Mobile Conductance in Sparse Networks and Mobility-Connectivity Tradeoff

In this paper, our recently proposed mobile-conductance based analytical framework is extended to the sparse settings, thus offering a unified tool for analyzing information spreading in mobile networks. A penalty factor is identified for information spreading in sparse networks as compared to the connected scenario, which is then intuitively interpreted and verified by simulations. With the analytical results obtained, the mobility-connectivity tradeoff is quantitatively analyzed to determine how much mobility may be exploited to make up for network connectivity deficiency.Comment: Accepted to ISIT 201

Managing Complex Social Housing Urban Redevelopment Projects through Improved Project Management and Value Generation

Book of abstracts of the workshop:Managing Complex Social Housing Urban Redevelopment Projects through Improved Project Management and Value Generatio

A Contextual Bandit Approach for Value-oriented Prediction Interval Forecasting

Prediction interval (PI) is an effective tool to quantify uncertainty and usually serves as an input to downstream robust optimization. Traditional approaches focus on improving the quality of PI in the view of statistical scores and assume the improvement in quality will lead to a higher value in the power systems operation. However, such an assumption cannot always hold in practice. In this paper, we propose a value-oriented PI forecasting approach, which aims at reducing operational costs in downstream operations. For that, it is required to issue PIs with the guidance of operational costs in robust optimization, which is addressed within the contextual bandit framework here. Concretely, the agent is used to select the optimal quantile proportion, while the environment reveals the costs in operations as rewards to the agent. As such, the agent can learn the policy of quantile proportion selection for minimizing the operational cost. The numerical study regarding a two-timescale operation of a virtual power plant verifies the superiority of the proposed approach in terms of operational value. And it is especially evident in the context of extensive penetration of wind power.Comment: submitted to IEEE Transactions on Smart Gri

Optimal Vehicle Charging in Bilevel Power-Traffic Networks via Charging Demand Function

Electric vehicle (EV) charging couples the operation of power and traffic networks. Specifically, the power network determines the charging price at various locations, while EVs on the traffic network optimize the charging power given the price, acting as price-takers. We model such decision-making processes by a bilevel program, with the power network at the upper-level and the traffic network at the lower-level. However, since the two networks are managed by separate entities and the charging expense term, calculated as the product of charging price and charging demand, is nonlinear. Solving the bilevel program is nontrivial. To overcome these challenges, we derive the charging demand function using multiparametric programming theory. This function establishes a piecewise linear relationship between the charging price and the optimal charging power, enabling the power network operator to manage EV charging power independently while accounting for the coupling between the two networks. With the derived function, we are also able to replace the nonlinear charging expense term with a piecewise quadratic one, thus guaranteeing solution optimality. Our numerical studies demonstrate that different traffic demands can have an impact on charging patterns and the power network can effectively incentivize charging at low-price nodes through price setting.Comment: submitted to IEEE Transactions on Smart Gri

Value-oriented Renewable Energy Forecasting for Coordinated Energy Dispatch Problems at Two Stages

Energy forecasting is deemed an essential task in power system operations. Operators usually issue forecasts and leverage them to schedule energy dispatch ahead of time (referred to as the 'predict, then optimize' paradigm). However, forecast models are often developed via optimizing statistical scores while overlooking the value of the forecasts in operation. In this paper, we design a value-oriented point forecasting approach for energy dispatch problems with renewable energy sources (RESs). At the training phase, this approach incorporates forecasting with day-ahead/real-time operations for power systems, thereby achieving reduced operation costs of the two stages. To this end, we formulate the forecast model parameter estimation as a bilevel program at the training phase, where the lower level solves the day-ahead and real-time energy dispatch problems, with the forecasts as parameters; the optimal solutions of the lower level are then returned to the upper level, which optimizes the model parameters given the contextual information and minimizes the expected operation cost of the two stages. Under mild assumptions, we propose a novel iterative solution strategy for this bilevel program. Under such an iterative scheme, we show that the upper level objective is locally linear regarding the forecast model output, and can act as the loss function. Numerical experiments demonstrate that, compared to commonly used point forecasting methods, the forecasts obtained by the proposed approach result in lower operation costs in the subsequent energy dispatch problems. Meanwhile, the proposed approach is more computationally efficient than traditional two-stage stochastic program.Comment: submitted to European Journal of Operational Researc

3D Automatic Segmentation Method for Retinal Optical Coherence Tomography Volume Data Using Boundary Surface Enhancement

With the introduction of spectral-domain optical coherence tomography (SDOCT), much larger image datasets are routinely acquired compared to what was possible using the previous generation of time-domain OCT. Thus, there is a critical need for the development of 3D segmentation methods for processing these data. We present here a novel 3D automatic segmentation method for retinal OCT volume data. Briefly, to segment a boundary surface, two OCT volume datasets are obtained by using a 3D smoothing filter and a 3D differential filter. Their linear combination is then calculated to generate new volume data with an enhanced boundary surface, where pixel intensity, boundary position information, and intensity changes on both sides of the boundary surface are used simultaneously. Next, preliminary discrete boundary points are detected from the A-Scans of the volume data. Finally, surface smoothness constraints and a dynamic threshold are applied to obtain a smoothed boundary surface by correcting a small number of error points. Our method can extract retinal layer boundary surfaces sequentially with a decreasing search region of volume data. We performed automatic segmentation on eight human OCT volume datasets acquired from a commercial Spectralis OCT system, where each volume of data consisted of 97 OCT images with a resolution of 496 512; experimental results show that this method can accurately segment seven layer boundary surfaces in normal as well as some abnormal eyes.Comment: 27 pages, 19 figure

How evidence-based design supports better value generation for end-users

Interest in Evidence based design (EBD) has been growing extensively. Proper design decisions can not only maximise the occupant's health benefits, but also improve service delivery. There is a clear link between the concept of EBD and that of value generation to guide decision making for better healthcare design. Through an expensive literature review, a conceptual framework is presented, illustrating important decision making steps, considering EBD as means, with an emphasis on how it helps increase the end-users value generation. The paper concludes by identifying limitations and potential future studies

A field survey on the indoor environmental quality of the UK primary school classroom

The indoor environmental quality (thermal, visual, acoustic and air quality) of the primary school classroom has an impact on pupils' learning and wellbeing. A field study, conducted on random days from 2012 to 2013, was carried out in 203 classrooms from 30 primary schools in 3 areas within the UK. Physical parameters were measured at the site: illuminance, air temperature, relative humidity, CO2 concentration and noise level. It was inferred that: 30.9% classrooms did not meet the proposed standards limiting the level of CO2 levels; light levels were found to be notably low (less than 500lux) because the pedagogy was reliant on smart boards; high noise levels (between 40 to 80dBA) were caused mainly from the adjacent activity areas to the classroom. Based on the findings, practical suggestions are proposed to maximise the environmental benefit to the pupil

Blockchain Network Analysis: A Comparative Study of Decentralized Banks

Decentralized finance (DeFi) is known for its unique mechanism design, which applies smart contracts to facilitate peer-to-peer transactions. The decentralized bank is a typical DeFi application. Ideally, a decentralized bank should be decentralized in the transaction. However, many recent studies have found that decentralized banks have not achieved a significant degree of decentralization. This research conducts a comparative study among mainstream decentralized banks. We apply core-periphery network features analysis using the transaction data from four decentralized banks, Liquity, Aave, MakerDao, and Compound. We extract six features and compare the banks' levels of decentralization cross-sectionally. According to the analysis results, we find that: 1) MakerDao and Compound are more decentralized in the transactions than Aave and Liquity. 2) Although decentralized banking transactions are supposed to be decentralized, the data show that four banks have primary external transaction core addresses such as Huobi, Coinbase, Binance, etc. We also discuss four design features that might affect network decentralization. Our research contributes to the literature at the interface of decentralized finance, financial technology (Fintech), and social network analysis and inspires future protocol designs to live up to the promise of decentralized finance for a truly peer-to-peer transaction network