Modeling and Simulations of 4H-SiC/6H-SiC/4H-SiC Single Quantum-Well Light Emitting Diode Using Diffusion Bonding Technique

In the last decade, silicon carbide (SiC) has emerged as a potential material for high-frequency electronics and optoelectronics applications that may require elevated temperature processing. SiC exists in more than 200 different crystallographic forms, referred to as polytypes. Based on their remarkable physical and electrical characteristics, such as better thermal and electrical conductivities, 3C-SiC, 4H-SiC, and 6H-SiC are considered as the most distinguished polytypes of SiC. In this article, physical device simulation of a light-emitting diode (LED) based on the unique structural configuration of 4H-SiC and 6H-SiC layers has been performed which corresponds to a novel material joining technique, called diffusion welding/bonding. The proposed single quantum well (SQW) edge-emitting SiC-based LED has been simulated using a commercially available semiconductor device simulator, SILVACO TCAD. Moreover, by varying different design parameters, the current-voltage characteristics, luminous power, and power spectral density have been calculated. Our proposed LED device exhibited promising results in terms of luminous power efficiency and external quantum efficiency (EQE). The device numerically achieved a luminous efficiency of 25% and EQE of 16.43%, which is at par performance for a SQW LED. The resultant LED structure can be customized by choosing appropriate materials of varying bandgaps to extract the light emission spectrum in the desired wavelength range. It is anticipated that the physical fabrication of our proposed LED by direct bonding of SiC-SiC wafers will pave the way for the future development of efficient and cost-effective SiC-based LEDs

Novel Adaptive Bayesian Regularization Networks for Peristaltic Motion of a Third-Grade Fluid in a Planar Channel

In this presented communication, a novel design of intelligent Bayesian regularization backpropagation networks (IBRBNs) based on stochastic numerical computing is presented. The dynamics of peristaltic motion of a third-grade fluid in a planar channel is examined by IBRBNs using multilayer structure modeling competency of neural networks trained with efficient optimization ability of Bayesian regularization method. The reference dataset used as inputs and targets parameters of IBRBN has been obtained via the state-of-the-art Adams numerical method. The data of solution dynamics is created for multiple scenarios of the peristaltic transport model by varying the volume flow rate, material parametric of a third-grade fluid model, wave amplitude, and inclination angles. The designed integrated IBRBNs are constructed by exploiting training, testing, and validation operations at each epoch via optimization of a figure of merit on mean square error sense. Exhaustive simulation of IBRBNs with comparison on mean square error, histograms, and regression index substantiated the precision, stability, and reliability to solve the peristaltic transport model

Novel Adaptive Bayesian Regularization Networks for Peristaltic Motion of a Third-Grade Fluid in a Planar Channel

In this presented communication, a novel design of intelligent Bayesian regularization backpropagation networks (IBRBNs) based on stochastic numerical computing is presented. The dynamics of peristaltic motion of a third-grade fluid in a planar channel is examined by IBRBNs using multilayer structure modeling competency of neural networks trained with efficient optimization ability of Bayesian regularization method. The reference dataset used as inputs and targets parameters of IBRBN has been obtained via the state-of-the-art Adams numerical method. The data of solution dynamics is created for multiple scenarios of the peristaltic transport model by varying the volume flow rate, material parametric of a third-grade fluid model, wave amplitude, and inclination angles. The designed integrated IBRBNs are constructed by exploiting training, testing, and validation operations at each epoch via optimization of a figure of merit on mean square error sense. Exhaustive simulation of IBRBNs with comparison on mean square error, histograms, and regression index substantiated the precision, stability, and reliability to solve the peristaltic transport model

Genetic Divergence in Brassica napus L. Germplasm as Determined by Quantitative Attributes

Brassica napus L., a candidate with high yield and good quality oil potential was evaluated for genetic divergence for two years on two locations. A collection of 328 lines belonging to various origins along with a check variety Faisal Canola was sown in the field following augmented design and phenotyped for eighteen quantitative traits. The recorded data when statistically analyzed inferred that, days to flower initiation, 50 % flowering, flower completion, 50 % maturity were main contributors of variations in the germplasm and were highly related with pod dehiscence and yield. Furthermore, BN328, BN371, BN494, BN618, BN625 and BN627 were found diverse lines in both years. The outcomes from this study are very helpful to proceed for any Oilseed rape breeding programs to improve yield

Evaluation of prognostic risk models for postoperative pulmonary complications in adult patients undergoing major abdominal surgery: a systematic review and international external validation cohort study

Background Stratifying risk of postoperative pulmonary complications after major abdominal surgery allows clinicians to modify risk through targeted interventions and enhanced monitoring. In this study, we aimed to identify and validate prognostic models against a new consensus definition of postoperative pulmonary complications. Methods We did a systematic review and international external validation cohort study. The systematic review was done in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched MEDLINE and Embase on March 1, 2020, for articles published in English that reported on risk prediction models for postoperative pulmonary complications following abdominal surgery. External validation of existing models was done within a prospective international cohort study of adult patients (≥18 years) undergoing major abdominal surgery. Data were collected between Jan 1, 2019, and April 30, 2019, in the UK, Ireland, and Australia. Discriminative ability and prognostic accuracy summary statistics were compared between models for the 30-day postoperative pulmonary complication rate as defined by the Standardised Endpoints in Perioperative Medicine Core Outcome Measures in Perioperative and Anaesthetic Care (StEP-COMPAC). Model performance was compared using the area under the receiver operating characteristic curve (AUROCC). Findings In total, we identified 2903 records from our literature search; of which, 2514 (86·6%) unique records were screened, 121 (4·8%) of 2514 full texts were assessed for eligibility, and 29 unique prognostic models were identified. Nine (31·0%) of 29 models had score development reported only, 19 (65·5%) had undergone internal validation, and only four (13·8%) had been externally validated. Data to validate six eligible models were collected in the international external validation cohort study. Data from 11 591 patients were available, with an overall postoperative pulmonary complication rate of 7·8% (n=903). None of the six models showed good discrimination (defined as AUROCC ≥0·70) for identifying postoperative pulmonary complications, with the Assess Respiratory Risk in Surgical Patients in Catalonia score showing the best discrimination (AUROCC 0·700 [95% CI 0·683–0·717]). Interpretation In the pre-COVID-19 pandemic data, variability in the risk of pulmonary complications (StEP-COMPAC definition) following major abdominal surgery was poorly described by existing prognostication tools. To improve surgical safety during the COVID-19 pandemic recovery and beyond, novel risk stratification tools are required. Funding British Journal of Surgery Society