Materials and Components for Low Temperature Solid Oxide Fuel Cells – an Overview

This article summarizes the recent advancements made in the area of materials and components for low temperature solid oxide fuel cells (LT-SOFCs). LT-SOFC is a new trend in SOFCtechnology since high temperature SOFC puts very high demands on the materials and too expensive to match marketability. The current status of the electrolyte and electrode materials used in SOFCs, their specific features and the need for utilizing them for LT-SOFC are presented precisely in this review article. The section on electrolytes gives an overview of zirconia, lanthanum gallate and ceria based materials. Also, this review article explains the application of different anode, cathode and interconnect materials used for SOFC systems. SOFC can result in better performance with the application of liquid fuels such methanol and ethanol. As a whole, this review article discusses the novel materials suitable for operation of SOFC systems especially for low temperature operation

Kikuchi-Fujimoto disease presenting as pyrexia of unknown origin

Background: Kikuchi-Fujimoto disease, a benign self-limited lymphadenopathy is an uncommon cause of pyrexia of unknown origin (PUO). Methods: We retrospectively studied the case-records of 13 patients presenting with PUO who were diagnosed to have Kikuchi-Fujimoto disease on peripheral lymph node excision biopsy and report the salient clinical manifestations and histopathological findings in them. All of them received symptomatic treatment. Results: Their median age was 28 [interquartile range (IQR) 18.5-38.0] years. Women (11/13, 84.6%) were more frequently affected. All of them were human immunodeficiency virus (HIV) seronegative. Prior to presenting to us, two were being treated for lymph node tuberculosis with DOTS. Cervical lymph nodes were predominantly involved, the distribution being: right cervical (n=10, 76.9%); left cervical (n=4); and bilateral cervical (n=2). Axillary and generalized lymphadenopathy were rare being seen in 2 and 1 patient respectively. The median (IQR) erythrocyte sedimentation rate (n=11) was 53 (35-89) mm at the end of first hour. Salient histopathological features were paracortical patchy zones of eosinophilic fibrinoid necrosis with karyorrhectic debris, large numbers of histiocytes, including histiocytes with peripherally placed “crescentic” nuclei. Spontaneous regression of fever and lymphadenopathy was observed over a median (IQR) period of 8 (6.75-10.25) months in all of them. Conclusions: Kikuchi-Fujimoto disease is a rare but important cause of PUO presenting with peripheral lymphadenopathy. Women are most often affected and cervical lymph nodes are the most frequently involved site. Clinical suspicion and thoughtful collaboration between clinicians and pathologists are essential for accurate diagnosis, and to minimize unnecessary investigations and inappropriate aggressive treatment

The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset

Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression. Methods: A dataset partition consisting of 3D knee MRI from 88 subjects at two timepoints with ground-truth articular (femoral, tibial, patellar) cartilage and meniscus segmentations was standardized. Challenge submissions and a majority-vote ensemble were evaluated using Dice score, average symmetric surface distance, volumetric overlap error, and coefficient of variation on a hold-out test set. Similarities in network segmentations were evaluated using pairwise Dice correlations. Articular cartilage thickness was computed per-scan and longitudinally. Correlation between thickness error and segmentation metrics was measured using Pearson's coefficient. Two empirical upper bounds for ensemble performance were computed using combinations of model outputs that consolidated true positives and true negatives. Results: Six teams (T1-T6) submitted entries for the challenge. No significant differences were observed across all segmentation metrics for all tissues (p=1.0) among the four top-performing networks (T2, T3, T4, T6). Dice correlations between network pairs were high (>0.85). Per-scan thickness errors were negligible among T1-T4 (p=0.99) and longitudinal changes showed minimal bias (<0.03mm). Low correlations (<0.41) were observed between segmentation metrics and thickness error. The majority-vote ensemble was comparable to top performing networks (p=1.0). Empirical upper bound performances were similar for both combinations (p=1.0). Conclusion: Diverse networks learned to segment the knee similarly where high segmentation accuracy did not correlate to cartilage thickness accuracy. Voting ensembles did not outperform individual networks but may help regularize individual models.Comment: Submitted to Radiology: Artificial Intelligence; Fixed typo

Misusability Measure Based Sanitization of Big Data for Privacy Preserving MapReduce Programming

Leakage and misuse of sensitive data is a challenging problem to enterprises. It has become more serious problem with the advent of cloud and big data. The rationale behind this is the increase in outsourcing of data to public cloud and publishing data for wider visibility. Therefore Privacy Preserving Data Publishing (PPDP), Privacy Preserving Data Mining (PPDM) and Privacy Preserving Distributed Data Mining (PPDM) are crucial in the contemporary era. PPDP and PPDM can protect privacy at data and process levels respectively. Therefore, with big data privacy to data became indispensable due to the fact that data is stored and processed in semi-trusted environment. In this paper we proposed a comprehensive methodology for effective sanitization of data based on misusability measure for preserving privacy to get rid of data leakage and misuse. We followed a hybrid approach that caters to the needs of privacy preserving MapReduce programming. We proposed an algorithm known as Misusability Measure-Based Privacy serving Algorithm (MMPP) which considers level of misusability prior to choosing and application of appropriate sanitization on big data. Our empirical study with Amazon EC2 and EMR revealed that the proposed methodology is useful in realizing privacy preserving Map Reduce programming