Theoretical Modeling for Predicting Material Removal Rate through Interelectrode Gap

Abstract In EDM, the thermal energy of the discharge causing material erosion which is supplied by the power source unit as electrical input. The discharge energy may be recognized by the current and voltage pulses on time transient discharge characteristic curve (V-I curve) during machining. However, the plasma resistance is very short for a smaller interelectrode gap in micro-EDM compared to the impedance of the circuit. Hence, direct probe-based measurement of current and voltage pulses may include the voltage drop across the stray impedance which causes variation in its exact value. Here, a modeling-based approach may help to analyze the energy interaction with the interelectrode gap. This article presents a theoretical modeling approach to predict the interelectrode gap based on gap voltage, gap current, and plasma characteristics. Initially, a simplified two-dimensional heat conduction equation (cylindrical form) was studied to understand the asymmetry of heat flow in Gaussian distribution. A numerical analysis of a single discharge pulse was considered by applying some basic assumptions. A numerical model has been developed to predict gap distance and MRR considering gap voltage, gap current, and plasma properties. The predicted model was validated against previously reported data from the literature. Later on, the impact of gap voltage on gap distance, plasma resistance, and material erosion rate was analyzed and discussed briefly.

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Relative contributions of family history and a polygenic risk score on COPD and related outcomes: COPDGene and ECLIPSE studies

BMJ Open Respiratory Research ◽

10.1136/bmjresp-2020-000755 ◽

2020 ◽

Vol 7 (1) ◽

pp. e000755

Author(s):

Matthew Moll ◽

Sharon M. Lutz ◽

Auyon J. Ghosh ◽

Phuwanat Sakornsakolpat ◽

Craig P. Hersh ◽

...

Keyword(s):

Family History ◽

Risk Score ◽

Characteristic Curve ◽

Significant Proportion ◽

Chronic Obstructive ◽

Polygenic Risk Score ◽

Positive Interaction ◽

Polygenic Risk ◽

Mediation Analyses ◽

The Impact

IntroductionFamily history is a risk factor for chronic obstructive pulmonary disease (COPD). We previously developed a COPD risk score from genome-wide genetic markers (Polygenic Risk Score, PRS). Whether the PRS and family history provide complementary or redundant information for predicting COPD and related outcomes is unknown.MethodsWe assessed the predictive capacity of family history and PRS on COPD and COPD-related outcomes in non-Hispanic white (NHW) and African American (AA) subjects from COPDGene and ECLIPSE studies. We also performed interaction and mediation analyses.ResultsIn COPDGene, family history and PRS were significantly associated with COPD in a single model (PFamHx <0.0001; PPRS<0.0001). Similar trends were seen in ECLIPSE. The area under the receiver operator characteristic curve for a model containing family history and PRS was significantly higher than a model with PRS (p=0.00035) in NHWs and a model with family history (p<0.0001) alone in NHWs and AAs. Both family history and PRS were significantly associated with measures of quantitative emphysema and airway thickness. There was a weakly positive interaction between family history and the PRS under the additive, but not multiplicative scale in NHWs (relative excess risk due to interaction=0.48, p=0.04). Mediation analyses found that a significant proportion of the effect of family history on COPD was mediated through PRS in NHWs (16.5%, 95% CI 9.4% to 24.3%), but not AAs.ConclusionFamily history and the PRS provide complementary information for predicting COPD and related outcomes. Future studies can address the impact of obtaining both measures in clinical practice.

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Increased Construct Stiffness With Meniscal Repair Sutures and Devices Increases the Risk of Cheese-Wiring During Biomechanical Load-to-Failure Testing

Orthopaedic Journal of Sports Medicine ◽

10.1177/23259671211015674 ◽

2021 ◽

Vol 9 (6) ◽

pp. 232596712110156

Author(s):

Sebastian Müller ◽

Tanja Schwenk ◽

Michael de Wild ◽

Dimitris Dimitriou ◽

Claudio Rosso

Keyword(s):

Characteristic Curve ◽

Meniscal Repair ◽

Suture Fixation ◽

Contributing Factors ◽

Mode Of Failure ◽

Load To Failure ◽

Pull Through ◽

Optimal Stiffness ◽

The Impact ◽

Inside Out

Background: Cheese-wiring, the suture that cuts through the meniscus, is a well-known issue in meniscal repair. So far, contributing factors are neither fully understood nor sufficiently studied. Hypothesis/Purpose: To investigate whether the construct stiffness of repair sutures and devices correlates with suture cut-through (cheese-wiring) during load-to-failure testing. Study Design: Controlled laboratory study. Methods: In 131 porcine menisci, longitudinal bucket-handle tears were repaired using either inside-out sutures (n = 66; No. 0 Ultrabraid, 2-0 Orthocord, 2-0 FiberWire, and 2-0 Ethibond) or all-inside devices (n = 65; FastFix360, Omnispan, and Meniscal Cinch). After cyclic loading, load-to-failure testing was performed. The mode of failure and construct stiffness were recorded. A receiver operating characteristic curve analysis was performed to define the optimal stiffness threshold for predicting meniscal repair failure by cheese-wiring. The 2-tailed t test and analysis of variance were used to test significance. Results: Loss of suture fixation was the most common mode of failure in all specimens (58%), except for the Omnispan, which failed most commonly because of anchor pull-through. The Omnispan demonstrated the highest construct stiffness (30.8 ± 3.5 N/mm), whereas the Meniscal Cinch (18.0 ± 8.8 N/mm) and Ethibond (19.4 ± 7.8 N/mm) demonstrated the lowest construct stiffness. The Omnispan showed significantly higher stiffness compared with the Meniscal Cinch ( P < .001) and Ethibond ( P = .02), whereas the stiffness of the Meniscal Cinch was significantly lower compared with that of the FiberWire ( P = .01), Ultrabraid ( P = .04), and FastFix360 ( P = .03). While meniscal repair with a high construct stiffness more often failed by cheese-wiring, meniscal repair with a lower stiffness failed by loss of suture fixation, knot slippage, or anchor pull-through. Meniscal repair with a stiffness >26.5 N/mm had a 3.6 times higher risk of failure due to cheese-wiring during load-to-failure testing (95% CI, 1.4-8.2; P < .0001). Conclusion: Meniscal repair using inside-out sutures and all-inside devices with a higher construct stiffness (>26.5 N/mm) was more likely to fail through suture cut-through (cheese-wiring) than that with a lower stiffness (≤26.5 N/mm). Clinical Relevance: This is the first study investigating the impact of construct stiffness on meniscal repair failure by suture cut-through (cheese-wiring).

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The Experiment on Soil Water Characteristic Curve Considering the Effect of Urea Concentration

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.919-921.795 ◽

2014 ◽

Vol 919-921 ◽

pp. 795-799

Author(s):

Gai Qing Dai ◽

Dong Fang Tian ◽

Yao Ruan ◽

Lang Tian ◽

You Le Wang

Keyword(s):

Water Content ◽

Soil Water ◽

Characteristic Curve ◽

Urea Concentration ◽

Test Method ◽

Materials Selection ◽

Valuable Data ◽

Soil Water Characteristic Curve ◽

Concentration Test ◽

The Impact

A new soil water characteristic curve (SWCC) experiment contemplating urea concentration is presented in the paper. We focus on the impact of the SWCC considering urea concentration test method for materials selection and introduction, experimental results, and finally, we have conducted some experiments of SWCC and obtained some valuable data which could affect urea concentration. By using linear fitting, an exponential function between water content and suction and urea concentration is established.

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Reduction of Harmonics Cause of Inrush Current for Transformer

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.d1868.039520 ◽

2020 ◽

Vol 9 (5) ◽

pp. 1-4

Keyword(s):

Voltage Drop ◽

Inrush Current ◽

Source Impedance ◽

Different Types ◽

Current Reduction ◽

Switching Method ◽

The Impact ◽

Transformer Core ◽

Secondary Winding ◽

Sequential Switching

Due to the impact of inrush current of transformer, made a voltage drop by source impedance as well as by sensitive loads. That’s why, necessary action is required to reduce the inrush current. Many factors like, secondary winding load, material of transformer core, Saturation flux/residual flux of transformer core, capacity of transformer, supply impedance, voltage-starting phase angle, are affecting the inrush current of transformer. Some different types of methods for inrush current reduction are listed in this paper. Out of that, sequential switching method is adopted for the reduction inrush current.

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Experimental Study of the Chaotic Jerk Circuit Application for Chaos Shift Keying

Latvian Journal of Physics and Technical Sciences ◽

10.2478/lpts-2021-0033 ◽

2021 ◽

Vol 58 (4) ◽

pp. 55-68

Author(s):

F. Capligins ◽

A. Litvinenko ◽

A. Aboltins ◽

E. Austrums ◽

A. Rusins ◽

...

Keyword(s):

Experimental Study ◽

Voltage Drop ◽

Experimental Studies ◽

Chaotic Synchronization ◽

Channel Noise ◽

Error Feedback ◽

Shift Keying ◽

The Stability ◽

Chaos Shift Keying ◽

The Impact

Abstract The paper presents a study of the chaotic jerk circuit (CJC) employment capabilities for digital communications. The concept of coherent chaos shift keying (CSK) communication system with controlled error feedback chaotic synchronization is proposed for a specific CJC in two modifications. The stability of chaotic synchronization between the two CJCs was evaluated in terms of voltage drop at the input of the slave circuit and the impact of channel noise using simulations and experimental studies.

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Solidification Enhancement in a Multi-Tube Latent Heat Storage System for Efficient and Economical Production: Effect of Number, Position and Temperature of the Tubes

Nanomaterials ◽

10.3390/nano11123211 ◽

2021 ◽

Vol 11 (12) ◽

pp. 3211

Author(s):

Min Li ◽

Jasim M. Mahdi ◽

Hayder I. Mohammed ◽

Dmitry Olegovich Bokov ◽

Mustafa Z. Mahmoud ◽

...

Keyword(s):

Free Convection ◽

Heat Storage ◽

Liquid Fraction ◽

Removal Rate ◽

Heat Removal ◽

Vertical Direction ◽

Heat Transfer Fluid ◽

Latent Heat Storage ◽

Solidification Mode ◽

The Impact

Thermal energy storage is an important component in energy units to decrease the gap between energy supply and demand. Free convection and the locations of the tubes carrying the heat-transfer fluid (HTF) have a significant influence on both the energy discharging potential and the buoyancy effect during the solidification mode. In the present study, the impact of the tube position was examined during the discharging process. Liquid-fraction evolution and energy removal rate with thermo-fluid contour profiles were used to examine the performance of the unit. Heat exchanger tubes are proposed with different numbers and positions in the unit for various cases including uniform and non-uniform tubes distribution. The results show that moving the HTF tubes to medium positions along the vertical direction is relatively better for enhancing the solidification of PCM with multiple HTF tubes. Repositioning of the HTF tubes on the left side of the unit can slightly improve the heat removal rate by about 0.2 in the case of p5-u-1 and decreases by 1.6% in the case of p5-u-2. It was found also that increasing the distance between the tubes in the vertical direction has a detrimental effect on the PCM solidification mode. Replacing the HTF tubes on the left side of the unit negatively reduces the heat removal rate by about 1.2 and 4.4%, respectively. Further, decreasing the HTF temperature from 15 °C to 10 and 5 °C can increase the heat removal rate by around 7 and 16%, respectively. This paper indicates that the specific concern to the HTF tube arrangement should be made to improve the discharging process attending free convection impact in phase change heat storage.

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Radiomics of high-resolution computed tomography for the differentiation between cholesteatoma and middle ear inflammation: effects of post-reconstruction methods in a dual-center study

European Radiology ◽

10.1007/s00330-020-07564-4 ◽

2020 ◽

Author(s):

Christophe T. Arendt ◽

Doris Leithner ◽

Marius E. Mayerhoefer ◽

Peter Gibbs ◽

Christian Czerny ◽

...

Keyword(s):

Computed Tomography ◽

High Resolution ◽

Middle Ear ◽

Characteristic Curve ◽

High Resolution Computed Tomography ◽

Probability Of Error ◽

Haar Wavelet Transform ◽

Reconstruction Methods ◽

The Impact ◽

Center Study

Abstract Objectives To evaluate the performance of radiomic features extracted from high-resolution computed tomography (HRCT) for the differentiation between cholesteatoma and middle ear inflammation (MEI), and to investigate the impact of post-reconstruction harmonization and data resampling. Methods One hundred patients were included in this retrospective dual-center study: 48 with histology-proven cholesteatoma (center A: 23; center B: 25) and 52 with MEI (A: 27; B: 25). Radiomic features (co-occurrence and run-length matrix, absolute gradient, autoregressive model, Haar wavelet transform) were extracted from manually defined 2D-ROIs. The ten best features for lesion differentiation were selected using probability of error and average correlation coefficients. A multi-layer perceptron feed-forward artificial neural network (MLP-ANN) was used for radiomics-based classification, with histopathology serving as the reference standard (70% of cases for training, 30% for validation). The analysis was performed five times each on (a) unmodified data and on data that were (b) resampled to the same matrix size, and (c) corrected for acquisition protocol differences using ComBat harmonization. Results Using unmodified data, the MLP-ANN classification yielded an overall median area under the receiver operating characteristic curve (AUC) of 0.78 (0.72–0.84). Using original data from center A and resampled data from center B, an overall median AUC of 0.88 (0.82–0.99) was yielded, while using ComBat harmonized data, an overall median AUC of 0.89 (0.79–0.92) was revealed. Conclusion Radiomic features extracted from HRCT differentiate between cholesteatoma and MEI. When using multi-centric data obtained with differences in CT acquisition parameters, data resampling and ComBat post-reconstruction harmonization clearly improve radiomics-based lesion classification. Key Points • Unenhanced high-resolution CT coupled with radiomics analysis may be useful for the differentiation between cholesteatoma and middle ear inflammation. • Pooling of data extracted from inhomogeneous CT datasets does not appear meaningful without further post-processing. • When using multi-centric CT data obtained with differences in acquisition parameters, post-reconstruction harmonization and data resampling clearly improve radiomics-based soft-tissue differentiation.

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The Diagnostic Value of Mir-133a in ST Elevation and Non-ST Elevation Myocardial Infarction: A Meta-Analysis

Cells ◽

10.3390/cells9040793 ◽

2020 ◽

Vol 9 (4) ◽

pp. 793

Author(s):

Yehuda Wexler ◽

Udi Nussinovitch

Keyword(s):

Myocardial Infarction ◽

Cardiovascular Disease ◽

Diagnostic Performance ◽

Characteristic Curve ◽

Diagnostic Value ◽

Receiver Operator Characteristic Curve ◽

St Elevation Myocardial Infarction ◽

Diagnostic Potential ◽

St Elevation ◽

The Impact

Numerous studies have reported correlations between plasma microRNA signatures and cardiovascular disease. MicroRNA-133a (Mir-133a) has been researched extensively for its diagnostic value in acute myocardial infarction (AMI). While initial results seemed promising, more recent studies cast doubt on the diagnostic utility of Mir-133a, calling its clinical prospects into question. Here, the diagnostic potential of Mir-133a was analyzed using data from multiple papers. Medline, Embase, and Web of Science were systematically searched for publications containing “Cardiovascular Disease”, “MicroRNA”, “Mir-133a” and their synonyms. Diagnostic performance was assessed using area under the summary receiver operator characteristic curve (AUC), while examining the impact of age, sex, final diagnosis, and time. Of the 753 identified publications, 9 were included in the quantitative analysis. The pooled AUC for Mir-133a was 0.73. Analyses performed separately on studies using healthy vs. symptomatic controls yielded pooled AUCs of 0.89 and 0.68, respectively. Age and sex were not found to significantly affect diagnostic performance. Our findings indicate that control characteristics and methodological inconsistencies are likely the causes of incongruent reports, and that Mir-133a may have limited use in distinguishing symptomatic patients from those suffering AMI. Lastly, we hypothesized that Mir-133a may find a new use as a risk stratification biomarker in patients with specific subsets of non-ST elevation myocardial infarction (NSTEMI).

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Engineering the Interaction Forces to Optimize CMP Performance

MRS Proceedings ◽

10.1557/proc-732-i3.5 ◽

2002 ◽

Vol 732 ◽

Author(s):

G. B. Basim ◽

I. Vakarelski ◽

P. Singh ◽

B. M. Moudgil

Keyword(s):

Material Removal ◽

Surface Deformation ◽

Defect Density ◽

Removal Rate ◽

Particle Interactions ◽

Interaction Forces ◽

High Performing ◽

Optimal Material ◽

Slurry Stability ◽

The Impact

AbstractThe main objective of Chemical Mechanical Polishing (CMP) process is to planarize the metal or dielectric layers deposited on the wafer surfaces in microelectronics device manufacturing. In CMP, slurries containing submicrometer size particles and chemicals are used to achieve planarization. An effective polishing requires an optimal material removal rate with minimal surface deformation. Therefore, it is important to control the particle-substrate interactions that are responsible for the material removal and the particle-particle interactions, which control the slurry stability and consequently the defect density. This paper discusses the impact of interaction forces on polishing, and underlines the scientific guidelines to formulate consistently high performing CMP slurries.

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Assessing the Clinical Impact of Risk Prediction Models With Decision Curves: Guidance for Correct Interpretation and Appropriate Use

Journal of Clinical Oncology ◽

10.1200/jco.2015.65.5654 ◽

2016 ◽

Vol 34 (21) ◽

pp. 2534-2540 ◽

Cited By ~ 92

Author(s):

Kathleen F. Kerr ◽

Marshall D. Brown ◽

Kehao Zhu ◽

Holly Janes

Keyword(s):

Prostate Cancer ◽

Risk Prediction ◽

Prediction Models ◽

Risk Model ◽

Characteristic Curve ◽

Clinical Impact ◽

Adjuvant Radiation ◽

Risk Models ◽

Personal Benefit ◽

The Impact

The decision curve is a graphical summary recently proposed for assessing the potential clinical impact of risk prediction biomarkers or risk models for recommending treatment or intervention. It was applied recently in an article in Journal of Clinical Oncology to measure the impact of using a genomic risk model for deciding on adjuvant radiation therapy for prostate cancer treated with radical prostatectomy. We illustrate the use of decision curves for evaluating clinical- and biomarker-based models for predicting a man’s risk of prostate cancer, which could be used to guide the decision to biopsy. Decision curves are grounded in a decision-theoretical framework that accounts for both the benefits of intervention and the costs of intervention to a patient who cannot benefit. Decision curves are thus an improvement over purely mathematical measures of performance such as the area under the receiver operating characteristic curve. However, there are challenges in using and interpreting decision curves appropriately. We caution that decision curves cannot be used to identify the optimal risk threshold for recommending intervention. We discuss the use of decision curves for miscalibrated risk models. Finally, we emphasize that a decision curve shows the performance of a risk model in a population in which every patient has the same expected benefit and cost of intervention. If every patient has a personal benefit and cost, then the curves are not useful. If subpopulations have different benefits and costs, subpopulation-specific decision curves should be used. As a companion to this article, we released an R software package called DecisionCurve for making decision curves and related graphics.

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