanning electron microscopy techniques. Injection molding experiments show that the thickness of the skin layer increases with decreasing injection speed and decreasing melt and mold temperatures. The trends in morphological developments in the injection molded specimens correlate with the measured tensile moduli.     

Statistical process control of acoustic emission for cutting tool monitoring

The problem of cutting process monitoring has been investigated in recent years, with encouraging results, using pattern recognition analysis of acoustic emission (AE) signals. The analyses are based on linear discriminant functions, which assume that the observed data (from each class) are independent random samples from multivariate normal distributions with equal covariance matrices. However, in a number of practical situations some (or all) of these assumptions may not necessarily hold, resulting in errors in the analysis.In this paper, the distributions of AE spectra generated in earlier work are first analysed, and the results indicate departure from the assumptions, although the lack of normality was not too severe. Relaxing the assumption of equality of the covariance matrices, quadratic discriminant function analysis produced improved results for tool wear and chip noise monitoring while degrading tool fracture detection. The latter is due to inadequacy of the amount of data used in training the system. It is expected that increasing the data base would improve the results for all classes.The analysis until now has focused on reducing the dimensionality of the feature space by eliminating the features with the least discriminatory power. Even though this inevitably reduces the performance of the system, it is a necessary compromise for increased computational speed. To make use of the entire feature set with a reduced matrix rank, a principal component analysis is investigated. The result is a substantial improvement in correct classification of AE signals, even under different cuting conditions.     

Zooplankton partitioning in a tropical alkaline–saline endorheic Lake Nakuru,Kenya: Spatial and temporal trends in relation to the environment

Spatial and temporal zooplankton variations were studied for 1 year in tropical alkaline–saline Lake Nakuru to determine how they partition in the habitat, relative to environmental variables. Monthly samples were collected at 10 sampling sites, with subsurface tows, using 33.5‐μm mesh plankton nets. Physicochemical parameters displayed clear seasonal variations associated with precipitation patterns. Nine species, belonging to two main zooplankton taxonomic groups (ciliates; rotifers), were identified in the samples. Brachionus dimidiatus dominated the samples, accounting for 80% of the total zooplankton abundance. Kruskall–Wallis tests indicated significant (P < 0.05) temporal and spatial variations among all taxonomic groups. Different zooplankton species displayed a clear succession throughout the year. The total abundance of the rotifers and ciliates peaked at sampling sites near inlets during the long rainy seasons, while those in the inshore sites displayed variable succession patterns. Spatiotemporal structure of the zooplankton assemblages, and its correlation with environmental variables, indicated each species displayed distinct niche‐based partitioning. The ciliates niche was associated with increasing soluble reactive phosphorus, total phosphorus and nitrite–nitrogen (NO₂–N) concentrations. Niche partitioning in rotifers was associated with nitrate–nitrogen (NO₃–N), conductivity and pH. These results indicate physical niche separation, even in a small, relatively homogenous lake among species of rotifers and ciliates, providing information from which future changes in their abundance and spatial distributions can be predicted, given continuous water quality changes.     

二冲程柴油机机座表面振动信号的特征分析

为监测主轴承的磨损情况,对船用二冲程低速柴油主机机座表面的振动信号开展测试与分析。介绍了测试系统的组成和振动测点的布置。通过静态敲击试验,证实了缸内燃气爆发冲击和较远档主轴承的冲击对主轴承处机座表面的振动影响较小。通过对柴油机机座表面振动信号的时域波形分析,证实了对各档主轴承座附近机座表面的冲击振动进行监测,可以了解各档主轴承的磨损状态。研究结果为船用二冲程柴油机的振动监测提供了一个新的方法。