Topography of the flank wear surface

In many applications, topography represents the main external features of a surface. This paper describes the topography of the flank wear surface and also presents the relationship between the maximum flank wear and the topography parameters (roughness parameters) of the flank wear surface during the turning operation. A modern CNC lathe machine (Okuma LH35-N) was used for the machine turning operation. Three-dimensional surface roughness parameters of the flank wear surface were measured by a surface texture instrument (from Talysurf series) using surface topography software (Talymap). Based on the resulting experimental data, it is found that as the flank wear increases, the roughness parameters (sR_a, sR_q, and sR_t) on the flank surface increase significantly. The greater the roughness value of the flank wear surface, the higher the friction of the tool on the workpiece and the greater the heat generation that will occur, thus ultimately causing tool failure. On the other hand, positive skewness (sR_sk) indicates the presence of a small number of spikes on the flank surface of the cutting tool, which could quickly wear off during the machining process.     

A Review of Element-Based Galerkin Methods for Numerical Weather Prediction: Finite Elements,Spectral Elements,and Discontinuous Galerkin

Numerical weather prediction (NWP) is in a period of transition. As resolutions increase, global models are moving towards fully nonhydrostatic dynamical cores, with the local and global models using the same governing equations; therefore we have reached a point where it will be necessary to use a single model for both applications. The new dynamical cores at the heart of these unified models are designed to scale efficiently on clusters with hundreds of thousands or even millions of CPU cores and GPUs. Operational and research NWP codes currently use a wide range of numerical methods: finite differences, spectral transform, finite volumes and, increasingly, finite/spectral elements and discontinuous Galerkin, which constitute element-based Galerkin (EBG) methods. Due to their important role in this transition, will EBGs be the dominant power behind NWP in the next 10 years, or will they just be one of many methods to choose from? One decade after the review of numerical methods for atmospheric modeling by Steppeler et al. (Meteorol Atmos Phys 82:287–301, 2003), this review discusses EBG methods as a viable numerical approach for the next-generation NWP models. One well-known weakness of EBG methods is the generation of unphysical oscillations in advection-dominated flows; special attention is hence devoted to dissipation-based stabilization methods. Since EBGs are geometrically flexible and allow both conforming and non-conforming meshes, as well as grid adaptivity, this review is concluded with a short overview of how mesh generation and dynamic mesh refinement are becoming as important for atmospheric modeling as they have been for engineering applications for many years.     

Notes on spiking neural P systems and finite automata

Spiking neural P systems (in short, SN P systems) are membrane computing models inspired by the pulse coding of information in biological neurons. SN P systems with standard rules have neurons that emit at most one spike (the pulse) each step, and have either an input or output neuron connected to the environment. A variant known as SN P modules generalize SN P systems by using extended rules (more than one spike can be emitted each step) and a set of input and output neurons. In this work we continue relating SN P modules and finite automata. In particular, we amend and improve previous constructions for the simulatons of deterministic finite automata and state transducers. Our improvements reduce the number of neurons from three down to one, so our results are optimal. We also simulate finite automata with output, and we use these simulations to generate automatic sequences.     

A scalable constraint-based Q-hash indexing for moving objects

In this paper, we develop a Q-hash index structure to efficiently store the position of moving objects. An environment of moving objects contains continuously changing locations which are hard to index using traditional index structures such as R-trees, QuadTrees and their variants. In order to answer the queries accurately, one of the problems faced in storing these positions is the number of updates that have to be made to the database whenever locations change. The high maintenance overhead on updates leads to performance degradation of these index structures; additionally, it makes the database very bulky which results in very poor performance in terms of query execution time. One of the main objectives of the structure we propose is to minimize the number of updates to the database to an optimal number so that the accuracy and response time of the query result are not compromised and at the same time the number of wireless communications can be reduced. The indexing is done using a hashing technique where the hashing function makes use of a region based QuadTree structure. To improve the efficiency of the query processing our index structure helps us define constraints over speed, direction and location of the moving object at the device level which controls the number of updates. In addition, in order to answer different query types efficiently at all levels we propose a three-tier (moving object, regional server, central repository) architecture. Our extensive performance evaluation and comparison of the proposed technique concludes that our scheme outperforms existing Q + R-tree and QuadTree in terms of range query execution time by a high order of magnitude.     

Multi-rate model predictive control of particle size distribution in a semibatch emulsion copolymerization reactor

A multi-rate model predictive controller algorithm is presented for the in-batch closed-loop control of the full particle size distribution (PSD) in a semibatch emulsion copolymerization system. The lack of frequent measurements of the PSD and the measurement delay of these measurements are addressed through the use of frequent density measurements from which the current conditions of the system are estimated. The high dimensionality of the discretized full PSD is reduced by the use of model order reduction based on principal component analysis. This method effectively reduces the size of the problem while preserving the main characteristics of the population balance system. Disturbances that perturb the surfactant and monomer amounts inside the semibatch vinyl acetate–butyl acrylate reactor are considered to demonstrate the performance of the proposed control algorithm.     

Parallel evolution using multi-chromosome cartesian genetic programming

Parallel and distributed methods for evolutionary algorithms have concentrated on maintaining multiple populations of genotypes, where each genotype in a population encodes a potential solution to the problem. In this paper, we investigate the parallelisation of the genotype itself into a collection of independent chromosomes which can be evaluated in parallel. We call this multi-chromosomal evolution (MCE). We test this approach using Cartesian Genetic Programming and apply MCE to a series of digital circuit design problems to compare the efficacy of MCE with a conventional single chromosome approach (SCE). MCE can be readily used for many digital circuits because they have multiple outputs. In MCE, an independent chromosome is assigned to each output. When we compare MCE with SCE we find that MCE allows us to evolve solutions much faster. In addition, in some cases we were able to evolve solutions with MCE that we unable to with SCE. In a case-study, we investigate how MCE can be applied to to a single objective problem in the domain of image classification, namely, the classification of breast X-rays for cancer. To apply MCE to this problem, we identify regions of interest (RoI) from the mammograms, divide the RoI into a collection of sub-images and use a chromosome to classify each sub-image. This problem allows us to evaluate various evolutionary mutation operators which can pairwise swap chromosomes either randomly or topographically or reuse chromosomes in place of other chromosomes.     

Proteomic Helicobacter pylori biomarkers discriminative of low-grade gastric MALT lymphoma and duodenal ulcer

To date no reliable diagnostic method exists to predict, among the very large and clinically heterogeneous group of Helicobacter pylori‐infected patients, the extremely small group at risk for developing low‐grade gastric MALT lymphoma (LG‐MALT). Search of proteomic biomarkers holds promise for the classification of the H. pylori strains with regard to this severe clinical outcome. In the present study 69 H. pylori strains isolated from patients with two different H. pylori‐associated diseases, duodenal ulcer (DU, n=29) and LG‐MALT (n=40) were used. Protein expression patterns of the strains were analyzed by using the high‐throughput methodology SELDI. Selected proteins were purified by means of chromatographic and electrophoretic methods in view of further sequencing by LC‐MS/MS. Univariate analysis (Mann–Whitney test) of the protein expression patterns generated nine significant biomarkers that can discriminate between H. pylori strains from patients with DU and LG‐MALT. These biomarkers are of low molecular weight, ranging from 6 to 26.6 kDa. Among them, two are overexpressed in LG‐MALT strains and seven – in DU strains. Two biomarker proteins, one overexpressed in LG‐MALT strains (13.2 kDa) and another one – overexpressed in DU strains (26.6 kDa), were purified to homogeneity and identified by using LC‐MS/MS as a 50S ribosomal protein L7/L12 and a urease subunit, respectively. These biomarkers can be included in novel protein arrays for the differential diagnosis of H. pylori‐associated clinical outcomes.     

Technology transfer in digital mammography. Report of the Joint National Cancer Institute-National Aeronautics and Space Administration workshop of May 19-20, 1993

Digital mammography is one of the most promising novel technologies for further improvement of early detection of breast cancer, offering important potential advantages: 1) improved image quality; 2) digital image processing for improved lesion contrast; 3) computer-aided diagnosis for enhanced radiologic interpretation; and 4) teleradiology for facilitated radiologic consultation. The Diagnostic Imaging Research Branch of the National Cancer Institute (NCI) recently funded an international, multidisciplinary, multi-institutional Digital Mammography Development Group for collaborations between NCI, the academic community, and industry to facilitate the integrated development and implementation of digital mammographic systems. Currently, however, digital mammography faces a number of fundamental technological roadblocks: 1) cost-effective digital detectors and displays for imaging systems; 2) the need for novel algorithms for image processing and computer-aided diagnosis; and 3) high performance, low cost digital networks to provide an "information superhighway" for teleradiology. To solve some of these technological problems, the Diagnostic Imaging Research Branch of NCI joined efforts with the Technology Transfer Division of the National Aeronautics and Space Administration to pursue a federal technology transfer program in digital mammography. The authors discuss the findings and recommendations of the workshop entitled "Technology Transfer in Digital Mammography," which was organized and held jointly by the NCI and the National Aeronautics and Space Administration in May, 1993. Numerous innovative technologies of varying degree of promise for digital mammography were presented at the conference. In this article, specific technologies presented at the workshop by the federal and federally-supported laboratories are described, and critiques of these technologies by the leaders of the medical imaging community are presented.     

Disruption of epithelial cell-matrix interactions induces apoptosis

Cell-matrix interactions have major effects upon phenotypic features such as gene regulation, cytoskeletal structure, differentiation, and aspects of cell growth control. Programmed cell death (apoptosis) is crucial for maintaining appropriate cell number and tissue organization. It was therefore of interest to determine whether cell-matrix interactions affect apoptosis. The present report demonstrates that apoptosis was induced by disruption of the interactions between normal epithelial cells and extracellular matrix. We have termed this phenomenon "anoikis." Overexpression of bcl-2 protected cells against anoikis. Cellular sensitivity to anoikis was apparently regulated: (a) anoikis did not occur in normal fibroblasts; (b) it was abrogated in epithelial cells by transformation with v-Ha-ras, v-src, or treatment with phorbol ester; (c) sensitivity to anoikis was conferred upon HT1080 cells or v-Ha-ras-transformed MDCK cells by reverse-transformation with adenovirus E1a; (d) anoikis in MDCK cells was alleviated by the motility factor, scatter factor. The results suggest that the circumvention of anoikis accompanies the acquisition of anchorage independence or cell motility.     

Model-based 3-D segmentation of multiple sclerosis lesions in magnetic resonance brain images

Human investigators instinctively segment medical images into their anatomical components, drawing upon prior knowledge of anatomy to overcome image artifacts, noise, and lack of tissue contrast. The authors describe: 1) the development and use of a brain tissue probability model for the segmentation of multiple sclerosis (MS) lesions in magnetic resonance (MR) brain images, and 2) an empirical comparison of the performance of statistical and decision tree classifiers, applied to MS lesion segmentation. Based on MR image data obtained from healthy volunteers, the model provides prior probabilities of brain tissue distribution per unit voxel in a standardized 3-D "brain space". In comparison to purely data-driven segmentation, the use of the model to guide the segmentation of MS lesions reduced the volume of false positive lesions by 50-80%