Direct insight to SRAM array devices in advanced FinFET nodes by large scale ultra-fast in-situ characterization

Author(s):  
Yuncheng Song ◽  
Randy Mann ◽  
Sheng Xie ◽  
Lucile C. Teague Sheridan ◽  
Joseph Versaggi ◽  
...  
Nanoscale ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 3346-3373
Author(s):  
Christos Tsakonas ◽  
Marinos Dimitropoulos ◽  
Anastasios C. Manikas ◽  
Costas Galiotis

In this review we highlight the recent progress in 2DM growth on LMCat, which in combination with in situ characterization presents a viable and large-scale sustainable direction that has the prospect of achieving defect-free 2D materials.


Reproduction ◽  
2000 ◽  
pp. 325-335 ◽  
Author(s):  
A Calvo ◽  
LM Pastor ◽  
S Bonet ◽  
E Pinart ◽  
M Ventura

Lectin histochemistry was used to perform in situ characterization of the glycoconjugates present in boar testis and epididymis. Thirteen horseradish peroxidase- or digoxigenin-labelled lectins were used in samples obtained from healthy fertile boars. The acrosomes of the spermatids were stained intensely by lectins with affinity for galactose and N-acetyl-galactosamine residues, these being soybean, peanut and Ricinus communis agglutinins. Sertoli cells were stained selectively by Maackia ammurensis agglutinin. The lamina propria of seminiferous tubules showed the most intense staining with fucose-binding lectins. The Golgi area and the apical part of the principal cells of the epididymis were stained intensely with many lectins and their distribution was similar in the three zones of the epididymis. On the basis of lectin affinity, both testis and epididymis appear to have N- and O-linked glycoconjugates. Spermatozoa from different epididymal regions showed different expression of terminal galactose and N-acetyl-galactosamine. Sialic acid (specifically alpha2,3 neuraminic-5 acid) was probably incorporated into spermatozoa along the extratesticular ducts. These findings indicate that the development and maturation of boar spermatozoa are accompanied by changes in glycoconjugates. As some lectins stain cellular or extracellular compartments specifically, these lectins could be useful markers in histopathological evaluation of diseases of boar testis and epididymis.


1983 ◽  
Author(s):  
K. Arulanandan ◽  
Y. Dafalias ◽  
L. R. Herrmann ◽  
A. Anandarajah ◽  
N. Meegoda

2018 ◽  
Vol 23 (suppl_1) ◽  
pp. e16-e16
Author(s):  
Ahmed Moussa ◽  
Audrey Larone-Juneau ◽  
Laura Fazilleau ◽  
Marie-Eve Rochon ◽  
Justine Giroux ◽  
...  

Abstract BACKGROUND Transitions to new healthcare environments can negatively impact patient care and threaten patient safety. Immersive in situ simulation conducted in newly constructed single family room (SFR) Neonatal Intensive Care Units (NICUs) prior to occupancy, has been shown to be effective in testing new environments and identifying latent safety threats (LSTs). These simulations overlay human factors to identify LSTs as new and existing process and systems are implemented in the new environment OBJECTIVES We aimed to demonstrate that large-scale, immersive, in situ simulation prior to the transition to a new SFR NICU improves: 1) systems readiness, 2) staff preparedness, 3) patient safety, 4) staff comfort with simulation, and 5) staff attitude towards culture change. DESIGN/METHODS Multidisciplinary teams of neonatal healthcare providers (HCP) and parents of former NICU patients participated in large-scale, immersive in-situ simulations conducted in the new NICU prior to occupancy. One eighth of the NICU was outfitted with equipment and mannequins and staff performed in their native roles. Multidisciplinary debriefings, which included parents, were conducted immediately after simulations to identify LSTs. Through an iterative process issues were resolved and additional simulations conducted. Debriefings were documented and debriefing transcripts transcribed and LSTs classified using qualitative methods. To assess systems readiness and staff preparedness for transition into the new NICU, HCPs completed surveys prior to transition, post-simulation and post-transition. Systems readiness and staff preparedness were rated on a 5-point Likert scale. Average survey responses were analyzed using dependent samples t-tests and repeated measures ANOVAs. RESULTS One hundred eight HCPs and 24 parents participated in six half-day simulation sessions. A total of 75 LSTs were identified and were categorized into eight themes: 1) work organization, 2) orientation and parent wayfinding, 3) communication devices/systems, 4) nursing and resuscitation equipment, 5) ergonomics, 6) parent comfort; 7) work processes, and 8) interdepartmental interactions. Prior to the transition to the new NICU, 76% of the LSTs were resolved. Survey response rate was 31%, 16%, 7% for baseline, post-simulation and post-move surveys, respectively. System readiness at baseline was 1.3/5,. Post-simulation systems readiness was 3.5/5 (p = 0.0001) and post-transition was 3.9/5 (p = 0.02). Staff preparedness at baseline was 1.4/5. Staff preparedness post-simulation was 3.3/5 (p = 0.006) and post-transition was 3.9/5 (p = 0.03). CONCLUSION Large-scale, immersive in situ simulation is a feasible and effective methodology for identifying LSTs, improving systems readiness and staff preparedness in a new SFR NICU prior to occupancy. However, to optimize patient safety, identified LSTs must be mitigated prior to occupancy. Coordinating large-scale simulations is worth the time and cost investment necessary to optimize systems and ensure patient safety prior to transition to a new SFR NICU.


2016 ◽  
Vol 108 (21) ◽  
pp. 211902 ◽  
Author(s):  
Xian Chen ◽  
Nobumichi Tamura ◽  
Alastair MacDowell ◽  
Richard D. James

ACS Catalysis ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 1464-1484 ◽  
Author(s):  
Yong Han ◽  
Hui Zhang ◽  
Yi Yu ◽  
Zhi Liu

2021 ◽  
Vol 13 (2) ◽  
pp. 228
Author(s):  
Jian Kang ◽  
Rui Jin ◽  
Xin Li ◽  
Yang Zhang

In recent decades, microwave remote sensing (RS) has been used to measure soil moisture (SM). Long-term and large-scale RS SM datasets derived from various microwave sensors have been used in environmental fields. Understanding the accuracies of RS SM products is essential for their proper applications. However, due to the mismatched spatial scale between the ground-based and RS observations, the truth at the pixel scale may not be accurately represented by ground-based observations, especially when the spatial density of in situ measurements is low. Because ground-based observations are often sparsely distributed, temporal upscaling was adopted to transform a few in situ measurements into SM values at a pixel scale of 1 km by introducing the temperature vegetation dryness index (TVDI) related to SM. The upscaled SM showed high consistency with in situ SM observations and could accurately capture rainfall events. The upscaled SM was considered as the reference data to evaluate RS SM products at different spatial scales. In regard to the validation results, in addition to the correlation coefficient (R) of the Soil Moisture Active Passive (SMAP) SM being slightly lower than that of the Climate Change Initiative (CCI) SM, SMAP had the best performance in terms of the root-mean-square error (RMSE), unbiased RMSE and bias, followed by the CCI. The Soil Moisture and Ocean Salinity (SMOS) products were in worse agreement with the upscaled SM and were inferior to the R value of the X-band SM of the Advanced Microwave Scanning Radiometer 2 (AMSR2). In conclusion, in the study area, the SMAP and CCI SM are more reliable, although both products were underestimated by 0.060 cm3 cm−3 and 0.077 cm3 cm−3, respectively. If the biases are corrected, then the improved SMAP with an RMSE of 0.043 cm3 cm−3 and the CCI with an RMSE of 0.039 cm3 cm−3 will hopefully reach the application requirement for an accuracy with an RMSE less than 0.040 cm3 cm−3.


2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sungmin O. ◽  
Rene Orth

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.


Sign in / Sign up

Export Citation Format

Share Document