Relational Learning Based Happiness Intensity Analysis in a Group

2016 ◽  
Author(s):  
Tuoerhongjiang Yusufu ◽  
Naifan Zhuang ◽  
Kai Li ◽  
Kien A. Hua
Keyword(s):  
Relational Learning ◽  
Intensity Analysis

Penentuan Metode Intensitas Hujan Berdasarkan Karakteristik Hujan dari Stasiun Pengamat Hujan Disekitar Kecamatan Karawang Timur

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Melisa Permatasari ◽  
M. Candra Nugraha ◽  
Etih Hartati
Keyword(s):  
Drainage System ◽  
Daily Rainfall ◽  
Intensity Analysis ◽  
Rain Intensity ◽  
Rainy Period ◽  
Discharge Plan ◽  
Calculation Results ◽  
Value Determination ◽  
Maximum Daily Rainfall ◽  
Method Approach

<p>The rain intensity is the high rainfall in unit of time. The length of rain will be reversed by the amount rain intensity. The shorter time the rain lasts, the greater of the intensity and re-period of its rain. The value of rain intensity is required to calculate the flood discharge plan on the drainage system planning area in East Karawang district. Determining the value rain intensity is required the maximum daily rainfall data obtained from the main observer stations in the Plawad station planning area. The method of determination rain intensity analysis can be done with three methods: Van Breen, Bell Tanimoto and Hasper der Weduwen. Selected method is based on the smallest deviation value. Determination deviation value is determined by comparing rain intensity value of Van Breen method, Bell Tanimoto, Hasper der Weduwen. By comparing rain intensity value of the Van Breen method, Bell Tanimoto, Hasper der Weduwen with the results of calculating three methods through the method approach Talbot, Sherman and Ishiguro. Calculation results show that the method of rain has smallest deviation standard is method Van Breen with Talbot approach for rainy period (PUH) 2, 5, 10, 25, 50 and 100 years.</p>

Whole-Field Indocyanine Green Intensity Analysis to Intraoperatively Predict Cerebral Hyperperfusion Syndrome Following Superficial Temporal Artery-Middle Cerebral Artery Bypass: A Retrospective Case-Control Study in 7-Year Experience With 112 Cases

2019 ◽  
Author(s):  
Yosuke Sato ◽  
Tatsuya Sugiyama ◽  
Tohru Mizutani
Keyword(s):  
Case Control Study ◽  
Superficial Temporal Artery ◽  
Temporal Artery ◽  
Case Control ◽  
Control Group ◽  
Intensity Analysis ◽  
Retrospective Case ◽  
Cerebral Hyperperfusion Syndrome ◽  
Cerebral Hyperperfusion ◽  
Control Study

Abstract BACKGROUND Intraoperative prediction of postoperative cerebral hyperperfusion syndrome (CHS) after cerebrovascular bypass surgery is challenging. OBJECTIVE To conduct a retrospective case-control study with indocyanine green (ICG) intensity analysis of the superficial temporal artery-middle cerebral artery (STA-MCA) bypass and investigate whether its washout pattern might be a marker for intraoperative prediction of CHS. METHODS Between 2012 and 2018, 6 of 112 patients (5.4%) that underwent STA-MCA bypass exhibited CHS. We selected 5 patients with CHS (3 with atherosclerotic cerebrovascular disease [ASCVD] and 2 with moyamoya) and 15 patients without CHS (60% ASCVD and 40% moyamoya) as a matched control group. During prebypass and postbypass, washout times (WTs) for the first 10%, 25%, 50%, and 75% of maximum ICG intensity measured in the whole-camera field were compared between groups. The changes in WT (ΔWT) from prebypass to postbypass for each ICG intensity level were compared between groups. The cutoff ΔWTs, sensitivities, and specificities were also calculated. RESULTS Postbypass WTs were significantly longer in the CHS group than the control group at all ICG intensities (P < .05). ΔWT was significantly greater in the CHS group than the control group for the first 10%, 25%, and 50% ICG intensities (P < .001). A cutoff ΔWT of ≥2.66 s for the first 50% ICG intensity showed a sensitivity of 100% and specificity of 100%. CONCLUSION We found that a ΔWT ≥2.66 s for the first 50% ICG intensity could be an intraoperative predictive factor for CHS.

Dirichlet enhanced relational learning

2005 ◽  
Author(s):  
Zhao Xu ◽  
Volker Tresp ◽  
Kai Yu ◽  
Shipeng Yu ◽  
Hans-Peter Kriegel
Keyword(s):  
Relational Learning

scholarly journals Relational Learning Improves Prediction of Mortality in COVID-19 in the Intensive Care Unit

2021 ◽  
pp. 1-1
Author(s):  
Tingyi Wanyan ◽  
Akhil Vaid ◽  
Jessica K De Freitas ◽  
Sulaiman Somani ◽  
Riccardo Miotto ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Relational Learning ◽  
Prediction Of Mortality

CHEMICAL RECONSTRUCTION OF THE TiAl(010) SURFACE

1995 ◽  
Vol 02 (02) ◽  
pp. 183-189 ◽  
Author(s):  
C.P. WANG ◽  
S.K. KIM ◽  
F. JONA ◽  
D.R. STRONGIN ◽  
B.-R. SHEU ◽  
...  
Keyword(s):  
Electron Diffraction ◽  
Atomic Structure ◽  
Atomic Layer ◽  
Interlayer Spacing ◽  
Low Energy ◽  
Intensity Analysis ◽  
Low Energy Electron Diffraction ◽  
Surface Phases ◽  
Low Energy Electron ◽  
Bulk Structure

The atomic structure of a clean (010) surface of the ordered binary alloy TiAl (with tetragonal bulk structure of the CuAu I type) is studied with quantitative low-energy electron diffraction (QLEED). Two different surface phases are found depending on the preparation procedure. After a cleaning step in vacuo by means of Ar-ion bombardments, anneals at 750−850°C produce a 2×1 surface and anneals at about 900° C produce a 1×1 surface. A QLEED intensity analysis of the 1×1 structure reveals the occurrence of chemical reconstruction, whereby the Ti atoms in the first layer exchange places with the Al atoms in the second layer. Thus, while any bulk (010) plane contains 50% Al and 50% Ti , the top atomic layer of a (010) surface contains 100% Al and the second atomic layer contains 100% Ti . Both layers are slightly buckled and the first interlayer distance is compressed about 7.1% while the second interlayer spacing is expanded about 7.4% with respect to the bulk value.

scholarly journals 1P142 Configuration of Myosin Crossbridges in Relaxed and Contracting Muscles Determined by Intensity Analysis of Myosin-based Reflections

2005 ◽  
Vol 45 (supplement) ◽  
pp. S67
Author(s):  
K. Oshima ◽  
Y. Takazawa ◽  
T. Kobayashi ◽  
C. Irving Thomas ◽  
K. Wakabayashi
Keyword(s):  
Intensity Analysis

scholarly journals Estimation of coronary wave intensity analysis using noninvasive techniques and its application to exercise physiology

2016 ◽  
Vol 310 (5) ◽  
pp. H619-H627 ◽  
Author(s):  
Christopher J. Broyd ◽  
Sukhjinder Nijjer ◽  
Sayan Sen ◽  
Ricardo Petraco ◽  
Siana Jones ◽  
...  
Keyword(s):  
Coronary Circulation ◽  
Exercise Physiology ◽  
Left Ventricular ◽  
Wave Intensity ◽  
Concordance Correlation Coefficient ◽  
Intensity Analysis ◽  
Wave Intensity Analysis ◽  
Concordance Correlation ◽  
Decompression Wave ◽  
Pulsed Wave Doppler

Wave intensity analysis (WIA) has found particular applicability in the coronary circulation where it can quantify traveling waves that accelerate and decelerate blood flow. The most important wave for the regulation of flow is the backward-traveling decompression wave (BDW). Coronary WIA has hitherto always been calculated from invasive measures of pressure and flow. However, recently it has become feasible to obtain estimates of these waveforms noninvasively. In this study we set out to assess the agreement between invasive and noninvasive coronary WIA at rest and measure the effect of exercise. Twenty-two patients (mean age 60) with unobstructed coronaries underwent invasive WIA in the left anterior descending artery (LAD). Immediately afterwards, noninvasive LAD flow and pressure were recorded and WIA calculated from pulsed-wave Doppler coronary flow velocity and central blood pressure waveforms measured using a cuff-based technique. Nine of these patients underwent noninvasive coronary WIA assessment during exercise. A pattern of six waves were observed in both modalities. The BDW was similar between invasive and noninvasive measures [peak: 14.9 ± 7.8 vs. −13.8 ± 7.1 × 104 W·m−2·s−2, concordance correlation coefficient (CCC): 0.73, P < 0.01; cumulative: −64.4 ± 32.8 vs. −59.4 ± 34.2 × 102 W·m−2·s−1, CCC: 0.66, P < 0.01], but smaller waves were underestimated noninvasively. Increased left ventricular mass correlated with a decreased noninvasive BDW fraction ( r = −0.48, P = 0.02). Exercise increased the BDW: at maximum exercise peak BDW was −47.0 ± 29.5 × 104 W·m−2·s−2 ( P < 0.01 vs. rest) and cumulative BDW −19.2 ± 12.6 × 103 W·m−2·s−1 ( P < 0.01 vs. rest). The BDW can be measured noninvasively with acceptable reliably potentially simplifying assessments and increasing the applicability of coronary WIA.

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