Hydrogen production by steam reforming of DME in a large scale CFB reactor. Part I:computational model and predictions

This study presents a computational fluid dynamic (CFD) study of Dimethyl Ether steam reforming (DME-SR) in a large scale Circulating Fluidized Bed (CFB) reactor. The CFD model is based on Eulerian-Eulerian dispersed flow and solved using commercial software (ANSYS FLUENT). The DME-SR reactions scheme and kinetics in the presence of a bifunctional catalyst of CuO/ZnO/Al2O3+ZSM-5 were incorporated in the model using in-house developed user-defined function. The model was validated by comparing the predictions with experimental data from the literature. The results revealed for the first time detailed CFB reactor hydrodynamics, gas residence time, temperature distribution and product gas composition at a selected operating condition of 300 °C and steam to DME mass ratio of 3 (molar ratio of 7.62). The spatial variation in the gas species concentrations suggests the existence of three distinct reaction zones but limited temperature variations. The DME conversion and hydrogen yield were found to be 87% and 59% respectively, resulting in a product gas consisting of 72 mol% hydrogen. In part II of this study, the model presented here will be used to optimize the reactor design and study the effect of operating conditions on the reactor performance and products.

Large-scale molecular dynamics simulations of HLA-A*0201 complexed with a tumor-specific antigenic peptide:can the α3 and β2m domains be neglected?

Large-scale massively parallel molecular dynamics (MD) simulations of the human class I major histo-compatibility complex (MHC) protein HLA-A*0201 bound to a decameric tumor-specific antigenic peptide GVY-DGREHTV were performed using a scalable MD code on high-performance computing platforms. Such computational capabilities put us in reach of simulations of various scales and complexities. The supercomputing resources available Large-scale massively parallel molecular dynamics (MD) simulations of the human class I major histocompatibility complex (MHC) protein HLA-A*0201 bound to a decameric tumor-specific antigenic peptide GVYDGREHTV were performed using a scalable MD code on high-performance computing platforms. Such computational capabilities put us in reach of simulations of various scales and complexities. The supercomputing resources available for this study allow us to compare directly differences in the behavior of very large molecular models; in this case, the entire extracellular portion of the peptide–MHC complex vs. the isolated peptide binding domain. Comparison of the results from the partial and the whole system simulations indicates that the peptide is less tightly bound in the partial system than in the whole system. From a detailed study of conformations, solvent-accessible surface area, the nature of the water network structure, and the binding energies, we conclude that, when considering the conformation of the α1–α2 domain, the α3 and β2m domains cannot be neglected. © 2004 Wiley Periodicals, Inc. J Comput Chem 25: 1803–1813, 2004

An improved memory management scheme for large scale graph computing engine GraphChi

GraphChi is the first reported disk-based graph engine that can handle billion-scale graphs on a single PC efficiently. GraphChi is able to execute several advanced data mining, graph mining and machine learning algorithms on very large graphs. With the novel technique of parallel sliding windows (PSW) to load subgraph from disk to memory for vertices and edges updating, it can achieve data processing performance close to and even better than those of mainstream distributed graph engines. GraphChi mentioned that its memory is not effectively utilized with large dataset, which leads to suboptimal computation performances. In this paper we are motivated by the concepts of 'pin ' from TurboGraph and 'ghost' from GraphLab to propose a new memory utilization mode for GraphChi, which is called Part-in-memory mode, to improve the GraphChi algorithm performance. The main idea is to pin a fixed part of data inside the memory during the whole computing process. Part-in-memory mode is successfully implemented with only about 40 additional lines of code to the original GraphChi engine. Extensive experiments are performed with large real datasets (including Twitter graph with 1.4 billion edges). The preliminary results show that Part-in-memory mode memory management approach effectively reduces the GraphChi running time by up to 60% in PageRank algorithm. Interestingly it is found that a larger portion of data pinned in memory does not always lead to better performance in the case that the whole dataset cannot be fitted in memory. There exists an optimal portion of data which should be kept in the memory to achieve the best computational performance.

A quantitative approach for understanding small-scale human mesenchymal stem cell culture - implications for large-scale bioprocess development

Human mesenchymal stem cell (hMSC) therapies have the potential to revolutionise the healthcare industry and replicate the success of the therapeutic protein industry; however, for this to be achieved there is a need to apply key bioprocessing engineering principles and adopt a quantitative approach for large-scale reproducible hMSC bioprocess development. Here we provide a quantitative analysis of the changes in concentration of glucose, lactate and ammonium with time during hMSC monolayer culture over 4 passages, under 100% and 20% dissolved oxgen (dO2), where either a 100%, 50% or 0% growth medium exchange was performed after 72h in culture. Yield coefficients, specific growth rates (h-1) and doubling times (h) were calculated for all cases. The 100% dO2 flasks outperformed the 20% dO2 flasks with respect to cumulative cell number, with the latter consuming more glucose and producing more lactate and ammonium. Furthermore, the 100% and 50% medium exchange conditions resulted in similar cumulative cell numbers, whilst the 0% conditions were significantly lower. Cell immunophenotype and multipotency were not affected by the experimental culture conditions. This study demonstrates the importance of determining optimal culture conditions for hMSC expansion and highlights a potential cost savings from only making a 50% medium exchange, which may prove significant for large-scale bioprocessing. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Characterization of human mesenchymal stem cells from multiple donors and the implications for large scale bioprocess development

Cell-based therapies have the potential to contribute to global healthcare, whereby the use of living cells and tissues can be used as medicinal therapies. Despite this potential, many challenges remain before the full value of this emerging field can be realized. The characterization of input material for cell-based therapy bioprocesses from multiple donors is necessary to identify and understand the potential implications of input variation on process development. In this work, we have characterized bone marrow derived human mesenchymal stem cells (BM-hMSCs) from multiple donors and discussed the implications of the measurable input variation on the development of autologous and allogeneic cell-based therapy manufacturing processes. The range of cumulative population doublings across the five BM-hMSC lines over 30 days of culture was 5.93, with an 18.2% range in colony forming efficiency at the end of the culture process and a 55.1% difference in the production of interleukin-6 between these cell lines. It has been demonstrated that this variation results in a range in the process time between these donor hMSC lines for a hypothetical product of over 13 days, creating potential batch timing issues when manufacturing products from multiple patients. All BM-hMSC donor lines demonstrated conformity to the ISCT criteria but showed a difference in cell morphology. Metabolite analysis showed that hMSCs from the different donors have a range in glucose consumption of 26.98 pmol cell−1 day−1, Lactate production of 29.45 pmol cell−1 day−1 and ammonium production of 1.35 pmol cell−1 day−1, demonstrating the extent of donor variability throughout the expansion process. Measuring informative product attributes during process development will facilitate progress towards consistent manufacturing processes, a critical step in the translation cell-based therapies.

Responsive fixture design using dynamic product inspection and monitoring technologies for the precision machining of large-scale aerospace parts

When machining a large-scale aerospace part, the part is normally located and clamped firmly until a set of features are machined. When the part is released, its size and shape may deform beyond the tolerance limits due to stress release. This paper presents the design of a new fixing method and flexible fixtures that would automatically respond to workpiece deformation during machining. Deformation is inspected and monitored on-line, and part location and orientation can be adjusted timely to ensure follow-up operations are carried out under low stress and with respect to the related datum defined in the design models.

A new paradigm in large-scale assembly-research priorities in measurement assisted assembly

This paper presents for the first time the concept of measurement assisted assembly (MAA) and outlines the research priorities of the realisation of this concept in the industry. MAA denotes a paradigm shift in assembly for high value and complex products and encompasses the development and use of novel metrology processes for the holistic integration and capability enhancement of key assembly and ancillary processes. A complete framework for MAA is detailed showing how this can facilitate a step change in assembly process capability and efficiency for large and complex products, such as airframes, where traditional assembly processes exhibit the requirement for rectification and rework, use inflexible tooling and are largely manual, resulting in cost and cycle time pressures. The concept of MAA encompasses a range of innovativemeasurement- assisted processes which enable rapid partto- part assembly, increased use of flexible automation, traceable quality assurance and control, reduced structure weight and improved levels of precision across the dimensional scales. A full scale industrial trial of MAA technologies has been carried out on an experimental aircraft wing demonstrating the viability of the approach while studies within 140 smaller companies have highlighted the need for better adoption of existing process capability and quality control standards. The identified research priorities for MAA include the development of both frameless and tooling embedded automated metrology networks. Other research priorities relate to the development of integrated dimensional variation management, thermal compensation algorithms as well as measurement planning and inspection of algorithms linking design to measurement and process planning. © Springer-Verlag London 2013.

Recent developments in large-scale dimensional metrology

With ever-more demanding requirements for the accurate manufacture of large components, dimensional measuring techniques are becoming progressively more sophisticated. This review describes some of the more recently developed techniques and the state-of-the-art in the more well-known large-scale dimensional metrology methods. In some cases, the techniques are described in detail, or, where relevant specialist review papers exist, these are cited as further reading. The traceability of the measurement data collected is discussed with reference to new international standards that are emerging. In some cases, hybrid measurement techniques are finding specialized applications and these are referred to where appropriate. © IMechE 2009.

Large-scale complex IT systems

Society depends on complex IT systems created by integrating and orchestrating independently managed systems. The incredible increase in scale and complexity in them over the past decade means new software-engineering techniques are needed to help us cope with their inherent complexity. The key characteristic of these systems is that they are assembled from other systems that are independently controlled and managed. While there is increasing awareness in the software engineering community of related issues, the most relevant background work comes from systems engineering. The interacting algos that led to the Flash Crash represent an example of a coalition of systems, serving the purposes of their owners and cooperating only because they have to. The owners of the individual systems were competing finance companies that were often mutually hostile. Each system jealously guarded its own information and could change without consulting any other system.

Large-scale production of secreted proteins in Pichia pastoris

The production of recombinant therapeutic proteins is an active area of research in drug development. These bio-therapeutic drugs target nearly 150 disease states and promise to bring better treatments to patients. However, if new bio-therapeutics are to be made more accessible and affordable, improvements in production performance and optimization of processes are necessary. A major challenge lies in controlling the effect of process conditions on production of intact functional proteins. To achieve this, improved tools are needed for bio-processing. For example, implementation of process modeling and high-throughput technologies can be used to achieve quality by design, leading to improvements in productivity. Commercially, the most sought after targets are secreted proteins due to the ease of handling in downstream procedures. This chapter outlines different approaches for production and optimization of secreted proteins in the host Pichia pastoris. © 2012 Springer Science+business Media, LLC.

Machine learning for large-scale wearable sensor data in Parkinson disease:concepts, promises, pitfalls, and futures

For the treatment and monitoring of Parkinson's disease (PD) to be scientific, a key requirement is that measurement of disease stages and severity is quantitative, reliable, and repeatable. The last 50 years in PD research have been dominated by qualitative, subjective ratings obtained by human interpretation of the presentation of disease signs and symptoms at clinical visits. More recently, “wearable,” sensor-based, quantitative, objective, and easy-to-use systems for quantifying PD signs for large numbers of participants over extended durations have been developed. This technology has the potential to significantly improve both clinical diagnosis and management in PD and the conduct of clinical studies. However, the large-scale, high-dimensional character of the data captured by these wearable sensors requires sophisticated signal processing and machine-learning algorithms to transform it into scientifically and clinically meaningful information. Such algorithms that “learn” from data have shown remarkable success in making accurate predictions for complex problems in which human skill has been required to date, but they are challenging to evaluate and apply without a basic understanding of the underlying logic on which they are based. This article contains a nontechnical tutorial review of relevant machine-learning algorithms, also describing their limitations and how these can be overcome. It discusses implications of this technology and a practical road map for realizing the full potential of this technology in PD research and practice. © 2016 International Parkinson and Movement Disorder Society.

Segmental body composition determination through a bioelectrical very large scale integration (VLSI) system

The purpose of this investigation was to develop new techniques to generate segmental assessments of body composition based on Segmental Bioelectrical Impedance Analysis (SBIA). An equally important consideration was the design, simulation, development, and the software and hardware integration of the SBIA system. This integration was carried out with a Very Large Scale Integration (VLSI) Field Programmable Gate Array (FPGA) microcontroller that analyzed the measurements obtained from segments of the body, and provided full body and segmental Fat Free Mass (FFM) and Fat Mass (FM) percentages. Also, the issues related to the estimate of the body's composition in persons with spinal cord injury (SCI) were addressed and investigated. This investigation demonstrated that the SBIA methodology provided accurate segmental body composition measurements. Disabled individuals are expected to benefit from these SBIA evaluations, as they are non-invasive methods, suitable for paralyzed individuals. The SBIA VLSI system may replace bulky, non flexible electronic modules attached to human bodies. ^

Internet Protocol Television (IPTV) controller realization with Very Large Scale Integration (VLSI) modules

Internet Protocol Television (IPTV) is a system where a digital television service is delivered by using Internet Protocol over a network infrastructure. There is considerable confusion and concern about the IPTV, since two different technologies have to be mended together to provide the end customers with some thing better than the conventional television. In this research, functional architecture of the IPTV system was investigated. Very Large Scale Integration based system for streaming server controller were designed and different ways of hosting a web server which can be used to send the control signals to the streaming server controller were studied. The web server accepts inputs from the keyboard and FPGA board switches and depending on the preset configuration the server will open a selected web page and also sends the control signals to the streaming server controller. It was observed that the applications run faster on PowerPC since it is embedded into the FPGA. Commercial market and Global deployment of IPTV were discussed.

Large scale data processing using MapReduce

As massive data sets become increasingly available, people are facing the problem of how to effectively process and understand these data. Traditional sequential computing models are giving way to parallel and distributed computing models, such as MapReduce, both due to the large size of the data sets and their high dimensionality. This dissertation, as in the same direction of other researches that are based on MapReduce, tries to develop effective techniques and applications using MapReduce that can help people solve large-scale problems. Three different problems are tackled in the dissertation. The first one deals with processing terabytes of raster data in a spatial data management system. Aerial imagery files are broken into tiles to enable data parallel computation. The second and third problems deal with dimension reduction techniques that can be used to handle data sets of high dimensionality. Three variants of the nonnegative matrix factorization technique are scaled up to factorize matrices of dimensions in the order of millions in MapReduce based on different matrix multiplication implementations. Two algorithms, which compute CANDECOMP/PARAFAC and Tucker tensor decompositions respectively, are parallelized in MapReduce based on carefully partitioning the data and arranging the computation to maximize data locality and parallelism.

A conceptual ecological model to facilitate understanding the role of invasive species in large-scale ecosystem restoration

We developed a conceptual ecological model (CEM) for invasive species to help understand the role invasive exotics have in ecosystem ecology and their impacts on restoration activities. Our model, which can be applied to any invasive species, grew from the eco-regional conceptual models developed for Everglades restoration. These models identify ecological drivers, stressors, effects and attributes; we integrated the unique aspects of exotic species invasions and effects into this conceptual hierarchy. We used the model to help identify important aspects of invasion in the development of an invasive exotic plant ecological indicator, which is described a companion paper in this special issue journal. A key aspect of the CEM is that it is a general ecological model that can be tailored to specific cases and species, as the details of any invasion are unique to that invasive species. Our model encompasses the temporal and spatial changes that characterize invasion, identifying the general conditions that allow a species to become invasive in a de novo environment; it then enumerates the possible effects exotic species may have collectively and individually at varying scales and for different ecosystem properties, once a species becomes invasive. The model provides suites of characteristics and processes, as well as hypothesized causal relationships to consider when thinking about the effects or potential effects of an invasive exotic and how restoration efforts will affect these characteristics and processes. In order to illustrate how to use the model as a blueprint for applying a similar approach to other invasive species and ecosystems, we give two examples of using this conceptual model to evaluate the status of two south Florida invasive exotic plant species (melaleuca and Old World climbing fern) and consider potential impacts of these invasive species on restoration.