Effect of Microfabric on Mechanical Behavior of Kaolin Clay Using Cubical True Triaxial Testing

Various aspects of the mechanical behavior of kaolin clay are discussed in light of experimental observations from a series of strain controlled true triaxial undrained tests performed on cubical kaolin clay specimens with flocculated and dispersed microfabric, using a fully automated flexible boundary experimental setup with real-time feedback control system. The laboratory procedures used to prepare flocculated and dispersed microfabric specimens are presented. Mercury intrusion porosimetry is used to evaluate the pore structure of these specimens. The influence of microfabric on the consolidation behavior of kaolin clay is evaluated based on the data obtained from K0 consolidation during constant rate of strain tests and the isotropic consolidation during true triaxial tests. Undrained tests on kaolin clay show that the following vary with microfabric of specimen: The shear stiffness, excess pore pressure generated during shear, and strength and strain to failure. For both microfabrics, the observed strength behavior using cubical triaxial testing shows a similar pattern of variation with applied stress anisotropy; hence, only a marginal influence of fabric-induced anisotropy.     

Gambling, health and age: Data from the National Epidemiologic Survey on Alcohol and Related Conditions.

The health effects of recreational gambling are presently unclear, particularly across age groups. Theories of healthy aging suggest that social activities, including gambling, may be beneficial to the health of older adults. Using cross-sectional data from the National Epidemiologic Survey on Alcohol and Related Conditions (N=43,093), the authors examined associations between gambling (categorized as nongambling, recreational gambling, or problem/pathological gambling) and health and functioning measures stratified by age (40-64 years and ≥65). Problem/pathological gambling was uniformly associated with poorer health measures among both younger and older adults. Among younger respondents, poorer health measures were also found among recreational gamblers. However, among older respondents, recreational gambling was associated not only with some negative measures (e.g., obesity) but also with some positive measures (e.g., better physical and mental functioning). Longitudinal studies are needed to clarify the relationship between gambling and health in older adults in the context of healthy aging. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A framework for medical image retrieval using machine learning and statistical similarity matching techniques with relevance feedback.

A content-based image retrieval (CBIR) framework for diverse collection of medical images of different imaging modalities, anatomic regions with different orientations and biological systems is proposed. Organization of images in such a database (DB) is well defined with predefined semantic categories; hence, it can be useful for category-specific searching. The proposed framework consists of machine learning methods for image prefiltering, similarity matching using statistical distance measures, and a relevance feedback (RF) scheme. To narrow down the semantic gap and increase the retrieval efficiency, we investigate both supervised and unsupervised learning techniques to associate low-level global image features (e.g., color, texture, and edge) in the projected PCA-based eigenspace with their high-level semantic and visual categories. Specially, we explore the use of a probabilistic multiclass support vector machine (SVM) and fuzzy c-mean (FCM) clustering for categorization and prefiltering of images to reduce the search space. A category-specific statistical similarity matching is proposed in a finer level on the prefiltered images. To incorporate a better perception subjectivity, an RF mechanism is also added to update the query parameters dynamically and adjust the proposed matching functions. Experiments are based on a ground-truth DB consisting of 5000 diverse medical images of 20 predefined categories. Analysis of results based on cross-validation (CV) accuracy and precision-recall for image categorization and retrieval is reported. It demonstrates the improvement, effectiveness, and efficiency achieved by the proposed framework.     

Thermal impact of an active 3-D microelectrode array implanted in the brain.

A chronically implantable, wireless neural interface device will require integrating electronic circuitry with the interfacing microelectrodes in order to eliminate wired connections. Since the integrated circuit (IC) dissipates a certain amount of power, it will raise the temperature in surrounding tissues where it is implanted. In this paper, the thermal influence of the integrated 3-D Utah electrode array (UEA) device implanted in the brain was investigated by numerical simulation using finite element analysis (FEA) and by experimental measurement in vitro as well as in vivo. The numerically calculated and experimentally measured temperature increases due to the UEA implantation were in good agreement. The experimentally validated numerical model predicted that the temperature increases linearly with power dissipation through the UEA, with a slope of 0.029 degree C/mW over the power dissipation levels expected to be used. The influences of blood perfusion, brain metabolism, and UEA geometry on tissue heating were also investigated using the numerical model.     

Adaptive value function approximation for continuous-state stochastic dynamic programming

Approximate dynamic programming (ADP) commonly employs value function approximation to numerically solve complex dynamic programming problems. A statistical perspective of value function approximation employs a design and analysis of computer experiments (DACE) approach, where the “computer experiment” yields points on the value function curve. The DACE approach has been used to numerically solve high-dimensional, continuous-state stochastic dynamic programming, and performs two tasks primarily: (1) design of experiments and (2) statistical modeling. The use of design of experiments enables more efficient discretization. However, identifying the appropriate sample size is not straightforward. Furthermore, identifying the appropriate model structure is a well-known problem in the field of statistics. In this paper, we present a sequential method that can adaptively determine both sample size and model structure. Number-theoretic methods (NTM) are used to sequentially grow the experimental design because of their ability to fill the design space. Feed-forward neural networks (NNs) are used for statistical modeling because of their adjustability in structure-complexity . This adaptive value function approximation (AVFA) method must be automated to enable efficient implementation within ADP. An AVFA algorithm is introduced, that increments the size of the state space training data in each sequential step, and for each sample size a successive model search process is performed to find an optimal NN model. The new algorithm is tested on a nine-dimensional inventory forecasting problem.     

Linoleic Acid Rich Oil Seeds

The fatty acid composition of Cupressus semprevirens, Cupressus torulosa, Cupressus funebris, Cuppressus lusitanica, Cupressus macrocarpa and Cupressus arizonica seed oils had been determined.     

Discriminative Models for Multi-Class Object Layout

Many state-of-the-art approaches for object recognition reduce the problem to a 0-1 classification task. This allows one to leverage sophisticated machine learning techniques for training classifiers from labeled examples. However, these models are typically trained independently for each class using positive and negative examples cropped from images. At test-time, various post-processing heuristics such as non-maxima suppression (NMS) are required to reconcile multiple detections within and between different classes for each image. Though crucial to good performance on benchmarks, this post-processing is usually defined heuristically.     

A Cure for Bioengineering? A New Undergraduate Core Curriculum

Student interest, industry growth, and medical advances highlight a growing need for baccalaureate trained bioengineers. However, undergraduate bioengineering curricula have not kept pace with the recent rapid growth of biotechnology, bioinformatics, and cellular level bioengineering. It is the premise of this paper that a new baccalaureate core curriculum in cell and tissue engineering is needed to foster the full development of bioengineering as a core engineering discipline. Employment opportunities for baccalaureate Bioengineering graduates in the emerging biologically‐based engineering industry (e.g. bioinformatics, cell and tissue engineering, neural engineering) are increasing. We describe a new undergraduate B.S. bioengineering curriculum in cell and tissue engineering with an emphasis in interfacial molecular approaches. This curriculum is integrated with traditional bioengineering topics and has its technical emphasis in molecular biology, nanotechnology, and computational modeling. Thus, the future bioengineer can be expected to design, model, fabricate, and control living systems and their fundamental constituents at the same level of detail as an electrical engineer uses SPICE and CAD tools to develop a microelectronic or micro‐electro‐mechanical device. Such engineers would be expected to take their place in industry, graduate school, or medicine with a clear mastery of cell and tissue‐based bioengineering.