Theoretical and numerical analysis of large-scale heat transfer problems with temperature-dependent pore-fluid densities

Purpose - In many scientific and engineering fields, large-scale heat transfer problems with temperature-dependent pore-fluid densities are commonly encountered. For example, heat transfer from the mantle into the upper crust of the Earth is a typical problem of them. The main purpose of this paper is to develop and present a new combined methodology to solve large-scale heat transfer problems with temperature-dependent pore-fluid densities in the lithosphere and crust scales. Design/methodology/approach - The theoretical approach is used to determine the thickness and the related thermal boundary conditions of the continental crust on the lithospheric scale, so that some important information can be provided accurately for establishing a numerical model of the crustal scale. The numerical approach is then used to simulate the detailed structures and complicated geometries of the continental crust on the crustal scale. The main advantage in using the proposed combination method of the theoretical and numerical approaches is that if the thermal distribution in the crust is of the primary interest, the use of a reasonable numerical model on the crustal scale can result in a significant reduction in computer efforts. Findings - From the ore body formation and mineralization points of view, the present analytical and numerical solutions have demonstrated that the conductive-and-advective lithosphere with variable pore-fluid density is the most favorite lithosphere because it may result in the thinnest lithosphere so that the temperature at the near surface of the crust can be hot enough to generate the shallow ore deposits there. The upward throughflow (i.e. mantle mass flux) can have a significant effect on the thermal structure within the lithosphere. In addition, the emplacement of hot materials from the mantle may further reduce the thickness of the lithosphere. Originality/value - The present analytical solutions can be used to: validate numerical methods for solving large-scale heat transfer problems; provide correct thermal boundary conditions for numerically solving ore body formation and mineralization problems on the crustal scale; and investigate the fundamental issues related to thermal distributions within the lithosphere. The proposed finite element analysis can be effectively used to consider the geometrical and material complexities of large-scale heat transfer problems with temperature-dependent fluid densities.

Towards the preparative and large-scale precision manafacture of virus-like particles

Virus-like particles (VLPs) are of interest in vaccination, gene therapy and drug delivery, but their potential has yet to be fully realized. This is because existing laboratory processes, when scaled, do not easily give a compositionally and architecturally consistent product. Research suggests that new process routes might ultimately be based on chemical processing by self-assembly, involving the precision manufacture of precursor capsomeres followed by in vitro VLP self-assembly and scale-up to required levels. A synergistic interaction of biomolecular design and bioprocess engineering (i.e. biomolecular engineering) is required if these alternative process routes and, thus, the promise of new VLP products, are to be realized.

Large scale experiments on gravel and mixed beaches: Experimental procedure, data documentation and initial results

This paper provides information on the experimental set-up, data collection methods and results to date for the project Large scale modelling of coarse grained beaches, undertaken at the Large Wave Channel (GWK) of FZK in Hannover by an international group of researchers in Spring 2002. The main objective of the experiments was to provide full scale measurements of cross-shore processes on gravel and mixed beaches for the verification and further development of cross-shore numerical models of gravel and mixed sediment beaches. Identical random and regular wave tests were undertaken for a gravel beach and a mixed sand/gravel beach set up in the flume. Measurements included profile development, water surface elevation along the flume, internal pressures in the swash zone, piezometric head levels within the beach, run-up, flow velocities in the surf-zone and sediment size distributions. The purpose of the paper is to present to the scientific community the experimental procedure, a summary of the data collected, some initial results, as well as a brief outline of the on-going research being carried out with the data by different research groups. The experimental data is available to all the scientific community following submission of a statement of objectives, specification of data requirements and an agreement to abide with the GWK and EU protocols. (C) 2005 Elsevier B.V. All rights reserved.

Maternal health during pregnancy and perinatal mortality in Bangladesh: evidence form a large-scale community-based clinical trial

Perinatal mortality is very high in Bangladesh. In this setting, few community-level studies have assessed the influence of underlying maternal health factors on perinatal outcomes. We used the data from a community-based clinical controlled trial conducted between 1994 and 1997 in the catchment areas of a large MCH/FP hospital located in Mirpur, a suburban area of Dhaka in Bangladesh, to investigate the levels of perinatal mortality and its associated maternal health factors during pregnancy. A total of 2007 women were followed after recruitment up to delivery, maternal death, or until they dropped out of the study. Of these, 1584 who gave birth formed our study subjects. The stillbirth rate was 39.1 per 1000 births [95% confidence interval (CI) 39.0, 39.3] and the perinatal mortality rate (up to 3 days) was 54.3 per 1000 births [95% CI 54.0, 54.6] among the study population. In the fully adjusted logistic regression model, the risk of perinatal mortality was as high as 2.7 times [95% CI 1.5, 4.9] more likely for women with hypertensive disorders, 5.0 times [95% CI 2.3, 10.8] as high for women who had antepartum haemorrhage and 2.6 times [95% CI 1.2, 5.8] as high for women who had higher haemoglobin levels in pregnancy when compared with their counterparts. The inclusion of potential confounding variables such as poor obstetric history, sociodemographic characteristics and preterm delivery influenced only marginally the net effect of important maternal health factors associated with perinatal mortality. Perinatal mortality in the study setting was significantly associated with poor maternal health conditions during pregnancy. The results of this study point towards the urgent need for monitoring complications in high-risk pregnancies, calling for the specific components of the safe motherhood programme interventions that are designed to manage these complications of pregnancy.

Parallel implementation of stochastic simulation for large scale cellular processes

Experimental and theoretical studies have shown the importance of stochastic processes in genetic regulatory networks and cellular processes. Cellular networks and genetic circuits often involve small numbers of key proteins such as transcriptional factors and signaling proteins. In recent years stochastic models have been used successfully for studying noise in biological pathways, and stochastic modelling of biological systems has become a very important research field in computational biology. One of the challenge problems in this field is the reduction of the huge computing time in stochastic simulations. Based on the system of the mitogen-activated protein kinase cascade that is activated by epidermal growth factor, this work give a parallel implementation by using OpenMP and parallelism across the simulation. Special attention is paid to the independence of the generated random numbers in parallel computing, that is a key criterion for the success of stochastic simulations. Numerical results indicate that parallel computers can be used as an efficient tool for simulating the dynamics of large-scale genetic regulatory networks and cellular processes

A combined neural network and DEA for measuring efficiency of large scale datasets

Data Envelopment Analysis (DEA) is one of the most widely used methods in the measurement of the efficiency and productivity of Decision Making Units (DMUs). DEA for a large dataset with many inputs/outputs would require huge computer resources in terms of memory and CPU time. This paper proposes a neural network back-propagation Data Envelopment Analysis to address this problem for the very large scale datasets now emerging in practice. Neural network requirements for computer memory and CPU time are far less than that needed by conventional DEA methods and can therefore be a useful tool in measuring the efficiency of large datasets. Finally, the back-propagation DEA algorithm is applied to five large datasets and compared with the results obtained by conventional DEA.