2174-PUB: Profiling of Young Diabetes in India: A Cross-Sectional Analysis Report

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 2174-PUB
Author(s):  
NARAYANAN NK ◽  
CS DWARAKANATH ◽  
VENKATARAMAN S ◽  
MANIKANDAN RM ◽  
NARENDRA BS ◽  
...  
2012 ◽  
Vol 58 (4) ◽  
pp. 472-476 ◽  
Author(s):  
Caroline Filla Rosaneli ◽  
Flavia Auler ◽  
Carla Barreto Manfrinato ◽  
Claudine Filla Rosaneli ◽  
Caroline Sganzerla ◽  
...  

2017 ◽  
Vol 48 (S 01) ◽  
pp. S1-S45
Author(s):  
M. Zielonka ◽  
S. Garbade ◽  
S. Kölker ◽  
G. Hoffmann ◽  
M. Ries

2019 ◽  
Author(s):  
Patricia Clark ◽  
Annarella Barbato ◽  
Miguel Angel Guagnelli ◽  
Jose Alberto Rascon ◽  
Edgar Denova ◽  
...  

Author(s):  
B. Domengès ◽  
P. Poirier

Abstract In this study, the resistance of FIB prepared vias was characterized by the Kelvin probe technique and their physical characteristics studied using cross-sectional analysis. Two domains of resistivity were isolated in relation to the ion beam current used for the deposition of the via metal (Pt). Also submicrometer vias were investigated on 4.2 µm deep metal lines of a BiCMOS aluminum based design and a CMOS 090 copper based one. It is shown that the controlling parameter is the shape and volume of the contact, and that the contact formation is favored by the amount of over-mill of the via into the metal line it will contact.


2019 ◽  
Author(s):  
Yanink Caro-Vega ◽  
Pablo F. Belaunzarán-Zamudio ◽  
Jesús Alegre-Díaz ◽  
Brenda Crabtree-Ramírez ◽  
Raúl Ramírez-Reyes ◽  
...  

2000 ◽  
Vol 19 (2) ◽  
pp. 159-174 ◽  
Author(s):  
B. Charlene Henderson ◽  
Steven E. Kaplan

This study investigates the determinants of audit report lag (ARL) for a sample of banks. Researchers have been interested in the determinants of ARL, in part, because it impacts the timeliness of public disclosures. However, prior ARL research has relied exclusively on regression analysis of cross-sectional samples of companies from many industries. In addition to focusing exclusively on banks, panel data analysis is introduced and compared with cross-sectional analysis to demonstrate its power in dynamic settings and its potential to improve estimation. Results reveal important differences between cross-sectional analysis and panel data analysis. First, bank size is negatively related to ARL in cross-section but positively related to ARL using panel data analysis. The cross-sectional size estimate is subject to omitted variables bias, and furthermore, cross-sectional analysis fails to capture variation in size over time in relation to ARL. Panel data analysis both accounts for omitted variables and captures the dynamics of the relationship between size and ARL. As well, the panel data model's explanatory power far exceeds that of the cross-sectional model. This is primarily due to the panel model's use of firm-specific intercepts that both capture the role of reporting tradition and eliminate heterogeneity bias. Thus, panel data analysis proves to be a powerful tool in the analysis of ARL.


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