scholarly journals Characteristic curve of the relation of cutting conditions and the results of metal machining

2021 ◽  
Vol 21 (2) ◽  
pp. 255-260
Author(s):  
Karol Vasilko ◽  
Zuzana Murčinková
Keyword(s):  
Characteristic Curve ◽  
Cutting Conditions ◽  
Metal Machining

The Role of a Decision Rule in Symptomatic Pulmonary Embolism Patients with a Non-high Probability Ventilation-perfusion Scan

1997 ◽  
Vol 78 (02) ◽  
pp. 794-798 ◽  
Author(s):  
Bowine C Michel ◽  
Philomeen M M Kuijer ◽  
Joseph McDonnell ◽  
Edwin J R van Beek ◽  
Frans F H Rutten ◽  
...  
Keyword(s):  
Pulmonary Embolism ◽  
Decision Rule ◽  
Predictive Value ◽  
Patient Population ◽  
High Probability ◽  
Characteristic Curve ◽  
Stepwise Logistic Regression ◽  
Pulmonary Angiogram ◽  
Symptomatic Pulmonary Embolism ◽  
Perfusion Scan

Summary Background: In order to improve the use of information contained in the medical history and physical examination in patients with suspected pulmonary embolism and a non-high probability ventilation-perfusion scan, we assessed whether a simple, quantitative decision rule could be derived for the diagnosis or exclusion of pulmonary embolism. Methods: In 140 consecutive symptomatic patients with a non- high probability ventilation-perfusion scan and an interpretable pulmonary angiogram, various clinical and lung scan items were collected prospectively and analyzed by multivariate stepwise logistic regression analysis to identify the most informative combination of items. Results: The prevalence of proven pulmonary embolism in the patient population was 27.1%. A decision rule containing the presence of wheezing, previous deep venous thrombosis, recently developed or worsened cough, body temperature above 37° C and multiple defects on the perfusion scan was constructed. For the rule the area under the Receiver Operating Characteristic curve was larger than that of the prior probability of pulmonary embolism as assessed by the physician at presentation (0.76 versus 0.59; p = 0.0097). At the cut-off point with the maximal positive predictive value 2% of the patients scored positive, at the cut-off point with the maximal negative predictive value pulmonary embolism could be excluded in 16% of the patients. Conclusions: We derived a simple decision rule containing 5 easily interpretable variables for the patient population specified. The optimal use of the rule appears to be in the exclusion of pulmonary embolism. Prospective validation of this rule is indicated to confirm its clinical utility.

Dynamics of the processes in metal machining

1998 ◽  
Vol 2 ◽  
pp. 115-122
Author(s):  
Donatas Švitra ◽  
Jolanta Janutėnienė
Keyword(s):  
High Frequency ◽  
Machine Tools ◽  
Metal Cutting ◽  
Cutting Tool ◽  
Low Frequency ◽  
Metal Cutting Machine ◽  
Cutting Machine ◽  
Metal Machining

In the practice of processing of metals by cutting it is necessary to overcome the vibration of the cutting tool, the processed detail and units of the machine tool. These vibrations in many cases are an obstacle to increase the productivity and quality of treatment of details on metal-cutting machine tools. Vibration at cutting of metals is a very diverse phenomenon due to both it’s nature and the form of oscillatory motion. The most general classification of vibrations at cutting is a division them into forced vibration and autovibrations. The most difficult to remove and poorly investigated are the autovibrations, i.e. vibrations arising at the absence of external periodic forces. The autovibrations, stipulated by the process of cutting on metalcutting machine are of two types: the low-frequency autovibrations and high-frequency autovibrations. When the low-frequency autovibration there appear, the cutting process ought to be terminated and the cause of the vibrations eliminated. Otherwise, there is a danger of a break of both machine and tool. In the case of high-frequency vibration the machine operates apparently quiently, but the processed surface feature small-sized roughness. The frequency of autovibrations can reach 5000 Hz and more.

The role of serum periostin in the diagnosis of asthma: A meta-analysis

2020 ◽  
Vol 41 (4) ◽  
pp. 240-247
Author(s):  
Lei Yang ◽  
Qingtao Zhao ◽  
Shuyu Wang
Keyword(s):  
Diagnostic Accuracy ◽  
Meta Analysis ◽  
Characteristic Curve ◽  
False Negative ◽  
Summary Receiver Operating Characteristic ◽  
True Negative ◽  
Negative Results ◽  
Noninvasive Biomarker ◽  
Sensitivity Specificity ◽  
Serum Periostin

Background: Serum periostin has been proposed as a noninvasive biomarker for asthma diagnosis and management. However, its accuracy for the diagnosis of asthma in different populations is not completely clear. Methods: This meta-analysis aimed to evaluate the diagnostic accuracy of periostin level in the clinical determination of asthma. Several medical literature data bases were searched for relevant studies through December 1, 2019. The numbers of patients with true-positive, false-positive, false-negative, and true-negative results for the periostin level were extracted from each individual study. We assessed the risk of bias by using Quality Assessment of Diagnostic Accuracy Studies 2. We used the meta-analysis to produce summary estimates of accuracy. Results: In total, nine studies with 1757 subjects met the inclusion criteria. The pooled estimates of sensitivity, specificity, and diagnostic odds ratios for the detection of asthma were 0.58 (95% confidence interval [CI], 0.38‐0.76), 0.86 (95% CI, 0.74‐0.93), and 8.28 (95% CI, 3.67‐18.68), respectively. The area under the summary receiver operating characteristic curve was 0.82 (95% CI, 0.79‐0.85). And significant publication bias was found in this meta‐analysis (p = 0.39). Conclusion: Serum periostin may be used for the diagnosis of asthma, with moderate diagnostic accuracy.

Proliferation-dominant high-grade astrocytoma: survival benefit associated with extensive resection of FLAIR abnormality region

2020 ◽  
Vol 132 (4) ◽  
pp. 998-1005 ◽  
Author(s):  
Haihui Jiang ◽  
Yong Cui ◽  
Xiang Liu ◽  
Xiaohui Ren ◽  
Mingxiao Li ◽  
...  
Keyword(s):  
Survival Benefit ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Characteristic Curve ◽  
Extent Of Resection ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
High Grade ◽  
Extensive Resection ◽  
High Grade Astrocytoma

OBJECTIVEThe aim of this study was to investigate the relationship between extent of resection (EOR) and survival in terms of clinical, molecular, and radiological factors in high-grade astrocytoma (HGA).METHODSClinical and radiological data from 585 cases of molecularly defined HGA were reviewed. In each case, the EOR was evaluated twice: once according to contrast-enhanced T1-weighted images (CE-T1WI) and once according to fluid attenuated inversion recovery (FLAIR) images. The ratio of the volume of the region of abnormality in CE-T1WI to that in FLAIR images (VFLAIR/VCE-T1WI) was calculated and a receiver operating characteristic curve was used to determine the optimal cutoff value for that ratio. Univariate and multivariate analyses were performed to identify the prognostic value of each factor.RESULTSBoth the EOR evaluated from CE-T1WI and the EOR evaluated from FLAIR could divide the whole cohort into 4 subgroups with different survival outcomes (p < 0.001). Cases were stratified into 2 subtypes based on VFLAIR/VCE-T1WIwith a cutoff of 10: a proliferation-dominant subtype and a diffusion-dominant subtype. Kaplan-Meier analysis showed a significant survival advantage for the proliferation-dominant subtype (p < 0.0001). The prognostic implication has been further confirmed in the Cox proportional hazards model (HR 1.105, 95% CI 1.078–1.134, p < 0.0001). The survival of patients with proliferation-dominant HGA was significantly prolonged in association with extensive resection of the FLAIR abnormality region beyond contrast-enhancing tumor (p = 0.03), while no survival benefit was observed in association with the extensive resection in the diffusion-dominant subtype (p=0.86).CONCLUSIONSVFLAIR/VCE-T1WIis an important classifier that could divide the HGA into 2 subtypes with distinct invasive features. Patients with proliferation-dominant HGA can benefit from extensive resection of the FLAIR abnormality region, which provides the theoretical basis for a personalized resection strategy.

Comparison of PROMIS Anxiety and Depression, PHQ-8, and GAD-7 to screen for anxiety and depression among patients presenting for spine surgery

2019 ◽  
Vol 30 (4) ◽  
pp. 524-531
Author(s):  
Taylor E. Purvis ◽  
Brian J. Neuman ◽  
Lee H. Riley ◽  
Richard L. Skolasky
Keyword(s):  
Major Depression ◽  
Spine Surgery ◽  
Characteristic Curve ◽  
Depression Scale ◽  
Data Driven ◽  
Anxiety And Depression ◽  
Measurement Information ◽  
Generalized Anxiety ◽  
Cut Points ◽  
Patient Reported

OBJECTIVEIn this paper, the authors demonstrate to spine surgeons the prevalence and severity of anxiety and depression among patients presenting for surgery and explore the relationships between different legacy and Patient-Reported Outcomes Measurement Information System (PROMIS) screening measures.METHODSA total of 512 adult spine surgery patients at a single institution completed the 7-item Generalized Anxiety Disorder questionnaire (GAD-7), 8-item Patient Health Questionnaire (PHQ-8) depression scale, and PROMIS Anxiety and Depression computer-adaptive tests (CATs) preoperatively. Correlation coefficients were calculated between PROMIS scores and GAD-7 and PHQ-8 scores. Published reference tables were used to determine the presence of anxiety or depression using GAD-7 and PHQ-8. Sensitivity and specificity of published guidance on the PROMIS Anxiety and Depression CATs were compared. Guidance from 3 sources was compared: published GAD-7 and PHQ-8 crosswalk tables, American Psychiatric Association scales, and expert clinical consensus. Receiver operator characteristic curves were used to determine data-driven cut-points for PROMIS Anxiety and Depression. Significance was accepted as p < 0.05.RESULTSIn 512 spine surgery patients, anxiety and depression were prevalent preoperatively (5% with any anxiety, 24% with generalized anxiety screen-positive; and 54% with any depression, 24% with probable major depression). Correlations were moderately strong between PROMIS Anxiety and GAD-7 scores (r = 0.72; p < 0.001) and between PROMIS Depression and PHQ-8 scores (r = 0.74; p < 0.001). The observed correlation of the PROMIS Depression score was greater with the PHQ-8 cognitive/affective score (r = 0.766) than with the somatic score (r = 0.601) (p < 0.001). PROMIS Anxiety and Depression CATs were able to detect the presence of generalized anxiety screen-positive (sensitivity, 86.0%; specificity, 81.6%) and of probable major depression (sensitivity, 82.3%; specificity, 81.4%). Receiver operating characteristic curve analysis demonstrated data-driven cut-points for these groups.CONCLUSIONSPROMIS Anxiety and Depression CATs are reliable tools for identifying generalized anxiety screen-positive spine surgery patients and those with probable major depression.

Determination and Compare Analysis for Soil Water Characteristic Curve Model in Planting Soil in Saline Region of Tianjin

2010 ◽  
Vol 12 (3) ◽  
pp. 336-341
Author(s):  
Fei CAI ◽  
Xiaohou SHAO ◽  
Zhenyu WANG ◽  
Mingyong HUANG ◽  
Yaming ZHAI ◽  
...  
Keyword(s):  
Soil Water ◽  
Characteristic Curve ◽  
Soil Water Characteristic Curve ◽  
Curve Model ◽  
Compare Analysis

Identify Compounds' Target Against Alzheimer's Disease Based on In-Silico Approach

2019 ◽  
Vol 16 (3) ◽  
pp. 193-208 ◽  
Author(s):  
Yan Hu ◽  
Guangya Zhou ◽  
Chi Zhang ◽  
Mengying Zhang ◽  
Qin Chen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Small Molecule ◽  
Characteristic Curve ◽  
Machine Learning Algorithms ◽  
Learner Model ◽  
Mao B ◽  
Number Of Patients ◽  
Sar Model ◽  
Set Up

Background: Alzheimer's disease swept every corner of the globe and the number of patients worldwide has been rising. At present, there are as many as 30 million people with Alzheimer's disease in the world, and it is expected to exceed 80 million people by 2050. Consequently, the study of Alzheimer’s drugs has become one of the most popular medical topics. Methods: In this study, in order to build a predicting model for Alzheimer’s drugs and targets, the attribute discriminators CfsSubsetEval, ConsistencySubsetEval and FilteredSubsetEval are combined with search methods such as BestFirst, GeneticSearch and Greedystepwise to filter the molecular descriptors. Then the machine learning algorithms such as BayesNet, SVM, KNN and C4.5 are used to construct the 2D-Structure Activity Relationship(2D-SAR) model. Its modeling results are utilized for Receiver Operating Characteristic curve(ROC) analysis. Results: The prediction rates of correctness using Randomforest for AChE, BChE, MAO-B, BACE1, Tau protein and Non-inhibitor are 77.0%, 79.1%, 100.0%, 94.2%, 93.2% and 94.9%, respectively, which are overwhelming as compared to those of BayesNet, BP, SVM, KNN, AdaBoost and C4.5. Conclusion: In this paper, we conclude that Random Forest is the best learner model for the prediction of Alzheimer’s drugs and targets. Besides, we set up an online server to predict whether a small molecule is the inhibitor of Alzheimer's target at http://47.106.158.30:8080/AD/. Furthermore, it can distinguish the target protein of a small molecule.

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