Pond aquaculture is the major freshwater aquaculture method in China. Ammonia-oxidizing communities inhabiting pond sediments play an important role in controlling culture water quality. However, the distribution and activities of ammonia-oxidizing microbial communities along sediment profiles are poorly understood in this specific environment. Vertical variations in the abundance, transcription, potential ammonia oxidizing rate, and community composition of ammonia-oxidizing bacteria (AOB) and ammonia-oxidizing archaea (AOA) in sediment samples (0–50 cm depth) collected from a freshwater aquaculture pond were investigated. The concentrations of the AOA amoA gene were higher than those of the AOB by an order of magnitude, which suggested that AOA, as opposed to AOB, were the numerically predominant ammonia-oxidizing organisms in the surface sediment. This could be attributed to the fact that AOA are more resistant to low levels of dissolved oxygen. However, the concentrations of the AOB amoA mRNA were higher than those of the AOA by 2.5- to 39.9-fold in surface sediments (0–10 cm depth), which suggests that the oxidation of ammonia was mainly performed by AOB in the surface sediments, and by AOA in the deeper sediments, where only AOA could be detected. Clone libraries of AOA and AOB amoA sequences indicated that the diversity of AOA and AOB decreased with increasing depth. The AOB community consisted of two groups: the Nitrosospira and Nitrosomonas clusters, and Nitrosomonas were predominant in the freshwater pond sediment. All AOA amoA gene sequences in the 0–2 cm deep sediment were grouped into the Nitrososphaera cluster, while other AOA sequences in deeper sediments (10–15 and 20–25 cm depths) were grouped into the Nitrosopumilus cluster.

Digital assets have come under various network security threats in the digital age. As a kind of security equipment to protect digital assets, intrusion detection system (IDS) is less efficient if the alert is not timely and IDS is useless if the accuracy cannot meet the requirements. Therefore, an intrusion detection model that combines machine learning with deep learning is proposed in this paper. The model uses the kmeans and the random forest (RF) algorithms for the binary classification, and distributed computing of these algorithms is implemented on the Spark platform to quickly classify normal events and attack events. Then, by using the convolutional neural network (CNN), long short-term memory (LSTM), and other deep learning algorithms, the events judged as abnormal are further classified into different attack types finally. At this stage, adaptive synthetic sampling (ADASYN) is adopted to solve the unbalanced dataset. The NSL-KDD and CIS-IDS2017 datasets are used to evaluate the performance of the proposed model. The experimental results show that the proposed model has better TPR for most of attack events, faster data preprocessing speed, and potentially less training time. In particular, the accuracy of multi-target classification can reach as high as 85.24% in the NSL-KDD dataset and 99.91% in the CIC-IDS2017 dataset.INDEX TERMS Intrusion detection system, machine learning algorithm, k-means, random forest, deep learning algorithm.

Pond aquaculture undeniably offers the potential for food production worldwide. In China, 45.83% of aquatic production is currently from pond aquaculture. However, with the continuous expansion of this practice, environmental problems such as a high level of water consumption, aquaculture water deterioration, pollution from effluent and aquatic product quality decline seriously restrict the sustainable development of pond aquaculture. In this review, we summarise the (i) the impacts of pond aquaculture on the environment, (ii) research progress in pond aquaculture ecological engineering, (iii) existing technologies regarding pond aquaculture ecological engineering systems, (iv) effects of applying pond aquaculture ecological engineering and (v) summary and prospects. Moreover, we discuss the merits and drawbacks of each method and technology, and future research priorities are reviewed. With this, an understanding of the role played by ecological engineering in pond aquaculture is provided, as well as guidance for precisely managing aquaculture water and effluent, aquaculture practices, and technological developments. In summary, the pond aquaculture ecological engineering can be managed so as to improve animal welfare and the stability of water treatment systems, reducing the adverse effects on the environment and public health, and enabling the sustainable development of pond aquaculture.

Cybersecurity risk assessment is an important means of effective response to network attacks on industrial control systems. However, cybersecurity risk assessment process is susceptible to subjective and objective effects. To solve this problem, this paper introduced cybersecurity risk assessment method based on fuzzy theory of Attack-Defense Tree model and probability cybersecurity risk assessment technology, and applied it to airport automatic fuel supply control system. Firstly, an Attack-Defense Tree model was established based on the potential cybersecurity threat of the system and deployed security equipment. Secondly, the interval probability of the attack path was calculated using the triangular fuzzy quantification of the interval probabilities of the attack leaf nodes and defensive leaf nodes. Next, the interval probability of the final path was defuzzified. Finally, the occurrence probability of each final attack path was obtained and a reference for the deployment of security equipment was provided. The main contributions of this paper are as follows: (1) considering the distribution of equipment in industrial control system, a new cybersecurity risk evaluation model of industrial control system is proposed. (2) The experimental results of this article are compared with other assessment technologies, and the trend is similar to that of other evaluation methods, which proves that the method was introduced in this paper is scientific. However, this method reduces the subjective impact of experts on cybersecurity risk assessment, and the assessment results are more objective and reasonable. (3) Applying this model to the airport oil supply automatic control system can comprehensively evaluate risk, solve the practical problems faced by the airport, and also provide an important basis for the cybersecurity protection scheme of the energy industry.

The algal succession in Microcystis blooms of varying biomass under continuous aeration was studied in a greenhouse. There were four treatments (control, Low, Medium, and High) with initial chlorophyll a (Chl-a) of 32.5, 346.8, 1413.7, and 14,250.0 μg L−1, respectively. During the experiment, Cyanophyta biomass was the lowest in the Medium treatment (P < 0.05), while its Chlorophyta biomass was the highest (P < 0.05). Both Chlorophyta and Bacillariophyta biomass were the lowest in the High treatment (P < 0.05). Bacillariophyta biomass, particularly the diatom Nitzschia palea was the highest in the Low treatment (P < 0.05), and Nitzschia palea cells were attached to the Microcystis colonies. Thus, the algal shift in Microcystis blooms under aeration disturbance depends on its initial biomass, and it shift to green algae or/and diatom dominance in the control, Low, Medium treatments. Diatom cells, particularly N. palea, grew in an attached form on Microcystis colonies in treatment Low, in which the colonies provided media for the adherence. The mechanism of the algal shift with different biomass must be related to the nutrient level, low light and aerobic conditions under aeration disturbance as well as the aeration itself, which destroyed the Microcystis colonies’ advantage of floating on the water.