The rise in popularity of permissioned blockchain platforms in recent time is significant. Hyperledger Fabric is one such permissioned blockchain platform and one of the Hyperledger projects hosted by the Linux Foundation [13]. The Fabric comprises of various components such as smartcontracts, endorsers, committers, validators, and orderers. As the performance of blockchain platform is a major concern for enterprise applications, in this work, we perform a comprehensive empirical study to characterize the performance of Hyperledger Fabric and identify potential performance bottlenecks to gain a better understanding of the system.We follow a two-phased approach. In the first phase, our goal is to understand the impact of various configuration parameters such as block size, endorsement policy, channels, resource allocation, state database choice on the transaction throughput & latency to provide various guidelines on configuring these parameters. In addition, we also aim to identify performance bottlenecks and hotspots. We observed that (1) endorsement policy verification, (2) sequential policy validation of transactions in a block, and (3) state validation and commit (with CouchDB) were the three major bottlenecks.In the second phase, we focus on optimizing Hyperledger Fabric v1.0 based on our observations. We introduced and studied various simple optimizations such as aggressive caching for endorsement policy verification in the cryptography component (3× improvement in the performance) and parallelizing endorsement policy verification (7× improvement). Further, we enhanced and measured the effect of an existing bulk read/write optimization for CouchDB during state validation & commit phase (2.5× improvement). By combining all three optimizations 1 , we improved the overall throughput by 16× (i.e., from 140 tps to 2250 tps).
Social Network Analysis has emerged as a key paradigm in modern sociology, technology, and information sciences. The paradigm stems from the view that the attributes of an individual in a network are less important than their ties (relationships) with other individuals in the network. Exploring the nature and strength of these ties can help understand the structure and dynamics of social networks and explain real-world phenomena, ranging from organizational efficiency to the spread of information and disease.In this paper, we examine the communication patterns of millions of mobile phone users, allowing us to study the underlying social network in a large-scale communication network. Our primary goal is to address the role of social ties in the formation and growth of groups, or communities, in a mobile network. In particular, we study the evolution of churners in an operator's network spanning over a period of four months. Our analysis explores the propensity of a subscriber to churn out of a service provider's network depending on the number of ties (friends) that have already churned. Based on our findings, we propose a spreading activation-based technique that predicts potential churners by examining the current set of churners and their underlying social network. The efficiency of the prediction is expressed as a lift curve, which indicates the fraction of all churners that can be caught when a certain fraction of subscribers were contacted.
Social Network Analysis has emerged as a key paradigm in modern sociology, technology, and information sciences. The paradigm stems from the view that the attributes of an individual in a network are less important than their ties (relationships) with other individuals in the network. Exploring the nature and strength of these ties can help understand the structure and dynamics of social networks and explain real-world phenomena, ranging from organizational efficiency to the spread of information and disease.In this paper, we examine the communication patterns of millions of mobile phone users, allowing us to study the underlying social network in a large-scale communication network. Our primary goal is to address the role of social ties in the formation and growth of groups, or communities, in a mobile network. In particular, we study the evolution of churners in an operator's network spanning over a period of four months. Our analysis explores the propensity of a subscriber to churn out of a service provider's network depending on the number of ties (friends) that have already churned. Based on our findings, we propose a spreading activation-based technique that predicts potential churners by examining the current set of churners and their underlying social network. The efficiency of the prediction is expressed as a lift curve, which indicates the fraction of all churners that can be caught when a certain fraction of subscribers were contacted.
Cysticercosis is an infection with larval cysts of the cestode Taenia solium. Through pathways that are incompletely understood, dying parasites initiate a granulomatous reaction that, in the brain, causes seizures. Substance P (SP), a neuropeptide involved in pain-transmission, contributes to inflammation and previously was detected in granulomas associated with dead T. crassiceps cysts. To determine if SP contributes to granuloma formation, we measured granuloma-size and levels of IL-1β, TNF-α, and IL-6 within granulomas in T. crassiceps-infected wild type (WT) mice and mice deficient in SP-precursor (SPP) or the SP-receptor (neurokinin 1, NK1). Granuloma volumes of infected SPP- and NK1-knockout mice were reduced by 31 and 36%, respectively, compared to WT mice (P < .05 for both) and produced up to 5-fold less IL-1β, TNF-α, and IL-6 protein. Thus, SP signaling contributes to granuloma development and proinflammatory cytokine production in T. crassiceps infection and suggests a potential role for this mediator in human cystercercosis.
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