Quantitative Analysis of the Growth of Salmonella Stanley during Alfalfa Sprouting and Evaluation of Enterobacter aerogenes as Its Surrogate

2007 ◽  
Vol 70 (2) ◽  
pp. 316-322 ◽  
Author(s):  
BIN LIU ◽  
DONALD W. SCHAFFNER
Keyword(s):  
Nalidixic Acid ◽  
Growth Rates ◽  
Irrigation Water ◽  
Large Scale ◽  
Food Poisoning ◽  
Enterobacter Aerogenes ◽  
Process Data ◽  
Inoculum Concentration ◽  
Logistic Regression Models ◽  
Water Testing

Raw seed sprouts have been implicated in several food poisoning outbreaks in the last 10 years. Few studies have included investigations of factors influencing the effectiveness of testing spent irrigation water, and in no studies to date has a nonpathogenic surrogate been identified as suitable for large-scale irrigation water testing trials. Alfalfa seeds were inoculated with Salmonella Stanley or its presumptive surrogate (nalidixic acid–resistant Enterobacter aerogenes) at three concentrations (∼3, ∼30, and ∼300 CFU/g) and were then transferred into either flasks or a bench top–scale sprouting chamber. Microbial concentrations were determined in seeds, sprouts, and irrigation water at various times during a 4-day sprouting process. Data were fit to logistic regression models, and growth rates and maximum concentrations were compared using the generalized linear model procedure of SAS. No significant differences in growth rates were observed among samples taken from flasks or the chamber. Microbial concentrations in irrigation water were not significantly different from concentrations in sprout samples obtained at the same time. E. aerogenes concentrations were similar to those of Salmonella Stanley at corresponding time points for all three inoculum concentrations. Growth rates were also constant regardless of inoculum concentration or strain, except that lower inoculum concentrations resulted in lower final concentrations proportional to their initial concentrations. This research demonstrated that a nonpathogenic easy-to-isolate surrogate (nalidixic acid–resistant E. aerogenes) provides results similar to those obtained with Salmonella Stanley, supporting the use of this surrogate in future large-scale experiments.

Mathematical Modeling and Assessment of Microbial Migration during the Sprouting of Alfalfa in Trays in a Nonuniformly Contaminated Seed Batch Using Enterobacter aerogenes as a Surrogate for Salmonella Stanley

2007 ◽  
Vol 70 (11) ◽  
pp. 2602-2605 ◽  
Author(s):  
BIN LIU ◽  
DONALD W. SCHAFFNER
Keyword(s):  
Irrigation Water ◽  
Membrane Filtration ◽  
Microbial Contamination ◽  
Food Poisoning ◽  
Enterobacter Aerogenes ◽  
Probability Of Detection ◽  
Microbial Populations ◽  
Water Sampling ◽  
The Past ◽  
Low Concentrations

Raw seed sprouts have been implicated in several food poisoning outbreaks in the past 10 years. The U.S. Food and Drug Administration recommends that sprout growers use interventions (such as testing of spent irrigation water) to control the presence of pathogens in the finished product. During the sprouting process, initially low concentrations of pathogen may increase, and contamination may spread within a batch of sprouting seeds. A model of pathogen growth as a function of time and distance from the contamination spot during the sprouting of alfalfa in trays has been developed with Enterobacter aerogenes. The probability of detecting contamination was assessed by logistic regression at various time points and distances by sampling from sprouts or irrigation water. Our results demonstrate that microbial populations and possibility of detection were greatly reduced at distances of ≥20 cm from the point of contamination in a seed batch during tray sprouting; however, the probability of detecting microbial contamination at distances less than 10 cm from the point of inoculation was almost 100% at the end of the sprouting process. Our results also show that sampling irrigation water, especially large volumes of water, is highly effective at detecting contamination: by collecting 100 ml of irrigation water for membrane filtration, the probability of detection was increased by three to four times during the first6hof seed germination. Our findings have quantified the degree to which a small level of contamination will spread throughout a tray of sprouting alfalfa seeds and subsequently be detected by either sprout or irrigation water sampling.

scholarly journals Using process data to understand problem-solving strategies and processes for drag-and-drop items in a large-scale mathematics assessment

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Yang Jiang ◽  
Tao Gong ◽  
Luis E. Saldivia ◽  
Gabrielle Cayton-Hodges ◽  
Christopher Agard
Keyword(s):  
Problem Solving ◽  
Time Allocation ◽  
Large Scale ◽  
Solution Process ◽  
Process Data ◽  
Mathematics Assessments ◽  
Assessment Design ◽  
Eighth Grade Students ◽  
Problem Solving Strategies ◽  
Drag And Drop

AbstractIn 2017, the mathematics assessments that are part of the National Assessment of Educational Progress (NAEP) program underwent a transformation shifting the administration from paper-and-pencil formats to digitally-based assessments (DBA). This shift introduced new interactive item types that bring rich process data and tremendous opportunities to study the cognitive and behavioral processes that underlie test-takers’ performances in ways that are not otherwise possible with the response data alone. In this exploratory study, we investigated the problem-solving processes and strategies applied by the nation’s fourth and eighth graders by analyzing the process data collected during their interactions with two technology-enhanced drag-and-drop items (one item for each grade) included in the first digital operational administration of the NAEP’s mathematics assessments. Results from this research revealed how test-takers who achieved different levels of accuracy on the items engaged in various cognitive and metacognitive processes (e.g., in terms of their time allocation, answer change behaviors, and problem-solving strategies), providing insights into the common mathematical misconceptions that fourth- and eighth-grade students held and the steps where they may have struggled during their solution process. Implications of the findings for educational assessment design and limitations of this research are also discussed.

scholarly journals Advantageous Description of Short Fatigue Crack Growth Rates in Austenitic Stainless Steels with Distinct Properties

Metals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 475
Author(s):  
Lukáš Trávníček ◽  
Ivo Kuběna ◽  
Veronika Mazánová ◽  
Tomáš Vojtek ◽  
Jaroslav Polák ◽  
...  
Keyword(s):  
Fatigue Crack ◽  
Fatigue Crack Growth ◽  
Crack Growth ◽  
Stainless Steels ◽  
Growth Rates ◽  
Large Scale ◽  
J Integral ◽  
Scale Yielding ◽  
Residual Fatigue ◽  
Crack Growth Rates

In this work two approaches to the description of short fatigue crack growth rate under large-scale yielding condition were comprehensively tested: (i) plastic component of the J-integral and (ii) Polák model of crack propagation. The ability to predict residual fatigue life of bodies with short initial cracks was studied for stainless steels Sanicro 25 and 304L. Despite their coarse microstructure and very different cyclic stress–strain response, the employed continuum mechanics models were found to give satisfactory results. Finite element modeling was used to determine the J-integrals and to simulate the evolution of crack front shapes, which corresponded to the real cracks observed on the fracture surfaces of the specimens. Residual fatigue lives estimated by these models were in good agreement with the number of cycles to failure of individual test specimens strained at various total strain amplitudes. Moreover, the crack growth rates of both investigated materials fell onto the same curve that was previously obtained for other steels with different properties. Such a “master curve” was achieved using the plastic part of J-integral and it has the potential of being an advantageous tool to model the fatigue crack propagation under large-scale yielding regime without a need of any additional experimental data.

scholarly journals Selecting Optimal Combination of Data Channels for Semantic Segmentation in City Information Modelling (CIM)

2021 ◽  
Vol 13 (7) ◽  
pp. 1367
Author(s):  
Yuanzhi Cai ◽  
Hong Huang ◽  
Kaiyang Wang ◽  
Cheng Zhang ◽  
Lei Fan ◽  
...  
Keyword(s):  
Large Scale ◽  
Semantic Segmentation ◽  
Optimal Combination ◽  
Reconstruction Technique ◽  
Process Data ◽  
Redundant Data ◽  
Multiple Data ◽  
Information Models ◽  
Efficient Data

Over the last decade, a 3D reconstruction technique has been developed to present the latest as-is information for various objects and build the city information models. Meanwhile, deep learning based approaches are employed to add semantic information to the models. Studies have proved that the accuracy of the model could be improved by combining multiple data channels (e.g., XYZ, Intensity, D, and RGB). Nevertheless, the redundant data channels in large-scale datasets may cause high computation cost and time during data processing. Few researchers have addressed the question of which combination of channels is optimal in terms of overall accuracy (OA) and mean intersection over union (mIoU). Therefore, a framework is proposed to explore an efficient data fusion approach for semantic segmentation by selecting an optimal combination of data channels. In the framework, a total of 13 channel combinations are investigated to pre-process data and the encoder-to-decoder structure is utilized for network permutations. A case study is carried out to investigate the efficiency of the proposed approach by adopting a city-level benchmark dataset and applying nine networks. It is found that the combination of IRGB channels provide the best OA performance, while IRGBD channels provide the best mIoU performance.

scholarly journals Projecting Basin-Scale Distributed Irrigation and Domestic Water Demands and Values: A Generic Method for Large-Scale Modeling

2016 ◽  
Vol 02 (04) ◽  
pp. 1650023 ◽  
Author(s):  
Noémie Neverre ◽  
Patrice Dumas
Keyword(s):  
Irrigation Water ◽  
Large Scale ◽  
Potential Evapotranspiration ◽  
Demand Curve ◽  
Economic Valuation ◽  
Economic Value ◽  
Expansion Method ◽  
Domestic Water ◽  
Water Demands ◽  
Basin Scale

This paper presents a methodology to project irrigation and domestic water demands on a regional to global scale, in terms of both quantity and economic value. Projections are distributed at the water basin scale. Irrigation water demand is projected under climate change. It is simply computed as the difference between crop potential evapotranspiration for the different stages of the growing season and available precipitation. Irrigation water economic value is based on a yield comparison approach between rainfed and irrigated crops using average yields. For the domestic sector, we project the combined effects of demographic growth, economic development and water cost evolution on future demands. The method consists in building three-part inverse demand functions in which volume limits of the blocks evolve with the level of GDP per capita. The value of water along the demand curve is determined from price-elasticity, price and demand data from the literature, using the point-expansion method, and from water cost data. This generic methodology can be easily applied to large-scale regions, in particular developing regions where reliable data are scarce. As an illustration, it is applied to Algeria, at the 2050 horizon, for demands associated to reservoirs. Our results show that domestic demand is projected to become a major water consumption sector. The methodology is meant to be integrated into large-scale hydroeconomic models, to determine inter-sectorial and inter-temporal water allocation based on economic valuation.

Model-Based Analysis of Transient Behavior of Large Scale CFB Boilers

2003 ◽  
Author(s):  
Ari Kettunen ◽  
Timo Hyppa¨nen ◽  
Ari-Pekka Kirkinen ◽  
Esa Maikkola
Keyword(s):  
Flow Rate ◽  
Large Scale ◽  
Air Flow ◽  
Model Parameters ◽  
Air Flow Rate ◽  
Process Data ◽  
Cfb Boiler ◽  
Flow Rates ◽  
Load Change ◽  
Secondary Air

The main objective of this study was to investigate the load change capability and effect of the individual control variables, such as fuel, primary air and secondary air flow rates, on the dynamics of large-scale CFB boilers. The dynamics of the CFB process were examined by dynamic process tests and by simulation studies. A multi-faceted set of transient process tests were performed at a commercial 235 MWe CFB unit. Fuel reactivity and interaction between gas flow rates, solid concentration profiles and heat transfer were studied by step changes of the following controllable variables: fuel feed rate, primary air flow rate, secondary air flow rate and primary to secondary air flow ratio. Load change performance was tested using two different types of tests: open and closed loop load changes. A tailored dynamic simulator for the CFB boiler was built and fine-tuned by determining the model parameters and by validating the models of each process component against measured process data of the transient test program. The know-how about the boiler dynamics obtained from the model analysis and the developed CFB simulator were utilized in designing the control systems of three new 262 MWe CFB units, which are now under construction. Further, the simulator was applied for the control system development and transient analysis of the supercritical OTU CFB boiler.

scholarly journals Analysis of Factors Influencing Effective Utilization Coefficient of Irrigation Water in the Manas River Basin

Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 189
Author(s):  
Lili Yang ◽  
Tong Heng ◽  
Guang Yang ◽  
Xinchen Gu ◽  
Jiaxin Wang ◽  
...  
Keyword(s):  
Irrigation Water ◽  
Large Scale ◽  
Grey Model ◽  
Analytic Hierarchy ◽  
Effective Utilization ◽  
Factors Affecting ◽  
Factors Influencing ◽  
Utilization Coefficient ◽  
Using Data ◽  
Effective Use

The factors influencing the effective utilization coefficient of irrigation water are not understood well. It is usually considered that this coefficient is lower in areas with large-scale irrigation. With this background, we analyzed the effective utilization coefficient of irrigation water using the analytic hierarchy process using data from 2014 to 2019 in Shihezi City, Xinjiang. The weights of the influencing factors on the effective utilization coefficient of irrigation water in different irrigation areas were analyzed. Predictions of the coefficient’s values for different years were made by understanding the trends based on the grey model. The results show that the scale of the irrigation area is not the only factor determining the effective utilization coefficient of irrigation water. Irrigation technology, organizational integrity, crop types, water price management, local economic level, and channel seepage prevention are the most critical factors affecting the effective use of irrigation water. The grey model prediction results show that the effective utilization coefficient of farmland irrigation water will continuously increase and reach 0.7204 in 2029. This research can serve as a reference for government authorities to make scientific decisions on water-saving projects in irrigation districts in terms of management, operation, and investment.

45 Application of Machine Learning Models to Thermal Burn Patient Outcome Predictions in the Aftermath of a Nuclear Event

2021 ◽  
Vol 42 (Supplement_1) ◽  
pp. S33-S34
Author(s):  
Morgan A Taylor ◽  
Randy D Kearns ◽  
Jeffrey E Carter ◽  
Mark H Ebell ◽  
Curt A Harris
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Length Of Stay ◽  
Regression Models ◽  
Large Scale ◽  
Prediction Models ◽  
Burn Patients ◽  
Thermal Burn ◽  
Logistic Regression Models ◽  
Burn Patient

Abstract Introduction A nuclear disaster would generate an unprecedented volume of thermal burn patients from the explosion and subsequent mass fires (Figure 1). Prediction models characterizing outcomes for these patients may better equip healthcare providers and other responders to manage large scale nuclear events. Logistic regression models have traditionally been employed to develop prediction scores for mortality of all burn patients. However, other healthcare disciplines have increasingly transitioned to machine learning (ML) models, which are automatically generated and continually improved, potentially increasing predictive accuracy. Preliminary research suggests ML models can predict burn patient mortality more accurately than commonly used prediction scores. The purpose of this study is to examine the efficacy of various ML methods in assessing thermal burn patient mortality and length of stay in burn centers. Methods This retrospective study identified patients with fire/flame burn etiologies in the National Burn Repository between the years 2009 – 2018. Patients were randomly partitioned into a 67%/33% split for training and validation. A random forest model (RF) and an artificial neural network (ANN) were then constructed for each outcome, mortality and length of stay. These models were then compared to logistic regression models and previously developed prediction tools with similar outcomes using a combination of classification and regression metrics. Results During the study period, 82,404 burn patients with a thermal etiology were identified in the analysis. The ANN models will likely tend to overfit the data, which can be resolved by ending the model training early or adding additional regularization parameters. Further exploration of the advantages and limitations of these models is forthcoming as metric analyses become available. Conclusions In this proof-of-concept study, we anticipate that at least one ML model will predict the targeted outcomes of thermal burn patient mortality and length of stay as judged by the fidelity with which it matches the logistic regression analysis. These advancements can then help disaster preparedness programs consider resource limitations during catastrophic incidents resulting in burn injuries.

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