Influence of Boron on the Hardenability of Unalloyed and Low Alloyed Steel

Abstract Boron exerts a large influence on mechanical properties of steel through microstructural control. It increases the hardenability of steel by retard ing the heterogeneous nucleation of ferrite at the austenite grain surfaces and the decomposition kinetics of austenite to ferrite t ransformat ion are governed by its location and chemical state. To understand the effect of boron (~ 25 pp m) on microstructural evolution and change in continuous cooling transformat ion (CCT) diagram , a systematic study has been carried out using Gleeble thermo-mechanical simulator in unalloyed (C: 0.05 wt%; Mn : 0.2 wt%) and low alloyed (C: 0.2 wt%; Mn : 1.2 wt%: Cr: 0.15 wt%) steels. CCT diagrams, plotted for the onset and end of pearlit ic, bainitic and martensitic reactions, consisted essentially of two C-curves and a remarkable d ifference was observed on comparing the results for both steels. It is interesting to note that bainite and martensite are co mpletely absent even at higher cooling rate o f 70°C/sec in the unalloyed steel with boron addition. In contrast, the addition of boron was observed to promote significant bainite format ion even at a slower cooling rate of only 20°C/sec in the low alloyed steel. These contradicting effects on hardenability can be explained by the effect of boron in shifting only the upper C curve to the right for reconstructive transformation

Deep learning frameworks for point cloud reconstruction

Rapid advancements have been made in the field of surface reconstruction over the last two decades. Nonetheless, traditional approaches to reconstructing surface representations, although robust for a plethora of objects, fail to scale well for 3D point cloud datasets available today that contain a diverse class of shapes. Due to the widely acknowledged success of deep learning-based methods on 2D images, there has been a growing interest in using deep learning for obtaining surface representations from 3D point clouds. While several such methods have been proposed, many are not easy to adapt for applications in fields like computer-aided design and agriculture. This thesis aims to develop robust deep learning frameworks for explicit and implicit surface reconstruction that can be seamlessly integrated into research pipelines of such fields. Boundary representations (B-reps) using Non-Uniform Rational B-splines (NURBS) are the de facto standard used in CAD, but their utility in deep learning-based approaches is not well researched. In our first work, we propose a differentiable NURBS module to integrate the NURBS representation of CAD models with deep learning methods. We mathematically define the derivatives of the NURBS curves or surfaces with respect to the input parameters, which are then used to perform the “backward” evaluation performed while training deep learning models. This allows NURBS to be incorporated with the modern differentiable programming paradigm used in deep learning, making it more easily integrated with modern deep learning frameworks. Reconstructing the geometry of crops from 3D point cloud data is useful for various plant phenotyping applications. Due to very thin and slender segments, obtaining accurate surface geometry representations from the 3D point cloud data of plants is challenging. Further, defects in the point cloud data might produce errors in the reconstructed plant structures. In our second work, we leverage deep learning frameworks that learn neural implicit representations to reconstruct the surfaces of fully developed maize plants using data acquired from Terrestrial Laser Scanners (TLS)

Deep learning frameworks for point cloud reconstruction

Rapid advancements have been made in the field of surface reconstruction over the last two decades. Nonetheless, traditional approaches to reconstructing surface representations, although robust for a plethora of objects, fail to scale well for 3D point cloud datasets available today that contain a diverse class of shapes. Due to the widely acknowledged success of deep learning-based methods on 2D images, there has been a growing interest in using deep learning for obtaining surface representations from 3D point clouds. While several such methods have been proposed, many are not easy to adapt for applications in fields like computer-aided design and agriculture. This thesis aims to develop robust deep learning frameworks for explicit and implicit surface reconstruction that can be seamlessly integrated into research pipelines of such fields. Boundary representations (B-reps) using Non-Uniform Rational B-splines (NURBS) are the de facto standard used in CAD, but their utility in deep learning-based approaches is not well researched. In our first work, we propose a differentiable NURBS module to integrate the NURBS representation of CAD models with deep learning methods. We mathematically define the derivatives of the NURBS curves or surfaces with respect to the input parameters, which are then used to perform the “backward” evaluation performed while training deep learning models. This allows NURBS to be incorporated with the modern differentiable programming paradigm used in deep learning, making it more easily integrated with modern deep learning frameworks. Reconstructing the geometry of crops from 3D point cloud data is useful for various plant phenotyping applications. Due to very thin and slender segments, obtaining accurate surface geometry representations from the 3D point cloud data of plants is challenging. Further, defects in the point cloud data might produce errors in the reconstructed plant structures. In our second work, we leverage deep learning frameworks that learn neural implicit representations to reconstruct the surfaces of fully developed maize plants using data acquired from Terrestrial Laser Scanners (TLS)

Deep learning frameworks for point cloud reconstruction

Rapid advancements have been made in the field of surface reconstruction over the last two decades. Nonetheless, traditional approaches to reconstructing surface representations, although robust for a plethora of objects, fail to scale well for 3D point cloud datasets available today that contain a diverse class of shapes. Due to the widely acknowledged success of deep learning-based methods on 2D images, there has been a growing interest in using deep learning for obtaining surface representations from 3D point clouds. While several such methods have been proposed, many are not easy to adapt for applications in fields like computer-aided design and agriculture. This thesis aims to develop robust deep learning frameworks for explicit and implicit surface reconstruction that can be seamlessly integrated into research pipelines of such fields. Boundary representations (B-reps) using Non-Uniform Rational B-splines (NURBS) are the de facto standard used in CAD, but their utility in deep learning-based approaches is not well researched. In our first work, we propose a differentiable NURBS module to integrate the NURBS representation of CAD models with deep learning methods. We mathematically define the derivatives of the NURBS curves or surfaces with respect to the input parameters, which are then used to perform the “backward” evaluation performed while training deep learning models. This allows NURBS to be incorporated with the modern differentiable programming paradigm used in deep learning, making it more easily integrated with modern deep learning frameworks. Reconstructing the geometry of crops from 3D point cloud data is useful for various plant phenotyping applications. Due to very thin and slender segments, obtaining accurate surface geometry representations from the 3D point cloud data of plants is challenging. Further, defects in the point cloud data might produce errors in the reconstructed plant structures. In our second work, we leverage deep learning frameworks that learn neural implicit representations to reconstruct the surfaces of fully developed maize plants using data acquired from Terrestrial Laser Scanners (TLS)

Management and Outcomes Following Surgery for Gastrointestinal Typhoid: An International, Prospective, Multicentre Cohort Study

Background: Gastrointestinal perforation is the most serious complication of typhoid fever, with a high disease burden in low-income countries. Reliable, prospective, contemporary surgical outcome data are scarce in these settings. This study aimed to investigate surgical outcomes following surgery for intestinal typhoid. Methods: Two multicentre, international prospective cohort studies of consecutive patients undergoing surgery for gastrointestinal typhoid perforation were conducted. Outcomes were measured at 30 days and included mortality, surgical site infection, organ space infection and reintervention rate. Multilevel logistic regression models were used to adjust for clinically plausible explanatory variables. Effect estimates are expressed as odds ratios (ORs) alongside their corresponding 95% confidence intervals. Results: A total of 88 patients across the GlobalSurg 1 and GlobalSurg 2 studies were included, from 11 countries. Children comprised 38.6% (34/88) of included patients. Most patients (87/88) had intestinal perforation. The 30-day mortality rate was 9.1% (8/88), which was higher in children (14.7 vs. 5.6%). Surgical site infection was common, at 67.0% (59/88). Organ site infection was common, with 10.2% of patients affected. An ASA grade of III and above was a strong predictor of 30-day post-operative mortality, at the univariable level and following adjustment for explanatory variables (OR 15.82, 95% CI 1.53–163.57, p = 0.021). Conclusions: With high mortality and complication rates, outcomes from surgery for intestinal typhoid remain poor. Future studies in this area should focus on sustainable interventions which can reduce perioperative morbidity. At a policy level, improving these outcomes will require both surgical and public health system advances

Abstracts of National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020

This book presents the abstracts of the papers presented to the Online National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020 (RDMPMC-2020) held on 26th and 27th August 2020 organized by the Department of Metallurgical and Materials Science in Association with the Department of Production and Industrial Engineering, National Institute of Technology Jamshedpur, Jharkhand, India. Conference Title: National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020Conference Acronym: RDMPMC-2020Conference Date: 26–27 August 2020Conference Location: Online (Virtual Mode)Conference Organizer: Department of Metallurgical and Materials Engineering, National Institute of Technology JamshedpurCo-organizer: Department of Production and Industrial Engineering, National Institute of Technology Jamshedpur, Jharkhand, IndiaConference Sponsor: TEQIP-

Global economic burden of unmet surgical need for appendicitis

Background There is a substantial gap in provision of adequate surgical care in many low- and middle-income countries. This study aimed to identify the economic burden of unmet surgical need for the common condition of appendicitis. Methods Data on the incidence of appendicitis from 170 countries and two different approaches were used to estimate numbers of patients who do not receive surgery: as a fixed proportion of the total unmet surgical need per country (approach 1); and based on country income status (approach 2). Indirect costs with current levels of access and local quality, and those if quality were at the standards of high-income countries, were estimated. A human capital approach was applied, focusing on the economic burden resulting from premature death and absenteeism. Results Excess mortality was 4185 per 100 000 cases of appendicitis using approach 1 and 3448 per 100 000 using approach 2. The economic burden of continuing current levels of access and local quality was US $92 492 million using approach 1 and$73 141 million using approach 2. The economic burden of not providing surgical care to the standards of high-income countries was $95 004 million using approach 1 and$75 666 million using approach 2. The largest share of these costs resulted from premature death (97.7 per cent) and lack of access (97.0 per cent) in contrast to lack of quality. Conclusion For a comparatively non-complex emergency condition such as appendicitis, increasing access to care should be prioritized. Although improving quality of care should not be neglected, increasing provision of care at current standards could reduce societal costs substantially

Global economic burden of unmet surgical need for appendicitis

Background There is a substantial gap in provision of adequate surgical care in many low- and middle-income countries. This study aimed to identify the economic burden of unmet surgical need for the common condition of appendicitis. Methods Data on the incidence of appendicitis from 170 countries and two different approaches were used to estimate numbers of patients who do not receive surgery: as a fixed proportion of the total unmet surgical need per country (approach 1); and based on country income status (approach 2). Indirect costs with current levels of access and local quality, and those if quality were at the standards of high-income countries, were estimated. A human capital approach was applied, focusing on the economic burden resulting from premature death and absenteeism. Results Excess mortality was 4185 per 100 000 cases of appendicitis using approach 1 and 3448 per 100 000 using approach 2. The economic burden of continuing current levels of access and local quality was US $92 492 million using approach 1 and$73 141 million using approach 2. The economic burden of not providing surgical care to the standards of high-income countries was $95 004 million using approach 1 and$75 666 million using approach 2. The largest share of these costs resulted from premature death (97.7 per cent) and lack of access (97.0 per cent) in contrast to lack of quality. Conclusion For a comparatively non-complex emergency condition such as appendicitis, increasing access to care should be prioritized. Although improving quality of care should not be neglected, increasing provision of care at current standards could reduce societal costs substantially