Stability Recovery in London Plane Trees Eight Years After Primary Anchorage Failure

2019 ◽  
Vol 45 (6) ◽  
Author(s):  
Andreas Detter ◽  
Philip van Wassenaer ◽  
Steffen Rust
Keyword(s):  
Urban Trees ◽  
Small Data ◽  
Rotational Stiffness ◽  
Data Set ◽  
Tree Stability ◽  
Ultimate Failure ◽  
Anchoring Strength ◽  
Original Strength ◽  
London Plane ◽  
Observation Period

As the intensity and frequency of strong storms increase, the potential for damage to urban trees also increases. So far, the risk of ultimate failure for partially uprooted trees and how they may recover their stability is not well understood. This study sets out to explore if and to what extent trees can regain anchoring strength after their root systems have been overloaded. In 2010, ten London Plane (Platanus × acerifolia) trees were subjected to destructive winching tests. Two trees were pulled to the ground while eight were loaded until primary anchorage failure occurred and were left standing with inclined stems. In 2013, two trees had failed and six were re-tested nondestructively. By 2018, another tree had failed, and we tested the remaining five again. Rotational stiffness was derived for all trials and served as a nondestructive proxy for anchoring strength (R² = 0.91). After eight years, one tree had regained its original strength, while four had reached between 71 and 82% of their initial rotational stiffness. However, three trees failed during the observation period. The results indicate that partially uprooted trees may re-establish stability over time, but some will not and may fail. In our small data set, it was not possible to identify visual criteria that could provide a reliable indication of tree stability recovery, but our data support the assumption that nondestructive pulling tests can be successfully employed to determine good vigorous candidates for retention after partial uprooting.

The Lyapunov Exponent Variance of an Electronic Manufacturing Enterprise’s Daily Qualified Rate Time Series by Improved Small Data Sets Approach

2012 ◽  
Vol 197 ◽  
pp. 271-277
Author(s):  
Zhu Ping Gong
Keyword(s):  
Time Series ◽  
Lyapunov Exponent ◽  
Chaotic Time Series ◽  
Quality System ◽  
Quality Level ◽  
Largest Lyapunov Exponent ◽  
Small Data ◽  
Data Set ◽  
Electronic Manufacturing ◽  
Small Data Set

Small data set approach is used for the estimation of Largest Lyapunov Exponent (LLE). Primarily, the mean period drawback of Small data set was corrected. On this base, the LLEs of daily qualified rate time series of HZ, an electronic manufacturing enterprise, were estimated and all positive LLEs were taken which indicate that this time series is a chaotic time series and the corresponding produce process is a chaotic process. The variance of the LLEs revealed the struggle between the divergence nature of quality system and quality control effort. LLEs showed sharp increase in getting worse quality level coincide with the company shutdown. HZ’s daily qualified rate, a chaotic time series, shows us the predictable nature of quality system in a short-run.

scholarly journals Classification of jujube defects in small data sets based on transfer learning

2021 ◽  
Author(s):  
Jianping Ju ◽  
Hong Zheng ◽  
Xiaohang Xu ◽  
Zhongyuan Guo ◽  
Zhaohui Zheng ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Loss Function ◽  
Training Model ◽  
Parameter Distribution ◽  
Test Accuracy ◽  
Small Data ◽  
Data Sets ◽  
Data Set ◽  
Small Data Sets

AbstractAlthough convolutional neural networks have achieved success in the field of image classification, there are still challenges in the field of agricultural product quality sorting such as machine vision-based jujube defects detection. The performance of jujube defect detection mainly depends on the feature extraction and the classifier used. Due to the diversity of the jujube materials and the variability of the testing environment, the traditional method of manually extracting the features often fails to meet the requirements of practical application. In this paper, a jujube sorting model in small data sets based on convolutional neural network and transfer learning is proposed to meet the actual demand of jujube defects detection. Firstly, the original images collected from the actual jujube sorting production line were pre-processed, and the data were augmented to establish a data set of five categories of jujube defects. The original CNN model is then improved by embedding the SE module and using the triplet loss function and the center loss function to replace the softmax loss function. Finally, the depth pre-training model on the ImageNet image data set was used to conduct training on the jujube defects data set, so that the parameters of the pre-training model could fit the parameter distribution of the jujube defects image, and the parameter distribution was transferred to the jujube defects data set to complete the transfer of the model and realize the detection and classification of the jujube defects. The classification results are visualized by heatmap through the analysis of classification accuracy and confusion matrix compared with the comparison models. The experimental results show that the SE-ResNet50-CL model optimizes the fine-grained classification problem of jujube defect recognition, and the test accuracy reaches 94.15%. The model has good stability and high recognition accuracy in complex environments.

scholarly journals 2D–3D reconstruction of distal forearm bone from actual X-ray images of the wrist using convolutional neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ryoya Shiode ◽  
Mototaka Kabashima ◽  
Yuta Hiasa ◽  
Kunihiro Oka ◽  
Tsuyoshi Murase ◽  
...  
Keyword(s):  
Wrist Joint ◽  
High Accuracy ◽  
Training Data ◽  
Small Data ◽  
Data Set ◽  
Accuracy Estimation ◽  
X Ray ◽  
Learning Network ◽  
Forearm Bone ◽  
Deep Learning Network

AbstractThe purpose of the study was to develop a deep learning network for estimating and constructing highly accurate 3D bone models directly from actual X-ray images and to verify its accuracy. The data used were 173 computed tomography (CT) images and 105 actual X-ray images of a healthy wrist joint. To compensate for the small size of the dataset, digitally reconstructed radiography (DRR) images generated from CT were used as training data instead of actual X-ray images. The DRR-like images were generated from actual X-ray images in the test and adapted to the network, and high-accuracy estimation of a 3D bone model from a small data set was possible. The 3D shape of the radius and ulna were estimated from actual X-ray images with accuracies of 1.05 ± 0.36 and 1.45 ± 0.41 mm, respectively.

A Comparison Study of Mahalanobis-Taguchi System and Neural Network for Multivariate Pattern Recognition

2005 ◽  
Author(s):  
Jungeui Hong ◽  
Elizabeth A. Cudney ◽  
Genichi Taguchi ◽  
Rajesh Jugulum ◽  
Kioumars Paryani ◽  
...  
Keyword(s):  
Neural Network ◽  
Small Data ◽  
Data Sets ◽  
Comparison Study ◽  
Data Set ◽  
Set Size ◽  
Breast Cancer Study ◽  
Discriminant Ability ◽  
Small Data Sets ◽  
Multivariate Pattern

The Mahalanobis-Taguchi System is a diagnosis and predictive method for analyzing patterns in multivariate cases. The goal of this study is to compare the ability of the Mahalanobis-Taguchi System and a neural network to discriminate using small data sets. We examine the discriminant ability as a function of data set size using an application area where reliable data is publicly available. The study uses the Wisconsin Breast Cancer study with nine attributes and one class.

P–184 Assisted hatching does not improve live birth rates, a prospective double-blinded randomized study

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
V Montalvo ◽  
J Masso ◽  
A Garcia-Faura ◽  
B Marques ◽  
M Lopez-Teijon
Keyword(s):  
Live Birth ◽  
Human Embryos ◽  
Assisted Hatching ◽  
Small Data ◽  
Success Rates ◽  
Birth Rates ◽  
Data Set ◽  
Positive Effects ◽  
Live Birth Rates ◽  
Frozen Embryo Transfers

Abstract Study question Does Assisted hatching (AH) improve success rates when applied to frozen embryo transfers? Summary answer AH does not improve implantation, ongoing pregnancy or live birth rates when applied to thawed embryos. What is known already Vitrification has been proven to be the most efficient technique to preserve human embryos. However, vitrification has some consequences for the embryos, zona pellucida (ZP) hardening being one of them. Multiple studies suggest the need to apply laser Assisted hatching or ZP thinning to thawed embryos in order to improve success rates. Still, there is not enough evidence to ensure the utility of AH, and considering the great variation in design between studies more evidence is needed. Study design, size, duration Study performed from October 2019 and January 2020. Disregarding embryos with natural Hatching and PGT-A. Embryos that, immediately after thawing, were completely expanded (trophectoderm in contact with ZP) were also excluded from the study. We applied a randomization to choose in which embryos we had to perform AH. Neither the gynecologist nor the embryologist performing the embryo transfer knew whether the embryo had AH performed or not. Participants/materials, setting, methods 353 frozen embryo transfers of one blastocist were considered for the study, 71 excluded for expansion after thawing, 65 excluded because of PGT-A, 103 in which we performed AH (AH+) and 114 without AH (AH-). In the AH+ group we performed laser-AH of 1/3 of the ZP, avoiding to damage the trophectoderm and performing the laser shots as far away to the ICM as possible. We used Chi-square testing to assess the effects of AH. Main results and the role of chance We assessed all relevant clinical data parameters. No statistical differences were found in egg age, maternal age, embryo quality, nor endometrial thickness between groups. Implantation and miscarriage rates were equivalent between AH+ group (40.9%; 20.5%) and AH- group (47.4%; 18.5%). The main outcome of this study was live birth rates. No statistical differences were found between groups (AH-= 38.6%; AH + = 30.1%; p = 03221) proving that making it easier to get out of the ZP does not affect success rates. Analyzing the data from the excluded embryos we found no improvement on live birth rates when embryos were expanded just after thawing (38.0%; p = 0.457). As expected, PGT-A embryos yielded higher live birth rates (52.3%; p < 0,05) Limitations, reasons for caution Preliminary study with a small data set. Wider implications of the findings: This study suggest that thawed embryos have the capacity to get out of the ZP regardless if AH was performed or not. Having no positive effects, AH seems to be unnecessary in this scenario. Trial registration number Not applicable

In vitro effectiveness of biapenem against IMP-producing Enterobacteriaceae

2021 ◽  
Vol 70 (10) ◽  
Author(s):  
Kazuyoshi Gotoh ◽  
Makoto Miyoshi ◽  
I Putu Bayu Mayura ◽  
Koji Iio ◽  
Osamu Matsushita ◽  
...  
Keyword(s):  
Therapeutic Option ◽  
Small Data ◽  
Limited Data ◽  
Data Set ◽  
Content Type ◽  
Link Type ◽  
Almost All ◽  
New Treatments ◽  
Time Kill

The options available for treating infections with carbapenemase-producing Enterobacteriaceae (CPE) are limited; with the increasing threat of these infections, new treatments are urgently needed. Biapenem (BIPM) is a carbapenem, and limited data confirming its in vitro killing effect against CPE are available. In this study, we examined the minimum inhibitory concentrations (MICs) and minimum bactericidal concentrations (MBCs) of BIPM for 14 IMP-1-producing Enterobacteriaceae strains isolated from the Okayama region in Japan. The MICs against almost all the isolates were lower than 0.5 µg ml−1, indicating susceptibility to BIPM, while approximately half of the isolates were confirmed to be bacteriostatic to BIPM. However, initial killing to a 99.9 % reduction was observed in seven out of eight strains in a time–kill assay. Despite the small data set, we concluded that the in vitro efficacy of BIPM suggests that the drug could be a new therapeutic option against infection with IMP-producing CPE.

Causal ascription of rework in building and civil engineering projects

2014 ◽  
Vol 21 (1) ◽  
pp. 111-126 ◽  
Author(s):  
Palaneeswaran Ekambaram ◽  
Peter E.D. Love ◽  
Mohan M. Kumaraswamy ◽  
Thomas S.T. Ng
Keyword(s):  
Construction Projects ◽  
Civil Engineering ◽  
Small Data ◽  
Data Set ◽  
Content Type ◽  
Management Measures ◽  
Significant Difference ◽  
Quantitative Impact ◽  
Engineering Projects

Purpose – Rework is an endemic problem in construction projects and has been identified as being a significant factor contributing cost and schedule overruns. Causal ascription is necessary to obtain knowledge about the underlying nature of rework so that appropriate prevention mechanisms can be put in place. The paper aims to discuss these issues. Design/methodology/approach – Using a supervised questionnaire survey and case-study interviews, data from 112 building and engineering projects about the sources and causes of rework in projects were obtained. A multivariate exploration was conducted to examine the underlying relationships between rework variables. Findings – The analysis revealed that there was a significant difference between rework causes for building and civil engineering projects. The set of associations explored in the analyses will be useful to develop a generic causal model to examine the quantitative impact of rework on project performance so that appropriate prevention strategies can be identified and developed. Research limitations/implications – The limitations include: small data set (112 projects), which include 75 from building and 37 from civil engineering projects. Practical implications – Meaningful insights into the rework occurrences in construction projects will pave pathways for rational mitigation and effective management measures. Originality/value – To date there has been limited empirical research that has sought to determine the causal ascription of rework, particularly in Hong Kong.

scholarly journals Finding COVID-19 from Chest X-rays using Deep Learning on a Small Dataset

2020 ◽  
Author(s):  
Lawrence Hall ◽  
Dmitry Goldgof ◽  
Rahul Paul ◽  
Gregory M. Goldgof
Keyword(s):  
False Negative ◽  
True Positive Rate ◽  
Small Data ◽  
Good Resolution ◽  
X Rays ◽  
True Positive ◽  
Data Set ◽  
X Ray ◽  
Negative Rate ◽  
Positive Rate

<p>Testing for COVID-19 has been unable to keep up with the demand. Further, the false negative rate is projected to be as high as 30% and test results can take some time to obtain. X-ray machines are widely available and provide images for diagnosis quickly. This paper explores how useful chest X-ray images can be in diagnosing COVID-19 disease. We have obtained 135 chest X-rays of COVID-19 and 320 chest X-rays of viral and bacterial pneumonia. </p><p> A pre-trained deep convolutional neural network, Resnet50 was tuned on 102 COVID-19 cases and 102 other pneumonia cases in a 10-fold cross validation. The results were </p><p> an overall accuracy of 89.2% with a COVID-19 true positive rate of 0.8039 and an AUC of 0.95. Pre-trained Resnet50 and VGG16 plus our own small CNN were tuned or trained on a balanced set of COVID-19 and pneumonia chest X-rays. An ensemble of the three types of CNN classifiers was applied to a test set of 33 unseen COVID-19 and 218 pneumonia cases. The overall accuracy was 91.24% with the true positive rate for COVID-19 of 0.7879 with 6.88% false positives for a true negative rate of 0.9312 and AUC of 0.94. </p><p> This preliminary study has flaws, most critically a lack of information about where in the disease process the COVID-19 cases were and the small data set size. More COVID-19 case images at good resolution will enable a better answer to the question of how useful chest X-rays can be for diagnosing COVID-19.</p>

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