Cross-Validation of Numerical and Experimental Studies of Transitional Airfoil Performance

2015 ◽  
Author(s):  
Ariane Frere ◽  
Koen Hillewaert ◽  
Hamid S. Chivaee ◽  
Robert F. Mikkelsen ◽  
Philippe Chatelain
Keyword(s):  
Cross Validation ◽  
Experimental Studies

scholarly journals Chemical Gas Sensors: Recent Developments, Challenges, and the Potential of Machine Learning—A Review

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2877
Author(s):  
Usman Yaqoob ◽  
Mohammad I. Younis
Keyword(s):  
Machine Learning ◽  
Gas Sensors ◽  
Cross Validation ◽  
Experimental Studies ◽  
Future Generation ◽  
Machine Learning Techniques ◽  
Healthcare Applications ◽  
Highly Sensitive ◽  
Metal Metal ◽  
Smart Machine

Nowadays, there is increasing interest in fast, accurate, and highly sensitive smart gas sensors with excellent selectivity boosted by the high demand for environmental safety and healthcare applications. Significant research has been conducted to develop sensors based on novel highly sensitive and selective materials. Computational and experimental studies have been explored in order to identify the key factors in providing the maximum active location for gas molecule adsorption including bandgap tuning through nanostructures, metal/metal oxide catalytic reactions, and nano junction formations. However, there are still great challenges, specifically in terms of selectivity, which raises the need for combining interdisciplinary fields to build smarter and high-performance gas/chemical sensing devices. This review discusses current major gas sensing performance-enhancing methods, their advantages, and limitations, especially in terms of selectivity and long-term stability. The discussion then establishes a case for the use of smart machine learning techniques, which offer effective data processing approaches, for the development of highly selective smart gas sensors. We highlight the effectiveness of static, dynamic, and frequency domain feature extraction techniques. Additionally, cross-validation methods are also covered; in particular, the manipulation of the k-fold cross-validation is discussed to accurately train a model according to the available datasets. We summarize different chemresistive and FET gas sensors and highlight their shortcomings, and then propose the potential of machine learning as a possible and feasible option. The review concludes that machine learning can be very promising in terms of building the future generation of smart, sensitive, and selective sensors.

scholarly journals MiRNA-disease association prediction via hypergraph learning based on high-dimensionality features

2021 ◽  
Vol 21 (S1) ◽  
Author(s):  
Yu-Tian Wang ◽  
Qing-Wen Wu ◽  
Zhen Gao ◽  
Jian-Cheng Ni ◽  
Chun-Hou Zheng
Keyword(s):  
Computational Models ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Experimental Studies ◽  
Prediction Method ◽  
Disease Association ◽  
High Dimensionality ◽  
K Nearest Neighbor ◽  
Hypergraph Learning ◽  
Disease Associations

Abstract Background MicroRNAs (miRNAs) have been confirmed to have close relationship with various human complex diseases. The identification of disease-related miRNAs provides great insights into the underlying pathogenesis of diseases. However, it is still a big challenge to identify which miRNAs are related to diseases. As experimental methods are in general expensive and time‐consuming, it is important to develop efficient computational models to discover potential miRNA-disease associations. Methods This study presents a novel prediction method called HFHLMDA, which is based on high-dimensionality features and hypergraph learning, to reveal the association between diseases and miRNAs. Firstly, the miRNA functional similarity and the disease semantic similarity are integrated to form an informative high-dimensionality feature vector. Then, a hypergraph is constructed by the K-Nearest-Neighbor (KNN) method, in which each miRNA-disease pair and its k most relevant neighbors are linked as one hyperedge to represent the complex relationships among miRNA-disease pairs. Finally, the hypergraph learning model is designed to learn the projection matrix which is used to calculate uncertain miRNA-disease association score. Result Compared with four state-of-the-art computational models, HFHLMDA achieved best results of 92.09% and 91.87% in leave-one-out cross validation and fivefold cross validation, respectively. Moreover, in case studies on Esophageal neoplasms, Hepatocellular Carcinoma, Breast Neoplasms, 90%, 98%, and 96% of the top 50 predictions have been manually confirmed by previous experimental studies. Conclusion MiRNAs have complex connections with many human diseases. In this study, we proposed a novel computational model to predict the underlying miRNA-disease associations. All results show that the proposed method is effective for miRNA–disease association predication.

scholarly journals A Novel Approach Based on a Weighted Interactive Network to Predict Associations of MiRNAs and Diseases

2018 ◽  
Vol 20 (1) ◽  
pp. 110 ◽  
Author(s):  
Haochen Zhao ◽  
Linai Kuang ◽  
Xiang Fen ◽  
Quan Zou ◽  
Lei Wang
Keyword(s):  
Cross Validation ◽  
Experimental Studies ◽  
Prostate Neoplasms ◽  
Gastric Neoplasms ◽  
Operating Characteristics ◽  
Disease Etiology ◽  
Colon Neoplasms ◽  
Interactive Network ◽  
Disease Associations ◽  
Fold Cross Validation

Accumulating evidence progressively indicated that microRNAs (miRNAs) play a significant role in the pathogenesis of diseases through many experimental studies; therefore, developing powerful computational models to identify potential human miRNA–disease associations is vital for an understanding of the disease etiology and pathogenesis. In this paper, a weighted interactive network was firstly constructed by combining known miRNA–disease associations, as well as the integrated similarity between diseases and the integrated similarity between miRNAs. Then, a new computational method implementing the newly weighted interactive network was developed for discovering potential miRNA–disease associations (WINMDA) by integrating the T most similar neighbors and the shortest path algorithm. Simulation results show that WINMDA can achieve reliable area under the receiver operating characteristics (ROC) curve (AUC) results of 0.9183 ± 0.0007 in 5-fold cross-validation, 0.9200 ± 0.0004 in 10-fold cross-validation, 0.9243 in global leave-one-out cross-validation (LOOCV), and 0.8856 in local LOOCV. Furthermore, case studies of colon neoplasms, gastric neoplasms, and prostate neoplasms based on the Human microRNA Disease Database (HMDD) database were implemented, for which 94% (colon neoplasms), 96% (gastric neoplasms), and 96% (prostate neoplasms) of the top 50 predicting miRNAs were confirmed by recent experimental reports, which also demonstrates that WINMDA can effectively uncover potential miRNA–disease associations.

3-D reconstruction and analysis of mammalian mitotic spindle structure

1992 ◽  
Vol 50 (1) ◽  
pp. 578-579
Author(s):  
Kent McDonald ◽  
David Mastronarde ◽  
Rubai Ding ◽  
Eileen O'Toole ◽  
J. Richard McIntosh
Keyword(s):  
Cross Sections ◽  
Mitotic Spindle ◽  
Experimental Studies ◽  
Video Camera ◽  
Three Dimensions ◽  
Mitotic Spindles ◽  
Black And White ◽  
Reconstruction Methods ◽  
Spindle Structure ◽  
Tissue Culture Line

Mammalian spindles are generally large and may contain over a thousand microtubules (MTs). For this reason they are difficult to reconstruct in three dimensions and many researchers have chosen to study the smaller and simpler spindles of lower eukaryotes. Nevertheless, the mammalian spindle is used for many experimental studies and it would be useful to know its detailed structure.We have been using serial cross sections and computer reconstruction methods to analyze MT distributions in mitotic spindles of PtK cells, a mammalian tissue culture line. Images from EM negatives are digtized on a light box by a Dage MTI video camera containing a black and white Saticon tube. The signal is digitized by a Parallax 1280 graphics device in a MicroVax III computer. Microtubules are digitized at a magnification such that each is 10-12 pixels in diameter.

Effects of Attachment Styles on the Experience of Equity in Heterosexual Couples Relationships

2003 ◽  
Vol 50 (4) ◽  
pp. 298-310 ◽  
Author(s):  
Ina Grau ◽  
Jörg Doll
Keyword(s):  
Attachment Theory ◽  
Attachment Style ◽  
Intimate Relationships ◽  
Attachment Styles ◽  
Experimental Studies ◽  
The Other ◽  
Heterosexual Couples ◽  
Secure Attachment ◽  
Dependent Variables ◽  
Equity Ratio

Abstract. Employing one correlational and two experimental studies, this paper examines the influence of attachment styles (secure, anxious, avoidant) on a person’s experience of equity in intimate relationships. While one experimental study employed a priming technique to stimulate the different attachment styles, the other involved vignettes describing fictitious characters with typical attachment styles. As the specific hypotheses about the single equity components have been developed on the basis of the attachment theory, the equity ratio itself and the four equity components (own outcome, own input, partner’s outcome, partner’s input) are analyzed as dependent variables. While partners with a secure attachment style tend to describe their relationship as equitable (i.e., they give and take extensively), partners who feel anxious about their relationship generally see themselves as being in an inequitable, disadvantaged position (i.e., they receive little from their partner). The hypothesis that avoidant partners would feel advantaged as they were less committed was only supported by the correlational study. Against expectations, the results of both experiments indicate that avoidant partners generally see themselves (or see avoidant vignettes) as being treated equitably, but that there is less emotional exchange than is the case with secure partners. Avoidant partners give and take less than secure ones.

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