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Sensor nodes are widely distributed in the Internet of Things and communicate with each other to form a wireless sensor network (WSN), which plays a vital role in people's productivity and life. However, the energy of WSN nodes is limited, so this paper proposes a two-layer WSN system based on edge computing to solve the problems of high energy consumption and short life cycle of WSN data transmission and establishes wireless energy consumption and distance optimization models for sensor networks. Specifically, we propose the optimization objective of balancing load and distance factors. We adopt an improved sparrow search algorithm to evenly distribute sensor nodes in the system to reduce resource consumption, consumption, and network life. Through the simulation experiment, our method is illustrated, effectively reducing the network's energy consumption by 26.8% and prolonging the network's life cycle. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low accuracy of network attack prediction and long response time of attack detection, bidirectional long short-term memory (BiLSTM) was used to predict network attacks. However, BiLSTM has the problems of difficulty in parameter setting and low accuracy of the prediction model. This paper first proposes the Improved Grey Wolf algorithm (IGWO) to optimize the BiLSTM (IGWO-BiLSTM). First, IGWO uses Dimension Learning Hunting (DLH) strategy to construct the wolf neighborhood. In the established wolf neighborhood, the BiLSTM parameters are iteratively optimized to obtain a prediction model with fast convergence speed and small reconstruction error. Secondly, the dataset is preprocessed, and the IP packet statistical signature (IPDCF) is defined according to the characteristics of denial of service (DOS) and distributed denial of service (DDOS) attacks. IPDCF was used to establish the time series model and network traffic time series data were input into IGWO-BiLSTM to get the prediction results. Finally, the DOS and DDOS network packets were input into the trained prediction model to obtain the prediction results of attack data. By comparing the predicted values of IGWO-BiLSTM normal network packets and attack packets, a reasonable threshold is set to provide the basis for the subsequent attack prediction. Experiments show that the IGWO-BiLSTM can reach 99.05% of the fitting degree and accurately distinguish network attacks from normal network demand increases. [ABSTRACT FROM AUTHOR]

Currently, research on the evolution of heterogeneous combat networks (HCNs) mainly focuses on the modeling process, with little attention paid to the impact of changes in network topology on operational capabilities. Link prediction can provide a fair and unified comparison standard for network evolution mechanisms. This paper uses link prediction methods to study the evolution of HCNs. Firstly, according to the characteristics of HCNs, a link prediction index based on frequent subgraphs (LPFS) is proposed. LPFS have been demonstrated on a real combat network to be superior to 26 baseline methods. The main driving force of research on evolution is to improve the operational capabilities of combat networks. Adding the same number of nodes and edges, 100 iterative experiments demonstrate that the evolutionary method (HCNE) proposed in this paper outperforms random evolution and preferential evolution in improving the operational capabilities of combat networks. Furthermore, the new network generated after evolution is more consistent with the characteristics of a real network. [ABSTRACT FROM AUTHOR]

With the rapid increase of smart Internet of Things (IoT) devices, edge networks generate a large number of computing tasks, which require edge-computing resource devices to complete the calculations. However, unreasonable edge-computing resource allocation suffers from high-power consumption and resource waste. Therefore, when user tasks are offloaded to the edge-computing system, reasonable resource allocation is an important issue. Thus, this paper proposes a digital-twin-(DT)-assisted edge-computing resource-allocation model and establishes a joint-optimization function of power consumption, delay, and unbalanced resource-allocation rate. Then, we develop a solution based on the improved whale optimization scheme. Specifically, we propose an improved whale optimization algorithm and design a greedy initialization strategy to improve the convergence speed for the DT-assisted edge-computing resource-allocation problem. Additionally, we redesign the whale search strategy to improve the allocation results. Several simulation experiments demonstrate that the improved whale optimization algorithm reduces the resource allocation and allocation objective function value, the power consumption, and the average resource allocation imbalance rate by 12.6%, 15.2%, and 15.6%, respectively. Overall, the power consumption with the assistance of the DT is reduced to 89.6% of the power required without DT assistance, thus, improving the efficiency of the edge-computing resource allocation. [ABSTRACT FROM AUTHOR]

Moso bamboo (Phyllostachys edulis (Carrière) J.Houz.) is famous for its fast growth and biomass accumulation, as well as high annual output for timber and bamboo shoots. Organic mulches are widely used to improve shoots' production in moso bamboo forests. However, continuous mulching management may cause bamboo forest degradation and affect sustainable development. The objective of this study was to identify the degradation mechanism and to provide a theoretical basis for recovery. A complete randomized block design with four treatments was conducted, including mulching for one year (M1), two years (M2), three years (M3) and no-mulching management (NM). Soil nutrient contents, enzyme activities and microbial biomass were determined. With the increase of mulching years, the soil pH value gradually reduced, causing soil acidification, but the content of soil organic matter was inclined to ascend. Soil total nitrogen (TN), total phosphorus (TP) and total potassium (TK) contents showed an increasing trend, and they were significantly higher in mulching stands than those in NM (p < 0.05). Contents of soil available nutrients (AN, AP and AK) increased, then decreased with the increase of mulching years and peaked in M1. With the increase of mulching years, the soil stoichiometry ratio (C/N, C/P and N/P) gradually increased. Soil invertase, urease and acid phosphatase activities presented a single-peak curve and reached the maximum within one year after mulching. Total microbial biomass and that of individual groups changed greatly after mulching. Soil microbial biomass increased first and then decreased, and it was the largest in M1. The fungi:bacteria ratio decreased in the first year and then began to rise, while the aerobic:anaerobic ratio showed the opposite trend. According to the overall results, M3 leads to soil acidification, imbalance of the nutrients' proportion, abnormal enzyme activity and change of soil microbial flora, and rotated mulching management (mulching one year and then recuperating one year) should be recommended in practice. [ABSTRACT FROM AUTHOR]

The root of Moso bamboo (Phyllostachys edulis (Carrière) J. Houz.) develops extremely rapidly at seedling phase and is highly sensitive to water content in soil, but its response patterns and adaptation strategies of its root to drought are little known. The aim of this study was to investigate the response of root morphology and architecture of Moso bamboo to drought at seedling phase and then to explore the drought adaptation strategies of its root. One-year-old potted seedlings of Moso bamboo were planted under three drought treatments (control, moderate drought and severe drought) for three months. Seedling growth, specific root length (SRL), root architecture (fractal dimension (FD), root branching angle (RBA) and root topological index (TI)) and non-structural carbohydrate (NSC) concentrations in roots were measured every month. The results are as follows: (i) The dry weight of root and shoot decreased significantly under drought stress. (ii) The SRL decreased under drought stress in the early duration (the first month), and then increased in the late duration (the third month). Both FD and RBA decreased, while TI and the concentrations of NSCs increased under drought stress. (iii) The NSC concentrations were positively correlated with SRL and TI, but exhibited an inverse relationship to FD and RBA. Our results indicated that Moso bamboo seedlings formed a "steeper, simpler, expensive (low SRL and high TI)" root architecture to adapt to a short-term drought (one month), and formed a "cheaper (high SRL)" root to adapt to a long-term drought (three months). Increase of NSC concentrations supported the root architecture plasticity to some extent. [ABSTRACT FROM AUTHOR]

A total of 38 volatile components were identified in Rhodobryum giganteum (Schwaegr.) Par. collected from two different geographic regions by headspace solid-phase microextraction (HS-SPME), combined with gas chromatography/mass spectrometry (GC/MS). The volatile components included some aliphatic and aromatic aldehydes, monoterpene hydrocarbons and a sesquiterpene (alpha-farnesene), with 1-methoxy-2-propyl acetate and n-hexanal being found to be the most abundant volatile components. Analysis of the chemical constituents in the volatile oil of the two samples showed that ten compounds were shared.