Towards Joint Electricity and Data Trading: A Scalable Cooperative Game Theoretic Approach

This paper, for the first time, proposes a joint electricity and data trading mechanism based on cooperative game theory. All prosumers first submit the parameters associated with both electricity and data to the market operator. The operator utilizes the public and prosumers' private data to forecast the distributed renewable generators (DRGs) and quantify the improvement driven by prosumers' private data in terms of reduced uncertainty set. Then, the operator maximizes the grand coalition's total payoff considering the uncertain generation of DRGs and imputes the payoff to each prosumer based on their contribution to electricity and data sharing. The mathematical formulation of the grand coalition is developed and converted into a second order cone programming problem by using an affinepolicy based robust approach. The stability of such a grand coalition is mathematically proved, i.e., all prosumers are willing to cooperate. Furthermore, to address the scalability challenge of existing payoff imputation methods in the cooperative game, a two stage optimization based approach is proposed, which is converted into a mixed integer second order cone programming and solved by the Benders decomposition. Case studies illustrate all prosumers are motivated to trade electricity and data under the joint trading framework and the proposed imputation method significantly enhances the scalability

Hurricanes on tidally locked terrestrial planets: Fixed surface temperature experiments

In this work, we study the presence of hurricanes on exoplanets. Tidally locked terrestrial planets around M dwarfs are the main targets of space missions looking to discover habitable exoplanets. The question of whether hurricanes can form on this kind of planet is important for determining their climate and habitability. Using a high-resolution global atmospheric circulation model, we investigated whether there are hurricanes on tidally locked terrestrial planets under fixed surface temperatures. The relevant effects of the planetary rotation rate, surface temperature, and bulk atmospheric compositions were examined. We find that hurricanes can form on the planets but not on all of them. For planets near the inner edge of the habitable zone of late M dwarfs, there are more numerous and stronger hurricanes on both day and night sides. For planets in the middle and outer ranges of the habitable zone, the possibility of hurricane formation is low or even close to zero, as has been suggested in recent studies. Earth-based hurricane theories are applicable to tidally locked planets only when the atmospheric compositions are similar to that of Earth. However, if the background atmosphere is lighter than H2O, hurricanes can hardly be produced because convection is always inhibited due to the effect of the mean molecular weight, similarly to the case of Saturn. These results have broad implications on the precipitation, ocean mixing, climate, and atmospheric characterization of tidally locked planets. Finally, A test with a coupled slab ocean and an Earth-like atmosphere in a tide-locked orbit of ten Earth days demonstrates that there are also hurricanes present in the experiment

Prevalence of allergic rhinitis among adults in urban and rural areas of China : a population-based cross-sectional survey

Purpose: The aim of the present study was to compare the prevalence of self-reported and confirmable allergic rhinitis (AR) with positive skin prick test (SPT) results among adults living in urban and rural areas of China. Methods: Adults from a community in Beijing and a village in Baoding were selected as representative urban and rural dwellers, respectively. All eligible residents were enrolled from the population register and received a face-to-face interview using modified validated questionnaires. Equal sets of randomly selected self-reporting AR-positive and AR-negative participants who responded to the questionnaires were also investigated using skin prick tests. Results: A total of 803 participants in the rural area and a total of 1,499 participants in the urban area completed the questionnaires, with response rates being 75.9% and 81.5% respectively. The prevalence of self-reported AR of the rural area (19.1%) was significantly higher than that of the urban area (13.5%). The elementary school of educational level increased the risk of having AR (adjusted OR=2.198, 95% CI=1.072-2.236). The positive SET rates among subjects with self-reported AR in the rural and urban areas were 32.5% and 53.3%, respectively; the confirmable AR prevalence of 6.2% and 7.2% among the rural and urban adults, respectively. Conclusions: The prevalence of confirmable AR is similar between rural and urban areas in China, although there is a higher prevalence of self-reported AR in the former

Cyber-physical interdependent restoration scheduling for active distribution network via ad hoc wireless communication

This paper proposes a post-disaster cyber-physical interdependent restoration scheduling (CPIRS) framework for active distribution networks (ADN) where the simultaneous damages on cyber and physical networks are considered. The ad hoc wireless device-to-device (D2D) communication is leveraged, for the first time, to establish cyber networks instantly after the disaster to support ADN restoration. The repair and operation crew dispatching, the remote-controlled network reconfiguration and the system operation with DERs can be effectively coordinated under the cyber-physical interactions. The uncertain outputs of renewable energy resources (RESs) are represented by budget-constrained polyhedral uncertainty sets. Through implementing linearization techniques on disjunctive expressions, a monolithic mixed-integer linear programming (MILP) based two-stage robust optimization model is formulated and subsequently solved by a customized column-and-constraint generation (C&CG) algorithm. Numerical results on the IEEE 123-node distribution system demonstrate the effectiveness and superiorities of the proposed CPIRS method for ADN

Simple and Efficient Heterogeneous Graph Neural Network

Heterogeneous graph neural networks (HGNNs) have powerful capability to embed rich structural and semantic information of a heterogeneous graph into node representations. Existing HGNNs inherit many mechanisms from graph neural networks (GNNs) over homogeneous graphs, especially the attention mechanism and the multi-layer structure. These mechanisms bring excessive complexity, but seldom work studies whether they are really effective on heterogeneous graphs. This paper conducts an in-depth and detailed study of these mechanisms and proposes Simple and Efficient Heterogeneous Graph Neural Network (SeHGNN). To easily capture structural information, SeHGNN pre-computes the neighbor aggregation using a light-weight mean aggregator, which reduces complexity by removing overused neighbor attention and avoiding repeated neighbor aggregation in every training epoch. To better utilize semantic information, SeHGNN adopts the single-layer structure with long metapaths to extend the receptive field, as well as a transformer-based semantic fusion module to fuse features from different metapaths. As a result, SeHGNN exhibits the characteristics of simple network structure, high prediction accuracy, and fast training speed. Extensive experiments on five real-world heterogeneous graphs demonstrate the superiority of SeHGNN over the state-of-the-arts on both accuracy and training speed.Comment: Accepted by AAAI 202

Implementing a new fully stepwise decomposition-based sampling technique for the hybrid water level forecasting model in real-world application

Various time variant non-stationary signals need to be pre-processed properly in hydrological time series forecasting in real world, for example, predictions of water level. Decomposition method is a good candidate and widely used in such a pre-processing problem. However, decomposition methods with an inappropriate sampling technique may introduce future data which is not available in practical applications, and result in incorrect decomposition-based forecasting models. In this work, a novel Fully Stepwise Decomposition-Based (FSDB) sampling technique is well designed for the decomposition-based forecasting model, strictly avoiding introducing future information. This sampling technique with decomposition methods, such as Variational Mode Decomposition (VMD) and Singular spectrum analysis (SSA), is applied to predict water level time series in three different stations of Guoyang and Chaohu basins in China. Results of VMD-based hybrid model using FSDB sampling technique show that Nash-Sutcliffe Efficiency (NSE) coefficient is increased by 6.4%, 28.8% and 7.0% in three stations respectively, compared with those obtained from the currently most advanced sampling technique. In the meantime, for series of SSA-based experiments, NSE is increased by 3.2%, 3.1% and 1.1% respectively. We conclude that the newly developed FSDB sampling technique can be used to enhance the performance of decomposition-based hybrid model in water level time series forecasting in real world

Intelligent ZHENG Classification of Hypertension Depending on ML-kNN and Information Fusion

Hypertension is one of the major causes of heart cerebrovascular diseases. With a good accumulation of hypertension clinical data on hand, research on hypertension's ZHENG differentiation is an important and attractive topic, as Traditional Chinese Medicine (TCM) lies primarily in “treatment based on ZHENG differentiation.” From the view of data mining, ZHENG differentiation is modeled as a classification problem. In this paper, ML-kNN—a multilabel learning model—is used as the classification model for hypertension. Feature-level information fusion is also used for further utilization of all information. Experiment results show that ML-kNN can model the hypertension's ZHENG differentiation well. Information fusion helps improve models' performance