Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 3

Search results for: Yu-Sheng Lu

3 The Influence of Feedgas Ratio on the Ethene Hydroformylation using Rh-Co Bimetallic Catalyst Supported by Reduced Graphene Oxide

Authors: Jianli Chang, Yusheng Zhang, Yali Yao, Diane Hildebrandt, Xinying Liu


The influence of feed-gas ratio on the ethene hydroformylation over an Rh-Co bimetallic catalyst supported by reduced graphene oxide (RGO) has been investigated in a tubular fixed bed reactor. Argon was used as balance gas when the feed-gas ratio was changed, which can keep the partial pressure of the other two kinds of gas constant while the ratio of one component in feed-gas was changed. First, the effect of single-component gas ratio on the performance of ethene hydroformylation was studied one by one (H₂, C₂H₄ and CO). Then an optimized ratio was found to obtain a high selectivity to C₃ oxygenates. The results showed that: (1) 0.5%Rh-20%Co/RGO is a promising heterogeneous catalyst for ethene hydroformylation. (2) H₂ and CO have a more significant influence than C₂H₄ on selectivity to oxygenates. (3) A lower H₂ ratio and a higher CO ratio in feed-gas can lead to a higher selectivity to oxygenates. (4) The highest selectivity to oxygenates, 61.70%, was obtained at the feed-gas ratio CO: C₂H₄: H₂ = 4: 2: 1.

Keywords: ethene hydroformylation, reduced graphene oxide, rhodium cobalt bimetallic catalyst, the effect of feed-gas ratio

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2 Determinants of Mobile Payment Adoption among Retailers in Ghana

Authors: Ibrahim Masud, Yusheng Kong, Adam Diyawu Rahman


Mobile payment variously referred to as mobile money, mobile money transfer, and mobile wallet refers to payment services operated under financial regulation and performed from or via a mobile device. Mobile payment systems have come to augment and to some extent try to replace the conventional payment methods like cash, cheque, or credit cards. This study examines mobile payment adoption factors among retailers in Ghana. A conceptual framework was adopted from the extant literature using the Technology Acceptance Model and the Theory of Reasoned action as the theoretical bases. Data for the study was obtained from a sample of 240 respondents through a structured questionnaire. The PLS-SEM was used to analyze the data through SPSS v.22 and SmartPLS v.3. The findings indicate that factors such as perceived usefulness, perceived ease of use, perceived security, competitive pressure and facilitating conditions are the main determinants of mobile payment adoption among retailers in Ghana. The study contributes to the literature on mobile payment adoption from developing country context.

Keywords: mobile payment, retailers, structural equation modeling, technology acceptance model

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1 Novel Two-Level Graph Causality Analysis and Mathematical Modeling for Cybersecurity Data

Authors: Van Trieu-Do, Shouhuai Xu, Yusheng Feng


Tracking attack trajectories can be difficult with limited information about the nature of the attack. Even more difficult as attack information is collected by Intrusion Detection Systems (IDSs), but the current IDSs have some limitations in identifying malicious and anomalous traffic. Moreover, IDSs only point out the suspicious events but do not show how the events relate to each other or which event cause the other event to happen. Because of this, it is important to investigate new methods that can perform the tracking attack trajectories task quickly with less attack information and dependency on IDSs, to prioritize actions during incident responds. This paper proposes a novel two-level graph causality framework for tracking attack trajectories in internet networks by leveraging observable malicious behaviors to detect what is the most probable attack event that can cause another event occurred in the system. Technically, given the timeseries of malicious events, the framework will filter events with useful features, such as attack time and port number, to apply into the conditional independent tests to detect the relationship between attack events. Using the two academic datasets collected by IDSs, experimental results show that the framework can quickly detect the causal pairs that offer meaningful insights into the nature of the internet network, given only reasonable restrictions on network size and structure. Without the framework’s guidance, these insights would not be able to discover by the existing tools, such as IDSs, and it would cost expert human analysts a significant time to find out. The computational results from the proposed two-level graph network model reveal the obvious pattern and trends. In fact, more than 80% of causal pairs have the average time difference between the caused events and the being caused events in both computed and observed data are equivalent. This result can be used as a preventive measure against future attacks. Although the forecast may be short, from 0.2 second to 4.5 seconds, it is long enough to design a prevention protocol to block those attacks.

Keywords: causality, multilevel graph, cybersecurity, mathematical modeling, visualization

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