Transmission electron microscopy studies of thermomechanically control processed multiphase medium-carbon microalloyed steel: Forged, rolled, and low-cycle fatigued microstructures

A ferrite-bainite-martensite (F-B-M) microstructure was produced in a medium-carbon microalloyed (MA) steel through two routes, namely, low-temperature finish forging and rolling, followed by a two-step cooling (TSC) and annealing. Transmission electron microscopy (TEM) was employed to study the microstructural evolution in control forged and rolled material after TSC followed by annealing (TSCA). A TEM investigation was also carried out on samples low-cycle fatigue (LCF) tested at low and high total strain amplitudes of 0.4 and 0.7 pct in case of the forged steel (F-B-M(F)TSCA) and 0.55 and 0.8 pct for the rolled steel (F-B-M(R)TSCA), respectively. Microstructural changes accompanying the LCF testing were identified. The two-step cooled microstructure processed through forging (F-B-M(F)TSC) as well as rolling (F-B-M(R)TSC) revealed a complex multiphase microstructure, along with films and blocks of retained austenite. In both microstructural conditions, vanadium carbide precipitates were too fine to be identified after the TSC treatment. Annealing after TSC produced a stress-free microstructure. The F-B-M(F)TSCA microstructure predominantly consisted of granular/lower bainite, lath martensite, and polygonal ferrite with interlath films as well as blocks of retained austenite, while the F-B-M(R)TSCA microstructure predominantly consisted of lath martensite, granular/lower bainite, and polygonal ferrite with interlath strips/films of retained austenite. Lath martensite content was higher in the F-B-M(R)TSCA condition than in the F-B-M(R)TSCA condition. In both conditions, vanadium carbide precipitates could be seen after annealing. Fatigue-tested F-B-M(F)TSCA microstructure up to a total strain amplitude of 0.4 pct and F-B-M(F)TSCA microstructure up to a total strain amplitude of 0.55 pct were stable. Lath martensite did not undergo deformation and in both microstructural conditions dislocation cell structures were not observed in the ferrite or bainite regions. The interlath retained austenite strips/films played a significant role in preventing the softening during fatigue loading. First, it was stable up to a total strain amplitude of 0.4 and 0.55 pct in the respective microstructures. Second, it underwent heavy deformation during fatigue loading at high total strain amplitudes, thereby accommodating the strain. Fatigue-tested F-B-M(F)TSCA microstructure at a total strain amplitude of 0.7 pct and F-B-M(R)TSCA microstructure at a total strain amplitude of 0.8 pct revealed deformed bainite/martensite laths, dislocation cells, and slip bands in the ferrite regions, which are characteristic features of cyclic softening. The retained austenite transformed to martensite through a strain-induced transformation mechanism and, at that stage, the microstructure contained in addition dislocation-rich bainite and ferrite.     

Polarization discrimination of coherently propagating light in turbid media

We describe the use of degree of polarization to discriminate unscattered and weakly scattered light from multiply scattered light in an optically turbid material. We use spatially resolved measurements of the degree of polarization to compare how well linearly and circularly polarized light survives in a sample. Experiments were performed on common tissue phantoms consisting of polystyrene and Intralipid microsphere suspensions and on adipose and arterial tissue. The results indicate that polarization is maintained even after unpolarized irradiance through each sample has been extinguished by several orders of magnitude. The results also show that polarized light propagation in common tissue phantoms is distinctly different from polarized light propagation in the two tissues investigated. Further, these experiments illustrate when polarization is an effective discrimination criterion and when it is not. The potential of a polarization-based discrimination scheme to image through the biological and nonbiological samples investigated here is also discussed.     

Native and oxidized low density lipoproteins modulate mesangial cell apoptosis

Hyperlipidemia has been demonstrated to contribute to hypercellularity of the mesangium in experimental animal models of glomerulosclerosis. We studied whether it also has the potential to convert a hypercellular mesangium into a hypocellular one by inducing mesangial cell (MC) apoptosis. Low density lipoprotein (LDL) enhanced (P < 0.001) mouse mesangial cell (MMC) proliferation at lower concentrations (control, 10.3 +/- 0.3 vs. LDL 100 micrograms/ml, 24.2 +/- 0.3 x 10(4) cells/ml) but augmented (P < 0.001) apoptosis at higher concentrations (control, 5.6 +/- 0.5% vs. LDL, 500 micrograms/ml 26.2 +/- 3.4% apoptotic cells/field). Oxidized (OX) LDL enhanced MMC apoptosis in concentrations of 50 to 200 micrograms/dl. There was a direct relationship between MMC apoptosis and oxidation of LDL as judged by measuring thiobarbituric acid reactive species (TBARS). Since superoxide dismutase (SOD) attenuated (P < 0.001) LDL-induced MMC apoptosis, it seems to be mediated through the generation of free radicals by mesangial cells (control, 4.3 +/- 1.5%; LDL, 200 micrograms/ml, 19.4 +/- 0.5%; LDL + SOD, 8.1 +/- 1.3% apoptotic cells/field). LDL also induced a similar effect on human mesangial cells. These studies were further confirmed by DNA fragment assays and ELISA for programmed cell death. LDL treated cells also showed enhanced mRNA expression for RSG-2, a marker for active cell death. These in vitro results provide a basis for the speculation that LDL has the potential to cause an initial hypercellular and subsequent hypocellular mesangium in the course of the development of glomerulosclerosis.     

Electronic error compensation of a current transformer

A scheme is presented employing two precision resistors and an electronic amplifier for reducing the ratio and phase-angle errors in a current transformer. The amplifier supplies only the error current. The proposed arrangement facilitates the adaptability of inexpensive low ampere-turn c.t.s for calibration work in the laboratory.     

Mining gene–sample–time microarray data: a coherent gene cluster discovery approach

Extensive studies have shown that mining microarray data sets is important in bioinformatics research and biomedical applications. In this paper, we explore a novel type of gene–sample–time microarray data sets that records the expression levels of various genes under a set of samples during a series of time points. In particular, we propose the mining of coherent gene clusters from such data sets. Each cluster contains a subset of genes and a subset of samples such that the genes are coherent on the samples along the time series. The coherent gene clusters may identify the samples corresponding to some phenotypes (e.g., diseases), and suggest the candidate genes correlated to the phenotypes. We present two efficient algorithms, namely the Sample-Gene Search and the Gene–Sample Search, to mine the complete set of coherent gene clusters. We empirically evaluate the performance of our approaches on both a real microarray data set and synthetic data sets. The test results have shown that our approaches are both efficient and effective to find meaningful coherent gene clusters. Daxin Jiang received the Ph.D. degree in computer science and engineering from the State University of New York at Buffalo in 2005. He received the B.S. degree in computer science from the University of Science and Technology of China. From 1998 to 2000, he was a M.S. student in Software Institute, Chinese Academy of Sciences. He is currently an assistant professor at the School of Computer Engineering, Nanyang Technology University, Singapore. His research interests include data mining, bioinformatics, machine learning, and information retrieval. Jian Pei received the Ph.D. degree in computing science from Simon Fraser University, Canada, in 2002, under Dr. Jiawei Han's supervision. He also received the B.Eng. and the M.Eng. degrees from Shanghai Jiao Tong University, China, in 1991 and 1993, respectively, both in Computer Science. He is currently an assistant professor of computing science at Simon Fraser University. His research interests include developing effective and efficient data analysis techniques for novel data intensive applications. He is currently interested in various techniques of data mining, data warehousing, online analytical processing, and database systems, as well as their applications in bioinformatics. His current research is supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) and the National Science Foundation (NSF) of the United States. Since 2000, he has published over 70 research papers in refereed journals, conferences, and workshops, has served in the organization committees and the program committees of over 60 international conferences and workshops, and has been a reviewer for some leading academic journals. He is a member of the ACM, the ACM SIGMOD, and the ACM SIGKDD. Murali Ramanathan is an associate professor of pharmaceutical sciences and neurology. He received the B.Tech. (Honors) in chemical engineering from the Indian Institute of Technology, India, in 1983. After a 4-year stint in the chemical industry, he obtained the M.S. degree in chemical engineering from Iowa State University, Ames, IA, in 1987, and the Ph.D. degree in bioengineering from the University of California-San Francisco and University of California-Berkeley Joint Program in Bioengineering in 1994. Dr. Ramanathan research interests are primarily focused on the treatment of multiple sclerosis (MS), an inflammatory-demyelinating disease of the central nervous system that affects over 1 million patients worldwide. MS is a complex, variable disease that causes physical and cognitive disability and nearly 50% of patients diagnosed with MS are unable to walk after 15 years. The etiology and pathogenesis of MS remains poorly understood. Dr. Ramanathan's research interests include stochastic modeling of pharmaceutical systems and novel approaches to analyzing and using genetic and genomic data for improving patient care and optimizing therapy. Chuan Lin is currently a Ph.D. student in the Department of Computer Science and Engineering, State University of New York at Buffalo. She received the B.E. and the M.S. degrees in computer science and technology from Tsinghua University in China. Her research interests include bioinformatics, data mining, and machine learning. Chun Tang received the B.S. and M.S. degrees from Peking University, China, in 1996 and 1999, respectively, and the Ph.D. degree from State University of New York at Buffalo, USA, in 2005, all in computer science. Currently, she is a postdoctoral associate of Center for Medical Informatics, Yale University. Her research interests include bioinformatics, data mining, machine learning, database, and information retrieval. Aidong Zhang received the Ph.D. degree in computer science from Purdue University, West Lafayette, Indiana, in 1994. She was an assistant professor from 1994 to 1999, an associate professor from 1999 to 2002, and has been a professor since 2002 in the Department of Computer Science and Engineering at State University of New York at Buffalo. Her research interests include multimedia systems, content-based image retrieval, bioinformatics, and data mining. She is an author of over 140 research publications in these areas. Dr. Zhang's research has been funded by NSF, NIH, NIMA, and Xerox. Zhang serves on the editorial boards of International Journal of Bioinformatics Research and Applications (IJBRA), ACM Multimedia Systems, International Journal of Multimedia Tools and Applications, and International Journal of Distributed and Parallel Databases. She was the editor for ACM SIGMOD DiSC (Digital Symposium Collection) from 2001 to 2003. She was co-chair of the technical program committee for ACM Multimedia in 2001. She has also served on various conference program committees. Dr. Zhang is a recipient of the National Science Foundation CAREER award and SUNY Chancellor's Research Recognition award.     

An InGaP/GaAs Merged HBT-FET (BiFET) Technology and Applications to the Design of Handset Power Amplifiers

The last decade has seen GaAs HBTs emerge as the dominant technology in wireless handset power amplifiers. Modern application requirements and size limitations have driven industry leaders towards the co-integration of depletion mode n-FET and GaAs HBT. The merger of Bipolar and FET, or BiFET, gives an additional degree of freedom in the design of advanced power amplifiers independent of a silicon controller. This paper provides an overview of the various techniques that can be used to join the two device technologies and then shows how a merged epitaxial structure, where an FET is formed in the emitter layers of an HBT, combines functional versatility with the high volume manufacturability needed to supply millions of power amplifiers at low cost. A large-signal model of the FET structure is developed which takes into account the unique physics and geometries of the device, including voltage-dependant parameters and charges on all four electrical terminals. Specific handset applications that can benefit or be enabled by BiFET are presented, such as on-off switching, low voltage bias controllers , Auto-Bias power amplifiers, and bias circuits with low or no voltage reference. When npn-only bias circuitry is limited to low voltage reference levels, HBT power amplifiers with BiFET bias stages are shown to have superior RF performance to their npn-only counterparts.     

Collocation-based stochastic finite element analysis for random field problems

A stochastic response surface method (SRSM) which has been previously proposed for problems dealing only with random variables is extended in this paper for problems in which physical properties exhibit spatial random variation and may be modeled as random fields. The formalism of the extended SRSM is similar to the spectral stochastic finite element method (SSFEM) in the sense that both of them utilize Karhunen–Loeve (K–L) expansion to represent the input, and polynomial chaos expansion to represent the output. However, the coefficients in the polynomial chaos expansion are calculated using a probabilistic collocation approach in SRSM. This strategy helps us to decouple the finite element and stochastic computations, and the finite element code can be treated as a black box, as in the case of a commercial code. The collocation-based SRSM approach is compared in this paper with an existing analytical SSFEM approach, which uses a Galerkin-based weighted residual formulation, and with a black-box SSFEM approach, which uses Latin Hypercube sampling for the design of experiments. Numerical examples are used to illustrate the features of the extended SRSM and to compare its efficiency and accuracy with the existing analytical and black-box versions of SSFEM.     

Studies on slip casting behavior of lanthanum strontium manganite

Dispersion conditions for slip (slurry) formulation of a powder mixture of lanthanum strontium manganite (La_0.84Sr_0.16MnO₃ - LSM) and carbon (pore former) in water was studied through detailed zeta-potential and rheological measurements. The zeta potential variation with pH for the aqueous suspensions of only LSM or carbon exhibited a maximum value in alkaline medium (−40 mV to −50 mV at a pH of 10-11), establishing the pH window for their co-dispersion for slurry formulation. A study of the viscosity variation with shear rate for the slurries with varying solid content (in the range of 45-65 wt.%) exhibited pseudo-plastic flow behavior, indicating presence of flocculates in them. The yield stress values obtained from the Casson equation reduced with decreasing solid content, indicating reduction in the flocculate strength. The slip with solid content of 50 wt.% exhibited optimum flow characteristics to form long tubes with uniform wall thickness (wall thickness 2-4 mm and length of 150-200 mm). The tubular specimens formed after controlled carbon burn out and sintering at 1400 °C for 1 h possessed about 35% open porosity. The porosity remained the same upon further sintering at 1400 °C for 8 h.     

Study on the Effect of Loading Rate on Flexural Strength of Glass/Polyester Composites as a Function of Span-to-Depth Ratio and Fiber Volume Fraction

The influence of loading rate, span-to-thickness ratio and fiber volume fraction on composites was studied by three-point bend test on glass fiber reinforced in polyester composite laminate fabricated by compression moulding method. For lower L/h ratio, shear fracture was observed. Tensile fracture was observed for the specimen having higher L/h ratio irrespective of loading rate. Shear fracture dominates at high loading rates. This behavior was interpreted as being due to the fact that the increase in loading rate increases the brittleness of materials, subsequently increasing the defect sensitivity and leading to shear fracture.     

A molecular mechanism for electrical tuning of cochlear hair cells

Cochlear frequency selectivity in lower vertebrates arises in part from electrical tuning intrinsic to the sensory hair cells. The resonant frequency is determined largely by the gating kinetics of calcium-activated potassium (BK) channels encoded by the slo gene. Alternative splicing of slo from chick cochlea generated kinetically distinct BK channels. Combination with accessory beta subunits slowed the gating kinetics of alpha splice variants but preserved relative differences between them. In situ hybridization showed that the beta subunit is preferentially expressed by low-frequency (apical) hair cells in the avian cochlea. Interaction of beta with alpha splice variants could provide the kinetic range needed for electrical tuning of cochlear hair cells.