China is on the track tackling Enteromorpha spp forming green tide

Green tide management is supposed to be a long term fight rather than an episode during the 29th Olympic Games for China, since it has been gaining in scale and frequency during the past 3 decades in both marine and estuary environment all over the world. A number of rapid-responding studies including oceanographic comprehensive surveys along the coastline have been conducted during the bloom and post-bloom periods in 2008 by Chinese marine scientists. The preliminary results are as below: (1) phylogenetic analysis indicates that the bloom forming alga forms a clade with representatives of the green seaweed Enteromorpha linza, though, the alga has been identified as E. proliera by means of morphological; (2) the present data suggest that the bloom was originated from south of Yellow Sea, but not the severely affected area near Qingdao City; (3) pathways of reproduction for E. prolifera have approved to be multifarious, including sexual, asexual and vegetative propagation; (4) somatic cells may act as a propagule bank, which is supposed to be a very dangerous transmitting way for its marked movability, adaptability and viability; (5) pyrolysis of the alga showed that three stages appeared during the process, which are dehydration (18–20^o^C), main devolatilization (200–450^o^C) and residual decomposition (450–750^o^C), and activation energy of the alga was determined at 237.23 KJ•mol^-1^. Although the scarce knowlegde on E. prolifera not yet allow a fully understanding of the green tide, some of the results suggests possible directions in further green tide research and management

Link Prediction on Heterophilic Graphs via Disentangled Representation Learning

Link prediction is an important task that has wide applications in various domains. However, the majority of existing link prediction approaches assume the given graph follows homophily assumption, and designs similarity-based heuristics or representation learning approaches to predict links. However, many real-world graphs are heterophilic graphs, where the homophily assumption does not hold, which challenges existing link prediction methods. Generally, in heterophilic graphs, there are many latent factors causing the link formation, and two linked nodes tend to be similar in one or two factors but might be dissimilar in other factors, leading to low overall similarity. Thus, one way is to learn disentangled representation for each node with each vector capturing the latent representation of a node on one factor, which paves a way to model the link formation in heterophilic graphs, resulting in better node representation learning and link prediction performance. However, the work on this is rather limited. Therefore, in this paper, we study a novel problem of exploring disentangled representation learning for link prediction on heterophilic graphs. We propose a novel framework DisenLink which can learn disentangled representations by modeling the link formation and perform factor-aware message-passing to facilitate link prediction. Extensive experiments on 13 real-world datasets demonstrate the effectiveness of DisenLink for link prediction on both heterophilic and hemophiliac graphs. Our codes are available at https://github.com/sjz5202/DisenLin

Improving Fairness of Graph Neural Networks: A Graph Counterfactual Perspective

Graph neural networks have shown great ability in representation (GNNs) learning on graphs, facilitating various tasks. Despite their great performance in modeling graphs, recent works show that GNNs tend to inherit and amplify the bias from training data, causing concerns of the adoption of GNNs in high-stake scenarios. Hence, many efforts have been taken for fairness-aware GNNs. However, most existing fair GNNs learn fair node representations by adopting statistical fairness notions, which may fail to alleviate bias in the presence of statistical anomalies. Motivated by causal theory, there are several attempts utilizing graph counterfactual fairness to mitigate root causes of unfairness. However, these methods suffer from non-realistic counterfactuals obtained by perturbation or generation. In this paper, we take a causal view on fair graph learning problem. Guided by the casual analysis, we propose a novel framework CAF, which can select counterfactuals from training data to avoid non-realistic counterfactuals and adopt selected counterfactuals to learn fair node representations for node classification task. Extensive experiments on synthetic and real-world datasets show the effectiveness of CAF

A Robust Planning Model for Offshore Microgrid Considering Tidal Power and Desalination

Increasing attention has been paid to resources on islands, thus microgrids on islands need to be invested. Different from onshore microgrids, offshore microgrids (OM) are usually abundant in ocean renewable energy (ORE), such as offshore wind, tidal power generation (TPG), etc. Moreover, some special loads such as seawater desalination unit (SDU) should be included. In this sense, this paper proposes a planning method for OM to minimize the investment cost while the ORE's fluctuation could be accommodated with robustness. First, a deterministic planning model (DPM) is formulated for the OM with TPG and SDU. A robust planning model (RPM) is then developed considering the uncertainties from both TPG and load demand. The Column-and-constraint generation (C&CG) algorithm is then employed to solve the RPM, producing planning results for the OM that is robust against the worst scenario. Results of the case studies show that the investment and operation decisions of the proposed model are robust, and TPG shows good complementarity with the other RESs