Fuzzy TOPSIS Approach in Selection of Optimal Noise Barrier for Traffic Noise Abatement

The paper presents a retrospective study for selection of noise barrier for road traffic noise abatement. The work proposes the application of Fuzzy TOPSIS (Technique for order preference by similarity to an ideal solution) approach is selection of optimal road traffic noise barrier. The present work utilizes the fuzzy TOPSIS model proposed by Mahdavi et al. (2008) in determination of ranking order of various types of noise barriers with respect to the various criteria considered. It is suggested that application of this approach can be very helpful in selection and application of optimal noise barrier for road traffic noise abatement

Swarm Learning for decentralized and confidential clinical machine learning

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine

Swarm Learning for decentralized and confidential clinical machine learning

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine

Bibliometric analysis and visualization of the extrahepatic portal venous obstruction publication landscape

Introduction: A scientometric analysis was conducted to characterize the global research publications in extrahepatic portal venous obstruction (EHPVO), and state-of-the-art visualization graphics were generated to provide insight into specific bibliometric variables. Materials and Methods: The Web of Science database was accessed for research productivity and bibliometric variables of countries, institutions, authors, journals, and content analysis of top-20 cited documents were performed. Collaborative networks and co-occurrence of keywords map were generated using VOSviewer software. Results: Two hundred and sixteen records were retrieved with an annual growth rate of 2.53%. India is the leading country in productivity (n = 4339), followed by the USA and China. Post Graduate Institute of Medical Education and Research, Chandigarh, was the top productive institute. Sarin SK was the most prolific author, having the highest citations received and h-index. The hotspot topics were “portal hypertension,” “cirrhosis,” “children,” “biliopathy/cholangiopathy,” “liver fibrosis,” and “liver transplantation” as per keyword co-occurrence networking. J Gastroenterol Hepatol had the most publications of EHPVO research as well the h-index. Regarding collaborative network mapping, the USA and Primignani M were the significant nodes among country and author, respectively. Conclusion: EHPVO research publication volume is low but is gradually progressing with dominant contributions from Indian institutes and authors. Most highly cited articles are of low level of evidence, and multi-institutional collaborative research can be the way forward