scholarly journals Development of Tree Vigor Prediction Method at an Early Stage Based on Stem Hydraulic Conductance of Seedlings in Citrus Rootstocks

2011 ◽  
Vol 80 (4) ◽  
pp. 390-395 ◽  
Author(s):  
Mitsunori Iwasaki ◽  
Hiroshi Fukamachi ◽  
Keiko Satoh ◽  
Hirohisa Nesumi ◽  
Terutaka Yoshioka
Keyword(s):  
Early Stage ◽  
Prediction Method ◽  
Hydraulic Conductance

Studying the nonlinear properties and strain-rate sensitivity of SiC short fiber-reinforced Al matrix composites

2017 ◽  
Vol 24 (4) ◽  
pp. 521-529
Author(s):  
Junjie Ye ◽  
Yuanying Qiu ◽  
Yumin He ◽  
Juan Ma ◽  
Xinglong Zhang ◽  
...  
Keyword(s):  
Residual Stress ◽  
Composite Structures ◽  
Early Stage ◽  
Strain Analysis ◽  
Prediction Method ◽  
Short Fiber ◽  
Thermal Residual Stress ◽  
Fiber Reinforced ◽  
Nonlinear Properties ◽  
Stress Influences

AbstractStress-strain analysis has been an interesting issue for the mechanical design of composite structures. In this paper, a three-dimensional mechanical model based on generalized method of cells is presented to study the thermal residual stress and loading rates influence on the mechanical responses of short fiber-reinforced (SFR) composites. The effects of the fiber shape on the elastic constant of the SFR were investigated. To verify the prediction method, the calculated elastic modulus was compared with the results of finite element method. On this basis, a unified constitutive model is used to acquire the nonlinear properties of matrix materials. For comparison, SFR composites with and without consideration of thermal residual stress influences on the nonlinear responses are both considered. The results show that the distinct difference for SFR composites can be found at an early stage of loading. Meanwhile, the thermal residual stress influences on the mechanical behaviors present two characteristic stages.

scholarly journals Identification of Chemical Toxicity Using Ontology Information of Chemicals

2015 ◽  
Vol 2015 ◽  
pp. 1-5 ◽  
Author(s):  
Zhanpeng Jiang ◽  
Rui Xu ◽  
Changchun Dong
Keyword(s):  
Combinatorial Chemistry ◽  
Early Stage ◽  
Prediction Method ◽  
Previous Method ◽  
The Other ◽  
New Drugs ◽  
Chemical Toxicity ◽  
Synthetic Compounds ◽  
Limited Knowledge ◽  
Other Hand

With the advance of the combinatorial chemistry, a large number of synthetic compounds have surged. However, we have limited knowledge about them. On the other hand, the speed of designing new drugs is very slow. One of the key causes is the unacceptable toxicities of chemicals. If one can correctly identify the toxicity of chemicals, the unsuitable chemicals can be discarded in early stage, thereby accelerating the study of new drugs and reducing the R&D costs. In this study, a new prediction method was built for identification of chemical toxicities, which was based on ontology information of chemicals. By comparing to a previous method, our method is quite effective. We hope that the proposed method may give new insights to study chemical toxicity and other attributes of chemicals.

Creep-Fatigue Damage and Life Evaluation for Boiler Header Stub Weldment

2002 ◽  
Author(s):  
Takuya Ito ◽  
Isamu Nonaka ◽  
Hideo Umaki ◽  
Hidetaka Nishida ◽  
Shizuma Shintani
Keyword(s):  
Fatigue Damage ◽  
Early Stage ◽  
Crack Depth ◽  
Prediction Method ◽  
Fatigue Tests ◽  
Short Cracks ◽  
Micro Cracks ◽  
Creep Fatigue ◽  
Damage Process ◽  
Maximum Crack

In order to clarify the creep-fatigue damage process and to evaluate the creep-fatigue life for boiler 2.25Cr-1Mo header stub welds, a series of creep-fatigue tests were performed on partial mock-up specimens of actual plant under simulated plant loading conditions. Creep voids and micro-cracks occurred along the weld toes at an early stage of life and grew to form many short cracks. These short cracks grew both on the surface and through the wall of the stub tube and later coalesced to form one crack. It was proved that there was a correlation between the maximum crack depth and life ratio and also that there was a correlation between the maximum crack depth and the maximum crack length on the surface. A life prediction method was proposed based on these two correlations.

3D Temperature Field Prediction and Experiment Verification of Concrete Diaphragm of Box-Girder Bridge in Early Hardening Period

2013 ◽  
Vol 671-674 ◽  
pp. 947-951
Author(s):  
Yi Shu Zhou ◽  
Jing Hong Liu
Keyword(s):  
Temperature Field ◽  
Early Stage ◽  
Prediction Method ◽  
Hydration Heat ◽  
Measured Temperature ◽  
Element Analysis ◽  
Box Girder ◽  
Box Girder Bridge ◽  
Girder Bridge ◽  
Temperature Field Prediction

Diaphragm is often used in box-girder bridge for controlling warping stress such those in midspan or transferring strong bearing reactions such those in ends of span. The results of a crack investigation of box-girder bridges showed that vertical cracks can be found on most diaphragms and formed in early stage of the concrete hardening. Temperature caused by hydration heat is an important factor for these cracking. Therefore temperature field prediction for the diaphragm is significant to prevent the concrete diaphragm cracking. In this paper, three-dimensional finite element analysis software ANSYS is used for simulating 3D temperature field of diaphragm of the concrete box girder bridge in all stages of construction. By calculating space temperature field of the diaphragm in different time hydration heat of the law is analyzed, combined with the measured temperature a comparative analysis to verify the validity of the temperature prediction method is conducted. The results show that simulation method is effective and accurate enough to predict the time-varying temperature field of the diaphragm.

Sequential Prediction for Large-Scale Traffic Incident Duration: Application and Comparison of Survival Models

2020 ◽  
Vol 2674 (1) ◽  
pp. 79-93 ◽  
Author(s):  
Xiaobing Li ◽  
Jun Liu ◽  
Asad Khattak ◽  
Shashi Nambisan
Keyword(s):  
Large Scale ◽  
Early Stage ◽  
Prediction Method ◽  
Finite Mixture Model ◽  
Finite Mixture ◽  
Survival Models ◽  
Incident Management ◽  
Traffic Incident ◽  
Sequential Prediction ◽  
Incident Duration

A quick and accurate traffic incident duration prediction could greatly facilitate traffic incident management. However, at the very early stage of an incident, limited information is available for prediction. Information gathering for large-scale traffic incidents is a chronological process when a multi-agency response is required. At the early stage, information such as incident start time and roadway and weather conditions may be available, but information about response agencies and incident management solutions (e.g., lane closures) remains unknown. The objective of this study is to develop a sequential prediction method to handle the chronological process of incident information gathering. The method is based upon parametric survival modeling, which is often utilized to predict incident duration. This study took advantage of a unique incident database and identified over 600 large-scale incidents in the East Tennessee area from 2015 to 2016. A five-stage prediction method is proposed according to the chronological process by which information becomes available during incident operations. Using the data, this study compared three survival models: frailty model, multilevel mixed-effects model, and finite mixture model. Generally, with more information becoming available for modeling from the first to the last stage, the models’ performance improved according to the root mean square error and mean absolute percent error. The finite mixture model outperforms the other two models and its mean absolute percentage error is between 10% and 15%. Incident-associated factors at each stage are discussed and implications based on the study outcomes are also covered in the paper.

scholarly journals A Thermal-Based Fuel-Prediction Method for Intelligent Fire Extinguisher in an Indoor Environment

2021 ◽  
Vol 15 (3) ◽  
pp. 362-373
Author(s):  
Teerapong Suejantra ◽  
Kosin Chamnongthai
Keyword(s):  
Indoor Environment ◽  
Short Term Memory ◽  
Early Stage ◽  
Prediction Method ◽  
Thermal Image ◽  
Fire Extinguisher ◽  
Short Term ◽  
Long Short Term Memory ◽  
Fire Flame

Classification of fuel in the early stage of fire is important to choose the appropriate type of extinguisher for extinguishing fire. This paper proposes a method of fuel prediction based on heat information for intelligent fire extinguisher in an indoor environment. Fire flame in the early stage is first detected based on patterns of differences between consecutive thermal image frames in which temperature grows up rapidly and reveals a sharp positive slope. Then candidate flame boundaries are detected in the thermal image frames during the early stage, and boundary matching is performed among the frames. These matched boundaries are classified as fire flame and fuel class based on LSTM (Long short-term memory) for extinguisher selection. Experiments were performed with 300 samples for classification into four classes of fuel, and the results based on 9:1 training and testing ratio showed 92.142% accuracy.

scholarly journals Basic prediction methodology for covid-19: estimation and sensitivity considerations

2020 ◽  
Author(s):  
Tom Britton
Keyword(s):  
Preventive Measures ◽  
Early Stage ◽  
Prediction Method ◽  
Important Conclusion ◽  
Fatality Risk ◽  
Estimation And Prediction ◽  
Simple Estimation ◽  
Carry Over ◽  
Basic Prediction ◽  
High Uncertainty

SummaryThe purpose of the present paper is to present simple estimation and prediction methods for basic quantities in an emerging epidemic like the ongoing covid-10 pandemic. The simple methods have the advantage that relations between basic quantities become more transparent, thus shedding light to which quantities have biggest impact on predictions, with the additional conclusion that uncertainties in these quantities carry over to high uncertainty also in predictions.A simple non-parametric prediction method for future cumulative case fatalities, as well as future cumulative incidence of infections (assuming a given infection fatality risk f), is presented. The method uses cumulative reported case fatalities up to present time as input data. It is also described how the introduction of preventive measures of a given magnitude ρ will affect the two incidence predictions, using basic theory of epidemic models. This methodology is then reversed, thus enabling estimation of the preventive magnitude ρ, and of the resulting effective reproduction number RE. However, the effects of preventive measures only start affecting case fatalities some 3-4 weeks later, so estimates are only available after this time has elapsed. The methodology is applicable in the early stage of an outbreak, before, say, 10% of the community have been infected.Beside giving simple estimation and prediction tools for an ongoing epidemic, another important conclusion lies in the observation that the two quantities f (infection fatality risk) and ρ (the magnitude of preventive measures) have very big impact on predictions. Further, both of these quantities currently have very high uncertainty: current estimates of f lie in the range 0.2% up to 2% ([9], [7]), and the overall effect of several combined preventive measures is clearly very uncertain.The two main findings from the paper are hence that, a) any prediction containing f, and/or some preventive measures, contain a large amount of uncertainty (which is usually not acknowledged well enough), and b) obtaining more accurate estimates of in particular f, should be highly prioritized. Seroprevalence testing of random samples in a community where the epidemic has ended are urgently needed.

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