scholarly journals Studies on Axial Deviation of Hole in Deep Machine-Boring of Wood

10.5109/4648 ◽  
2005 ◽  
Vol 50 (2) ◽  
pp. 353-362
Author(s):  
Takeshi Ohuchi ◽  
Nobuhiko Suzuki ◽  
Yasuhide Murase
Keyword(s):  
Axial Deviation

Stability of Unsymmetrical Rigid Rotor Supported by Self-Acting Gas-Lubricated Journal Bearings

1983 ◽  
Vol 105 (4) ◽  
pp. 656-661 ◽  
Author(s):  
S. Yoshimoto ◽  
Y. Nakano
Keyword(s):  
Theoretical Approach ◽  
Journal Bearings ◽  
Center Of Gravity ◽  
Rigid Rotor ◽  
Load Bearing ◽  
Bearing Clearance ◽  
Axial Deviation ◽  
Bearing Load ◽  
Theoretical Results ◽  
Good Agreement

In order to determine the threshold of instability for an unsymmetrical rigid rotor supported by two identical self-acting, plain-cylindrical gas journal bearings, a theoretical approach is made by the use of the quasi-static nonliner PH method. Influence of various parameters affecting the threshold of instability of the unsymmetrical rotor is experimentally shown and compared with the theoretical results. The experimental variables considered in this paper include bearing load, bearing length, bearing clearance, the axial deviation of the center of gravity from the center of the rotor span. Good agreement was obtained between the experimental and theoretical results.

scholarly journals Correlation of brachycephaly grade with level of exophthalmos, reduced airway passages and degree of dental malalignment’ in Persian cats

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254420
Author(s):  
Jana Sieslack ◽  
Daniela Farke ◽  
Klaus Failing ◽  
Martin Kramer ◽  
Martin J. Schmidt
Keyword(s):  
Computed Tomography ◽  
Three Dimensional ◽  
Direct Relation ◽  
Flow Area ◽  
Nasal Airways ◽  
Axial Deviation ◽  
Cranial Index ◽  
Upper Respiratory ◽  
Upper Jaw ◽  
Lower Height

For many years, there has been a trend to breed cats with an increasing degree of brachycephalic head features, which are known to have a severe impact on the animals’ health and welfare. The direct relation between different grades of brachycephaly and their negative implications have not been researched in this species. The aim of this study was therefore to establish correlations between the different grades of brachycephaly and reduced upper respiratory airways, exophthalmos of the eye globes and malalignment of the teeth in Persian cats. Sixty-nine Persian cats of various skull dimensions and ten Domestic shorthair cats were recruited for the study. The cats’ skulls were examined using three-dimensional reconstructions created from Computed Tomography datasets. Brachycephaly was graded using established craniometric measurements (facial index, cranial index, skull index, craniofacial angle). The flow area of the nasal passageways at different locations, the amount of the eye globe not supported by the bony orbit and the axial deviation of the teeth were quantified and evaluated for a correlation with the grade of brachycephaly. The results of this study clearly show that increased grades of brachycephaly in Persian cats resulted in larger extra-orbital parts of the ocular bulbs. The brachycephalic skull dimension also resulted in a lower height of the naso-osseal aperture, while other areas of the nasal airways were not correlated with the severity of brachycephaly. Persian cats showed a significantly increased occurrence of premolar tooth displacement in the upper jaw with increasing brachycephaly grades. It was interesting to note that the measured values had a broad range and values of some individual Persian cats showed an overlap with those of Domestic shorthair cats.

Effects of Different Deviations of Helical Inlet Port on Performance

2012 ◽  
Vol 516-517 ◽  
pp. 888-891 ◽  
Author(s):  
Xue Dong Zhang ◽  
Ping Sun ◽  
Wen Xia Lu ◽  
Li Hua Ye
Keyword(s):  
Numerical Simulation ◽  
Diesel Engine ◽  
Flow Characteristics ◽  
Inlet Port ◽  
Axial Deviation ◽  
Solid Models

In this paper, the helical inlet port of CZ4B26 diesel engine is studied. Its3-D solid models of port with the axial deviation and rotated port are made to research the effect on helical inlet port performance. Based on the validation of CFD precision by test, the numerical simulation of flow characteristics in ports-valve-cylinder system is carried out. The results showed that the axial deviation of the port and rotated port have a range of effects on the performance of inlet port.

Which Axial Deviation Results in Limitations of Pro- and Supination Following Diaphyseal Lower Arm Fracture in Childhood?

2001 ◽  
Vol 27 (6) ◽  
pp. 309-316 ◽  
Author(s):  
Annelie-Martina Weinberg ◽  
Philipp Kasten ◽  
Christoph Castellani ◽  
Manfred Jablonski ◽  
Ulrich Hofmann ◽  
...  
Keyword(s):  
Axial Deviation

scholarly journals Axial Deviation of Hole in Deep Drilling (1st Renort)

1983 ◽  
Vol 49 (10) ◽  
pp. 1379-1384
Author(s):  
Keizo SAKUMA ◽  
Koichi TAGUCHI ◽  
Akio KATSUKI
Keyword(s):  
Deep Drilling ◽  
Axial Deviation

Clinical experiences with axial deviation of the aryepiglottic folds in 52 racehorses

2001 ◽  
Vol 30 (2) ◽  
pp. 151-160 ◽  
Author(s):  
Dana S. King ◽  
Eric Tulleners ◽  
Benson B. Martin ◽  
Eric J. Parente ◽  
Ray Boston
Keyword(s):  
Clinical Experiences ◽  
Axial Deviation

scholarly journals Axial deviation of a hole in deep drilling - Influence of tool geometry (comparison of single and multi-edged tools)

1987 ◽  
Vol 53 (486) ◽  
pp. 471-477 ◽  
Author(s):  
Akio KATSUKI ◽  
keizo SAKUMA ◽  
Koichi TAGUCHI ◽  
Hiromichi ONIKURA ◽  
Hisashi AKIYOSHI ◽  
...  
Keyword(s):  
Tool Geometry ◽  
Deep Drilling ◽  
Axial Deviation

scholarly journals 372 Machine learning and pulmonary hypertension

2021 ◽  
Vol 23 (Supplement_G) ◽  
Author(s):  
Lucia Soriente ◽  
Luigi Cerulo ◽  
Giovanna Mercurio ◽  
Giuseppe Iuliano ◽  
Francesco Paolo Mancini
Keyword(s):  
Machine Learning ◽  
Pulmonary Hypertension ◽  
Decision Tree ◽  
Learning Algorithm ◽  
Right Atrial ◽  
Validation Dataset ◽  
Machine Learning Algorithm ◽  
Acceleration Time ◽  
Axial Deviation ◽  
The Right

Abstract Aims Pulmonary hypertension (IP) characterized by an average resting pulmonary pressure ≥20 mmHg can sustain various clinical conditions that differ in physiopathological, haemodynamic, and therefore therapeutic aspects. The goal of our work was to apply a machine learning algorithm that could accurately distinguish pre- and post-heart pulmonary hypertension through non-invasive methods (medical history, clinical, and echocardiographic data). Methods and results In order to achieve our goal we used the ‘decision tree’ machine learning algorithm implemented in the C5.0 package of the R development environment. The first step was the preparation of the data. The dataset of patients with IP was composed of 85 patients divided into XX precapillary IP (1) and YY postcapillary (2). Each patient is described by 11 features: some comorbidities (arterial hypertension and atrial fibrillation), BMI, right axial deviation on ECG, DLCO, and some echocardiographic measurements (e/e′, right atrial area, S wave at TDI, acceleration time on the pulmonary, inferior vena cava, diameters of the right ventricle). The dataset was divided into a data.train training subset (45 patients) and an evaluation subset (40 patients), maintaining the proportion between classes. Starting from the training dataset, the C5.0 algorithm generated the decision tree shown in Figure 1. The root node was made up of the mitral pattern e/e′, followed by the right axis deviation on the ECG and the acceleration rate on the lung that the algorithm considered the best discriminated features. The model was then validated in the validation dataset and through the Caret package and the Confusion matrix function we calculated the performance metrics of the algorithm obtaining an accuracy of 0.87, a kappa statistic of 0.742, a sensitivity of 0.913, and a specificity of 0.823. The true positive rate was 0.87 while the true negative rate was 0.87. The performance of the model was also measured using the ROC curve, obtaining an area under the curve of 0.916. Conclusions Our results show that the ‘decision tree’ algorithm starting from echocardiographic data and the ECG has a good ability to discriminate between the precapillary and postcapillary IP. In particular, the decision chain consisting of: mitral pattern and / and ratio ≤8, right axial deviation on the ECG and acceleration time on the lung ≤80 ms seems to predict the IP class with reasonable accuracy. Our results confirm that the probability of prediction and the prediction itself depend, however, on what degree of purity the partitions learned during the decision tree construction process are made up. To improve the estimation of the algorithm’s performance and thus generalize the results obtained, we believe to evaluate this approach on larger datasets also considering different machine learning algorithms. 372 Figure

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