The fractal correlation between relaxation dynamics and atomic-level structures observed in metallic glasses by computer simulation

2017 ◽  
Vol 19 (25) ◽  
pp. 16850-16856 ◽  
Author(s):  
M. H. Yang ◽  
J. H. Li ◽  
B. X. Liu
Keyword(s):  
Computer Simulation ◽  
Hierarchical Clustering ◽  
Metallic Glasses ◽  
Clustering Analysis ◽  
Atomic Level ◽  
Hierarchical Clustering Analysis ◽  
Relaxation Dynamics

Hierarchical clustering analysis shows that the activating atoms are excited in a cooperative and avalanche-like model to form activating units. Interestingly, a fractal correlation is found between the number and size of the activating units.

scholarly journals The visual categorization of production automotive seats on descriptors of comfort

Work ◽  
2021 ◽  
Vol 68 (s1) ◽  
pp. S69-S85
Author(s):  
Tugra Erol ◽  
Cyriel Diels ◽  
James Shippen ◽  
Dale Richards
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Analysis ◽  
The Other ◽  
Visual Design ◽  
Hierarchical Clustering Analysis ◽  
Card Sorting ◽  
Design Attributes ◽  
Comfort Perception ◽  
Automotive Production

BACKGROUND: The role of appearance of automotive seats on perceived comfort and comfort expectancy has been acknowledged in previous research but it has not been investigated in depth. OBJECTIVE: To identify the effects of the appearance of production automotive seats, based on the hypothesis that visual design differentiations are affective in creating comfort expectations. The significance of the descriptors Sporty, Luxurious and Comfortable and the associated visual design attributes was of interest. METHOD: Images from 38 automotive production seats were used in an image-based card sorting app (qCard) with a total of 24 participants. Participants were asked to categorize the different seat designs varying from 1: least, to 9: most for all three descriptors.The resulting data was analyzed using hierarchical clustering analysis. RESULTS: The results indicated that the perceived Sporty, Luxurious and Comfortable were descriptor items that significantly differentiated seats with certain design attributes. It was found that for the Sporty perception the integrated headrest design and angular shapes were key. On the other hand, the Comfort perception was characterised by seating with a separate headrest and rounded seat back/cushion shapes. CONCLUSIONS: For seat design processes, the method enables a practical way to identify elements conveying Sporty, Comfortable and Luxurious perception.

scholarly journals High plasma C-terminal FGF-23 levels predict poor outcomes in patients with chronic kidney disease superimposed with acute kidney injury

2020 ◽  
Vol 11 ◽  
pp. 204062232096416
Author(s):  
Yu-Hsing Chang ◽  
Che-Hsiung Wu ◽  
Nai-Kuan Chou ◽  
Li-Jung Tseng ◽  
i-Ping Huang ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Acute Kidney Injury ◽  
Kidney Disease ◽  
Hospital Discharge ◽  
Risk Prediction ◽  
Hierarchical Clustering ◽  
Risk Score ◽  
Clustering Analysis ◽  
Kidney Injury ◽  
Hierarchical Clustering Analysis

Background: Elevated plasma C-terminal fibroblast growth factor-23 (cFGF-23) levels are associated with higher mortality in patients with chronic kidney disease (CKD) and acute kidney injury (AKI). Our study explored the outcome forecasting accuracy of cFGF-23 in critically ill patients with CKD superimposed with AKI (ACKD). Methods: Urine and plasma biomarkers from 149 CKD patients superimposed with AKI before dialysis were checked in this multicenter prospective observational cohort study. Endpoints were 90-day mortality and 90 days free from dialysis after hospital discharge. Associations with study endpoints were assessed using hierarchical clustering analysis, the generalized additive model, the Cox proportional hazard model, competing risk analysis, and discrimination evaluation. Results: Over a median follow up of 40 days, 67 (45.0%) patients died before the 90th day after hospital discharge and 39 (26.2%) progressed to kidney failure with replacement therapy (KFRT). Hierarchical clustering analysis demonstrated that cFGF-23 levels had better predictive ability for 90-day mortality than did other biomarkers. Higher serum cFGF-23 levels were independently associated with greater risk for 90-day mortality [hazard ratio (HR): 2.5; 95% confidence interval (CI) 1.5–4.1; p < 0.001]. Moreover, adding plasma cFGF-23 to the Demirjian AKI risk score model substantially improved risk prediction for 90-day mortality than the Demirjian model alone (integrated discrimination improvement: 0.06; p < 0.05; 95% CI 0.02–0.10). The low plasma cFGF-23 group was predicted having more weaning from dialysis in surviving patients (HR = 0.53, 95% CI, 0.29–0.95, p = 0.05). Conclusions: In patients with ACKD, plasma cFGF-23 levels are an independent risk factor to forecast 90-day mortality and 90-day progression to KFRT. In combination with the clinical risk score, plasma cFGF-23 levels could substantially improve mortality risk prediction.

scholarly journals Investigating the Significant Individual Historical Factors of Driving Risk Using Hierarchical Clustering Analysis and Quasi-Poisson Regression Model

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2331
Author(s):  
Hasan A.H. Naji ◽  
Qingji Xue ◽  
Ke Zheng ◽  
Nengchao Lyu
Keyword(s):  
Regression Model ◽  
Hierarchical Clustering ◽  
Clustering Analysis ◽  
Poisson Regression ◽  
Individual Risk ◽  
Hierarchical Clustering Analysis ◽  
Poisson Regression Model ◽  
Historical Factors ◽  
Driving Risk ◽  
Naturalistic Driving

Driving risk varies substantially according to many factors related to the driven vehicle, environmental conditions, and drivers. This study explores the contributing historical factors of driving risk with hierarchical clustering analysis and the quasi-Poisson regression model. The dataset of the study was collected from two sources: naturalistic driving experiments and self-reports. The drivers who participated in the naturalistic driving experiment were categorized into four risk groups according to their near-crash frequency with the hierarchical clustering method. Moreover, a quasi-Poisson model was used to identify the essential factors of individual driving risk. The findings of this study indicated that historical driving factors have substantial impacts on individual risk of drivers. These factors include the total number of miles driven, the driver’s age, the number of illegal parking (past three years), the number of over-speeding (past three years) and passing red lights (past three years). The outcome of the study can help transportation officials, educators, and researchers to consider the influencing factors on individual driving risk and can give insights and provide suggestions to improve driving safety.

Molecular Classification of HPV-Associated Head Neck Cancer

2008 ◽  
Vol 139 (2_suppl) ◽  
pp. P91-P91
Author(s):  
Paul M. Weinberger ◽  
Ziwei Yu ◽  
Panteleimon Kountourakis ◽  
Clarence T Sasaki ◽  
Diane Kowalski ◽  
...  
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Analysis ◽  
Class Ii ◽  
Class I ◽  
Hierarchical Clustering Analysis ◽  
Class Iii ◽  
Class Model ◽  
Hpv Dna ◽  
Clustering Patterns ◽  
P16 Expression

Problem We previously proposed that head and neck squamous cell carcinoma (HNSCC) presents in three histopathologically similar but molecularly distinct types where p16 expression combined with high-risk Human Papillomavirus (HPV) presence defines three distinct subgroups: Class I (HPV-), Class II (HPV16+/p16-), Class III (HPV16+/p16+). In this study we sought to further explore and validate this classification schema. Methods Paraffin-embedded specimens from 77 patients with OSCC classified into the 3-class model based on p16 expression and HPV DNA presence were previously queried for expression of 14 target proteins with known involvement in tumor progression using AQUA(tm). We calculated pairwise associations by Spearman correlation and performed hierarchical clustering analysis to assess global expression patterns of class I, II and III tumors. Results Hierarchical clustering analysis demonstrated class I and class II tumors displayed similar clustering patterns. Class III tumors (HPV+/p16+) displayed unique clustering patterns distinct from both class I and class II tumors. In class I/II tumors ERK2, p27, p14, ki67 and PCNA were tightly associated. This contrasted with class III tumors which displayed two separate clusters (EGFR, RB, pAKT and CND1), and (ERK2, p16 and p14). Conclusion We demonstrated that HNSCC classified by a combination of HPV DNA presence and p16 expression status have distinct molecular phenotypes. Class III tumors, distinct from HPV+ but p16-negative class II tumors, have a unique molecular clustering profile, lending support to the proposed 3-class model. Significance These findings may have significant implications in patient selection for HPV-specific therapies. Support AAO/HNSF Resident Research Grant 26357 (PMW).

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