Authoring Data-Driven Videos with DataClips.

Science.gov (United States)

Amini, Fereshteh; Riche, Nathalie Henry; Lee, Bongshin; Monroy-Hernandez, Andres; Irani, Pourang

2017-01-01

Data videos, or short data-driven motion graphics, are an increasingly popular medium for storytelling. However, creating data videos is difficult as it involves pulling together a unique combination of skills. We introduce DataClips, an authoring tool aimed at lowering the barriers to crafting data videos. DataClips allows non-experts to assemble data-driven "clips" together to form longer sequences. We constructed the library of data clips by analyzing the composition of over 70 data videos produced by reputable sources such as The New York Times and The Guardian. We demonstrate that DataClips can reproduce over 90% of our data videos corpus. We also report on a qualitative study comparing the authoring process and outcome achieved by (1) non-experts using DataClips, and (2) experts using Adobe Illustrator and After Effects to create data-driven clips. Results indicated that non-experts are able to learn and use DataClips with a short training period. In the span of one hour, they were able to produce more videos than experts using a professional editing tool, and their clips were rated similarly by an independent audience.

Data-driven storytelling

CERN Document Server

Hurter, Christophe; Diakopoulos, Nicholas ed.; Carpendale, Sheelagh

2018-01-01

This book is an accessible introduction to data-driven storytelling, resulting from discussions between data visualization researchers and data journalists. This book will be the first to define the topic, present compelling examples and existing resources, as well as identify challenges and new opportunities for research.

A new statistic for identifying batch effects in high-throughput genomic data that uses guided principal component analysis.

Science.gov (United States)

Reese, Sarah E; Archer, Kellie J; Therneau, Terry M; Atkinson, Elizabeth J; Vachon, Celine M; de Andrade, Mariza; Kocher, Jean-Pierre A; Eckel-Passow, Jeanette E

2013-11-15

Batch effects are due to probe-specific systematic variation between groups of samples (batches) resulting from experimental features that are not of biological interest. Principal component analysis (PCA) is commonly used as a visual tool to determine whether batch effects exist after applying a global normalization method. However, PCA yields linear combinations of the variables that contribute maximum variance and thus will not necessarily detect batch effects if they are not the largest source of variability in the data. We present an extension of PCA to quantify the existence of batch effects, called guided PCA (gPCA). We describe a test statistic that uses gPCA to test whether a batch effect exists. We apply our proposed test statistic derived using gPCA to simulated data and to two copy number variation case studies: the first study consisted of 614 samples from a breast cancer family study using Illumina Human 660 bead-chip arrays, whereas the second case study consisted of 703 samples from a family blood pressure study that used Affymetrix SNP Array 6.0. We demonstrate that our statistic has good statistical properties and is able to identify significant batch effects in two copy number variation case studies. We developed a new statistic that uses gPCA to identify whether batch effects exist in high-throughput genomic data. Although our examples pertain to copy number data, gPCA is general and can be used on other data types as well. The gPCA R package (Available via CRAN) provides functionality and data to perform the methods in this article. reesese@vcu.edu

Removing Batch Effects from Longitudinal Gene Expression - Quantile Normalization Plus ComBat as Best Approach for Microarray Transcriptome Data.

Directory of Open Access Journals (Sweden)

Christian Müller

Full Text Available Technical variation plays an important role in microarray-based gene expression studies, and batch effects explain a large proportion of this noise. It is therefore mandatory to eliminate technical variation while maintaining biological variability. Several strategies have been proposed for the removal of batch effects, although they have not been evaluated in large-scale longitudinal gene expression data. In this study, we aimed at identifying a suitable method for batch effect removal in a large study of microarray-based longitudinal gene expression. Monocytic gene expression was measured in 1092 participants of the Gutenberg Health Study at baseline and 5-year follow up. Replicates of selected samples were measured at both time points to identify technical variability. Deming regression, Passing-Bablok regression, linear mixed models, non-linear models as well as ReplicateRUV and ComBat were applied to eliminate batch effects between replicates. In a second step, quantile normalization prior to batch effect correction was performed for each method. Technical variation between batches was evaluated by principal component analysis. Associations between body mass index and transcriptomes were calculated before and after batch removal. Results from association analyses were compared to evaluate maintenance of biological variability. Quantile normalization, separately performed in each batch, combined with ComBat successfully reduced batch effects and maintained biological variability. ReplicateRUV performed perfectly in the replicate data subset of the study, but failed when applied to all samples. All other methods did not substantially reduce batch effects in the replicate data subset. Quantile normalization plus ComBat appears to be a valuable approach for batch correction in longitudinal gene expression data.

Temporal Data-Driven Sleep Scheduling and Spatial Data-Driven Anomaly Detection for Clustered Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Gang Li

2016-09-01

Full Text Available The spatial–temporal correlation is an important feature of sensor data in wireless sensor networks (WSNs. Most of the existing works based on the spatial–temporal correlation can be divided into two parts: redundancy reduction and anomaly detection. These two parts are pursued separately in existing works. In this work, the combination of temporal data-driven sleep scheduling (TDSS and spatial data-driven anomaly detection is proposed, where TDSS can reduce data redundancy. The TDSS model is inspired by transmission control protocol (TCP congestion control. Based on long and linear cluster structure in the tunnel monitoring system, cooperative TDSS and spatial data-driven anomaly detection are then proposed. To realize synchronous acquisition in the same ring for analyzing the situation of every ring, TDSS is implemented in a cooperative way in the cluster. To keep the precision of sensor data, spatial data-driven anomaly detection based on the spatial correlation and Kriging method is realized to generate an anomaly indicator. The experiment results show that cooperative TDSS can realize non-uniform sensing effectively to reduce the energy consumption. In addition, spatial data-driven anomaly detection is quite significant for maintaining and improving the precision of sensor data.

Consistent data-driven computational mechanics

Science.gov (United States)

González, D.; Chinesta, F.; Cueto, E.

2018-05-01

We present a novel method, within the realm of data-driven computational mechanics, to obtain reliable and thermodynamically sound simulation from experimental data. We thus avoid the need to fit any phenomenological model in the construction of the simulation model. This kind of techniques opens unprecedented possibilities in the framework of data-driven application systems and, particularly, in the paradigm of industry 4.0.

Production of nattokinase by batch and fed-batch culture of Bacillus subtilis.

Science.gov (United States)

Cho, Young-Han; Song, Jae Yong; Kim, Kyung Mi; Kim, Mi Kyoung; Lee, In Young; Kim, Sang Bum; Kim, Hyeon Shup; Han, Nam Soo; Lee, Bong Hee; Kim, Beom Soo

2010-09-30

Nattokinase was produced by batch and fed-batch culture of Bacillus subtilis in flask and fermentor. Effect of supplementing complex media (peptone, yeast extract, or tryptone) was investigated on the production of nattokinase. In flask culture, the highest cell growth and nattokinase activity were obtained with 50 g/L of peptone supplementation. In this condition, nattokinase activity was 630 unit/ml at 12 h. In batch culture of B. subtilis in fermentor, the highest nattokinase activity of 3400 unit/ml was obtained at 10h with 50 g/L of peptone supplementation. From the batch kinetics data, it was shown that nattokinase production was growth-associated and culture should be harvested before stationary phase for maximum nattokinase production. In fed-batch culture of B. subtilis using pH-stat feeding strategy, cell growth (optical density monitored at 600 nm) increased to ca. 100 at 22 h, which was 2.5 times higher than that in batch culture. The highest nattokinase activity was 7100 unit/ml at 19 h, which was also 2.1 times higher than that in batch culture. Copyright 2010 Elsevier B.V. All rights reserved.

Knowledge-Driven Versus Data-Driven Logics

Czech Academy of Sciences Publication Activity Database

Dubois, D.; Hájek, Petr; Prade, H.

2000-01-01

Roč. 9, č. 1 (2000), s. 65-89 ISSN 0925-8531 R&D Projects: GA AV ČR IAA1030601 Grant - others:CNRS(FR) 4008 Institutional research plan: AV0Z1030915 Keywords : epistemic logic * possibility theory * data-driven reasoning * deontic logic Subject RIV: BA - General Mathematics

Data Driven Broiler Weight Forecasting using Dynamic Neural Network Models

DEFF Research Database (Denmark)

Johansen, Simon Vestergaard; Bendtsen, Jan Dimon; Riisgaard-Jensen, Martin

2017-01-01

In this article, the dynamic influence of environmental broiler house conditions and broiler growth is investigated. Dynamic neural network forecasting models have been trained on farm-scale broiler batch production data from 12 batches from the same house. The model forecasts future broiler weight...... and uses environmental conditions such as heating, ventilation, and temperature along with broiler behavior such as feed and water consumption. Training data and forecasting data is analyzed to explain when the model might fail at generalizing. We present ensemble broiler weight forecasts to day 7, 14, 21...

Data-driven architectural production and operation

NARCIS (Netherlands)

Bier, H.H.; Mostafavi, S.

2014-01-01

Data-driven architectural production and operation as explored within Hyperbody rely heavily on system thinking implying that all parts of a system are to be understood in relation to each other. These relations are increasingly established bi-directionally so that data-driven architecture is not

Batch calculations in CalcHEP

International Nuclear Information System (INIS)

Pukhov, A.

2003-01-01

CalcHEP is a clone of the CompHEP project which is developed by the author outside of the CompHEP group. CompHEP/CalcHEP are packages for automatic calculations of elementary particle decay and collision properties in the lowest order of perturbation theory. The main idea prescribed into the packages is to make available passing on from the Lagrangian to the final distributions effectively with a high level of automation. According to this, the packages were created as a menu driven user friendly programs for calculations in the interactive mode. From the other side, long-time calculations should be done in the non-interactive regime. Thus, from the beginning CompHEP has a problem of batch calculations. In CompHEP 33.23 the batch session was realized by mean of interactive menu which allows to the user to formulate the task for batch. After that the not-interactive session was launched. This way is too restricted, not flexible, and leads to doubling in programming. In this article I discuss another approach how one can force an interactive program to work in non-interactive mode. This approach was realized in CalcHEP 2.1 disposed on http://theory.sinp.msu.ru/~pukhov/calchep.html

Heuristics for batching and sequencing in batch processing machines

Directory of Open Access Journals (Sweden)

Chuda Basnet

2016-12-01

Full Text Available In this paper, we discuss the “batch processing” problem, where there are multiple jobs to be processed in flow shops. These jobs can however be formed into batches and the number of jobs in a batch is limited by the capacity of the processing machines to accommodate the jobs. The processing time required by a batch in a machine is determined by the greatest processing time of the jobs included in the batch. Thus, the batch processing problem is a mix of batching and sequencing – the jobs need to be grouped into distinct batches, the batches then need to be sequenced through the flow shop. We apply certain newly developed heuristics to the problem and present computational results. The contributions of this paper are deriving a lower bound, and the heuristics developed and tested in this paper.

Simultaneous Transformation of Commingled Trichloroethylene, Tetrachloroethylene, and 1,4-Dioxane by a Microbially Driven Fenton Reaction in Batch Liquid Cultures

Science.gov (United States)

Sekar, Ramanan; Taillefert, Martial

2016-01-01

ABSTRACT Improper disposal of 1,4-dioxane and the chlorinated organic solvents trichloroethylene (TCE) and tetrachloroethylene (also known as perchloroethylene [PCE]) has resulted in widespread contamination of soil and groundwater. In the present study, a previously designed microbially driven Fenton reaction system was reconfigured to generate hydroxyl (HO˙) radicals for simultaneous transformation of source zone levels of single, binary, and ternary mixtures of TCE, PCE, and 1,4-dioxane. The reconfigured Fenton reaction system was driven by fed batch cultures of the Fe(III)-reducing facultative anaerobe Shewanella oneidensis amended with lactate, Fe(III), and contaminants and exposed to alternating anaerobic and aerobic conditions. To avoid contaminant loss due to volatility, the Fe(II)-generating, hydrogen peroxide-generating, and contaminant transformation phases of the microbially driven Fenton reaction system were separated. The reconfigured Fenton reaction system transformed TCE, PCE, and 1,4-dioxane either as single contaminants or as binary and ternary mixtures. In the presence of equimolar concentrations of PCE and TCE, the ratio of the experimentally derived rates of PCE and TCE transformation was nearly identical to the ratio of the corresponding HO˙ radical reaction rate constants. The reconfigured Fenton reaction system may be applied as an ex situ platform for simultaneous degradation of commingled TCE, PCE, and 1,4-dioxane and provides valuable information for future development of in situ remediation technologies. IMPORTANCE A microbially driven Fenton reaction system [driven by the Fe(III)-reducing facultative anaerobe S. oneidensis] was reconfigured to transform source zone levels of TCE, PCE, and 1,4-dioxane as single contaminants or as binary and ternary mixtures. The microbially driven Fenton reaction may thus be applied as an ex situ platform for simultaneous degradation of at least three (and potentially more) commingled contaminants

KNMI DataLab experiences in serving data-driven innovations

Science.gov (United States)

Noteboom, Jan Willem; Sluiter, Raymond

2016-04-01

Climate change research and innovations in weather forecasting rely more and more on (Big) data. Besides increasing data from traditional sources (such as observation networks, radars and satellites), the use of open data, crowd sourced data and the Internet of Things (IoT) is emerging. To deploy these sources of data optimally in our services and products, KNMI has established a DataLab to serve data-driven innovations in collaboration with public and private sector partners. Big data management, data integration, data analytics including machine learning and data visualization techniques are playing an important role in the DataLab. Cross-domain data-driven innovations that arise from public-private collaborative projects and research programmes can be explored, experimented and/or piloted by the KNMI DataLab. Furthermore, advice can be requested on (Big) data techniques and data sources. In support of collaborative (Big) data science activities, scalable environments are offered with facilities for data integration, data analysis and visualization. In addition, Data Science expertise is provided directly or from a pool of internal and external experts. At the EGU conference, gained experiences and best practices are presented in operating the KNMI DataLab to serve data-driven innovations for weather and climate applications optimally.

Data-Driven and Expectation-Driven Discovery of Empirical Laws.

Science.gov (United States)

1982-10-10

occurred in small integer proportions to each other. In 1809, Joseph Gay- Lussac found evidence for his law of combining volumes, which stated that a...of Empirical Laws Patrick W. Langley Gary L. Bradshaw Herbert A. Simon T1he Robotics Institute Carnegie-Mellon University Pittsburgh, Pennsylvania...Subtitle) S. TYPE OF REPORT & PERIOD COVERED Data-Driven and Expectation-Driven Discovery Interim Report 2/82-10/82 of Empirical Laws S. PERFORMING ORG

BatchJS: Implementing Batches in JavaScript

NARCIS (Netherlands)

D. Kasemier

2014-01-01

htmlabstractNone of our popular programming languages know how to handle distribution well. Yet our programs interact more and more with each other and our data resorts in databases and web services. Batches are a new addition to languages that can finally bring native support for distribution to

NDA BATCH 2002-02

Energy Technology Data Exchange (ETDEWEB)

Lawrence Livermore National Laboratory

2009-12-09

QC sample results (daily background checks, 20-gram and 100-gram SGS drum checks) were within acceptable criteria established by WIPP's Quality Assurance Objectives for TRU Waste Characterization. Replicate runs were performed on 5 drums with IDs LL85101099TRU, LL85801147TRU, LL85801109TRU, LL85300999TRU and LL85500979TRU. All replicate measurement results are identical at the 95% confidence level as established by WIPP criteria. Note that the batch covered 5 weeks of SGS measurements from 23-Jan-2002 through 22-Feb-2002. Data packet for SGS Batch 2002-02 generated using gamma spectroscopy with the Pu Facility SGS unit is technically reasonable. All QC samples are in compliance with established control limits. The batch data packet has been reviewed for correctness, completeness, consistency and compliance with WIPP's Quality Assurance Objectives and determined to be acceptable. An Expert Review was performed on the data packet between 28-Feb-02 and 09-Jul-02 to check for potential U-235, Np-237 and Am-241 interferences and address drum cases where specific scan segments showed Se gamma ray transmissions for the 136-keV gamma to be below 0.1 %. Two drums in the batch showed Pu-238 at a relative mass ratio more than 2% of all the Pu isotopes.

Data-driven modeling of nano-nose gas sensor arrays

DEFF Research Database (Denmark)

Alstrøm, Tommy Sonne; Larsen, Jan; Nielsen, Claus Højgård

2010-01-01

We present a data-driven approach to classification of Quartz Crystal Microbalance (QCM) sensor data. The sensor is a nano-nose gas sensor that detects concentrations of analytes down to ppm levels using plasma polymorized coatings. Each sensor experiment takes approximately one hour hence...... the number of available training data is limited. We suggest a data-driven classification model which work from few examples. The paper compares a number of data-driven classification and quantification schemes able to detect the gas and the concentration level. The data-driven approaches are based on state...

Data Exchanges and Verifications Online (DEVO)

Data.gov (United States)

Social Security Administration — DEVO is the back-end application for processing SSN verifications and data exchanges. DEVO uses modern technology for parameter driven processing of both batch and...

Alternative approach to estimate the hydrolysis rate constant of particulate material from batch data

International Nuclear Information System (INIS)

Koch, Konrad; Drewes, Jörg E.

2014-01-01

Highlights: • An alternative to the commonly used first-order approach is presented. • A relationship between k h and the 1% criterion of the VDI 4630 is deduced. • Equation is proposed to directly calculate k h without the need for data fitting. • Hydrolysis constant k h can then easily be read-off from a table. - Abstract: As anaerobic batch tests are easy to conduct, they are commonly used to assess the effects of different operational factors on the anaerobic digestion process. Hydrolysis of particulate material is often assumed to be the rate limiting step in anaerobic digestion. Its velocity is often estimated by data fitting from batch tests. In this study, a Monod-type alternative to the commonly used first-order approach is presented. The approach was adapted from balancing a continuously stirred-tank reactor and better accommodates the fact that even after a long incubation time, some of the methane potential of the substrate remains untapped in the digestate. In addition, an equation is proposed to directly calculate the hydrolysis constant from the time when the daily gas production is less than 1% of the total gas production. The hydrolysis constant can then easily be read-off from a table when the batch test duration is known

Dynamic Data-Driven UAV Network for Plume Characterization

Science.gov (United States)

2016-05-23

AFRL-AFOSR-VA-TR-2016-0203 Dynamic Data-Driven UAV Network for Plume Characterization Kamran Mohseni UNIVERSITY OF FLORIDA Final Report 05/23/2016...AND SUBTITLE Dynamic Data-Driven UAV Network for Plume Characterization 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER FA9550-13-1-0090 5c.  PROGRAM ELEMENT...studied a dynamic data driven (DDD) approach to operation of a heterogeneous team of unmanned aerial vehicles ( UAVs ) or micro/miniature aerial

A Model-based B2B (Batch to Batch) Control for An Industrial Batch Polymerization Process

Science.gov (United States)

Ogawa, Morimasa

This paper describes overview of a model-based B2B (batch to batch) control for an industrial batch polymerization process. In order to control the reaction temperature precisely, several methods based on the rigorous process dynamics model are employed at all design stage of the B2B control, such as modeling and parameter estimation of the reaction kinetics which is one of the important part of the process dynamics model. The designed B2B control consists of the gain scheduled I-PD/II2-PD control (I-PD with double integral control), the feed-forward compensation at the batch start time, and the model adaptation utilizing the results of the last batch operation. Throughout the actual batch operations, the B2B control provides superior control performance compared with that of conventional control methods.

Data mining, knowledge discovery and data-driven modelling

NARCIS (Netherlands)

Solomatine, D.P.; Velickov, S.; Bhattacharya, B.; Van der Wal, B.

2003-01-01

The project was aimed at exploring the possibilities of a new paradigm in modelling - data-driven modelling, often referred as "data mining". Several application areas were considered: sedimentation problems in the Port of Rotterdam, automatic soil classification on the basis of cone penetration

Data Report on the Newest Batch of PCEA Graphite for the VHTR Baseline Graphite Characterization Program

Energy Technology Data Exchange (ETDEWEB)

Carroll, Mark Christopher [Idaho National Lab. (INL), Idaho Falls, ID (United States); Cottle, David Lynn [Idaho National Lab. (INL), Idaho Falls, ID (United States); Rohrbaugh, David Thomas [Idaho National Lab. (INL), Idaho Falls, ID (United States)

2016-08-01

This report details a comparison of mechanical and physical properties from the first billet of extruded PCEA nuclear-grade graphite from the newest batch of PCEA procured from GrafTech. Testing has largely been completed on three of the billets from the original batch of PCEA, with data distributions for those billets exhibiting a much wider range of values when compared to the distributions of properties from other grades. A higher propensity for extremely low values or specimens that broke while machining or handling was also characteristic of the billets from the first batch, owing to unusually large fissures or disparate flaws in the billets in an as-manufactured state. Coordination with GrafTech prior to placing the order for a second batch of PCEA included discussions on these large disparate flaws and how to prevent them during the manufacturing process. This report provides a comparison of the observed data distributions from properties measured in the first billet from the new batch of PCEA with those observed in the original batch, in order that an evaluation of tighter control of the manufacturing process and the outcome of these controls on final properties can be ascertained. Additionally, this billet of PCEA is the first billet to formally include measurements from two alternate test techniques that will become part of the Baseline Graphite Characterization database – the three-point bend test on sub-sized cylinders and the Brazilian disc splitting tensile strength test. As the program moves forward, property distributions from these two tests will be based on specimen geometries that match specimen geometries being used in the irradiated Advanced Graphite Creep (AGC) program. This will allow a more thorough evaluation of both the utility of the test and expected variability in properties when using those approaches on the constrained geometries of specimens irradiated in the Advanced Test Reactor as part of the AGC experiment.

Using Two Different Approaches to Assess Dietary Patterns: Hypothesis-Driven and Data-Driven Analysis

Directory of Open Access Journals (Sweden)

Ágatha Nogueira Previdelli

2016-09-01

Full Text Available The use of dietary patterns to assess dietary intake has become increasingly common in nutritional epidemiology studies due to the complexity and multidimensionality of the diet. Currently, two main approaches have been widely used to assess dietary patterns: data-driven and hypothesis-driven analysis. Since the methods explore different angles of dietary intake, using both approaches simultaneously might yield complementary and useful information; thus, we aimed to use both approaches to gain knowledge of adolescents’ dietary patterns. Food intake from a cross-sectional survey with 295 adolescents was assessed by 24 h dietary recall (24HR. In hypothesis-driven analysis, based on the American National Cancer Institute method, the usual intake of Brazilian Healthy Eating Index Revised components were estimated. In the data-driven approach, the usual intake of foods/food groups was estimated by the Multiple Source Method. In the results, hypothesis-driven analysis showed low scores for Whole grains, Total vegetables, Total fruit and Whole fruits, while, in data-driven analysis, fruits and whole grains were not presented in any pattern. High intakes of sodium, fats and sugars were observed in hypothesis-driven analysis with low total scores for Sodium, Saturated fat and SoFAA (calories from solid fat, alcohol and added sugar components in agreement, while the data-driven approach showed the intake of several foods/food groups rich in these nutrients, such as butter/margarine, cookies, chocolate powder, whole milk, cheese, processed meat/cold cuts and candies. In this study, using both approaches at the same time provided consistent and complementary information with regard to assessing the overall dietary habits that will be important in order to drive public health programs, and improve their efficiency to monitor and evaluate the dietary patterns of populations.

Evaluation of vitrification factors from DWPF's macro-batch 1

International Nuclear Information System (INIS)

Edwards, T.B.

2000-01-01

The Defense Waste Processing Facility (DWPF) is evaluating new sampling and analytical methods that may be used to support future Slurry Mix Evaporator (SME) batch acceptability decisions. This report uses data acquired during DWPF's processing of macro-batch 1 to determine a set of vitrification factors covering several SME and Melter Feed Tank (MFT) batches. Such values are needed for converting the cation measurements derived from the new methods to a ''glass'' basis. The available data from macro-batch 1 were used to examine the stability of these vitrification factors, to estimate their uncertainty over the course of a macro-batch, and to provide a recommendation on the use of a single factor for an entire macro-batch. The report is in response to Technical Task Request HLW/DWPF/TTR-980015

Data-driven architectural design to production and operation

NARCIS (Netherlands)

Bier, H.H.; Mostafavi, S.

2015-01-01

Data-driven architectural production and operation explored within Hyperbody rely heavily on system thinking implying that all parts of a system are to be understood in relation to each other. These relations are established bi-directionally so that data-driven architecture is not only produced

The Structural Consequences of Big Data-Driven Education.

Science.gov (United States)

Zeide, Elana

2017-06-01

Educators and commenters who evaluate big data-driven learning environments focus on specific questions: whether automated education platforms improve learning outcomes, invade student privacy, and promote equality. This article puts aside separate unresolved-and perhaps unresolvable-issues regarding the concrete effects of specific technologies. It instead examines how big data-driven tools alter the structure of schools' pedagogical decision-making, and, in doing so, change fundamental aspects of America's education enterprise. Technological mediation and data-driven decision-making have a particularly significant impact in learning environments because the education process primarily consists of dynamic information exchange. In this overview, I highlight three significant structural shifts that accompany school reliance on data-driven instructional platforms that perform core school functions: teaching, assessment, and credentialing. First, virtual learning environments create information technology infrastructures featuring constant data collection, continuous algorithmic assessment, and possibly infinite record retention. This undermines the traditional intellectual privacy and safety of classrooms. Second, these systems displace pedagogical decision-making from educators serving public interests to private, often for-profit, technology providers. They constrain teachers' academic autonomy, obscure student evaluation, and reduce parents' and students' ability to participate or challenge education decision-making. Third, big data-driven tools define what "counts" as education by mapping the concepts, creating the content, determining the metrics, and setting desired learning outcomes of instruction. These shifts cede important decision-making to private entities without public scrutiny or pedagogical examination. In contrast to the public and heated debates that accompany textbook choices, schools often adopt education technologies ad hoc. Given education

Data-driven workflows for microservices

DEFF Research Database (Denmark)

Safina, Larisa; Mazzara, Manuel; Montesi, Fabrizio

2016-01-01

Microservices is an architectural style inspired by service-oriented computing that has recently started gainingpopularity. Jolie is a programming language based on the microservices paradigm: the main building block of Jolie systems are services, in contrast to, e.g., functions or objects....... The primitives offered by the Jolie language elicit many of the recurring patterns found in microservices, like load balancers and structured processes. However, Jolie still lacks some useful constructs for dealing with message types and data manipulation that are present in service-oriented computing......). We show the impact of our implementation on some of the typical scenarios found in microservice systems. This shows how computation can move from a process-driven to a data-driven approach, and leads to the preliminary identification of recurring communication patterns that can be shaped as design...

Challenges of Data-driven Healthcare Management

DEFF Research Database (Denmark)

Bossen, Claus; Danholt, Peter; Ubbesen, Morten Bonde

This paper describes the new kind of data-work involved in developing data-driven healthcare based on two cases from Denmark: The first case concerns a governance infrastructure based on Diagnose-Related Groups (DRG), which was introduced in Denmark in the 1990s. The DRG-system links healthcare...... activity and financing and relies of extensive data entry, reporting and calculations. This has required the development of new skills, work and work roles. The second case concerns a New Governance project aimed at developing new performance indicators for healthcare delivery as an alternative to DRG....... Here, a core challenge is select indicators and actually being able to acquire data upon them. The two cases point out that data-driven healthcare requires more and new kinds of work for which new skills, functions and work roles have to be developed....

Batch-to-batch quality consistency evaluation of botanical drug products using multivariate statistical analysis of the chromatographic fingerprint.

Science.gov (United States)

Xiong, Haoshu; Yu, Lawrence X; Qu, Haibin

2013-06-01

Botanical drug products have batch-to-batch quality variability due to botanical raw materials and the current manufacturing process. The rational evaluation and control of product quality consistency are essential to ensure the efficacy and safety. Chromatographic fingerprinting is an important and widely used tool to characterize the chemical composition of botanical drug products. Multivariate statistical analysis has showed its efficacy and applicability in the quality evaluation of many kinds of industrial products. In this paper, the combined use of multivariate statistical analysis and chromatographic fingerprinting is presented here to evaluate batch-to-batch quality consistency of botanical drug products. A typical botanical drug product in China, Shenmai injection, was selected as the example to demonstrate the feasibility of this approach. The high-performance liquid chromatographic fingerprint data of historical batches were collected from a traditional Chinese medicine manufacturing factory. Characteristic peaks were weighted by their variability among production batches. A principal component analysis model was established after outliers were modified or removed. Multivariate (Hotelling T(2) and DModX) control charts were finally successfully applied to evaluate the quality consistency. The results suggest useful applications for a combination of multivariate statistical analysis with chromatographic fingerprinting in batch-to-batch quality consistency evaluation for the manufacture of botanical drug products.

Acquisition of data from on-line laser turbidimeter and calculation of some kinetic variables in computer-coupled automated fed-batch culture

International Nuclear Information System (INIS)

Kadotani, Y.; Miyamoto, K.; Mishima, N.; Kominami, M.; Yamane, T.

1995-01-01

Output signals of a commercially available on-line laser turbidimeter exhibit fluctuations due to air and/or CO 2 bubbles. A simple data processing algorithm and a personal computer software have been developed to smooth the noisy turbidity data acquired, and to utilize them for the on-line calculations of some kinetic variables involved in batch and fed-batch cultures of uniformly dispersed microorganisms. With this software, about 10 3 instantaneous turbidity data acquired over 55 s are averaged and convert it to dry cell concentration, X, every minute. Also, volume of the culture broth, V, is estimated from the averaged output data of weight loss of feed solution reservoir, W, using an electronic balance on which the reservoir is placed. Then, the computer software is used to perform linear regression analyses over the past 30 min of the total biomass, VX, the natural logarithm of the total biomass, ln(VX), and the weight loss, W, in order to calculate volumetric growth rate, d(VX)/dt, specific growth rate, μ [ = dln(VX)/dt] and the rate of W, dW/dt, every minute in a fed-batch culture. The software used to perform the first-order regression analyses of VX, ln(VX) and W was applied to batch or fed-batch cultures of Escherichia coli on minimum synthetic or natural complex media. Sample determination coefficients of the three different variables (VX, ln(VX) and W) were close to unity, indicating that the calculations are accurate. Furthermore, growth yield, Y x/s , and specific substrate consumption rate, q sc , were approximately estimated from the data, dW/dt and in a ‘balanced’ fed-batch culture of E. coli on the minimum synthetic medium where the computer-aided substrate-feeding system automatically matches well with the cell growth. (author)

Acceptance Test Data for BWXT Coated Particle Batches 93172B and 93173B—Defective IPyC and Pyrocarbon Anisotropy

Energy Technology Data Exchange (ETDEWEB)

Hunn, John D. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Helmreich, Grant W. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Dyer, John A. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Schumacher, Austin T. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Skitt, Darren J. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

2017-08-01

Coated particle batches J52O-16-93172B and J52O-16-93173B were produced by Babcock and Wilcox Technologies (BWXT) as part of the production campaign for the Advanced Gas Reactor Fuel Development and Qualification (AGR) Program’s AGR-5/6/7 irradiation test in the Idaho National Laboratory (INL) Advanced Test Reactor (ATR), but were not used in the final fuel composite. However, these batches may be used as demonstration production-scale coated particle fuel for other experiments. Each batch was coated in a 150-mm-diameter production-scale fluidized-bed chemical vapor deposition (CVD) furnace. Tristructural isotropic (TRISO) coatings were deposited on 425-μm-nominal-diameter spherical kernels from BWXT lot J52R-16-69317 containing a mixture of 15.5%-enriched uranium carbide and uranium oxide (UCO). The TRISO coatings consisted of four consecutive CVD layers: a ~50% dense carbon buffer layer with 100-μm-nominal thickness, a dense inner pyrolytic carbon (IPyC) layer with 40-μm-nominal thickness, a silicon carbide (SiC) layer with 35-μm-nominal thickness, and a dense outer pyrolytic carbon (OPyC) layer with 40-μm-nominal thickness. The TRISO-coated particle batches were sieved to upgrade the particles by removing over-sized and under-sized material, and the upgraded batches were designated by appending the letter A to the end of the batch number (e.g., 93172A). Secondary upgrading by sieving was performed on the A-designated batches to remove particles with missing or very-thin buffer layers that were identified during previous analysis of the individual batches for defective IPyC, as reported in the acceptance test data report for the AGR-5/6/7 production batches [Hunn et al. 2017b]. The additionally-upgraded batches were designated by appending the letter B to the end of the batch number (e.g., 93172B).

Acceptance Test Data for Candidate AGR-5/6/7 TRISO Particle Batches BWXT Coater Batches 93165 93172 Defective IPyC Fraction and Pyrocarbon Anisotropy

Energy Technology Data Exchange (ETDEWEB)

Helmreich, Grant W. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Hunn, John D. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Skitt, Darren J. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Dyer, John A. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Schumacher, Austin T. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

2017-03-01

Coated particle fuel batches J52O-16-93165, 93166, 93168, 93169, 93170, and 93172 were produced by Babcock and Wilcox Technologies (BWXT) for possible selection as fuel for the Advanced Gas Reactor Fuel Development and Qualification (AGR) Program’s AGR-5/6/7 irradiation test in the Idaho National Laboratory (INL) Advanced Test Reactor (ATR). Some of these batches may alternately be used as demonstration coated particle fuel for other experiments. Each batch was coated in a 150-mm-diameter production-scale fluidized-bed chemical vapor deposition (CVD) furnace. Tristructural isotropic (TRISO) coatings were deposited on 425-μm-nominal-diameter spherical kernels from BWXT lot J52R-16-69317 containing a mixture of 15.5%-enriched uranium carbide and uranium oxide (UCO). The TRISO coatings consisted of four consecutive CVD layers: a ~50% dense carbon buffer layer with 100-μm-nominal thickness, a dense inner pyrolytic carbon (IPyC) layer with 40-μm-nominal thickness, a silicon carbide (SiC) layer with 35-μm-nominal thickness, and a dense outer pyrolytic carbon (OPyC) layer with 40-μmnominal thickness. The TRISO-coated particle batches were sieved to upgrade the particles by removing over-sized and under-sized material, and the upgraded batches were designated by appending the letter A to the end of the batch number (e.g., 93165A).

Energy efficiency of batch and semi-batch (CCRO) reverse osmosis desalination.

Science.gov (United States)

Warsinger, David M; Tow, Emily W; Nayar, Kishor G; Maswadeh, Laith A; Lienhard V, John H

2016-12-01

As reverse osmosis (RO) desalination capacity increases worldwide, the need to reduce its specific energy consumption becomes more urgent. In addition to the incremental changes attainable with improved components such as membranes and pumps, more significant reduction of energy consumption can be achieved through time-varying RO processes including semi-batch processes such as closed-circuit reverse osmosis (CCRO) and fully-batch processes that have not yet been commercialized or modelled in detail. In this study, numerical models of the energy consumption of batch RO (BRO), CCRO, and the standard continuous RO process are detailed. Two new energy-efficient configurations of batch RO are analyzed. Batch systems use significantly less energy than continuous RO over a wide range of recovery ratios and source water salinities. Relative to continuous RO, models predict that CCRO and batch RO demonstrate up to 37% and 64% energy savings, respectively, for brackish water desalination at high water recovery. For batch RO and CCRO, the primary reductions in energy use stem from atmospheric pressure brine discharge and reduced streamwise variation in driving pressure. Fully-batch systems further reduce energy consumption by not mixing streams of different concentrations, which CCRO does. These results demonstrate that time-varying processes can significantly raise RO energy efficiency. Copyright © 2016 Elsevier Ltd. All rights reserved.

Data driven marketing for dummies

CERN Document Server

Semmelroth, David

2013-01-01

Embrace data and use it to sell and market your products Data is everywhere and it keeps growing and accumulating. Companies need to embrace big data and make it work harder to help them sell and market their products. Successful data analysis can help marketing professionals spot sales trends, develop smarter marketing campaigns, and accurately predict customer loyalty. Data Driven Marketing For Dummies helps companies use all the data at their disposal to make current customers more satisfied, reach new customers, and sell to their most important customer segments more efficiently. Identifyi

Characterization of a prototype batch of long polyimide cables designed for fast data transmission on ATLAS ITk strip staves

CERN Document Server

Dopke, Jens; The ATLAS collaboration; Sawyer, Craig; Sullivan, Stephanie W

2018-01-01

The silicon-strip system in the ATLAS ITk detector has individual sensor modules mounted on staves to provide integrated solution for mechanical support, power, cooling, and data transmission. The data and power are transmitted to individual modules on polyimide tapes placed on thermo-mechanical stave cores. The 1.4 m long tapes transmit module data at the rate of 640 Mbps, along with providing several multi-drop clock and command links, and power lines. The first batch of 25 tapes has been produced. We characterized the line impedance and its variation across the batch, examined the tape cross-section, and assessed the variation between design and fabrication.

Scenario driven data modelling: a method for integrating diverse sources of data and data streams

Science.gov (United States)

2011-01-01

Background Biology is rapidly becoming a data intensive, data-driven science. It is essential that data is represented and connected in ways that best represent its full conceptual content and allows both automated integration and data driven decision-making. Recent advancements in distributed multi-relational directed graphs, implemented in the form of the Semantic Web make it possible to deal with complicated heterogeneous data in new and interesting ways. Results This paper presents a new approach, scenario driven data modelling (SDDM), that integrates multi-relational directed graphs with data streams. SDDM can be applied to virtually any data integration challenge with widely divergent types of data and data streams. In this work, we explored integrating genetics data with reports from traditional media. SDDM was applied to the New Delhi metallo-beta-lactamase gene (NDM-1), an emerging global health threat. The SDDM process constructed a scenario, created a RDF multi-relational directed graph that linked diverse types of data to the Semantic Web, implemented RDF conversion tools (RDFizers) to bring content into the Sematic Web, identified data streams and analytical routines to analyse those streams, and identified user requirements and graph traversals to meet end-user requirements. Conclusions We provided an example where SDDM was applied to a complex data integration challenge. The process created a model of the emerging NDM-1 health threat, identified and filled gaps in that model, and constructed reliable software that monitored data streams based on the scenario derived multi-relational directed graph. The SDDM process significantly reduced the software requirements phase by letting the scenario and resulting multi-relational directed graph define what is possible and then set the scope of the user requirements. Approaches like SDDM will be critical to the future of data intensive, data-driven science because they automate the process of converting

Data-Driven Methods to Diversify Knowledge of Human Psychology

OpenAIRE

Jack, Rachael E.; Crivelli, Carlos; Wheatley, Thalia

2017-01-01

open access article Psychology aims to understand real human behavior. However, cultural biases in the scientific process can constrain knowledge. We describe here how data-driven methods can relax these constraints to reveal new insights that theories can overlook. To advance knowledge we advocate a symbiotic approach that better combines data-driven methods with theory.

Extracting surface diffusion coefficients from batch adsorption measurement data: application of the classic Langmuir kinetics model.

Science.gov (United States)

Chu, Khim Hoong

2017-11-09

Surface diffusion coefficients may be estimated by fitting solutions of a diffusion model to batch kinetic data. For non-linear systems, a numerical solution of the diffusion model's governing equations is generally required. We report here the application of the classic Langmuir kinetics model to extract surface diffusion coefficients from batch kinetic data. The use of the Langmuir kinetics model in lieu of the conventional surface diffusion model allows derivation of an analytical expression. The parameter estimation procedure requires determining the Langmuir rate coefficient from which the pertinent surface diffusion coefficient is calculated. Surface diffusion coefficients within the 10 -9 to 10 -6  cm 2 /s range obtained by fitting the Langmuir kinetics model to experimental kinetic data taken from the literature are found to be consistent with the corresponding values obtained from the traditional surface diffusion model. The virtue of this simplified parameter estimation method is that it reduces the computational complexity as the analytical expression involves only an algebraic equation in closed form which is easily evaluated by spreadsheet computation.

Increasing the production of desulfurizing biocatalysts by means of fed - batch culture

International Nuclear Information System (INIS)

Berdugo, C I; Mena, J A; Acero, J R; Mogollon, L

2001-01-01

Over the past years, environmental regulations have driven a lot of effort for the development of new technologies for the upgrading of fossil fuels. Biotechnology offers an alternative way to process fossil fuels by means of a biodesulfurization technology where the production of the biocatalyst is one of the key topics. Traditionally, the production is carried out in batch culture where the maximum cellular concentration is restricted by inherent limitations of the culture type and the microorganism growth rate. This work addresses the production of two desulfurizing microorganisms: Rhodococcus erythropolis IGTS8 and gordona rubropertinctus ICP172 using fed-batch culture. Fed-batch cultures were conducted in a 12 L fermentor using ICP 4 medium containing glucose and DMSO as carbon and sulfur sources. As a result, cell concentration was increased 1.5 and 3 times with fed-batch cultures using constant and exponential flow respectively, achieving a maximum cell concentration of 7.3 g DCW/L of biocatalyst igts8 and 12.85 gGDCW/L of the new biocatalyst ICP172. Both biocatalysts presented biodesulfurization activity in a spiked matrix DBT/HXD and in diesel matrix with the detection of 2-HBP which is the end-product of DBT degradation pathway

Data-Driven Innovation through Open Government Data

DEFF Research Database (Denmark)

Jetzek, Thorhildur; Avital, Michel; Bjørn-Andersen, Niels

2014-01-01

The exponentially growing production of data and the social trend towards openness and sharing are power-ful forces that are changing the global economy and society. Governments around the world have become active participants in this evolution, opening up their data for access and reuse by public...... and private agents alike. The phenomenon of Open Government Data has spread around the world in the last four years, driven by the widely held belief that use of Open Government Data has the ability to generate both economic and social value. However, a cursory review of the popular press, as well...... as an investigation of academic research and empirical data, reveals the need to further understand the relationship between Open Government Data and value. In this paper, we focus on how use of Open Government Data can bring about new innovative solutions that can generate social and economic value. We apply...

Effects of region, demography, and protection from fishing on batch fecundity of common coral trout ( Plectropomus leopardus)

Science.gov (United States)

Carter, Alex B.; Davies, Campbell R.; Mapstone, Bruce D.; Russ, Garry R.; Tobin, Andrew J.; Williams, Ashley J.

2014-09-01

Batch fecundity of female Plectropomus leopardus, a coral reef fish targeted by commercial and recreational fishing, was compared between reefs open to fishing and reefs within no-take marine reserves within three regions of the Great Barrier Reef (GBR), Australia. Length, weight, and age had positive effects on batch fecundity of spawners from northern and central reefs but negligible effects on spawners from southern reefs. Females were least fecund for a given length, weight, and age in the southern GBR. Batch fecundity of a 500-mm fork length female was 430 % greater on central reefs and 207 % greater on northern reefs than on southern reefs. The effects of length and age on batch fecundity did not differ significantly between reserve and fished reefs in any region, but weight-specific fecundity was 100 % greater for large 2.0 kg females on reserve reefs compared with fished reefs in the central GBR. We hypothesize that regional variation in batch fecundity is likely driven by water temperature and prey availability. Significant regional variation in batch fecundity highlights the need for understanding spatial variation in reproductive output where single conservation or fishery management strategies cover large, potentially diverse, spatial scales.

Comparison of the release of constituents from granular materials under batch and column testing.

Science.gov (United States)

Lopez Meza, Sarynna; Garrabrants, Andrew C; van der Sloot, Hans; Kosson, David S

2008-01-01

Column leaching testing can be considered a better basis for assessing field impact data than any other available batch test method and thus provides a fundamental basis from which to estimate constituent release under a variety of field conditions. However, column testing is time-intensive compared to the more simplified batch testing, and may not always be a viable option when making decisions for material reuse. Batch tests are used most frequently as a simple tool for compliance or quality control reasons. Therefore, it is important to compare the release that occurs under batch and column testing, and establish conservative interpretation protocols for extrapolation from batch data when column data are not available. Five different materials (concrete, construction debris, aluminum recycling residue, coal fly ash and bottom ash) were evaluated via batch and column testing, including different column flow regimes (continuously saturated and intermittent unsaturated flow). Constituent release data from batch and column tests were compared. Results showed no significant difference between the column flow regimes when constituent release data from batch and column tests were compared. In most cases batch and column testing agreed when presented in the form of cumulative release. For arsenic in carbonated materials, however, batch testing underestimates the column constituent release for most LS ratios and also on a cumulative basis. For cases when As is a constituent of concern, column testing may be required.

Solving a chemical batch scheduling problem by local search

NARCIS (Netherlands)

Brucker, P.; Hurink, Johann L.

1999-01-01

A chemical batch scheduling problem is modelled in two different ways as a discrete optimization problem. Both models are used to solve the batch scheduling problem in a two-phase tabu search procedure. The method is tested on real-world data.

Batching System for Superior Service

Science.gov (United States)

2001-01-01

Veridian's Portable Batch System (PBS) was the recipient of the 1997 NASA Space Act Award for outstanding software. A batch system is a set of processes for managing queues and jobs. Without a batch system, it is difficult to manage the workload of a computer system. By bundling the enterprise's computing resources, the PBS technology offers users a single coherent interface, resulting in efficient management of the batch services. Users choose which information to package into "containers" for system-wide use. PBS also provides detailed system usage data, a procedure not easily executed without this software. PBS operates on networked, multi-platform UNIX environments. Veridian's new version, PBS Pro,TM has additional features and enhancements, including support for additional operating systems. Veridian distributes the original version of PBS as Open Source software via the PBS website. Customers can register and download the software at no cost. PBS Pro is also available via the web and offers additional features such as increased stability, reliability, and fault tolerance.A company using PBS can expect a significant increase in the effective management of its computing resources. Tangible benefits include increased utilization of costly resources and enhanced understanding of computational requirements and user needs.

Data Driven Constraints for the SVM

DEFF Research Database (Denmark)

Darkner, Sune; Clemmensen, Line Katrine Harder

2012-01-01

We propose a generalized data driven constraint for support vector machines exemplified by classification of paired observations in general and specifically on the human ear canal. This is particularly interesting in dynamic cases such as tissue movement or pathologies developing over time. Assum...

PROOF on a Batch System

International Nuclear Information System (INIS)

Behrenhoff, W; Ehrenfeld, W; Samson, J; Stadie, H

2011-01-01

The 'parallel ROOT facility' (PROOF) from the ROOT framework provides a mechanism to distribute the load of interactive and non-interactive ROOT sessions on a set of worker nodes optimising the overall execution time. While PROOF is designed to work on a dedicated PROOF cluster, the benefits of PROOF can also be used on top of another batch scheduling system with the help of temporary per user PROOF clusters. We will present a lightweight tool which starts a temporary PROOF cluster on a SGE based batch cluster or, via a plugin mechanism, e.g. on a set of bare desktops via ssh. Further, we will present the result of benchmarks which compare the data throughput for different data storage back ends available at the German National Analysis Facility (NAF) at DESY.

Data-driven regionalization of housing markets

NARCIS (Netherlands)

Helbich, M.; Brunauer, W.; Hagenauer, J.; Leitner, M.

2013-01-01

This article presents a data-driven framework for housing market segmentation. Local marginal house price surfaces are investigated by means of mixed geographically weighted regression and are reduced to a set of principal component maps, which in turn serve as input for spatial regionalization. The

Locative media and data-driven computing experiments

Directory of Open Access Journals (Sweden)

Sung-Yueh Perng

2016-06-01

Full Text Available Over the past two decades urban social life has undergone a rapid and pervasive geocoding, becoming mediated, augmented and anticipated by location-sensitive technologies and services that generate and utilise big, personal, locative data. The production of these data has prompted the development of exploratory data-driven computing experiments that seek to find ways to extract value and insight from them. These projects often start from the data, rather than from a question or theory, and try to imagine and identify their potential utility. In this paper, we explore the desires and mechanics of data-driven computing experiments. We demonstrate how both locative media data and computing experiments are ‘staged’ to create new values and computing techniques, which in turn are used to try and derive possible futures that are ridden with unintended consequences. We argue that using computing experiments to imagine potential urban futures produces effects that often have little to do with creating new urban practices. Instead, these experiments promote Big Data science and the prospect that data produced for one purpose can be recast for another and act as alternative mechanisms of envisioning urban futures.

Concept evaluation of nuclear fusion driven symbiotic energy systems

International Nuclear Information System (INIS)

Renier, J.P.; Hoffman, T.J.

1979-01-01

This paper analyzes systems based on D-T and semi-catalyzed D-D fusion-powered U233 breeders. Two different blanket types were used: metallic thorium pebble-bed blankets with a batch reprocessing mode and a molten salt blanket with on-line continuous or batch reprocessing. All fusion-driven blankets are assumed to have spherical geometries, with a 85% closure. Neutronics depletion calculations were performed with a revised version of the discrete ordinates code XSDRN-PM, using multigroup (100 neutron, 21 gamma-ray groups) coupled cross-section libraries. These neutronics calculations are coupled with a scenario optimization and cost analysis code. Also, the fusion burn was shaped so as to keep the blanket maximum power density below a preset value, and to improve the performance of the fusion-driven systems. The fusion-driven symbiotes are compared with LMFBR-driven energy systems. The nuclear fission breeders that were used as drivers have parameters characteristic of heterogeneous, oxide LMFBRs. They are net plutonium users - the plutonium is obtained from the discharges of LWRs - and U233 is bred in the fission breeder thorium blankets. The analyses of the symbiotic energy systems were performed at equilibrium, at maximum rate of grid expansion, and for a given nuclear power demand

Writing through Big Data: New Challenges and Possibilities for Data-Driven Arguments

Science.gov (United States)

Beveridge, Aaron

2017-01-01

As multimodal writing continues to shift and expand in the era of Big Data, writing studies must confront the new challenges and possibilities emerging from data mining, data visualization, and data-driven arguments. Often collected under the broad banner of "data literacy," students' experiences of data visualization and data-driven…

SPS batch spacing optimisation

CERN Document Server

Velotti, F M; Carlier, E; Goddard, B; Kain, V; Kotzian, G

2017-01-01

Until 2015, the LHC filling schemes used the batch spac-ing as specified in the LHC design report. The maximumnumber of bunches injectable in the LHC directly dependson the batch spacing at injection in the SPS and hence onthe MKP rise time.As part of the LHC Injectors Upgrade project for LHCheavy ions, a reduction of the batch spacing is needed. In thisdirection, studies to approach the MKP design rise time of150ns(2-98%) have been carried out. These measurementsgave clear indications that such optimisation, and beyond,could be done also for higher injection momentum beams,where the additional slower MKP (MKP-L) is needed.After the successful results from 2015 SPS batch spacingoptimisation for the Pb-Pb run [1], the same concept wasthought to be used also for proton beams. In fact, thanksto the SPS transverse feed back, it was already observedthat lower batch spacing than the design one (225ns) couldbe achieved. For the 2016 p-Pb run, a batch spacing of200nsfor the proton beam with100nsbunch spacing wasreque...

Retrospective data-driven respiratory gating for PET/CT

International Nuclear Information System (INIS)

Schleyer, Paul J; O'Doherty, Michael J; Barrington, Sally F; Marsden, Paul K

2009-01-01

Respiratory motion can adversely affect both PET and CT acquisitions. Respiratory gating allows an acquisition to be divided into a series of motion-reduced bins according to the respiratory signal, which is typically hardware acquired. In order that the effects of motion can potentially be corrected for, we have developed a novel, automatic, data-driven gating method which retrospectively derives the respiratory signal from the acquired PET and CT data. PET data are acquired in listmode and analysed in sinogram space, and CT data are acquired in cine mode and analysed in image space. Spectral analysis is used to identify regions within the CT and PET data which are subject to respiratory motion, and the variation of counts within these regions is used to estimate the respiratory signal. Amplitude binning is then used to create motion-reduced PET and CT frames. The method was demonstrated with four patient datasets acquired on a 4-slice PET/CT system. To assess the accuracy of the data-derived respiratory signal, a hardware-based signal was acquired for comparison. Data-driven gating was successfully performed on PET and CT datasets for all four patients. Gated images demonstrated respiratory motion throughout the bin sequences for all PET and CT series, and image analysis and direct comparison of the traces derived from the data-driven method with the hardware-acquired traces indicated accurate recovery of the respiratory signal.

Spatial and interannual variability in Baltic sprat batch fecundity

DEFF Research Database (Denmark)

Haslob, H.; Tomkiewicz, Jonna; Hinrichsen, H.H.

2011-01-01

in the central Baltic Sea, namely the Bornholm Basin, Gdansk Deep and Southern Gotland Basin. Environmental parameters such as hydrography, fish condition and stock density were tested in order to investigate the observed variability in sprat fecundity. Absolute batch fecundity was found to be positively related...... to fish length and weight. Significant differences in absolute and relative batch fecundity of Baltic sprat among areas and years were detected, and could partly be explained by hydrographic features of the investigated areas. A non-linear multiple regression model taking into account fish length...... and ambient temperature explained 70% of variability in absolute batch fecundity. Oxygen content and fish condition were not related to sprat batch fecundity. Additionally, a negative effect of stock size on sprat batch fecundity in the Bornholm Basin was revealed. The obtained data and results are important...

Data-Driven Learning of Q-Matrix

Science.gov (United States)

Liu, Jingchen; Xu, Gongjun; Ying, Zhiliang

2012-01-01

The recent surge of interests in cognitive assessment has led to developments of novel statistical models for diagnostic classification. Central to many such models is the well-known "Q"-matrix, which specifies the item-attribute relationships. This article proposes a data-driven approach to identification of the "Q"-matrix and estimation of…

Modelling and Simulation of the Batch Hydrolysis of Acetic ...

African Journals Online (AJOL)

The kinetic modelling of the batch synthesis of acetic acid from acetic anhydride was investigated. The kinetic data of the reaction was obtained by conducting the hydrolysis reaction in a batch reactor. A dynamic model was formulated for this process and simulation was carried out using gPROMS® an advanced process ...

A Data-driven Concept Schema for Defining Clinical Research Data Needs

Science.gov (United States)

Hruby, Gregory W.; Hoxha, Julia; Ravichandran, Praveen Chandar; Mendonça, Eneida A.; Hanauer, David A; Weng, Chunhua

2016-01-01

OBJECTIVES The Patient, Intervention, Control/Comparison, and Outcome (PICO) framework is an effective technique for framing a clinical question. We aim to develop the counterpart of PICO to structure clinical research data needs. METHODS We use a data-driven approach to abstracting key concepts representing clinical research data needs by adapting and extending an expert-derived framework originally developed for defining cancer research data needs. We annotated clinical trial eligibility criteria, EHR data request logs, and data queries to electronic health records (EHR), to extract and harmonize concept classes representing clinical research data needs. We evaluated the class coverage, class preservation from the original framework, schema generalizability, schema understandability, and schema structural correctness through a semi-structured interview with eight multidisciplinary domain experts. We iteratively refined the schema based on the evaluations. RESULTS Our data-driven schema preserved 68% of the 63 classes from the original framework and covered 88% (73/82) of the classes proposed by evaluators. Class coverage for participants of different backgrounds ranged from 60% to 100% with a median value of 95% agreement among the individual evaluators. The schema was found understandable and structurally sound. CONCLUSIONS Our proposed schema may serve as the counterpart to PICO for improving the research data needs communication between researchers and informaticians. PMID:27185504

Farm batch system and Fermi inter-process communication and synchronization toolkit

International Nuclear Information System (INIS)

Mandrichenko, I.V.

2001-01-01

Farms Batch System (FBS) was developed as a batch process management system for off-line Run II data processing at Fermilab. FBS will manage PC farms composed of up to 250 nodes and scalable to 1000 nodes with disk capacity of up to several TB. FBS allows users to start arrays of parallel processes on multiple computers. It uses a simplified resource counting method load balancing. FBS has been successfully used for more than a year at Fermilab by fixed target experiments and will be used for collider experiment off-line data processing. Fermi Inter-Process Communication toolkit (FIPC) was designed as a supplement product for FBS that helps establish synchronization and communication between processes running in a distributed batch environment. However, FIPC is an independent package, and can be used with other batch systems, as well as in a non-batch environment. FIPC provides users with a variety of global distributed objects such as semaphores, queues and string variables. Other types of objects can be easily added to FIPC. FIPC has been running on several PC farms at Fermilab for half a year and is going to be used by CDF for off-line data processing

Modelling of Batch Process Operations

DEFF Research Database (Denmark)

Abdul Samad, Noor Asma Fazli; Cameron, Ian; Gani, Rafiqul

2011-01-01

Here a batch cooling crystalliser is modelled and simulated as is a batch distillation system. In the batch crystalliser four operational modes of the crystalliser are considered, namely: initial cooling, nucleation, crystal growth and product removal. A model generation procedure is shown that s...

Spring batch essentials

CERN Document Server

Rao, P Raja Malleswara

2015-01-01

If you are a Java developer with basic knowledge of Spring and some experience in the development of enterprise applications, and want to learn about batch application development in detail, then this book is ideal for you. This book will be perfect as your next step towards building simple yet powerful batch applications on a Java-based platform.

A Model-Driven Methodology for Big Data Analytics-as-a-Service

OpenAIRE

Damiani, Ernesto; Ardagna, Claudio Agostino; Ceravolo, Paolo; Bellandi, Valerio; Bezzi, Michele; Hebert, Cedric

2017-01-01

The Big Data revolution has promised to build a data-driven ecosystem where better decisions are supported by enhanced analytics and data management. However, critical issues still need to be solved in the road that leads to commodization of Big Data Analytics, such as the management of Big Data complexity and the protection of data security and privacy. In this paper, we focus on the first issue and propose a methodology based on Model Driven Engineering (MDE) that aims to substantially lowe...

General Purpose Data-Driven Monitoring for Space Operations

Science.gov (United States)

Iverson, David L.; Martin, Rodney A.; Schwabacher, Mark A.; Spirkovska, Liljana; Taylor, William McCaa; Castle, Joseph P.; Mackey, Ryan M.

2009-01-01

As modern space propulsion and exploration systems improve in capability and efficiency, their designs are becoming increasingly sophisticated and complex. Determining the health state of these systems, using traditional parameter limit checking, model-based, or rule-based methods, is becoming more difficult as the number of sensors and component interactions grow. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. System health can be monitored by comparing real-time operating data with these nominal characterizations, providing detection of anomalous data signatures indicative of system faults or failures. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. The scope of IMS based data-driven monitoring applications continues to expand with current development activities. Successful IMS deployment in the International Space Station (ISS) flight control room to monitor ISS attitude control systems has led to applications in other ISS flight control disciplines, such as thermal control. It has also generated interest in data-driven monitoring capability for Constellation, NASA's program to replace the Space Shuttle with new launch vehicles and spacecraft capable of returning astronauts to the moon, and then on to Mars. Several projects are currently underway to evaluate and mature the IMS technology and complementary tools for use in the Constellation program. These include an experiment on board the Air Force TacSat-3 satellite, and ground systems monitoring for NASA's Ares I-X and Ares I launch vehicles. The TacSat-3 Vehicle System Management (TVSM) project is a software experiment to integrate fault

Improving groundwater storage and soil moisture estimates by assimilating GRACE, SMOS, and SMAP data into CABLE using ensemble Kalman batch smoother and particle batch smoother frameworks

Science.gov (United States)

Han, S. C.; Tangdamrongsub, N.; Yeo, I. Y.; Dong, J.

2017-12-01

Soil moisture and groundwater storage are important information for comprehensive understanding of the climate system and accurate assessment of the regional/global water resources. It is possible to derive the water storage from land surface models but the outputs are commonly biased by inaccurate forcing data, inefficacious model physics, and improper model parameter calibration. To mitigate the model uncertainty, the observation (e.g., from remote sensing as well as ground in-situ data) are often integrated into the models via data assimilation (DA). This study aims to improve the estimation of soil moisture and groundwater storage by simultaneously assimilating satellite observations from the Gravity Recovery And Climate Experiment (GRACE), the Soil Moisture Ocean Salinity (SMOS), and the Soil Moisture Active Passive (SMAP) into the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model using the ensemble Kalman batch smoother (EnBS) and particle batch smoother (PBS) frameworks. The uncertainty of GRACE observation is obtained rigorously from the full error variance-covariance matrix of the GRACE data product. This method demonstrates the use of a realistic representative of GRACE uncertainty, which is spatially correlated in nature, leads to a higher accuracy of water storage computation. Additionally, the comparison between EnBS and PBS results is discussed to understand the filter's performance, limitation, and suitability. The joint DA is demonstrated in the Goulburn catchment, South-East Australia, where diverse ground observations (surface soil moisture, root-zone soil moisture, and groundwater level) are available for evaluation of our DA results. Preliminary results show that both smoothers provide significant improvement of surface soil moisture and groundwater storage estimates. Importantly, our developed DA scheme disaggregates the catchment-scale GRACE information into finer vertical and spatial scales ( 25 km). We present an

Data-Driven H∞ Control for Nonlinear Distributed Parameter Systems.

Science.gov (United States)

Luo, Biao; Huang, Tingwen; Wu, Huai-Ning; Yang, Xiong

2015-11-01

The data-driven H∞ control problem of nonlinear distributed parameter systems is considered in this paper. An off-policy learning method is developed to learn the H∞ control policy from real system data rather than the mathematical model. First, Karhunen-Loève decomposition is used to compute the empirical eigenfunctions, which are then employed to derive a reduced-order model (ROM) of slow subsystem based on the singular perturbation theory. The H∞ control problem is reformulated based on the ROM, which can be transformed to solve the Hamilton-Jacobi-Isaacs (HJI) equation, theoretically. To learn the solution of the HJI equation from real system data, a data-driven off-policy learning approach is proposed based on the simultaneous policy update algorithm and its convergence is proved. For implementation purpose, a neural network (NN)- based action-critic structure is developed, where a critic NN and two action NNs are employed to approximate the value function, control, and disturbance policies, respectively. Subsequently, a least-square NN weight-tuning rule is derived with the method of weighted residuals. Finally, the developed data-driven off-policy learning approach is applied to a nonlinear diffusion-reaction process, and the obtained results demonstrate its effectiveness.

Monte Carlo simulation on kinetics of batch and semi-batch free radical polymerization

KAUST Repository

Shao, Jing; Tang, Wei; Xia, Ru; Feng, Xiaoshuang; Chen, Peng; Qian, Jiasheng; Song, Changjiang

2015-01-01

experimental and simulation studies, we showed the capability of our Monte Carlo scheme on representing polymerization kinetics in batch and semi-batch processes. Various kinetics information, such as instant monomer conversion, molecular weight

Scalable data-driven short-term traffic prediction

NARCIS (Netherlands)

Friso, K.; Wismans, L. J.J.; Tijink, M. B.

2017-01-01

Short-term traffic prediction has a lot of potential for traffic management. However, most research has traditionally focused on either traffic models-which do not scale very well to large networks, computationally-or on data-driven methods for freeways, leaving out urban arterials completely. Urban

Monte Carlo simulation on kinetics of batch and semi-batch free radical polymerization

KAUST Repository

Shao, Jing

2015-10-27

Based on Monte Carlo simulation technology, we proposed a hybrid routine which combines reaction mechanism together with coarse-grained molecular simulation to study the kinetics of free radical polymerization. By comparing with previous experimental and simulation studies, we showed the capability of our Monte Carlo scheme on representing polymerization kinetics in batch and semi-batch processes. Various kinetics information, such as instant monomer conversion, molecular weight, and polydispersity etc. are readily calculated from Monte Carlo simulation. The kinetic constants such as polymerization rate k p is determined in the simulation without of “steady-state” hypothesis. We explored the mechanism for the variation of polymerization kinetics those observed in previous studies, as well as polymerization-induced phase separation. Our Monte Carlo simulation scheme is versatile on studying polymerization kinetics in batch and semi-batch processes.

From Fed-batch to Continuous Enzymatic Biodiesel Production

DEFF Research Database (Denmark)

Price, Jason Anthony; Nordblad, Mathias; Woodley, John M.

2015-01-01

In this this paper, we use mechanistic modelling to guide the development of acontinuous enzymatic process that is performed as a fed-batch operation. In this workwe use the enzymatic biodiesel process as a case study. A mechanistic model developedin our previous work was used to determine...... measured components (triglycerides, diglycerides, monoglycerides, free fatty acid and fatty acid methyl esters(biodiesel)) much better than using fed-batch data alone given the smaller residuals. We also observe a reduction in the correlation between the parameters.The model was then used to predict that 5...... reactors are required (with a combined residence time of 30 hours) to reach a final biodiesel concentration within 2 % of the95.6 mass % achieved in a fed-batch operation, for 24 hours....

Data-Driven Planning: Using Assessment in Strategic Planning

Science.gov (United States)

Bresciani, Marilee J.

2010-01-01

Data-driven planning or evidence-based decision making represents nothing new in its concept. For years, business leaders have claimed they have implemented planning informed by data that have been strategically and systematically gathered. Within higher education and student affairs, there may be less evidence of the actual practice of…

Geographically distributed Batch System as a Service: the INDIGO-DataCloud approach exploiting HTCondor

Science.gov (United States)

Aiftimiei, D. C.; Antonacci, M.; Bagnasco, S.; Boccali, T.; Bucchi, R.; Caballer, M.; Costantini, A.; Donvito, G.; Gaido, L.; Italiano, A.; Michelotto, D.; Panella, M.; Salomoni, D.; Vallero, S.

2017-10-01

One of the challenges a scientific computing center has to face is to keep delivering well consolidated computational frameworks (i.e. the batch computing farm), while conforming to modern computing paradigms. The aim is to ease system administration at all levels (from hardware to applications) and to provide a smooth end-user experience. Within the INDIGO- DataCloud project, we adopt two different approaches to implement a PaaS-level, on-demand Batch Farm Service based on HTCondor and Mesos. In the first approach, described in this paper, the various HTCondor daemons are packaged inside pre-configured Docker images and deployed as Long Running Services through Marathon, profiting from its health checks and failover capabilities. In the second approach, we are going to implement an ad-hoc HTCondor framework for Mesos. Container-to-container communication and isolation have been addressed exploring a solution based on overlay networks (based on the Calico Project). Finally, we have studied the possibility to deploy an HTCondor cluster that spans over different sites, exploiting the Condor Connection Broker component, that allows communication across a private network boundary or firewall as in case of multi-site deployments. In this paper, we are going to describe and motivate our implementation choices and to show the results of the first tests performed.

Enabling Data-Driven Methodologies Across the Data Lifecycle and Ecosystem

Science.gov (United States)

Doyle, R. J.; Crichton, D.

2017-12-01

NASA has unlocked unprecedented scientific knowledge through exploration of the Earth, our solar system, and the larger universe. NASA is generating enormous amounts of data that are challenging traditional approaches to capturing, managing, analyzing and ultimately gaining scientific understanding from science data. New architectures, capabilities and methodologies are needed to span the entire observing system, from spacecraft to archive, while integrating data-driven discovery and analytic capabilities. NASA data have a definable lifecycle, from remote collection point to validated accessibility in multiple archives. Data challenges must be addressed across this lifecycle, to capture opportunities and avoid decisions that may limit or compromise what is achievable once data arrives at the archive. Data triage may be necessary when the collection capacity of the sensor or instrument overwhelms data transport or storage capacity. By migrating computational and analytic capability to the point of data collection, informed decisions can be made about which data to keep; in some cases, to close observational decision loops onboard, to enable attending to unexpected or transient phenomena. Along a different dimension than the data lifecycle, scientists and other end-users must work across an increasingly complex data ecosystem, where the range of relevant data is rarely owned by a single institution. To operate effectively, scalable data architectures and community-owned information models become essential. NASA's Planetary Data System is having success with this approach. Finally, there is the difficult challenge of reproducibility and trust. While data provenance techniques will be part of the solution, future interactive analytics environments must support an ability to provide a basis for a result: relevant data source and algorithms, uncertainty tracking, etc., to assure scientific integrity and to enable confident decision making. Advances in data science offer

Application of ''Confirm tank T is an appropriate feed source for High-Level waste feed batch X'' to specific feed batches

International Nuclear Information System (INIS)

JO, J.

1999-01-01

This document addresses the characterization needs of tanks as set forth in the Data Quality Objectives for TWRS Privatization Phase I: Confirm Tank T is an Appropriate Feed Source for High-Level Waste Feed Batch X (Crawford et al. 1998). The primary purpose of this document is to collect existing data and identify the data needed to determine whether or not the feed source(s) are appropriate for a specific batch. To answer these questions, the existing tank data must be collected and a detailed review performed. If the existing data are insufficient to complete a full comparison, additional data must be obtained from the feed source(s). Additional information requirements need to be identified and formally documented, then the source tank waste must be sampled or resampled and analyzed. Once the additional data are obtained, the data shall be incorporated into the existing database for the source tank and a reevaluation of the data against the Data Quality Objective (DQO) must be made

Data Driven Tuning of Inventory Controllers

DEFF Research Database (Denmark)

Huusom, Jakob Kjøbsted; Santacoloma, Paloma Andrade; Poulsen, Niels Kjølstad

2007-01-01

A systematic method for criterion based tuning of inventory controllers based on data-driven iterative feedback tuning is presented. This tuning method circumvent problems with modeling bias. The process model used for the design of the inventory control is utilized in the tuning...... as an approximation to reduce time required on experiments. The method is illustrated in an application with a multivariable inventory control implementation on a four tank system....

Application of ''Confirm tank T is an appropriate feed source for Low-Activity waste feed batch X'' to specific feed batches

International Nuclear Information System (INIS)

JO, J.

1999-01-01

This document addresses the characterization needs of tanks as set forth in the ''Confirm Tank T is an Appropriate Feed Source for Low-Activity Waste Feed Batch X'' Data Quality Objective (DQO) (Certa and Jo 1998). The primary purpose of this document is to collect existing data and identify the data needed to determine whether or not the feed source(s) are appropriate for a specific batch before transfer is made to the feed staging tanks. To answer these questions, the existing tank data must be collected and a detailed review performed. If the existing data are insufficient to complete a full comparison, additional data must be obtained from the feed source(s). Additional information requirements need to be identified and formally documented, then the source tank waste must be sampled or resampled and analyzed. Once the additional data are obtained, the data shall be incorporated into the existing database for the source tank and a reevaluation of the data against the DQO must be made

Prototype Development: Context-Driven Dynamic XML Ophthalmologic Data Capture Application

Science.gov (United States)

Schwei, Kelsey M; Kadolph, Christopher; Finamore, Joseph; Cancel, Efrain; McCarty, Catherine A; Okorie, Asha; Thomas, Kate L; Allen Pacheco, Jennifer; Pathak, Jyotishman; Ellis, Stephen B; Denny, Joshua C; Rasmussen, Luke V; Tromp, Gerard; Williams, Marc S; Vrabec, Tamara R; Brilliant, Murray H

2017-01-01

Background The capture and integration of structured ophthalmologic data into electronic health records (EHRs) has historically been a challenge. However, the importance of this activity for patient care and research is critical. Objective The purpose of this study was to develop a prototype of a context-driven dynamic extensible markup language (XML) ophthalmologic data capture application for research and clinical care that could be easily integrated into an EHR system. Methods Stakeholders in the medical, research, and informatics fields were interviewed and surveyed to determine data and system requirements for ophthalmologic data capture. On the basis of these requirements, an ophthalmology data capture application was developed to collect and store discrete data elements with important graphical information. Results The context-driven data entry application supports several features, including ink-over drawing capability for documenting eye abnormalities, context-based Web controls that guide data entry based on preestablished dependencies, and an adaptable database or XML schema that stores Web form specifications and allows for immediate changes in form layout or content. The application utilizes Web services to enable data integration with a variety of EHRs for retrieval and storage of patient data. Conclusions This paper describes the development process used to create a context-driven dynamic XML data capture application for optometry and ophthalmology. The list of ophthalmologic data elements identified as important for care and research can be used as a baseline list for future ophthalmologic data collection activities. PMID:28903894

Big Data: An Opportunity for Collaboration with Computer Scientists on Data-Driven Science

Science.gov (United States)

Baru, C.

2014-12-01

Big data technologies are evolving rapidly, driven by the need to manage ever increasing amounts of historical data; process relentless streams of human and machine-generated data; and integrate data of heterogeneous structure from extremely heterogeneous sources of information. Big data is inherently an application-driven problem. Developing the right technologies requires an understanding of the applications domain. Though, an intriguing aspect of this phenomenon is that the availability of the data itself enables new applications not previously conceived of! In this talk, we will discuss how the big data phenomenon creates an imperative for collaboration among domain scientists (in this case, geoscientists) and computer scientists. Domain scientists provide the application requirements as well as insights about the data involved, while computer scientists help assess whether problems can be solved with currently available technologies or require adaptaion of existing technologies and/or development of new technologies. The synergy can create vibrant collaborations potentially leading to new science insights as well as development of new data technologies and systems. The area of interface between geosciences and computer science, also referred to as geoinformatics is, we believe, a fertile area for interdisciplinary research.

In Vitro Growth of Curcuma longa L. in Response to Five Mineral Elements and Plant Density in Fed-Batch Culture Systems

Science.gov (United States)

El-Hawaz, Rabia F.; Bridges, William C.; Adelberg, Jeffrey W.

2015-01-01

Plant density was varied with P, Ca, Mg, and KNO3 in a multifactor experiment to improve Curcuma longa L. micropropagation, biomass and microrhizome development in fed-batch liquid culture. The experiment had two paired D-optimal designs, testing sucrose fed-batch and nutrient sucrose fed-batch techniques. When sucrose became depleted, volume was restored to 5% m/v sucrose in 200 ml of modified liquid MS medium by adding sucrose solutions. Similarly, nutrient sucrose fed-batch was restored to set points with double concentration of treatments’ macronutrient and MS micronutrient solutions, along with sucrose solutions. Changes in the amounts of water and sucrose supplementations were driven by the interaction of P and KNO3 concentrations. Increasing P from 1.25 to 6.25 mM increased both multiplication and biomass. The multiplication ratio was greatest in the nutrient sucrose fed-batch technique with the highest level of P, 6 buds/vessel, and the lowest level of Ca and KNO3. The highest density (18 buds/vessel) produced the highest fresh biomass at the highest concentrations of KNO3 and P with nutrient sucrose fed-batch, and moderate Ca and Mg concentrations. However, maximal rhizome dry biomass required highest P, sucrose fed-batch, and a moderate plant density. Different media formulations and fed-batch techniques were identified to maximize the propagation and storage organ responses. A single experimental design was used to optimize these dual purposes. PMID:25830292

In vitro growth of Curcuma longa L. in response to five mineral elements and plant density in fed-batch culture systems.

Science.gov (United States)

El-Hawaz, Rabia F; Bridges, William C; Adelberg, Jeffrey W

2015-01-01

Plant density was varied with P, Ca, Mg, and KNO3 in a multifactor experiment to improve Curcuma longa L. micropropagation, biomass and microrhizome development in fed-batch liquid culture. The experiment had two paired D-optimal designs, testing sucrose fed-batch and nutrient sucrose fed-batch techniques. When sucrose became depleted, volume was restored to 5% m/v sucrose in 200 ml of modified liquid MS medium by adding sucrose solutions. Similarly, nutrient sucrose fed-batch was restored to set points with double concentration of treatments' macronutrient and MS micronutrient solutions, along with sucrose solutions. Changes in the amounts of water and sucrose supplementations were driven by the interaction of P and KNO3 concentrations. Increasing P from 1.25 to 6.25 mM increased both multiplication and biomass. The multiplication ratio was greatest in the nutrient sucrose fed-batch technique with the highest level of P, 6 buds/vessel, and the lowest level of Ca and KNO3. The highest density (18 buds/vessel) produced the highest fresh biomass at the highest concentrations of KNO3 and P with nutrient sucrose fed-batch, and moderate Ca and Mg concentrations. However, maximal rhizome dry biomass required highest P, sucrose fed-batch, and a moderate plant density. Different media formulations and fed-batch techniques were identified to maximize the propagation and storage organ responses. A single experimental design was used to optimize these dual purposes.

Kinetic study of batch and fed-batch enzymatic saccharification of pretreated substrate and subsequent fermentation to ethanol

Directory of Open Access Journals (Sweden)

Gupta Rishi

2012-03-01

Full Text Available Abstract Background Enzymatic hydrolysis, the rate limiting step in the process development for biofuel, is always hampered by its low sugar concentration. High solid enzymatic saccharification could solve this problem but has several other drawbacks such as low rate of reaction. In the present study we have attempted to enhance the concentration of sugars in enzymatic hydrolysate of delignified Prosopis juliflora, using a fed-batch enzymatic hydrolysis approach. Results The enzymatic hydrolysis was carried out at elevated solid loading up to 20% (w/v and a comparison kinetics of batch and fed-batch enzymatic hydrolysis was carried out using kinetic regimes. Under batch mode, the actual sugar concentration values at 20% initial substrate consistency were found deviated from the predicted values and the maximum sugar concentration obtained was 80.78 g/L. Fed-batch strategy was implemented to enhance the final sugar concentration to 127 g/L. The batch and fed-batch enzymatic hydrolysates were fermented with Saccharomyces cerevisiae and ethanol production of 34.78 g/L and 52.83 g/L, respectively, were achieved. Furthermore, model simulations showed that higher insoluble solids in the feed resulted in both smaller reactor volume and shorter residence time. Conclusion Fed-batch enzymatic hydrolysis is an efficient procedure for enhancing the sugar concentration in the hydrolysate. Restricting the process to suitable kinetic regimes could result in higher conversion rates.

Kinetic study of batch and fed-batch enzymatic saccharification of pretreated substrate and subsequent fermentation to ethanol

Science.gov (United States)

2012-01-01

Background Enzymatic hydrolysis, the rate limiting step in the process development for biofuel, is always hampered by its low sugar concentration. High solid enzymatic saccharification could solve this problem but has several other drawbacks such as low rate of reaction. In the present study we have attempted to enhance the concentration of sugars in enzymatic hydrolysate of delignified Prosopis juliflora, using a fed-batch enzymatic hydrolysis approach. Results The enzymatic hydrolysis was carried out at elevated solid loading up to 20% (w/v) and a comparison kinetics of batch and fed-batch enzymatic hydrolysis was carried out using kinetic regimes. Under batch mode, the actual sugar concentration values at 20% initial substrate consistency were found deviated from the predicted values and the maximum sugar concentration obtained was 80.78 g/L. Fed-batch strategy was implemented to enhance the final sugar concentration to 127 g/L. The batch and fed-batch enzymatic hydrolysates were fermented with Saccharomyces cerevisiae and ethanol production of 34.78 g/L and 52.83 g/L, respectively, were achieved. Furthermore, model simulations showed that higher insoluble solids in the feed resulted in both smaller reactor volume and shorter residence time. Conclusion Fed-batch enzymatic hydrolysis is an efficient procedure for enhancing the sugar concentration in the hydrolysate. Restricting the process to suitable kinetic regimes could result in higher conversion rates. PMID:22433563

Medication waste reduction in pediatric pharmacy batch processes.

Science.gov (United States)

Toerper, Matthew F; Veltri, Michael A; Hamrock, Eric; Mollenkopf, Nicole L; Holt, Kristen; Levin, Scott

2014-04-01

To inform pediatric cart-fill batch scheduling for reductions in pharmaceutical waste using a case study and simulation analysis. A pre and post intervention and simulation analysis was conducted during 3 months at a 205-bed children's center. An algorithm was developed to detect wasted medication based on time-stamped computerized provider order entry information. The algorithm was used to quantify pharmaceutical waste and associated costs for both preintervention (1 batch per day) and postintervention (3 batches per day) schedules. Further, simulation was used to systematically test 108 batch schedules outlining general characteristics that have an impact on the likelihood for waste. Switching from a 1-batch-per-day to a 3-batch-per-day schedule resulted in a 31.3% decrease in pharmaceutical waste (28.7% to 19.7%) and annual cost savings of $183,380. Simulation results demonstrate how increasing batch frequency facilitates a more just-in-time process that reduces waste. The most substantial gains are realized by shifting from a schedule of 1 batch per day to at least 2 batches per day. The simulation exhibits how waste reduction is also achievable by avoiding batch preparation during daily time periods where medication administration or medication discontinuations are frequent. Last, the simulation was used to show how reducing batch preparation time per batch provides some, albeit minimal, opportunity to decrease waste. The case study and simulation analysis demonstrate characteristics of batch scheduling that may support pediatric pharmacy managers in redesign toward minimizing pharmaceutical waste.

Cadmium removal using Cladophora in batch, semi-batch and flow reactors.

Science.gov (United States)

Sternberg, Steven P K; Dorn, Ryan W

2002-02-01

This study presents the results of using viable algae to remove cadmium from a synthetic wastewater. In batch and semi-batch tests, a local strain of Cladophora algae removed 80-94% of the cadmium introduced. The flow experiments that followed were conducted using non-local Cladophora parriaudii. Results showed that the alga removed only 12.7(+/-6.4)% of the cadmium introduced into the reactor. Limited removal was the result of insufficient algal quantities and poor contact between the algae and cadmium solution.

Batched Triangular Dense Linear Algebra Kernels for Very Small Matrix Sizes on GPUs

KAUST Repository

Charara, Ali; Keyes, David E.; Ltaief, Hatem

2017-01-01

Batched dense linear algebra kernels are becoming ubiquitous in scientific applications, ranging from tensor contractions in deep learning to data compression in hierarchical low-rank matrix approximation. Within a single API call, these kernels are capable of simultaneously launching up to thousands of similar matrix computations, removing the expensive overhead of multiple API calls while increasing the occupancy of the underlying hardware. A challenge is that for the existing hardware landscape (x86, GPUs, etc.), only a subset of the required batched operations is implemented by the vendors, with limited support for very small problem sizes. We describe the design and performance of a new class of batched triangular dense linear algebra kernels on very small data sizes using single and multiple GPUs. By deploying two-sided recursive formulations, stressing the register usage, maintaining data locality, reducing threads synchronization and fusing successive kernel calls, the new batched kernels outperform existing state-of-the-art implementations.

Batched Triangular Dense Linear Algebra Kernels for Very Small Matrix Sizes on GPUs

KAUST Repository

Charara, Ali

2017-03-06

Batched dense linear algebra kernels are becoming ubiquitous in scientific applications, ranging from tensor contractions in deep learning to data compression in hierarchical low-rank matrix approximation. Within a single API call, these kernels are capable of simultaneously launching up to thousands of similar matrix computations, removing the expensive overhead of multiple API calls while increasing the occupancy of the underlying hardware. A challenge is that for the existing hardware landscape (x86, GPUs, etc.), only a subset of the required batched operations is implemented by the vendors, with limited support for very small problem sizes. We describe the design and performance of a new class of batched triangular dense linear algebra kernels on very small data sizes using single and multiple GPUs. By deploying two-sided recursive formulations, stressing the register usage, maintaining data locality, reducing threads synchronization and fusing successive kernel calls, the new batched kernels outperform existing state-of-the-art implementations.

Kubernetes as a batch scheduler

OpenAIRE

Souza, Clenimar; Brito Da Rocha, Ricardo

2017-01-01

This project aims at executing a CERN batch use case using Kubernetes, in order to figure out what are the advantages and disadvantages, as well as the functionality that can be replicated or is missing. The reference for the batch system is the CERN Batch System, which uses HTCondor. Another goal of this project is to evaluate the current status of federated resources in Kubernetes, in comparison to the single-cluster API resources. Finally, the last goal of this project is to implement buil...

Data-driven Regulation and Governance in Smart Cities

NARCIS (Netherlands)

Ranchordás, Sofia; Klop, Abram; Mak, Vanessa; Berlee, Anna; Tjong Tjin Tai, Eric

2018-01-01

This chapter discusses the concept of data-driven regulation and governance in the context of smart cities by describing how these urban centres harness these technologies to collect and process information about citizens, traffic, urban planning or waste production. It describes how several smart

Statistical Data Processing with R – Metadata Driven Approach

Directory of Open Access Journals (Sweden)

Rudi SELJAK

2016-06-01

Full Text Available In recent years the Statistical Office of the Republic of Slovenia has put a lot of effort into re-designing its statistical process. We replaced the classical stove-pipe oriented production system with general software solutions, based on the metadata driven approach. This means that one general program code, which is parametrized with process metadata, is used for data processing for a particular survey. Currently, the general program code is entirely based on SAS macros, but in the future we would like to explore how successfully statistical software R can be used for this approach. Paper describes the metadata driven principle for data validation, generic software solution and main issues connected with the use of statistical software R for this approach.

Data-driven design of fault diagnosis and fault-tolerant control systems

CERN Document Server

Ding, Steven X

2014-01-01

Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems presents basic statistical process monitoring, fault diagnosis, and control methods, and introduces advanced data-driven schemes for the design of fault diagnosis and fault-tolerant control systems catering to the needs of dynamic industrial processes. With ever increasing demands for reliability, availability and safety in technical processes and assets, process monitoring and fault-tolerance have become important issues surrounding the design of automatic control systems. This text shows the reader how, thanks to the rapid development of information technology, key techniques of data-driven and statistical process monitoring and control can now become widely used in industrial practice to address these issues. To allow for self-contained study and facilitate implementation in real applications, important mathematical and control theoretical knowledge and tools are included in this book. Major schemes are presented in algorithm form and...

Data to Decisions: Creating a Culture of Model-Driven Drug Discovery.

Science.gov (United States)

Brown, Frank K; Kopti, Farida; Chang, Charlie Zhenyu; Johnson, Scott A; Glick, Meir; Waller, Chris L

2017-09-01

Merck & Co., Inc., Kenilworth, NJ, USA, is undergoing a transformation in the way that it prosecutes R&D programs. Through the adoption of a "model-driven" culture, enhanced R&D productivity is anticipated, both in the form of decreased attrition at each stage of the process and by providing a rational framework for understanding and learning from the data generated along the way. This new approach focuses on the concept of a "Design Cycle" that makes use of all the data possible, internally and externally, to drive decision-making. These data can take the form of bioactivity, 3D structures, genomics, pathway, PK/PD, safety data, etc. Synthesis of high-quality data into models utilizing both well-established and cutting-edge methods has been shown to yield high confidence predictions to prioritize decision-making and efficiently reposition resources within R&D. The goal is to design an adaptive research operating plan that uses both modeled data and experiments, rather than just testing, to drive project decision-making. To support this emerging culture, an ambitious information management (IT) program has been initiated to implement a harmonized platform to facilitate the construction of cross-domain workflows to enable data-driven decision-making and the construction and validation of predictive models. These goals are achieved through depositing model-ready data, agile persona-driven access to data, a unified cross-domain predictive model lifecycle management platform, and support for flexible scientist-developed workflows that simplify data manipulation and consume model services. The end-to-end nature of the platform, in turn, not only supports but also drives the culture change by enabling scientists to apply predictive sciences throughout their work and over the lifetime of a project. This shift in mindset for both scientists and IT was driven by an early impactful demonstration of the potential benefits of the platform, in which expert-level early discovery

Systematic Methodology for Reproducible Optimizing Batch Operation

DEFF Research Database (Denmark)

Bonné, Dennis; Jørgensen, Sten Bay

2006-01-01

This contribution presents a systematic methodology for rapid acquirement of discrete-time state space model representations of batch processes based on their historical operation data. These state space models are parsimoniously parameterized as a set of local, interdependent models. The present...

Product design pattern based on big data-driven scenario

Directory of Open Access Journals (Sweden)

Conggang Yu

2016-07-01

Full Text Available This article discusses about new product design patterns in the big data era, gives designer a new rational thinking way, and is a new way to understand the design of the product. Based on the key criteria of the product design process, category, element, and product are used to input the data, which comprises concrete data and abstract data as an enlargement of the criteria of product design process for the establishment of a big data-driven product design pattern’s model. Moreover, an experiment and a product design case are conducted to verify the feasibility of the new pattern. Ultimately, we will conclude that the data-driven product design has two patterns: one is the concrete data supporting the product design, namely “product–data–product” pattern, and the second is based on the value of the abstract data for product design, namely “data–product–data” pattern. Through the data, users are involving themselves in the design development process. Data and product form a huge network, and data plays a role of connection or node. So the essence of the design is to find a new connection based on element, and to find a new node based on category.

Data-driven remaining useful life prognosis techniques stochastic models, methods and applications

CERN Document Server

Si, Xiao-Sheng; Hu, Chang-Hua

2017-01-01

This book introduces data-driven remaining useful life prognosis techniques, and shows how to utilize the condition monitoring data to predict the remaining useful life of stochastic degrading systems and to schedule maintenance and logistics plans. It is also the first book that describes the basic data-driven remaining useful life prognosis theory systematically and in detail. The emphasis of the book is on the stochastic models, methods and applications employed in remaining useful life prognosis. It includes a wealth of degradation monitoring experiment data, practical prognosis methods for remaining useful life in various cases, and a series of applications incorporated into prognostic information in decision-making, such as maintenance-related decisions and ordering spare parts. It also highlights the latest advances in data-driven remaining useful life prognosis techniques, especially in the contexts of adaptive prognosis for linear stochastic degrading systems, nonlinear degradation modeling based pro...

Batch statistical process control of a fluid bed granulation process using in-line spatial filter velocimetry and product temperature measurements.

Science.gov (United States)

Burggraeve, A; Van den Kerkhof, T; Hellings, M; Remon, J P; Vervaet, C; De Beer, T

2011-04-18

Fluid bed granulation is a batch process, which is characterized by the processing of raw materials for a predefined period of time, consisting of a fixed spraying phase and a subsequent drying period. The present study shows the multivariate statistical modeling and control of a fluid bed granulation process based on in-line particle size distribution (PSD) measurements (using spatial filter velocimetry) combined with continuous product temperature registration using a partial least squares (PLS) approach. Via the continuous in-line monitoring of the PSD and product temperature during granulation of various reference batches, a statistical batch model was developed allowing the real-time evaluation and acceptance or rejection of future batches. Continuously monitored PSD and product temperature process data of 10 reference batches (X-data) were used to develop a reference batch PLS model, regressing the X-data versus the batch process time (Y-data). Two PLS components captured 98.8% of the variation in the X-data block. Score control charts in which the average batch trajectory and upper and lower control limits are displayed were developed. Next, these control charts were used to monitor 4 new test batches in real-time and to immediately detect any deviations from the expected batch trajectory. By real-time evaluation of new batches using the developed control charts and by computation of contribution plots of deviating process behavior at a certain time point, batch losses or reprocessing can be prevented. Immediately after batch completion, all PSD and product temperature information (i.e., a batch progress fingerprint) was used to estimate some granule properties (density and flowability) at an early stage, which can improve batch release time. Individual PLS models relating the computed scores (X) of the reference PLS model (based on the 10 reference batches) and the density, respectively, flowabililty as Y-matrix, were developed. The scores of the 4 test

Batch-to-batch uniformity of bacterial community succession and flavor formation in the fermentation of Zhenjiang aromatic vinegar.

Science.gov (United States)

Wang, Zong-Min; Lu, Zhen-Ming; Yu, Yong-Jian; Li, Guo-Quan; Shi, Jin-Song; Xu, Zheng-Hong

2015-09-01

Solid-state fermentation of traditional Chinese vinegar is a mixed-culture refreshment process that proceeds for many centuries without spoilage. Here, we investigated bacterial community succession and flavor formation in three batches of Zhenjiang aromatic vinegar using pyrosequencing and metabolomics approaches. Temporal patterns of bacterial succession in the Pei (solid-state vinegar culture) showed no significant difference (P > 0.05) among three batches of fermentation. In all the batches investigated, the average number of community operational taxonomic units (OTUs) decreased dramatically from 119 ± 11 on day 1 to 48 ± 16 on day 3, and then maintained in the range of 61 ± 9 from day 5 to the end of fermentation. We confirmed that, within a batch of fermentation process, the patterns of bacterial diversity between the starter (took from the last batch of vinegar culture on day 7) and the Pei on day 7 were similar (90%). The relative abundance dynamics of two dominant members, Lactobacillus and Acetobacter, showed high correlation (coefficient as 0.90 and 0.98 respectively) among different batches. Furthermore, statistical analysis revealed dynamics of 16 main flavor metabolites were stable among different batches. The findings validate the batch-to-batch uniformity of bacterial community succession and flavor formation accounts for the quality of Zhenjiang aromatic vinegar. Based on our understanding, this is the first study helps to explain the rationality of age-old artistry from a scientific perspective. Copyright © 2015 Elsevier Ltd. All rights reserved.

Production of ethanol in batch and fed-batch fermentation of soluble sugar

International Nuclear Information System (INIS)

Chaudhary, M.Y.; Shah, M.A.; Shah, F.H.

1991-01-01

Keeping in view of the demand and need for alternate energy source, especially liquid fuels and the availability of raw materials in Pakistan, we have carried out biochemical and technological studies for ethanol through fermentation of renewable substrates. Molasses and sugar cane have been used as substrate for yeast fermentation. Selected yeast were used in both batch and semi continuous fermentation of molasses. Clarified dilute molasses were fermented with different strains of Saccharomyces cerevisiae. Ethanol concentration after 64 hours batch fermentation reached 9.4% with 90% yield based on sugar content. During feed batch system similar results were obtained after a fermentation cycle of 48 hours resulting in higher productivity. Similarly carbohydrates in fruit juices and hydro lysates of biomass can be economically fermented to ethanol to be used as feed stock for other chemicals. (author)

Data-driven analysis of blood glucose management effectiveness

NARCIS (Netherlands)

Nannings, B.; Abu-Hanna, A.; Bosman, R. J.

2005-01-01

The blood-glucose-level (BGL) of Intensive Care (IC) patients requires close monitoring and control. In this paper we describe a general data-driven analytical method for studying the effectiveness of BGL management. The method is based on developing and studying a clinical outcome reflecting the

Data-Driven Controller Design The H2 Approach

CERN Document Server

Sanfelice Bazanella, Alexandre; Eckhard, Diego

2012-01-01

Data-driven methodologies have recently emerged as an important paradigm alternative to model-based controller design and several such methodologies are formulated as an H2 performance optimization. This book presents a comprehensive theoretical treatment of the H2 approach to data-driven control design. The fundamental properties implied by the H2 problem formulation are analyzed in detail, so that common features to all solutions are identified. Direct methods (VRFT) and iterative methods (IFT, DFT, CbT) are put under a common theoretical framework. The choice of the reference model, the experimental conditions, the optimization method to be used, and several other designer’s choices are crucial to the quality of the final outcome, and firm guidelines for all these choices are derived from the theoretical analysis presented. The practical application of the concepts in the book is illustrated with a large number of practical designs performed for different classes of processes: thermal, fluid processing a...

Batch-batch stable microbial community in the traditional fermentation process of huyumei broad bean pastes.

Science.gov (United States)

Zhu, Linjiang; Fan, Zihao; Kuai, Hui; Li, Qi

2017-09-01

During natural fermentation processes, a characteristic microbial community structure (MCS) is naturally formed, and it is interesting to know about its batch-batch stability. This issue was explored in a traditional semi-solid-state fermentation process of huyumei, a Chinese broad bean paste product. The results showed that this MCS mainly contained four aerobic Bacillus species (8 log CFU per g), including B. subtilis, B. amyloliquefaciens, B. methylotrophicus, and B. tequilensis, and the facultative anaerobe B. cereus with a low concentration (4 log CFU per g), besides a very small amount of the yeast Zygosaccharomyces rouxii (2 log CFU per g). The dynamic change of the MCS in the brine fermentation process showed that the abundance of dominant species varied within a small range, and in the beginning of process the growth of lactic acid bacteria was inhibited and Staphylococcus spp. lost its viability. Also, the MCS and its dynamic change were proved to be highly reproducible among seven batches of fermentation. Therefore, the MCS naturally and stably forms between different batches of the traditional semi-solid-state fermentation of huyumei. Revealing microbial community structure and its batch-batch stability is helpful for understanding the mechanisms of community formation and flavour production in a traditional fermentation. This issue in a traditional semi-solid-state fermentation of huyumei broad bean paste was firstly explored. This fermentation process was revealed to be dominated by a high concentration of four aerobic species of Bacillus, a low concentration of B. cereus and a small amount of Zygosaccharomyces rouxii. Lactic acid bacteria and Staphylococcus spp. lost its viability at the beginning of fermentation. Such the community structure was proved to be highly reproducible among seven batches. © 2017 The Society for Applied Microbiology.

Pipe break prediction based on evolutionary data-driven methods with brief recorded data

International Nuclear Information System (INIS)

Xu Qiang; Chen Qiuwen; Li Weifeng; Ma Jinfeng

2011-01-01

Pipe breaks often occur in water distribution networks, imposing great pressure on utility managers to secure stable water supply. However, pipe breaks are hard to detect by the conventional method. It is therefore necessary to develop reliable and robust pipe break models to assess the pipe's probability to fail and then to optimize the pipe break detection scheme. In the absence of deterministic physical models for pipe break, data-driven techniques provide a promising approach to investigate the principles underlying pipe break. In this paper, two data-driven techniques, namely Genetic Programming (GP) and Evolutionary Polynomial Regression (EPR) are applied to develop pipe break models for the water distribution system of Beijing City. The comparison with the recorded pipe break data from 1987 to 2005 showed that the models have great capability to obtain reliable predictions. The models can be used to prioritize pipes for break inspection and then improve detection efficiency.

PENENTUAN PRODUCTION LOT SIZES DAN TRANSFER BATCH SIZES DENGAN PENDEKATAN MULTISTAGE

Directory of Open Access Journals (Sweden)

Purnawan Adi W

2012-02-01

Full Text Available Pengendalian dan perawatan inventori merupakan suatu permasalahan yang sering dihadapi seluruh organisasi dalam berbagai sektor ekonomi. Salah satu tantangan yang yang harus dihadapi dalam pengendalian inventori adalah bagaimana menentukan ukuran lot yang optimal pada suatu sistem produksi dengan berbagai tipe. Analisis batch produksi (production lot dengan pendekatan hybrid simulasi analitik merupakan salah satu penelitian mengenai ukuran lot optimal. Penelitian tersebut menggunakan pendekatan sistem singlestage dimana tidak adanya hubungan antar proses di setiap stage atau dengan kata lain, proses yang satu independen terhadap proses yang lain. Dengan menggunakan objek penelitian yang sama dengan objek penelitian diatas, penelitian ini kemudian mengangkat permasalahan penentuan ukuran production lot dengan pendekatan multistage. Pertama, dengan menggunakan data-data yang sama dengan penelitian sebelumnya ditentukan ukuran production lot yang optimal dengan metode programa linier. Selanjutnya ukuran production lot digunakan sebegai input simulasi untuk menentukan ukuran transfer batch. Rata-rata panjang antrian dan waktu tunggu menjadi ukuran performansi yang digunakan sebagai acuan penentuan ukuran transfer batch dari beberapa alternatif ukuran yang ada. Pada penelitian ini, ukuran production lot yang dihasilkan sama besarnya dengan demand tiap periode. Sedangkan untuk ukuran transfer batch, hasil penentuan dengan menggunakan simulasi kemudian dimplementasikan ke dalam model. Hasilnya adalah adanya penurunan inventori yang terjadi sebesar 76,35% untuk produk connector dan 50,59% untuk produk box connector dari inventori yang dihasilkan dengan pendekatan singlestage. Kata kunci : multistage, production lot, transfer batch     Abstract   Inventory maintenance and inventory control is a problem that often faced by all organization in many economic sectors. One of challenges that must be faced in inventory control is how to determine the

Data-driven asthma endotypes defined from blood biomarker and gene expression data.

Directory of Open Access Journals (Sweden)

Barbara Jane George

Full Text Available The diagnosis and treatment of childhood asthma is complicated by its mechanistically distinct subtypes (endotypes driven by genetic susceptibility and modulating environmental factors. Clinical biomarkers and blood gene expression were collected from a stratified, cross-sectional study of asthmatic and non-asthmatic children from Detroit, MI. This study describes four distinct asthma endotypes identified via a purely data-driven method. Our method was specifically designed to integrate blood gene expression and clinical biomarkers in a way that provides new mechanistic insights regarding the different asthma endotypes. For example, we describe metabolic syndrome-induced systemic inflammation as an associated factor in three of the four asthma endotypes. Context provided by the clinical biomarker data was essential in interpreting gene expression patterns and identifying putative endotypes, which emphasizes the importance of integrated approaches when studying complex disease etiologies. These synthesized patterns of gene expression and clinical markers from our research may lead to development of novel serum-based biomarker panels.

Data-driven non-linear elasticity: constitutive manifold construction and problem discretization

Science.gov (United States)

Ibañez, Ruben; Borzacchiello, Domenico; Aguado, Jose Vicente; Abisset-Chavanne, Emmanuelle; Cueto, Elias; Ladeveze, Pierre; Chinesta, Francisco

2017-11-01

The use of constitutive equations calibrated from data has been implemented into standard numerical solvers for successfully addressing a variety problems encountered in simulation-based engineering sciences (SBES). However, the complexity remains constantly increasing due to the need of increasingly detailed models as well as the use of engineered materials. Data-Driven simulation constitutes a potential change of paradigm in SBES. Standard simulation in computational mechanics is based on the use of two very different types of equations. The first one, of axiomatic character, is related to balance laws (momentum, mass, energy,\\ldots ), whereas the second one consists of models that scientists have extracted from collected, either natural or synthetic, data. Data-driven (or data-intensive) simulation consists of directly linking experimental data to computers in order to perform numerical simulations. These simulations will employ laws, universally recognized as epistemic, while minimizing the need of explicit, often phenomenological, models. The main drawback of such an approach is the large amount of required data, some of them inaccessible from the nowadays testing facilities. Such difficulty can be circumvented in many cases, and in any case alleviated, by considering complex tests, collecting as many data as possible and then using a data-driven inverse approach in order to generate the whole constitutive manifold from few complex experimental tests, as discussed in the present work.

Prototype Development: Context-Driven Dynamic XML Ophthalmologic Data Capture Application.

Science.gov (United States)

Peissig, Peggy; Schwei, Kelsey M; Kadolph, Christopher; Finamore, Joseph; Cancel, Efrain; McCarty, Catherine A; Okorie, Asha; Thomas, Kate L; Allen Pacheco, Jennifer; Pathak, Jyotishman; Ellis, Stephen B; Denny, Joshua C; Rasmussen, Luke V; Tromp, Gerard; Williams, Marc S; Vrabec, Tamara R; Brilliant, Murray H

2017-09-13

The capture and integration of structured ophthalmologic data into electronic health records (EHRs) has historically been a challenge. However, the importance of this activity for patient care and research is critical. The purpose of this study was to develop a prototype of a context-driven dynamic extensible markup language (XML) ophthalmologic data capture application for research and clinical care that could be easily integrated into an EHR system. Stakeholders in the medical, research, and informatics fields were interviewed and surveyed to determine data and system requirements for ophthalmologic data capture. On the basis of these requirements, an ophthalmology data capture application was developed to collect and store discrete data elements with important graphical information. The context-driven data entry application supports several features, including ink-over drawing capability for documenting eye abnormalities, context-based Web controls that guide data entry based on preestablished dependencies, and an adaptable database or XML schema that stores Web form specifications and allows for immediate changes in form layout or content. The application utilizes Web services to enable data integration with a variety of EHRs for retrieval and storage of patient data. This paper describes the development process used to create a context-driven dynamic XML data capture application for optometry and ophthalmology. The list of ophthalmologic data elements identified as important for care and research can be used as a baseline list for future ophthalmologic data collection activities. ©Peggy Peissig, Kelsey M Schwei, Christopher Kadolph, Joseph Finamore, Efrain Cancel, Catherine A McCarty, Asha Okorie, Kate L Thomas, Jennifer Allen Pacheco, Jyotishman Pathak, Stephen B Ellis, Joshua C Denny, Luke V Rasmussen, Gerard Tromp, Marc S Williams, Tamara R Vrabec, Murray H Brilliant. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 13.09.2017.

Dynamically adaptive data-driven simulation of extreme hydrological flows

KAUST Repository

Kumar Jain, Pushkar; Mandli, Kyle; Hoteit, Ibrahim; Knio, Omar; Dawson, Clint

2017-01-01

evacuation in real-time and through the development of resilient infrastructure based on knowledge of how systems respond to extreme events. Data-driven computational modeling is a critical technology underpinning these efforts. This investigation focuses

Multi-objective optimization of glycopeptide antibiotic production in batch and fed batch processes

DEFF Research Database (Denmark)

Maiti, Soumen K.; Eliasson Lantz, Anna; Bhushan, Mani

2011-01-01

batch operations using process model for Amycolatopsis balhimycina, a glycopeptide antibiotic producer. This resulted in a set of several pareto optimal solutions with the two objectives ranging from (0.75gl−1, 3.97g$-1) to (0.44gl−1, 5.19g$-1) for batch and from (1.5gl−1, 5.46g$-1) to (1.1gl−1, 6.34g...

Data triggered data processing at MFTF-B

International Nuclear Information System (INIS)

Jackson, R.J.; Balch, T.R.; Preckshot, G.G.

1985-01-01

A primary characteristic of most batch systems is that the input data files must exist before jobs are scheduled. On the Mirror Fusion Test Facility (MFTF-B) at Lawrence Livermore National Laboratory we schedule jobs to process experimental data to be collected during a five minute shot cycle. Our data-driven processing system emulates a coarsely granular data flow architecture. Processing jobs are scheduled before the experimental data is collected. Processing jobs ''fire'', or execute, as input data becomes available. Similar to UNIX ''pipes'', data produced by upstream processing nodes may be used as inputs by following nodes. Users, working on our networked SUN workstations, specify data processing templates which define processes and their data dependencies. Data specifications indicate the source of data; actual associations with specific data instantiations are made when the jobs are scheduled. We report here on details of diagnostic data processing and our experiences

Data-Driven Learning: Reasonable Fears and Rational Reassurance

Science.gov (United States)

Boulton, Alex

2009-01-01

Computer corpora have many potential applications in teaching and learning languages, the most direct of which--when the learners explore a corpus themselves--has become known as data-driven learning (DDL). Despite considerable enthusiasm in the research community and interest in higher education, the approach has not made major inroads to…

Data-Driven Exercises for Chemistry: A New Digital Collection

Science.gov (United States)

Grubbs, W. Tandy

2007-01-01

The analysis presents a new digital collection for various data-driven exercises that are used for teaching chemistry to the students. Such methods are expected to help the students to think in a more scientific manner.

Data-Driven Model Order Reduction for Bayesian Inverse Problems

KAUST Repository

Cui, Tiangang; Youssef, Marzouk; Willcox, Karen

2014-01-01

One of the major challenges in using MCMC for the solution of inverse problems is the repeated evaluation of computationally expensive numerical models. We develop a data-driven projection- based model order reduction technique to reduce

Data-Intensive Science Meets Inquiry-Driven Pedagogy: Interactive Big Data Exploration, Threshold Concepts, and Liminality

Science.gov (United States)

Ramachandran, R.; Nair, U. S.; Word, A.

2014-12-01

Threshold concepts in any discipline are the core concepts an individual must understand in order to master a discipline. By their very nature, these concepts are troublesome, irreversible, integrative, bounded, discursive, and reconstitutive. Although grasping threshold concepts can be extremely challenging for each learner as s/he moves through stages of cognitive development relative to a given discipline, the learner's grasp of these concepts determines the extent to which s/he is prepared to work competently and creatively within the field itself. The movement of individuals from a state of ignorance of these core concepts to one of mastery occurs not along a linear path but in iterative cycles of knowledge creation and adjustment in liminal spaces - conceptual spaces through which learners move from the vaguest awareness of concepts to mastery, accompanied by understanding of their relevance, connectivity, and usefulness relative to questions and constructs in a given discipline. With the explosive growth of data available in atmospheric science, driven largely by satellite Earth observations and high-resolution numerical simulations, paradigms such as that of data-intensive science have emerged. These paradigm shifts are based on the growing realization that current infrastructure, tools and processes will not allow us to analyze and fully utilize the complex and voluminous data that is being gathered. In this emerging paradigm, the scientific discovery process is driven by knowledge extracted from large volumes of data. In this presentation, we contend that this paradigm naturally lends to inquiry-driven pedagogy where knowledge is discovered through inductive engagement with large volumes of data rather than reached through traditional, deductive, hypothesis-driven analyses. In particular, data-intensive techniques married with an inductive methodology allow for exploration on a scale that is not possible in the traditional classroom with its typical

Design of two-column batch-to-batch recirculation to enhance performance in ion-exchange chromatography.

Science.gov (United States)

Persson, Oliver; Andersson, Niklas; Nilsson, Bernt

2018-01-05

Preparative liquid chromatography is a separation technique widely used in the manufacturing of fine chemicals and pharmaceuticals. A major drawback of traditional single-column batch chromatography step is the trade-off between product purity and process performance. Recirculation of impure product can be utilized to make the trade-off more favorable. The aim of the present study was to investigate the usage of a two-column batch-to-batch recirculation process step to increase the performance compared to single-column batch chromatography at a high purity requirement. The separation of a ternary protein mixture on ion-exchange chromatography columns was used to evaluate the proposed process. The investigation used modelling and simulation of the process step, experimental validation and optimization of the simulated process. In the presented case the yield increases from 45.4% to 93.6% and the productivity increases 3.4 times compared to the performance of a batch run for a nominal case. A rapid concentration build-up product can be seen during the first cycles, before the process reaches a cyclic steady-state with reoccurring concentration profiles. The optimization of the simulation model predicts that the recirculated salt can be used as a flying start of the elution, which would enhance the process performance. The proposed process is more complex than a batch process, but may improve the separation performance, especially while operating at cyclic steady-state. The recirculation of impure fractions reduces the product losses and ensures separation of product to a high degree of purity. Copyright © 2017 Elsevier B.V. All rights reserved.

Meeting report: batch-to-batch variability in estrogenic activity in commercial animal diets--importance and approaches for laboratory animal research.

Science.gov (United States)

Heindel, Jerrold J; vom Saal, Frederick S

2008-03-01

We report information from two workshops sponsored by the National Institutes of Health that were held to a) assess whether dietary estrogens could significantly impact end points in experimental animals, and b) involve program participants and feed manufacturers to address the problems associated with measuring and eliminating batch-to-batch variability in rodent diets that may lead to conflicting findings in animal experiments within and between laboratories. Data were presented at the workshops showing that there is significant batch-to-batch variability in estrogenic content of commercial animal diets, and that this variability results in differences in experimental outcomes. A combination of methods were proposed to determine levels of total estrogenic activity and levels of specific estrogenic constituents in soy-containing, casein-containing, and other soy-free rodent diets. Workshop participants recommended that researchers pay greater attention to the type of diet being used in animal studies and choose a diet whose estrogenic activity (or lack thereof) is appropriate for the experimental model and end points of interest. Information about levels of specific phytoestrogens, as well as estrogenic activity caused by other contaminants and measured by bioassay, should be disclosed in scientific publications. This will require laboratory animal diet manufacturers to provide investigators with information regarding the phytoestrogen content and other estrogenic compounds in commercial diets used in animal research.

Developing Annotation Solutions for Online Data Driven Learning

Science.gov (United States)

Perez-Paredes, Pascual; Alcaraz-Calero, Jose M.

2009-01-01

Although "annotation" is a widely-researched topic in Corpus Linguistics (CL), its potential role in Data Driven Learning (DDL) has not been addressed in depth by Foreign Language Teaching (FLT) practitioners. Furthermore, most of the research in the use of DDL methods pays little attention to annotation in the design and implementation…

7 CFR 58.728 - Cooking the batch.

Science.gov (United States)

2010-01-01

... 7 Agriculture 3 2010-01-01 2010-01-01 false Cooking the batch. 58.728 Section 58.728 Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (Standards... Procedures § 58.728 Cooking the batch. Each batch of cheese within the cooker, including the optional...

Nuclear data requirements for accelerator driven sub-critical systems

Indian Academy of Sciences (India)

The development of accelerator driven sub-critical systems (ADSS) require significant amount of new nuclear data in extended energy regions as well as for a variety of new materials. This paper reviews these perspectives in the Indian context.

Data-driven methods towards learning the highly nonlinear inverse kinematics of tendon-driven surgical manipulators.

Science.gov (United States)

Xu, Wenjun; Chen, Jie; Lau, Henry Y K; Ren, Hongliang

2017-09-01

Accurate motion control of flexible surgical manipulators is crucial in tissue manipulation tasks. The tendon-driven serpentine manipulator (TSM) is one of the most widely adopted flexible mechanisms in minimally invasive surgery because of its enhanced maneuverability in torturous environments. TSM, however, exhibits high nonlinearities and conventional analytical kinematics model is insufficient to achieve high accuracy. To account for the system nonlinearities, we applied a data driven approach to encode the system inverse kinematics. Three regression methods: extreme learning machine (ELM), Gaussian mixture regression (GMR) and K-nearest neighbors regression (KNNR) were implemented to learn a nonlinear mapping from the robot 3D position states to the control inputs. The performance of the three algorithms was evaluated both in simulation and physical trajectory tracking experiments. KNNR performed the best in the tracking experiments, with the lowest RMSE of 2.1275 mm. The proposed inverse kinematics learning methods provide an alternative and efficient way to accurately model the tendon driven flexible manipulator. Copyright © 2016 John Wiley & Sons, Ltd.

NGBAuth - Next Generation Batch Authentication for long running batch jobs.

CERN Document Server

Juto, Zakarias

2015-01-01

This document describes the prototyping of a new solution for the CERN batch authentication of long running jobs. While the job submission requires valid user credentials, these have to be renewed due to long queuing and execution times. Described within is a new system which will guarantee a similar level of security as the old LSFAuth while simplifying the implementation and the overall architecture. The new system is being built on solid, streamlined and tested components (notably OpenSSL) and a priority has been to make it more generic in order to facilitate the evolution of the current system such as for the expected migration from LSF to Condor as backend batch system.

On Mixed Data and Event Driven Design for Adaptive-Critic-Based Nonlinear $H_{\\infty}$ Control.

Science.gov (United States)

Wang, Ding; Mu, Chaoxu; Liu, Derong; Ma, Hongwen

2018-04-01

In this paper, based on the adaptive critic learning technique, the control for a class of unknown nonlinear dynamic systems is investigated by adopting a mixed data and event driven design approach. The nonlinear control problem is formulated as a two-player zero-sum differential game and the adaptive critic method is employed to cope with the data-based optimization. The novelty lies in that the data driven learning identifier is combined with the event driven design formulation, in order to develop the adaptive critic controller, thereby accomplishing the nonlinear control. The event driven optimal control law and the time driven worst case disturbance law are approximated by constructing and tuning a critic neural network. Applying the event driven feedback control, the closed-loop system is built with stability analysis. Simulation studies are conducted to verify the theoretical results and illustrate the control performance. It is significant to observe that the present research provides a new avenue of integrating data-based control and event-triggering mechanism into establishing advanced adaptive critic systems.

Data and Dynamics Driven Approaches for Modelling and Forecasting the Red Sea Chlorophyll

KAUST Repository

Dreano, Denis

2017-01-01

concentration and have practical applications for fisheries operation and harmful algae blooms monitoring. Modelling approaches can be divided between physics- driven (dynamical) approaches, and data-driven (statistical) approaches. Dynamical models are based

Data-driven execution of fast multipole methods

KAUST Repository

Ltaief, Hatem

2013-09-17

Fast multipole methods (FMMs) have O (N) complexity, are compute bound, and require very little synchronization, which makes them a favorable algorithm on next-generation supercomputers. Their most common application is to accelerate N-body problems, but they can also be used to solve boundary integral equations. When the particle distribution is irregular and the tree structure is adaptive, load balancing becomes a non-trivial question. A common strategy for load balancing FMMs is to use the work load from the previous step as weights to statically repartition the next step. The authors discuss in the paper another approach based on data-driven execution to efficiently tackle this challenging load balancing problem. The core idea consists of breaking the most time-consuming stages of the FMMs into smaller tasks. The algorithm can then be represented as a directed acyclic graph where nodes represent tasks and edges represent dependencies among them. The execution of the algorithm is performed by asynchronously scheduling the tasks using the queueing and runtime for kernels runtime environment, in a way such that data dependencies are not violated for numerical correctness purposes. This asynchronous scheduling results in an out-of-order execution. The performance results of the data-driven FMM execution outperform the previous strategy and show linear speedup on a quad-socket quad-core Intel Xeon system.Copyright © 2013 John Wiley & Sons, Ltd. Copyright © 2013 John Wiley & Sons, Ltd.

Data-driven Discovery: A New Era of Exploiting the Literature and Data

Directory of Open Access Journals (Sweden)

Ying Ding

2016-11-01

Full Text Available In the current data-intensive era, the traditional hands-on method of conducting scientific research by exploring related publications to generate a testable hypothesis is well on its way of becoming obsolete within just a year or two. Analyzing the literature and data to automatically generate a hypothesis might become the de facto approach to inform the core research efforts of those trying to master the exponentially rapid expansion of publications and datasets. Here, viewpoints are provided and discussed to help the understanding of challenges of data-driven discovery.

Articulatory Distinctiveness of Vowels and Consonants: A Data-Driven Approach

Science.gov (United States)

Wang, Jun; Green, Jordan R.; Samal, Ashok; Yunusova, Yana

2013-01-01

Purpose: To quantify the articulatory distinctiveness of 8 major English vowels and 11 English consonants based on tongue and lip movement time series data using a data-driven approach. Method: Tongue and lip movements of 8 vowels and 11 consonants from 10 healthy talkers were

Data driven information system for supervision of judicial open

Directory of Open Access Journals (Sweden)

Ming LI

2016-08-01

Full Text Available Aiming at the four outstanding problems of informationized supervision for judicial publicity, the judicial public data is classified based on data driven to form the finally valuable data. Then, the functional structure, technical structure and business structure of the data processing system are put forward, including data collection module, data reduction module, data analysis module, data application module and data security module, etc. The development of the data processing system based on these structures can effectively reduce work intensity of judicial open iformation management, summarize the work state, find the problems, and promote the level of judicial publicity.

Building Data-Driven Pathways From Routinely Collected Hospital Data: A Case Study on Prostate Cancer

Science.gov (United States)

Clark, Jeremy; Cooper, Colin S; Mills, Robert; Rayward-Smith, Victor J; de la Iglesia, Beatriz

2015-01-01

Background Routinely collected data in hospitals is complex, typically heterogeneous, and scattered across multiple Hospital Information Systems (HIS). This big data, created as a byproduct of health care activities, has the potential to provide a better understanding of diseases, unearth hidden patterns, and improve services and cost. The extent and uses of such data rely on its quality, which is not consistently checked, nor fully understood. Nevertheless, using routine data for the construction of data-driven clinical pathways, describing processes and trends, is a key topic receiving increasing attention in the literature. Traditional algorithms do not cope well with unstructured processes or data, and do not produce clinically meaningful visualizations. Supporting systems that provide additional information, context, and quality assurance inspection are needed. Objective The objective of the study is to explore how routine hospital data can be used to develop data-driven pathways that describe the journeys that patients take through care, and their potential uses in biomedical research; it proposes a framework for the construction, quality assessment, and visualization of patient pathways for clinical studies and decision support using a case study on prostate cancer. Methods Data pertaining to prostate cancer patients were extracted from a large UK hospital from eight different HIS, validated, and complemented with information from the local cancer registry. Data-driven pathways were built for each of the 1904 patients and an expert knowledge base, containing rules on the prostate cancer biomarker, was used to assess the completeness and utility of the pathways for a specific clinical study. Software components were built to provide meaningful visualizations for the constructed pathways. Results The proposed framework and pathway formalism enable the summarization, visualization, and querying of complex patient-centric clinical information, as well as the

Product design pattern based on big data-driven scenario

OpenAIRE

Conggang Yu; Lusha Zhu

2016-01-01

This article discusses about new product design patterns in the big data era, gives designer a new rational thinking way, and is a new way to understand the design of the product. Based on the key criteria of the product design process, category, element, and product are used to input the data, which comprises concrete data and abstract data as an enlargement of the criteria of product design process for the establishment of a big data-driven product design pattern’s model. Moreover, an exper...

Data-Driven Cyber-Physical Systems via Real-Time Stream Analytics and Machine Learning

OpenAIRE

Akkaya, Ilge

2016-01-01

Emerging distributed cyber-physical systems (CPSs) integrate a wide range of heterogeneous components that need to be orchestrated in a dynamic environment. While model-based techniques are commonly used in CPS design, they be- come inadequate in capturing the complexity as systems become larger and extremely dynamic. The adaptive nature of the systems makes data-driven approaches highly desirable, if not necessary.Traditionally, data-driven systems utilize large volumes of static data sets t...

The development of an industrial-scale fed-batch fermentation simulation.

Science.gov (United States)

Goldrick, Stephen; Ştefan, Andrei; Lovett, David; Montague, Gary; Lennox, Barry

2015-01-10

This paper describes a simulation of an industrial-scale fed-batch fermentation that can be used as a benchmark in process systems analysis and control studies. The simulation was developed using a mechanistic model and validated using historical data collected from an industrial-scale penicillin fermentation process. Each batch was carried out in a 100,000 L bioreactor that used an industrial strain of Penicillium chrysogenum. The manipulated variables recorded during each batch were used as inputs to the simulator and the predicted outputs were then compared with the on-line and off-line measurements recorded in the real process. The simulator adapted a previously published structured model to describe the penicillin fermentation and extended it to include the main environmental effects of dissolved oxygen, viscosity, temperature, pH and dissolved carbon dioxide. In addition the effects of nitrogen and phenylacetic acid concentrations on the biomass and penicillin production rates were also included. The simulated model predictions of all the on-line and off-line process measurements, including the off-gas analysis, were in good agreement with the batch records. The simulator and industrial process data are available to download at www.industrialpenicillinsimulation.com and can be used to evaluate, study and improve on the current control strategy implemented on this facility. Crown Copyright © 2014. Published by Elsevier B.V. All rights reserved.

Estimating the Probability of Wind Ramping Events: A Data-driven Approach

OpenAIRE

Wang, Cheng; Wei, Wei; Wang, Jianhui; Qiu, Feng

2016-01-01

This letter proposes a data-driven method for estimating the probability of wind ramping events without exploiting the exact probability distribution function (PDF) of wind power. Actual wind data validates the proposed method.

Exploring Techniques of Developing Writing Skill in IELTS Preparatory Courses: A Data-Driven Study

Science.gov (United States)

Ostovar-Namaghi, Seyyed Ali; Safaee, Seyyed Esmail

2017-01-01

Being driven by the hypothetico-deductive mode of inquiry, previous studies have tested the effectiveness of theory-driven interventions under controlled experimental conditions to come up with universally applicable generalizations. To make a case in the opposite direction, this data-driven study aims at uncovering techniques and strategies…

Observer and data-driven model based fault detection in Power Plant Coal Mills

DEFF Research Database (Denmark)

Fogh Odgaard, Peter; Lin, Bao; Jørgensen, Sten Bay

2008-01-01

model with motor power as the controlled variable, data-driven methods for fault detection are also investigated. Regression models that represent normal operating conditions (NOCs) are developed with both static and dynamic principal component analysis and partial least squares methods. The residual...... between process measurement and the NOC model prediction is used for fault detection. A hybrid approach, where a data-driven model is employed to derive an optimal unknown input observer, is also implemented. The three methods are evaluated with case studies on coal mill data, which includes a fault......This paper presents and compares model-based and data-driven fault detection approaches for coal mill systems. The first approach detects faults with an optimal unknown input observer developed from a simplified energy balance model. Due to the time-consuming effort in developing a first principles...

Leaching behavior of mineral processing waste: Comparison of batch and column investigations

Energy Technology Data Exchange (ETDEWEB)

Al-Abed, Souhail R. [National Risk Management Research Laboratory, U.S. Environmental Protection Agency, 26 West Martin Luther King Drive, Cincinnati, OH 45268 (United States)], E-mail: al-abed.souhail@epa.gov; Jegadeesan, G. [Pegasus Technical Services Inc., 46 East Hollister Street, Cincinnati, OH 45219 (United States); Purandare, J. [Englandgeosystem Inc., 15375 Barranca Pkwy, Suite F-106, Irvine, CA 92618 (United States); Allen, D. [National Risk Management Research Laboratory, U.S. Environmental Protection Agency, 26 West Martin Luther King Drive, Cincinnati, OH 45268 (United States)

2008-05-30

In this study, a comparison of laboratory batch and column experiments on metal release profile from a mineral processing waste (MPW) is presented. Batch (equilibrium) and column (dynamic) leaching tests were conducted on ground MPW at different liquid-solid ratios (LS) to determine the mechanisms controlling metal release. Additionally, the effect of pH on metal release is also discussed. It was observed that acidic pH conditions induced dissolution of As, Zn and Cu. Negligible leaching at alkaline pH was observed. However, Se depicted amphoteric behavior with high release at low and high pH. The batch and column data showed that As and Se release increased with LS ratio, while that of Cu and Zn increased initially and tapered towards equilibrium values at high LS ratios. The results on metal release from the MPW suggested that dissolution of the metal was the controlling mechanism. Leaching profiles from the batch and column data corresponded well for most LS ratios. This is most likely due to the acidic character of the waste, minimal changes in pH during the column operation and granular structure of the waste. From a waste management perspective, low cost batch equilibrium studies in lieu of high cost column experiments can be used for decision making on its disposal only when the waste exhibits characteristics similar to the mineral processing waste.

Leaching behavior of mineral processing waste: Comparison of batch and column investigations

International Nuclear Information System (INIS)

Al-Abed, Souhail R.; Jegadeesan, G.; Purandare, J.; Allen, D.

2008-01-01

In this study, a comparison of laboratory batch and column experiments on metal release profile from a mineral processing waste (MPW) is presented. Batch (equilibrium) and column (dynamic) leaching tests were conducted on ground MPW at different liquid-solid ratios (LS) to determine the mechanisms controlling metal release. Additionally, the effect of pH on metal release is also discussed. It was observed that acidic pH conditions induced dissolution of As, Zn and Cu. Negligible leaching at alkaline pH was observed. However, Se depicted amphoteric behavior with high release at low and high pH. The batch and column data showed that As and Se release increased with LS ratio, while that of Cu and Zn increased initially and tapered towards equilibrium values at high LS ratios. The results on metal release from the MPW suggested that dissolution of the metal was the controlling mechanism. Leaching profiles from the batch and column data corresponded well for most LS ratios. This is most likely due to the acidic character of the waste, minimal changes in pH during the column operation and granular structure of the waste. From a waste management perspective, low cost batch equilibrium studies in lieu of high cost column experiments can be used for decision making on its disposal only when the waste exhibits characteristics similar to the mineral processing waste

Kinetic studies on batch cultivation of Trichoderma reesei and application to enhance cellulase production by fed-batch fermentation.

Science.gov (United States)

Ma, Lijuan; Li, Chen; Yang, Zhenhua; Jia, Wendi; Zhang, Dongyuan; Chen, Shulin

2013-07-20

Reducing the production cost of cellulase as the key enzyme for cellulose hydrolysis to fermentable sugars remains a major challenge for biofuel production. Because of the complexity of cellulase production, kinetic modeling and mass balance calculation can be used as effective tools for process design and optimization. In this study, kinetic models for cell growth, substrate consumption and cellulase production in batch fermentation were developed, and then applied in fed-batch fermentation to enhance cellulase production. Inhibition effect of substrate was considered and a modified Luedeking-Piret model was developed for cellulase production and substrate consumption according to the growth characteristics of Trichoderma reesei. The model predictions fit well with the experimental data. Simulation results showed that higher initial substrate concentration led to decrease of cellulase production rate. Mass balance and kinetic simulation results were applied to determine the feeding strategy. Cellulase production and its corresponding productivity increased by 82.13% after employing the proper feeding strategy in fed-batch fermentation. This method combining mathematics and chemometrics by kinetic modeling and mass balance can not only improve cellulase fermentation process, but also help to better understand the cellulase fermentation process. The model development can also provide insight to other similar fermentation processes. Copyright © 2013 Elsevier B.V. All rights reserved.

Data-driven importance distributions for articulated tracking

DEFF Research Database (Denmark)

Hauberg, Søren; Pedersen, Kim Steenstrup

2011-01-01

We present two data-driven importance distributions for particle filterbased articulated tracking; one based on background subtraction, another on depth information. In order to keep the algorithms efficient, we represent human poses in terms of spatial joint positions. To ensure constant bone le...... filter, where they improve both accuracy and efficiency of the tracker. In fact, they triple the effective number of samples compared to the most commonly used importance distribution at little extra computational cost....

Dynamically adaptive data-driven simulation of extreme hydrological flows

Science.gov (United States)

Kumar Jain, Pushkar; Mandli, Kyle; Hoteit, Ibrahim; Knio, Omar; Dawson, Clint

2018-02-01

Hydrological hazards such as storm surges, tsunamis, and rainfall-induced flooding are physically complex events that are costly in loss of human life and economic productivity. Many such disasters could be mitigated through improved emergency evacuation in real-time and through the development of resilient infrastructure based on knowledge of how systems respond to extreme events. Data-driven computational modeling is a critical technology underpinning these efforts. This investigation focuses on the novel combination of methodologies in forward simulation and data assimilation. The forward geophysical model utilizes adaptive mesh refinement (AMR), a process by which a computational mesh can adapt in time and space based on the current state of a simulation. The forward solution is combined with ensemble based data assimilation methods, whereby observations from an event are assimilated into the forward simulation to improve the veracity of the solution, or used to invert for uncertain physical parameters. The novelty in our approach is the tight two-way coupling of AMR and ensemble filtering techniques. The technology is tested using actual data from the Chile tsunami event of February 27, 2010. These advances offer the promise of significantly transforming data-driven, real-time modeling of hydrological hazards, with potentially broader applications in other science domains.

Dynamically adaptive data-driven simulation of extreme hydrological flows

KAUST Repository

Kumar Jain, Pushkar

2017-12-27

Hydrological hazards such as storm surges, tsunamis, and rainfall-induced flooding are physically complex events that are costly in loss of human life and economic productivity. Many such disasters could be mitigated through improved emergency evacuation in real-time and through the development of resilient infrastructure based on knowledge of how systems respond to extreme events. Data-driven computational modeling is a critical technology underpinning these efforts. This investigation focuses on the novel combination of methodologies in forward simulation and data assimilation. The forward geophysical model utilizes adaptive mesh refinement (AMR), a process by which a computational mesh can adapt in time and space based on the current state of a simulation. The forward solution is combined with ensemble based data assimilation methods, whereby observations from an event are assimilated into the forward simulation to improve the veracity of the solution, or used to invert for uncertain physical parameters. The novelty in our approach is the tight two-way coupling of AMR and ensemble filtering techniques. The technology is tested using actual data from the Chile tsunami event of February 27, 2010. These advances offer the promise of significantly transforming data-driven, real-time modeling of hydrological hazards, with potentially broader applications in other science domains.

Response variation in a batch of TLDS

International Nuclear Information System (INIS)

Burrage, J.; Campbell, A.

2004-01-01

Full text: At Royal Perth Hospital, LiF thermoluminescent dosimeter rods (TLDs) are handled in batches of 50. Rods in each batch are always annealed together to ensure the same thermal history and an individual batch is used with the same type and energy of radiation. A subset of a batch is used for calibration purposes by exposing them to a range of known doses and their output is used to calculate the dose received by other rods used for a dose measurement. Variation in TLD response is addressed by calculating 95% certainty levels from the calibration rods and applying this to the dose measurement rods. This approach relies on the sensitivity of rods within each batch being similar. This work investigates the validity of this assumption and considers possible benefits of applying individual rod sensitivities. The variation in response of TLD rods was assessed using 25 TLD-100 rods (Harshaw/Bicron) which were uniformly exposed to 1 Gy using 6 MeV photons in a linear accelerator on 5 separate occasions. Rods were read with a Harshaw 5500 reader. During the read process the Harshaw reader periodically checks for noise and PMT gain drift and the data were corrected for these parameters. Replicate exposure data were analysed using 1-way Analysis of Variance (ANOVA) to determine whether the between rod variations were significantly different to the variations within a single rod. A batch of 50 rods was also exposed on three occasions using the above technique. Individual TLD rod sensitivity values were determined using the rod responses from 2 exposures and these values were applied to correct charges on a rod-by-rod basis for the third exposure. ANOVA results on the 5 exposures of 25 rods showed the variance between rods was significantly greater than the within rod variance (p < 0.001). The precision of an individual rod was estimated to have a standard deviation of 2.8%. This suggests that the 95% confidence limits for repeated measurements using the same dose and

An Open Framework for Dynamic Big-data-driven Application Systems (DBDDAS) Development

KAUST Repository

Douglas, Craig

2014-01-01

In this paper, we outline key features that dynamic data-driven application systems (DDDAS) have. A DDDAS is an application that has data assimilation that can change the models and/or scales of the computation and that the application controls the data collection based on the computational results. The term Big Data (BD) has come into being in recent years that is highly applicable to most DDDAS since most applications use networks of sensors that generate an overwhelming amount of data in the lifespan of the application runs. We describe what a dynamic big-data-driven application system (DBDDAS) toolkit must have in order to provide all of the essential building blocks that are necessary to easily create new DDDAS without re-inventing the building blocks.

An Open Framework for Dynamic Big-data-driven Application Systems (DBDDAS) Development

KAUST Repository

Douglas, Craig

2014-06-06

In this paper, we outline key features that dynamic data-driven application systems (DDDAS) have. A DDDAS is an application that has data assimilation that can change the models and/or scales of the computation and that the application controls the data collection based on the computational results. The term Big Data (BD) has come into being in recent years that is highly applicable to most DDDAS since most applications use networks of sensors that generate an overwhelming amount of data in the lifespan of the application runs. We describe what a dynamic big-data-driven application system (DBDDAS) toolkit must have in order to provide all of the essential building blocks that are necessary to easily create new DDDAS without re-inventing the building blocks.

Bioprocess iterative batch-to-batch optimization based on hybrid parametric/nonparametric models.

Science.gov (United States)

Teixeira, Ana P; Clemente, João J; Cunha, António E; Carrondo, Manuel J T; Oliveira, Rui

2006-01-01

This paper presents a novel method for iterative batch-to-batch dynamic optimization of bioprocesses. The relationship between process performance and control inputs is established by means of hybrid grey-box models combining parametric and nonparametric structures. The bioreactor dynamics are defined by material balance equations, whereas the cell population subsystem is represented by an adjustable mixture of nonparametric and parametric models. Thus optimizations are possible without detailed mechanistic knowledge concerning the biological system. A clustering technique is used to supervise the reliability of the nonparametric subsystem during the optimization. Whenever the nonparametric outputs are unreliable, the objective function is penalized. The technique was evaluated with three simulation case studies. The overall results suggest that the convergence to the optimal process performance may be achieved after a small number of batches. The model unreliability risk constraint along with sampling scheduling are crucial to minimize the experimental effort required to attain a given process performance. In general terms, it may be concluded that the proposed method broadens the application of the hybrid parametric/nonparametric modeling technique to "newer" processes with higher potential for optimization.

Model Driven Development of Data Sensitive Systems

DEFF Research Database (Denmark)

Olsen, Petur

2014-01-01

storage systems, where the actual values of the data is not relevant for the behavior of the system. For many systems the values are important. For instance the control flow of the system can be dependent on the input values. We call this type of system data sensitive, as the execution is sensitive...... to the values of variables. This theses strives to improve model-driven development of such data-sensitive systems. This is done by addressing three research questions. In the first we combine state-based modeling and abstract interpretation, in order to ease modeling of data-sensitive systems, while allowing...... efficient model-checking and model-based testing. In the second we develop automatic abstraction learning used together with model learning, in order to allow fully automatic learning of data-sensitive systems to allow learning of larger systems. In the third we develop an approach for modeling and model-based...

Glucoamylase production in batch, chemostat and fed-batch cultivations by an industrial strain of Aspergillus niger

DEFF Research Database (Denmark)

Pedersen, Henrik; Beyer, Michael; Nielsen, Jens

2000-01-01

The Aspergillus niger strain BO-1 was grown in batch, continuous (chemostat) and fed-batch cultivations in order to study the production of the extracellular enzyme glucoamylase under different growth conditions. In the pH range 2.5-6.0, the specific glucoamylase productivity and the specific...

Data-Driven Security-Constrained OPF

DEFF Research Database (Denmark)

Thams, Florian; Halilbasic, Lejla; Pinson, Pierre

2017-01-01

considerations, while being less conservative than current approaches. Our approach can be scalable for large systems, accounts explicitly for power system security, and enables the electricity market to identify a cost-efficient dispatch avoiding redispatching actions. We demonstrate the performance of our......In this paper we unify electricity market operations with power system security considerations. Using data-driven techniques, we address both small signal stability and steady-state security, derive tractable decision rules in the form of line flow limits, and incorporate the resulting constraints...... in market clearing algorithms. Our goal is to minimize redispatching actions, and instead allow the market to determine the most cost-efficient dispatch while considering all security constraints. To maintain tractability of our approach we perform our security assessment offline, examining large datasets...

Advances in industrial biopharmaceutical batch process monitoring: Machine-learning methods for small data problems.

Science.gov (United States)

Tulsyan, Aditya; Garvin, Christopher; Ündey, Cenk

2018-04-06

Biopharmaceutical manufacturing comprises of multiple distinct processing steps that require effective and efficient monitoring of many variables simultaneously in real-time. The state-of-the-art real-time multivariate statistical batch process monitoring (BPM) platforms have been in use in recent years to ensure comprehensive monitoring is in place as a complementary tool for continued process verification to detect weak signals. This article addresses a longstanding, industry-wide problem in BPM, referred to as the "Low-N" problem, wherein a product has a limited production history. The current best industrial practice to address the Low-N problem is to switch from a multivariate to a univariate BPM, until sufficient product history is available to build and deploy a multivariate BPM platform. Every batch run without a robust multivariate BPM platform poses risk of not detecting potential weak signals developing in the process that might have an impact on process and product performance. In this article, we propose an approach to solve the Low-N problem by generating an arbitrarily large number of in silico batches through a combination of hardware exploitation and machine-learning methods. To the best of authors' knowledge, this is the first article to provide a solution to the Low-N problem in biopharmaceutical manufacturing using machine-learning methods. Several industrial case studies from bulk drug substance manufacturing are presented to demonstrate the efficacy of the proposed approach for BPM under various Low-N scenarios. © 2018 Wiley Periodicals, Inc.

Data-Driven Model Order Reduction for Bayesian Inverse Problems

KAUST Repository

Cui, Tiangang

2014-01-06

One of the major challenges in using MCMC for the solution of inverse problems is the repeated evaluation of computationally expensive numerical models. We develop a data-driven projection- based model order reduction technique to reduce the computational cost of numerical PDE evaluations in this context.

Robust Data-Driven Inference for Density-Weighted Average Derivatives

DEFF Research Database (Denmark)

Cattaneo, Matias D.; Crump, Richard K.; Jansson, Michael

This paper presents a new data-driven bandwidth selector compatible with the small bandwidth asymptotics developed in Cattaneo, Crump, and Jansson (2009) for density- weighted average derivatives. The new bandwidth selector is of the plug-in variety, and is obtained based on a mean squared error...

Data-driven modelling of LTI systems using symbolic regression

NARCIS (Netherlands)

Khandelwal, D.; Toth, R.; Van den Hof, P.M.J.

2017-01-01

The aim of this project is to automate the task of data-driven identification of dynamical systems. The underlying goal is to develop an identification tool that models a physical system without distinguishing between classes of systems such as linear, nonlinear or possibly even hybrid systems. Such

"Batch" kinetics in flow: online IR analysis and continuous control.

Science.gov (United States)

Moore, Jason S; Jensen, Klavs F

2014-01-07

Currently, kinetic data is either collected under steady-state conditions in flow or by generating time-series data in batch. Batch experiments are generally considered to be more suitable for the generation of kinetic data because of the ability to collect data from many time points in a single experiment. Now, a method that rapidly generates time-series reaction data from flow reactors by continuously manipulating the flow rate and reaction temperature has been developed. This approach makes use of inline IR analysis and an automated microreactor system, which allowed for rapid and tight control of the operating conditions. The conversion/residence time profiles at several temperatures were used to fit parameters to a kinetic model. This method requires significantly less time and a smaller amount of starting material compared to one-at-a-time flow experiments, and thus allows for the rapid generation of kinetic data. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

CONVERSION OF PINEAPPLE JUICE WASTE INTO LACTIC ACID IN BATCH AND FED – BATCH FERMENTATION SYSTEMS

Directory of Open Access Journals (Sweden)

Abdullah Mochamad Busairi

2012-01-01

Full Text Available Pineapple juice waste contains valuable components, which are mainly sucrose, glucose, and fructose. Recently, lactic acid has been considered to be an important raw material for the production of biodegradable lactide polymer. The fermentation experiments were carried out in a 3 litres fermentor (Biostat B Model under anaerobic condition with stirring speed of 50 rpm, temperature at 40oC, and pH of 6.00. Effect of feed concentration on lactic acid production, bacterial growth, substrate utilisation and productivity was studied. The results obtained from fed- batch culture fermentation showed that the maximum lactic acid productivity was 0.44 g/L.h for feed concentration of 90 g/L at 48 hours. Whereas the lactic acid productivity obtained from fed-batch culture was twice and half fold higher than that of batch culture productivity.  Buangan jus nanas mengandung komponen yang berharga terutama sukrosa, glukosa, dan fruktosa. Asam laktat adalah bahan baku yang terbaru dan penting untuk dibuat sebagai polimer laktat yang dapat terdegradasi oleh lingkungan. Percobaan dilakukan pada fermentor 3 liter (Model Biostat B di bawah kondisi anaerob dengan kecepatan pengadukan 50 rpm, temperatur 40oC, dan pH 6,00. Pengaruh konsentrasi umpan terhadap produksi asam laktat, pertumbuhan mikroba, pengggunaan substrat dan produktivitas telah dipelajari. Hasil yang didapatkan pada fermentasi dengan menggunakan sistem fed-batch menunjukkan bahwa produktivitas asam laktat maksimum adalah 0.44 g/L,jam dengan konsentrasi umpan, 90 g/L pada waktu 48 jam. Bahkan produktivitas asam laktat yang didapat pada kultur fed-batch lebih tinggi 2,5 kali dari pada proses menggunakan sistem batch

Passing in Command Line Arguments and Parallel Cluster/Multicore Batching in R with batch.

Science.gov (United States)

Hoffmann, Thomas J

2011-03-01

It is often useful to rerun a command line R script with some slight change in the parameters used to run it - a new set of parameters for a simulation, a different dataset to process, etc. The R package batch provides a means to pass in multiple command line options, including vectors of values in the usual R format, easily into R. The same script can be setup to run things in parallel via different command line arguments. The R package batch also provides a means to simplify this parallel batching by allowing one to use R and an R-like syntax for arguments to spread a script across a cluster or local multicore/multiprocessor computer, with automated syntax for several popular cluster types. Finally it provides a means to aggregate the results together of multiple processes run on a cluster.

Pollution prevention applications in batch manufacturing operations

Science.gov (United States)

Sykes, Derek W.; O'Shaughnessy, James

2004-02-01

Older, "low-tech" batch manufacturing operations are often fertile grounds for gains resulting from pollution prevention techniques. This paper presents a pollution prevention technique utilized for wastewater discharge permit compliance purposes at a batch manufacturer of detergents, deodorants, and floor-care products. This manufacturer generated industrial wastewater as a result of equipment rinses required after each product batch changeover. After investing a significant amount of capital on end of pip-line wastewater treatment technology designed to address existing discharge limits, this manufacturer chose to investigate alternate, low-cost approaches to address anticipated new permit limits. Mass balances using spreadsheets and readily available formulation and production data were conducted on over 300 products to determine how each individual product contributed to the total wastewater pollutant load. These mass balances indicated that 22 products accounted for over 55% of the wastewater pollutant. Laboratory tests were conducted to determine whether these same products could accept their individual changeover rinse water as make-up water in formulations without sacrificing product quality. This changeover reuse technique was then implement at the plant scale for selected products. Significant reductions in wastewater volume (25%) and wastewater pollutant loading (85+%) were realized as a direct result of this approach.

Fork-join and data-driven execution models on multi-core architectures: Case study of the FMM

KAUST Repository

Amer, Abdelhalim

2013-01-01

Extracting maximum performance of multi-core architectures is a difficult task primarily due to bandwidth limitations of the memory subsystem and its complex hierarchy. In this work, we study the implications of fork-join and data-driven execution models on this type of architecture at the level of task parallelism. For this purpose, we use a highly optimized fork-join based implementation of the FMM and extend it to a data-driven implementation using a distributed task scheduling approach. This study exposes some limitations of the conventional fork-join implementation in terms of synchronization overheads. We find that these are not negligible and their elimination by the data-driven method, with a careful data locality strategy, was beneficial. Experimental evaluation of both methods on state-of-the-art multi-socket multi-core architectures showed up to 22% speed-ups of the data-driven approach compared to the original method. We demonstrate that a data-driven execution of FMM not only improves performance by avoiding global synchronization overheads but also reduces the memory-bandwidth pressure caused by memory-intensive computations. © 2013 Springer-Verlag.

Acceptance Test Data for BWXT Coated Particle Batch 93164A Defective IPyC Fraction and Pyrocarbon Anisotropy

Energy Technology Data Exchange (ETDEWEB)

Helmreich, Grant W. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Hunn, John D. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Skitt, Darren J. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Dyer, John A. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

2017-02-01

Coated particle fuel batch J52O-16-93164 was produced by Babcock and Wilcox Technologies (BWXT) for possible selection as fuel for the Advanced Gas Reactor Fuel Development and Qualification (AGR) Program’s AGR-5/6/7 irradiation test in the Idaho National Laboratory (INL) Advanced Test Reactor (ATR), or may be used as demonstration production-scale coated particle fuel for other experiments. The tristructural-isotropic (TRISO) coatings were deposited in a 150-mm-diameter production-scale fluidizedbed chemical vapor deposition (CVD) furnace onto 425-μm-nominal-diameter spherical kernels from BWXT lot J52L-16-69316. Each kernel contained a mixture of 15.5%-enriched uranium carbide and uranium oxide (UCO) and was coated with four consecutive CVD layers: a ~50% dense carbon buffer layer with 100-μm-nominal thickness, a dense inner pyrolytic carbon (IPyC) layer with 40-μm-nominal thickness, a silicon carbide (SiC) layer with 35-μm-nominal thickness, and a dense outer pyrolytic carbon (OPyC) layer with 40-μm-nominal thickness. The TRISO-coated particle batch was sieved to upgrade the particles by removing over-sized and under-sized material, and the upgraded batch was designated by appending the letter A to the end of the batch number (i.e., 93164A).

On-line Scheduling Of Multi-Server Batch Operations

NARCIS (Netherlands)

van der Zee, D.J.; van Harten, A.; Schuur, P.C.

1999-01-01

Batching jobs in a manufacturing system is a very common policy in most industries. Main reasons for batching are avoidance of setups and/or facilitation of material handling. Good examples of batch-wise production systems are ovens found in aircraft industry and in semiconductor manufacturing.

[Batch release of immunoglobulin and monoclonal antibody products].

Science.gov (United States)

Gross, S

2014-10-01

The Paul-Ehrlich Institute (PEI) is an independent institution of the Federal Republic of Germany responsible for performing official experimental batch testing of sera. The institute decides about the release of each batch and performs experimental research in the field. The experimental quality control ensures the potency of the product and also the absence of harmful impurities. For release of an immunoglobulin batch the marketing authorization holder has to submit the documentation of the manufacture and the results of quality control measures together with samples of the batch to the PEI. Experimental testing is performed according to the approved specifications regarding the efficacy and safety. Since implementation of the 15th German drug law amendment, the source of antibody is not defined anymore. According to § 32 German drug law, all batches of sera need to be released by an official control laboratory. Sera are medicinal products, which contain antibodies, antibody fragments or fusion proteins with a functional antibody portion. Therefore, all batches of monoclonal antibodies and derivatives must also be released by the PEI and the marketing authorization holder has to submit a batch release application. Under certain circumstances a waiver for certain products can be issued with regard to batch release. The conditions for such a waiver apply to the majority of monoclonal antibodies.

Dose and batch-dependent hepatobiliary toxicity of 10 nm silver nanoparticles

Directory of Open Access Journals (Sweden)

Marcella De Maglie

2015-07-01

Full Text Available Silver nanoparticles (AgNPs are widely used because of their antimicrobial properties in medical devices and in a variety of consumer products. The extensive use of AgNPs raises concerns about their potential toxicity, although it is still difficult to draw definite conclusions about their toxicity based on published data. Our preliminary studies performed to compare the effect of the AgNPs size (10-40-100 nm on toxicity, demonstrated that the smallest AgNPs determine the most severe toxicological effects. In order to best investigate the impact of physicochemical characteristics of 10 nm AgNPs on toxicity, we compare three different batches of 10 nm AgNPs slightly different in size distribution (Batch A: 8.8±1.7 nm; Batch B: 9.4±1.7 nm; Batch C: 10.0±1.8 nm. Mice were intravenously treated with two doses (5 and 10 mg/kg of the 3 AgNPs. 24 hours after the treatment, mice were euthanized and underwent complete necropsy. Tissues were collected for histopathological examination and total silver content was determined in tissues by inductively coupled plasma mass spectrometry (ICP-MS. All batches induced severe hepatobiliary lesions, i.e. marked hepatocellular necrosis and massive hemorrhage of the gall bladder. The toxicity was dose-dependent and interestingly, the toxic effects were more severe in mice treated with batches A and B that contained smaller AgNPs. Since the total silver mass concentration was similar, the observed batch-dependent toxicity suggest that even subtle differences in size may contribute to relevant changes in the toxicological outcomes, confirming the fundamental involvement of physicochemical features with respect to toxicity.

LSF usage for batch at CERN

CERN Multimedia

Schwickerath, Ulrich

2007-01-01

Contributed poster to the CHEP07. Original abstract: LSF 7, the latest version of Platform's batch workload management system, addresses many issues which limited the ability of LSF 6.1 to support large scale batch farms, such as the lxbatch service at CERN. In this paper we will present the status of the evaluation and deployment of LSF 7 at CERN, including issues concerning the integration of LSF 7 with the gLite grid middleware suite and, in particular, the steps taken to endure an efficient reporting of the local batch system status and usage to the Grid Information System

Data triggered data processing at the Mirror Fusion Test Facility

International Nuclear Information System (INIS)

Jackson, R.J.; Balch, T.R.; Preckshot, G.G.

1986-01-01

A primary characteristic of most batch systems is that the input data files must exist before jobs are scheduled. On the Mirror Fusion Test Facility (MFTF-B) at Lawrence Livermore National Laboratory the authors schedule jobs to process experimental data to be collected during a five minute shot cycle. The data driven processing system emulates a coarsely granular data flow architecture. Processing jobs are scheduled before the experimental data is collected. Processing jobs ''fire'', or execute, as input data becomes available. Similar to UNIX ''pipes'', data produced by upstream processing nodes may be used as inputs by following nodes. Users, working on the networked SUN workstations, specify data processing templates which define processes and their data dependencies. Data specifications indicate the source of data; actual associations with specific data instantiations are made when the jobs are scheduled. The authors report here on details of diagnostic data processing and their experiences

Hierarchical Bayesian models to assess between- and within-batch variability of pathogen contamination in food.

Science.gov (United States)

Commeau, Natalie; Cornu, Marie; Albert, Isabelle; Denis, Jean-Baptiste; Parent, Eric

2012-03-01

Assessing within-batch and between-batch variability is of major interest for risk assessors and risk managers in the context of microbiological contamination of food. For example, the ratio between the within-batch variability and the between-batch variability has a large impact on the results of a sampling plan. Here, we designed hierarchical Bayesian models to represent such variability. Compatible priors were built mathematically to obtain sound model comparisons. A numeric criterion is proposed to assess the contamination structure comparing the ability of the models to replicate grouped data at the batch level using a posterior predictive loss approach. Models were applied to two case studies: contamination by Listeria monocytogenes of pork breast used to produce diced bacon and contamination by the same microorganism on cold smoked salmon at the end of the process. In the first case study, a contamination structure clearly exists and is located at the batch level, that is, between batches variability is relatively strong, whereas in the second a structure also exists but is less marked. © 2012 Society for Risk Analysis.

Data-Driven Iterative Vibration Signal Enhancement Strategy Using Alpha Stable Distribution

Directory of Open Access Journals (Sweden)

Grzegorz Żak

2017-01-01

Full Text Available The authors propose a novel procedure for enhancement of the signal to noise ratio in vibration data acquired from machines working in mining industry environment. Proposed method allows performing data-driven reduction of the deterministic, high energy, and low frequency components. Furthermore, it provides a way to enhance signal of interest. Procedure incorporates application of the time-frequency decomposition, α-stable distribution based signal modeling, and stability parameter in the time domain as a stoppage criterion for iterative part of the procedure. An advantage of the proposed algorithm is data-driven, automative detection of the informative frequency band as well as band with high energy due to the properties of the used distribution. Furthermore, there is no need to have knowledge regarding kinematics, speed, and so on. The proposed algorithm is applied towards real data acquired from the belt conveyor pulley drive’s gearbox.

Controller synthesis for negative imaginary systems: a data driven approach

KAUST Repository

Mabrok, Mohamed; Petersen, Ian R.

2016-01-01

-driven controller synthesis methodology for NI systems is presented. In this approach, measured frequency response data of the plant is used to construct the controller frequency response at every frequency by minimising a cost function. Then, this controller

Combining engineering and data-driven approaches

DEFF Research Database (Denmark)

Fischer, Katharina; De Sanctis, Gianluca; Kohler, Jochen

2015-01-01

Two general approaches may be followed for the development of a fire risk model: statistical models based on observed fire losses can support simple cost-benefit studies but are usually not detailed enough for engineering decision-making. Engineering models, on the other hand, require many assump...... to the calibration of a generic fire risk model for single family houses to Swiss insurance data. The example demonstrates that the bias in the risk estimation can be strongly reduced by model calibration.......Two general approaches may be followed for the development of a fire risk model: statistical models based on observed fire losses can support simple cost-benefit studies but are usually not detailed enough for engineering decision-making. Engineering models, on the other hand, require many...... assumptions that may result in a biased risk assessment. In two related papers we show how engineering and data-driven modelling can be combined by developing generic risk models that are calibrated to statistical data on observed fire events. The focus of the present paper is on the calibration procedure...

Pengembangan Data Warehouse Menggunakan Pendekatan Data-Driven untuk Membantu Pengelolaan SDM

Directory of Open Access Journals (Sweden)

Mujiono Mujiono

2016-01-01

Full Text Available The basis of bureaucratic reform is the reform of human resources management. One supporting factor is the development of an employee database. To support the management of human resources required including data warehouse and business intelligent tools. The data warehouse is an integrated concept of reliable data storage to provide support to all the needs of the data analysis. In this study developed a data warehouse using the data-driven approach to the source data comes from SIMPEG, SAPK and electronic presence. Data warehouses are designed using the nine steps methodology and unified modeling language (UML notation. Extract transform load (ETL is done by using Pentaho Data Integration by applying transformation maps. Furthermore, to help human resource management, the system is built to perform online analytical processing (OLAP to facilitate web-based information. In this study generated BI application development framework with Model-View-Controller (MVC architecture and OLAP operations are built using the dynamic query generation, PivotTable, and HighChart to present information about PNS, CPNS, Retirement, Kenpa and Presence

Automated Creation of Datamarts from a Clinical Data Warehouse, Driven by an Active Metadata Repository

Science.gov (United States)

Rogerson, Charles L.; Kohlmiller, Paul H.; Stutman, Harris

1998-01-01

A methodology and toolkit are described which enable the automated metadata-driven creation of datamarts from clinical data warehouses. The software uses schema-to-schema transformation driven by an active metadata repository. Tools for assessing datamart data quality are described, as well as methods for assessing the feasibility of implementing specific datamarts. A methodology for data remediation and the re-engineering of operational data capture is described.

Family based dispatching with batch availability

NARCIS (Netherlands)

van der Zee, D.J.

2013-01-01

Family based dispatching rules seek to lower set-up frequencies by grouping (batching) similar types of jobs for joint processing. Hence shop flow times may be improved, as less time is spent on set-ups. Motivated by an industrial project we study the control of machines with batch availability,

On-line scheduling of multi-server batch operations

NARCIS (Netherlands)

Zee, Durk Jouke van der; Harten, Aart van; Schuur, Peter

The batching of jobs in a manufacturing system is a very common policy in many industries. The main reasons for batching are the avoidance of setups and/or facilitation of material handling. Good examples of batch-wise production systems are the ovens that are found in the aircraft industry and in

DeDaL: Cytoscape 3 app for producing and morphing data-driven and structure-driven network layouts.

Science.gov (United States)

Czerwinska, Urszula; Calzone, Laurence; Barillot, Emmanuel; Zinovyev, Andrei

2015-08-14

Visualization and analysis of molecular profiling data together with biological networks are able to provide new mechanistic insights into biological functions. Currently, it is possible to visualize high-throughput data on top of pre-defined network layouts, but they are not always adapted to a given data analysis task. A network layout based simultaneously on the network structure and the associated multidimensional data might be advantageous for data visualization and analysis in some cases. We developed a Cytoscape app, which allows constructing biological network layouts based on the data from molecular profiles imported as values of node attributes. DeDaL is a Cytoscape 3 app, which uses linear and non-linear algorithms of dimension reduction to produce data-driven network layouts based on multidimensional data (typically gene expression). DeDaL implements several data pre-processing and layout post-processing steps such as continuous morphing between two arbitrary network layouts and aligning one network layout with respect to another one by rotating and mirroring. The combination of all these functionalities facilitates the creation of insightful network layouts representing both structural network features and correlation patterns in multivariate data. We demonstrate the added value of applying DeDaL in several practical applications, including an example of a large protein-protein interaction network. DeDaL is a convenient tool for applying data dimensionality reduction methods and for designing insightful data displays based on data-driven layouts of biological networks, built within Cytoscape environment. DeDaL is freely available for downloading at http://bioinfo-out.curie.fr/projects/dedal/.

Data-driven haemodynamic response function extraction using Fourier-wavelet regularised deconvolution

NARCIS (Netherlands)

Wink, Alle Meije; Hoogduin, Hans; Roerdink, Jos B.T.M.

2008-01-01

Background: We present a simple, data-driven method to extract haemodynamic response functions (HRF) from functional magnetic resonance imaging (fMRI) time series, based on the Fourier-wavelet regularised deconvolution (ForWaRD) technique. HRF data are required for many fMRI applications, such as

Data-driven haemodynamic response function extraction using Fourier-wavelet regularised deconvolution

NARCIS (Netherlands)

Wink, Alle Meije; Hoogduin, Hans; Roerdink, Jos B.T.M.

2010-01-01

Background: We present a simple, data-driven method to extract haemodynamic response functions (HRF) from functional magnetic resonance imaging (fMRI) time series, based on the Fourier-wavelet regularised deconvolution (ForWaRD) technique. HRF data are required for many fMRI applications, such as

Efficient Feature-Driven Visualization of Large-Scale Scientific Data

Energy Technology Data Exchange (ETDEWEB)

Lu, Aidong

2012-12-12

Very large, complex scientific data acquired in many research areas creates critical challenges for scientists to understand, analyze, and organize their data. The objective of this project is to expand the feature extraction and analysis capabilities to develop powerful and accurate visualization tools that can assist domain scientists with their requirements in multiple phases of scientific discovery. We have recently developed several feature-driven visualization methods for extracting different data characteristics of volumetric datasets. Our results verify the hypothesis in the proposal and will be used to develop additional prototype systems.

A data-driven prediction method for fast-slow systems

Science.gov (United States)

Groth, Andreas; Chekroun, Mickael; Kondrashov, Dmitri; Ghil, Michael

2016-04-01

In this work, we present a prediction method for processes that exhibit a mixture of variability on low and fast scales. The method relies on combining empirical model reduction (EMR) with singular spectrum analysis (SSA). EMR is a data-driven methodology for constructing stochastic low-dimensional models that account for nonlinearity and serial correlation in the estimated noise, while SSA provides a decomposition of the complex dynamics into low-order components that capture spatio-temporal behavior on different time scales. Our study focuses on the data-driven modeling of partial observations from dynamical systems that exhibit power spectra with broad peaks. The main result in this talk is that the combination of SSA pre-filtering with EMR modeling improves, under certain circumstances, the modeling and prediction skill of such a system, as compared to a standard EMR prediction based on raw data. Specifically, it is the separation into "fast" and "slow" temporal scales by the SSA pre-filtering that achieves the improvement. We show, in particular that the resulting EMR-SSA emulators help predict intermittent behavior such as rapid transitions between specific regions of the system's phase space. This capability of the EMR-SSA prediction will be demonstrated on two low-dimensional models: the Rössler system and a Lotka-Volterra model for interspecies competition. In either case, the chaotic dynamics is produced through a Shilnikov-type mechanism and we argue that the latter seems to be an important ingredient for the good prediction skills of EMR-SSA emulators. Shilnikov-type behavior has been shown to arise in various complex geophysical fluid models, such as baroclinic quasi-geostrophic flows in the mid-latitude atmosphere and wind-driven double-gyre ocean circulation models. This pervasiveness of the Shilnikow mechanism of fast-slow transition opens interesting perspectives for the extension of the proposed EMR-SSA approach to more realistic situations.

Data-driven Inference and Investigation of Thermosphere Dynamics and Variations

Science.gov (United States)

Mehta, P. M.; Linares, R.

2017-12-01

This paper presents a methodology for data-driven inference and investigation of thermosphere dynamics and variations. The approach uses data-driven modal analysis to extract the most energetic modes of variations for neutral thermospheric species using proper orthogonal decomposition, where the time-independent modes or basis represent the dynamics and the time-depedent coefficients or amplitudes represent the model parameters. The data-driven modal analysis approach combined with sparse, discrete observations is used to infer amplitues for the dynamic modes and to calibrate the energy content of the system. In this work, two different data-types, namely the number density measurements from TIMED/GUVI and the mass density measurements from CHAMP/GRACE are simultaneously ingested for an accurate and self-consistent specification of the thermosphere. The assimilation process is achieved with a non-linear least squares solver and allows estimation/tuning of the model parameters or amplitudes rather than the driver. In this work, we use the Naval Research Lab's MSIS model to derive the most energetic modes for six different species, He, O, N2, O2, H, and N. We examine the dominant drivers of variations for helium in MSIS and observe that seasonal latitudinal variation accounts for about 80% of the dynamic energy with a strong preference of helium for the winter hemisphere. We also observe enhanced helium presence near the poles at GRACE altitudes during periods of low solar activity (Feb 2007) as previously deduced. We will also examine the storm-time response of helium derived from observations. The results are expected to be useful in tuning/calibration of the physics-based models.

Unifying cancer and normal RNA sequencing data from different sources

Science.gov (United States)

Wang, Qingguo; Armenia, Joshua; Zhang, Chao; Penson, Alexander V.; Reznik, Ed; Zhang, Liguo; Minet, Thais; Ochoa, Angelica; Gross, Benjamin E.; Iacobuzio-Donahue, Christine A.; Betel, Doron; Taylor, Barry S.; Gao, Jianjiong; Schultz, Nikolaus

2018-01-01

Driven by the recent advances of next generation sequencing (NGS) technologies and an urgent need to decode complex human diseases, a multitude of large-scale studies were conducted recently that have resulted in an unprecedented volume of whole transcriptome sequencing (RNA-seq) data, such as the Genotype Tissue Expression project (GTEx) and The Cancer Genome Atlas (TCGA). While these data offer new opportunities to identify the mechanisms underlying disease, the comparison of data from different sources remains challenging, due to differences in sample and data processing. Here, we developed a pipeline that processes and unifies RNA-seq data from different studies, which includes uniform realignment, gene expression quantification, and batch effect removal. We find that uniform alignment and quantification is not sufficient when combining RNA-seq data from different sources and that the removal of other batch effects is essential to facilitate data comparison. We have processed data from GTEx and TCGA and successfully corrected for study-specific biases, enabling comparative analysis between TCGA and GTEx. The normalized datasets are available for download on figshare. PMID:29664468

Modeling of Fusarium redolens Dzf2 mycelial growth kinetics and optimal fed-batch fermentation for beauvericin production.

Science.gov (United States)

Xu, Li-Jian; Liu, Yuan-Shuai; Zhou, Li-Gang; Wu, Jian-Yong

2011-09-01

Beauvericin (BEA) is a cyclic hexadepsipeptide mycotoxin with notable phytotoxic and insecticidal activities. Fusarium redolens Dzf2 is a highly BEA-producing fungus isolated from a medicinal plant. The aim of the current study was to develop a simple and valid kinetic model for F. redolens Dzf2 mycelial growth and the optimal fed-batch operation for efficient BEA production. A modified Monod model with substrate (glucose) and product (BEA) inhibition was constructed based on the culture characteristics of F. redolens Dzf2 mycelia in a liquid medium. Model parameters were derived by simulation of the experimental data from batch culture. The model fitted closely with the experimental data over 20-50 g l(-1) glucose concentration range in batch fermentation. The kinetic model together with the stoichiometric relationships for biomass, substrate and product was applied to predict the optimal feeding scheme for fed-batch fermentation, leading to 54% higher BEA yield (299 mg l(-1)) than in the batch culture (194 mg l(-1)). The modified Monod model incorporating substrate and product inhibition was proven adequate for describing the growth kinetics of F. redolens Dzf2 mycelial culture at suitable but not excessive initial glucose levels in batch and fed-batch cultures.

Data-driven HR how to use analytics and metrics to drive performance

CERN Document Server

Marr, Bernard

2018-01-01

Traditionally seen as a purely people function unconcerned with numbers, HR is now uniquely placed to use company data to drive performance, both of the people in the organization and the organization as a whole. Data-driven HR is a practical guide which enables HR practitioners to leverage the value of the vast amount of data available at their fingertips. Covering how to identify the most useful sources of data, how to collect information in a transparent way that is in line with data protection requirements and how to turn this data into tangible insights, this book marks a turning point for the HR profession. Covering all the key elements of HR including recruitment, employee engagement, performance management, wellbeing and training, Data-driven HR examines the ways data can contribute to organizational success by, among other things, optimizing processes, driving performance and improving HR decision making. Packed with case studies and real-life examples, this is essential reading for all HR profession...

Data Driven Modelling of the Dynamic Wake Between Two Wind Turbines

DEFF Research Database (Denmark)

Knudsen, Torben; Bak, Thomas

2012-01-01

turbine. This paper establishes flow models relating the wind speeds at turbines in a farm. So far, research in this area has been mainly based on first principles static models and the data driven modelling done has not included the loading of the upwind turbine and its impact on the wind speed downwind......Wind turbines in a wind farm, influence each other through the wind flow. Downwind turbines are in the wake of upwind turbines and the wind speed experienced at downwind turbines is hence a function of the wind speeds at upwind turbines but also the momentum extracted from the wind by the upwind....... This paper is the first where modern commercial mega watt turbines are used for data driven modelling including the upwind turbine loading by changing power reference. Obtaining the necessary data is difficult and data is therefore limited. A simple dynamic extension to the Jensen wake model is tested...

Batch-To-Batch Rational Feedforward Control : From Iterative Learning to Identification Approaches, with Application to a Wafer Stage

NARCIS (Netherlands)

Blanken, L.; Boeren, F.A.J.; Bruijnen, D.J.H.; Oomen, T.A.E.

2017-01-01

Feedforward control enables high performance for industrial motion systems that perform nonrepeating motion tasks. Recently, learning techniques have been proposed that improve both performance and flexibility to nonrepeating tasks in a batch-To-batch fashion by using a rational parameterization in

Lipid production in batch and fed-batch cultures of Rhodosporidium toruloides from 5 and 6 carbon carbohydrates

Directory of Open Access Journals (Sweden)

Wiebe Marilyn G

2012-05-01

Full Text Available Abstract Background Microbial lipids are a potential source of bio- or renewable diesel and the red yeast Rhodosporidium toruloides is interesting not only because it can accumulate over 50% of its dry biomass as lipid, but also because it utilises both five and six carbon carbohydrates, which are present in plant biomass hydrolysates. Methods R. toruloides was grown in batch and fed-batch cultures in 0.5 L bioreactors at pH 4 in chemically defined, nitrogen restricted (C/N 40 to 100 media containing glucose, xylose, arabinose, or all three carbohydrates as carbon source. Lipid was extracted from the biomass using chloroform-methanol, measured gravimetrically and analysed by GC. Results Lipid production was most efficient with glucose (up to 25 g lipid L−1, 48 to 75% lipid in the biomass, at up to 0.21 g lipid L−1 h−1 as the sole carbon source, but high lipid concentrations were also produced from xylose (36 to 45% lipid in biomass. Lipid production was low (15–19% lipid in biomass with arabinose as sole carbon source and was lower than expected (30% lipid in biomass when glucose, xylose and arabinose were provided simultaneously. The presence of arabinose and/or xylose in the medium increased the proportion of palmitic and linoleic acid and reduced the proportion of oleic acid in the fatty acids, compared to glucose-grown cells. High cell densities were obtained in both batch (37 g L−1, with 49% lipid in the biomass and fed-batch (35 to 47 g L−1, with 50 to 75% lipid in the biomass cultures. The highest proportion of lipid in the biomass was observed in cultures given nitrogen during the batch phase but none with the feed. However, carbohydrate consumption was incomplete when the feed did not contain nitrogen and the highest total lipid and best substrate consumption were observed in cultures which received a constant low nitrogen supply. Conclusions Lipid production in R. toruloides was lower from arabinose and mixed

A copula-based sampling method for data-driven prognostics

International Nuclear Information System (INIS)

Xi, Zhimin; Jing, Rong; Wang, Pingfeng; Hu, Chao

2014-01-01

This paper develops a Copula-based sampling method for data-driven prognostics. The method essentially consists of an offline training process and an online prediction process: (i) the offline training process builds a statistical relationship between the failure time and the time realizations at specified degradation levels on the basis of off-line training data sets; and (ii) the online prediction process identifies probable failure times for online testing units based on the statistical model constructed in the offline process and the online testing data. Our contributions in this paper are three-fold, namely the definition of a generic health index system to quantify the health degradation of an engineering system, the construction of a Copula-based statistical model to learn the statistical relationship between the failure time and the time realizations at specified degradation levels, and the development of a simulation-based approach for the prediction of remaining useful life (RUL). Two engineering case studies, namely the electric cooling fan health prognostics and the 2008 IEEE PHM challenge problem, are employed to demonstrate the effectiveness of the proposed methodology. - Highlights: • We develop a novel mechanism for data-driven prognostics. • A generic health index system quantifies health degradation of engineering systems. • Off-line training model is constructed based on the Bayesian Copula model. • Remaining useful life is predicted from a simulation-based approach

Sojourn time distributions in a Markovian G-queue with batch arrival and batch removal

Directory of Open Access Journals (Sweden)

Yang Woo Shin

1999-01-01

Full Text Available We consider a single server Markovian queue with two types of customers; positive and negative, where positive customers arrive in batches and arrivals of negative customers remove positive customers in batches. Only positive customers form a queue and negative customers just reduce the system congestion by removing positive ones upon their arrivals. We derive the LSTs of sojourn time distributions for a single server Markovian queue with positive customers and negative customers by using the first passage time arguments for Markov chains.

Stochastic growth logistic model with aftereffect for batch fermentation process

Energy Technology Data Exchange (ETDEWEB)

Rosli, Norhayati; Ayoubi, Tawfiqullah [Faculty of Industrial Sciences and Technology, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300 Gambang, Pahang (Malaysia); Bahar, Arifah; Rahman, Haliza Abdul [Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor (Malaysia); Salleh, Madihah Md [Department of Biotechnology Industry, Faculty of Biosciences and Bioengineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor (Malaysia)

2014-06-19

In this paper, the stochastic growth logistic model with aftereffect for the cell growth of C. acetobutylicum P262 and Luedeking-Piret equations for solvent production in batch fermentation system is introduced. The parameters values of the mathematical models are estimated via Levenberg-Marquardt optimization method of non-linear least squares. We apply Milstein scheme for solving the stochastic models numerically. The effciency of mathematical models is measured by comparing the simulated result and the experimental data of the microbial growth and solvent production in batch system. Low values of Root Mean-Square Error (RMSE) of stochastic models with aftereffect indicate good fits.

Stochastic growth logistic model with aftereffect for batch fermentation process

Science.gov (United States)

Rosli, Norhayati; Ayoubi, Tawfiqullah; Bahar, Arifah; Rahman, Haliza Abdul; Salleh, Madihah Md

2014-06-01

In this paper, the stochastic growth logistic model with aftereffect for the cell growth of C. acetobutylicum P262 and Luedeking-Piret equations for solvent production in batch fermentation system is introduced. The parameters values of the mathematical models are estimated via Levenberg-Marquardt optimization method of non-linear least squares. We apply Milstein scheme for solving the stochastic models numerically. The effciency of mathematical models is measured by comparing the simulated result and the experimental data of the microbial growth and solvent production in batch system. Low values of Root Mean-Square Error (RMSE) of stochastic models with aftereffect indicate good fits.

Stochastic growth logistic model with aftereffect for batch fermentation process

International Nuclear Information System (INIS)

Rosli, Norhayati; Ayoubi, Tawfiqullah; Bahar, Arifah; Rahman, Haliza Abdul; Salleh, Madihah Md

2014-01-01

In this paper, the stochastic growth logistic model with aftereffect for the cell growth of C. acetobutylicum P262 and Luedeking-Piret equations for solvent production in batch fermentation system is introduced. The parameters values of the mathematical models are estimated via Levenberg-Marquardt optimization method of non-linear least squares. We apply Milstein scheme for solving the stochastic models numerically. The effciency of mathematical models is measured by comparing the simulated result and the experimental data of the microbial growth and solvent production in batch system. Low values of Root Mean-Square Error (RMSE) of stochastic models with aftereffect indicate good fits

Recommendation of ruthenium source for sludge batch flowsheet studies

Energy Technology Data Exchange (ETDEWEB)

Woodham, W. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL)

2017-09-13

Included herein is a preliminary analysis of previously-generated data from sludge batches 7a, 7b, 8, and 9 sludge simulant and real-waste testing, performed to recommend a form of ruthenium for future sludge batch simulant testing under the nitric-formic flowsheet. Focus is given to reactions present in the Sludge Receipt and Adjustment Tank cycle, given that this cycle historically produces the most changes in chemical composition during Chemical Process Cell processing. Data is presented and analyzed for several runs performed under the nitric-formic flowsheet, with consideration given to effects on the production of hydrogen gas, nitrous oxide gas, consumption of formate, conversion of nitrite to nitrate, and the removal and recovery of mercury during processing. Additionally, a brief discussion is given to the effect of ruthenium source selection under the nitric-glycolic flowsheet. An analysis of data generated from scaled demonstration testing, sludge batch 9 qualification testing, and antifoam degradation testing under the nitric-glycolic flowsheet is presented. Experimental parameters of interest under the nitric-glycolic flowsheet include N2O production, glycolate destruction, conversion of glycolate to formate and oxalate, and the conversion of nitrite to nitrate. To date, the number of real-waste experiments that have been performed under the nitric-glycolic flowsheet is insufficient to provide a complete understanding of the effects of ruthenium source selection in simulant experiments with regard to fidelity to real-waste testing. Therefore, a determination of comparability between the two ruthenium sources as employed under the nitric-glycolic flowsheet is made based on available data in order to inform ruthenium source selection for future testing under the nitric-glycolic flowsheet.

Data driven CAN node reliability assessment for manufacturing system

Science.gov (United States)

Zhang, Leiming; Yuan, Yong; Lei, Yong

2017-01-01

The reliability of the Controller Area Network(CAN) is critical to the performance and safety of the system. However, direct bus-off time assessment tools are lacking in practice due to inaccessibility of the node information and the complexity of the node interactions upon errors. In order to measure the mean time to bus-off(MTTB) of all the nodes, a novel data driven node bus-off time assessment method for CAN network is proposed by directly using network error information. First, the corresponding network error event sequence for each node is constructed using multiple-layer network error information. Then, the generalized zero inflated Poisson process(GZIP) model is established for each node based on the error event sequence. Finally, the stochastic model is constructed to predict the MTTB of the node. The accelerated case studies with different error injection rates are conducted on a laboratory network to demonstrate the proposed method, where the network errors are generated by a computer controlled error injection system. Experiment results show that the MTTB of nodes predicted by the proposed method agree well with observations in the case studies. The proposed data driven node time to bus-off assessment method for CAN networks can successfully predict the MTTB of nodes by directly using network error event data.

Queue Length and Server Content Distribution in an Infinite-Buffer Batch-Service Queue with Batch-Size-Dependent Service

Directory of Open Access Journals (Sweden)

U. C. Gupta

2015-01-01

Full Text Available We analyze an infinite-buffer batch-size-dependent batch-service queue with Poisson arrival and arbitrarily distributed service time. Using supplementary variable technique, we derive a bivariate probability generating function from which the joint distribution of queue and server content at departure epoch of a batch is extracted and presented in terms of roots of the characteristic equation. We also obtain the joint distribution of queue and server content at arbitrary epoch. Finally, the utility of analytical results is demonstrated by the inclusion of some numerical examples which also includes the investigation of multiple zeros.

STATISTICAL EVALUATION OF SMALL SCALE MIXING DEMONSTRATION SAMPLING AND BATCH TRANSFER PERFORMANCE - 12093

Energy Technology Data Exchange (ETDEWEB)

GREER DA; THIEN MG

2012-01-12

The ability to effectively mix, sample, certify, and deliver consistent batches of High Level Waste (HLW) feed from the Hanford Double Shell Tanks (DST) to the Waste Treatment and Immobilization Plant (WTP) presents a significant mission risk with potential to impact mission length and the quantity of HLW glass produced. DOE's Tank Operations Contractor, Washington River Protection Solutions (WRPS) has previously presented the results of mixing performance in two different sizes of small scale DSTs to support scale up estimates of full scale DST mixing performance. Currently, sufficient sampling of DSTs is one of the largest programmatic risks that could prevent timely delivery of high level waste to the WTP. WRPS has performed small scale mixing and sampling demonstrations to study the ability to sufficiently sample the tanks. The statistical evaluation of the demonstration results which lead to the conclusion that the two scales of small DST are behaving similarly and that full scale performance is predictable will be presented. This work is essential to reduce the risk of requiring a new dedicated feed sampling facility and will guide future optimization work to ensure the waste feed delivery mission will be accomplished successfully. This paper will focus on the analytical data collected from mixing, sampling, and batch transfer testing from the small scale mixing demonstration tanks and how those data are being interpreted to begin to understand the relationship between samples taken prior to transfer and samples from the subsequent batches transferred. An overview of the types of data collected and examples of typical raw data will be provided. The paper will then discuss the processing and manipulation of the data which is necessary to begin evaluating sampling and batch transfer performance. This discussion will also include the evaluation of the analytical measurement capability with regard to the simulant material used in the demonstration tests. The

Polynomial Batch Codes for Efficient IT-PIR

Directory of Open Access Journals (Sweden)

Henry Ryan

2016-10-01

Full Text Available Private information retrieval (PIR is a way for clients to query a remote database without the database holder learning the clients’ query terms or the responses they generate. Compelling applications for PIR are abound in the cryptographic and privacy research literature, yet existing PIR techniques are notoriously inefficient. Consequently, no such PIRbased application to date has seen real-world at-scale deployment. This paper proposes new “batch coding” techniques to help address PIR’s efficiency problem. The new techniques exploit the connection between ramp secret sharing schemes and efficient information-theoretically secure PIR (IT-PIR protocols. This connection was previously observed by Henry, Huang, and Goldberg (NDSS 2013, who used ramp schemes to construct efficient “batch queries” with which clients can fetch several database records for the same cost as fetching a single record using a standard, non-batch query. The new techniques in this paper generalize and extend those of Henry et al. to construct “batch codes” with which clients can fetch several records for only a fraction the cost of fetching a single record using a standard non-batch query over an unencoded database. The batch codes are highly tuneable, providing a means to trade off (i lower server-side computation cost, (ii lower server-side storage cost, and/or (iii lower uni- or bi-directional communication cost, in exchange for a comparatively modest decrease in resilience to Byzantine database servers.

Citric acid production from hydrolysate of pretreated straw cellulose by Yarrowia lipolytica SWJ-1b using batch and fed-batch cultivation.

Science.gov (United States)

Liu, Xiaoyan; Lv, Jinshun; Zhang, Tong; Deng, Yuanfang

2015-01-01

In this study, crude cellulase produced by Trichoderma reesei Rut-30 was used to hydrolyze pretreated straw. After the compositions of the hydrolysate of pretreated straw were optimized, the study showed that natural components of pretreated straw without addition of any other components such as (NH4)2SO4, KH2PO4, or Mg(2+) were suitable for citric acid production by Yarrowia lipolytica SWJ-1b, and the optimal ventilatory capacity was 10.0 L/min/L medium. Batch and fed-batch production of citric acid from the hydrolysate of pretreated straw by Yarrowia lipolytica SWJ-1b has been investigated. In the batch cultivation, 25.4 g/L and 26.7 g/L citric acid were yields from glucose and hydrolysate of straw cellulose, respectively, while the cultivation time was 120 hr. In the three-cycle fed-batch cultivation, citric acid (CA) production was increased to 42.4 g/L and the cultivation time was extended to 240 hr. However, iso-citric acid (ICA) yield in fed-batch cultivation (4.0 g/L) was similar to that during the batch cultivation (3.9 g/L), and only 1.6 g/L of reducing sugar was left in the medium at the end of fed-batch cultivation, suggesting that most of the added carbon was used in the cultivation.

Data-Driven Healthcare: Challenges and Opportunities for Interactive Visualization.

Science.gov (United States)

Gotz, David; Borland, David

2016-01-01

The healthcare industry's widespread digitization efforts are reshaping one of the largest sectors of the world's economy. This transformation is enabling systems that promise to use ever-improving data-driven evidence to help doctors make more precise diagnoses, institutions identify at risk patients for intervention, clinicians develop more personalized treatment plans, and researchers better understand medical outcomes within complex patient populations. Given the scale and complexity of the data required to achieve these goals, advanced data visualization tools have the potential to play a critical role. This article reviews a number of visualization challenges unique to the healthcare discipline.

Batch and multi-step fed-batch enzymatic saccharification of Formiline-pretreated sugarcane bagasse at high solid loadings for high sugar and ethanol titers.

Science.gov (United States)

Zhao, Xuebing; Dong, Lei; Chen, Liang; Liu, Dehua

2013-05-01

Formiline pretreatment pertains to a biomass fractionation process. In the present work, Formiline-pretreated sugarcane bagasse was hydrolyzed with cellulases by batch and multi-step fed-batch processes at 20% solid loading. For wet pulp, after 144 h incubation with cellulase loading of 10 FPU/g dry solid, fed-batch process obtained ~150 g/L glucose and ~80% glucan conversion, while batch process obtained ~130 g/L glucose with corresponding ~70% glucan conversion. Solid loading could be further increased to 30% for the acetone-dried pulp. By fed-batch hydrolysis of the dried pulp in pH 4.8 buffer solution, glucose concentration could be 247.3±1.6 g/L with corresponding 86.1±0.6% glucan conversion. The enzymatic hydrolyzates could be well converted to ethanol by a subsequent fermentation using Saccharomices cerevisiae with ethanol titer of 60-70 g/L. Batch and fed-batch SSF indicated that Formiline-pretreated substrate showed excellent fermentability. The final ethanol concentration was 80 g/L with corresponding 82.7% of theoretical yield. Copyright © 2012 Elsevier Ltd. All rights reserved.

A data-driven framework for investigating customer retention

OpenAIRE

Mgbemena, Chidozie Simon

2016-01-01

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London. This study presents a data-driven simulation framework in order to understand customer behaviour and therefore improve customer retention. The overarching system design methodology used for this study is aligned with the design science paradigm. The Social Media Domain Analysis (SoMeDoA) approach is adopted and evaluated to build a model on the determinants of customer satisfaction ...

Data-driven Development of ROTEM and TEG Algorithms for the Management of Trauma Hemorrhage

DEFF Research Database (Denmark)

Baksaas-Aasen, Kjersti; Van Dieren, Susan; Balvers, Kirsten

2018-01-01

for ROTEM, TEG, and CCTs to be used in addition to ratio driven transfusion and tranexamic acid. CONCLUSIONS: We describe a systematic approach to define threshold parameters for ROTEM and TEG. These parameters were incorporated into algorithms to support data-driven adjustments of resuscitation...

The Role of Guided Induction in Paper-Based Data-Driven Learning

Science.gov (United States)

Smart, Jonathan

2014-01-01

This study examines the role of guided induction as an instructional approach in paper-based data-driven learning (DDL) in the context of an ESL grammar course during an intensive English program at an American public university. Specifically, it examines whether corpus-informed grammar instruction is more effective through inductive, data-driven…

External radioactive markers for PET data-driven respiratory gating in positron emission tomography.

Science.gov (United States)

Büther, Florian; Ernst, Iris; Hamill, James; Eich, Hans T; Schober, Otmar; Schäfers, Michael; Schäfers, Klaus P

2013-04-01

Respiratory gating is an established approach to overcoming respiration-induced image artefacts in PET. Of special interest in this respect are raw PET data-driven gating methods which do not require additional hardware to acquire respiratory signals during the scan. However, these methods rely heavily on the quality of the acquired PET data (statistical properties, data contrast, etc.). We therefore combined external radioactive markers with data-driven respiratory gating in PET/CT. The feasibility and accuracy of this approach was studied for [(18)F]FDG PET/CT imaging in patients with malignant liver and lung lesions. PET data from 30 patients with abdominal or thoracic [(18)F]FDG-positive lesions (primary tumours or metastases) were included in this prospective study. The patients underwent a 10-min list-mode PET scan with a single bed position following a standard clinical whole-body [(18)F]FDG PET/CT scan. During this scan, one to three radioactive point sources (either (22)Na or (18)F, 50-100 kBq) in a dedicated holder were attached the patient's abdomen. The list mode data acquired were retrospectively analysed for respiratory signals using established data-driven gating approaches and additionally by tracking the motion of the point sources in sinogram space. Gated reconstructions were examined qualitatively, in terms of the amount of respiratory displacement and in respect of changes in local image intensity in the gated images. The presence of the external markers did not affect whole-body PET/CT image quality. Tracking of the markers led to characteristic respiratory curves in all patients. Applying these curves for gated reconstructions resulted in images in which motion was well resolved. Quantitatively, the performance of the external marker-based approach was similar to that of the best intrinsic data-driven methods. Overall, the gain in measured tumour uptake from the nongated to the gated images indicating successful removal of respiratory motion

Batch Computed Tomography Analysis of Projectiles

Science.gov (United States)

2016-05-01

ARL-TR-7681 ● MAY 2016 US Army Research Laboratory Batch Computed Tomography Analysis of Projectiles by Michael C Golt, Chris M...Laboratory Batch Computed Tomography Analysis of Projectiles by Michael C Golt and Matthew S Bratcher Weapons and Materials Research...values to account for projectile variability in the ballistic evaluation of armor. 15. SUBJECT TERMS computed tomography , CT, BS41, projectiles

Data-driven warehouse optimization : Deploying skills of order pickers

NARCIS (Netherlands)

M. Matusiak (Marek); M.B.M. de Koster (René); J. Saarinen (Jari)

2015-01-01

textabstractBatching orders and routing order pickers is a commonly studied problem in many picker-to-parts warehouses. The impact of individual differences in picking skills on performance has received little attention. In this paper, we show that taking into account differences in the skills of

Helioseismic and neutrino data-driven reconstruction of solar properties

Science.gov (United States)

Song, Ningqiang; Gonzalez-Garcia, M. C.; Villante, Francesco L.; Vinyoles, Nuria; Serenelli, Aldo

2018-06-01

In this work, we use Bayesian inference to quantitatively reconstruct the solar properties most relevant to the solar composition problem using as inputs the information provided by helioseismic and solar neutrino data. In particular, we use a Gaussian process to model the functional shape of the opacity uncertainty to gain flexibility and become as free as possible from prejudice in this regard. With these tools we first readdress the statistical significance of the solar composition problem. Furthermore, starting from a composition unbiased set of standard solar models (SSMs) we are able to statistically select those with solar chemical composition and other solar inputs which better describe the helioseismic and neutrino observations. In particular, we are able to reconstruct the solar opacity profile in a data-driven fashion, independently of any reference opacity tables, obtaining a 4 per cent uncertainty at the base of the convective envelope and 0.8 per cent at the solar core. When systematic uncertainties are included, results are 7.5 per cent and 2 per cent, respectively. In addition, we find that the values of most of the other inputs of the SSMs required to better describe the helioseismic and neutrino data are in good agreement with those adopted as the standard priors, with the exception of the astrophysical factor S11 and the microscopic diffusion rates, for which data suggests a 1 per cent and 30 per cent reduction, respectively. As an output of the study we derive the corresponding data-driven predictions for the solar neutrino fluxes.

Accelerator driven systems for energy production and waste incineration: Physics, design and related nuclear data

International Nuclear Information System (INIS)

Herman, M.; Stanculescu, A.; Paver, N.

2003-01-01

This volume contains the notes of lectures given at the workshops 'Hybrid Nuclear Systems for Energy Production, Utilisation of Actinides and Transmutation of Long-lived Radioactive Waste' and 'Nuclear Data for Science and Technology: Accelerator Driven Waste Incineration', held at the Abdus Salam ICTP in September 2001. The subject of the first workshop was focused on the so-called Accelerator Driven Systems, and covered the most important physics and technological aspects of this innovative field. The second workshop was devoted to an exhaustive survey on the acquisition, evaluation, retrieval and validation of the nuclear data relevant to the design of Accelerator Driven Systems

Accelerator driven systems for energy production and waste incineration: Physics, design and related nuclear data

Energy Technology Data Exchange (ETDEWEB)

Herman, M; Stanculescu, A [International Atomic Energy Agency, Vienna (Austria); Paver, N [University of Trieste and INFN, Trieste (Italy)

2003-06-15

This volume contains the notes of lectures given at the workshops 'Hybrid Nuclear Systems for Energy Production, Utilisation of Actinides and Transmutation of Long-lived Radioactive Waste' and 'Nuclear Data for Science and Technology: Accelerator Driven Waste Incineration', held at the Abdus Salam ICTP in September 2001. The subject of the first workshop was focused on the so-called Accelerator Driven Systems, and covered the most important physics and technological aspects of this innovative field. The second workshop was devoted to an exhaustive survey on the acquisition, evaluation, retrieval and validation of the nuclear data relevant to the design of Accelerator Driven Systems.

Data driven propulsion system weight prediction model

Science.gov (United States)

Gerth, Richard J.

1994-10-01

The objective of the research was to develop a method to predict the weight of paper engines, i.e., engines that are in the early stages of development. The impetus for the project was the Single Stage To Orbit (SSTO) project, where engineers need to evaluate alternative engine designs. Since the SSTO is a performance driven project the performance models for alternative designs were well understood. The next tradeoff is weight. Since it is known that engine weight varies with thrust levels, a model is required that would allow discrimination between engines that produce the same thrust. Above all, the model had to be rooted in data with assumptions that could be justified based on the data. The general approach was to collect data on as many existing engines as possible and build a statistical model of the engines weight as a function of various component performance parameters. This was considered a reasonable level to begin the project because the data would be readily available, and it would be at the level of most paper engines, prior to detailed component design.

A scalable architecture for online anomaly detection of WLCG batch jobs

Science.gov (United States)

Kuehn, E.; Fischer, M.; Giffels, M.; Jung, C.; Petzold, A.

2016-10-01

For data centres it is increasingly important to monitor the network usage, and learn from network usage patterns. Especially configuration issues or misbehaving batch jobs preventing a smooth operation need to be detected as early as possible. At the GridKa data and computing centre we therefore operate a tool BPNetMon for monitoring traffic data and characteristics of WLCG batch jobs and pilots locally on different worker nodes. On the one hand local information itself are not sufficient to detect anomalies for several reasons, e.g. the underlying job distribution on a single worker node might change or there might be a local misconfiguration. On the other hand a centralised anomaly detection approach does not scale regarding network communication as well as computational costs. We therefore propose a scalable architecture based on concepts of a super-peer network.

qPortal: A platform for data-driven biomedical research.

Science.gov (United States)

Mohr, Christopher; Friedrich, Andreas; Wojnar, David; Kenar, Erhan; Polatkan, Aydin Can; Codrea, Marius Cosmin; Czemmel, Stefan; Kohlbacher, Oliver; Nahnsen, Sven

2018-01-01

Modern biomedical research aims at drawing biological conclusions from large, highly complex biological datasets. It has become common practice to make extensive use of high-throughput technologies that produce big amounts of heterogeneous data. In addition to the ever-improving accuracy, methods are getting faster and cheaper, resulting in a steadily increasing need for scalable data management and easily accessible means of analysis. We present qPortal, a platform providing users with an intuitive way to manage and analyze quantitative biological data. The backend leverages a variety of concepts and technologies, such as relational databases, data stores, data models and means of data transfer, as well as front-end solutions to give users access to data management and easy-to-use analysis options. Users are empowered to conduct their experiments from the experimental design to the visualization of their results through the platform. Here, we illustrate the feature-rich portal by simulating a biomedical study based on publically available data. We demonstrate the software's strength in supporting the entire project life cycle. The software supports the project design and registration, empowers users to do all-digital project management and finally provides means to perform analysis. We compare our approach to Galaxy, one of the most widely used scientific workflow and analysis platforms in computational biology. Application of both systems to a small case study shows the differences between a data-driven approach (qPortal) and a workflow-driven approach (Galaxy). qPortal, a one-stop-shop solution for biomedical projects offers up-to-date analysis pipelines, quality control workflows, and visualization tools. Through intensive user interactions, appropriate data models have been developed. These models build the foundation of our biological data management system and provide possibilities to annotate data, query metadata for statistics and future re-analysis on

Using Shape Memory Alloys: A Dynamic Data Driven Approach

KAUST Repository

Douglas, Craig C.

2013-06-01

Shape Memory Alloys (SMAs) are capable of changing their crystallographic structure due to changes of either stress or temperature. SMAs are used in a number of aerospace devices and are required in some devices in exotic environments. We are developing dynamic data driven application system (DDDAS) tools to monitor and change SMAs in real time for delivering payloads by aerospace vehicles. We must be able to turn on and off the sensors and heating units, change the stress on the SMA, monitor on-line data streams, change scales based on incoming data, and control what type of data is generated. The application must have the capability to be run and steered remotely as an unmanned feedback control loop.

A canned food scheduling problem with batch due date

Science.gov (United States)

Chung, Tsui-Ping; Liao, Ching-Jong; Smith, Milton

2014-09-01

This article considers a canned food scheduling problem where jobs are grouped into several batches. Jobs can be sent to the next operation only when all the jobs in the same batch have finished their processing, i.e. jobs in a batch, have a common due date. This batch due date problem is quite common in canned food factories, but there is no efficient heuristic to solve the problem. The problem can be formulated as an identical parallel machine problem with batch due date to minimize the total tardiness. Since the problem is NP hard, two heuristics are proposed to find the near-optimal solution. Computational results comparing the effectiveness and efficiency of the two proposed heuristics with an existing heuristic are reported and discussed.

Data-driven non-Markovian closure models

Science.gov (United States)

Kondrashov, Dmitri; Chekroun, Mickaël D.; Ghil, Michael

2015-03-01

This paper has two interrelated foci: (i) obtaining stable and efficient data-driven closure models by using a multivariate time series of partial observations from a large-dimensional system; and (ii) comparing these closure models with the optimal closures predicted by the Mori-Zwanzig (MZ) formalism of statistical physics. Multilayer stochastic models (MSMs) are introduced as both a generalization and a time-continuous limit of existing multilevel, regression-based approaches to closure in a data-driven setting; these approaches include empirical model reduction (EMR), as well as more recent multi-layer modeling. It is shown that the multilayer structure of MSMs can provide a natural Markov approximation to the generalized Langevin equation (GLE) of the MZ formalism. A simple correlation-based stopping criterion for an EMR-MSM model is derived to assess how well it approximates the GLE solution. Sufficient conditions are derived on the structure of the nonlinear cross-interactions between the constitutive layers of a given MSM to guarantee the existence of a global random attractor. This existence ensures that no blow-up can occur for a broad class of MSM applications, a class that includes non-polynomial predictors and nonlinearities that do not necessarily preserve quadratic energy invariants. The EMR-MSM methodology is first applied to a conceptual, nonlinear, stochastic climate model of coupled slow and fast variables, in which only slow variables are observed. It is shown that the resulting closure model with energy-conserving nonlinearities efficiently captures the main statistical features of the slow variables, even when there is no formal scale separation and the fast variables are quite energetic. Second, an MSM is shown to successfully reproduce the statistics of a partially observed, generalized Lotka-Volterra model of population dynamics in its chaotic regime. The challenges here include the rarity of strange attractors in the model's parameter

Prunus dulcis, Batch

African Journals Online (AJOL)

STORAGESEVER

2010-06-07

Jun 7, 2010 ... almond (Prunus dulcis, Batch) genotypes as revealed by PCR analysis. Yavar Sharafi1*, Jafar Hajilou1, Seyed AbolGhasem Mohammadi2, Mohammad Reza Dadpour1 and Sadollah Eskandari3. 1Department of Horticulture, Faculty of Agriculture, University of Tabriz, Tabriz, 5166614766, Iran.

Data-Intensive Science meets Inquiry-Driven Pedagogy: Interactive Big Data Exploration, Threshold Concepts, and Liminality

Science.gov (United States)

Ramachandran, Rahul; Word, Andrea; Nair, Udasysankar

2014-01-01

Threshold concepts in any discipline are the core concepts an individual must understand in order to master a discipline. By their very nature, these concepts are troublesome, irreversible, integrative, bounded, discursive, and reconstitutive. Although grasping threshold concepts can be extremely challenging for each learner as s/he moves through stages of cognitive development relative to a given discipline, the learner's grasp of these concepts determines the extent to which s/he is prepared to work competently and creatively within the field itself. The movement of individuals from a state of ignorance of these core concepts to one of mastery occurs not along a linear path but in iterative cycles of knowledge creation and adjustment in liminal spaces - conceptual spaces through which learners move from the vaguest awareness of concepts to mastery, accompanied by understanding of their relevance, connectivity, and usefulness relative to questions and constructs in a given discipline. For example, challenges in the teaching and learning of atmospheric science can be traced to threshold concepts in fluid dynamics. In particular, Dynamic Meteorology is one of the most challenging courses for graduate students and undergraduates majoring in Atmospheric Science. Dynamic Meteorology introduces threshold concepts - those that prove troublesome for the majority of students but that are essential, associated with fundamental relationships between forces and motion in the atmosphere and requiring the application of basic classical statics, dynamics, and thermodynamic principles to the three dimensionally varying atmospheric structure. With the explosive growth of data available in atmospheric science, driven largely by satellite Earth observations and high-resolution numerical simulations, paradigms such as that of dataintensive science have emerged. These paradigm shifts are based on the growing realization that current infrastructure, tools and processes will not allow

Comparison of Batch Assay and Random Assay Using Automatic Dispenser in Radioimmunoassay

Energy Technology Data Exchange (ETDEWEB)

Moon, Seung Hwan; Jang, Su Jin; Kang, Ji Yeon; Lee, Dong Soo; Chung, June Key; Lee, Myung Chul [Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul (Korea, Republic of); Lee, Ho Young; Shin, Sun Young; Min, Gyeong Sun; Lee, Hyun Joo [Seoul National University college of Medicine, Seoul (Korea, Republic of)

2009-08-15

Radioimmunoassay (RIA) was usually performed by the batch assay. To improve the efficiency of RIA without increase of the cost and time, random assay could be a choice. We investigated the possibility of the random assay using automatic dispenser by assessing the agreement between batch assay and random assay. The experiments were performed with four items; Triiodothyronine (T3), free thyroxine (fT4), Prostate specific antigen (PSA), Carcinoembryonic antigen (CEA). In each item, the sera of twenty patients, the standard, and the control samples were used. The measurements were done 4 times with 3 hour time intervals by random assay and batch assay. The coefficient of variation (CV) of the standard samples and patients' data in T3, fT4, PSA, and CEA were assessed. ICC (Intraclass correlation coefficient) and coefficient of correlation were measured to assessing the agreement between two methods. The CVs (%) of T3, fT4, PSA, and CEA measured by batch assay were 3.2+-1.7%, 3.9+-2.1%, 7.1+-6.2%, 11.2+-7.2%. The CVs by random assay were 2.1+-1.7%, 4.8+-3.1%, 3.6+-4.8%, and 7.4+-6.2%. The ICC between the batch assay and random assay were 0.9968 (T3), 0.9973 (fT4), 0.9996 (PSA), and 0.9901 (CEA). The coefficient of correlation between the batch assay and random assay were 0.9924(T3), 0.9974 (fT4), 0.9994 (PSA), and 0.9989 (CEA) (p<0.05). The results of random assay showed strong agreement with the batch assay in a day. These results suggest that random assay using automatic dispenser could be used in radioimmunoassay

Comparison of Batch Assay and Random Assay Using Automatic Dispenser in Radioimmunoassay

International Nuclear Information System (INIS)

Moon, Seung Hwan; Jang, Su Jin; Kang, Ji Yeon; Lee, Dong Soo; Chung, June Key; Lee, Myung Chul; Lee, Ho Young; Shin, Sun Young; Min, Gyeong Sun; Lee, Hyun Joo

2009-01-01

Radioimmunoassay (RIA) was usually performed by the batch assay. To improve the efficiency of RIA without increase of the cost and time, random assay could be a choice. We investigated the possibility of the random assay using automatic dispenser by assessing the agreement between batch assay and random assay. The experiments were performed with four items; Triiodothyronine (T3), free thyroxine (fT4), Prostate specific antigen (PSA), Carcinoembryonic antigen (CEA). In each item, the sera of twenty patients, the standard, and the control samples were used. The measurements were done 4 times with 3 hour time intervals by random assay and batch assay. The coefficient of variation (CV) of the standard samples and patients' data in T3, fT4, PSA, and CEA were assessed. ICC (Intraclass correlation coefficient) and coefficient of correlation were measured to assessing the agreement between two methods. The CVs (%) of T3, fT4, PSA, and CEA measured by batch assay were 3.2±1.7%, 3.9±2.1%, 7.1±6.2%, 11.2±7.2%. The CVs by random assay were 2.1±1.7%, 4.8±3.1%, 3.6±4.8%, and 7.4±6.2%. The ICC between the batch assay and random assay were 0.9968 (T3), 0.9973 (fT4), 0.9996 (PSA), and 0.9901 (CEA). The coefficient of correlation between the batch assay and random assay were 0.9924(T3), 0.9974 (fT4), 0.9994 (PSA), and 0.9989 (CEA) (p<0.05). The results of random assay showed strong agreement with the batch assay in a day. These results suggest that random assay using automatic dispenser could be used in radioimmunoassay

Data-Driven Anomaly Detection Performance for the Ares I-X Ground Diagnostic Prototype

Science.gov (United States)

Martin, Rodney A.; Schwabacher, Mark A.; Matthews, Bryan L.

2010-01-01

In this paper, we will assess the performance of a data-driven anomaly detection algorithm, the Inductive Monitoring System (IMS), which can be used to detect simulated Thrust Vector Control (TVC) system failures. However, the ability of IMS to detect these failures in a true operational setting may be related to the realistic nature of how they are simulated. As such, we will investigate both a low fidelity and high fidelity approach to simulating such failures, with the latter based upon the underlying physics. Furthermore, the ability of IMS to detect anomalies that were previously unknown and not previously simulated will be studied in earnest, as well as apparent deficiencies or misapplications that result from using the data-driven paradigm. Our conclusions indicate that robust detection performance of simulated failures using IMS is not appreciably affected by the use of a high fidelity simulation. However, we have found that the inclusion of a data-driven algorithm such as IMS into a suite of deployable health management technologies does add significant value.

40 CFR 63.1408 - Aggregate batch vent stream provisions.

Science.gov (United States)

2010-07-01

... 40 Protection of Environment 11 2010-07-01 2010-07-01 true Aggregate batch vent stream provisions... § 63.1408 Aggregate batch vent stream provisions. (a) Emission standards. Owners or operators of aggregate batch vent streams at a new or existing affected source shall comply with either paragraph (a)(1...

Service and Data Driven Multi Business Model Platform in a World of Persuasive Technologies

DEFF Research Database (Denmark)

Andersen, Troels Christian; Bjerrum, Torben Cæsar Bisgaard

2016-01-01

companies in establishing a service organization that delivers, creates and captures value through service and data driven business models by utilizing their network, resources and customers and/or users. Furthermore, based on literature and collaboration with the case company, the suggestion of a new...... framework provides the necessary construction of how the manufac- turing companies can evolve their current business to provide multi service and data driven business models, using the same resources, networks and customers....

Objective, Quantitative, Data-Driven Assessment of Chemical Probes.

Science.gov (United States)

Antolin, Albert A; Tym, Joseph E; Komianou, Angeliki; Collins, Ian; Workman, Paul; Al-Lazikani, Bissan

2018-02-15

Chemical probes are essential tools for understanding biological systems and for target validation, yet selecting probes for biomedical research is rarely based on objective assessment of all potential compounds. Here, we describe the Probe Miner: Chemical Probes Objective Assessment resource, capitalizing on the plethora of public medicinal chemistry data to empower quantitative, objective, data-driven evaluation of chemical probes. We assess >1.8 million compounds for their suitability as chemical tools against 2,220 human targets and dissect the biases and limitations encountered. Probe Miner represents a valuable resource to aid the identification of potential chemical probes, particularly when used alongside expert curation. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Privacy in Sensor-Driven Human Data Collection: A Guide for Practitioners

OpenAIRE

Stopczynski, Arkadiusz; Pietri, Riccardo; Pentland, Alex; Lazer, David; Lehmann, Sune

2014-01-01

In recent years, the amount of information collected about human beings has increased dramatically. This development has been partially driven by individuals posting and storing data about themselves and friends using online social networks or collecting their data for self-tracking purposes (quantified-self movement). Across the sciences, researchers conduct studies collecting data with an unprecedented resolution and scale. Using computational power combined with mathematical models, such r...

ISDMS, Inel Scientific Data Management System

International Nuclear Information System (INIS)

Bruestle, H.R.; Russell, K.D.; Snider, D.M.; Stewart, H.D.

1993-01-01

Description of program or function: The Idaho National Engineering Laboratory (INEL) Scientific Data Management System, ISDMS, is a generalized scientific data processing system designed to meet the needs of the various organizations at the INEL. It consists of a set of general and specific processors running under the control of an executive processor which serves as the interface between the system and the user. The data requirements at the INEL are primarily for times series analyses. Data acquired at various site facilities are processed on the central CDC CYBER computers. This processing includes: data conversion, data calibration, computed parameter calculations, time series plots, and sundry other applications. The data structure used in ISDMS is CWAF, a common word addressable format. A table driven command language serves as the ISDMS control language. Execution in both batch and interactive mode is possible. All commands and their input arguments are specified in free form. ISDMS is a modular system both at the top executive or MASTER level and in the independent lower or sub-level modules. ISDMS processors were designed and isolated according to their function. This release of ISDMS, identified as 1.3A by the developers, includes processors for data conversion and reformatting for applications programs (e.g. RELAP4), interactive and batch graphics, data analysis, data storage, and archival and development aids

Data driven profiting from your most important business asset

CERN Document Server

Redman, Thomas C

2008-01-01

Your company's data has the potential to add enormous value to every facet of the organization -- from marketing and new product development to strategy to financial management. Yet if your company is like most, it's not using its data to create strategic advantage. Data sits around unused -- or incorrect data fouls up operations and decision making. In Data Driven, Thomas Redman, the "Data Doc," shows how to leverage and deploy data to sharpen your company's competitive edge and enhance its profitability. The author reveals: · The special properties that make data such a powerful asset · The hidden costs of flawed, outdated, or otherwise poor-quality data · How to improve data quality for competitive advantage · Strategies for exploiting your data to make better business decisions · The many ways to bring data to market · Ideas for dealing with political struggles over data and concerns about privacy rights Your company's data is a key business asset, and you need to manage it aggressively and professi...

Data-Driven Assistance Functions for Industrial Automation Systems

International Nuclear Information System (INIS)

Windmann, Stefan; Niggemann, Oliver

2015-01-01

The increasing amount of data in industrial automation systems overburdens the user in process control and diagnosis tasks. One possibility to cope with these challenges consists of using smart assistance systems that automatically monitor and optimize processes. This article deals with aspects of data-driven assistance systems such as assistance functions, process models and data acquisition. The paper describes novel approaches for self-diagnosis and self-optimization, and shows how these assistance functions can be integrated in different industrial environments. The considered assistance functions are based on process models that are automatically learned from process data. Fault detection and isolation is based on the comparison of observations of the real system with predictions obtained by application of the process models. The process models are further employed for energy efficiency optimization of industrial processes. Experimental results are presented for fault detection and energy efficiency optimization of a drive system. (paper)

Effect of glass-batch makeup on the melting process

International Nuclear Information System (INIS)

Hrma, Pavel R.; Schweiger, Michael J.; Humrickhouse, Carissa J.; Moody, J. Adam; Tate, Rachel M.; Rainsdon, Timothy T.; Tegrotenhuis, Nathan E.; Arrigoni, Benjamin M.; Marcial, Jose; Rodriguez, Carmen P.; Tincher, Benjamin

2010-01-01

The response of a glass batch to heating is determined by the batch makeup and in turn determines the rate of melting. Batches formulated for a high-alumina nuclear waste to be vitrified in an all-electric melter were heated at a constant temperature-increase rate to determine changes in melting behavior in response to the selection of batch chemicals and silica grain-size as well as the addition of heat-generating reactants. The type of batch materials and the size of silica grains determine how much, if any, primary foam occurs during melting. Small quartz grains, 5 (micro)m in size, caused extensive foaming because their major portion dissolved at temperatures 800 C when batch gases no longer evolved. The exothermal reaction of nitrates with sucrose was ignited at a temperature as low as 160 C and caused a temporary jump in temperature of several hundred degrees. Secondary foam, the source of which is oxygen from redox reactions, occurred in all batches of a limited composition variation involving five oxides, B 2 O 3 , CaO, Li 2 O, MgO, and Na 2 O. The foam volume at the maximum volume-increase rate was a weak function of temperature and melt basicity. Neither the batch makeup nor the change in glass composition had a significant impact on the dissolution of silica grains. The impacts of primary foam generation on glass homogeneity and the rate of melting in large-scale continuous furnaces have yet to be established via mathematical modeling and melter experiments.

Effect Of Glass-Batch Makeup On The Melting Process

International Nuclear Information System (INIS)

Kruger, A.A.; Hrma, P.

2010-01-01

The response of a glass batch to heating is determined by the batch makeup and in turn determines the rate of melting. Batches formulated for a high-alumina nuclear waste to be vitrified in an all-electric melter were heated at a constant temperature-increase rate to determine changes in melting behavior in response to the selection of batch chemicals and silica grain-size as well as the addition of heat-generating reactants. The type of batch materials and the size of silica grains determine how much, if any, primary foam occurs during melting. Small quartz grains, 5 (micro)m in size, caused extensive foaming because their major portion dissolved at temperatures 800 C when batch gases no longer evolved. The exothermal reaction of nitrates with sucrose was ignited at a temperature as low as 160 C and caused a temporary jump in temperature of several hundred degrees. Secondary foam, the source of which is oxygen from redox reactions, occurred in all batches of a limited composition variation involving five oxides, B 2 O 3 , CaO, Li 2 O, MgO, and Na 2 O. The foam volume at the maximum volume-increase rate was a weak function of temperature and melt basicity. Neither the batch makeup nor the change in glass composition had a significant impact on the dissolution of silica grains. The impacts of primary foam generation on glass homogeneity and the rate of melting in large-scale continuous furnaces have yet to be established via mathematical modeling and melter experiments.

Autonomous Soil Assessment System: A Data-Driven Approach to Planetary Mobility Hazard Detection

Science.gov (United States)

Raimalwala, K.; Faragalli, M.; Reid, E.

2018-04-01

The Autonomous Soil Assessment System predicts mobility hazards for rovers. Its development and performance are presented, with focus on its data-driven models, machine learning algorithms, and real-time sensor data fusion for predictive analytics.

Fuzzy batch controller for granular materials

OpenAIRE

Zamyatin Nikolaj; Smirnov Gennadij; Fedorchuk Yuri; Rusina Olga

2018-01-01

The paper focuses on batch control of granular materials in production of building materials from fluorine anhydrite. Batching equipment is intended for smooth operation and timely feeding of supply hoppers at a required level. Level sensors and a controller of an asynchronous screw drive motor are used to control filling of the hopper with industrial anhydrite binders. The controller generates a required frequency and ensures required productivity of a feed conveyor. Mamdani-type fuzzy infer...

Research on AO/FO batch management technology in aircraft production

Directory of Open Access Journals (Sweden)

Yin Haijun

2018-01-01

Full Text Available Based on the analysis of the characteristics and significance of AO/FO in the process of aircraft production, this paper analyzes the format rules of AO/FO batch management from the perspective of technology realization, and details the AO/FO The change of the query and the change status tracking, introduces the AO/FO single-stand status display in the batch management, increases the structure definition of the attribute table in the batch management, and designs the relevant algorithm to store and calculate the batch information. Finally, based on the above theory support AO/FO batch management system successfully used in the production of a machine.

A data-driven approach to reverse engineering customer engagement models: towards functional constructs.

Science.gov (United States)

de Vries, Natalie Jane; Carlson, Jamie; Moscato, Pablo

2014-01-01

Online consumer behavior in general and online customer engagement with brands in particular, has become a major focus of research activity fuelled by the exponential increase of interactive functions of the internet and social media platforms and applications. Current research in this area is mostly hypothesis-driven and much debate about the concept of Customer Engagement and its related constructs remains existent in the literature. In this paper, we aim to propose a novel methodology for reverse engineering a consumer behavior model for online customer engagement, based on a computational and data-driven perspective. This methodology could be generalized and prove useful for future research in the fields of consumer behaviors using questionnaire data or studies investigating other types of human behaviors. The method we propose contains five main stages; symbolic regression analysis, graph building, community detection, evaluation of results and finally, investigation of directed cycles and common feedback loops. The 'communities' of questionnaire items that emerge from our community detection method form possible 'functional constructs' inferred from data rather than assumed from literature and theory. Our results show consistent partitioning of questionnaire items into such 'functional constructs' suggesting the method proposed here could be adopted as a new data-driven way of human behavior modeling.

A data-driven approach to reverse engineering customer engagement models: towards functional constructs.

Directory of Open Access Journals (Sweden)

Natalie Jane de Vries

Full Text Available Online consumer behavior in general and online customer engagement with brands in particular, has become a major focus of research activity fuelled by the exponential increase of interactive functions of the internet and social media platforms and applications. Current research in this area is mostly hypothesis-driven and much debate about the concept of Customer Engagement and its related constructs remains existent in the literature. In this paper, we aim to propose a novel methodology for reverse engineering a consumer behavior model for online customer engagement, based on a computational and data-driven perspective. This methodology could be generalized and prove useful for future research in the fields of consumer behaviors using questionnaire data or studies investigating other types of human behaviors. The method we propose contains five main stages; symbolic regression analysis, graph building, community detection, evaluation of results and finally, investigation of directed cycles and common feedback loops. The 'communities' of questionnaire items that emerge from our community detection method form possible 'functional constructs' inferred from data rather than assumed from literature and theory. Our results show consistent partitioning of questionnaire items into such 'functional constructs' suggesting the method proposed here could be adopted as a new data-driven way of human behavior modeling.

Data-free and data-driven spectral perturbations for RANS UQ

Science.gov (United States)

Edeling, Wouter; Mishra, Aashwin; Iaccarino, Gianluca

2017-11-01

Despite recent developments in high-fidelity turbulent flow simulations, RANS modeling is still vastly used by industry, due to its inherent low cost. Since accuracy is a concern in RANS modeling, model-form UQ is an essential tool for assessing the impacts of this uncertainty on quantities of interest. Applying the spectral decomposition to the modeled Reynolds-Stress Tensor (RST) allows for the introduction of decoupled perturbations into the baseline intensity (kinetic energy), shape (eigenvalues), and orientation (eigenvectors). This constitutes a natural methodology to evaluate the model form uncertainty associated to different aspects of RST modeling. In a predictive setting, one frequently encounters an absence of any relevant reference data. To make data-free predictions with quantified uncertainty we employ physical bounds to a-priori define maximum spectral perturbations. When propagated, these perturbations yield intervals of engineering utility. High-fidelity data opens up the possibility of inferring a distribution of uncertainty, by means of various data-driven machine-learning techniques. We will demonstrate our framework on a number of flow problems where RANS models are prone to failure. This research was partially supported by the Defense Advanced Research Projects Agency under the Enabling Quantification of Uncertainty in Physical Systems (EQUiPS) project (technical monitor: Dr Fariba Fahroo), and the DOE PSAAP-II program.

Data-driven system to predict academic grades and dropout

Science.gov (United States)

Rovira, Sergi; Puertas, Eloi

2017-01-01

Nowadays, the role of a tutor is more important than ever to prevent students dropout and improve their academic performance. This work proposes a data-driven system to extract relevant information hidden in the student academic data and, thus, help tutors to offer their pupils a more proactive personal guidance. In particular, our system, based on machine learning techniques, makes predictions of dropout intention and courses grades of students, as well as personalized course recommendations. Moreover, we present different visualizations which help in the interpretation of the results. In the experimental validation, we show that the system obtains promising results with data from the degree studies in Law, Computer Science and Mathematics of the Universitat de Barcelona. PMID:28196078

Data-Driven Model Reduction and Transfer Operator Approximation

Science.gov (United States)

Klus, Stefan; Nüske, Feliks; Koltai, Péter; Wu, Hao; Kevrekidis, Ioannis; Schütte, Christof; Noé, Frank

2018-06-01

In this review paper, we will present different data-driven dimension reduction techniques for dynamical systems that are based on transfer operator theory as well as methods to approximate transfer operators and their eigenvalues, eigenfunctions, and eigenmodes. The goal is to point out similarities and differences between methods developed independently by the dynamical systems, fluid dynamics, and molecular dynamics communities such as time-lagged independent component analysis, dynamic mode decomposition, and their respective generalizations. As a result, extensions and best practices developed for one particular method can be carried over to other related methods.

Perspectives of data-driven LPV modeling of high-purity distillation columns

NARCIS (Netherlands)

Bachnas, A.A.; Toth, R.; Mesbah, A.; Ludlage, J.H.A.

2013-01-01

Abstract—This paper investigates data-driven, Linear- Parameter-Varying (LPV) modeling of a high-purity distillation column. Two LPV modeling approaches are studied: a local approach, corresponding to the interpolation of Linear Time- Invariant (LTI) models identified at steady-state purity levels,

Batch Attribute-Based Encryption for Secure Clouds

Directory of Open Access Journals (Sweden)

Chen Yang

2015-10-01

Full Text Available Cloud storage is widely used by organizations due to its advantage of allowing universal access with low cost. Attribute-based encryption (ABE is a kind of public key encryption suitable for cloud storage. The secret key of each user and the ciphertext are associated with an access policy and an attribute set, respectively; in addition to holding a secret key, one can decrypt a ciphertext only if the associated attributes match the predetermined access policy, which allows one to enforce fine-grained access control on outsourced files. One issue in existing ABE schemes is that they are designed for the users of a single organization. When one wants to share the data with the users of different organizations, the owner needs to encrypt the messages to the receivers of one organization and then repeats this process for another organization. This situation is deteriorated with more and more mobile devices using cloud services, as the ABE encryption process is time consuming and may exhaust the power supplies of the mobile devices quickly. In this paper, we propose a batch attribute-based encryption (BABE approach to address this problem in a provably-secure way. With our approach, the data owner can outsource data in batches to the users of different organizations simultaneously. The data owner is allowed to decide the receiving organizations and the attributes required for decryption. Theoretical and experimental analyses show that our approach is more efficient than traditional encryption implementations in computation and communication.

Data driven modelling of vertical atmospheric radiation

International Nuclear Information System (INIS)

Antoch, Jaromir; Hlubinka, Daniel

2011-01-01

In the Czech Hydrometeorological Institute (CHMI) there exists a unique set of meteorological measurements consisting of the values of vertical atmospheric levels of beta and gamma radiation. In this paper a stochastic data-driven model based on nonlinear regression and on nonhomogeneous Poisson process is suggested. In the first part of the paper, growth curves were used to establish an appropriate nonlinear regression model. For comparison we considered a nonhomogeneous Poisson process with its intensity based on growth curves. In the second part both approaches were applied to the real data and compared. Computational aspects are briefly discussed as well. The primary goal of this paper is to present an improved understanding of the distribution of environmental radiation as obtained from the measurements of the vertical radioactivity profiles by the radioactivity sonde system. - Highlights: → We model vertical atmospheric levels of beta and gamma radiation. → We suggest appropriate nonlinear regression model based on growth curves. → We compare nonlinear regression modelling with Poisson process based modeling. → We apply both models to the real data.

A data driven nonlinear stochastic model for blood glucose dynamics.

Science.gov (United States)

Zhang, Yan; Holt, Tim A; Khovanova, Natalia

2016-03-01

The development of adequate mathematical models for blood glucose dynamics may improve early diagnosis and control of diabetes mellitus (DM). We have developed a stochastic nonlinear second order differential equation to describe the response of blood glucose concentration to food intake using continuous glucose monitoring (CGM) data. A variational Bayesian learning scheme was applied to define the number and values of the system's parameters by iterative optimisation of free energy. The model has the minimal order and number of parameters to successfully describe blood glucose dynamics in people with and without DM. The model accounts for the nonlinearity and stochasticity of the underlying glucose-insulin dynamic process. Being data-driven, it takes full advantage of available CGM data and, at the same time, reflects the intrinsic characteristics of the glucose-insulin system without detailed knowledge of the physiological mechanisms. We have shown that the dynamics of some postprandial blood glucose excursions can be described by a reduced (linear) model, previously seen in the literature. A comprehensive analysis demonstrates that deterministic system parameters belong to different ranges for diabetes and controls. Implications for clinical practice are discussed. This is the first study introducing a continuous data-driven nonlinear stochastic model capable of describing both DM and non-DM profiles. Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Data-driven integration of genome-scale regulatory and metabolic network models

Science.gov (United States)

Imam, Saheed; Schäuble, Sascha; Brooks, Aaron N.; Baliga, Nitin S.; Price, Nathan D.

2015-01-01

Microbes are diverse and extremely versatile organisms that play vital roles in all ecological niches. Understanding and harnessing microbial systems will be key to the sustainability of our planet. One approach to improving our knowledge of microbial processes is through data-driven and mechanism-informed computational modeling. Individual models of biological networks (such as metabolism, transcription, and signaling) have played pivotal roles in driving microbial research through the years. These networks, however, are highly interconnected and function in concert—a fact that has led to the development of a variety of approaches aimed at simulating the integrated functions of two or more network types. Though the task of integrating these different models is fraught with new challenges, the large amounts of high-throughput data sets being generated, and algorithms being developed, means that the time is at hand for concerted efforts to build integrated regulatory-metabolic networks in a data-driven fashion. In this perspective, we review current approaches for constructing integrated regulatory-metabolic models and outline new strategies for future development of these network models for any microbial system. PMID:25999934

Data-driven diagnostics of terrestrial carbon dynamics over North America

Science.gov (United States)

Jingfeng Xiao; Scott V. Ollinger; Steve Frolking; George C. Hurtt; David Y. Hollinger; Kenneth J. Davis; Yude Pan; Xiaoyang Zhang; Feng Deng; Jiquan Chen; Dennis D. Baldocchi; Bevery E. Law; M. Altaf Arain; Ankur R. Desai; Andrew D. Richardson; Ge Sun; Brian Amiro; Hank Margolis; Lianhong Gu; Russell L. Scott; Peter D. Blanken; Andrew E. Suyker

2014-01-01

The exchange of carbon dioxide is a key measure of ecosystem metabolism and a critical intersection between the terrestrial biosphere and the Earth's climate. Despite the general agreement that the terrestrial ecosystems in North America provide a sizeable carbon sink, the size and distribution of the sink remain uncertain. We use a data-driven approach to upscale...

Perfusion cell culture decreases process and product heterogeneity in a head-to-head comparison with fed-batch.

Science.gov (United States)

Walther, Jason; Lu, Jiuyi; Hollenbach, Myles; Yu, Marcella; Hwang, Chris; McLarty, Jean; Brower, Kevin

2018-05-30

In this study, we compared the impacts of fed-batch and perfusion platforms on process and product attributes for IgG1- and IgG4-producing cell lines. A "plug-and-play" approach was applied to both platforms at bench scale, using commercially available basal and feed media, a standard feed strategy for fed-batch, and ATF filtration for perfusion. Product concentration in fed-batch was 2.5 times greater than perfusion, while average productivity in perfusion was 7.5 times greater than fed-batch. PCA revealed more variability in the cell environment and metabolism during the fed-batch run. LDH measurements showed that exposure of product to cell lysate was 7-10 times greater in fed-batch. Product analysis shows larger abundances of neutral species in perfusion, likely due to decreased bioreactor residence times and extracellular exposure. The IgG1 perfusion product also had higher purity and lower half-antibody. Glycosylation was similar across both culture modes. The first perfusion harvest slice for both product types showed different glycosylation than subsequent harvests, suggesting that product quality lags behind metabolism. In conclusion, process and product data indicate that intra-lot heterogeneity is decreased in perfusion cultures. Additional data and discussion is required to understand the developmental, clinical and commercial implications, and in what situations increased uniformity would be beneficial. This article is protected by copyright. All rights reserved.

Data Science and its Relationship to Big Data and Data-Driven Decision Making.

Science.gov (United States)

Provost, Foster; Fawcett, Tom

2013-03-01

Companies have realized they need to hire data scientists, academic institutions are scrambling to put together data-science programs, and publications are touting data science as a hot-even "sexy"-career choice. However, there is confusion about what exactly data science is, and this confusion could lead to disillusionment as the concept diffuses into meaningless buzz. In this article, we argue that there are good reasons why it has been hard to pin down exactly what is data science. One reason is that data science is intricately intertwined with other important concepts also of growing importance, such as big data and data-driven decision making. Another reason is the natural tendency to associate what a practitioner does with the definition of the practitioner's field; this can result in overlooking the fundamentals of the field. We believe that trying to define the boundaries of data science precisely is not of the utmost importance. We can debate the boundaries of the field in an academic setting, but in order for data science to serve business effectively, it is important (i) to understand its relationships to other important related concepts, and (ii) to begin to identify the fundamental principles underlying data science. Once we embrace (ii), we can much better understand and explain exactly what data science has to offer. Furthermore, only once we embrace (ii) should we be comfortable calling it data science. In this article, we present a perspective that addresses all these concepts. We close by offering, as examples, a partial list of fundamental principles underlying data science.

Variance bias analysis for the Gelbard's batch method

Energy Technology Data Exchange (ETDEWEB)

Seo, Jae Uk; Shim, Hyung Jin [Seoul National Univ., Seoul (Korea, Republic of)

2014-05-15

In this paper, variances and the bias will be derived analytically when the Gelbard's batch method is applied. And then, the real variance estimated from this bias will be compared with the real variance calculated from replicas. Variance and the bias were derived analytically when the batch method was applied. If the batch method was applied to calculate the sample variance, covariance terms between tallies which exist in the batch were eliminated from the bias. With the 2 by 2 fission matrix problem, we could calculate real variance regardless of whether or not the batch method was applied. However as batch size got larger, standard deviation of real variance was increased. When we perform a Monte Carlo estimation, we could get a sample variance as the statistical uncertainty of it. However, this value is smaller than the real variance of it because a sample variance is biased. To reduce this bias, Gelbard devised the method which is called the Gelbard's batch method. It has been certificated that a sample variance get closer to the real variance when the batch method is applied. In other words, the bias get reduced. This fact is well known to everyone in the MC field. However, so far, no one has given the analytical interpretation on it.

Batch and Continuous Packed Column Studies Biosorption by Yeast Supported onto Granular Pozzolana

OpenAIRE

A. Djafer; S. Kouadri Moustefai; A. Idou; M. Douani

2013-01-01

The removal of chromium by living yeast biomass immobilized onto pozzolana was studied. The results obtained in batch experiments indicate that the immobilized yeast on to pozzolana is a excellent biosorbent of Cr(V) with a good removal rates of 85–90%. The initial concentration solution and agitation speed affected Cr(V) removal. The batch studies data were described using the Freundlich and Langmuir models, but the best fit was obtained with Langmuir model. The breakthrough curve from the c...

Facilitating Data Driven Business Model Innovation - A Case study

DEFF Research Database (Denmark)

Bjerrum, Torben Cæsar Bisgaard; Andersen, Troels Christian; Aagaard, Annabeth

2016-01-01

. The businesses interdisciplinary capabilities come into play in the BMI process, where knowledge from the facilitation strategy and knowledge from phases of the BMI process needs to be present to create new knowledge, hence new BMs and innovations. Depending on the environment and shareholders, this also exposes......This paper aims to understand the barriers that businesses meet in understanding their current business models (BM) and in their attempt at innovating new data driven business models (DDBM) using data. The interdisciplinary challenge of knowledge exchange occurring outside and/or inside businesses......, that gathers knowledge is of great importance. The SMEs have little, if no experience, within data handling, data analytics, and working with structured Business Model Innovation (BMI), that relates to both new and conventional products, processes and services. This new frontier of data and BMI will have...

Fault Detection for Nonlinear Process With Deterministic Disturbances: A Just-In-Time Learning Based Data Driven Method.

Science.gov (United States)

Yin, Shen; Gao, Huijun; Qiu, Jianbin; Kaynak, Okyay

2017-11-01

Data-driven fault detection plays an important role in industrial systems due to its applicability in case of unknown physical models. In fault detection, disturbances must be taken into account as an inherent characteristic of processes. Nevertheless, fault detection for nonlinear processes with deterministic disturbances still receive little attention, especially in data-driven field. To solve this problem, a just-in-time learning-based data-driven (JITL-DD) fault detection method for nonlinear processes with deterministic disturbances is proposed in this paper. JITL-DD employs JITL scheme for process description with local model structures to cope with processes dynamics and nonlinearity. The proposed method provides a data-driven fault detection solution for nonlinear processes with deterministic disturbances, and owns inherent online adaptation and high accuracy of fault detection. Two nonlinear systems, i.e., a numerical example and a sewage treatment process benchmark, are employed to show the effectiveness of the proposed method.

40 CFR 63.1322 - Batch process vents-reference control technology.

Science.gov (United States)

2010-07-01

... 40 Protection of Environment 11 2010-07-01 2010-07-01 true Batch process vents-reference control technology. 63.1322 Section 63.1322 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR... Batch process vents—reference control technology. (a) Batch process vents. The owner or operator of a...

A Web Services Data Analysis Grid

Energy Technology Data Exchange (ETDEWEB)

William A Watson III; Ian Bird; Jie Chen; Bryan Hess; Andy Kowalski; Ying Chen

2002-07-01

The trend in large-scale scientific data analysis is to exploit compute, storage and other resources located at multiple sites, and to make those resources accessible to the scientist as if they were a single, coherent system. Web technologies driven by the huge and rapidly growing electronic commerce industry provide valuable components to speed the deployment of such sophisticated systems. Jefferson Lab, where several hundred terabytes of experimental data are acquired each year, is in the process of developing a web-based distributed system for data analysis and management. The essential aspects of this system are a distributed data grid (site independent access to experiment, simulation and model data) and a distributed batch system, augmented with various supervisory and management capabilities, and integrated using Java and XML-based web services.

A Web Services Data Analysis Grid

International Nuclear Information System (INIS)

William A Watson III; Ian Bird; Jie Chen; Bryan Hess; Andy Kowalski; Ying Chen

2002-01-01

The trend in large-scale scientific data analysis is to exploit compute, storage and other resources located at multiple sites, and to make those resources accessible to the scientist as if they were a single, coherent system. Web technologies driven by the huge and rapidly growing electronic commerce industry provide valuable components to speed the deployment of such sophisticated systems. Jefferson Lab, where several hundred terabytes of experimental data are acquired each year, is in the process of developing a web-based distributed system for data analysis and management. The essential aspects of this system are a distributed data grid (site independent access to experiment, simulation and model data) and a distributed batch system, augmented with various supervisory and management capabilities, and integrated using Java and XML-based web services

Data-Driven Design of Intelligent Wireless Networks: An Overview and Tutorial

Directory of Open Access Journals (Sweden)

Merima Kulin

2016-06-01

Full Text Available Data science or “data-driven research” is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i clarifies when, why and how to use data science in wireless network research; (ii provides a generic framework for applying data science in wireless networks; (iii gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v provides the reader the necessary datasets and scripts to go through the tutorial steps themselves.

Toward Data-Driven Design of Educational Courses: A Feasibility Study

Science.gov (United States)

Agrawal, Rakesh; Golshan, Behzad; Papalexakis, Evangelos

2016-01-01

A study plan is the choice of concepts and the organization and sequencing of the concepts to be covered in an educational course. While a good study plan is essential for the success of any course offering, the design of study plans currently remains largely a manual task. We present a novel data-driven method, which given a list of concepts can…

Data-driven models of dominantly-inherited Alzheimer's disease progression.

Science.gov (United States)

Oxtoby, Neil P; Young, Alexandra L; Cash, David M; Benzinger, Tammie L S; Fagan, Anne M; Morris, John C; Bateman, Randall J; Fox, Nick C; Schott, Jonathan M; Alexander, Daniel C

2018-03-22

Dominantly-inherited Alzheimer's disease is widely hoped to hold the key to developing interventions for sporadic late onset Alzheimer's disease. We use emerging techniques in generative data-driven disease progression modelling to characterize dominantly-inherited Alzheimer's disease progression with unprecedented resolution, and without relying upon familial estimates of years until symptom onset. We retrospectively analysed biomarker data from the sixth data freeze of the Dominantly Inherited Alzheimer Network observational study, including measures of amyloid proteins and neurofibrillary tangles in the brain, regional brain volumes and cortical thicknesses, brain glucose hypometabolism, and cognitive performance from the Mini-Mental State Examination (all adjusted for age, years of education, sex, and head size, as appropriate). Data included 338 participants with known mutation status (211 mutation carriers in three subtypes: 163 PSEN1, 17 PSEN2, and 31 APP) and a baseline visit (age 19-66; up to four visits each, 1.1 ± 1.9 years in duration; spanning 30 years before, to 21 years after, parental age of symptom onset). We used an event-based model to estimate sequences of biomarker changes from baseline data across disease subtypes (mutation groups), and a differential equation model to estimate biomarker trajectories from longitudinal data (up to 66 mutation carriers, all subtypes combined). The two models concur that biomarker abnormality proceeds as follows: amyloid deposition in cortical then subcortical regions (∼24 ± 11 years before onset); phosphorylated tau (17 ± 8 years), tau and amyloid-β changes in cerebrospinal fluid; neurodegeneration first in the putamen and nucleus accumbens (up to 6 ± 2 years); then cognitive decline (7 ± 6 years), cerebral hypometabolism (4 ± 4 years), and further regional neurodegeneration. Our models predicted symptom onset more accurately than predictions that used familial estimates: root mean squared error of 1

Batch-to-Batch Quality Consistency Evaluation of Botanical Drug Products Using Multivariate Statistical Analysis of the Chromatographic Fingerprint

OpenAIRE

Xiong, Haoshu; Yu, Lawrence X.; Qu, Haibin

2013-01-01

Botanical drug products have batch-to-batch quality variability due to botanical raw materials and the current manufacturing process. The rational evaluation and control of product quality consistency are essential to ensure the efficacy and safety. Chromatographic fingerprinting is an important and widely used tool to characterize the chemical composition of botanical drug products. Multivariate statistical analysis has showed its efficacy and applicability in the quality evaluation of many ...

Fuzzy batch controller for granular materials

Directory of Open Access Journals (Sweden)

Zamyatin Nikolaj

2018-01-01

Full Text Available The paper focuses on batch control of granular materials in production of building materials from fluorine anhydrite. Batching equipment is intended for smooth operation and timely feeding of supply hoppers at a required level. Level sensors and a controller of an asynchronous screw drive motor are used to control filling of the hopper with industrial anhydrite binders. The controller generates a required frequency and ensures required productivity of a feed conveyor. Mamdani-type fuzzy inference is proposed for controlling the speed of the screw that feeds mixture components. As related to production of building materials based on fluoride anhydrite, this method is used for the first time. A fuzzy controller is proven to be effective in controlling the filling level of the supply hopper. In addition, the authors determined optimal parameters of the batching process to ensure smooth operation and production of fluorine anhydrite materials of specified properties that can compete with gypsum-based products.

Extension of a data-driven gating technique to 3D, whole body PET studies

International Nuclear Information System (INIS)

Schleyer, Paul J; O'Doherty, Michael J; Marsden, Paul K

2011-01-01

Respiratory gating can be used to separate a PET acquisition into a series of near motion-free bins. This is typically done using additional gating hardware; however, software-based methods can derive the respiratory signal from the acquired data itself. The aim of this work was to extend a data-driven respiratory gating method to acquire gated, 3D, whole body PET images of clinical patients. The existing method, previously demonstrated with 2D, single bed-position data, uses a spectral analysis to find regions in raw PET data which are subject to respiratory motion. The change in counts over time within these regions is then used to estimate the respiratory signal of the patient. In this work, the gating method was adapted to only accept lines of response from a reduced set of axial angles, and the respiratory frequency derived from the lung bed position was used to help identify the respiratory frequency in all other bed positions. As the respiratory signal does not identify the direction of motion, a registration-based technique was developed to align the direction for all bed positions. Data from 11 clinical FDG PET patients were acquired, and an optical respiratory monitor was used to provide a hardware-based signal for comparison. All data were gated using both the data-driven and hardware methods, and reconstructed. The centre of mass of manually defined regions on gated images was calculated, and the overall displacement was defined as the change in the centre of mass between the first and last gates. The mean displacement was 10.3 mm for the data-driven gated images and 9.1 mm for the hardware gated images. No significant difference was found between the two gating methods when comparing the displacement values. The adapted data-driven gating method was demonstrated to successfully produce respiratory gated, 3D, whole body, clinical PET acquisitions.

Estimating animal abundance in ground beef batches assayed with molecular markers.

Directory of Open Access Journals (Sweden)

Xin-Sheng Hu

Full Text Available Estimating animal abundance in industrial scale batches of ground meat is important for mapping meat products through the manufacturing process and for effectively tracing the finished product during a food safety recall. The processing of ground beef involves a potentially large number of animals from diverse sources in a single product batch, which produces a high heterogeneity in capture probability. In order to estimate animal abundance through DNA profiling of ground beef constituents, two parameter-based statistical models were developed for incidence data. Simulations were applied to evaluate the maximum likelihood estimate (MLE of a joint likelihood function from multiple surveys, showing superiority in the presence of high capture heterogeneity with small sample sizes, or comparable estimation in the presence of low capture heterogeneity with a large sample size when compared to other existing models. Our model employs the full information on the pattern of the capture-recapture frequencies from multiple samples. We applied the proposed models to estimate animal abundance in six manufacturing beef batches, genotyped using 30 single nucleotide polymorphism (SNP markers, from a large scale beef grinding facility. Results show that between 411∼1367 animals were present in six manufacturing beef batches. These estimates are informative as a reference for improving recall processes and tracing finished meat products back to source.

Fed-batch production of vanillin by Bacillus aryabhattai BA03.

Science.gov (United States)

Paz, Alicia; Outeiriño, David; Pinheiro de Souza Oliveira, Ricardo; Domínguez, José Manuel

2018-01-25

Bacillus aryabhattai BA03, a strain isolated in our laboratory, has interesting properties related to the production of natural aromas and flavors. Specifically, we have found that it was able to produce vanillin from ferulic acid (FA). Furthermore, this strain produces high amounts of 4-vinylguaiacol in only 14h, this being the only intermediate metabolite observed in the process. FA is an inexpensive feedstock for the production of natural value-added compounds when extracted from lignocellulosic wastes. In this study, we optimized the operational conditions (temperature, pH and agitation), medium composition and bioconversion technology (batch or fed-batch) to produce vanillin. In a fed-batch process conducted with just one additional supplementation after 24h, the maximal concentration of vanillin (147.1±0.9mg/L) was observed after 216h (Q V =0.681mg/Lh; Y V/fFA =0.082mg/mg) after degrading 90.3% FA. In view of our data, we postulate that Bacillus aryabhattai BA03 carries out a decarboxylation of ferulic acid as a metabolic pathway. Copyright © 2017 Elsevier B.V. All rights reserved.

Ability Grouping and Differentiated Instruction in an Era of Data-Driven Decision Making

Science.gov (United States)

Park, Vicki; Datnow, Amanda

2017-01-01

Despite data-driven decision making being a ubiquitous part of policy and school reform efforts, little is known about how teachers use data for instructional decision making. Drawing on data from a qualitative case study of four elementary schools, we examine the logic and patterns of teacher decision making about differentiation and ability…

Data-driven modeling, control and tools for cyber-physical energy systems

Science.gov (United States)

Behl, Madhur

Energy systems are experiencing a gradual but substantial change in moving away from being non-interactive and manually-controlled systems to utilizing tight integration of both cyber (computation, communications, and control) and physical representations guided by first principles based models, at all scales and levels. Furthermore, peak power reduction programs like demand response (DR) are becoming increasingly important as the volatility on the grid continues to increase due to regulation, integration of renewables and extreme weather conditions. In order to shield themselves from the risk of price volatility, end-user electricity consumers must monitor electricity prices and be flexible in the ways they choose to use electricity. This requires the use of control-oriented predictive models of an energy system's dynamics and energy consumption. Such models are needed for understanding and improving the overall energy efficiency and operating costs. However, learning dynamical models using grey/white box approaches is very cost and time prohibitive since it often requires significant financial investments in retrofitting the system with several sensors and hiring domain experts for building the model. We present the use of data-driven methods for making model capture easy and efficient for cyber-physical energy systems. We develop Model-IQ, a methodology for analysis of uncertainty propagation for building inverse modeling and controls. Given a grey-box model structure and real input data from a temporary set of sensors, Model-IQ evaluates the effect of the uncertainty propagation from sensor data to model accuracy and to closed-loop control performance. We also developed a statistical method to quantify the bias in the sensor measurement and to determine near optimal sensor placement and density for accurate data collection for model training and control. Using a real building test-bed, we show how performing an uncertainty analysis can reveal trends about

Data driven innovations in structural health monitoring

Science.gov (United States)

Rosales, M. J.; Liyanapathirana, R.

2017-05-01

At present, substantial investments are being allocated to civil infrastructures also considered as valuable assets at a national or global scale. Structural Health Monitoring (SHM) is an indispensable tool required to ensure the performance and safety of these structures based on measured response parameters. The research to date on damage assessment has tended to focus on the utilization of wireless sensor networks (WSN) as it proves to be the best alternative over the traditional visual inspections and tethered or wired counterparts. Over the last decade, the structural health and behaviour of innumerable infrastructure has been measured and evaluated owing to several successful ventures of implementing these sensor networks. Various monitoring systems have the capability to rapidly transmit, measure, and store large capacities of data. The amount of data collected from these networks have eventually been unmanageable which paved the way to other relevant issues such as data quality, relevance, re-use, and decision support. There is an increasing need to integrate new technologies in order to automate the evaluation processes as well as to enhance the objectivity of data assessment routines. This paper aims to identify feasible methodologies towards the application of time-series analysis techniques to judiciously exploit the vast amount of readily available as well as the upcoming data resources. It continues the momentum of a greater effort to collect and archive SHM approaches that will serve as data-driven innovations for the assessment of damage through efficient algorithms and data analytics.

Input variable selection for data-driven models of Coriolis flowmeters for two-phase flow measurement

International Nuclear Information System (INIS)

Wang, Lijuan; Yan, Yong; Wang, Xue; Wang, Tao

2017-01-01

Input variable selection is an essential step in the development of data-driven models for environmental, biological and industrial applications. Through input variable selection to eliminate the irrelevant or redundant variables, a suitable subset of variables is identified as the input of a model. Meanwhile, through input variable selection the complexity of the model structure is simplified and the computational efficiency is improved. This paper describes the procedures of the input variable selection for the data-driven models for the measurement of liquid mass flowrate and gas volume fraction under two-phase flow conditions using Coriolis flowmeters. Three advanced input variable selection methods, including partial mutual information (PMI), genetic algorithm-artificial neural network (GA-ANN) and tree-based iterative input selection (IIS) are applied in this study. Typical data-driven models incorporating support vector machine (SVM) are established individually based on the input candidates resulting from the selection methods. The validity of the selection outcomes is assessed through an output performance comparison of the SVM based data-driven models and sensitivity analysis. The validation and analysis results suggest that the input variables selected from the PMI algorithm provide more effective information for the models to measure liquid mass flowrate while the IIS algorithm provides a fewer but more effective variables for the models to predict gas volume fraction. (paper)

City Connects Prompts Data-Driven Action in Community Schools in the Bronx

Science.gov (United States)

Haywoode, Alyssa

2018-01-01

Community schools have a long history of helping students succeed in school by addressing the problems they face outside of school. But without specific data on students and the full range of their needs, community schools cannot be as effective as they would like to be. Driven by the desire to make more data-informed decisions, the Children's Aid…

Data and Dynamics Driven Approaches for Modelling and Forecasting the Red Sea Chlorophyll

KAUST Repository

Dreano, Denis

2017-05-31

Phytoplankton is at the basis of the marine food chain and therefore play a fundamental role in the ocean ecosystem. However, the large-scale phytoplankton dynamics of the Red Sea are not well understood yet, mainly due to the lack of historical in situ measurements. As a result, our knowledge in this area relies mostly on remotely-sensed observations and large-scale numerical marine ecosystem models. Models are very useful to identify the mechanisms driving the variations in chlorophyll concentration and have practical applications for fisheries operation and harmful algae blooms monitoring. Modelling approaches can be divided between physics- driven (dynamical) approaches, and data-driven (statistical) approaches. Dynamical models are based on a set of differential equations representing the transfer of energy and matter between different subsets of the biota, whereas statistical models identify relationships between variables based on statistical relations within the available data. The goal of this thesis is to develop, implement and test novel dynamical and statistical modelling approaches for studying and forecasting the variability of chlorophyll concentration in the Red Sea. These new models are evaluated in term of their ability to efficiently forecast and explain the regional chlorophyll variability. We also propose innovative synergistic strategies to combine data- and physics-driven approaches to further enhance chlorophyll forecasting capabilities and efficiency.

Controller synthesis for negative imaginary systems: a data driven approach

KAUST Repository

Mabrok, Mohamed

2016-02-17

The negative imaginary (NI) property occurs in many important applications. For instance, flexible structure systems with collocated force actuators and position sensors can be modelled as negative imaginary systems. In this study, a data-driven controller synthesis methodology for NI systems is presented. In this approach, measured frequency response data of the plant is used to construct the controller frequency response at every frequency by minimising a cost function. Then, this controller response is used to identify the controller transfer function using system identification methods. © The Institution of Engineering and Technology 2016.

Data-driven integration of genome-scale regulatory and metabolic network models

Directory of Open Access Journals (Sweden)

Saheed eImam

2015-05-01

Full Text Available Microbes are diverse and extremely versatile organisms that play vital roles in all ecological niches. Understanding and harnessing microbial systems will be key to the sustainability of our planet. One approach to improving our knowledge of microbial processes is through data-driven and mechanism-informed computational modeling. Individual models of biological networks (such as metabolism, transcription and signaling have played pivotal roles in driving microbial research through the years. These networks, however, are highly interconnected and function in concert – a fact that has led to the development of a variety of approaches aimed at simulating the integrated functions of two or more network types. Though the task of integrating these different models is fraught with new challenges, the large amounts of high-throughput data sets being generated, and algorithms being developed, means that the time is at hand for concerted efforts to build integrated regulatory-metabolic networks in a data-driven fashion. In this perspective, we review current approaches for constructing integrated regulatory-metabolic models and outline new strategies for future development of these network models for any microbial system.

A framework for the automated data-driven constitutive characterization of composites

Science.gov (United States)

J.G. Michopoulos; John Hermanson; T. Furukawa; A. Iliopoulos

2010-01-01

We present advances on the development of a mechatronically and algorithmically automated framework for the data-driven identification of constitutive material models based on energy density considerations. These models can capture both the linear and nonlinear constitutive response of multiaxially loaded composite materials in a manner that accounts for progressive...

History based batch method preserving tally means

International Nuclear Information System (INIS)

Shim, Hyung Jin; Choi, Sung Hoon

2012-01-01

In the Monte Carlo (MC) eigenvalue calculations, the sample variance of a tally mean calculated from its cycle-wise estimates is biased because of the inter-cycle correlations of the fission source distribution (FSD). Recently, we proposed a new real variance estimation method named the history-based batch method in which a MC run is treated as multiple runs with small number of histories per cycle to generate independent tally estimates. In this paper, the history-based batch method based on the weight correction is presented to preserve the tally mean from the original MC run. The effectiveness of the new method is examined for the weakly coupled fissile array problem as a function of the dominance ratio and the batch size, in comparison with other schemes available

Examining Data Driven Decision Making via Formative Assessment: A Confluence of Technology, Data Interpretation Heuristics and Curricular Policy

Science.gov (United States)

Swan, Gerry; Mazur, Joan

2011-01-01

Although the term data-driven decision making (DDDM) is relatively new (Moss, 2007), the underlying concept of DDDM is not. For example, the practices of formative assessment and computer-managed instruction have historically involved the use of student performance data to guide what happens next in the instructional sequence (Morrison, Kemp, &…

Kinetics of sugars consumption and ethanol inhibition in carob pulp fermentation by Saccharomyces cerevisiae in batch and fed-batch cultures.

Science.gov (United States)

Lima-Costa, Maria Emília; Tavares, Catarina; Raposo, Sara; Rodrigues, Brígida; Peinado, José M

2012-05-01

The waste materials from the carob processing industry are a potential resource for second-generation bioethanol production. These by-products are small carob kibbles with a high content of soluble sugars (45-50%). Batch and fed-batch Saccharomyces cerevisiae fermentations of high density sugar from carob pods were analyzed in terms of the kinetics of sugars consumption and ethanol inhibition. In all the batch runs, 90-95% of the total sugar was consumed and transformed into ethanol with a yield close to the theoretical maximum (0.47-0.50 g/g), and a final ethanol concentration of 100-110 g/l. In fed-batch runs, fresh carob extract was added when glucose had been consumed. This addition and the subsequent decrease of ethanol concentrations by dilution increased the final ethanol production up to 130 g/l. It seems that invertase activity and yeast tolerance to ethanol are the main factors to be controlled in carob fermentations. The efficiency of highly concentrated carob fermentation makes it a very promising process for use in a second-generation ethanol biorefinery.

Short-term stream flow forecasting at Australian river sites using data-driven regression techniques

CSIR Research Space (South Africa)

Steyn, Melise

2017-09-01

Full Text Available This study proposes a computationally efficient solution to stream flow forecasting for river basins where historical time series data are available. Two data-driven modeling techniques are investigated, namely support vector regression...

Designing Data-Driven Battery Prognostic Approaches for Variable Loading Profiles: Some Lessons Learned

Data.gov (United States)

National Aeronautics and Space Administration — Among various approaches for implementing prognostic algorithms data-driven algorithms are popular in the industry due to their intuitive nature and relatively fast...

Numerical modeling of batch formation in waste incineration plants

Directory of Open Access Journals (Sweden)

Obroučka Karel

2015-03-01

Full Text Available The aim of this paper is a mathematical description of algorithm for controlled assembly of incinerated batch of waste. The basis for formation of batch is selected parameters of incinerated waste as its calorific value or content of pollutants or the combination of both. The numerical model will allow, based on selected criteria, to compile batch of wastes which continuously follows the previous batch, which is a prerequisite for optimized operation of incinerator. The model was prepared as for waste storage in containers, as well as for waste storage in continuously refilled boxes. The mathematical model was developed into the computer program and its functionality was verified either by practical measurements or by numerical simulations. The proposed model can be used in incinerators for hazardous and municipal waste.

Monitoring a PVC batch process with multivariate statistical process control charts

NARCIS (Netherlands)

Tates, A. A.; Louwerse, D. J.; Smilde, A. K.; Koot, G. L. M.; Berndt, H.

1999-01-01

Multivariate statistical process control charts (MSPC charts) are developed for the industrial batch production process of poly(vinyl chloride) (PVC). With these MSPC charts different types of abnormal batch behavior were detected on-line. With batch contribution plots, the probable causes of these

Spatial and temporal variation in Baltic sprat (Sprattus sprattus balticus S.) batch fecundity

DEFF Research Database (Denmark)

Haslob, Holger; Tomkiewicz, Jonna; Hinrichsen, Hans-Harald

,salinity, oxygen content as well as fish and stock size were tested as explanatory variables. The data obtained in this investigation were used to develop a predictive model of Balticsprat batch fecundity. Coupling these results with existing ichthyoplankton survey and stockstructure data will allow applying...

Retesting the Limits of Data-Driven Learning: Feedback and Error Correction

Science.gov (United States)

Crosthwaite, Peter

2017-01-01

An increasing number of studies have looked at the value of corpus-based data-driven learning (DDL) for second language (L2) written error correction, with generally positive results. However, a potential conundrum for language teachers involved in the process is how to provide feedback on students' written production for DDL. The study looks at…

Data-Driven Engineering of Social Dynamics: Pattern Matching and Profit Maximization.

Science.gov (United States)

Peng, Huan-Kai; Lee, Hao-Chih; Pan, Jia-Yu; Marculescu, Radu

2016-01-01

In this paper, we define a new problem related to social media, namely, the data-driven engineering of social dynamics. More precisely, given a set of observations from the past, we aim at finding the best short-term intervention that can lead to predefined long-term outcomes. Toward this end, we propose a general formulation that covers two useful engineering tasks as special cases, namely, pattern matching and profit maximization. By incorporating a deep learning model, we derive a solution using convex relaxation and quadratic-programming transformation. Moreover, we propose a data-driven evaluation method in place of the expensive field experiments. Using a Twitter dataset, we demonstrate the effectiveness of our dynamics engineering approach for both pattern matching and profit maximization, and study the multifaceted interplay among several important factors of dynamics engineering, such as solution validity, pattern-matching accuracy, and intervention cost. Finally, the method we propose is general enough to work with multi-dimensional time series, so it can potentially be used in many other applications.

Data-Driven Engineering of Social Dynamics: Pattern Matching and Profit Maximization.

Directory of Open Access Journals (Sweden)

Huan-Kai Peng

Full Text Available In this paper, we define a new problem related to social media, namely, the data-driven engineering of social dynamics. More precisely, given a set of observations from the past, we aim at finding the best short-term intervention that can lead to predefined long-term outcomes. Toward this end, we propose a general formulation that covers two useful engineering tasks as special cases, namely, pattern matching and profit maximization. By incorporating a deep learning model, we derive a solution using convex relaxation and quadratic-programming transformation. Moreover, we propose a data-driven evaluation method in place of the expensive field experiments. Using a Twitter dataset, we demonstrate the effectiveness of our dynamics engineering approach for both pattern matching and profit maximization, and study the multifaceted interplay among several important factors of dynamics engineering, such as solution validity, pattern-matching accuracy, and intervention cost. Finally, the method we propose is general enough to work with multi-dimensional time series, so it can potentially be used in many other applications.

Data-Driven Engineering of Social Dynamics: Pattern Matching and Profit Maximization

Science.gov (United States)

Peng, Huan-Kai; Lee, Hao-Chih; Pan, Jia-Yu; Marculescu, Radu

2016-01-01

In this paper, we define a new problem related to social media, namely, the data-driven engineering of social dynamics. More precisely, given a set of observations from the past, we aim at finding the best short-term intervention that can lead to predefined long-term outcomes. Toward this end, we propose a general formulation that covers two useful engineering tasks as special cases, namely, pattern matching and profit maximization. By incorporating a deep learning model, we derive a solution using convex relaxation and quadratic-programming transformation. Moreover, we propose a data-driven evaluation method in place of the expensive field experiments. Using a Twitter dataset, we demonstrate the effectiveness of our dynamics engineering approach for both pattern matching and profit maximization, and study the multifaceted interplay among several important factors of dynamics engineering, such as solution validity, pattern-matching accuracy, and intervention cost. Finally, the method we propose is general enough to work with multi-dimensional time series, so it can potentially be used in many other applications. PMID:26771830

Data-Driven Model Uncertainty Estimation in Hydrologic Data Assimilation

Science.gov (United States)

Pathiraja, S.; Moradkhani, H.; Marshall, L.; Sharma, A.; Geenens, G.

2018-02-01

The increasing availability of earth observations necessitates mathematical methods to optimally combine such data with hydrologic models. Several algorithms exist for such purposes, under the umbrella of data assimilation (DA). However, DA methods are often applied in a suboptimal fashion for complex real-world problems, due largely to several practical implementation issues. One such issue is error characterization, which is known to be critical for a successful assimilation. Mischaracterized errors lead to suboptimal forecasts, and in the worst case, to degraded estimates even compared to the no assimilation case. Model uncertainty characterization has received little attention relative to other aspects of DA science. Traditional methods rely on subjective, ad hoc tuning factors or parametric distribution assumptions that may not always be applicable. We propose a novel data-driven approach (named SDMU) to model uncertainty characterization for DA studies where (1) the system states are partially observed and (2) minimal prior knowledge of the model error processes is available, except that the errors display state dependence. It includes an approach for estimating the uncertainty in hidden model states, with the end goal of improving predictions of observed variables. The SDMU is therefore suited to DA studies where the observed variables are of primary interest. Its efficacy is demonstrated through a synthetic case study with low-dimensional chaotic dynamics and a real hydrologic experiment for one-day-ahead streamflow forecasting. In both experiments, the proposed method leads to substantial improvements in the hidden states and observed system outputs over a standard method involving perturbation with Gaussian noise.

Look-ahead strategies for controlling batch operations in industry - An overview

NARCIS (Netherlands)

Zee, Durk-Jouke van der; Chick, SE; Sanchez, PJ; Ferrin, D; Morrice, DJ

2003-01-01

Batching jobs in a manufacturing system is a very common policy in most industries. Main reasons for batching are avoidance of set ups and/or facilitation of material handling. Examples of batch-wise production systems are ovens found in aircraft industry and in semiconductor manufacturing. Starting

On-line runaway detection in batch reactors using chaos theory techniques.

NARCIS (Netherlands)

Strozzi, F.; Strozzi, F.; Zaldivar, J.M.; Zaldivar, J.M.; Kronberg, Alexandre E.; Westerterp, K.R.

1999-01-01

In this work nonlinear time-series analysis using delay coordinate embedding was applied to simulated temperature data from isoperibolic batch reactors to develop an early-warning detection system of the runaway. In the first part of this study an early-warning detection criterion, that is, when the

Data-driven algorithm to estimate friction in automobile engine

DEFF Research Database (Denmark)

Stotsky, Alexander A.

2010-01-01

Algorithms based on the oscillations of the engine angular rotational speed under fuel cutoff and no-load were proposed for estimation of the engine friction torque. The recursive algorithm to restore the periodic signal is used to calculate the amplitude of the engine speed signal at fuel cutoff....... The values of the friction torque in the corresponding table entries are updated at acquiring new measurements of the friction moment. A new, data-driven algorithm for table adaptation on the basis of stepwise regression was developed and verified using the six-cylinder Volvo engine....

Towards Data-Driven Simulations of Wildfire Spread using Ensemble-based Data Assimilation

Science.gov (United States)

Rochoux, M. C.; Bart, J.; Ricci, S. M.; Cuenot, B.; Trouvé, A.; Duchaine, F.; Morel, T.

2012-12-01

Real-time predictions of a propagating wildfire remain a challenging task because the problem involves both multi-physics and multi-scales. The propagation speed of wildfires, also called the rate of spread (ROS), is indeed determined by complex interactions between pyrolysis, combustion and flow dynamics, atmospheric dynamics occurring at vegetation, topographical and meteorological scales. Current operational fire spread models are mainly based on a semi-empirical parameterization of the ROS in terms of vegetation, topographical and meteorological properties. For the fire spread simulation to be predictive and compatible with operational applications, the uncertainty on the ROS model should be reduced. As recent progress made in remote sensing technology provides new ways to monitor the fire front position, a promising approach to overcome the difficulties found in wildfire spread simulations is to integrate fire modeling and fire sensing technologies using data assimilation (DA). For this purpose we have developed a prototype data-driven wildfire spread simulator in order to provide optimal estimates of poorly known model parameters [*]. The data-driven simulation capability is adapted for more realistic wildfire spread : it considers a regional-scale fire spread model that is informed by observations of the fire front location. An Ensemble Kalman Filter algorithm (EnKF) based on a parallel computing platform (OpenPALM) was implemented in order to perform a multi-parameter sequential estimation where wind magnitude and direction are in addition to vegetation properties (see attached figure). The EnKF algorithm shows its good ability to track a small-scale grassland fire experiment and ensures a good accounting for the sensitivity of the simulation outcomes to the control parameters. As a conclusion, it was shown that data assimilation is a promising approach to more accurately forecast time-varying wildfire spread conditions as new airborne-like observations of

Data-driven sensor placement from coherent fluid structures

Science.gov (United States)

Manohar, Krithika; Kaiser, Eurika; Brunton, Bingni W.; Kutz, J. Nathan; Brunton, Steven L.

2017-11-01

Optimal sensor placement is a central challenge in the prediction, estimation and control of fluid flows. We reinterpret sensor placement as optimizing discrete samples of coherent fluid structures for full state reconstruction. This permits a drastic reduction in the number of sensors required for faithful reconstruction, since complex fluid interactions can often be described by a small number of coherent structures. Our work optimizes point sensors using the pivoted matrix QR factorization to sample coherent structures directly computed from flow data. We apply this sampling technique in conjunction with various data-driven modal identification methods, including the proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD). In contrast to POD-based sensors, DMD demonstrably enables the optimization of sensors for prediction in systems exhibiting multiple scales of dynamics. Finally, reconstruction accuracy from pivot sensors is shown to be competitive with sensors obtained using traditional computationally prohibitive optimization methods.

Econophysics and Data Driven Modelling of Market Dynamics

CERN Document Server

Aoyama, Hideaki; Chakrabarti, Bikas; Chakraborti, Anirban; Ghosh, Asim; Econophysics and Data Driven Modelling of Market Dynamics

2015-01-01

This book presents the works and research findings of physicists, economists, mathematicians, statisticians, and financial engineers who have undertaken data-driven modelling of market dynamics and other empirical studies in the field of Econophysics. During recent decades, the financial market landscape has changed dramatically with the deregulation of markets and the growing complexity of products. The ever-increasing speed and decreasing costs of computational power and networks have led to the emergence of huge databases. The availability of these data should permit the development of models that are better founded empirically, and econophysicists have accordingly been advocating that one should rely primarily on the empirical observations in order to construct models and validate them. The recent turmoil in financial markets and the 2008 crash appear to offer a strong rationale for new models and approaches. The Econophysics community accordingly has an important future role to play in market modelling....

Manuscript 101: A Data-Driven Writing Exercise For Beginning Scientists

OpenAIRE

Ralston, Amy; Halbisen, Michael

2017-01-01

Learning to write a scientific manuscript is one of the most important and rewarding scientific training experiences, yet most young scientists only embark on this experience relatively late in graduate school, after gathering sufficient data in the lab. Yet, familiarity with the process of writing a scientific manuscript and receiving peer reviews, often leads to a more focused and driven experimental approach. To jump-start this training, we developed a protocol for teaching manuscript writ...

Data-Driven Predictive Direct Load Control of Refrigeration Systems

DEFF Research Database (Denmark)

Shafiei, Seyed Ehsan; Knudsen, Torben; Wisniewski, Rafal

2015-01-01

A predictive control using subspace identification is applied for the smart grid integration of refrigeration systems under a direct load control scheme. A realistic demand response scenario based on regulation of the electrical power consumption is considered. A receding horizon optimal control...... is proposed to fulfil two important objectives: to secure high coefficient of performance and to participate in power consumption management. Moreover, a new method for design of input signals for system identification is put forward. The control method is fully data driven without an explicit use of model...... against real data. The performance improvement results in a 22% reduction in the energy consumption. A comparative simulation is accomplished showing the superiority of the method over the existing approaches in terms of the load following performance....

Client and event driven data hub system at CDF

International Nuclear Information System (INIS)

Kilminster, Ben; McFarland, Kevin; Vaiciulis, Tony; Matsunaga, Hiroyuki; Shimojima, Makoto

2001-01-01

The Consumer-Server Logger (CSL) system at the Collider Detector at Fermilab is a client and event driven data hub capable of receiving physics events from multiple connections, and logging them to multiple streams while distributing them to multiple online analysis programs (consumers). Its multiple-partitioned design allows data flowing through different paths of the detector sub-systems to be processed separately. The CSL system, using a set of internal memory buffers and message queues mapped to the location of events within its programs, and running on an SGI 2200 Server, is able to process at least the required 20 MB/s of constant event logging (75 Hz of 250 KB events) while also filtering up to 10 MB/s to consumers requesting specific types of events

Comparative study of trapping parameters of LiF(TLD-100) from different production batches

Energy Technology Data Exchange (ETDEWEB)

Bos, A.J.J.; Piters, T.M.; Vries, W. de; Hoogenboom, J.E. (Delft Univ. of Technology (Netherlands). Interfaculty Reactor Institute)

1990-01-01

Computerised glow curve analysis has been used to determine the trapping parameters of the main peaks of the thermoluminescent (TL) material LiF(TLD-100). The TL material (solid state chips) originated from six different production batches with at least 19 chips per batch. The maxima of glow peaks 2 to 5 are found at the same temperature within very small limits. The activation energy and frequency factor of the main glow peak (peak 5) of TLD-100 originating from two batches differ significantly from those of the other four investigated batches. Nevertheless, the sensitivity of glow peak 5 is more or less the same for all batches. The trapping parameters of glow peaks 2 to 4 of TLD-100 vary little from batch to batch. The measured half-life of peak 2 differed strongly from batch to batch. For all investigated peaks no correlation has been found between glow peak sensitivity and trapping parameters. The results of this study suggest that both defect concentration and nature of the trapping centres vary from batch to batch. It would appear that as a consequence of selection by the manufacturer, the differences between the batches in terms of total light output are small. (author).

A data-driven approach for modeling post-fire debris-flow volumes and their uncertainty

Science.gov (United States)

Friedel, Michael J.

2011-01-01

This study demonstrates the novel application of genetic programming to evolve nonlinear post-fire debris-flow volume equations from variables associated with a data-driven conceptual model of the western United States. The search space is constrained using a multi-component objective function that simultaneously minimizes root-mean squared and unit errors for the evolution of fittest equations. An optimization technique is then used to estimate the limits of nonlinear prediction uncertainty associated with the debris-flow equations. In contrast to a published multiple linear regression three-variable equation, linking basin area with slopes greater or equal to 30 percent, burn severity characterized as area burned moderate plus high, and total storm rainfall, the data-driven approach discovers many nonlinear and several dimensionally consistent equations that are unbiased and have less prediction uncertainty. Of the nonlinear equations, the best performance (lowest prediction uncertainty) is achieved when using three variables: average basin slope, total burned area, and total storm rainfall. Further reduction in uncertainty is possible for the nonlinear equations when dimensional consistency is not a priority and by subsequently applying a gradient solver to the fittest solutions. The data-driven modeling approach can be applied to nonlinear multivariate problems in all fields of study.

Design and Implementation of Electronic Batch Record Systems for Pharmaceutical Manufacturing Documentation

International Nuclear Information System (INIS)

Abdul Jalil Abd Hamid; Shafii Khamis; Rehir Dahalan

2011-01-01

Paper batch records have been used for decades to record procedures, the types and quantities of each material used, and the status of each step in the manufacturing process for both pharmaceuticals and medical devices. Although paper batch records are well established in its implementation, the system is laborious to maintain and prone to human error, particularly as manufacturing operations become increasingly complicated. Many pharmaceutical manufacturers are currently evaluating the feasibility of Electronic Batch Record (EBR) system. An integrated EBR system has been developed by Medical Technology Division of Nuclear Malaysia to monitor process and equipment used in the manufacture of pharmaceuticals and medical devices. The system architecture consists of an iPAN7 data processing system operating under Microsoft Windows Embedded CE 6.0 R2. The system serves as a common data bank and an input/output device for the iPAN7 processors. Full traceability from component material to finished product is maintained. Properly implemented, EBR eliminate paperwork, speed up information distribution, and provide useful tools for improving quality and efficiency. This paper discusses the general system requirements and specifications along with the hardware and software required to implement those requirements and specifications. Also discussed are problems which were encountered after initial development and plans for future development, and a plan for extending and commercializing this technology. (author)

General Purpose Data-Driven Online System Health Monitoring with Applications to Space Operations

Science.gov (United States)

Iverson, David L.; Spirkovska, Lilly; Schwabacher, Mark

2010-01-01

Modern space transportation and ground support system designs are becoming increasingly sophisticated and complex. Determining the health state of these systems using traditional parameter limit checking, or model-based or rule-based methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. System health can be monitored by comparing real-time operating data with these nominal characterizations, providing detection of anomalous data signatures indicative of system faults, failures, or precursors of significant failures. The Inductive Monitoring System (IMS) is a general purpose, data-driven system health monitoring software tool that has been successfully applied to several aerospace applications and is under evaluation for anomaly detection in vehicle and ground equipment for next generation launch systems. After an introduction to IMS application development, we discuss these NASA online monitoring applications, including the integration of IMS with complementary model-based and rule-based methods. Although the examples presented in this paper are from space operations applications, IMS is a general-purpose health-monitoring tool that is also applicable to power generation and transmission system monitoring.

Inorganic fouling mitigation by salinity cycling in batch reverse osmosis

OpenAIRE

Maswadeh, Laith A.; Warsinger, David Elan Martin; Tow, Emily W.; Connors, Grace B.; Swaminathan, Jaichander; Lienhard, John H

2018-01-01

Enhanced fouling resistance has been observed in recent variants of reverse osmosis (RO) desalination which use time-varying batch or semi-batch processes, such as closed-circuit RO (CCRO) and pulse flow RO (PFRO). However, the mechanisms of batch processes' fouling resistance are not well-understood, and models have not been developed for prediction of their fouling performance. Here, a framework for predicting reverse osmosis fouling is developed by comparing the fluid residence time in bat...

Dynamic Scheduling Of Batch Operations With Non-Identical Machines

NARCIS (Netherlands)

van der Zee, D.J.; van Harten, A.; Schuur, P.C.

1997-01-01

Batch-wise production is found in many industries. A good example of production systems which process products batch-wise are the ovens found in aircraft industry and in semiconductor manufacturing. These systems mostly consist of multiple machines of different types, given the range and volumes of

Dynamic scheduling of batch operations with non-identical machines

NARCIS (Netherlands)

van der Zee, D.J.; van Harten, Aart; Schuur, Peter

1997-01-01

Batch-wise production is found in many industries. A good example of production systems which process products batch-wise are the ovens found in aircraft industry and in semiconductor manufacturing. These systems mostly consist of multiple machines of different types, given the range and volumes of

Some performance measures for vacation models with a batch Markovian arrival process

Directory of Open Access Journals (Sweden)

Sadrac K. Matendo

1994-01-01

Full Text Available We consider a single server infinite capacity queueing system, where the arrival process is a batch Markovian arrival process (BMAP. Particular BMAPs are the batch Poisson arrival process, the Markovian arrival process (MAP, many batch arrival processes with correlated interarrival times and batch sizes, and superpositions of these processes. We note that the MAP includes phase-type (PH renewal processes and non-renewal processes such as the Markov modulated Poisson process (MMPP.

VLAM-G: Interactive Data Driven Workflow Engine for Grid-Enabled Resources

Directory of Open Access Journals (Sweden)

Vladimir Korkhov

2007-01-01

Full Text Available Grid brings the power of many computers to scientists. However, the development of Grid-enabled applications requires knowledge about Grid infrastructure and low-level API to Grid services. In turn, workflow management systems provide a high-level environment for rapid prototyping of experimental computing systems. Coupling Grid and workflow paradigms is important for the scientific community: it makes the power of the Grid easily available to the end user. The paradigm of data driven workflow execution is one of the ways to enable distributed workflow on the Grid. The work presented in this paper is carried out in the context of the Virtual Laboratory for e-Science project. We present the VLAM-G workflow management system and its core component: the Run-Time System (RTS. The RTS is a dataflow driven workflow engine which utilizes Grid resources, hiding the complexity of the Grid from a scientist. Special attention is paid to the concept of dataflow and direct data streaming between distributed workflow components. We present the architecture and components of the RTS, describe the features of VLAM-G workflow execution, and evaluate the system by performance measurements and a real life use case.

Comparison of ERBS orbit determination accuracy using batch least-squares and sequential methods

Science.gov (United States)

Oza, D. H.; Jones, T. L.; Fabien, S. M.; Mistretta, G. D.; Hart, R. C.; Doll, C. E.

1991-10-01

The Flight Dynamics Div. (FDD) at NASA-Goddard commissioned a study to develop the Real Time Orbit Determination/Enhanced (RTOD/E) system as a prototype system for sequential orbit determination of spacecraft on a DOS based personal computer (PC). An overview is presented of RTOD/E capabilities and the results are presented of a study to compare the orbit determination accuracy for a Tracking and Data Relay Satellite System (TDRSS) user spacecraft obtained using RTOS/E on a PC with the accuracy of an established batch least squares system, the Goddard Trajectory Determination System (GTDS), operating on a mainframe computer. RTOD/E was used to perform sequential orbit determination for the Earth Radiation Budget Satellite (ERBS), and the Goddard Trajectory Determination System (GTDS) was used to perform the batch least squares orbit determination. The estimated ERBS ephemerides were obtained for the Aug. 16 to 22, 1989, timeframe, during which intensive TDRSS tracking data for ERBS were available. Independent assessments were made to examine the consistencies of results obtained by the batch and sequential methods. Comparisons were made between the forward filtered RTOD/E orbit solutions and definitive GTDS orbit solutions for ERBS; the solution differences were less than 40 meters after the filter had reached steady state.

Comparison of ERBS orbit determination accuracy using batch least-squares and sequential methods

Science.gov (United States)

Oza, D. H.; Jones, T. L.; Fabien, S. M.; Mistretta, G. D.; Hart, R. C.; Doll, C. E.

1991-01-01

The Flight Dynamics Div. (FDD) at NASA-Goddard commissioned a study to develop the Real Time Orbit Determination/Enhanced (RTOD/E) system as a prototype system for sequential orbit determination of spacecraft on a DOS based personal computer (PC). An overview is presented of RTOD/E capabilities and the results are presented of a study to compare the orbit determination accuracy for a Tracking and Data Relay Satellite System (TDRSS) user spacecraft obtained using RTOS/E on a PC with the accuracy of an established batch least squares system, the Goddard Trajectory Determination System (GTDS), operating on a mainframe computer. RTOD/E was used to perform sequential orbit determination for the Earth Radiation Budget Satellite (ERBS), and the Goddard Trajectory Determination System (GTDS) was used to perform the batch least squares orbit determination. The estimated ERBS ephemerides were obtained for the Aug. 16 to 22, 1989, timeframe, during which intensive TDRSS tracking data for ERBS were available. Independent assessments were made to examine the consistencies of results obtained by the batch and sequential methods. Comparisons were made between the forward filtered RTOD/E orbit solutions and definitive GTDS orbit solutions for ERBS; the solution differences were less than 40 meters after the filter had reached steady state.

A predictive estimation method for carbon dioxide transport by data-driven modeling with a physically-based data model

Science.gov (United States)

Jeong, Jina; Park, Eungyu; Han, Weon Shik; Kim, Kue-Young; Jun, Seong-Chun; Choung, Sungwook; Yun, Seong-Taek; Oh, Junho; Kim, Hyun-Jun

2017-11-01

In this study, a data-driven method for predicting CO2 leaks and associated concentrations from geological CO2 sequestration is developed. Several candidate models are compared based on their reproducibility and predictive capability for CO2 concentration measurements from the Environment Impact Evaluation Test (EIT) site in Korea. Based on the data mining results, a one-dimensional solution of the advective-dispersive equation for steady flow (i.e., Ogata-Banks solution) is found to be most representative for the test data, and this model is adopted as the data model for the developed method. In the validation step, the method is applied to estimate future CO2 concentrations with the reference estimation by the Ogata-Banks solution, where a part of earlier data is used as the training dataset. From the analysis, it is found that the ensemble mean of multiple estimations based on the developed method shows high prediction accuracy relative to the reference estimation. In addition, the majority of the data to be predicted are included in the proposed quantile interval, which suggests adequate representation of the uncertainty by the developed method. Therefore, the incorporation of a reasonable physically-based data model enhances the prediction capability of the data-driven model. The proposed method is not confined to estimations of CO2 concentration and may be applied to various real-time monitoring data from subsurface sites to develop automated control, management or decision-making systems.

A predictive estimation method for carbon dioxide transport by data-driven modeling with a physically-based data model.

Science.gov (United States)

Jeong, Jina; Park, Eungyu; Han, Weon Shik; Kim, Kue-Young; Jun, Seong-Chun; Choung, Sungwook; Yun, Seong-Taek; Oh, Junho; Kim, Hyun-Jun

2017-11-01

In this study, a data-driven method for predicting CO 2 leaks and associated concentrations from geological CO 2 sequestration is developed. Several candidate models are compared based on their reproducibility and predictive capability for CO 2 concentration measurements from the Environment Impact Evaluation Test (EIT) site in Korea. Based on the data mining results, a one-dimensional solution of the advective-dispersive equation for steady flow (i.e., Ogata-Banks solution) is found to be most representative for the test data, and this model is adopted as the data model for the developed method. In the validation step, the method is applied to estimate future CO 2 concentrations with the reference estimation by the Ogata-Banks solution, where a part of earlier data is used as the training dataset. From the analysis, it is found that the ensemble mean of multiple estimations based on the developed method shows high prediction accuracy relative to the reference estimation. In addition, the majority of the data to be predicted are included in the proposed quantile interval, which suggests adequate representation of the uncertainty by the developed method. Therefore, the incorporation of a reasonable physically-based data model enhances the prediction capability of the data-driven model. The proposed method is not confined to estimations of CO 2 concentration and may be applied to various real-time monitoring data from subsurface sites to develop automated control, management or decision-making systems. Copyright © 2017 Elsevier B.V. All rights reserved.

ATLAS job transforms: a data driven workflow engine

International Nuclear Information System (INIS)

Stewart, G A; Breaden-Madden, W B; Maddocks, H J; Harenberg, T; Sandhoff, M; Sarrazin, B

2014-01-01

The need to run complex workflows for a high energy physics experiment such as ATLAS has always been present. However, as computing resources have become even more constrained, compared to the wealth of data generated by the LHC, the need to use resources efficiently and manage complex workflows within a single grid job have increased. In ATLAS, a new Job Transform framework has been developed that we describe in this paper. This framework manages the multiple execution steps needed to 'transform' one data type into another (e.g., RAW data to ESD to AOD to final ntuple) and also provides a consistent interface for the ATLAS production system. The new framework uses a data driven workflow definition which is both easy to manage and powerful. After a transform is defined, jobs are expressed simply by specifying the input data and the desired output data. The transform infrastructure then executes only the necessary substeps to produce the final data products. The global execution cost of running the job is minimised and the transform can adapt to scenarios where data can be produced along different execution paths. Transforms for specific physics tasks which support up to 60 individual substeps have been successfully run. As the new transforms infrastructure has been deployed in production many features have been added to the framework which improve reliability, quality of error reporting and also provide support for multi-process jobs.

A Transition Towards a Data-Driven Business Model (DDBM)

DEFF Research Database (Denmark)

Zaki, Mohamed; Bøe-Lillegraven, Tor; Neely, Andy

2016-01-01

Nettavisen is a Norwegian online start-up that experienced a boost after the financial crisis of 2009. Since then, the firm has been able to increase its market share and profitability through the use of highly disruptive business models, allowing the relatively small staff to outcompete powerhouse...... legacy-publishing companies and new media players such as Facebook and Google. These disruptive business models have been successful, as Nettavisen captured a large market share in Norway early on, and was consistently one of the top-three online news sites in Norway. Capitalising on media data explosion...... and the recent acquisition of blogger network ‘Blog.no’, Nettavisen is moving towards a data-driven business model (DDBM). In particular, the firm aims to analyse huge volumes of user Web browsing and purchasing habits....

21 CFR 80.37 - Treatment of batch pending certification.

Science.gov (United States)

2010-04-01

... 21 Food and Drugs 1 2010-04-01 2010-04-01 false Treatment of batch pending certification. 80.37 Section 80.37 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES GENERAL COLOR ADDITIVE CERTIFICATION Certification Procedures § 80.37 Treatment of batch pending certification...

Big data-driven business how to use big data to win customers, beat competitors, and boost profits

CERN Document Server

Glass, Russell

2014-01-01

Get the expert perspective and practical advice on big data The Big Data-Driven Business: How to Use Big Data to Win Customers, Beat Competitors, and Boost Profits makes the case that big data is for real, and more than just big hype. The book uses real-life examples-from Nate Silver to Copernicus, and Apple to Blackberry-to demonstrate how the winners of the future will use big data to seek the truth. Written by a marketing journalist and the CEO of a multi-million-dollar B2B marketing platform that reaches more than 90% of the U.S. business population, this book is a comprehens

The Financial and Non-Financial Aspects of Developing a Data-Driven Decision-Making Mindset in an Undergraduate Business Curriculum

Science.gov (United States)

Bohler, Jeffrey; Krishnamoorthy, Anand; Larson, Benjamin

2017-01-01

Making data-driven decisions is becoming more important for organizations faced with confusing and often contradictory information available to them from their operating environment. This article examines one college of business' journey of developing a data-driven decision-making mindset within its undergraduate curriculum. Lessons learned may be…

Influence of coal batch preparation on the quality of metallurgical соkе

Directory of Open Access Journals (Sweden)

Катерина Олегівна Шмельцер

2015-10-01

Full Text Available To study the influence of coal batch properties on coke strength we have considered the quality of the coke produced at the plant in Krivoy Rog from 2008 till 2012. Such factors as the large number of coal suppliers, imprecise selection of the optimal degree of batch crushing result in the decline in coke quality, the batch density and contents of the lean class (<0,5 mm are not optimum; poor blending of the batch after crushing; increased moisture and ash content of the coking batch; and extreme fluctuation in the coal and batch characteristics. It was found that high humidity of coal batch and its large fluctuations has most profound effect on the mechanical properties of coke. Under deteriorating resource base the quality of the coking batch preparation is important, To have batch of proper quality the following key aspects must be taken into account: the batch must be crushed to an optimum degree that will result in leaning components decrease and increased contents of vitrivite in it which improves the sinterability and coking, and hence the quality of coke; the degree of mixing of the coking batch in all indices must be up to 98-99%, for uneven distribution in the coal chamber worsens the quality of coke

Data-driven outbreak forecasting with a simple nonlinear growth model.

Science.gov (United States)

Lega, Joceline; Brown, Heidi E

2016-12-01

Recent events have thrown the spotlight on infectious disease outbreak response. We developed a data-driven method, EpiGro, which can be applied to cumulative case reports to estimate the order of magnitude of the duration, peak and ultimate size of an ongoing outbreak. It is based on a surprisingly simple mathematical property of many epidemiological data sets, does not require knowledge or estimation of disease transmission parameters, is robust to noise and to small data sets, and runs quickly due to its mathematical simplicity. Using data from historic and ongoing epidemics, we present the model. We also provide modeling considerations that justify this approach and discuss its limitations. In the absence of other information or in conjunction with other models, EpiGro may be useful to public health responders. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Global retrieval of soil moisture and vegetation properties using data-driven methods

Science.gov (United States)

Rodriguez-Fernandez, Nemesio; Richaume, Philippe; Kerr, Yann

2017-04-01

Data-driven methods such as neural networks (NNs) are a powerful tool to retrieve soil moisture from multi-wavelength remote sensing observations at global scale. In this presentation we will review a number of recent results regarding the retrieval of soil moisture with the Soil Moisture and Ocean Salinity (SMOS) satellite, either using SMOS brightness temperatures as input data for the retrieval or using SMOS soil moisture retrievals as reference dataset for the training. The presentation will discuss several possibilities for both the input datasets and the datasets to be used as reference for the supervised learning phase. Regarding the input datasets, it will be shown that NNs take advantage of the synergy of SMOS data and data from other sensors such as the Advanced Scatterometer (ASCAT, active microwaves) and MODIS (visible and infra red). NNs have also been successfully used to construct long time series of soil moisture from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) and SMOS. A NN with input data from ASMR-E observations and SMOS soil moisture as reference for the training was used to construct a dataset sharing a similar climatology and without a significant bias with respect to SMOS soil moisture. Regarding the reference data to train the data-driven retrievals, we will show different possibilities depending on the application. Using actual in situ measurements is challenging at global scale due to the scarce distribution of sensors. In contrast, in situ measurements have been successfully used to retrieve SM at continental scale in North America, where the density of in situ measurement stations is high. Using global land surface models to train the NN constitute an interesting alternative to implement new remote sensing surface datasets. In addition, these datasets can be used to perform data assimilation into the model used as reference for the training. This approach has recently been tested at the European Centre

Data driven approaches for diagnostics and optimization of NPP operation

International Nuclear Information System (INIS)

Pliska, J.; Machat, Z.

2014-01-01

The efficiency and heat rate is an important indicator of both the health of the power plant equipment and the quality of power plant operation. To achieve this challenges powerful tool is a statistical data processing of large data sets which are stored in data historians. These large data sets contain useful information about process quality and equipment and sensor health. The paper discusses data-driven approaches for model building of main power plant equipment such as condenser, cooling tower and the overall thermal cycle as well using multivariate regression techniques based on so called a regression triplet - data, model and method. Regression models comprise a base for diagnostics and optimization tasks. Diagnostics and optimization tasks are demonstrated on practical cases - diagnostics of main power plant equipment to early identify equipment fault, and optimization task of cooling circuit by cooling water flow control to achieve for a given boundary conditions the highest power output. (authors)

Data-Driven Visualization and Group Analysis of Multichannel EEG Coherence with Functional Units

NARCIS (Netherlands)

Caat, Michael ten; Maurits, Natasha M.; Roerdink, Jos B.T.M.

2008-01-01

A typical data- driven visualization of electroencephalography ( EEG) coherence is a graph layout, with vertices representing electrodes and edges representing significant coherences between electrode signals. A drawback of this layout is its visual clutter for multichannel EEG. To reduce clutter,

Big-Data-Driven Stem Cell Science and Tissue Engineering: Vision and Unique Opportunities.

Science.gov (United States)

Del Sol, Antonio; Thiesen, Hans J; Imitola, Jaime; Carazo Salas, Rafael E

2017-02-02

Achieving the promises of stem cell science to generate precise disease models and designer cell samples for personalized therapeutics will require harnessing pheno-genotypic cell-level data quantitatively and predictively in the lab and clinic. Those requirements could be met by developing a Big-Data-driven stem cell science strategy and community. Copyright © 2017 Elsevier Inc. All rights reserved.

Data-Driven User Feedback: An Improved Neurofeedback Strategy considering the Interindividual Variability of EEG Features.

Science.gov (United States)

Han, Chang-Hee; Lim, Jeong-Hwan; Lee, Jun-Hak; Kim, Kangsan; Im, Chang-Hwan

2016-01-01

It has frequently been reported that some users of conventional neurofeedback systems can experience only a small portion of the total feedback range due to the large interindividual variability of EEG features. In this study, we proposed a data-driven neurofeedback strategy considering the individual variability of electroencephalography (EEG) features to permit users of the neurofeedback system to experience a wider range of auditory or visual feedback without a customization process. The main idea of the proposed strategy is to adjust the ranges of each feedback level using the density in the offline EEG database acquired from a group of individuals. Twenty-two healthy subjects participated in offline experiments to construct an EEG database, and five subjects participated in online experiments to validate the performance of the proposed data-driven user feedback strategy. Using the optimized bin sizes, the number of feedback levels that each individual experienced was significantly increased to 139% and 144% of the original results with uniform bin sizes in the offline and online experiments, respectively. Our results demonstrated that the use of our data-driven neurofeedback strategy could effectively increase the overall range of feedback levels that each individual experienced during neurofeedback training.

Huginot data of plastic foams obtained from leaser-driven shocks

Czech Academy of Sciences Publication Activity Database

Dezulian, R.; Canova, F.; Barbanotti, S.; Orsenigo, F.; Redaelli, R.; Vinci, T.; Lucchini, G.; Batani, D.; Rus, Bedřich; Polan, Jiří; Kozlová, Michaela; Stupka, Michal; Präg R., Ansgar; Homer, Pavel; Havlíček, Tomáš; Soukup, Miroslav; Krouský, Eduard; Skála, Jiří; Dudžák, Roman; Pfeifer, Miroslav; Nishimura, H.; Nagai, K.; Ito, F.; Norimatsu, T.; Kilpio, A.; Shashkov, E.; Stuchebrukhov, I.; Vovchenko, V.; Chernomyrdin, V.; Krasuyk, I.

2006-01-01

Roč. 73, č. 4 (2006), 047401/1-047401/4 ISSN 1539-3755 R&D Projects: GA MŠk(CZ) LC528; GA MŠk(CZ) LN00A100 Grant - others:6th FP of the E.U.(XE) RII3-CT-2003-506350; RFBR(XE) 03-02-17549 Program:FP6 Institutional research plan: CEZ:AV0Z10100523 Keywords : equation of state * laser-driven shock * Huginot data Subject RIV: BH - Optics, Masers, Lasers Impact factor: 2.438, year: 2006

A new data-driven controllability measure with application in intelligent buildings

DEFF Research Database (Denmark)

Shaker, Hamid Reza; Lazarova-Molnar, Sanja

2017-01-01

and instrumentation within today's intelligent buildings enable collecting high quality data which could be used directly in data-based analysis and control methods. The area of data-based systems analysis and control is concentrating on developing analysis and control methods that rely on data collected from meters...... and sensors, and information obtained by data processing. This differs from the traditional model-based approaches that are based on mathematical models of systems. We propose and describe a data-driven controllability measure for discrete-time linear systems. The concept is developed within a data......-based system analysis and control framework. Therefore, only measured data is used to obtain the proposed controllability measure. The proposed controllability measure not only shows if the system is controllable or not, but also reveals the level of controllability, which is the information its previous...

Following an Optimal Batch Bioreactor Operations Model

DEFF Research Database (Denmark)

Ibarra-Junquera, V.; Jørgensen, Sten Bay; Virgen-Ortíz, J.J.

2012-01-01

The problem of following an optimal batch operation model for a bioreactor in the presence of uncertainties is studied. The optimal batch bioreactor operation model (OBBOM) refers to the bioreactor trajectory for nominal cultivation to be optimal. A multiple-variable dynamic optimization of fed...... as the master system which includes the optimal cultivation trajectory for the feed flow rate and the substrate concentration. The “real” bioreactor, the one with unknown dynamics and perturbations, is considered as the slave system. Finally, the controller is designed such that the real bioreactor...

Limiting factors in Escherichia colifed-batch production of recombinant proteins

DEFF Research Database (Denmark)

Sanden, A.M.; Prytz, I.; Tubelekas, I.

2003-01-01

recombinant protein production, fed-batch, specific growth rate, feed profile, induction, mRNA, transcription, translation, acetic acid formation......recombinant protein production, fed-batch, specific growth rate, feed profile, induction, mRNA, transcription, translation, acetic acid formation...

A data-driven approach to quality risk management.

Science.gov (United States)

Alemayehu, Demissie; Alvir, Jose; Levenstein, Marcia; Nickerson, David

2013-10-01

An effective clinical trial strategy to ensure patient safety as well as trial quality and efficiency involves an integrated approach, including prospective identification of risk factors, mitigation of the risks through proper study design and execution, and assessment of quality metrics in real-time. Such an integrated quality management plan may also be enhanced by using data-driven techniques to identify risk factors that are most relevant in predicting quality issues associated with a trial. In this paper, we illustrate such an approach using data collected from actual clinical trials. Several statistical methods were employed, including the Wilcoxon rank-sum test and logistic regression, to identify the presence of association between risk factors and the occurrence of quality issues, applied to data on quality of clinical trials sponsored by Pfizer. ONLY A SUBSET OF THE RISK FACTORS HAD A SIGNIFICANT ASSOCIATION WITH QUALITY ISSUES, AND INCLUDED: Whether study used Placebo, whether an agent was a biologic, unusual packaging label, complex dosing, and over 25 planned procedures. Proper implementation of the strategy can help to optimize resource utilization without compromising trial integrity and patient safety.

A data-driven approach to quality risk management

Directory of Open Access Journals (Sweden)

Demissie Alemayehu

2013-01-01

Full Text Available Aim: An effective clinical trial strategy to ensure patient safety as well as trial quality and efficiency involves an integrated approach, including prospective identification of risk factors, mitigation of the risks through proper study design and execution, and assessment of quality metrics in real-time. Such an integrated quality management plan may also be enhanced by using data-driven techniques to identify risk factors that are most relevant in predicting quality issues associated with a trial. In this paper, we illustrate such an approach using data collected from actual clinical trials. Materials and Methods: Several statistical methods were employed, including the Wilcoxon rank-sum test and logistic regression, to identify the presence of association between risk factors and the occurrence of quality issues, applied to data on quality of clinical trials sponsored by Pfizer. Results: Only a subset of the risk factors had a significant association with quality issues, and included: Whether study used Placebo, whether an agent was a biologic, unusual packaging label, complex dosing, and over 25 planned procedures. Conclusion: Proper implementation of the strategy can help to optimize resource utilization without compromising trial integrity and patient safety.

Selection of the Sample for Data-Driven $Z \\to \

CERN Document Server

Krauss, Martin

2009-01-01

The topic of this study was to improve the selection of the sample for data-driven Z → ν ν background estimation, which is a major contribution in supersymmetric searches in ̄ a no-lepton search mode. The data is based on Z → + − samples using data created with ATLAS simulation software. This method works if two leptons are reconstructed, but using cuts that are typical for SUSY searches reconstruction efficiency for electrons and muons is rather low. For this reason it was tried to enhance the data sample. Therefore events were considered, where only one electron was reconstructed. In this case the invariant mass for the electron and each jet was computed to select the jet with the best match for the Z boson mass as not reconstructed electron. This way the sample can be extended but significantly looses purity because of also reconstructed background events. To improve this method other variables have to be considered which were not available for this study. Applying a similar method to muons using ...

40 CFR 63.1326 - Batch process vents-recordkeeping provisions.

Science.gov (United States)

2010-07-01

....1325(e) for aggregate batch vent streams; (ii) For a boiler or process heater, a description of the location at which the vent stream is introduced into the boiler or process heater; (iii) For a boiler or... process vents or halogenated aggregate batch vent streams, the percent reduction of total hydrogen halides...

Data-driven modeling and real-time distributed control for energy efficient manufacturing systems

International Nuclear Information System (INIS)

Zou, Jing; Chang, Qing; Arinez, Jorge; Xiao, Guoxian

2017-01-01

As manufacturers face the challenges of increasing global competition and energy saving requirements, it is imperative to seek out opportunities to reduce energy waste and overall cost. In this paper, a novel data-driven stochastic manufacturing system modeling method is proposed to identify and predict energy saving opportunities and their impact on production. A real-time distributed feedback production control policy, which integrates the current and predicted system performance, is established to improve the overall profit and energy efficiency. A case study is presented to demonstrate the effectiveness of the proposed control policy. - Highlights: • A data-driven stochastic manufacturing system model is proposed. • Real-time system performance and energy saving opportunity identification method is developed. • Prediction method for future potential system performance and energy saving opportunity is developed. • A real-time distributed feedback control policy is established to improve energy efficiency and overall system profit.

Structural analysis of magnetic fusion energy systems in a combined interactive/batch computer environment

International Nuclear Information System (INIS)

Johnson, N.E.; Singhal, M.K.; Walls, J.C.; Gray, W.H.

1979-01-01

A system of computer programs has been developed to aid in the preparation of input data for and the evaluation of output data from finite element structural analyses of magnetic fusion energy devices. The system utilizes the NASTRAN structural analysis computer program and a special set of interactive pre- and post-processor computer programs, and has been designed for use in an environment wherein a time-share computer system is linked to a batch computer system. In such an environment, the analyst must only enter, review and/or manipulate data through interactive terminals linked to the time-share computer system. The primary pre-processor programs include NASDAT, NASERR and TORMAC. NASDAT and TORMAC are used to generate NASTRAN input data. NASERR performs routine error checks on this data. The NASTRAN program is run on a batch computer system using data generated by NASDAT and TORMAC. The primary post-processing programs include NASCMP and NASPOP. NASCMP is used to compress the data initially stored on magnetic tape by NASTRAN so as to facilitate interactive use of the data. NASPOP reads the data stored by NASCMP and reproduces NASTRAN output for selected grid points, elements and/or data types

Simulation of kefiran production of Lactobacillus kefiranofaciens JCM6985 in fed-batch reactor

Directory of Open Access Journals (Sweden)

Benjamas Cheirsilp

2006-09-01

Full Text Available Kinetics of kefiran production by Lactobacillus kefiranofaciens JCM6985 has been investigated. A mathematical model taking into account the mechanism of exopolysaccharides production has been developed. Experiments were carried out in batch mode in order to obtain kinetic model parameters that were further applied to simulate fed-batch processes. A simplification of parameter fitting was also introduced for complicated model. The fed-batch mode allows more flexibility in the control of the substrate concentration as well as product concentration in the culture medium. Based on the batch mathematical model, a fed-batch model was developed and simulations were done. Simulation study in fed-batch reactor resulted that substrate concentration should be controlled at 20 g L-1 to soften the product inhibition and also to stimulate utilization of substrate and its hydrolysate. From simulation results of different feeding techniques, it was found that constant feeding at 0.01 L h-1 was most practically effective feeding profile for exopolysaccharides production in fed-batch mode.

Challenges and best practices for big data-driven healthcare innovations conducted by profit–non-profit partnerships – a quantitative prioritization

NARCIS (Netherlands)

Witjas-Paalberends, E. R.; van Laarhoven, L. P.M.; van de Burgwal, L. H.M.; Feilzer, J.; de Swart, J.; Claassen, H.J.H.M.; Jansen, W. T.M.

2017-01-01

Big data-driven innovations are key in improving healthcare system sustainability. Given the complexity, these are frequently conducted by public-private-partnerships (PPPs) between profit and non-profit parties. However, information on how to manage big data-driven healthcare innovations by PPPs is

40 CFR 63.487 - Batch front-end process vents-reference control technology.

Science.gov (United States)

2010-07-01

... § 63.487 Batch front-end process vents—reference control technology. (a) Batch front-end process vents... 40 Protection of Environment 9 2010-07-01 2010-07-01 false Batch front-end process vents-reference control technology. 63.487 Section 63.487 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY...

Data-driven, Interpretable Photometric Redshifts Trained on Heterogeneous and Unrepresentative Data

International Nuclear Information System (INIS)

Leistedt, Boris; Hogg, David W.

2017-01-01

We present a new method for inferring photometric redshifts in deep galaxy and quasar surveys, based on a data-driven model of latent spectral energy distributions (SEDs) and a physical model of photometric fluxes as a function of redshift. This conceptually novel approach combines the advantages of both machine learning methods and template fitting methods by building template SEDs directly from the spectroscopic training data. This is made computationally tractable with Gaussian processes operating in flux–redshift space, encoding the physics of redshifts and the projection of galaxy SEDs onto photometric bandpasses. This method alleviates the need to acquire representative training data or to construct detailed galaxy SED models; it requires only that the photometric bandpasses and calibrations be known or have parameterized unknowns. The training data can consist of a combination of spectroscopic and deep many-band photometric data with reliable redshifts, which do not need to entirely spatially overlap with the target survey of interest or even involve the same photometric bands. We showcase the method on the i -magnitude-selected, spectroscopically confirmed galaxies in the COSMOS field. The model is trained on the deepest bands (from SUBARU and HST ) and photometric redshifts are derived using the shallower SDSS optical bands only. We demonstrate that we obtain accurate redshift point estimates and probability distributions despite the training and target sets having very different redshift distributions, noise properties, and even photometric bands. Our model can also be used to predict missing photometric fluxes or to simulate populations of galaxies with realistic fluxes and redshifts, for example.

Data-driven, Interpretable Photometric Redshifts Trained on Heterogeneous and Unrepresentative Data

Energy Technology Data Exchange (ETDEWEB)

Leistedt, Boris; Hogg, David W., E-mail: boris.leistedt@nyu.edu, E-mail: david.hogg@nyu.edu [Center for Cosmology and Particle Physics, Department of Physics, New York University, New York, NY 10003 (United States)

2017-03-20

We present a new method for inferring photometric redshifts in deep galaxy and quasar surveys, based on a data-driven model of latent spectral energy distributions (SEDs) and a physical model of photometric fluxes as a function of redshift. This conceptually novel approach combines the advantages of both machine learning methods and template fitting methods by building template SEDs directly from the spectroscopic training data. This is made computationally tractable with Gaussian processes operating in flux–redshift space, encoding the physics of redshifts and the projection of galaxy SEDs onto photometric bandpasses. This method alleviates the need to acquire representative training data or to construct detailed galaxy SED models; it requires only that the photometric bandpasses and calibrations be known or have parameterized unknowns. The training data can consist of a combination of spectroscopic and deep many-band photometric data with reliable redshifts, which do not need to entirely spatially overlap with the target survey of interest or even involve the same photometric bands. We showcase the method on the i -magnitude-selected, spectroscopically confirmed galaxies in the COSMOS field. The model is trained on the deepest bands (from SUBARU and HST ) and photometric redshifts are derived using the shallower SDSS optical bands only. We demonstrate that we obtain accurate redshift point estimates and probability distributions despite the training and target sets having very different redshift distributions, noise properties, and even photometric bands. Our model can also be used to predict missing photometric fluxes or to simulate populations of galaxies with realistic fluxes and redshifts, for example.

Ontology-driven data integration and visualization for exploring regional geologic time and paleontological information

Science.gov (United States)

Wang, Chengbin; Ma, Xiaogang; Chen, Jianguo

2018-06-01

Initiatives of open data promote the online publication and sharing of large amounts of geologic data. How to retrieve information and discover knowledge from the big data is an ongoing challenge. In this paper, we developed an ontology-driven data integration and visualization pilot system for exploring information of regional geologic time, paleontology, and fundamental geology. The pilot system (http://www2.cs.uidaho.edu/%7Emax/gts/)

Data-driven motion correction in brain SPECT

International Nuclear Information System (INIS)

Kyme, A.Z.; Hutton, B.F.; Hatton, R.L.; Skerrett, D.W.

2002-01-01

Patient motion can cause image artifacts in SPECT despite restraining measures. Data-driven detection and correction of motion can be achieved by comparison of acquired data with the forward-projections. By optimising the orientation of the reconstruction, parameters can be obtained for each misaligned projection and applied to update this volume using a 3D reconstruction algorithm. Digital and physical phantom validation was performed to investigate this approach. Noisy projection data simulating at least one fully 3D patient head movement during acquisition were constructed by projecting the digital Huffman brain phantom at various orientations. Motion correction was applied to the reconstructed studies. The importance of including attenuation effects in the estimation of motion and the need for implementing an iterated correction were assessed in the process. Correction success was assessed visually for artifact reduction, and quantitatively using a mean square difference (MSD) measure. Physical Huffman phantom studies with deliberate movements introduced during the acquisition were also acquired and motion corrected. Effective artifact reduction in the simulated corrupt studies was achieved by motion correction. Typically the MSD ratio between the corrected and reference studies compared to the corrupted and reference studies was > 2. Motion correction could be achieved without inclusion of attenuation effects in the motion estimation stage, providing simpler implementation and greater efficiency. Moreover the additional improvement with multiple iterations of the approach was small. Improvement was also observed in the physical phantom data, though the technique appeared limited here by an object symmetry. Copyright (2002) The Australian and New Zealand Society of Nuclear Medicine Inc

Dynamic Extensions of Batch Systems with Cloud Resources

International Nuclear Information System (INIS)

Hauth, T; Quast, G; Büge, V; Scheurer, A; Kunze, M; Baun, C

2011-01-01

Compute clusters use Portable Batch Systems (PBS) to distribute workload among individual cluster machines. To extend standard batch systems to Cloud infrastructures, a new service monitors the number of queued jobs and keeps track of the price of available resources. This meta-scheduler dynamically adapts the number of Cloud worker nodes according to the requirement profile. Two different worker node topologies are presented and tested on the Amazon EC2 Cloud service.

Design and Data in Balance: Using Design-Driven Decision Making to Enable Student Success

Science.gov (United States)

Fairchild, Susan; Farrell, Timothy; Gunton, Brad; Mackinnon, Anne; McNamara, Christina; Trachtman, Roberta

2014-01-01

Data-driven approaches to school decision making have come into widespread use in the past decade, nationally and in New York City. New Visions has been at the forefront of those developments: in New Visions schools, teacher teams and school teams regularly examine student performance data to understand patterns and drive classroom- and…

An order batching algorithm for wave picking in a parallel-aisle warehouse

NARCIS (Netherlands)

Gademann, A.J.R.M.; Berg, van den J.P.; Hoff, van der H.H.

2001-01-01

In this paper we address the problem of batching orders in a parallel-aisle warehouse, with the objective to minimize the maximum lead time of any of the batches. This is a typical objective for a wave picking operation. Many heuristics have been suggested to solve order batching problems. We

Data-driven directions for effective footwear provision for the high-risk diabetic foot

NARCIS (Netherlands)

Arts, M. L. J.; de Haart, M.; Waaijman, R.; Dahmen, R.; Berendsen, H.; Nollet, F.; Bus, S. A.

2015-01-01

Custom-made footwear is used to offload the diabetic foot to prevent plantar foot ulcers. This prospective study evaluates the offloading effects of modifying custom-made footwear and aims to provide data-driven directions for the provision of effectively offloading footwear in clinical practice.

Preface [HD3-2015: International meeting on high-dimensional data-driven science

International Nuclear Information System (INIS)

2016-01-01

A never-ending series of innovations in measurement technology and evolutions in information and communication technologies have led to the ongoing generation and accumulation of large quantities of high-dimensional data every day. While detailed data-centric approaches have been pursued in respective research fields, situations have been encountered where the same mathematical framework of high-dimensional data analysis can be found in a wide variety of seemingly unrelated research fields, such as estimation on the basis of undersampled Fourier transform in nuclear magnetic resonance spectroscopy in chemistry, in magnetic resonance imaging in medicine, and in astronomical interferometry in astronomy. In such situations, bringing diverse viewpoints together therefore becomes a driving force for the creation of innovative developments in various different research fields. This meeting focuses on “Sparse Modeling” (SpM) as a methodology for creation of innovative developments through the incorporation of a wide variety of viewpoints in various research fields. The objective of this meeting is to offer a forum where researchers with interest in SpM can assemble and exchange information on the latest results and newly established methodologies, and discuss future directions of the interdisciplinary studies for High-Dimensional Data-Driven science (HD 3 ). The meeting was held in Kyoto from 14-17 December 2015. We are pleased to publish 22 papers contributed by invited speakers in this volume of Journal of Physics: Conference Series. We hope that this volume will promote further development of High-Dimensional Data-Driven science. (paper)

Fed-batch coculture of Lactobacillus kefiranofaciens with Saccharomyces cerevisiae for effective production of kefiran.

Science.gov (United States)

Tada, Shiori; Katakura, Yoshio; Ninomiya, Kazuaki; Shioya, Suteaki

2007-06-01

In a batch coculture of kefiran-producing lactic acid bacteria Lactobacillus kefiranofaciens and lactate-assimilating yeast Saccharomyces cerevisiae, lactate accumulation in the medium was observed, which inhibited kefiran production. To enhance kefiran productivity by preventing lactate accumulation, we conducted lactose-feeding batch operation with feedforward/feedback control during the coculture, so that the lactate production rate of L. kefiranofaciens was balanced with the lactate consumption rate of S. cerevisiae. The lactate concentration was maintained at less than 6 g l(-1) throughout the fed-batch coculture using a 5 l jar fermentor, although the concentration reached 33 g l(-1) in the batch coculture. Kefiran production was increased to 6.3 g in 102 h in the fed-batch coculture, whereas 4.5 g kefiran was produced in 97 h in the batch coculture. The kefiran yield on lactose basis was increased up to 0.033 g g(-1) in the fed-batch coculture, whereas that in the batch coculture was 0.027 g g(-1).

Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life

International Nuclear Information System (INIS)

Hu Chao; Youn, Byeng D.; Wang Pingfeng; Taek Yoon, Joung

2012-01-01

Prognostics aims at determining whether a failure of an engineered system (e.g., a nuclear power plant) is impending and estimating the remaining useful life (RUL) before the failure occurs. The traditional data-driven prognostic approach is to construct multiple candidate algorithms using a training data set, evaluate their respective performance using a testing data set, and select the one with the best performance while discarding all the others. This approach has three shortcomings: (i) the selected standalone algorithm may not be robust; (ii) it wastes the resources for constructing the algorithms that are discarded; (iii) it requires the testing data in addition to the training data. To overcome these drawbacks, this paper proposes an ensemble data-driven prognostic approach which combines multiple member algorithms with a weighted-sum formulation. Three weighting schemes, namely the accuracy-based weighting, diversity-based weighting and optimization-based weighting, are proposed to determine the weights of member algorithms. The k-fold cross validation (CV) is employed to estimate the prediction error required by the weighting schemes. The results obtained from three case studies suggest that the ensemble approach with any weighting scheme gives more accurate RUL predictions compared to any sole algorithm when member algorithms producing diverse RUL predictions have comparable prediction accuracy and that the optimization-based weighting scheme gives the best overall performance among the three weighting schemes.

Exploring the Transition From Batch to Online

DEFF Research Database (Denmark)

Jørgensen, Anker Helms

2010-01-01

of the truly interactive use of computers known today. The transition invoked changes in a number of areas: technological, such as hybrid forms between batch and online; organisational such as decentralization; and personal as users and developers alike had to adopt new technology, shape new organizational...... structures, and acquire new skills. This work-in-progress paper extends an earlier study of the transition from batch to online, based on oral history interviews with (ex)-employees in two large Danish Service Bureaus. The paper takes the next step by ana-lyzing a particular genre: the commercial computer...

Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies

Directory of Open Access Journals (Sweden)

Lifeng Wu

2016-05-01

Full Text Available Lithium-ion batteries are the primary power source in electric vehicles, and the prognosis of their remaining useful life is vital for ensuring the safety, stability, and long lifetime of electric vehicles. Accurately establishing a mechanism model of a vehicle lithium-ion battery involves a complex electrochemical process. Remaining useful life (RUL prognostics based on data-driven methods has become a focus of research. Current research on data-driven methodologies is summarized in this paper. By analyzing the problems of vehicle lithium-ion batteries in practical applications, the problems that need to be solved in the future are identified.

Workload-Driven Design and Evaluation of Large-Scale Data-Centric Systems

Science.gov (United States)

2012-05-09

in the batch zone in and out of a low-power state, e.g., sending a “ hibernate ” command via ssh and using Wake-on-LAN or related technologies [85]. If...parameter values for experiments with stand-alone jobs. The mapred.child.java.opts parameter sets the maximum virtual memory of the Java child pro- cesses

Data-Driven Based Asynchronous Motor Control for Printing Servo Systems

Science.gov (United States)

Bian, Min; Guo, Qingyun

Modern digital printing equipment aims to the environmental-friendly industry with high dynamic performances and control precision and low vibration and abrasion. High performance motion control system of printing servo systems was required. Control system of asynchronous motor based on data acquisition was proposed. Iterative learning control (ILC) algorithm was studied. PID control was widely used in the motion control. However, it was sensitive to the disturbances and model parameters variation. The ILC applied the history error data and present control signals to approximate the control signal directly in order to fully track the expect trajectory without the system models and structures. The motor control algorithm based on the ILC and PID was constructed and simulation results were given. The results show that data-driven control method is effective dealing with bounded disturbances for the motion control of printing servo systems.

Optimization of the Production of Polygalacturonase from Aspergillus kawachii Cloned in Saccharomyces cerevisiae in Batch and Fed-Batch Cultures

Directory of Open Access Journals (Sweden)

Diego Jorge Baruque

2011-01-01

Full Text Available Polygalacturonases (PG; EC 3.2.1.15 catalyze the hydrolysis of pectin and/or pectic acid and are useful for industrial applications such as juice clarification and pectin extraction. Growth and heterologous expression of recombinant Saccharomyces cerevisiae which expresses an acidic PG from Aspergillus kawachii has been studied in batch and fed-batch cultures. Kinetics and stoichiometric parameters of the recombinant yeast were determined in batch cultures in a synthetic medium. In these cultures, the total biomass concentration, protein concentration, and enzyme activity achieved were 2.2 g/L, 10 mg/L, and 3 U/mL, respectively, to give a productivity of 0.06 U/(mL·h. In fed-batch cultures, various strategies for galactose feeding were used: (i after a glucose growth phase, the addition of a single pulse of galactose which gave a productivity of 0.19 U/(mL·h; (ii after a glucose growth phase, a double pulse of galactose at the same final concentration was added, resulting in a productivity of 0.21 U/(mL·h; (iii a simultaneous feeding of glucose and galactose, yielding a productivity of 1.32 U/(mL·h. Based on these results, the simultaneous feeding of glucose and galactose was by far the most suitable strategy for the production of this enzyme. Moreover, some biochemical characteristics of the recombinant enzyme such as a molecular mass of ~60 kDa, an isoelectric point of 3.7 and its ability to hydrolyze polygalacturonic acid at pH=2.5 were determined.

Data-Driven User Feedback: An Improved Neurofeedback Strategy considering the Interindividual Variability of EEG Features

Directory of Open Access Journals (Sweden)

Chang-Hee Han

2016-01-01

Full Text Available It has frequently been reported that some users of conventional neurofeedback systems can experience only a small portion of the total feedback range due to the large interindividual variability of EEG features. In this study, we proposed a data-driven neurofeedback strategy considering the individual variability of electroencephalography (EEG features to permit users of the neurofeedback system to experience a wider range of auditory or visual feedback without a customization process. The main idea of the proposed strategy is to adjust the ranges of each feedback level using the density in the offline EEG database acquired from a group of individuals. Twenty-two healthy subjects participated in offline experiments to construct an EEG database, and five subjects participated in online experiments to validate the performance of the proposed data-driven user feedback strategy. Using the optimized bin sizes, the number of feedback levels that each individual experienced was significantly increased to 139% and 144% of the original results with uniform bin sizes in the offline and online experiments, respectively. Our results demonstrated that the use of our data-driven neurofeedback strategy could effectively increase the overall range of feedback levels that each individual experienced during neurofeedback training.

Data Driven Marketing in Apple and Back to School Campaign 2011

OpenAIRE

Bernátek, Martin

2011-01-01

Out of the campaign analysis the most important contribution is that Data-Driven Marketing makes sense only once it is already part of the marketing plan. So the team preparing the marketing plan defines the goals and sets the proper measurement matrix according to those goals. It enables to adjust the marketing plan to extract more value, watch the execution and do adjustments if necessary and evaluate at the end of the campaign.

A Model of Batch Scheduling for a Single Batch Processor with Additional Setups to Minimize Total Inventory Holding Cost of Parts of a Single Item Requested at Multi-due-date

Science.gov (United States)

Hakim Halim, Abdul; Ernawati; Hidayat, Nita P. A.

2018-03-01

This paper deals with a model of batch scheduling for a single batch processor on which a number of parts of a single items are to be processed. The process needs two kinds of setups, i. e., main setups required before processing any batches, and additional setups required repeatedly after the batch processor completes a certain number of batches. The parts to be processed arrive at the shop floor at the times coinciding with their respective starting times of processing, and the completed parts are to be delivered at multiple due dates. The objective adopted for the model is that of minimizing total inventory holding cost consisting of holding cost per unit time for a part in completed batches, and that in in-process batches. The formulation of total inventory holding cost is derived from the so-called actual flow time defined as the interval between arrival times of parts at the production line and delivery times of the completed parts. The actual flow time satisfies not only minimum inventory but also arrival and delivery just in times. An algorithm to solve the model is proposed and a numerical example is shown.

Simulated Batch Production of Penicillin

Science.gov (United States)

Whitaker, A.; Walker, J. D.

1973-01-01

Describes a program in applied biology in which the simulation of the production of penicillin in a batch fermentor is used as a teaching technique to give students experience before handling a genuine industrial fermentation process. Details are given for the calculation of minimum production cost. (JR)

Data driven parallelism in experimental high energy physics applications

International Nuclear Information System (INIS)

Pohl, M.

1987-01-01

I present global design principles for the implementation of high energy physics data analysis code on sequential and parallel processors with mixed shared and local memory. Potential parallelism in the structure of high energy physics tasks is identified with granularity varying from a few times 10 8 instructions all the way down to a few times 10 4 instructions. It follows the hierarchical structure of detector and data acquisition systems. To take advantage of this - yet preserving the necessary portability of the code - I propose a computational model with purely data driven concurrency in Single Program Multiple Data (SPMD) mode. The task granularity is defined by varying the granularity of the central data structure manipulated. Concurrent processes coordiate themselves asynchroneously using simple lock constructs on parts of the data structure. Load balancing among processes occurs naturally. The scheme allows to map the internal layout of the data structure closely onto the layout of local and shared memory in a parallel architecture. It thus allows to optimize the application with respect to synchronization as well as data transport overheads. I present a coarse top level design for a portable implementation of this scheme on sequential machines, multiprocessor mainframes (e.g. IBM 3090), tightly coupled multiprocessors (e.g. RP-3) and loosely coupled processor arrays (e.g. LCAP, Emulating Processor Farms). (orig.)

Data driven parallelism in experimental high energy physics applications

Science.gov (United States)

Pohl, Martin

1987-08-01

I present global design principles for the implementation of High Energy Physics data analysis code on sequential and parallel processors with mixed shared and local memory. Potential parallelism in the structure of High Energy Physics tasks is identified with granularity varying from a few times 10 8 instructions all the way down to a few times 10 4 instructions. It follows the hierarchical structure of detector and data acquisition systems. To take advantage of this - yet preserving the necessary portability of the code - I propose a computational model with purely data driven concurrency in Single Program Multiple Data (SPMD) mode. The Task granularity is defined by varying the granularity of the central data structure manipulated. Concurrent processes coordinate themselves asynchroneously using simple lock constructs on parts of the data structure. Load balancing among processes occurs naturally. The scheme allows to map the internal layout of the data structure closely onto the layout of local and shared memory in a parallel architecture. It thus allows to optimize the application with respect to synchronization as well as data transport overheads. I present a coarse top level design for a portable implementation of this scheme on sequential machines, multiprocessor mainframes (e.g. IBM 3090), tightly coupled multiprocessors (e.g. RP-3) and loosely coupled processor arrays (e.g. LCAP, Emulating Processor Farms).

Cloudweaver: Adaptive and Data-Driven Workload Manager for Generic Clouds

Science.gov (United States)

Li, Rui; Chen, Lei; Li, Wen-Syan

Cloud computing denotes the latest trend in application development for parallel computing on massive data volumes. It relies on clouds of servers to handle tasks that used to be managed by an individual server. With cloud computing, software vendors can provide business intelligence and data analytic services for internet scale data sets. Many open source projects, such as Hadoop, offer various software components that are essential for building a cloud infrastructure. Current Hadoop (and many others) requires users to configure cloud infrastructures via programs and APIs and such configuration is fixed during the runtime. In this chapter, we propose a workload manager (WLM), called CloudWeaver, which provides automated configuration of a cloud infrastructure for runtime execution. The workload management is data-driven and can adapt to dynamic nature of operator throughput during different execution phases. CloudWeaver works for a single job and a workload consisting of multiple jobs running concurrently, which aims at maximum throughput using a minimum set of processors.

Data-driven gradient algorithm for high-precision quantum control

Science.gov (United States)

Wu, Re-Bing; Chu, Bing; Owens, David H.; Rabitz, Herschel

2018-04-01

In the quest to achieve scalable quantum information processing technologies, gradient-based optimal control algorithms (e.g., grape) are broadly used for implementing high-precision quantum gates, but their performance is often hindered by deterministic or random errors in the system model and the control electronics. In this paper, we show that grape can be taught to be more effective by jointly learning from the design model and the experimental data obtained from process tomography. The resulting data-driven gradient optimization algorithm (d-grape) can in principle correct all deterministic gate errors, with a mild efficiency loss. The d-grape algorithm may become more powerful with broadband controls that involve a large number of control parameters, while other algorithms usually slow down due to the increased size of the search space. These advantages are demonstrated by simulating the implementation of a two-qubit controlled-not gate.

Minimization of energy consumption in HVAC systems with data-driven models and an interior-point method

International Nuclear Information System (INIS)

Kusiak, Andrew; Xu, Guanglin; Zhang, Zijun

2014-01-01

Highlights: • We study the energy saving of HVAC systems with a data-driven approach. • We conduct an in-depth analysis of the topology of developed Neural Network based HVAC model. • We apply interior-point method to solving a Neural Network based HVAC optimization model. • The uncertain building occupancy is incorporated in the minimization of HVAC energy consumption. • A significant potential of saving HVAC energy is discovered. - Abstract: In this paper, a data-driven approach is applied to minimize energy consumption of a heating, ventilating, and air conditioning (HVAC) system while maintaining the thermal comfort of a building with uncertain occupancy level. The uncertainty of arrival and departure rate of occupants is modeled by the Poisson and uniform distributions, respectively. The internal heating gain is calculated from the stochastic process of the building occupancy. Based on the observed and simulated data, a multilayer perceptron algorithm is employed to model and simulate the HVAC system. The data-driven models accurately predict future performance of the HVAC system based on the control settings and the observed historical information. An optimization model is formulated and solved with the interior-point method. The optimization results are compared with the results produced by the simulation models

Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks.

Science.gov (United States)

Vlachas, Pantelis R; Byeon, Wonmin; Wan, Zhong Y; Sapsis, Themistoklis P; Koumoutsakos, Petros

2018-05-01

We introduce a data-driven forecasting method for high-dimensional chaotic systems using long short-term memory (LSTM) recurrent neural networks. The proposed LSTM neural networks perform inference of high-dimensional dynamical systems in their reduced order space and are shown to be an effective set of nonlinear approximators of their attractor. We demonstrate the forecasting performance of the LSTM and compare it with Gaussian processes (GPs) in time series obtained from the Lorenz 96 system, the Kuramoto-Sivashinsky equation and a prototype climate model. The LSTM networks outperform the GPs in short-term forecasting accuracy in all applications considered. A hybrid architecture, extending the LSTM with a mean stochastic model (MSM-LSTM), is proposed to ensure convergence to the invariant measure. This novel hybrid method is fully data-driven and extends the forecasting capabilities of LSTM networks.

Analysis and modelling of the energy consumption of chemical batch plants

Energy Technology Data Exchange (ETDEWEB)

Bieler, P.S.

2004-07-01

This report for the Swiss Federal Office of Energy (SFOE) describes two different approaches for the energy analysis and modelling of chemical batch plants. A top-down model consisting of a linear equation based on the specific energy consumption per ton of production output and the base consumption of the plant is postulated. The model is shown to be applicable to single and multi-product batches for batch plants with constant production mix and multi-purpose batch plants in which only similar chemicals are produced. For multipurpose batch plants with highly varying production processes and changing production mix, the top-down model produced inaccurate results. A bottom-up model is postulated for such plants. The results obtained are discussed that show that the electricity consumption for infrastructure equipment was significant and responsible for about 50% of total electricity consumption. The specific energy consumption for the different buildings was related to the degree of automation and the production processes. Analyses of the results of modelling are presented. More detailed analyses of the energy consumption of this apparatus group show that about 30 to 40% of steam energy is lost and thus a large potential for optimisation exists. Various potentials for making savings, ranging from elimination of reflux conditions to the development of a new heating/cooling-system for a generic batch reactor, are identified.

Multivariate statistical process control (MSPC) using Raman spectroscopy for in-line culture cell monitoring considering time-varying batches synchronized with correlation optimized warping (COW).

Science.gov (United States)

Liu, Ya-Juan; André, Silvère; Saint Cristau, Lydia; Lagresle, Sylvain; Hannas, Zahia; Calvosa, Éric; Devos, Olivier; Duponchel, Ludovic

2017-02-01

Multivariate statistical process control (MSPC) is increasingly popular as the challenge provided by large multivariate datasets from analytical instruments such as Raman spectroscopy for the monitoring of complex cell cultures in the biopharmaceutical industry. However, Raman spectroscopy for in-line monitoring often produces unsynchronized data sets, resulting in time-varying batches. Moreover, unsynchronized data sets are common for cell culture monitoring because spectroscopic measurements are generally recorded in an alternate way, with more than one optical probe parallelly connecting to the same spectrometer. Synchronized batches are prerequisite for the application of multivariate analysis such as multi-way principal component analysis (MPCA) for the MSPC monitoring. Correlation optimized warping (COW) is a popular method for data alignment with satisfactory performance; however, it has never been applied to synchronize acquisition time of spectroscopic datasets in MSPC application before. In this paper we propose, for the first time, to use the method of COW to synchronize batches with varying durations analyzed with Raman spectroscopy. In a second step, we developed MPCA models at different time intervals based on the normal operation condition (NOC) batches synchronized by COW. New batches are finally projected considering the corresponding MPCA model. We monitored the evolution of the batches using two multivariate control charts based on Hotelling's T 2 and Q. As illustrated with results, the MSPC model was able to identify abnormal operation condition including contaminated batches which is of prime importance in cell culture monitoring We proved that Raman-based MSPC monitoring can be used to diagnose batches deviating from the normal condition, with higher efficacy than traditional diagnosis, which would save time and money in the biopharmaceutical industry. Copyright © 2016 Elsevier B.V. All rights reserved.

Cloning, multicopy expression and fed-batch production of Rhodotorula araucariae epoxide hydrolase in yarrowia lipolytica

CSIR Research Space (South Africa)

Ramduth, D

2008-05-01

Full Text Available demonstrated a 4 fold enhanced EH activity over the transformant. The transformant was then evaluated in batch and fed batch fermentations, where the batch fermentations resulted in - 50% improved EH activity from flask evaluations. In fed batch fermentations...

Data Driven - Android based displays on data acquisition and system status

CERN Document Server

Canilho, Paulo

2014-01-01

For years, both hardware and software engineers have struggled with the acquisition of device information in a flexible and fast perspective, numerous devices cannot have their status quickly tested due to time limitation associated with the travelling to a computer terminal. For instance, in order to test a scintillator status, one has to inject beam into the device and quickly return to a terminal to see the results, this is not only time demanding but extremely inconvenient for the person responsible, it consumes time that would be used in more pressing matters. In this train of thoughts, the proposal of creating an interface to bring a stable, flexible, user friendly and data driven solution to this problem was created. Being the most common operative system for mobile display, the Android API proved to have the best efficient in financing, since it is based on an open source software, and in implementation difficulty since it’s backend development resides in JAVA calls and XML for visual representation...

Stratified randomization controls better for batch effects in 450K methylation analysis: A cautionary tale

Directory of Open Access Journals (Sweden)

Olive D. Buhule

2014-10-01

Full Text Available Background: Batch effects in DNA methylation microarray experiments can lead to spurious results if not properly handled during the plating of samples. Methods: Two pilot studies examining the association of DNA methylation patterns across the genome with obesity in Samoan men were investigated for chip- and row-specific batch effects. For each study, the DNA of 46 obese men and 46 lean men were assayed using Illumina's Infinium HumanMethylation450 BeadChip. In the first study (Sample One, samples from obese and lean subjects were examined on separate chips. In the second study (Sample Two, the samples were balanced on the chips by lean/obese status, age group, and census region. We used methylumi, watermelon, and limma R packages, as well as ComBat, to analyze the data. Principal component analysis and linear regression were respectively employed to identify the top principal components and to test for their association with the batches and lean/obese status. To identify differentially methylated positions (DMPs between obese and lean males at each locus, we used a moderated t-test.Results: Chip effects were effectively removed from Sample Two but not Sample One. In addition, dramatic differences were observed between the two sets of DMP results. After removing'' batch effects with ComBat, Sample One had 94,191 probes differentially methylated at a q-value threshold of 0.05 while Sample Two had zero differentially methylated probes. The disparate results from Sample One and Sample Two likely arise due to the confounding of lean/obese status with chip and row batch effects.Conclusion: Even the best possible statistical adjustments for batch effects may not completely remove them. Proper study design is vital for guarding against spurious findings due to such effects.

Simple approximations for the batch-arrival MX/G/1 queue

NARCIS (Netherlands)

van Ommeren, Jan C.W.

1990-01-01

In this paper we consider the MX/G/I queueing system with batch arrivals. We give simple approximations for the waiting-time probabilities of individual customers. These approximations are checked numerically and they are found to perform very well for a wide variety of batch-size and service-timed

Data Driven Exploratory Attacks on Black Box Classifiers in Adversarial Domains

OpenAIRE

Sethi, Tegjyot Singh; Kantardzic, Mehmed

2017-01-01

While modern day web applications aim to create impact at the civilization level, they have become vulnerable to adversarial activity, where the next cyber-attack can take any shape and can originate from anywhere. The increasing scale and sophistication of attacks, has prompted the need for a data driven solution, with machine learning forming the core of many cybersecurity systems. Machine learning was not designed with security in mind, and the essential assumption of stationarity, requiri...

Data-driven automatic parking constrained control for four-wheeled mobile vehicles

OpenAIRE

Wenxu Yan; Jing Deng; Dezhi Xu

2016-01-01

In this article, a novel data-driven constrained control scheme is proposed for automatic parking systems. The design of the proposed scheme only depends on the steering angle and the orientation angle of the car, and it does not involve any model information of the car. Therefore, the proposed scheme-based automatic parking system is applicable to different kinds of cars. In order to further reduce the desired trajectory coordinate tracking errors, a coordinates compensation algorithm is als...

Random assay in radioimmunoassay: Feasibility and application compared with batch assay

Energy Technology Data Exchange (ETDEWEB)

Lee, Jung Min; Lee, Hwan Hee; Park, Sohyun; Kim, Tae Sung; Kim, Seok Ki [Dept. of Nuclear MedicineNational Cancer Center, Goyang (Korea, Republic of)

2016-12-15

The batch assay has been conventionally used for radioimmunoassay (RIA) because of its technical robustness and practical convenience. However, it has limitations in terms of the relative lag of report time due to the necessity of multiple assays in a small number of samples compared with the random assay technique. In this study, we aimed to verify whether the random assay technique can be applied in RIA and is feasible in daily practice. The coefficients of variation (CVs) of eight standard curves within a single kit were calculated in a CA-125 immunoradiometric assay (IRMA) for the reference of the practically ideal CV of the CA-125 kit. Ten standard curves of 10 kits from 2 prospectively collected lots (pLot) and 85 standard curves of 85 kits from 3 retrospectively collected lots (Lot) were obtained. Additionally, the raw measurement data of both 170 control references and 1123 patients' sera were collected retrospectively between December 2015 and January 2016. A standard curve of the first kit of each lot was used as a master standard curve for a random assay. The CVs of inter-kits were analyzed in each lot, respectively. All raw measurements were normalized by decay and radioactivity. The CA-125 values from control samples and patients' sera were compared using the original batch assay and random assay. In standard curve analysis, the CVs of inter-kits in pLots and Lots were comparable to those within a single kit. The CVs from the random assay with normalization were similar to those from the batch assay in the control samples (CVs % of low/high concentration; Lot1 2.71/1.91, Lot2 2.35/1.83, Lot3 2.83/2.08 vs. Lot1 2.05/1.21, Lot2 1.66/1.48, Lot3 2.41/2.14). The ICCs between the batch assay and random assay using patients' sera were satisfactory (Lot1 1.00, Lot2 0.999, Lot3 1.00). The random assay technique could be successfully applied to the conventional CA-125 IRMA kits. The random assay showed strong agreement with the batch assay. The

Look-ahead strategies for controlling batch operations in industry - overview, comparison and exploration

NARCIS (Netherlands)

Zee, D.J. van der; Harten, A. van; Schuur, P.C.; Joines, JA; Barton, RR; Kang, K; Fishwick, PA

2000-01-01

Batching jobs in a manufacturing system is a very common policy in most industries. The main reasons for batching are avoidance of set ups and/or facilitation of material handling. Good examples of batch-wise production systems are ovens found in aircraft industry and in semiconductor manufacturing.

Data-Driven Diffusion Of Innovations: Successes And Challenges In 3 Large-Scale Innovative Delivery Models.

Science.gov (United States)

Dorr, David A; Cohen, Deborah J; Adler-Milstein, Julia

2018-02-01

Failed diffusion of innovations may be linked to an inability to use and apply data, information, and knowledge to change perceptions of current practice and motivate change. Using qualitative and quantitative data from three large-scale health care delivery innovations-accountable care organizations, advanced primary care practice, and EvidenceNOW-we assessed where data-driven innovation is occurring and where challenges lie. We found that implementation of some technological components of innovation (for example, electronic health records) has occurred among health care organizations, but core functions needed to use data to drive innovation are lacking. Deficits include the inability to extract and aggregate data from the records; gaps in sharing data; and challenges in adopting advanced data functions, particularly those related to timely reporting of performance data. The unexpectedly high costs and burden incurred during implementation of the innovations have limited organizations' ability to address these and other deficits. Solutions that could help speed progress in data-driven innovation include facilitating peer-to-peer technical assistance, providing tailored feedback reports to providers from data aggregators, and using practice facilitators skilled in using data technology for quality improvement to help practices transform. Policy efforts that promote these solutions may enable more rapid uptake of and successful participation in innovative delivery system reforms.

Testing the Accuracy of Data-driven MHD Simulations of Active Region Evolution

Energy Technology Data Exchange (ETDEWEB)

Leake, James E.; Linton, Mark G. [U.S. Naval Research Laboratory, 4555 Overlook Avenue, SW, Washington, DC 20375 (United States); Schuck, Peter W., E-mail: james.e.leake@nasa.gov [NASA Goddard Space Flight Center, 8800 Greenbelt Road, Greenbelt, MD 20771 (United States)

2017-04-01

Models for the evolution of the solar coronal magnetic field are vital for understanding solar activity, yet the best measurements of the magnetic field lie at the photosphere, necessitating the development of coronal models which are “data-driven” at the photosphere. We present an investigation to determine the feasibility and accuracy of such methods. Our validation framework uses a simulation of active region (AR) formation, modeling the emergence of magnetic flux from the convection zone to the corona, as a ground-truth data set, to supply both the photospheric information and to perform the validation of the data-driven method. We focus our investigation on how the accuracy of the data-driven model depends on the temporal frequency of the driving data. The Helioseismic and Magnetic Imager on NASA’s Solar Dynamics Observatory produces full-disk vector magnetic field measurements at a 12-minute cadence. Using our framework we show that ARs that emerge over 25 hr can be modeled by the data-driving method with only ∼1% error in the free magnetic energy, assuming the photospheric information is specified every 12 minutes. However, for rapidly evolving features, under-sampling of the dynamics at this cadence leads to a strobe effect, generating large electric currents and incorrect coronal morphology and energies. We derive a sampling condition for the driving cadence based on the evolution of these small-scale features, and show that higher-cadence driving can lead to acceptable errors. Future work will investigate the source of errors associated with deriving plasma variables from the photospheric magnetograms as well as other sources of errors, such as reduced resolution, instrument bias, and noise.

Data-driven risk identification in phase III clinical trials using central statistical monitoring.

Science.gov (United States)

Timmermans, Catherine; Venet, David; Burzykowski, Tomasz

2016-02-01

Our interest lies in quality control for clinical trials, in the context of risk-based monitoring (RBM). We specifically study the use of central statistical monitoring (CSM) to support RBM. Under an RBM paradigm, we claim that CSM has a key role to play in identifying the "risks to the most critical data elements and processes" that will drive targeted oversight. In order to support this claim, we first see how to characterize the risks that may affect clinical trials. We then discuss how CSM can be understood as a tool for providing a set of data-driven key risk indicators (KRIs), which help to organize adaptive targeted monitoring. Several case studies are provided where issues in a clinical trial have been identified thanks to targeted investigation after the identification of a risk using CSM. Using CSM to build data-driven KRIs helps to identify different kinds of issues in clinical trials. This ability is directly linked with the exhaustiveness of the CSM approach and its flexibility in the definition of the risks that are searched for when identifying the KRIs. In practice, a CSM assessment of the clinical database seems essential to ensure data quality. The atypical data patterns found in some centers and variables are seen as KRIs under a RBM approach. Targeted monitoring or data management queries can be used to confirm whether the KRIs point to an actual issue or not.

Small Scale Mixing Demonstration Batch Transfer and Sampling Performance of Simulated HLW - 12307

Energy Technology Data Exchange (ETDEWEB)

Jensen, Jesse; Townson, Paul; Vanatta, Matt [EnergySolutions, Engineering and Technology Group, Richland, WA, 99354 (United States)

2012-07-01

The ability to effectively mix, sample, certify, and deliver consistent batches of High Level Waste (HLW) feed from the Hanford Double Shell Tanks (DST) to the Waste treatment Plant (WTP) has been recognized as a significant mission risk with potential to impact mission length and the quantity of HLW glass produced. At the end of 2009 DOE's Tank Operations Contractor, Washington River Protection Solutions (WRPS), awarded a contract to EnergySolutions to design, fabricate and operate a demonstration platform called the Small Scale Mixing Demonstration (SSMD) to establish pre-transfer sampling capacity, and batch transfer performance data at two different scales. This data will be used to examine the baseline capacity for a tank mixed via rotational jet mixers to transfer consistent or bounding batches, and provide scale up information to predict full scale operational performance. This information will then in turn be used to define the baseline capacity of such a system to transfer and sample batches sent to WTP. The Small Scale Mixing Demonstration (SSMD) platform consists of 43'' and 120'' diameter clear acrylic test vessels, each equipped with two scaled jet mixer pump assemblies, and all supporting vessels, controls, services, and simulant make up facilities. All tank internals have been modeled including the air lift circulators (ALCs), the steam heating coil, and the radius between the wall and floor. The test vessels are set up to simulate the transfer of HLW out of a mixed tank, and collect a pre-transfer sample in a manner similar to the proposed baseline configuration. The collected material is submitted to an NQA-1 laboratory for chemical analysis. Previous work has been done to assess tank mixing performance at both scales. This work involved a combination of unique instruments to understand the three dimensional distribution of solids using a combination of Coriolis meter measurements, in situ chord length distribution

Integrating PROOF Analysis in Cloud and Batch Clusters

International Nuclear Information System (INIS)

Rodríguez-Marrero, Ana Y; Fernández-del-Castillo, Enol; López García, Álvaro; Marco de Lucas, Jesús; Matorras Weinig, Francisco; González Caballero, Isidro; Cuesta Noriega, Alberto

2012-01-01

High Energy Physics (HEP) analysis are becoming more complex and demanding due to the large amount of data collected by the current experiments. The Parallel ROOT Facility (PROOF) provides researchers with an interactive tool to speed up the analysis of huge volumes of data by exploiting parallel processing on both multicore machines and computing clusters. The typical PROOF deployment scenario is a permanent set of cores configured to run the PROOF daemons. However, this approach is incapable of adapting to the dynamic nature of interactive usage. Several initiatives seek to improve the use of computing resources by integrating PROOF with a batch system, such as Proof on Demand (PoD) or PROOF Cluster. These solutions are currently in production at Universidad de Oviedo and IFCA and are positively evaluated by users. Although they are able to adapt to the computing needs of users, they must comply with the specific configuration, OS and software installed at the batch nodes. Furthermore, they share the machines with other workloads, which may cause disruptions in the interactive service for users. These limitations make PROOF a typical use-case for cloud computing. In this work we take profit from Cloud Infrastructure at IFCA in order to provide a dynamic PROOF environment where users can control the software configuration of the machines. The Proof Analysis Framework (PAF) facilitates the development of new analysis and offers a transparent access to PROOF resources. Several performance measurements are presented for the different scenarios (PoD, SGE and Cloud), showing a speed improvement closely correlated with the number of cores used.

A data-driven multiplicative fault diagnosis approach for automation processes.

Science.gov (United States)

Hao, Haiyang; Zhang, Kai; Ding, Steven X; Chen, Zhiwen; Lei, Yaguo

2014-09-01

This paper presents a new data-driven method for diagnosing multiplicative key performance degradation in automation processes. Different from the well-established additive fault diagnosis approaches, the proposed method aims at identifying those low-level components which increase the variability of process variables and cause performance degradation. Based on process data, features of multiplicative fault are extracted. To identify the root cause, the impact of fault on each process variable is evaluated in the sense of contribution to performance degradation. Then, a numerical example is used to illustrate the functionalities of the method and Monte-Carlo simulation is performed to demonstrate the effectiveness from the statistical viewpoint. Finally, to show the practical applicability, a case study on the Tennessee Eastman process is presented. Copyright © 2013. Published by Elsevier Ltd.

Design and evaluation of a data-driven scenario generation framework for game-based training

NARCIS (Netherlands)

Luo, L.; Yin, H.; Cai, W.; Zhong, J.; Lees, M.

Generating suitable game scenarios that can cater for individual players has become an emerging challenge in procedural content generation. In this paper, we propose a data-driven scenario generation framework for game-based training. An evolutionary scenario generation process is designed with a

40 CFR 1065.245 - Sample flow meter for batch sampling.

Science.gov (United States)

2010-07-01

... 40 Protection of Environment 32 2010-07-01 2010-07-01 false Sample flow meter for batch sampling... Sample flow meter for batch sampling. (a) Application. Use a sample flow meter to determine sample flow... difference between a diluted exhaust sample flow meter and a dilution air meter to calculate raw exhaust flow...

Actual Waste Demonstration of the Nitric-Glycolic Flowsheet for Sludge Batch 9 Qualification

Energy Technology Data Exchange (ETDEWEB)

Newell, J. D. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Pareizs, J. M. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Martino, C. J. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Reboul, S. H. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Coleman, C. J. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Edwards, T. B. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Johnson, F. C. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL)

2016-09-01

For each sludge batch that is processed in the Defense Waste Processing Facility (DWPF), the Savannah River National Laboratory (SRNL) performs qualification testing to demonstrate that the sludge batch is processable. Testing performed by the Savannah River National Laboratory has shown glycolic acid to be effective in replacing the function of formic acid in the DWPF chemical process. The nitric-glycolic flowsheet reduces mercury, significantly lowers the catalytic generation of hydrogen and ammonia which could allow purge reduction in the Sludge Receipt and Adjustment Tank (SRAT), stabilizes the pH and chemistry in the SRAT and the Slurry Mix Evaporator (SME), allows for effective rheology adjustment, and is favorable with respect to melter flammability. In order to implement the new flowsheet, SRAT and SME cycles, designated SC-18, were performed using a Sludge Batch (SB) 9 slurry blended from SB8 Tank 40H and Tank 51H samples. The SRAT cycle involved adding nitric and glycolic acids to the sludge, refluxing to steam strip mercury, and dewatering to a targeted solids concentration. Data collected during the SRAT cycle included offgas analyses, process temperatures, heat transfer, and pH measurements. The SME cycle demonstrated the addition of glass frit and the replication of six canister decontamination additions. The demonstration concluded with dewatering to a targeted solids concentration. Data collected during the SME cycle included offgas analyses, process temperatures, heat transfer, and pH measurements. Slurry and condensate samples were collected for subsequent analysis

A Data-Driven Stochastic Reactive Power Optimization Considering Uncertainties in Active Distribution Networks and Decomposition Method

DEFF Research Database (Denmark)

Ding, Tao; Yang, Qingrun; Yang, Yongheng

2018-01-01

To address the uncertain output of distributed generators (DGs) for reactive power optimization in active distribution networks, the stochastic programming model is widely used. The model is employed to find an optimal control strategy with minimum expected network loss while satisfying all......, in this paper, a data-driven modeling approach is introduced to assume that the probability distribution from the historical data is uncertain within a confidence set. Furthermore, a data-driven stochastic programming model is formulated as a two-stage problem, where the first-stage variables find the optimal...... control for discrete reactive power compensation equipment under the worst probability distribution of the second stage recourse. The second-stage variables are adjusted to uncertain probability distribution. In particular, this two-stage problem has a special structure so that the second-stage problem...

Removing batch effects for prediction problems with frozen surrogate variable analysis

Directory of Open Access Journals (Sweden)

Hilary S. Parker

2014-09-01

Full Text Available Batch effects are responsible for the failure of promising genomic prognostic signatures, major ambiguities in published genomic results, and retractions of widely-publicized findings. Batch effect corrections have been developed to remove these artifacts, but they are designed to be used in population studies. But genomic technologies are beginning to be used in clinical applications where samples are analyzed one at a time for diagnostic, prognostic, and predictive applications. There are currently no batch correction methods that have been developed specifically for prediction. In this paper, we propose an new method called frozen surrogate variable analysis (fSVA that borrows strength from a training set for individual sample batch correction. We show that fSVA improves prediction accuracy in simulations and in public genomic studies. fSVA is available as part of the sva Bioconductor package.

A Data-Driven Approach to Realistic Shape Morphing

KAUST Repository

Gao, Lin; Lai, Yu-Kun; Huang, Qi-Xing; Hu, Shi-Min

2013-01-01

Morphing between 3D objects is a fundamental technique in computer graphics. Traditional methods of shape morphing focus on establishing meaningful correspondences and finding smooth interpolation between shapes. Such methods however only take geometric information as input and thus cannot in general avoid producing unnatural interpolation, in particular for large-scale deformations. This paper proposes a novel data-driven approach for shape morphing. Given a database with various models belonging to the same category, we treat them as data samples in the plausible deformation space. These models are then clustered to form local shape spaces of plausible deformations. We use a simple metric to reasonably represent the closeness between pairs of models. Given source and target models, the morphing problem is casted as a global optimization problem of finding a minimal distance path within the local shape spaces connecting these models. Under the guidance of intermediate models in the path, an extended as-rigid-as-possible interpolation is used to produce the final morphing. By exploiting the knowledge of plausible models, our approach produces realistic morphing for challenging cases as demonstrated by various examples in the paper. © 2013 The Eurographics Association and Blackwell Publishing Ltd.

A Data-Driven Approach to Realistic Shape Morphing

KAUST Repository

Gao, Lin

2013-05-01

Morphing between 3D objects is a fundamental technique in computer graphics. Traditional methods of shape morphing focus on establishing meaningful correspondences and finding smooth interpolation between shapes. Such methods however only take geometric information as input and thus cannot in general avoid producing unnatural interpolation, in particular for large-scale deformations. This paper proposes a novel data-driven approach for shape morphing. Given a database with various models belonging to the same category, we treat them as data samples in the plausible deformation space. These models are then clustered to form local shape spaces of plausible deformations. We use a simple metric to reasonably represent the closeness between pairs of models. Given source and target models, the morphing problem is casted as a global optimization problem of finding a minimal distance path within the local shape spaces connecting these models. Under the guidance of intermediate models in the path, an extended as-rigid-as-possible interpolation is used to produce the final morphing. By exploiting the knowledge of plausible models, our approach produces realistic morphing for challenging cases as demonstrated by various examples in the paper. © 2013 The Eurographics Association and Blackwell Publishing Ltd.

Adaptation to high throughput batch chromatography enhances multivariate screening.

Science.gov (United States)

Barker, Gregory A; Calzada, Joseph; Herzer, Sibylle; Rieble, Siegfried

2015-09-01

High throughput process development offers unique approaches to explore complex process design spaces with relatively low material consumption. Batch chromatography is one technique that can be used to screen chromatographic conditions in a 96-well plate. Typical batch chromatography workflows examine variations in buffer conditions or comparison of multiple resins in a given process, as opposed to the assessment of protein loading conditions in combination with other factors. A modification to the batch chromatography paradigm is described here where experimental planning, programming, and a staggered loading approach increase the multivariate space that can be explored with a liquid handling system. The iterative batch chromatography (IBC) approach is described, which treats every well in a 96-well plate as an individual experiment, wherein protein loading conditions can be varied alongside other factors such as wash and elution buffer conditions. As all of these factors are explored in the same experiment, the interactions between them are characterized and the number of follow-up confirmatory experiments is reduced. This in turn improves statistical power and throughput. Two examples of the IBC method are shown and the impact of the load conditions are assessed in combination with the other factors explored. Copyright © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Keys to success for data-driven decision making: Lessons from participatory monitoring and collaborative adaptive management

Science.gov (United States)

Recent years have witnessed a call for evidence-based decisions in conservation and natural resource management, including data-driven decision-making. Adaptive management (AM) is one prevalent model for integrating scientific data into decision-making, yet AM has faced numerous challenges and limit...

Model Integrasi Penjadwalan Produksi Batch dan Penjadwalan Perawatan dengan Kendala Due Date

Directory of Open Access Journals (Sweden)

Zahedi .

2014-01-01

Full Text Available This paper discusses the integration model of batch production and preventive maintenance scheduling on a single machine producing an item to be delivered at a common due date. The machine is a deteriorating machine that requires preventive maintenance to ensure the availability of the machine at a desired service level. Decision variables of the model are the number of preventive maintenances, the schedule, length of production runs, as well as the number of batches, batch sizes and the production schedule of the resulting batches for each production run. The objective function of the model is to minimize the total cost consisting of inventory costs during parts processing, setup cost and cost of preventive maintenance. The results show three important points: First, the sequence of optimal batches always follows the SPT (short processing time. Second, variation of preventive maintenance unit cost does not influence the sequence of batches. Third, the first production run length from production starting time is smaller than the next production run length and this pattern continues until the due date. When in process inventory unit cost is increased, the pattern will continue until a specified cost limit, and beyond the limit the pattern will change to be the opposite pattern.

Adsorption thermal energy storage for cogeneration in industrial batch processes: Experiment, dynamic modeling and system analysis

International Nuclear Information System (INIS)

Schreiber, Heike; Graf, Stefan; Lanzerath, Franz; Bardow, André

2015-01-01

Adsorption thermal energy storage is investigated for heat supply with cogeneration in industrial batch processes. The feasibility of adsorption thermal energy storage is demonstrated with a lab-scale prototype. Based on these experiments, a dynamic model is developed and successfully calibrated to measurement data. Thereby, a reliable description of the dynamic behavior of the adsorption thermal energy storage unit is achieved. The model is used to study and benchmark the performance of adsorption thermal energy storage combined with cogeneration for batch process energy supply. As benchmark, we consider both a peak boiler and latent thermal energy storage based on a phase change material. Beer brewing is considered as an example of an industrial batch process. The study shows that adsorption thermal energy storage has the potential to increase energy efficiency significantly; primary energy consumption can be reduced by up to 25%. However, successful integration of adsorption thermal storage requires appropriate integration of low grade heat: Preferentially, low grade heat is available at times of discharging and in demand when charging the storage unit. Thus, adsorption thermal energy storage is most beneficial if applied to a batch process with heat demands on several temperature levels. - Highlights: • A highly efficient energy supply for industrial batch processes is presented. • Adsorption thermal energy storage (TES) is analyzed in experiment and simulation. • Adsorption TES can outperform both peak boilers and latent TES. • Performance of adsorption TES strongly depends on low grade heat temperature.

Towards efficient data exchange and sharing for big-data driven materials science: metadata and data formats

Science.gov (United States)

Ghiringhelli, Luca M.; Carbogno, Christian; Levchenko, Sergey; Mohamed, Fawzi; Huhs, Georg; Lüders, Martin; Oliveira, Micael; Scheffler, Matthias

2017-11-01

With big-data driven materials research, the new paradigm of materials science, sharing and wide accessibility of data are becoming crucial aspects. Obviously, a prerequisite for data exchange and big-data analytics is standardization, which means using consistent and unique conventions for, e.g., units, zero base lines, and file formats. There are two main strategies to achieve this goal. One accepts the heterogeneous nature of the community, which comprises scientists from physics, chemistry, bio-physics, and materials science, by complying with the diverse ecosystem of computer codes and thus develops "converters" for the input and output files of all important codes. These converters then translate the data of each code into a standardized, code-independent format. The other strategy is to provide standardized open libraries that code developers can adopt for shaping their inputs, outputs, and restart files, directly into the same code-independent format. In this perspective paper, we present both strategies and argue that they can and should be regarded as complementary, if not even synergetic. The represented appropriate format and conventions were agreed upon by two teams, the Electronic Structure Library (ESL) of the European Center for Atomic and Molecular Computations (CECAM) and the NOvel MAterials Discovery (NOMAD) Laboratory, a European Centre of Excellence (CoE). A key element of this work is the definition of hierarchical metadata describing state-of-the-art electronic-structure calculations.

Batch top-spray fluid bed coating: Scale-up insight using dynamic heat- and mass-transfer modelling

DEFF Research Database (Denmark)

Hede, Peter Dybdahl; Bach, P.; Jensen, Anker Degn

2009-01-01

A mathematical model was developed for batch top-spray fluid bed coating processes based on Ronsse et al. [2007a.b. Combined population balance and thermodynamic modelling of the batch top-spray fluidised bed coating process. Part I-model development and validation. journal of Food Engineering 78......, 296-307; Combined population balance and thermodynamic modelling of the batch top-spray fluidised bed coating process. Part II-model and process analysis. journal of Food Engineering 78, 308-322]. The model is based on one-dimensional discretisation of the fluid bed into a number of well-mixed control......-up principles by comparing simulation results with experimental temperature and humidity data obtained from inorganic salt coating of placebo cores in three pilot fluid bed scales being a 0.5kg small-scale (GEA Aeromatic-Fielder Strea-1), 4kg medium-scale (GEA Niro MP-1) and 24kg large-scale (GEA MP-2...

Data driven processor 'Vertex Trigger' for B experiments

International Nuclear Information System (INIS)

Hartouni, E.P.

1993-01-01

Data Driven Processors (DDP's) are specialized computation engines configured to solve specific numerical problems, such as vertex reconstruction. The architecture of the DDP which is the subject of this talk was designed and implemented by W. Sippach and B.C. Knapp at Nevis Lab. in the early 1980's. This particular implementation allows multiple parallel streams of data to provide input to a heterogenous collection of simple operators whose interconnection form an algorithm. The local data flow control allows this device to execute algorithms extremely quickly provided that care is taken in the layout of the algorithm. I/O rates of several hundred megabytes/second are routinely achieved thus making DDP's attractive candidates for complex online calculations. The original question was open-quote can a DDP reconstruct tracks in a Silicon Vertex Detector, find events with a separated vertex and do it fast enough to be used as an online trigger?close-quote Restating this inquiry as three questions and describing the answers to the questions will be the subject of this talk. The three specific questions are: (1) Can an algorithm be found which reconstructs tracks in a planar geometry and no magnetic field; (2) Can separated vertices be recognized in some way; (3) Can the algorithm be implemented in the Nevis-UMass and DDP and execute in 10-20 μs?

The influence of pH adjustment on kinetics parameters in tapioca wastewater treatment using aerobic sequencing batch reactor system

Science.gov (United States)

Mulyani, Happy; Budianto, Gregorius Prima Indra; Margono, Kaavessina, Mujtahid

2018-02-01

The present investigation deals with the aerobic sequencing batch reactor system of tapioca wastewater treatment with varying pH influent conditions. This project was carried out to evaluate the effect of pH on kinetics parameters of system. It was done by operating aerobic sequencing batch reactor system during 8 hours in many tapioca wastewater conditions (pH 4.91, pH 7, pH 8). The Chemical Oxygen Demand (COD) and Mixed Liquor Volatile Suspended Solids (MLVSS) of the aerobic sequencing batch reactor system effluent at steady state condition were determined at interval time of two hours to generate data for substrate inhibition kinetics parameters. Values of the kinetics constants were determined using Monod and Andrews models. There was no inhibition constant (Ki) detected in all process variation of aerobic sequencing batch reactor system for tapioca wastewater treatment in this study. Furthermore, pH 8 was selected as the preferred aerobic sequencing batch reactor system condition in those ranging pH investigated due to its achievement of values of kinetics parameters such µmax = 0.010457/hour and Ks = 255.0664 mg/L COD.

Study on the impact of transition from 3-batch to 4-batch loading at Loviisa NPP on the long-term decay heat and activity inventory

Energy Technology Data Exchange (ETDEWEB)

Lahtinen, Tuukka [Fortum Power and Heat Ltd., Fortum (Finland)

2017-09-15

The fuel economy of Loviisa NPP was improved by implementing a transition from 3-batch to 4-batch loading scheme between 2009 and 2013. Equilibrium cycle length as well as all process parameters were retained unchanged while the increase of fuel enrichment enabled to reduce the annual reload batch size from 102 to 84 assemblies. The fuel cycle transition obviously had an effect on the long-term decay heat and activity inventory. However, due to simultaneous change in several quantities the net effect over the relevant cooling time region is not self-evident. In this study the effect is analyzed properly, i. e. applying consistent calculation models and detailed description of assembly-wise irradiation histories. The study concludes that for the cooling time, foreseen typical prior to encapsulation of assemblies, the decay heat of discharge batch increases 2 - 3%. It is also concluded that, in order to maintain 100% filling degree of final disposal canisters, the cooling time prior to encapsulation needs to be prolonged by 10 - 15 years.

Data-driven identification of potential Zika virus vectors

Science.gov (United States)

Evans, Michelle V; Dallas, Tad A; Han, Barbara A; Murdock, Courtney C; Drake, John M

2017-01-01

Zika is an emerging virus whose rapid spread is of great public health concern. Knowledge about transmission remains incomplete, especially concerning potential transmission in geographic areas in which it has not yet been introduced. To identify unknown vectors of Zika, we developed a data-driven model linking vector species and the Zika virus via vector-virus trait combinations that confer a propensity toward associations in an ecological network connecting flaviviruses and their mosquito vectors. Our model predicts that thirty-five species may be able to transmit the virus, seven of which are found in the continental United States, including Culex quinquefasciatus and Cx. pipiens. We suggest that empirical studies prioritize these species to confirm predictions of vector competence, enabling the correct identification of populations at risk for transmission within the United States. DOI: http://dx.doi.org/10.7554/eLife.22053.001 PMID:28244371

TOWARDS DEMAND DRIVEN PUBLISHING: APPROCHES TO THE PRIORITISATION OF DIGITISATION OF NATURAL HISTORY COLLECTIONS DATA

Directory of Open Access Journals (Sweden)

Vishwas Chavan

2010-10-01

Full Text Available Natural history collections represent a vast repository of biodiversity data of international significance. There is an imperative to capture the data through digitisation projects in order to expose the data to new and established users of biodiversity data. On the basis of review of current state of digitization of natural history collections, a demand driven approach is advocated through the use of metadata to promote and increase access to natural history collection data.

Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study

OpenAIRE

Kim, Eun-Kyung; Kim, Hyo-Eun; Han, Kyunghwa; Kang, Bong Joo; Sohn, Yu-Mee; Woo, Ok Hee; Lee, Chan Wha

2018-01-01

We assessed the feasibility of a data-driven imaging biomarker based on weakly supervised learning (DIB; an imaging biomarker derived from large-scale medical image data with deep learning technology) in mammography (DIB-MG). A total of 29,107 digital mammograms from five institutions (4,339 cancer cases and 24,768 normal cases) were included. After matching patients’ age, breast density, and equipment, 1,238 and 1,238 cases were chosen as validation and test sets, respectively, and the remai...

A data driven method to measure electron charge mis-identification rate

CERN Document Server

Bakhshiansohi, Hamed

2009-01-01

Electron charge mis-measurement is an important challenge in analyses which depend on the charge of electron. To estimate the probability of {\\it electron charge mis-measurement} a data driven method is introduced and a good agreement with MC based methods is achieved.\\\\ The third moment of $\\phi$ distribution of hits in electron SuperCluster is studied. The correlation between this variable and the electron charge is also investigated. Using this `new' variable and some other variables the electron charge measurement is improved by two different approaches.

Retardation characteristics of radionuclides in geologic media through batch and packed column experiments

International Nuclear Information System (INIS)

Park, Hun Hwee; Han, Kyung Won; Han, Pil Soo; Lee, Jae Owan; Park, Chung Kyun; Yang, Ho Yeon

1988-03-01

Batch and packed column experiments are performed to investigate the retardation characteristics of radionuclide,i.e, Cs-137 in geologic media. In batch experiment, the effects of important parameters on the sorption of radionuclide in geologic media, such as nuclide concentration, pH, and particle size are examined. The Kd value obtained from breakthrough curve was compared with that from the batch sorption experiment to investigate the applicability of the Kd value from batch experiment to prediction of radionuclide migration in dynamic flow through porous media. The proposed model of radionuclide migration in porous media is also verified using the experimental results. (Author)

Model-Driven Policy Framework for Data Centers

DEFF Research Database (Denmark)

Caba, Cosmin Marius; Kentis, Angelos Mimidis; Soler, José

2016-01-01

. Moreover, the lack of simple solutions for managing the configuration and behavior of the DC components makes the DC hard to configure and slow in adapting to changes in business needs. In this paper, we propose a model-driven framework for policy-based management for DCs, to simplify not only the service...

Personalized mortality prediction driven by electronic medical data and a patient similarity metric.

Science.gov (United States)

Lee, Joon; Maslove, David M; Dubin, Joel A

2015-01-01

Clinical outcome prediction normally employs static, one-size-fits-all models that perform well for the average patient but are sub-optimal for individual patients with unique characteristics. In the era of digital healthcare, it is feasible to dynamically personalize decision support by identifying and analyzing similar past patients, in a way that is analogous to personalized product recommendation in e-commerce. Our objectives were: 1) to prove that analyzing only similar patients leads to better outcome prediction performance than analyzing all available patients, and 2) to characterize the trade-off between training data size and the degree of similarity between the training data and the index patient for whom prediction is to be made. We deployed a cosine-similarity-based patient similarity metric (PSM) to an intensive care unit (ICU) database to identify patients that are most similar to each patient and subsequently to custom-build 30-day mortality prediction models. Rich clinical and administrative data from the first day in the ICU from 17,152 adult ICU admissions were analyzed. The results confirmed that using data from only a small subset of most similar patients for training improves predictive performance in comparison with using data from all available patients. The results also showed that when too few similar patients are used for training, predictive performance degrades due to the effects of small sample sizes. Our PSM-based approach outperformed well-known ICU severity of illness scores. Although the improved prediction performance is achieved at the cost of increased computational burden, Big Data technologies can help realize personalized data-driven decision support at the point of care. The present study provides crucial empirical evidence for the promising potential of personalized data-driven decision support systems. With the increasing adoption of electronic medical record (EMR) systems, our novel medical data analytics contributes to

Personalized mortality prediction driven by electronic medical data and a patient similarity metric.

Directory of Open Access Journals (Sweden)

Joon Lee

Full Text Available Clinical outcome prediction normally employs static, one-size-fits-all models that perform well for the average patient but are sub-optimal for individual patients with unique characteristics. In the era of digital healthcare, it is feasible to dynamically personalize decision support by identifying and analyzing similar past patients, in a way that is analogous to personalized product recommendation in e-commerce. Our objectives were: 1 to prove that analyzing only similar patients leads to better outcome prediction performance than analyzing all available patients, and 2 to characterize the trade-off between training data size and the degree of similarity between the training data and the index patient for whom prediction is to be made.We deployed a cosine-similarity-based patient similarity metric (PSM to an intensive care unit (ICU database to identify patients that are most similar to each patient and subsequently to custom-build 30-day mortality prediction models. Rich clinical and administrative data from the first day in the ICU from 17,152 adult ICU admissions were analyzed. The results confirmed that using data from only a small subset of most similar patients for training improves predictive performance in comparison with using data from all available patients. The results also showed that when too few similar patients are used for training, predictive performance degrades due to the effects of small sample sizes. Our PSM-based approach outperformed well-known ICU severity of illness scores. Although the improved prediction performance is achieved at the cost of increased computational burden, Big Data technologies can help realize personalized data-driven decision support at the point of care.The present study provides crucial empirical evidence for the promising potential of personalized data-driven decision support systems. With the increasing adoption of electronic medical record (EMR systems, our novel medical data analytics

Monitoring and Characterization of Crystal Nucleation and Growth during Batch Crystallization

NARCIS (Netherlands)

Kadam, S.S.

2012-01-01

Batch crystallization is commonly used in pharmaceutical, agrochemical, specialty and fine chemicals industry. The advantages of batch crystallization lie in its ease of operation and the relatively simple equipment that can be used. On the other hand a major disadvantage associated with it is the

The effects of data-driven learning activities on EFL learners' writing development.

Science.gov (United States)

Luo, Qinqin

2016-01-01

Data-driven learning has been proved as an effective approach in helping learners solve various writing problems such as correcting lexical or grammatical errors, improving the use of collocations and generating ideas in writing, etc. This article reports on an empirical study in which data-driven learning was accomplished with the assistance of the user-friendly BNCweb, and presents the evaluation of the outcome by comparing the effectiveness of BNCweb and a search engine Baidu which is most commonly used as reference resource by Chinese learners of English as a foreign language. The quantitative results about 48 Chinese college students revealed that the experimental group which used BNCweb performed significantly better in the post-test in terms of writing fluency and accuracy, as compared with the control group which used the search engine Baidu. However, no significant difference was found between the two groups in terms of writing complexity. The qualitative results about the interview revealed that learners generally showed a positive attitude toward the use of BNCweb but there were still some problems of using corpora in the writing process, thus the combined use of corpora and other types of reference resource was suggested as a possible way to counter the potential barriers for Chinese learners of English.

Teacher Talk about Student Ability and Achievement in the Era of Data-Driven Decision Making

Science.gov (United States)

Datnow, Amanda; Choi, Bailey; Park, Vicki; St. John, Elise

2018-01-01

Background: Data-driven decision making continues to be a common feature of educational reform agendas across the globe. In many U.S. schools, the teacher team meeting is a key setting in which data use is intended to take place, with the aim of planning instruction to address students' needs. However, most prior research has not examined how the…

Batched Triangular DLA for Very Small Matrices on GPUs

KAUST Repository

Charara, Ali

2017-03-13

In several scientific applications, like tensor contractions in deep learning computation or data compression in hierarchical low rank matrix approximation, the bulk of computation typically resides in performing thousands of independent dense linear algebra operations on very small matrix sizes (usually less than 100). Batched dense linear algebra kernels are becoming ubiquitous for such scientific computations. Within a single API call, these kernels are capable of simultaneously launching a large number of similar matrix computations, removing the expensive overhead of multiple API calls while increasing the utilization of the underlying hardware.

A Knowledge-Driven Geospatially Enabled Framework for Geological Big Data

Directory of Open Access Journals (Sweden)

Liang Wu

2017-06-01

Full Text Available Geologic survey procedures accumulate large volumes of structured and unstructured data. Fully exploiting the knowledge and information that are included in geological big data and improving the accessibility of large volumes of data are important endeavors. In this paper, which is based on the architecture of the geological survey information cloud-computing platform (GSICCP and big-data-related technologies, we split geologic unstructured data into fragments and extract multi-dimensional features via geological domain ontology. These fragments are reorganized into a NoSQL (Not Only SQL database, and then associations between the fragments are added. A specific class of geological questions was analyzed and transformed into workflow tasks according to the predefined rules and associations between fragments to identify spatial information and unstructured content. We establish a knowledge-driven geologic survey information smart-service platform (GSISSP based on previous work, and we detail a study case for our research. The study case shows that all the content that has known relationships or semantic associations can be mined with the assistance of multiple ontologies, thereby improving the accuracy and comprehensiveness of geological information discovery.

Optimal operation of batch membrane processes

CERN Document Server

Paulen, Radoslav

2016-01-01

This study concentrates on a general optimization of a particular class of membrane separation processes: those involving batch diafiltration. Existing practices are explained and operational improvements based on optimal control theory are suggested. The first part of the book introduces the theory of membrane processes, optimal control and dynamic optimization. Separation problems are defined and mathematical models of batch membrane processes derived. The control theory focuses on problems of dynamic optimization from a chemical-engineering point of view. Analytical and numerical methods that can be exploited to treat problems of optimal control for membrane processes are described. The second part of the text builds on this theoretical basis to establish solutions for membrane models of increasing complexity. Each chapter starts with a derivation of optimal operation and continues with case studies exemplifying various aspects of the control problems under consideration. The authors work their way from th...

Defining datasets and creating data dictionaries for quality improvement and research in chronic disease using routinely collected data: an ontology-driven approach

Directory of Open Access Journals (Sweden)

Simon de Lusignan

2011-06-01

Conclusion Adopting an ontology-driven approach to case finding could improve the quality of disease registers and of research based on routine data. It would offer considerable advantages over using limited datasets to define cases. This approach should be considered by those involved in research and quality improvement projects which utilise routine data.

Application of gain scheduling to the control of batch bioreactors

Science.gov (United States)

Cardello, Ralph; San, Ka-Yiu

1987-01-01

The implementation of control algorithms to batch bioreactors is often complicated by the inherent variations in process dynamics during the course of fermentation. Such a wide operating range may render the performance of fixed gain PID controllers unsatisfactory. In this work, a detailed study on the control of batch fermentation is performed. Furthermore, a simple batch controller design is proposed which incorporates the concept of gain-scheduling, a subclass of adaptive control, with oxygen uptake rate as an auxiliary variable. The control of oxygen tension in the biorector is used as a vehicle to convey the proposed idea, analysis and results. Simulation experiments indicate significant improvement in controller performance can be achieved by the proposed approach even in the presence of measurement noise.

Evaluation of Respondent-Driven Sampling

Science.gov (United States)

McCreesh, Nicky; Frost, Simon; Seeley, Janet; Katongole, Joseph; Tarsh, Matilda Ndagire; Ndunguse, Richard; Jichi, Fatima; Lunel, Natasha L; Maher, Dermot; Johnston, Lisa G; Sonnenberg, Pam; Copas, Andrew J; Hayes, Richard J; White, Richard G

2012-01-01

Background Respondent-driven sampling is a novel variant of link-tracing sampling for estimating the characteristics of hard-to-reach groups, such as HIV prevalence in sex-workers. Despite its use by leading health organizations, the performance of this method in realistic situations is still largely unknown. We evaluated respondent-driven sampling by comparing estimates from a respondent-driven sampling survey with total-population data. Methods Total-population data on age, tribe, religion, socioeconomic status, sexual activity and HIV status were available on a population of 2402 male household-heads from an open cohort in rural Uganda. A respondent-driven sampling (RDS) survey was carried out in this population, employing current methods of sampling (RDS sample) and statistical inference (RDS estimates). Analyses were carried out for the full RDS sample and then repeated for the first 250 recruits (small sample). Results We recruited 927 household-heads. Full and small RDS samples were largely representative of the total population, but both samples under-represented men who were younger, of higher socioeconomic status, and with unknown sexual activity and HIV status. Respondent-driven-sampling statistical-inference methods failed to reduce these biases. Only 31%-37% (depending on method and sample size) of RDS estimates were closer to the true population proportions than the RDS sample proportions. Only 50%-74% of respondent-driven-sampling bootstrap 95% confidence intervals included the population proportion. Conclusions Respondent-driven sampling produced a generally representative sample of this well-connected non-hidden population. However, current respondent-driven-sampling inference methods failed to reduce bias when it occurred. Whether the data required to remove bias and measure precision can be collected in a respondent-driven sampling survey is unresolved. Respondent-driven sampling should be regarded as a (potentially superior) form of convenience

Evaluation of respondent-driven sampling.

Science.gov (United States)

McCreesh, Nicky; Frost, Simon D W; Seeley, Janet; Katongole, Joseph; Tarsh, Matilda N; Ndunguse, Richard; Jichi, Fatima; Lunel, Natasha L; Maher, Dermot; Johnston, Lisa G; Sonnenberg, Pam; Copas, Andrew J; Hayes, Richard J; White, Richard G

2012-01-01

Respondent-driven sampling is a novel variant of link-tracing sampling for estimating the characteristics of hard-to-reach groups, such as HIV prevalence in sex workers. Despite its use by leading health organizations, the performance of this method in realistic situations is still largely unknown. We evaluated respondent-driven sampling by comparing estimates from a respondent-driven sampling survey with total population data. Total population data on age, tribe, religion, socioeconomic status, sexual activity, and HIV status were available on a population of 2402 male household heads from an open cohort in rural Uganda. A respondent-driven sampling (RDS) survey was carried out in this population, using current methods of sampling (RDS sample) and statistical inference (RDS estimates). Analyses were carried out for the full RDS sample and then repeated for the first 250 recruits (small sample). We recruited 927 household heads. Full and small RDS samples were largely representative of the total population, but both samples underrepresented men who were younger, of higher socioeconomic status, and with unknown sexual activity and HIV status. Respondent-driven sampling statistical inference methods failed to reduce these biases. Only 31%-37% (depending on method and sample size) of RDS estimates were closer to the true population proportions than the RDS sample proportions. Only 50%-74% of respondent-driven sampling bootstrap 95% confidence intervals included the population proportion. Respondent-driven sampling produced a generally representative sample of this well-connected nonhidden population. However, current respondent-driven sampling inference methods failed to reduce bias when it occurred. Whether the data required to remove bias and measure precision can be collected in a respondent-driven sampling survey is unresolved. Respondent-driven sampling should be regarded as a (potentially superior) form of convenience sampling method, and caution is required

Data-Driven Hint Generation in Vast Solution Spaces: A Self-Improving Python Programming Tutor

Science.gov (United States)

Rivers, Kelly; Koedinger, Kenneth R.

2017-01-01

To provide personalized help to students who are working on code-writing problems, we introduce a data-driven tutoring system, ITAP (Intelligent Teaching Assistant for Programming). ITAP uses state abstraction, path construction, and state reification to automatically generate personalized hints for students, even when given states that have not…

Sludge Batch 7B Qualification Activities With SRS Tank Farm Sludge

International Nuclear Information System (INIS)

Pareizs, J.; Click, D.; Lambert, D.; Reboul, S.

2011-01-01

Waste Solidification Engineering (WSE) has requested that characterization and a radioactive demonstration of the next batch of sludge slurry - Sludge Batch 7b (SB7b) - be completed in the Shielded Cells Facility of the Savannah River National Laboratory (SRNL) via a Technical Task Request (TTR). This characterization and demonstration, or sludge batch qualification process, is required prior to transfer of the sludge from Tank 51 to the Defense Waste Processing Facility (DWPF) feed tank (Tank 40). The current WSE practice is to prepare sludge batches in Tank 51 by transferring sludge from other tanks. Discharges of nuclear materials from H Canyon are often added to Tank 51 during sludge batch preparation. The sludge is washed and transferred to Tank 40, the current DWPF feed tank. Prior to transfer of Tank 51 to Tank 40, SRNL typically simulates the Tank Farm and DWPF processes with a Tank 51 sample (referred to as the qualification sample). With the tight schedule constraints for SB7b and the potential need for caustic addition to allow for an acceptable glass processing window, the qualification for SB7b was approached differently than past batches. For SB7b, SRNL prepared a Tank 51 and a Tank 40 sample for qualification. SRNL did not receive the qualification sample from Tank 51 nor did it simulate all of the Tank Farm washing and decanting operations. Instead, SRNL prepared a Tank 51 SB7b sample from samples of Tank 7 and Tank 51, along with a wash solution to adjust the supernatant composition to the final SB7b Tank 51 Tank Farm projections. SRNL then prepared a sample to represent SB7b in Tank 40 by combining portions of the SRNL-prepared Tank 51 SB7b sample and a Tank 40 Sludge Batch 7a (SB7a) sample. The blended sample was 71% Tank 40 (SB7a) and 29% Tank 7/Tank 51 on an insoluble solids basis. This sample is referred to as the SB7b Qualification Sample. The blend represented the highest projected Tank 40 heel (as of May 25, 2011), and thus, the highest

Look-ahead strategies for controlling batch operations in industry : basic insights in rule construction

NARCIS (Netherlands)

van der Zee, D.J.; Sullivan, W.A.; Ahmad, M.M.; Fichtner, D.; Sauer, W.; Weigert, G.; Zerna, T.

2002-01-01

Batching jobs in a manufacturing system is a very common policy in most industries. Main reasons for batching are avoidance of set ups and/or facilitation of material handling. Examples of batch-wise production systems are ovens found in aircraft industry and in semiconductor manufacturing. Starting

Control of polymer network topology in semi-batch systems

Science.gov (United States)

Wang, Rui; Olsen, Bradley; Johnson, Jeremiah

Polymer networks invariably possess topological defects: loops of different orders. Since small loops (primary loops and secondary loops) both lower the modulus of network and lead to stress concentration that causes material failure at low deformation, it is desirable to greatly reduce the loop fraction. We have shown that achieving loop fraction close to zero is extremely difficult in the batch process due to the slow decay of loop fraction with the polymer concentration and chain length. Here, we develop a modified kinetic graph theory that can model network formation reactions in semi-batch systems. We demonstrate that the loop fraction is not sensitive to the feeding policy if the reaction volume maintains constant during the network formation. However, if we initially put concentrated solution of small junction molecules in the reactor and continuously adding polymer solutions, the fractions of both primary loop and higher-order loops will be significantly reduced. There is a limiting value (nonzero) of loop fraction that can be achieved in the semi-batch system in condition of extremely slow feeding rate. This minimum loop fraction only depends on a single dimensionless variable, the product of concentration and with single chain pervaded volume, and defines an operating zone in which the loop fraction of polymer networks can be controlled through adjusting the feeding rate of the semi-batch process.

Nursing Theory, Terminology, and Big Data: Data-Driven Discovery of Novel Patterns in Archival Randomized Clinical Trial Data.

Science.gov (United States)

Monsen, Karen A; Kelechi, Teresa J; McRae, Marion E; Mathiason, Michelle A; Martin, Karen S

The growth and diversification of nursing theory, nursing terminology, and nursing data enable a convergence of theory- and data-driven discovery in the era of big data research. Existing datasets can be viewed through theoretical and terminology perspectives using visualization techniques in order to reveal new patterns and generate hypotheses. The Omaha System is a standardized terminology and metamodel that makes explicit the theoretical perspective of the nursing discipline and enables terminology-theory testing research. The purpose of this paper is to illustrate the approach by exploring a large research dataset consisting of 95 variables (demographics, temperature measures, anthropometrics, and standardized instruments measuring quality of life and self-efficacy) from a theory-based perspective using the Omaha System. Aims were to (a) examine the Omaha System dataset to understand the sample at baseline relative to Omaha System problem terms and outcome measures, (b) examine relationships within the normalized Omaha System dataset at baseline in predicting adherence, and (c) examine relationships within the normalized Omaha System dataset at baseline in predicting incident venous ulcer. Variables from a randomized clinical trial of a cryotherapy intervention for the prevention of venous ulcers were mapped onto Omaha System terms and measures to derive a theoretical framework for the terminology-theory testing study. The original dataset was recoded using the mapping to create an Omaha System dataset, which was then examined using visualization to generate hypotheses. The hypotheses were tested using standard inferential statistics. Logistic regression was used to predict adherence and incident venous ulcer. Findings revealed novel patterns in the psychosocial characteristics of the sample that were discovered to be drivers of both adherence (Mental health Behavior: OR = 1.28, 95% CI [1.02, 1.60]; AUC = .56) and incident venous ulcer (Mental health Behavior

Monitoring of batch processes using spectroscopy

NARCIS (Netherlands)

Gurden, S. P.; Westerhuis, J. A.; Smilde, A. K.

2002-01-01

There is an increasing need for new techniques for the understanding, monitoring and the control of batch processes. Spectroscopy is now becoming established as a means of obtaining real-time, high-quality chemical information at frequent time intervals and across a wide range of industrial

Classification Systems, their Digitization and Consequences for Data-Driven Decision Making

DEFF Research Database (Denmark)

Stein, Mari-Klara; Newell, Sue; Galliers, Robert D.

2013-01-01

Classification systems are foundational in many standardized software tools. This digitization of classification systems gives them a new ‘materiality’ that, jointly with the social practices of information producers/consumers, has significant consequences on the representational quality of such ...... and the foundational role of representational quality in understanding the success and consequences of data-driven decision-making.......-narration and meta-narration), and three different information production/consumption situations. We contribute to the relational theorization of representational quality and extend classification systems research by drawing explicit attention to the importance of ‘materialization’ of classification systems...

New data-driven estimation of terrestrial CO2 fluxes in Asia using a standardized database of eddy covariance measurements, remote sensing data, and support vector regression

Science.gov (United States)

Ichii, Kazuhito; Ueyama, Masahito; Kondo, Masayuki; Saigusa, Nobuko; Kim, Joon; Alberto, Ma. Carmelita; Ardö, Jonas; Euskirchen, Eugénie S.; Kang, Minseok; Hirano, Takashi; Joiner, Joanna; Kobayashi, Hideki; Marchesini, Luca Belelli; Merbold, Lutz; Miyata, Akira; Saitoh, Taku M.; Takagi, Kentaro; Varlagin, Andrej; Bret-Harte, M. Syndonia; Kitamura, Kenzo; Kosugi, Yoshiko; Kotani, Ayumi; Kumar, Kireet; Li, Sheng-Gong; Machimura, Takashi; Matsuura, Yojiro; Mizoguchi, Yasuko; Ohta, Takeshi; Mukherjee, Sandipan; Yanagi, Yuji; Yasuda, Yukio; Zhang, Yiping; Zhao, Fenghua

2017-04-01

The lack of a standardized database of eddy covariance observations has been an obstacle for data-driven estimation of terrestrial CO2 fluxes in Asia. In this study, we developed such a standardized database using 54 sites from various databases by applying consistent postprocessing for data-driven estimation of gross primary productivity (GPP) and net ecosystem CO2 exchange (NEE). Data-driven estimation was conducted by using a machine learning algorithm: support vector regression (SVR), with remote sensing data for 2000 to 2015 period. Site-level evaluation of the estimated CO2 fluxes shows that although performance varies in different vegetation and climate classifications, GPP and NEE at 8 days are reproduced (e.g., r2 = 0.73 and 0.42 for 8 day GPP and NEE). Evaluation of spatially estimated GPP with Global Ozone Monitoring Experiment 2 sensor-based Sun-induced chlorophyll fluorescence shows that monthly GPP variations at subcontinental scale were reproduced by SVR (r2 = 1.00, 0.94, 0.91, and 0.89 for Siberia, East Asia, South Asia, and Southeast Asia, respectively). Evaluation of spatially estimated NEE with net atmosphere-land CO2 fluxes of Greenhouse Gases Observing Satellite (GOSAT) Level 4A product shows that monthly variations of these data were consistent in Siberia and East Asia; meanwhile, inconsistency was found in South Asia and Southeast Asia. Furthermore, differences in the land CO2 fluxes from SVR-NEE and GOSAT Level 4A were partially explained by accounting for the differences in the definition of land CO2 fluxes. These data-driven estimates can provide a new opportunity to assess CO2 fluxes in Asia and evaluate and constrain terrestrial ecosystem models.

Production of tea vinegar by batch and semicontinuous fermentation

OpenAIRE

Kaur, Pardeep; Kocher, G. S.; Phutela, R. P.

2010-01-01

The fermented tea vinegar combines the beneficial properties of tea and vinegar. The complete fermentation takes 4 to 5 weeks in a batch culture and thus can be shortened by semi continuous/ continuous fermentation using immobilized bacterial cells. In the present study, alcoholic fermentation of 1.0 and 1.5% tea infusions using Saccharomyces cerevisae G was carried out that resulted in 84.3 and 84.8% fermentation efficiency (FE) respectively. The batch vinegar fermentation of these wines wit...

Human body segmentation via data-driven graph cut.

Science.gov (United States)

Li, Shifeng; Lu, Huchuan; Shao, Xingqing

2014-11-01

Human body segmentation is a challenging and important problem in computer vision. Existing methods usually entail a time-consuming training phase for prior knowledge learning with complex shape matching for body segmentation. In this paper, we propose a data-driven method that integrates top-down body pose information and bottom-up low-level visual cues for segmenting humans in static images within the graph cut framework. The key idea of our approach is first to exploit human kinematics to search for body part candidates via dynamic programming for high-level evidence. Then, by using the body parts classifiers, obtaining bottom-up cues of human body distribution for low-level evidence. All the evidence collected from top-down and bottom-up procedures are integrated in a graph cut framework for human body segmentation. Qualitative and quantitative experiment results demonstrate the merits of the proposed method in segmenting human bodies with arbitrary poses from cluttered backgrounds.

Ethanol production from Sorghum bicolor using both separate and simultaneous saccharification and fermentation in batch and fed batch systems

DEFF Research Database (Denmark)

Mehmood, Sajid; Gulfraz, M.; Rana, N. F.

2009-01-01

The objective of this work was to find the best combination of different experimental conditions during pre-treatment, enzymatic saccharification, detoxification of inhibitors and fermentation of Sorghum bicolor straw for ethanol production. The optimization of pre-treatment using different...... were used in order to increase the monomeric sugar during enzymatic hydrolysis and it has been observed that the addition of these surfactants contributed significantly in cellulosic conversion but no effect was shown on hemicellulosic hydrolysis. Fermentability of hydrolyzate was tested using...... Saccharomyces cerevisiae Ethanol Red (TM) and it was observed that simultaneous saccharification and fermentation ( SSF) with both batch and fed batch resulted in better ethanol yield as compared to separate hydrolysis and fermentation ( SHF). Detoxification of furan during SHF facilitated reduction...

Data-driven classification of patients with primary progressive aphasia.

Science.gov (United States)

Hoffman, Paul; Sajjadi, Seyed Ahmad; Patterson, Karalyn; Nestor, Peter J

2017-11-01

Current diagnostic criteria classify primary progressive aphasia into three variants-semantic (sv), nonfluent (nfv) and logopenic (lv) PPA-though the adequacy of this scheme is debated. This study took a data-driven approach, applying k-means clustering to data from 43 PPA patients. The algorithm grouped patients based on similarities in language, semantic and non-linguistic cognitive scores. The optimum solution consisted of three groups. One group, almost exclusively those diagnosed as svPPA, displayed a selective semantic impairment. A second cluster, with impairments to speech production, repetition and syntactic processing, contained a majority of patients with nfvPPA but also some lvPPA patients. The final group exhibited more severe deficits to speech, repetition and syntax as well as semantic and other cognitive deficits. These results suggest that, amongst cases of non-semantic PPA, differentiation mainly reflects overall degree of language/cognitive impairment. The observed patterns were scarcely affected by inclusion/exclusion of non-linguistic cognitive scores. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Optimal Operation of Industrial Batch Crystallizers : A Nonlinear Model-based Control Approach

NARCIS (Netherlands)

Mesbah, A.

2010-01-01

Batch crystallization is extensively employed in the chemical, pharmaceutical, and food industries to separate and purify high value-added chemical substances. Despite their widespread application, optimal operation of batch crystallizers is particularly challenging. The difficulties primarily

Three-batch reloading scheme for IRIS reactor extended cycles

International Nuclear Information System (INIS)

Jecmenica, R.; Pevec, D.; Grgic, D.

2004-01-01

To fully exploit the IRIS reactor optimized maintenance, and at the same time improve fuel utilization, a core design enabling a 4-year operating cycle together with a three-batch reloading scheme is desirable. However, this requires not only the increased allowed burnup but also use of fuel with uranium oxide enriched beyond 5%. This paper considers three-batch reloading scheme for a 4-year operating cycle with the assumptions of increased discharge burnup and fuel enrichment beyond 5%. Calculational model of IRIS reactor core has been developed based on FER FA2D code for group constants generation and NRC's PARCS nodal code for global core analysis. Studies have been performed resulting in a preliminary design of a three-batch core configuration for the first cycle. It must be emphasized that this study is outside the current IRIS licensing efforts, which rely on the present fuel technology (enrichment below 5%), but it is of long-term interest for potential future IRIS design upgrades. (author)

Modeling and optimization of energy consumption in multipurpose batch plants - 2006 Annual report

Energy Technology Data Exchange (ETDEWEB)

Szijjarto, A.

2006-12-15

This annual report for the Swiss Federal Office of Energy (SFOE) takes a look at the work done in 2006 on the development of a model that is able to make prognoses concerning the energy consumption of chemical batch processes and thus enable these to be optimised. In the year under review, reliable models and software modelling tools were developed. The tools are based on commercially available simulation software. The authors note that the bottom-up model presented in the previous reports is powerful and robust enough to treat a significant amount of the process data in reasonable time. The model was tested for the modelling of energy consumption in the case-study plant during a period of two months. Up to 30 batches of 9 different products were produced in this period. The resolution of the model is discussed, which is very useful for identification of the process steps with the highest energy consumption. Energy-saving potential is noted. Based on these results, one product was chosen which is to be investigated in the final stage of the project in order to optimise the energy consumption of the case-study plant. The authors note that the methodology and software tools developed can be later applied for other products or chemical batch plants.

A data-driven weighting scheme for multivariate phenotypic endpoints recapitulates zebrafish developmental cascades

Energy Technology Data Exchange (ETDEWEB)

Zhang, Guozhu, E-mail: gzhang6@ncsu.edu [Bioinformatics Research Center, North Carolina State University, Raleigh, NC (United States); Roell, Kyle R., E-mail: krroell@ncsu.edu [Bioinformatics Research Center, North Carolina State University, Raleigh, NC (United States); Truong, Lisa, E-mail: lisa.truong@oregonstate.edu [Department of Environmental and Molecular Toxicology, Sinnhuber Aquatic Research Laboratory, Oregon State University, Corvallis, OR (United States); Tanguay, Robert L., E-mail: robert.tanguay@oregonstate.edu [Department of Environmental and Molecular Toxicology, Sinnhuber Aquatic Research Laboratory, Oregon State University, Corvallis, OR (United States); Reif, David M., E-mail: dmreif@ncsu.edu [Bioinformatics Research Center, North Carolina State University, Raleigh, NC (United States); Department of Biological Sciences, Center for Human Health and the Environment, North Carolina State University, Raleigh, NC (United States)

2017-01-01

Zebrafish have become a key alternative model for studying health effects of environmental stressors, partly due to their genetic similarity to humans, fast generation time, and the efficiency of generating high-dimensional systematic data. Studies aiming to characterize adverse health effects in zebrafish typically include several phenotypic measurements (endpoints). While there is a solid biomedical basis for capturing a comprehensive set of endpoints, making summary judgments regarding health effects requires thoughtful integration across endpoints. Here, we introduce a Bayesian method to quantify the informativeness of 17 distinct zebrafish endpoints as a data-driven weighting scheme for a multi-endpoint summary measure, called weighted Aggregate Entropy (wAggE). We implement wAggE using high-throughput screening (HTS) data from zebrafish exposed to five concentrations of all 1060 ToxCast chemicals. Our results show that our empirical weighting scheme provides better performance in terms of the Receiver Operating Characteristic (ROC) curve for identifying significant morphological effects and improves robustness over traditional curve-fitting approaches. From a biological perspective, our results suggest that developmental cascade effects triggered by chemical exposure can be recapitulated by analyzing the relationships among endpoints. Thus, wAggE offers a powerful approach for analysis of multivariate phenotypes that can reveal underlying etiological processes. - Highlights: • Introduced a data-driven weighting scheme for multiple phenotypic endpoints. • Weighted Aggregate Entropy (wAggE) implies differential importance of endpoints. • Endpoint relationships reveal developmental cascade effects triggered by exposure. • wAggE is generalizable to multi-endpoint data of different shapes and scales.

Practical options for selecting data-driven or physics-based prognostics algorithms with reviews

International Nuclear Information System (INIS)

An, Dawn; Kim, Nam H.; Choi, Joo-Ho

2015-01-01

This paper is to provide practical options for prognostics so that beginners can select appropriate methods for their fields of application. To achieve this goal, several popular algorithms are first reviewed in the data-driven and physics-based prognostics methods. Each algorithm’s attributes and pros and cons are analyzed in terms of model definition, model parameter estimation and ability to handle noise and bias in data. Fatigue crack growth examples are then used to illustrate the characteristics of different algorithms. In order to suggest a suitable algorithm, several studies are made based on the number of data sets, the level of noise and bias, availability of loading and physical models, and complexity of the damage growth behavior. Based on the study, it is concluded that the Gaussian process is easy and fast to implement, but works well only when the covariance function is properly defined. The neural network has the advantage in the case of large noise and complex models but only with many training data sets. The particle filter and Bayesian method are superior to the former methods because they are less affected by noise and model complexity, but work only when physical model and loading conditions are available. - Highlights: • Practical review of data-driven and physics-based prognostics are provided. • As common prognostics algorithms, NN, GP, PF and BM are introduced. • Algorithms’ attributes, pros and cons, and applicable conditions are discussed. • This will be helpful to choose the best algorithm for different applications

Results on testing pilot industrial batch of SC magnets for the UNK

International Nuclear Information System (INIS)

Ageev, A.I.; Andreev, N.I.; Balbekov, V.I.; Chirkov, P.N.; Dolzhenkov, V.I.; Gertsev, K.F.; Gridasov, V.I.; Myznikov, K.P.; Smirnov, N.L.; Sychev, V.A.

1992-01-01

IHEP has developed and studied the superconducting dipoles and quadrupoles of the regular part of the UNK main ring which satisfy the requirements imposed on them. The pilot-industrial batch of the UNK SC magnets has been produced now. The reproducibility of the magnet characteristics is studied and the mass production technology is optimized with this batch. The results of the cryogenic tests and the magnetic field measurements for the UNK SC dipoles of the pilot-industrial batch are presented. (author) 5 refs.; 6 figs.; 1 tab

The influence of habitat structure on energy allocation tactics in an estuarine batch spawner

Science.gov (United States)

Brigolin, D.; Cavraro, F.; Zanatta, V.; Pastres, R.; Malavasi, S.

2016-04-01

Trade-off between fecundity and survival was tested in a batch spawner, the Mediterranean killifish Aphanius fasciatus, using an integrated modelling-data approach based on previously collected empirical data. Two sites of the lagoon of Venice (Northern Adriatic sea, Italy) were selected in order to compare the energy allocation between growth and reproduction in two contrasting habitats. These were characterised by high and comparable level of richness in basal resources, but showed two different mortality schedules: an open natural salt marsh, exposed to high level of predation, and a confined artificial site protected from piscivorous predation. By means of a bioenergetic Scope for Growth model, developed and calibrated for the specific goals of this work, we compared the average individual life history between the two habitats. The average individual life history is characterised by a higher number of spawning events and lower per-spawning investment in the confined site exposed to lower predation risk, compared to the site connected with the open lagoon. Thus, model predictions suggest that habitat structure with different extrinsic mortality schedules may shape the life history strategy in modulating the pattern of energy allocation. Model application highlights the central role of energy partitioning through batch spawning, in determining the life history strategy. The particular ovary structure of a batch spawner seems therefore to allow the fish to modulate timing and investment of spawning events, shaping the optimal life history in relation to the environmental conditions.

An evaluation of data-driven motion estimation in comparison to the usage of external-surrogates in cardiac SPECT imaging

International Nuclear Information System (INIS)

Mukherjee, Joyeeta Mitra; Johnson, Karen L; Pretorius, P Hendrik; King, Michael A; Hutton, Brian F

2013-01-01

Motion estimation methods in single photon emission computed tomography (SPECT) can be classified into methods which depend on just the emission data (data-driven), or those that use some other source of information such as an external surrogate. The surrogate-based methods estimate the motion exhibited externally which may not correlate exactly with the movement of organs inside the body. The accuracy of data-driven strategies on the other hand is affected by the type and timing of motion occurrence during acquisition, the source distribution, and various degrading factors such as attenuation, scatter, and system spatial resolution. The goal of this paper is to investigate the performance of two data-driven motion estimation schemes based on the rigid-body registration of projections of motion-transformed source distributions to the acquired projection data for cardiac SPECT studies. Comparison is also made of six intensity based registration metrics to an external surrogate-based method. In the data-driven schemes, a partially reconstructed heart is used as the initial source distribution. The partially-reconstructed heart has inaccuracies due to limited angle artifacts resulting from using only a part of the SPECT projections acquired while the patient maintained the same pose. The performance of different cost functions in quantifying consistency with the SPECT projection data in the data-driven schemes was compared for clinically realistic patient motion occurring as discrete pose changes, one or two times during acquisition. The six intensity-based metrics studied were mean-squared difference, mutual information, normalized mutual information (NMI), pattern intensity (PI), normalized cross-correlation and entropy of the difference. Quantitative and qualitative analysis of the performance is reported using Monte-Carlo simulations of a realistic heart phantom including degradation factors such as attenuation, scatter and system spatial resolution. Further the

A universal piezo-driven ultrasonic cell microinjection system.

Science.gov (United States)

Huang, Haibo; Mills, James K; Lu, Cong; Sun, Dong

2011-08-01

Over the past decade, the rapid development of biotechnologies such as gene injection, in-vitro fertilization, intracytoplasmic sperm injection (ICSI) and drug development have led to great demand for highly automated, high precision equipment for microinjection. Recently a new cell injection technology using piezo-driven pipettes with a very small mercury column was proposed and successfully applied in ICSI to a variety of mammal species. Although this technique significantly improves the survival rates of the ICSI process, shortcomings due to the toxicity of mercury and damage to the cell membrane due to large lateral tip oscillations of the injector pipette may limit its application. In this paper, a new cell injection system for automatic batch injection of suspended cells is developed. A new design of the piezo-driven cell injector is proposed for automated suspended cell injection. This new piezo-driven cell injector design relocates the piezo oscillation actuator to the injector pipette which eliminates the vibration effect on other parts of the micromanipulator. A small piezo stack is sufficient to perform the cell injection process. Harmful lateral tip oscillations of the injector pipette are reduced substantially without the use of a mercury column. Furthermore, ultrasonic vibration micro-dissection (UVM) theory is utilized to analyze the piezo-driven cell injection process, and the source of the lateral oscillations of the injector pipette is investigated. From preliminary experiments of cell injection of a large number of zebrafish embryos (n = 200), the injector pipette can easily pierce through the cell membrane at a low injection speed and almost no deformation of the cell wall, and with a high success rate(96%) and survival rate(80.7%) This new injection approach shows good potential for precision injection with less damage to the injected cells.

A Novel Online Data-Driven Algorithm for Detecting UAV Navigation Sensor Faults

OpenAIRE

Rui Sun; Qi Cheng; Guanyu Wang; Washington Yotto Ochieng

2017-01-01

The use of Unmanned Aerial Vehicles (UAVs) has increased significantly in recent years. On-board integrated navigation sensors are a key component of UAVs’ flight control systems and are essential for flight safety. In order to ensure flight safety, timely and effective navigation sensor fault detection capability is required. In this paper, a novel data-driven Adaptive Neuron Fuzzy Inference System (ANFIS)-based approach is presented for the detection of on-board navigation sensor faults in ...

Modeling of oxide reduction in repeated-batch pyroprocessing

International Nuclear Information System (INIS)

Lee, Hyo Jik; Im, Hun Suk; Park, Geun Il

2016-01-01

Highlights: • Pyroprocessing is a complicated batch-type operation. • Discrete event system modeling was used to create an integrated operation model. • Simulation showed that could be accomplished. • The dynamic material flow helps us understand the process operation. • We showed that complex material flow could be simulated in terms of mass balance. - Abstract: Pyroprocessing is a complicated batch-type operation, involving a highly complex material flow logic with a huge number of unit processes. Discrete event system modeling was used to create an integrated operation model for which simulation showed that dynamic material flow could be accomplished to provide considerable insight into the process operation. In the model simulation, the amount of material transported upstream and downstream in the process satisfies a mass balance equation while considering the hold-up incurred by every batch operation. This study also simulated, in detail, an oxide reduction group process embracing electrolytic reduction, cathode processing, and salt purification. Based on the default operation scenario, it showed that complex material flows could be precisely simulated in terms of the mass balance. Specifically, the amount of high-heat elements remaining in the molten salt bath is analyzed to evaluate the operation scenario.

Near-infrared spectroscopic monitoring of a series of industrial batch processes using a bilinear grey model.

Science.gov (United States)

van Sprang, Eric N M; Ramaker, Henk-Jan; Westerhuis, Johan A; Smilde, Age K; Gurden, Stephen P; Wienke, Dietrich

2003-08-01

A good process understanding is the foundation for process optimization, process monitoring, end-point detection, and estimation of the end-product quality. Performing good process measurements and the construction of process models will contribute to a better process understanding. To improve the process knowledge it is common to build process models. These models are often based on first principles such as kinetic rates or mass balances. These types of models are also known as hard or white models. White models are characterized by being generally applicable but often having only a reasonable fit to real process data. Other commonly used types of models are empirical or black-box models such as regression and neural nets. Black-box models are characterized by having a good data fit but they lack a chemically meaningful model interpretation. Alternative models are grey models, which are combinations of white models and black models. The aim of a grey model is to combine the advantages of both black-box models and white models. In a qualitative case study of monitoring industrial batches using near-infrared (NIR) spectroscopy, it is shown that grey models are a good tool for detecting batch-to-batch variations and an excellent tool for process diagnosis compared to common spectroscopic monitoring tools.

Establishing column batch repeatability according to Quality by Design (QbD) principles using modeling software.

Science.gov (United States)

Rácz, Norbert; Kormány, Róbert; Fekete, Jenő; Molnár, Imre

2015-04-10

Column technology needs further improvement even today. To get information of batch-to-batch repeatability, intelligent modeling software was applied. Twelve columns from the same production process, but from different batches were compared in this work. In this paper, the retention parameters of these columns with real life sample solutes were studied. The following parameters were selected for measurements: gradient time, temperature and pH. Based on calculated results, batch-to-batch repeatability of BEH columns was evaluated. Two parallel measurements on two columns from the same batch were performed to obtain information about the quality of packing. Calculating the average of individual working points at the highest critical resolution (R(s,crit)) it was found that the robustness, calculated with a newly released robustness module, had a success rate >98% among the predicted 3(6) = 729 experiments for all 12 columns. With the help of retention modeling all substances could be separated independently from the batch and/or packing, using the same conditions, having high robustness of the experiments. Copyright © 2015 Elsevier B.V. All rights reserved.

Data-Driven Baseline Estimation of Residential Buildings for Demand Response

Directory of Open Access Journals (Sweden)

Saehong Park

2015-09-01

Full Text Available The advent of advanced metering infrastructure (AMI generates a large volume of data related with energy service. This paper exploits data mining approach for customer baseline load (CBL estimation in demand response (DR management. CBL plays a significant role in measurement and verification process, which quantifies the amount of demand reduction and authenticates the performance. The proposed data-driven baseline modeling is based on the unsupervised learning technique. Specifically we leverage both the self organizing map (SOM and K-means clustering for accurate estimation. This two-level approach efficiently reduces the large data set into representative weight vectors in SOM, and then these weight vectors are clustered by K-means clustering to find the load pattern that would be similar to the potential load pattern of the DR event day. To verify the proposed method, we conduct nationwide scale experiments where three major cities’ residential consumption is monitored by smart meters. Our evaluation compares the proposed solution with the various types of day matching techniques, showing that our approach outperforms the existing methods by up to a 68.5% lower error rate.

A Data-Driven Reliability Estimation Approach for Phased-Mission Systems

Directory of Open Access Journals (Sweden)

Hua-Feng He

2014-01-01

Full Text Available We attempt to address the issues associated with reliability estimation for phased-mission systems (PMS and present a novel data-driven approach to achieve reliability estimation for PMS using the condition monitoring information and degradation data of such system under dynamic operating scenario. In this sense, this paper differs from the existing methods only considering the static scenario without using the real-time information, which aims to estimate the reliability for a population but not for an individual. In the presented approach, to establish a linkage between the historical data and real-time information of the individual PMS, we adopt a stochastic filtering model to model the phase duration and obtain the updated estimation of the mission time by Bayesian law at each phase. At the meanwhile, the lifetime of PMS is estimated from degradation data, which are modeled by an adaptive Brownian motion. As such, the mission reliability can be real time obtained through the estimated distribution of the mission time in conjunction with the estimated lifetime distribution. We demonstrate the usefulness of the developed approach via a numerical example.

Fructose production by Zymomonas mobilis in fed-batch culture with minimal sorbitol formation

Energy Technology Data Exchange (ETDEWEB)

Edye, L A; Johns, M R; Ewings, K N

1989-08-01

Fed-batch cultures of Zymomonas mobilis (UQM 2864), a mutant unable to metabolise fructose, grown on diluted sugar cane syrup (200 g/l sucrose) achieved yields of 90.5 g/l fructose and 48.3 g/l ethanol with minimal sorbitol formation and complete utilization of the substrate. The effect of inoculum size on sorbitol formation in the batch stage of fed-batch fermentation are reported. Fermentation of sucrose (350 g/l) supplemented with nutrients yielded 142 g/l fructose and 76.5 g/l ethanol. Some fructose product loss at high fructose concentrations was observed. The fed-batch fermentation process offers a method for obtaining high concentrations of fructose and ethanol from sucrose materials. (orig.).

Development of a mathematical model for the growth associated Polyhydroxybutyrate fermentation by Azohydromonas australica and its use for the design of fed-batch cultivation strategies.

Science.gov (United States)

Gahlawat, Geeta; Srivastava, Ashok K

2013-06-01

In the present investigation, batch cultivation of Azohydromonas australica DSM 1124 was carried out in a bioreactor for growth associated PHB production. The observed batch PHB production kinetics data was then used for the development of a mathematical model which adequately described the substrate limitation and inhibition during the cultivation. The statistical validity test demonstrated that the proposed mathematical model predictions were significant at 99% confidence level. The model was thereafter extrapolated to fed-batch to identify various nutrients feeding regimes during the bioreactor cultivation to improve the PHB accumulation. The distinct capability of the mathematical model to predict highly dynamic fed-batch cultivation strategies was demonstrated by experimental implementation of two fed-batch cultivation strategies. A significantly high PHB concentration of 22.65 g/L & an overall PHB content of 76% was achieved during constant feed rate fed-batch cultivation which is the highest PHB content reported so far using A. australica. Copyright © 2013 Elsevier Ltd. All rights reserved.

Data-driven approach for assessing utility of medical tests using electronic medical records.

Science.gov (United States)

Skrøvseth, Stein Olav; Augestad, Knut Magne; Ebadollahi, Shahram

2015-02-01

To precisely define the utility of tests in a clinical pathway through data-driven analysis of the electronic medical record (EMR). The information content was defined in terms of the entropy of the expected value of the test related to a given outcome. A kernel density classifier was used to estimate the necessary distributions. To validate the method, we used data from the EMR of the gastrointestinal department at a university hospital. Blood tests from patients undergoing surgery for gastrointestinal surgery were analyzed with respect to second surgery within 30 days of the index surgery. The information content is clearly reflected in the patient pathway for certain combinations of tests and outcomes. C-reactive protein tests coupled to anastomosis leakage, a severe complication show a clear pattern of information gain through the patient trajectory, where the greatest gain from the test is 3-4 days post index surgery. We have defined the information content in a data-driven and information theoretic way such that the utility of a test can be precisely defined. The results reflect clinical knowledge. In the case we used the tests carry little negative impact. The general approach can be expanded to cases that carry a substantial negative impact, such as in certain radiological techniques. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Batched Tile Low-Rank GEMM on GPUs

KAUST Repository

Charara, Ali

2018-02-01

Dense General Matrix-Matrix (GEMM) multiplication is a core operation of the Basic Linear Algebra Subroutines (BLAS) library, and therefore, often resides at the bottom of the traditional software stack for most of the scientific applications. In fact, chip manufacturers give a special attention to the GEMM kernel implementation since this is exactly where most of the high-performance software libraries extract the hardware performance. With the emergence of big data applications involving large data-sparse, hierarchically low-rank matrices, the off-diagonal tiles can be compressed to reduce the algorithmic complexity and the memory footprint. The resulting tile low-rank (TLR) data format is composed of small data structures, which retains the most significant information for each tile. However, to operate on low-rank tiles, a new GEMM operation and its corresponding API have to be designed on GPUs so that it can exploit the data sparsity structure of the matrix while leveraging the underlying TLR compression format. The main idea consists in aggregating all operations onto a single kernel launch to compensate for their low arithmetic intensities and to mitigate the data transfer overhead on GPUs. The new TLR GEMM kernel outperforms the cuBLAS dense batched GEMM by more than an order of magnitude and creates new opportunities for TLR advance algorithms.

Architectural Strategies for Enabling Data-Driven Science at Scale

Science.gov (United States)

Crichton, D. J.; Law, E. S.; Doyle, R. J.; Little, M. M.

2017-12-01

architectural strategies, including a 2015-2016 NASA AIST Study on Big Data, for evolving scientific research towards massively distributed data-driven discovery. It will include example use cases across earth science, planetary science, and other disciplines.

Employment relations: A data driven analysis of job markets using online job boards and online professional networks

CSIR Research Space (South Africa)

Marivate, Vukosi N

2017-08-01

Full Text Available Data from online job boards and online professional networks present an opportunity to understand job markets as well as how professionals transition from one job/career to another. We propose a data driven approach to begin to understand a slice...

From batch to continuous extractive distillation using thermodynamic insight: class 1.0-2 case B

OpenAIRE

Shen, Weifeng; Benyounes, Hassiba; Gerbaud, Vincent

2011-01-01

A systematic feasibility analysis is presented for the separation azeotropic mixtures by batch and continuous extractive distillation. Based on batch feasibility knowledge, batch and continuous separation feasibility is studied under reflux ratio and entrainer flow-rate for the ternary system chloroform-vinyl acetate-butyl acetate, which belongs to the class 1.0-2 separating maximum boiling temperature azeotropes using a heavy entrainer. How information on feasibility of batch mode could be e...

The power of event-driven analytics in Large Scale Data Processing

CERN Multimedia

CERN. Geneva; Marques, Paulo

2011-01-01

FeedZai is a software company specialized in creating high-­‐throughput low-­‐latency data processing solutions. FeedZai develops a product called "FeedZai Pulse" for continuous event-­‐driven analytics that makes application development easier for end users. It automatically calculates key performance indicators and baselines, showing how current performance differ from previous history, creating timely business intelligence updated to the second. The tool does predictive analytics and trend analysis, displaying data on real-­‐time web-­‐based graphics. In 2010 FeedZai won the European EBN Smart Entrepreneurship Competition, in the Digital Models category, being considered one of the "top-­‐20 smart companies in Europe". The main objective of this seminar/workshop is to explore the topic for large-­‐scale data processing using Complex Event Processing and, in particular, the possible uses of Pulse in...

Fed-batch CHO cell culture for lab-scale antibody production

DEFF Research Database (Denmark)

Fan, Yuzhou; Ley, Daniel; Andersen, Mikael Rørdam

2017-01-01

Fed-batch culture is the most commonly used upstream process in industry today for recombinant monoclonal antibody production using Chinese hamster ovary cells. Developing and optimizing this process in the lab is crucial for establishing process knowledge, which enable rapid and predictable tech......-transfer to manufacturing scale. In this chapter, we will describe stepwise how to carry out fed-batch CHO cell culture for lab-scale antibody production....

Improving cellulase productivity of Penicillium oxalicum RE-10 by repeated fed-batch fermentation strategy.

Science.gov (United States)

Han, Xiaolong; Song, Wenxia; Liu, Guodong; Li, Zhonghai; Yang, Piao; Qu, Yinbo

2017-03-01

Medium optimization and repeated fed-batch fermentation were performed to improve the cellulase productivity by P. oxalicum RE-10 in submerged fermentation. First, Plackett-Burman design (PBD) and central composite design (CCD) were used to optimize the medium for cellulase production. PBD demonstrated wheat bran and NaNO 3 had significant influences on cellulase production. The CCD results showed the maximum filter paper activity (FPA) production of 8.61U/mL could be achieved in Erlenmeyer flasks. The maximal FPA reached 12.69U/mL by submerged batch fermentation in a 7.5-L stirred tank, 1.76-fold higher than that on the original medium. Then, the repeated fed-batch fermentation strategy was performed successfully for increasing the cellulase productivity from 105.75U/L/h in batch fermentation to 158.38U/L/h. The cellulase activity and the glucan conversion of delignined corn cob residue hydrolysis had no significant difference between the enzymes sampled from different cycles of the repeated fed-batch fermentation and that from batch culture. Copyright © 2016 Elsevier Ltd. All rights reserved.

Heat transfer system safety: Comparing the effectiveness of batch venting and a light-ends removal kit (LERK

Directory of Open Access Journals (Sweden)

Christopher Ian Wright

2014-11-01

Full Text Available Heat transfer fluids (HTF should be analysed at least once per year to determine the extent of thermal degradation. Under normal operating conditions, mineral-based HTFs will thermally degrade and the bonds between hydrocarbons break to form shorter-chain hydrocarbons known as “light-ends”. These light-ends accumulate in a HTF system and present a future potential fire risk. Light-ends can be removed from a HTF system via a batch vent or installation of a temporary or permanently installed light-ends removal kit (LERK. Data was collected prior to and following batch venting or installation of a LERK. The main study parameter was closed flash temperature as open flash temperature and fire point did not change considerably. Analysis showed that both methods increased closed flash temperature in excess of 130 °C three months after the intervention, so both methods were deemed effective. Data showed that the percentage change achieved with the LERK, compared to batch venting, was 2-fold higher at three months and 10-fold higher at 6 months. The duration of effect was longer with the LERK with closed flash temperature being stable and consistently above 130 °C for 50 months after being permanently installed. This case highlights the effectiveness of a permanently fitted LERK which is effective for the longer-term control of closed flash temperature. However, mobile LERKs could be an option for manufacturers looking to manage closed flash temperature on a shorter-term basis or as an alternative to batch venting.

Optimization of heat-liberating batches for ash residue stabilization

International Nuclear Information System (INIS)

Karlina, O.K.; Varlackova, G.A.; Ojovan, M.I.; Tivansky, V.M.; Dmitriev, S.A.

1999-01-01

The ash residue obtained after incineration of solid radioactive waste is a dusting poly-dispersed powder like material that contains radioactive nuclides ( 137 Cs, 90 Sr, 239 Pu, hor ( ellipsis)). Specific radioactivity of the ash can be about 10 5 --10 7 Bq/kg. In order to dispose of the ash, residue shall be stabilized by producing a monolith material. The ash residue can be either vitrified or stabilized into a ceramic matrix. For this purpose the ash residue is mixed with fluxing agents followed by melting of obtained composition in the different type melters. As a rule this requires both significant energy consumption and complex melting equipment. A stabilization technology of ash residue was proposed recently by using heat liberating batches-compositions with redox properties. The ash residue is melted due to exothermic chemical reactions in the mixture with heat-liberating batch that occur with considerable release of heat. Stabilization method has three stages: (1) preparation of a mixture of heating batch and ash residue with or without glass forming batch (frit); (2) ignition and combustion of mixed composition; (3) cooling (quenching) of obtained vitreous material. Combustion of mixed composition occurs in the form of propagation of reacting wave. The heat released during exothermic chemical reactions provides melting of ash residue components and production of glass-like phase. The final product consists of a glass like matrix with embedded crystalline inclusions of infusible ash residue components

Production of carotenoids and lipids by Rhodococcus opacus PD630 in batch and fed-batch culture.

Science.gov (United States)

Thanapimmetha, Anusith; Suwaleerat, Tharatron; Saisriyoot, Maythee; Chisti, Yusuf; Srinophakun, Penjit

2017-01-01

Production of carotenoids by Rhodococcus opacus PD630 is reported. A modified mineral salt medium formulated with glycerol as an inexpensive carbon source was used for the fermentation. Ammonium acetate was the nitrogen source. A dry cell mass concentration of nearly 5.4 g/L could be produced in shake flasks with a carotenoid concentration of 0.54 mg/L. In batch culture in a 5 L bioreactor, without pH control, the maximum dry biomass concentration was ~30 % lower than in shake flasks and the carotenoids concentration was 0.09 mg/L. Both the biomass concentration and the carotenoids concentration could be raised using a fed-batch operation with a feed mixture of ammonium acetate and acetic acid. With this strategy, the final biomass concentration was 8.2 g/L and the carotenoids concentration was 0.20 mg/L in a 10-day fermentation. A control of pH proved to be unnecessary for maximizing the production of carotenoids in this fermentation.

A CATASTROPHIC-CUM-RESTORATIVE QUEUING SYSTEM WITH CORRELATED BATCH ARRIVALS AND VARIABLE CAPACITY

Directory of Open Access Journals (Sweden)

Rakesh Kumar

2008-07-01

Full Text Available In this paper, we study a catastrophic-cum-restorative queuing system with correlated batch arrivals and service in batches of variable sizes. We perform the transient analysis of the queuing model. We obtain the Laplace Transform of the probability generating function of system size. Finally, some particular cases of the model have been derived and discussed. Keywords: Queue length, Catastrophes, Correlated batch arrivals, Broadband services, Variable service capacity, and Restoration.

Verification Of The Defense Waste Processing Facility's (DWPF) Process Digestion Methods For The Sludge Batch 8 Qualification Sample

International Nuclear Information System (INIS)

Click, D. R.; Edwards, T. B.; Wiedenman, B. J.; Brown, L. W.

2013-01-01

This report contains the results and comparison of data generated from inductively coupled plasma atomic emission spectroscopy (ICP-AES) analysis of Aqua Regia (AR), Sodium Peroxide/Sodium Hydroxide Fusion Dissolution (PF) and Cold Chem (CC) method digestions and Cold Vapor Atomic Absorption analysis of Hg digestions from the DWPF Hg digestion method of Sludge Batch 8 (SB8) Sludge Receipt and Adjustment Tank (SRAT) Receipt and SB8 SRAT Product samples. The SB8 SRAT Receipt and SB8 SRAT Product samples were prepared in the SRNL Shielded Cells, and the SRAT Receipt material is representative of the sludge that constitutes the SB8 Batch or qualification composition. This is the sludge in Tank 51 that is to be transferred into Tank 40, which will contain the heel of Sludge Batch 7b (SB7b), to form the SB8 Blend composition

Data-driven nutrient analysis and reality check: Human inputs, catchment delivery and management effects

Science.gov (United States)

Destouni, G.

2017-12-01

Measures for mitigating nutrient loads to aquatic ecosystems should have observable effects, e.g, in the Baltic region after joint first periods of nutrient management actions under the Baltic Sea Action Plan (BASP; since 2007) and the EU Water Framework Directive (WFD; since 2009). Looking for such observable effects, all openly available water and nutrient monitoring data since 2003 are compiled and analyzed for Sweden as a case study. Results show that hydro-climatically driven water discharge dominates the determination of waterborne loads of both phosphorus and nitrogen. Furthermore, the nutrient loads and water discharge are all similarly well correlated with the ecosystem status classification of Swedish water bodies according to the WFD. Nutrient concentrations, which are hydro-climatically correlated and should thus reflect human effects better than loads, have changed only slightly over the study period (2003-2013) and even increased in moderate-to-bad status waters, where the WFD and BSAP jointly target nutrient decreases. These results indicate insufficient distinction and mitigation of human-driven nutrient components by the internationally harmonized applications of both the WFD and the BSAP. Aiming for better general identification of such components, nutrient data for the large transboundary catchments of the Baltic Sea and the Sava River are compared. The comparison shows cross-regional consistency in nutrient relationships to driving hydro-climatic conditions (water discharge) for nutrient loads, and socio-economic conditions (population density and farmland share) for nutrient concentrations. A data-driven screening methodology is further developed for estimating nutrient input and retention-delivery in catchments. Its first application to nested Sava River catchments identifies characteristic regional values of nutrient input per area and relative delivery, and hotspots of much larger inputs, related to urban high-population areas.

A Job Monitoring and Accounting Tool for the LSF Batch System

International Nuclear Information System (INIS)

Sarkar, Subir; Taneja, Sonia

2011-01-01

This paper presents a web based job monitoring and group-and-user accounting tool for the LSF Batch System. The user oriented job monitoring displays a simple and compact quasi real-time overview of the batch farm for both local and Grid jobs. For Grid jobs the Distinguished Name (DN) of the Grid users is shown. The overview monitor provides the most up-to-date status of a batch farm at any time. The accounting tool works with the LSF accounting log files. The accounting information is shown for a few pre-defined time periods by default. However, one can also compute the same information for any arbitrary time window. The tool already proved to be an extremely useful means to validate more extensive accounting tools available in the Grid world. Several sites have already been using the present tool and more sites running the LSF batch system have shown interest. We shall discuss the various aspects that make the tool essential for site administrators and end-users alike and outline the current status of development as well as future plans.

Batch variation between branchial cell cultures: An analysis of variance

DEFF Research Database (Denmark)

Hansen, Heinz Johs. Max; Grosell, M.; Kristensen, L.

2003-01-01

We present in detail how a statistical analysis of variance (ANOVA) is used to sort out the effect of an unexpected batch-to-batch variation between cell cultures. Two separate cultures of rainbow trout branchial cells were grown on permeable filtersupports ("inserts"). They were supposed...... and introducing the observed difference between batches as one of the factors in an expanded three-dimensional ANOVA, we were able to overcome an otherwisecrucial lack of sufficiently reproducible duplicate values. We could thereby show that the effect of changing the apical medium was much more marked when...... the radioactive lipid precursors were added on the apical, rather than on the basolateral, side. Theinsert cell cultures were obviously polarized. We argue that it is not reasonable to reject troublesome experimental results, when we do not know a priori that something went wrong. The ANOVA is a very useful...

Biological treatment of PAH-contaminated sediments in a Sequencing Batch Reactor

International Nuclear Information System (INIS)

Chiavola, Agostina; Baciocchi, Renato; Gavasci, Renato

2010-01-01

The technical feasibility of a sequential batch process for the biological treatment of sediments contaminated by polycyclic aromatic hydrocarbons (PAHs) was evaluated through an experimental study. A bench-scale Sediment Slurry Sequencing Batch Reactor (SS-SBR) was fed with river sediments contaminated by a PAH mixture made by fluorene, anthracene, pyrene and crysene. The process performance was evaluated under different operating conditions, obtained by modifying the influent organic load, the feed composition and the hydraulic residence time. Measurements of the Oxygen Uptake Rates (OURs) provided useful insights on the biological kinetics occurring in the SS-SBR, suggesting the minimum applied cycle time-length of 7 days could be eventually halved, as also confirmed by the trend observed in the volatile solid and total organic carbon data. The removal efficiencies gradually improved during the SS-SBR operation, achieving at the end of the study rather constant removal rates above 80% for both 3-rings PAHs (fluorene and anthracene) and 4-ring PAHs (pyrene and crysene) for an inlet total PAH concentration of 70 mg/kg as dry weight (dw).

Conjugated Polymers Via Direct Arylation Polymerization in Continuous Flow: Minimizing the Cost and Batch-to-Batch Variations for High-Throughput Energy Conversion

DEFF Research Database (Denmark)

Gobalasingham, Nemal S.; Carlé, Jon Eggert; Krebs, Frederik C

2017-01-01

of high-performance materials. To demonstrate the usefulness of the method, DArP-prepared PPDTBT via continuous flow synthesis is employed for the preparation of indium tin oxide (ITO)-free and flexible roll-coated solar cells to achieve a power conversion efficiency of 3.5% for 1 cm2 devices, which...... is comparable to the performance of PPDTBT polymerized through Stille cross coupling. These efforts demonstrate the distinct advantages of the continuous flow protocol with DArP avoiding use of toxic tin chemicals, reducing the associated costs of polymer upscaling, and minimizing batch-to-batch variations...

Data-driven discovery of Koopman eigenfunctions using deep learning

Science.gov (United States)

Lusch, Bethany; Brunton, Steven L.; Kutz, J. Nathan

2017-11-01