Stability of the fragments and thermalization at peak center-of-mass energy

We simulate the central reactions of nearly symmetric, and asymmetric systems, for the energies at which the maximum production of IMFs occurs (E$_{c.m.}^{peak}$).This study is carried out by using hard EOS along with cugnon cross section and employing MSTB method for clusterization. We study the various properties of fragments. The stability of fragments is checked through persistence coefficient and gain term. The information about the thermalization and stopping in heavy-ion collisions is obtained via relative momentum, anisotropy ratio, and rapidity distribution. We find that for a complete stopping of incoming nuclei very heavy systems are required. The mass dependence of various quantities (such as average and maximum central density, collision dynamics as well as the time zone for hot and dense nuclear matter) is also presented. In all cases (i.e., average and maximum central density, collision dynamics as well as the time zone for hot and dense nuclear matter) a power law dependence is obtained.Comment: 21 Pages, 8 Figure

Design and Implementation of a VoIP PBX Integrated Vietnamese Virtual Assistant: A Case Study

補正完畢TW

Experience of shame in service failure context among restaurant frontline employees : does industry tenure matter?

202107 bchyAccepted ManuscriptOthersP0033712Publishe

Hospitality employees’ affective experience of shame, self-efficacy beliefs and job behaviors : the alleviating role of error tolerance

202206 bckwNot applicableOthersThe Hong Kong Polytechnic UniversityPublished36 month

Collaborative Learning for Cyberattack Detection in Blockchain Networks

This article aims to study intrusion attacks and then develop a novel cyberattack detection framework to detect cyberattacks at the network layer (e.g., brute password and flooding of transactions) of blockchain networks. Specifically, we first design and implement a blockchain network in our laboratory. This blockchain network will serve two purposes, i.e., to generate the real traffic data (including both normal data and attack data) for our learning models and to implement real-time experiments to evaluate the performance of our proposed intrusion detection framework. To the best of our knowledge, this is the first dataset that is synthesized in a laboratory for cyberattacks in a blockchain network. We then propose a novel collaborative learning model that allows efficient deployment in the blockchain network to detect attacks. The main idea of the proposed learning model is to enable blockchain nodes to actively collect data, learn the knowledge from data using the Deep Belief Network, and then share the knowledge learned from its data with other blockchain nodes in the network. In this way, we can not only leverage the knowledge from all the nodes in the network but also do not need to gather all raw data for training at a centralized node like conventional centralized learning solutions. Such a framework can also avoid the risk of exposing local data’s privacy as well as excessive network overhead/congestion. Both intensive simulations and real-time experiments clearly show that our proposed intrusion detection framework can achieve an accuracy of up to 98.6% in detecting attacks