Multi-Layered Blockchain Governance Game

The research designs a new integrated system for the security enhancement of a decentralized network by preventing damages from attackers, particularly for the 51 percent attack. The concept of multiple layered design based on Blockchain Governance Games frameworks could handle multiple number of networks analytically. The Multi-Layered Blockchain Governance Game is an innovative analytical model to find the best strategies for executing a safety operation to protect whole multiple layered network systems from attackers. This research fully analyzes a complex network with the compact mathematical forms and theoretically tractable results for predicting the moment of a safety operation execution are fully obtained. Additionally, simulation results are demonstrated to obtain the optimal values of configuring parameters of a blockchain-based security network. The Matlab codes for the simulations are publicly available to help those whom are constructing an enhanced decentralized security network architecture through this proposed integrated theoretical framework.

Three-Vector-Based Low Complexity Model Predictive Control for Soft Open Point

Soft open point (SOP) can improve the flexibility and reliability of power supplies; thus, they are widely used in distribution network systems. Traditional single-vector model predictive control (SV-MPC) can quickly and flexibly control the power and current at both ports of the SOP. However, SV-MPC can only select one voltage vector in a sampling time, producing large current ripples, and power fluctuations. In order to solve the above problems, this paper proposes a three-vector-based low complexity model predictive control (TV-MPC). In the proposed control method, two effective voltage vectors and one zero voltage vector are selected in a sampling time. For the two-port SOP, methods are given to judge the sectors on both sides and select the voltage vectors. Furthermore, the calculation method of the distribution time is proposed as well. Finally, the effectiveness of the proposed method is verified by steady-state and dynamic-state simulation results compared with the SV-MPC.

XGB-RF: A Hybrid Machine Learning Approach for IoT Intrusion Detection

In the past few years, Internet of Things (IoT) devices have evolved faster and the use of these devices is exceedingly increasing to make our daily activities easier than ever. However, numerous security flaws persist on IoT devices due to the fact that the majority of them lack the memory and computing resources necessary for adequate security operations. As a result, IoT devices are affected by a variety of attacks. A single attack on network systems or devices can lead to significant damages in data security and privacy. However, machine-learning techniques can be applied to detect IoT attacks. In this paper, a hybrid machine learning scheme called XGB-RF is proposed for detecting intrusion attacks. The proposed hybrid method was applied to the N-BaIoT dataset containing hazardous botnet attacks. Random forest (RF) was used for the feature selection and eXtreme Gradient Boosting (XGB) classifier was used to detect different types of attacks on IoT environments. The performance of the proposed XGB-RF scheme is evaluated based on several evaluation metrics and demonstrates that the model successfully detects 99.94% of the attacks. After comparing it with state-of-the-art algorithms, our proposed model has achieved better performance for every metric. As the proposed scheme is capable of detecting botnet attacks effectively, it can significantly contribute to reducing the security concerns associated with IoT systems.

Silicon Photonic Cross-bar Switching and Wavelength Conversion in a WDM Network Testbed (Project Report CIAN2-3-Y1)

The goal of this research was to design, fabricate and test chip-scale opto-electronic network systems for access and data networks.

Supporting the Bidding Decisions of Smallholder Farmers in Public Calls in Brazil

In Brazil, the National School Feeding Program (PNAE) seeks to contribute to the socio-economic development of smallholder farmers, prioritizing them in supplying their products for preparing daily meals in public schools. However, farmers face challenges in determining which school calls to bid for and the potential benefits from their participation, due to the multiple quantitative and qualitative decision criteria involved. This paper presents a novel Data Envelopment Analysis (DEA)-based method for bidding priority setting, to support the decision making. The model was applied for a case study in Brazil. The academic contribution lies in the innovation of using a Double-Frontier Slack-Based Measure (SBM) DEA model for Hierarchical Network systems, i.e., applied to multiple levels and followed by a tie-breaking method. The practical contribution lies in the decision support of farmers by presenting the results at three levels, the first of which is a ranking by the town or urban cluster priority, the second by the school, and the third by the products. Thus, using the rankings of calls, farmers can make informed decisions regarding the feasibility of bidding for each PNAE public call. At the same time, the objective rankings can alleviate friction and conflict within co-operatives during the decision-making process.

An automated microfluidic platform integrating functional vascularized organoids-on-chip

The development of vascular networks on-chip is crucial for the long-term culture of three-dimensional cell aggregates such as organoids, spheroids, tumoroids, and tissue explants. Despite the rapid advancement of microvascular network systems and organoid technology, vascularizing organoids-on-chips remains a challenge in tissue engineering. Moreover, most existing microfluidic devices poorly reflect the complexity of in vivo flows and require complex technical settings to operate. Considering these constraints, we developed an innovative platform to establish and monitor the formation of endothelial networks around model spheroids of mesenchymal and endothelial cells as well as blood vessel organoids generated from pluripotent stem cells, cultured for up to 15 days on-chip. Importantly, these networks were functional, demonstrating intravascular perfusion within the spheroids or vascular organoids connected to neighbouring endothelial beds. This microphysiological system thus represents a viable organ-on-chip model to vascularize biological tissues and should allow to establish perfusion into organoids using advanced microfluidics.

Network infrastructure in the present-day realities

The development of digital technologies, the transformation of the Internet into a “communication medium” leads to the formation of a network society with the large-scale development of network culture and the invasion of network business and network forms of education. Replacement of the face-to-face contact by the network communication, destruction of personal space, openness of personal life, its “inclusion in the network”, simultaneous possibility of anonymity, protection and irresponsibility of users become a reality of modern life. Network systems are becoming the basic infrastructure of modern society.

Approximate approach for improving pressure attenuation accuracy during hydraulic transients

The quasi-steady friction model is generally adopted in water hammer simulation in pipe network systems, which cannot accurately reflect the attenuation of pressure, while the existing unsteady friction model is challenging to use in complex pipe network systems. In this study, a convenient method for treating the friction term is proposed based on the Moody diagram. The attenuation process of water hammer pressure can be accurately reflected by reading the relationship curve between Reynolds number and the Darcy friction factor in the pipeline transient process. Combined with the classical water hammer experiment and the long pipe valve closing experiment in our laboratory, the accuracy of this model is verified, and the influence of absolute roughness (e) and Reynolds number (Re) on the model was analyzed as well. The results show that the pressure attenuation using the Method of Characteristics (MOC) and the proposed friction model has a good agreement with the experimental data. The absolute roughness has little influence on the results in hydraulically smooth pipe, while the minimum Reynolds number has a significant influence. When selecting the minimum Reynolds number, 2% ∼ 5% of the initial flow rate is recommended for calculation.