Network Cleansing

2009 ◽  
pp. 80-97 ◽  
Author(s):  
Paolo Marcatili ◽  
Anna Tramontano
Keyword(s):  
Experimental Data ◽  
Computational Methods ◽  
Gold Standard ◽  
State Of The Art ◽  
Experimental Studies ◽  
The State ◽  
Biological Information ◽  
Ppi Network

This chapter provides an overview of the current computational methods for PPI network cleansing. The authors first present the issue of identifying reliable PPIs from noisy and incomplete experimental data. Next, they address the questions of which are the expected results of the different experimental studies, of what can be defined as true interactions, of which kind of data are to be integrated in assigning reliability levels to PPIs and which gold standard should the authors use in training and testing PPI filtering methods. Finally, Marcatili and Tramontano describe the state of the art in the field, presenting the different classes of algorithms and comparing their results. The aim of the chapter is to guide the reader in the choice of the most convenient methods, experiments and integrative data and to underline the most common biases and errors to obtain a portrait of PINs which is not only reliable but as well able to correctly retrieve the biological information contained in such data.

Nonlaminar Behavior in Bearings: A Critical Review of the Literature

1974 ◽  
Vol 96 (1) ◽  
pp. 174-181 ◽  
Author(s):  
E. A. Saibel ◽  
N. A. Macken
Keyword(s):  
Flow Field ◽  
Critical Review ◽  
State Of The Art ◽  
Experimental Studies ◽  
The State ◽  
Basic Flow ◽  
Review Of The Literature ◽  
Design Data

The state-of-the-art of nonlaminar behavior in bearings is presented. Analytical and experimental studies are discussed. It is pointed out that the basic flow field is still not clearly understood, and that there is much more information needed before design data can be accurately predicted.

scholarly journals Sequence biases in CLIP experimental data are incorporated in protein RNA-binding models

2016 ◽  
Author(s):  
Yaron Orenstein ◽  
Raghavendra Hosur ◽  
Sean Simmons ◽  
Jadwiga Bienkoswka ◽  
Bonnie Berger
Keyword(s):  
Experimental Data ◽  
Binding Sites ◽  
Rna Binding ◽  
State Of The Art ◽  
The State ◽  
Enzyme Specificity ◽  
Peak Calling ◽  
Binding Models ◽  
Downstream Analysis

We report a newly-identified bias in CLIP data that results from cleaving enzyme specificity. This bias is inadvertently incorporated into standard peak calling methods [1], which identify the most likely locations where proteins bind RNA. We further show how, in downstream analysis, this bias is incorporated into models inferred by the state-of-the-art GraphProt method to predict protein RNA-binding. We call for both experimental controls to measure enzyme specificities and algorithms to identify unbiased CLIP binding sites.

Experimental studies of the influence of technological heredity on the damage to wheels of high hardness

2020 ◽  
pp. 12-14
Author(s):  
M.I. Biserikan ◽  
S.V. Petrochenko ◽  
K.V. Averkov ◽  
A.A. Rauba
Keyword(s):  
Experimental Data ◽  
Experimental Studies ◽  
Contact Fatigue ◽  
High Hardness ◽  
The State ◽  
Maximum Height ◽  
Technological Heredity ◽  
Fatigue Defect

The influence of technological heredity on the interaction of the wheel and rail is considered. The state of the surface of the samples for the presence of contactfatigue defects is estimated. A relationship is established between the maximum height of waviness on the surface of the roller and the rate of damage to its surface. The dependence of the number of loading cycles of the rollers on the height of the waviness and the distribution of the run of the wheels of increased hardness between the turnings are constructed, which is consistent with experimental data. Keywords wheel, rails, contact-fatigue processes, technological heredity, wear, fatigue defect, wheel of increased hardness. [email protected]

scholarly journals Evaluating the state of the art in disorder recognition and normalization of the clinical narrative

2014 ◽  
Vol 22 (1) ◽  
pp. 143-154 ◽  
Author(s):  
Sameer Pradhan ◽  
Noémie Elhadad ◽  
Brett R South ◽  
David Martinez ◽  
Lee Christensen ◽  
...  
Keyword(s):  
Gold Standard ◽  
State Of The Art ◽  
Hybrid Approach ◽  
Standard Test ◽  
The State ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Clinical Text ◽  
Clinical Narrative ◽  
Community Evaluation

Abstract Objective The ShARe/CLEF eHealth 2013 Evaluation Lab Task 1 was organized to evaluate the state of the art on the clinical text in (i) disorder mention identification/recognition based on Unified Medical Language System (UMLS) definition (Task 1a) and (ii) disorder mention normalization to an ontology (Task 1b). Such a community evaluation has not been previously executed. Task 1a included a total of 22 system submissions, and Task 1b included 17. Most of the systems employed a combination of rules and machine learners. Materials and methods We used a subset of the Shared Annotated Resources (ShARe) corpus of annotated clinical text—199 clinical notes for training and 99 for testing (roughly 180 K words in total). We provided the community with the annotated gold standard training documents to build systems to identify and normalize disorder mentions. The systems were tested on a held-out gold standard test set to measure their performance. Results For Task 1a, the best-performing system achieved an F1 score of 0.75 (0.80 precision; 0.71 recall). For Task 1b, another system performed best with an accuracy of 0.59. Discussion Most of the participating systems used a hybrid approach by supplementing machine-learning algorithms with features generated by rules and gazetteers created from the training data and from external resources. Conclusions The task of disorder normalization is more challenging than that of identification. The ShARe corpus is available to the community as a reference standard for future studies.

Review—Computational Methods for Internal Flows With Emphasis on Turbomachinery

1985 ◽  
Vol 107 (1) ◽  
pp. 6-22 ◽  
Author(s):  
William D. McNally ◽  
Peter M. Sockol
Keyword(s):  
Stream Function ◽  
Computational Methods ◽  
State Of The Art ◽  
The State ◽  
The Other ◽  
Internal Flows ◽  
Other Hand

A review is given of current computational methods for analyzing flows in turbomachinery and other related internal propulsion components. The methods are divided primarily into two classes, inviscid and viscous. The inviscid methods deal specifically with turbomachinery applications. Viscous methods, on the other hand, due to the state-of-the-art, deal with generalized duct flows as well as flows in turbomachinery passages. Inviscid methods are categorized into the potential, stream function, and Euler approaches. Viscous methods are treated in terms of parabolic, partially parabolic, and elliptic procedures.

Investigation of Design Power Margin and Correlation Allowance for Surface Ships

1986 ◽  
Vol 23 (01) ◽  
pp. 35-54
Author(s):  
Grant R. Hagen ◽  
Edward N. Comstock ◽  
John J. Slager
Keyword(s):  
Experimental Data ◽  
State Of The Art ◽  
The State ◽  
Model Experiments ◽  
Current Correlation ◽  
Growing Body ◽  
Current Design ◽  
The Impact ◽  
Design Power ◽  
The U.S

This paper follows two earlier papers, published by the Society in 1962 and 1979, dealing with correlation allowance and design power margin. For some time it has been perceived that a need exists for changes in the numerical quantities which have been specified by the U.S. Navy for correlation allowance coefficients and design power margins. This perception results from the recognition of a growing body of experimental data, both from model experiments and from ship standardization trials, that provide the basis for both correlation and margin policies. In response to this need, an exhaustive investigation was undertaken to establish a sound basis for a revised correlation allowance policy and to evaluate its impact on design power margin policy. The investigation, which led to proposed revisions in both policies, provided the material for this paper. Presented herein are:a review of the state of the art in the areas of correlation allowance and speed-power margin;an updated database derived primarily from model experiments and standardization trials of U.S. Navy ships;an assessment and interpretation of the database;a proposed alternative to the current correlation allowance policy;an evaluation of the impact of applying the proposed policy in determining required speed-power margins for U.S. Navy ships; anda proposed alternative to the current design power margin policy for new U.S. Navy ships.

scholarly journals Approximating Integer Solution Counting via Space Quantification for Linear Constraints

2019 ◽  
Author(s):  
Cunjing Ge ◽  
Feifei Ma ◽  
Xutong Ma ◽  
Fan Zhang ◽  
Pei Huang ◽  
...  
Keyword(s):  
Experimental Data ◽  
State Of The Art ◽  
Solution Space ◽  
Linear Constraints ◽  
The State ◽  
Volume Estimation ◽  
Integer Solution ◽  
Counting Method ◽  
Volume Computation ◽  
Integer Solutions

Solution counting or solution space quantification (means volume computation and volume estimation) for linear constraints (LCs) has found interesting applications in various fields. Experimental data shows that integer solution counting is usually more expensive than quantifying volume of solution space while their output values are close. So it is helpful to approximate the number of integer solutions by the volume if the error is acceptable. In this paper, we present and prove a bound of such error for LCs. It is the first bound that can be used to approximate the integer solution counts. Based on this result, an approximate integer solution counting method for LCs is proposed. Experiments show that our approach is over 20x faster than the state-of-the-art integer solution counters. Moreover, such advantage increases with the problem scale.

scholarly journals Deep residual network framework for structural health monitoring

2020 ◽  
pp. 147592172091837 ◽  
Author(s):  
Ruhua Wang ◽  
Chencho ◽  
Senjian An ◽  
Jun Li ◽  
Ling Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Structural Health Monitoring ◽  
Convolutional Neural Network ◽  
Health Monitoring ◽  
Damage Identification ◽  
State Of The Art ◽  
Experimental Studies ◽  
The State ◽  
Residual Network ◽  
Structural Health

Convolutional neural networks have been widely employed for structural health monitoring and damage identification. The convolutional neural network is currently considered as the state-of-the-art method for structural damage identification due to its capabilities of efficient and robust feature learning in a hierarchical manner. It is a tendency to develop a convolutional neural network with a deeper architecture to gain a better performance. However, when the depth of the network increases to a certain level, the performance will degrade due to the gradient vanishing issue. Residual neural networks can avoid the problem of vanishing gradients by utilizing skip connections, which allows the information flowing to the next layer through identity mappings. In this article, a deep residual network framework is proposed for structural health monitoring of civil engineering structures. This framework is composed of purely residual blocks which operate as feature extractors and a fully connected layer as a regressor. It learns the damage-related features from the vibration characteristics such as mode shapes and maps them into the damage index labels, for example, stiffness reductions of structures. To evaluate the efficacy and robustness of the proposed framework, an intensive evaluation is conducted with both numerical and experimental studies. The comparison between the proposed approach and the state-of-the-art models, including a sparse autoencoder neural network, a shallow convolutional neural network and a convolutional neural network with the same structure but without skip connections, is conducted. In the numerical studies, a 7-storey steel frame is investigated. Four scenarios with considering measurement noise and finite element modelling errors in the data sets are studied. The proposed framework consistently outperforms the state-of-the-art models in all the scenarios, especially for the most challenging scenario, which includes both measurement noise and uncertainties. Experimental studies on a prestressed concrete bridge in the laboratory are conducted. The proposed framework demonstrates consistent damage prediction results on this beam with the state-of-the-art models.

State of the Art of Experimental Studies and Predictive Methods for Slug Liquid Holdup

2012 ◽  
Vol 134 (2) ◽  
Author(s):  
Eduardo Pereyra ◽  
Rosnayi Arismendi ◽  
Luis E. Gomez ◽  
Ram S. Mohan ◽  
Ovadia Shoham ◽  
...  
Keyword(s):  
Experimental Data ◽  
Data Base ◽  
State Of The Art ◽  
Experimental Studies ◽  
Mechanistic Models ◽  
Liquid Holdup ◽  
Predictive Methods ◽  
Improved Performance

A summary of all available correlations and mechanistic models for the prediction of slug liquid holdup is presented. Additionally, an experimental data base for slug liquid holdup has been collected from available literature. A comparison between the predictions of available models and correlations against the data base is presented, identifying the range of applicability of the different methods. The correlations have been tuned against the new data by calculating new values of their constant parameters, showing an improved performance. Also, the uncertainties of the correlations parameters are evaluated and presented. A recommendation for the best method of predicting the slug liquid holdup is provided.

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