Zhimei Zhang

Soil salinization leads to dehydration of plants, which seriously threatens ecologically sustainable development and food security guarantee. In the complex and diverse coastal wetland environment, the impervious surface and bare soil have similar spectral features with salinized soil, which make it difficult for traditional satellite data and algorithms to accurately and timely monitor the small surface features of salinization. This article presents a baseline-based soil salinity index (BSSI) for soil salinization monitoring using medium-resolution data. In BSSI, we construct a virtual salinization baseline by connecting the near-infrared (NIR) band and the short-wave infrared-2 (SWIR2) band to enhance the spectral feature of salinized soils which border on the impervious surface. In addition, we calculate the distance between the short-wave infrared-1 (SWIR1) band and the virtual salinization baseline as the BSSI, which can effectively improve the stability of salinity inversion for different soils. Through data comparison and model simulations, BSSI has shown advantages over a series of the traditional salinization spectral indices (SSIs). The results show that the saline soil extraction accuracy of BSSI exceeds 85% and the correlation coefficient of the BSSI and the degree of soil salinization exceeds 0.90. Since the related spectral bands, such as NIR, SWIR1, and SWIR2, are available on many existing satellite sensors such as Landsat TM/ETM+, OLI, and sentinel 2, the BSSI concept can be extended to establish long-term records for soil salinization monitoring.

Since modern smart grids have various and deeply coupled cyber-physical components, they are vulnerable to malicious cyber attacks. Although regular defenses including firewall and IDS are deployed, they may be weakened by zero-day vulnerabilities and sophisticated attack schemes. Therefore, defense strategies to mitigate the risk of blackouts during cyber attacks are necessary. This paper proposes a cyber-physical coordinated defense strategy to overcome the disruption and minimize the risk as much as possible. At the cyber layer, a zero-sum multilevel Markovian Stackelberg game is proposed to model sequential actions of the attacker and the defender. The defender distributes defensive resources to protect lines in a real-time manner, according to the attacker's action. If cyber attacks should result in physical outages, defense at the physical layer is then employed. A security-constrained optimal power flow reserving security margin of critical components will be performed to minimize the blackout scale and potential future risk. To solve the corresponding optimization problem and further get the optimal defense strategy, this paper devises a novel “water-pouring” algorithm. Lastly, test results show that the proposed dynamic defense strategy mitigates risk significantly and outperforms existing methods.

Since modern cyber-physical power systems are vulnerable to coordinated wide-area cyber attacks, it is necessary to mitigate the potential risk as much as possible. At the planning stage, the defender can utilize software diversity, which is a common phenomenon that the cyber software of different substations comes from different competing vendors. Therefore, different kinds of software may not be exposed to the same zero-day security loophole, preventing the attacker from taking charge of multiple substations at the same time. In this paper, the optimal scheme of software deployment considering long-term risk mitigation is studied. Firstly, the framework of diversity-based cyber defense against malicious attacks is formulated. Secondly, the risk index based on representative attack patterns is constructed, which is the objective to be minimized. Thirdly, considering that the deployment scheme is long-term stable while the operating mode varies with time, we construct a multiobjective nonlinear stochastic programming to mitigate the average risk of operating modes. Then the optimization problem is solved by the multiobjective genetic algorithm. Lastly, results of the IEEE 39-node CPPS and and the Virtual European Grid demonstrate that the proposed method can considerably reduce the attack risk.

Modern cyber-physical power systems are vulnerable to cyber attacks. Given that cyber and physical networks are coupled tightly, attacks in the cyber layer can penetrate the physical layer, causing the outage of transmission lines and other physical equipment, thus changing the topology of the grid. In some extreme scenarios, the topological change will disrupt the emergency response of the grid, eventually causing cascading outages along with a blackout. Therefore, as the defender, the operator of a cyber-physical power system should identify critical cyber attacks. In this paper, patterns of sequential cyber topological attacks are analyzed. Firstly, a coordinated attack process is established, including mechanism and probability analysis considering the different timescales. Secondly, the concept of patterns is defined as minimal attack sequences aimed at causing blackouts. Furthermore, the representativeness of patterns is illustrated, which can significantly reduce the storage of risky attack sequences. Thirdly, to address the problem that the identification of patterns is computationally intensive, a search strategy that selects the next attack target dynamically and increases the search depth gradually is proposed to avoid unnecessary search trials. Lastly, tests are carried out on the IEEE 39-node system using the AC power flow model, which validates the representativeness of patterns and the performance of the proposed search strategy.

In the analysis of cascading outages and blackouts in power systems, risky cascading fault chains should be accurately identified in order to do further block or alleviate blackouts. However, the huge computational burden makes online analysis difficult. In this paper, an online search method for representative risky fault chains based on reinforcement learning and knowledge transfer is proposed. This method aims at promoting efficiency by exploiting similarities of adjacent power flow snapshots in operations. After the “representative risky fault chain“ is defined, a framework of tree search based on Markov Decision Process and Q-learning is constructed. The knowledge in past runs is accumulated offline and then applied online, with a mechanism of knowledge transition and extension. The proposed learning based approach is verified on an illustrative 39-bus system with different loading levels, and simulations are carried out on a realworld 1000-bus power grid in China to show the effectiveness and efficiency of the proposed approach.

End-Stage Renal Disease (ESRD) is a worldwide public health problem. Currently, many genome-wide association studies have suggested a potential association between human leukocyte antigen (HLA) and ESRD by uncovering a causal relationship between HLA and glomerulonephritis. However, previous studies, which investigated the HLA polymorphism and its association with ESRD, were performed with the modest data sets and thus might be limited. On the other hand, few researches were conducted to tackle the Chinese population with ESRD. Therefore, this study aims to detect the susceptibilities of HLA polymorphism to ESRD within the Cantonese community, a representative southern population of China. From the same region, 4541 ESRD patients who were waiting for kidney transplantation and 3744 healthy volunteer bone marrow donors (controls) were randomly chosen for this study. Polymerase chain reaction-sequence specific primer method was used to analyze the HLA polymorphisms (including HLA-A, HLA-B and HLA-DRB1 loci) in both ESRD patients and controls. The frequencies of alleles at these loci and haplotypes were compared between ESRD patients and controls. A total of 88 distinct HLA alleles and 1361 HLA A-B-DRB1 haplotypes were detected. The frequencies of five alleles, HLA-A*24, HLA-B*55, HLA-B*54, HLA-B*40(60), HLA-DRB1*04, and one haplotype (HLA-A*11-B*27-DRB1*04) in ESRD patients are significantly higher than those in the controls, respectively. Five HLA alleles and one haplotype at the HLA-A, HLA-B and HLA-DRB1 loci appear to be associated with ESRD within the Cantonese population.