Slip line field analysis of a simple plane strain closed‐die forging

A slip line field is proposed for symmetrical single‐cavity closed‐die forging by rough dies. A compatible velocity field is shown to exist. Experiments were conducted using lead workpiece and rough dies. Experimentally observed flow and load were used to validate the proposed slip line field. The slip line field was used to simulate the process in the computer with the objective of studying the influence of flash geometry on cavity filling.

Transcriptional analysis of persistent Chlamydia pneumoniae infection in vitro

Chlamydia pneumoniae can cause acute respiratory infections including pneumonia. Repeated and persistent Chlamydia infections occur and persistent C. pneumoniae infection may have a role in the pathogenesis of atherosclerosis and coronary heart disease and may also contribute to the development of chronic inflammatory lung diseases like chronic obstructive pulmonary disease (COPD) and asthma. In this thesis in vitro models for persistent C. pneumonia infection were established in epithelial and monocyte/macrophage cell lines. Expression of host cell genes in the persistent C. pneumoniae infection model of epithelial cells was studied by microarray and RT-PCR. In the monocyte/macrophage infection model expression of selected C. pneumoniae genes were studied by RT-PCR and immunofluorescence microscopy. Chlamydia is able to modulate host cell gene expression and apoptosis of host cells, which may assist Chlamydia to evade the host cells' immune responses. This, in turn, may lead to extended survival of the organism inside epithelial cells and promote the development of persistent infection. To simulate persistent C. pneumoniae infection in vivo, we set up a persistent infection model exposing the HL cell cultures to IFN-gamma. When HL cell cultures were treated with moderate concentration of IFN-gamma, the replication of C. pneumoniae DNA was unaffected while differentiation into infectious elementary bodies (EB) was strongly inhibited. By transmission electron microscopy small atypical inclusions were identified in IFN-gamma treated cultures. No second cycle of infection was observed in cells exposed to IFN-gamma , whereas C. pneumoniae was able to undergo a second cycle of infection in unexposed HL cells. Although monocytic cells can naturally restrict chlamydial growth, IFN-gamma further reduced production of infectious C. pneumoniae in Mono Mac 6 cells. Under both studied conditions no second cycle of infection could be detected in monocytic cell line suggesting persistent infection in these cells. As a step toward understanding the role of host genes in the development and pathogenesis of persistent C. pneumoniae infection, modulation of host cell gene expression during IFN-gamma induced persistent infection was examined and compared to that seen during active C. pneumoniae infection or IFN-gamma treatment. Total RNA was collected at 6 to 150 h after infection of an epithelial cell line (HL) and analyzed by a cDNA array (available at that time) representing approximately 4000 human transcripts. In initial analysis 250 of the 4000 genes were identified as differentially expressed upon active and persistent chlamydial infection and IFN-gamma treatment. In persistent infection more potent up-regulation of many genes was observed in IFN-gamma induced persistent infection than in active infection or in IFN-gamma treated cell cultures. Also sustained up-regulation was observed for some genes. In addition, we could identify nine host cell genes whose transcription was specifically altered during the IFN-gamma induced persistent C. pneumoniae infection. Strongest up-regulation in persistent infection in relation to controls was identified for insulin like growth factor binding protein 6, interferon-stimulated protein 15 kDa, cyclin D1 and interleukin 7 receptor. These results suggest that during persistent infection, C. pneumoniae reprograms the host transcriptional machinery regulating a variety of cellular processes including adhesion, cell cycle regulation, growth and inflammatory response, all of which may play important roles in the pathogenesis of persistent C. pneumoniae infection. C. pneumoniae DNA can be detected in peripheral blood mononuclear cells indicating that the bacterium can also infect monocytic cells in vivo and thereby monocytes can assist the spread of infection from the lungs to other anatomical sites. Persistent infection established at these sites could promote inflammation and enhance pathology. Thus, the mononuclear cells are in a strategic position in the development of persistent infection. To investigate the intracellular replication and fate of C. pneumoniae in mononuclear cells we analyzed the transcription of 11 C. pneumoniae genes in Mono Mac 6 cells during infection by real time RT-PCR. Our results suggest that the transcriptional profile of the studied genes in monocytes is different from that seen in epithelial cells and that IFN-gamma has a less significant effect on C. pneumoniae transcription in monocytes. Furthermore, our study shows that type III secretion system (T3SS) related genes are transcribed and that Chlamydia possesses a functional T3SS during infection in monocytes. Since C. pneumoniae infection in monocytes has been implicated to have reduced antibiotic susceptibility, this creates opportunities for novel therapeutics targeting T3SS in the management of chlamydial infection in monocytes.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications. (C) 2005 Elsevier B. V. All rights reserved.

Sarveiskalvon permeabiliteetin ennustaminen QSPR-mallin avulla

QSPR-malli kuvaa kvantitatiivista riippuvuutta muuttujien ja biologisen ominaisuuden välillä. Näin ollen QSPR mallit ovat käyttökelpoisia lääkekehityksen apuvälineitä. Kirjallisessa osassa kerrotaan sarveiskalvon, suoliston ja veriaivoesteen permeabiliteetin malleista. Useimmin käytettyjä muuttujia ovat yhdisteen rasvaliukoisuus, polaarinen pinta-ala, vetysidosten muodostuminen ja varaus. Myös yhdisteen koko vaikuttaa läpäisevyyteen, vaikka tutkimuksissa onkin erilaista tietoa tämän merkittävyydestä. Malliin vaikuttaa myös muiden kuin mallissa mukana olevien muuttujien suuruusluokka esimerkkinä Lipinskin ‖rule of 5‖ luokittelu. Tässä luokittelussa yhdisteen ominaisuus ei saa ylittää tiettyjä raja-arvoja. Muussa tapauksessa sen imeytyminen suun kautta otettuna todennäköisesti vaarantuu. Lisäksi kirjallisessa osassa tutustuttiin kuljetinproteiineihin ja niiden toimintaan silmän sarveiskalvossa, suolistossa ja veriaivoesteessä. Nykyisin on kehitetty erilaisia QSAR-malleja kuljetinproteiineille ennustamaan mahdollisten substraatittien tai inhibiittorien vuorovaikutuksia kuljetinproteiinin kanssa. Kokeellisen osan tarkoitus oli rakentaa in silico -malli sarveiskalvon passiiviselle permeabiliteetille. Työssä tehtiin QSPR-malli 54 yhdisteen ACDLabs-ohjelmalla laskettujen muuttujien arvojen avulla. Permeabiliteettikertoimien arvot saatiin kirjallisuudesta kanin sarveiskalvon läpäisevyystutkimuksista. Lopullisen mallin muuttujina käytettiin oktanoli-vesijakaantumiskerrointa (logD) pH:ssa 7,4 ja vetysidosatomien kokonaismäärää. Yhtälö oli muotoa log10(permeabiliteettikerroin) = -3,96791 - 0,177842Htotal + 0,311963logD(pH7,4). R2-korrelaatiokerroin oli 0,77 ja Q2-korrelaatiokerroin oli 0,75. Lopullisen mallin hyvyyttä arvioitiin 15 yhdisteen ulkoisella testijoukolla, jolloin ennustettua permeabiliteettia verrattiin kokeelliseen permeabiliteettiin. QSPR-malli arvioitiin myös farmakokineettisen simulaation avulla. Simulaatiossa laskettiin seitsemän yhdisteen kammionestepitoisuudet in vivo vakaassa tilassa käyttäen simulaatioissa QSPR mallilla ennustettuja permeabiliteettikertoimia. Lisäksi laskettiin sarveiskalvon imeytymisen nopeusvakio (Kc) 13 yhdisteelle farmakokineettisen simulaation avulla ja verrattiin tätä lopullisella mallilla ennustettuun permeabiliteettiin. Tulosten perusteella saatiin tilastollisesti hyvä QSPR-malli kuvaamaan sarveiskalvon passiivista permeabiliteettia, jolloin tätä mallia voidaan käyttää lääkekehityksen alkuvaiheessa. QSPR-malli ennusti permeabiliteettikertoimet hyvin, mikä nähtiin vertaamalla mallilla ennustettuja arvoja kokeellisiin tuloksiin. Lisäksi yhdisteiden kammionestepitoisuudet voitiin simuloida käyttäen apuna QSPR-mallilla ennustettuja permeabiliteettikertoimien arvoja.

Effect of process variables on die-billet temperature history in a slow speed hot coining type process

A computer code is developed as a part of an ongoing project on computer aided process modelling of forging operation, to simulate heat transfer in a die-billet system. The code developed on a stage-by-stage technique is based on an Alternating Direction Implicit scheme. The experimentally validated code is used to study the effect of process specifics such as preheat die temperature, machine ascent time, rate of deformation, and dwell time on the thermal characteristics in a batch coining operation where deformation is restricted to surface level only.

Inelastic response of beams under sinusoidal and random loads

A new analytical model has been suggested for the hysteretic behaviour of beams. The model can be directly used in a response analysis without bothering to locate the precise point where the unloading commences. The model can efficiently simulate several types of realistic softening hysteretic loops. This is demonstrated by computing the response of cantilever beams under sinusoidal and random loadings. Results are presented in the form of graphs for maximum deflection, bending moment and shear

The 26 July 2005 heavy rainfall event over Mumbai: numerical modeling aspects

The performance of the Advanced Regional Prediction System (ARPS) in simulating an extreme rainfall event is evaluated, and subsequently the physical mechanisms leading to its initiation and sustenance are explored. As a case study, the heavy precipitation event that led to 65 cm of rainfall accumulation in a span of around 6 h (1430 LT-2030 LT) over Santacruz (Mumbai, India), on 26 July, 2005, is selected. Three sets of numerical experiments have been conducted. The first set of experiments (EXP1) consisted of a four-member ensemble, and was carried out in an idealized mode with a model grid spacing of 1 km. In spite of the idealized framework, signatures of heavy rainfall were seen in two of the ensemble members. The second set (EXP2) consisted of a five-member ensemble, with a four-level one-way nested integration and grid spacing of 54, 18, 6 and 1 km. The model was able to simulate a realistic spatial structure with the 54, 18, and 6 km grids; however, with the 1 km grid, the simulations were dominated by the prescribed boundary conditions. The third and final set of experiments (EXP3) consisted of a five-member ensemble, with a four-level one-way nesting and grid spacing of 54, 18, 6, and 2 km. The Scaled Lagged Average Forecasting (SLAF) methodology was employed to construct the ensemble members. The model simulations in this case were closer to observations, as compared to EXP2. Specifically, among all experiments, the timing of maximum rainfall, the abrupt increase in rainfall intensities, which was a major feature of this event, and the rainfall intensities simulated in EXP3 (at 6 km resolution) were closest to observations. Analysis of the physical mechanisms causing the initiation and sustenance of the event reveals some interesting aspects. Deep convection was found to be initiated by mid-tropospheric convergence that extended to lower levels during the later stage. In addition, there was a high negative vertical gradient of equivalent potential temperature suggesting strong atmospheric instability prior to and during the occurrence of the event. Finally, the presence of a conducive vertical wind shear in the lower and mid-troposphere is thought to be one of the major factors influencing the longevity of the event.

Performance of a small solar pond in the tropics

The performance of the 240 m2 solar pond in Bangalore is discussed. The problems of erosion of gradient zone and formation of internal convective zones is highlighted. The technique of passive salt addition is shown to be a viable alternative for salt recycling. Different techniques of heat extraction are discussed and the use of an immersed copper heat exchanger is shown to be most convenient. A two-zone model for prediction of the seasonal structure of the solar pond performance is proposed. The model is shown to simulate the seasonal structure of the observed variation of the temperature in the storage zone.

A proteomic view of probiotic Lactobacillus rhamnosus GG

Lactobacillus rhamnosus GG is a probiotic bacterium that is known worldwide. Since its discovery in 1985, the health effects and biology of this health-promoting strain have been researched at an increasing rate. However, knowledge of the molecular biology responsible for these health effects is limited, even though research in this area has continued to grow since the publication of the whole genome sequence of L. rhamnosus GG in 2009. In this thesis, the molecular biology of L. rhamnosus GG was explored by mapping the changes in protein levels in response to diverse stress factors and environmental conditions. The proteomics data were supplemented with transcriptome level mapping of gene expression. The harsh conditions of the gastro-intestinal tract, which involve acidic conditions and detergent-like bile acids, are a notable challenge to the survival of probiotic bacteria. To simulate these conditions, L. rhamnosus GG was exposed to a sudden bile stress, and several stress response mechanisms were revealed, among others various changes in the cell envelope properties. L. rhamnosus GG also responded in various ways to mild acid stress, which probiotic bacteria may face in dairy fermentations and product formulations. The acid stress response of L. rhamnosus GG included changes in central metabolism and specific responses related to the control of intracellular pH. Altogether, L. rhamnosus GG was shown to possess a large repertoire of mechanisms for responding to stress conditions, which is a beneficial character of a probiotic organism. Adaptation to different growth conditions was studied by comparing the proteome level responses of L. rhamnosus GG to divergent growth media and to different phases of growth. Comparing different growth phases revealed that the metabolism of L. rhamnosus GG is modified markedly during shift from the exponential to the stationary phase of growth. These changes were seen both at proteome and transcriptome levels and in various different cellular functions. When the growth of L. rhamnosus GG in a rich laboratory medium and in an industrial whey-based medium was compared, various differences in metabolism and in factors affecting the cell surface properties could be seen. These results led us to recommend that the industrial-type media should be used in laboratory studies of L. rhamnosus GG and other probiotic bacteria to achieve a similar physiological state for the bacteria as that found in industrial products, which would thus yield more relevant information about the bacteria. In addition, an interesting phenomenon of protein phosphorylation was observed in L. rhamnosus GG. Phosphorylation of several proteins of L. rhamnosus GG was detected, and there were hints that the degree of phosphorylation may be dependent on the growth pH.

Literature Review: Investigating Drug Transport at the Blood-Brain Barrier and the Effects of P-glycoprotein on the Brain Distribution of Drugs.

The blood-brain barrier (BBB) is a unique barrier that strictly regulates the entry of endogenous substrates and xenobiotics into the brain. This is due to its tight junctions and the array of transporters and metabolic enzymes that are expressed. The determination of brain concentrations in vivo is difficult, laborious and expensive which means that there is interest in developing predictive tools of brain distribution. Predicting brain concentrations is important even in early drug development to ensure efficacy of central nervous system (CNS) targeted drugs and safety of non-CNS drugs. The literature review covers the most common current in vitro, in vivo and in silico methods of studying transport into the brain, concentrating on transporter effects. The consequences of efflux mediated by p-glycoprotein, the most widely characterized transporter expressed at the BBB, is also discussed. The aim of the experimental study was to build a pharmacokinetic (PK) model to describe p-glycoprotein substrate drug concentrations in the brain using commonly measured in vivo parameters of brain distribution. The possibility of replacing in vivo parameter values with their in vitro counterparts was also studied. All data for the study was taken from the literature. A simple 2-compartment PK model was built using the Stella™ software. Brain concentrations of morphine, loperamide and quinidine were simulated and compared with published studies. Correlation of in vitro measured efflux ratio (ER) from different studies was evaluated in addition to studying correlation between in vitro and in vivo measured ER. A Stella™ model was also constructed to simulate an in vitro transcellular monolayer experiment, to study the sensitivity of measured ER to changes in passive permeability and Michaelis-Menten kinetic parameter values. Interspecies differences in rats and mice were investigated with regards to brain permeability and drug binding in brain tissue. Although the PK brain model was able to capture the concentration-time profiles for all 3 compounds in both brain and plasma and performed fairly well for morphine, for quinidine it underestimated and for loperamide it overestimated brain concentrations. Because the ratio of concentrations in brain and blood is dependent on the ER, it is suggested that the variable values cited for this parameter and its inaccuracy could be one explanation for the failure of predictions. Validation of the model with more compounds is needed to draw further conclusions. In vitro ER showed variable correlation between studies, indicating variability due to experimental factors such as test concentration, but overall differences were small. Good correlation between in vitro and in vivo ER at low concentrations supports the possibility of using of in vitro ER in the PK model. The in vitro simulation illustrated that in the simulation setting, efflux is significant only with low passive permeability, which highlights the fact that the cell model used to measure ER must have low enough paracellular permeability to correctly mimic the in vivo situation.

A simple method for potential flow simulation of cascades

A simple method using a combination of conformal mapping and vortex panel method to simulate potential flow in cascades is presented. The cascade is first transformed to a single body using a conformal mapping, and the potential flow over this body is solved using a simple higher order vortex panel method. The advantage of this method over existing methodologies is that it enables the use of higher order panel methods, as are used to solve flow past an isolated airfoil, to solve the cascade problem without the need for any numerical integrations or iterations. The fluid loading on the blades, such as the normal force and pitching moment, may be easily calculated from the resultant velocity field. The coefficient of pressure on cascade blades calculated with this methodology shows good agreement with previous numerical and experimental results.

Parallel Tempering Simulations of Liquid-Phase Adsorption of n-Alkane Mixtures in Zeolite LTA-5A

Adsorption of n-alkane mixtures in the zeolite LTA-5A under liquid-phase conditions has been studied using grand canonical Monte Carlo (GCMC) simulations combined with parallel tempering. Normal GCMC techniques fail for some of these systems due to the preference of linear molecules to coil within a single cage in the zeolite. The narrow zeolite windows severerly restrict interactions of the molecules, making it difficult to simulate cooperative rearrangements necessary to explore configuration space. Because of these reasons, normal GCMC simulations results show poor reproducibility in some cases. These problems were overcome with parallel tempering techniques. Even with parallel tempering, these are very challenging systems for molecular simulation. Similar problems may arise for other zeolites such as CHA, AFX, ERI, KFI, and RHO having cages connected by narrow windows. The simulations capture the complex selectivity behavior observed in experiments such as selectivity inversion and azeotrope formation.

Seasonal prediction of the Indian monsoon

Under the project `Seasonal Prediction of the Indian Monsoon' (SPIM), the prediction of Indian summer monsoon rainfall by five atmospheric general circulation models (AGCMs) during 1985-2004 was assessed. The project was a collaborative effort of the coordinators and scientists from the different modelling groups across the country. All the runs were made at the Centre for Development of Advanced Computing (CDAC) at Bangalore on the PARAM Padma supercomputing system. Two sets of simulations were made for this purpose. In the first set, the AGCMs were forced by the observed sea surface temperature (SST) for May-September during 1985-2004. In the second set, runs were made for 1987, 1988, 1994, 1997 and 2002 forced by SST which was obtained by assuming that the April anomalies persist during May-September. The results of the first set of runs show, as expected from earlier studies, that none of the models were able to simulate the correct sign of the anomaly of the Indian summer monsoon rainfall for all the years. However, among the five models, one simulated the correct sign in the largest number of years and the second model showed maximum skill in the simulation of the extremes (i.e. droughts or excess rainfall years). The first set of runs showed some common bias which could arise either from an excessive sensitivity of the models to El Nino Southern Oscillation (ENSO) or an inability of the models to simulate the link of the Indian monsoon rainfall to Equatorial Indian Ocean Oscillation (EQUINOO), or both. Analysis of the second set of runs showed that with a weaker ENSO forcing, some models could simulate the link with EQUINOO, suggesting that the errors in the monsoon simulations with observed SST by these models could be attributed to unrealistically high sensitivity to ENSO.

Barrier crossing in one and three dimensions by a long chain

We consider the Kramers problem for a long chain polymer trapped in a biased double-well potential. Initially the polymer is in the less stable well and it can escape from this well to the other well by the motion of its N beads across the barrier to attain the configuration having lower free energy. In one dimension we simulate the crossing and show that the results are in agreement with the kink mechanism suggested earlier. In three dimensions, it has not been possible to get an analytical `kink solution' for an arbitrary potential; however, one can assume the form of the solution of the nonlinear equation as a kink solution and then find a double-well potential in three dimensions. To verify the kink mechanism, simulations of the dynamics of a discrete Rouse polymer model in a double well in three dimensions are carried out. We find that the time of crossing is proportional to the chain length, which is in agreement with the results for the kink mechanism. The shape of the kink solution is also in agreement with the analytical solution in both one and three dimensions.