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Polar codes are closer to the Shannon limit with lower complexity in coding and decoding. As traditional decoding techniques suffer from high latency and low throughput, with the development of deep learning technology, some deep learning-based decoding methods have been proposed to solve these problems. Usually, the deep neural network is treated as a black box and learns to map the polar codes with noise to the original information code directly. In fact, it is difficult for the network to distinguish between valid and interfering information, which leads to limited BER performance. In this paper, a deep residual network based on information refinement (DIR-NET) is proposed for decoding polar-coded short packets. The proposed method works to fully distinguish the effective and interference information in the codewords, thus obtaining a lower bit error rate. To achieve this goal, we design a two-stage decoding network, including a denoising subnetwork and decoding subnetwork. This structure can further improve the accuracy of the decoding method. Furthermore, we construct the whole network solely on the basis of the attention mechanism. It has a stronger information extraction ability than the traditional neural network structure. Benefiting from cascaded attention modules, information can be filtered and refined step-by-step, thus obtaining a low bit error rate. The simulation results show that DIR-Net outperforms existing decoding methods in terms of BER performance under both AWGN channels and flat fading channels.

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It is critical to detect malicious code for the security of the Internet of Things (IoT). Therefore, this work proposes a malicious code detection algorithm based on the novel feature fusion-malware image convolutional neural network (FF-MICNN). This method combines a feature fusion algorithm with deep learning. First, the malicious code is transformed into grayscale image features by image technology, after which the opcode sequence features of the malicious code are extracted by the n-gram technique, and the global and local features are fused by feature fusion technology. The fused features are input into FF-MICNN for training, and an appropriate classifier is selected for detection. The results of experiments show that the proposed algorithm exhibits improvements in its detection speed, the comprehensiveness of features, and accuracy as compared with other algorithms. The accuracy rate of the proposed algorithm is also 0.2% better than that of a detection algorithm based on a single feature.

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In this study, a pilot-scale evaporation tower system was built to treat the desulfurization wastewater by spray evaporation. The distribution characteristics of Cl- in the wastewater evaporation process were investigated. Besides, the morphology and physicochemical property of solid evaporation products from desulfurization wastewater were analyzed. In addition, the emission characteristics of fine particulates were evaluated. The results indicated that the increase of salt content in desulfurization wastewater increased the mass concentrations of Cl- in three phases, but the proportions of it remained almost unchanged, which were about 10%, 55%, and 35% in the gas phase, outlet solid phase, and bottom solid phase respectively. The increase of flue gas temperature can improve the content of Cl- in the gas phase, while the increase of wastewater pH inhibited the formation of gaseous HCl. The solid evaporation products from desulfurization wastewater had a prismatic crystal structure, which mainly included the sulfate and chloride salts, and the main elements including O, Na, Mg, S, Cl, K, and Ca. Besides, the peak values of particle size distribution in the bottom solid phase and outlet solid phase were 7.67 and 0.32 µm, respectively. For the particulate matters in flue gas, the spray evaporation of desulfurization wastewater can reduce the particle concentration, promote particle agglomeration, reduce the number concentration of fine particles, and improve the removal effect of PM10. When the inlet particle concentration was 7.62 g/m3, it can reduce the particle concentration at the tower outlet to 4.59 g/m3 and reduce the number and mass concentrations of PM10 after ESP by about 43.8% and 36.8%.

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Integrated processes of whole plant cassava bioethanol production using full components including cellulosic C5 sugar are proposed. The impacts of different utilization patterns of cellulosic C5 sugar on bioethanol production are investigated by life cycle assessment. Results show that for cassava straw bioethanol, process using cellulosic C5 sugar performs better, and the NER, renewability and GWP (global warming potential) are 0.94, 1.09 and 2929 kg CO2 eq. The integrated process WPC-2 that the cellulosic C5 sugar mash is fermented together with the cassava starch, is a better cellulosic C5 sugar utilization pattern with NER 1.49, renewability 2.20 and GWP 1579 kg CO2 eq. The process WPC-2 shows the potential to approach cassava bioethanol in terms of energy and environmental emissions. The downstream products are investigated and the E85 fuel from WPC-2 has higher application potential.

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Tobacco, Nicotiana tabacum L., family Solanaceae, is grown for its leaves (mature period). It originated in the Mesoamericas but today is cultivated globally. China produces about one-third of the world´s supply of tobacco leaves and is the largest tobacco producer in the world (Hu et al. 2007). In the early summer of 2019, after tobacco has been topped, typical tobacco hollow stalk disease symptoms were observed in two to five percent of the fields in Shaowu and Jianyang counties of Nanping, Fujian province, China. Symptoms consisted of lesions extending from the point of topping downward, resulting in a black hollow stem (harvest period). The symptoms were similar to tobacco hollow stalk disease caused by Pectobacterium species. Four infected stems of different tobacco plants were collected from Nanping (N 27°37'48″, E 118°2'24″) for isolation. Small sections from the stems were surface sterilized with 1% sodium hypochlorite for 3 min, rinsed three times in sterile distilled water (SDW), then macerated in 400 µl of SDW. The suspensions were then streaked on Nutrient agar (NA) and incubated at 28°C in darkness. After 2 days, two predominant isolates that produced circular, convex, small (< 1 mm) colonies without pigmentation, were purified. The BIOLOG GEN III Microplates system was then used to identify the putative pathogenic bacteria based on the results of the biochemical assays read by the plate reader and its associated software and database. After culturing for 48 and 60 h,BIOLOG analysis identified the two isolates as Dickeya chrysanthemi (SIM=0.516, DIST=7.224 and SIM=0.589, DIST=5.984). To confirm its identification, the 16S rDNA gene from strain NPEc1 was amplified with the general primer 27F/1492R (Frank et al. 2008), A BLASTn search in GenBank of this sequence (1,465 bp, Accession No. MN640862) showed over 99% identity to the 16S rDNA of D. chrysanthemi (CP 001655). Partial sequences of housekeeping gene DNA polymerase III, subunits gamma and tau (dnaX), and the recombinase A (recA) gene were also analyzed according to Racix et al. (2014). The 511-bp dnaX sequence (MT597420) and 736-bp recA sequence (MT597421) respectively matched 98.22% and 96.19% with D. chrysanthemi (syn. Erwinia chrysanthemi) strain CFBP 2048 (JX434939) and ICMP 5703 (DQ859873). For pathogenicity testig, six tobacco seedlings of the 45-day-old cultivar Yunyan 87 were planted in nutriculture at room temperature. After three days, approximately 20 µl of each bacterial suspension (107 CFU/ml) of isolates NPEc1 and NPEc2 was injected into the leaf axils of six tobacco stems. As a control, similar seedlings were inoculated with SDW. Twelve hours after inoculation, symptoms of water-soaked decay appeared in the injected leaf axils. After 2 days, these symptoms developed into a severe rot similar to that of naturally infected plants. In contrast, the controls were symptomless. The bacterium was isolated from the rotten tissues and demonstrated a similar identity to the inoculants by the Biolog automated microbial identification system comparison, thus fulfilling Koch's postulates. The inoculation was repeated twice. On the basis of morphological, Biolog Gen III, molecular characteristics and pathogenicity, the causal agent of a hollow stalk rot on tobacco at Nanping, was identified as D. chrysanthemi. Previously, this pathogen was reported on tobacco in Cuba by Pérez (1981) and more recently isolated from poinsettia stems in China (Rungnapha et al. 2008). To our knowledge, this is the first report of Dickeya chrysanthemi causing a hollow stalk disease of toacco in China. Reference cultures have been deposited in CGMCC (NO. MT211276). Further spread of the pathogen may pose a threat to tobacco production in Nanping and other regions in China.

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3D topology control in underwater sensor networks is of great significance to ensuring reliable and efficient operation of the network. In this paper, by analyzing the characteristics of an underwater sensor network, we take the cube as the basic unit to perform 3D partition of the monitoring area, define the 3D partition unit and basic cluster structure of the underwater sensor network, and arrange rotating temporary control nodes in the cluster. Then, a cluster sleep-wake scheduling algorithm is proposed that compares the remaining node energy. It selects the node with the largest remaining energy as the working node, and the remaining nodes complete the transition of dormancy and waiting states as long as they reach the preset dormancy time. The node state settings of this phase are completed by the temporary control node. Temporary control nodes selecting and sleep-wake scheduling are used in the algorithm through 3D topology control, which reduces energy consumption and guarantees maximum sensing coverage of the entire network and the connection rate of active nodes. Simulation results further verify the effectiveness of the proposed algorithm.