Automated Quantitative Analyses of Fatigue-Induced Surface Damage by Deep Learning

The digitization of materials is the prerequisite for accelerating product development. However, technologically, this is only beneficial when reliability is maintained. This requires comprehension of the microstructure-driven fatigue damage mechanisms across scales. A substantial fraction of the lifetime for high performance materials is attributed to surface damage accumulation at the microstructural scale (e.g., extrusions and micro crack formation). Although, its modeling is impeded by a lack of comprehensive understanding of the related mechanisms. This makes statistical validation at the same scale by micromechanical experimentation a fundamental requirement. Hence, a large quantity of processed experimental data, which can only be acquired by automated experiments and data analyses, is crucial. Surface damage evolution is often accessed by imaging and subsequent image post-processing. In this work, we evaluated deep learning (DL) methodologies for semantic segmentation and different image processing approaches for quantitative slip trace characterization. Due to limited annotated data, a U-Net architecture was utilized. Three data sets of damage locations observed in scanning electron microscope (SEM) images of ferritic steel, martensitic steel, and copper specimens were prepared. In order to allow the developed models to cope with material-specific damage morphology and imaging-induced variance, a customized augmentation pipeline for the input images was developed. Material domain generalizability of ferritic steel and conjunct material trained models were tested successfully. Multiple image processing routines to detect slip trace orientation (STO) from the DL segmented extrusion areas were implemented and assessed. In conclusion, generalization to multiple materials has been achieved for the DL methodology, suggesting that extending it well beyond fatigue damage is feasible

ASSESSMENT, EVALUATION AND ANALYSIS OF THE MEDICATION ERRORS OF THE PATIENTS ADMITTED AT THE EMERGENCY DEPARTMENT OF A TERTIARY CARE TEACHING HOSPITAL OF A SOUTH INDIAN CITY

OBJECTIVE: The study was to assess, evaluate and analyze the medication errors of the patients admitted at the Emergency Department of a tertiary care teaching hospital.METHODS: The study was conducted for 6 months. Data was collected from the patients  admitted in the Emergency Department. The collected data was analyzed to identify medication errors and prescription errors in emergency unit in hospital by using drug information tools like Micromedex, Drug interaction checker, Stockley drug interaction text, BNF, Journals with good impact factor etc.RESULTS: A total of 200 patients were enrolled in the study according to the inclusion criteria and exclusion criteria in which 108 were males and 92 were females. 340 medication errors were obtained in 122 patients and 78 patients did not have any error. Medication errors were more commonly in the age group of 61-70 years (49%). In 340 medication errors, DDIs were the most (63.3%), followed by drug duplication (13.53%) and drugs given without indication (8.5%). In DDIs moderate interactions were the mostly seen error. On prescription analysis, drugs prescribed without strength (67.6%), omission error (16.4%), drugs prescribed without frequency (16%) was the most commonly seen. The most common pharmacological classification of drugs associated with medication errors were Antibiotics (25.6%), Anti-hypertensive drugs (13.65%) and Anti-platelet drugs (12.9%).CONCLUSION: Incidence of medication errors was mainly due to the use of Antibiotics. Due to the fast paced nature and overcrowding in ED, more number of prescription errors were obtained.   Â

Materials fatigue prediction using graph neural networks on microstructure representations

The local prediction of fatigue damage within polycrystals in a high-cycle fatigue setting is a long-lasting and challenging task. It requires identifying grains tending to accumulate plastic deformation under cyclic loading. We address this task by transcribing ferritic steel microtexture and damage maps from experiments into a microstructure graph. Here, grains constitute graph nodes connected by edges whenever grains share a common boundary. Fatigue loading causes some grains to develop slip markings, which can evolve into microcracks and lead to failure. This data set enables applying graph neural network variants on the task of binary grain-wise damage classification. The objective is to identify suitable data representations and models with an appropriate inductive bias to learn the underlying damage formation causes. Here, graph convolutional networks yielded the best performance with a balanced accuracy of 0.72 and a F$_1$-score of 0.34, outperforming phenomenological crystal plasticity (+ 68%) and conventional machine learning (+ 17%) models by large margins. Further, we present an interpretability analysis that highlights the grains along with features that are considered important by the graph model for the prediction of fatigue damage initiation, thus demonstrating the potential of such techniques to reveal underlying mechanisms and microstructural driving forces in critical grain ensembles

Copper complexes as chemical nucleases

Redox active mononuclear and binuclear copper(II) complexes have been prepared and structurally characterized. The complexes have planar N-donor heterocyclic bases like 1,10-phenanthroline (phen), dipyridoquinoxaline (dpq) and dipyridophenazine (dppz) ligands that are suitable for intercalation to B-DNA. Complexes studied for nuclease activity have the formulations [Cu(dpq)2(H2O)] (ClO4)2.H2O (1), [CuL(H2O)2(μ-ox)](ClO4)2 (L = bpy,2; phen,3; dpq,4; and dppz,5) and [Cu(L)(salgly)] (L = bpy,6; phen,7; dpq,8; and dppz,9), where salgly is a tridentate Schiff base obtained from the condensation of glycine and salicylaldehyde. The dpq complexes are efficient DNA binding and cleavage active species. The dppz complexes show good binding ability but poor nuclease activity. The cleavage activity of thebis-dpq complex is significantly higher than thebis-phen complex of copper(II). The nuclease activity is found to be dependent on the intercalating nature of the complex and on the redox potential of the copper(II)/copper(I) couple. The ancillary ligand plays a significant role in binding and cleavage activity

Rare occurrence of Bombay duck from Central Kerala

The occurrence of Bombay duck along south coast of India is very rare, especially Kerala coast. On 4th December, 2019, one specimen of Bombayduck, Harpadon nehereus measuring 252 mm in total length and weighing 114.5 g was landed at Chellanam landing centre, Kochi (9°47'56.8"N 76°16'32.2"E) by an outboard ringseiner unit operated at a depth of 30-50 m targeting Indian oil sardine. Morphometric and meristic characters of the specimen landed were recorde

Opportunities for and Limitations of Private Ordering in Family Law (Symposium Roundtable)

Symposium: Law and the New American Family Held at Indiana University School of Law - Bloomington Apr. 4, 199

Opportunities for and Limitations of Private Ordering in Family Law (Symposium Roundtable)

Symposium: Law and the New American Family Held at Indiana University School of Law - Bloomington Apr. 4, 199

Ontology Modelling for Materials Science Experiments

Materials are either enabler or bottleneck for the vast majority of technological innovations. The digitization of materials and processes is mandatory to create live production environments which represent physical entities and their aggregations and thus allow to represent, share, and understand materials changes. However, a common standard formalization for materials knowledge in the form of taxonomies, ontologies, or knowledge graphs has not been achieved yet. This paper sketches the e_orts in modelling an ontology prototype to describe Materials Science experiments. It describes what is expected from the ontology by introducing a use case where a process chain driven by the ontology enables the curation and understanding of experiments