Restructuring TCAD System: Teaching Traditional TCAD New Tricks

Traditional TCAD simulation has succeeded in predicting and optimizing the device performance; however, it still faces a massive challenge - a high computational cost. There have been many attempts to replace TCAD with deep learning, but it has not yet been completely replaced. This paper presents a novel algorithm restructuring the traditional TCAD system. The proposed algorithm predicts three-dimensional (3-D) TCAD simulation in real-time while capturing a variance, enables deep learning and TCAD to complement each other, and fully resolves convergence errors.Comment: In Proceedings of 2021 IEEE International Electron Devices Meeting (IEDM

Additional mechanical pleurodesis after thoracoscopic wedge resection and covering procedure for primary spontaneous pneumothorax

BACKGROUND: Additional mechanical pleurodesis for the treatment of primary spontaneous pneumothorax (PSP) is believed to reduce the recurrence of PSP, and a covering procedure with absorbable mesh also shows comparable results. This study was conducted to determine whether additional mechanical pleurodesis would be effective in reducing recurrence after thoracoscopic wedge resection and covering procedure. MATERIALS AND METHODS: Between May 2003 and August 2005, 99 patients underwent thoracoscopic bullectomy with staple line covering with absorbable cellulose mesh and fibrin glue followed by an additional mechanical pleurodesis. These patients were compared with 98 patients who underwent thoracoscopic bullectomy with staple line coverage alone. RESULTS: The additional mechanical pleurodesis group had findings comparable to those of the coverage group for duration of postoperative chest drainage, length of hospital stay, and complication rate. After median follow-up of 29.2 months, postoperative recurrence occurred in four patients (4.0%). CONCLUSIONS: Additional mechanical pleurodesis after covering procedure is also effective in decreasing postoperative recurrence of PSP

Genetic Drivers of Heterogeneity in Type 2 Diabetes Pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P \u3c 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P &lt; 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.</p

Suggestion for a framework for a sustainable infrastructure asset management manual in Korea

This study proposes a framework for an infrastructure asset management manual containing infrastructure asset management processes and operation techniques, which can be adjusted by different ordering authorities to develop their own manuals. The following conclusions were drawn in this study. First, the justification for implementation of asset management was examined through analysis of changes and status of asset management in domestic infrastructure, and the current status and insufficiencies in the asset management manuals of the government and ordering authorities were inspected. Second, the current status and systems of infrastructure asset management manuals in developed foreign nations such as Australia, the United Kingdom and the United States were examined, to analyze and compare the characteristics of asset management manuals among different nations. The directivity for composition of an infrastructure asset management manual in Korea was deduced for reference. Third, based on the directivity for composition of a domestic and foreign infrastructure asset management manual, a framework for an infrastructure asset management manual that can be utilized by the ordering authorities was proposed for (1) a general infrastructure asset management manual connected to global asset management manuals; (2) a manual that considers the asset management experience of the ordering authorities; (3) a systematic manual that takes user convenience into account; and (4) a circulatory process, which links infrastructure policy and strategy in the decision-making stage

Physics-augmented neural compact model for emerging device technologies

This paper proposes a novel compact modeling framework based on artificial neural networks and physics informed machine learning techniques. This physics- augmented neural compact model shows highly accurate fitting abilities and physically consistent inferences even at the unseen data. It is also scalable and technology independent, and consequently, is suitable for electrical modeling of new emerging devices. In addition, this neural compact model is able to cover both digital and analog circuit analysis due to the weight decay regularization as well as high order derivative losses. Finally, it is applied to promising DRAM and Logic technologies to be evaluated in terms of its scalability and fitting accuracy. The CMC&apos;s (Compact Model Coalition) standard model API (Application Programming Interface) supports the custom model implementation for SPICE. Therefore, this framework enables the circuit simulators to assess technology-independent PPA (Power, Performance, Area) and early-stage DTCO (Design Technology Cooptimization) for new emerging devices

PAC-Net: A Model Pruning Approach to Inductive Transfer Learning

Inductive transfer learning aims to learn from a small amount of training data for the target task by utilizing a pre-trained model from the source task. Most strategies that involve large-scale deep learning models adopt initialization with the pre-trained model and fine-tuning for the target task. However, when using over-parameterized models, we can often prune the model without sacrificing the accuracy of the source task. This motivates us to adopt model pruning for transfer learning with deep learning models. In this paper, we propose PAC-Net, a simple yet effective approach for transfer learning based on pruning. PAC-Net consists of three steps: Prune, Allocate, and Calibrate (PAC). The main idea behind these steps is to identify essential weights for the source task, fine-tune on the source task by updating the essential weights, and then calibrate on the target task by updating the remaining redundant weights. Under the various and extensive set of inductive transfer learning experiments, we show that our method achieves state-of-the-art performance by a large margin

Hybrid gel polymer electrolyte based on 1-methyl-1-Propylpyrrolidinium Bis(Trifluoromethanesulfonyl) imide for flexible and shape-variant lithium secondary batteries

Lithium ion conducting polymer electrolytes with broad electrochemical stability, good mechanical strength, high thermal stability, and easy processability are necessary for all-solid-state and shape-variant lithium secondary batteries. Hybrid gel polymer electrolytes incorporating an ionic liquid have been attracting attention for application in solid-state lithium secondary batteries owing to their superior thermal properties compared to conventional electrolyte systems. In this study, a variety of polymer electrolytes based on poly(vinylidene fluoride-co-hexafluoropropylene) (PVdF-HFP), lithium bis(trifluoromethanesulfonyl) imide (LiTFSI), and 1-methyl-1-propylpyrrolidinium bis(trifluoromethanesulfonyl) imide (PMPyrrTFSI) are prepared, and an in-depth study of their composition dependence and electrical properties is conducted to develop the optimum composition. The composition dependent ionic conductivity of the polymer electrolyte increases with increasing LiTFSI and PMPyrrTFSI and reaches a maximum value of 6.93 × 10−4 S cm−1 at room temperature (25 °C) when the polymer electrolyte contains 30 wt% LiTFSI and 60 wt% PMPyrrTFSI. In addition, the optimized gel polymer electrolytes consisting of PVdF-HFP/LiTFSI/PMPyrrTFSI (70/30/60 by weight, i.e., 70PVdF-HFP/30LiTFSI/60PMPyrrTFSI) look transparent and exhibit high mechanical stability and excellent thermal stability up to 420 °C. Finally, the lithium iron phosphate (LiFePO4)/lithium metal solid-state cells coupled with the optimized gel polymer electrolyte are prepared, and their discharge characteristics are studied. The 70PVdF-HFP/30LiTFSI/60PMPyrrTFSI based solid-state cell delivered a maximum discharge capacity of 151 mAh g−1 at room temperature with a good rate capability and cycling performance. © 2020 Elsevier B.V.1