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Characterization and biocontrol measures of Pseudomonas syringae pv. syringae associated with citrus blast disease

Vegetos 2020年第3期33卷 555-569页

作者： Islam, Md. Samiul Sultana, Razia Hasan, Md. Amit Alam, Md. Shah Sikdar, Biswanath Kamaruzzaman, Md. Islam, Md. Asadul Department of Plant Pathology College of Plant Science and Technology and the Key Lab of Crop Disease Monitoring & Safety Control in Hubei Province Huazhong Agricultural University Wuhan 430070 China Department of Genetic Engineering and Biotechnology Faculty of Life and Earth Science University of Rajshahi Rajshahi 6205 Rajshahi Bangladesh State Key Laboratory of Agricultural Microbiology Department of Microbiology College of Life Science and Technology Huazhong Agricultural University Wuhan 430070 China Department of Plant Pathology College of Plant Protection Nanjing Agricultural University and Key Laboratory of Integrated Management of Crop Diseases Ministry of Education Nanjing 210095 China

The present study was conducted to identify the two Pseudomonas syringae pv. syringae strains attained from the citrus blast infected tissues and also to find out the antimicrobial potentiality of plant extracts and antagonistic bacteria Bacillus spp. both under in vitro and in vivo conditions. Based on the microscopic observation and biochemical features including colouring, magnitude, size, and form of colonies displayed on LB medium, we speculate that ps17a and ps18a strains were similar to P. syringae pv syringae. For molecular identification, 16S rRNA and multi-locus sequence analysis (MLSA) with two housekeeping gene (rpoD and gyrB) sequences indicated an identical pattern to the corresponding sequences of other P. syringae pv. syringae strains which confirmed that a genetically homologous species of P. syringae pv. syringae was responsible for bacterial blast disease of citrus cultivars. Later, PCR amplification using two primer pairs (B1 and B2) yielded 752-bp fragments to confirm that syrB gene present on both isolated P. syringae pv syringae and reference strains (Pss16) can synthesize syringomycin, a potent toxin for plant pathogenicity induction. Based on the pathogenicity tests, two strains were similar in terms of virulence activity, however, were not analogous thus, having the same level of severity effect to induce bacterial blast. Moreover, antibiotic sensitivity assay showed disproportionate result for both strains which resemble susceptible strains determination. From the in vitro investigation of six plant extracts and three Bacillus isolates against ps17a and ps18a, the ethanolic extract of Allium sativum, Moringa oleifera, and Azadirachta indica showed the highest antibacterial activity against two isolated bacterial strains up to inhibition zone 23.4, 24.4 and 18.2 mm in diameter, respectively while Bacillus isolates proved to be better natural antimicrobial agents against the disease. Finally, based on in vivo experiment, the most effective p

关键词： Antagonistic activity Biological control In vivo management Molecular detection Pseudomonas syringae syrB Gene amplification

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Gas turbine rotor system fault diagnosis method based on improved deep convolutional generative adversarial networks

Research Square

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Research Square 2021年

作者： Liu, Shucong Wang, Hongjun Han, Fengxia Zhang, Xiang School of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing100192 China College of Mechanical Engineering and Applied Electronics Technology Beijing University of Technology Beijing100124 China Key Laboratory of Modern Measurement and Control Technology of Ministry of Education Beijing Information Science and Technology University Beijing100192 China

In gas turbine rotor system fault diagnosis intelligent method based on data-driven is an important means to monitor the health status of gas turbine, it is necessary to obtain sufficient effective fault data to train the intelligent diagnosis model. In the actual operation of gas turbine, the collected gas turbine fault data is limited, and the small and imbalanced fault samples seriously affect the accuracy of fault diagnosis method. Aiming at the imbalance of gas turbine fault data, an Improved Deep Convolutional Generative Adversarial Network (Improved DCGAN) suitable for gas turbine signal is proposed, an structural optimization on generator of Deep Convolutional Generative Adversarial Network (DCGAN) and gradient penalty improvement on the loss function are introduced to generate effective fault data and improve the classification accuracy. The experiment results of gas turbine test bench demonstrated that the proposed method generated effective fault samples as a supplementary set of fault samples to balance the dataset, effectively improved the fault classification and diagnosis performance of gas turbine rotor in the case of small samples, The proposed method can be used as a solution to the problems of small unbalanced fault samples, and provides an effective method for gas turbine fault diagnosis. © 2021, CC BY.

关键词： Failure analysis

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Co-Benefits Between PM2.5control and Carbon Reduction Revealed by Interpretable Machine Learning Methods

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Geophysical Research Letters 2025年第12期52卷

作者： Han Xu Jiahao Huang Dongheng Zhao Zhang Zhang Junbo Huang Feng Wang Jinxing Liu Shuai Jiang Yinchang Feng Shaojie Song Guoliang Shi Key Laboratory of Urban Air Particulate Pollution Prevention and Control of Ministry of Ecology and Environment Tianjin Key Laboratory of Urban Transport Emission Research College of Environmental Science and Engineering Nankai University Tianjin China CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER) College of Environmental Science and Engineering Nankai University Tianjin China School of Environmental Science and Safety Engineering Tianjin University of Technology Tianjin China State Key Laboratory of Precision Measuring Technology and Instruments Tianjin Environmental Monitoring Technology Enterprise Key Laboratory School of Precision Instrument and Optoelectronics Engineering Tianjin University Tianjin China Zhigan Technology (Tianjin) Co. Ltd Tianjin China

Air pollution and climate change, driven by fine particulate matter (PM 2.5 ) and carbon dioxide (CO 2 ), present critical challenges to human survival. Understanding the interaction between PM 2.5 control and carbon reduction-specifically, how mitigating PM 2.5 sources impacts CO 2 levels and vice versa-is essential for effective policy-making. To address this, we developed an Interpretable machine learning (ML) and source apportionment (IMSA) framework. The framework screens pollutant sources for PM 2.5 and CO 2 , and calculates their contributions, revealing that industrial emissions (IE) (11%, 29%), vehicle exhaust (VE) (13%, 19%), and coal combustion (19%, 15%) are major shared sources. By integrating interpretable ML methods, IMSA uncovers interaction effects, showing that reducing IE significantly lowers CO 2 , while targeting VE more effectively reduces PM 2.5 . The IMSA framework provides critical insights for co-beneficial strategies to improve air quality and mitigate climate change. Ensembled source apportionment method simultaneously assessed PM 2.5 and CO 2 source contributions The integration of feature importance schemes revealed the interaction patterns between the sources controlling PM 2.5 and those of CO 2 The study suggests that controlling industry, vehicles, and coal use contributes to local pollution reduction and carbon mitigation Grasping the interaction patterns between PM 2.5 control and carbon reduction-how reducing PM 2.5 sources impacts CO 2 levels and vice versa-is crucial for crafting effective policies that address both air pollution and climate change simultaneously. Therefore, an IMSA framework combining interpretable machine learning and source apportionment was proposed to identify sources of PM 2.5 and CO 2 , and analyze the interaction patterns of their reductions. The framework offered effective solutions for improving local air quality and tackling climate change.

关键词： machine learning fine particulate matter carbon dioxide coordinated control PMF

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Numerical Investigation of Distribution and Agglomeration Characteristics of Bendable Chain-Like Biomass Particles in a Fluidized Bed Reactor

SSRN

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SSRN 2022年

作者： Gu, Conghui Zhao, Haichao Xu, Bingyang Yang, Junjie Zhang, Jun Du, Mingpu Liu, Yuan Viktorovich, Tikhankin Dmitry Yuan, Zhulin School of Energy and Power Jiangsu University of Science and Technology Jiangsu Zhenjiang212100 China China Tobacco Jiangxi Industrial Co. Ltd. Jiangxi Nanchang330096 China Institute of Mechanics and Power Engineering Ogarev Mordovia State University Saransk430005 Russia Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education School of Energy and Environment Southeast University Jiangsu Nanjing210096 China

Abstract：Biomass is gradually perceived as an important carbon neutral and renewable energy source. In general, biomass particles are non-spherical and filamentous, and their efficiency of use is considerably reduced due to the high moisture content. As a result, the pretreatment dehydration of biomass particles is a crucial process in biomass production. The effectiveness of moisture removal, however, is directly affected by the fluidization characteristics of chain-like biomass particles in drying facilities. In this article, the distribution and agglomeration characteristics of chain-like biomass particles in a fluidized bed were studied according to the coupled model through the computational fluid dynamics-discrete element method. Based on the special structural feature of filamentous biomass particles, a new three-dimensional model of bendable chain-like particle was established. The effects of gas superficial velocity and mass flow rate of biomass particle on the distribution and agglomeration characteristics of chain-like biomass particles in the fluidized bed were experimentally and numerically investigated. The results demonstrated that the chain-like biomass particles were more likely to agglomerate near the wall, with a maximum of 0.11% of the volume fraction. The distribution of particle concentration showed an inverted C-shape in the cross section. Also, the velocity of biomass particles presented as an open downward parabolic distribution in the radial direction and reached a maximum at the central region of the fluidized bed. The particle velocity decreased with the increase of particle mass flow rate. Furthermore, chain-like biomass particles were more easily agglomerated at the corners of the fluidized bed due to wall effects. The maximum cluster area of chain-like biomass particles was 73.43 cm2 under the condition of ug=5.71 m/s, appearing in the corners and bottom of the fluidized bed. The band-like clusters transformed into irregular triangular

关键词： Fluidized beds

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Migration and transformation behavior of lattice nitrogen during chemical looping ammonia synthesis of tungsten-based nitrogen carrier

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International Journal of Hydrogen Energy 2025年 147卷

作者： Ziheng Han Tuo Guo Jingjing Ma Xiude Hu Qingjie Guo Zhongbin Liu State Key Laboratory of High efficiency Utiliztion of Coal and Green Chemical Engineering College of Chemistry and Chemical Engineering Ningxia University Yinchuan 750021 China Xinyuan Green Carbon Technology (Jiangmen) Co. Ltd Jiangmen 529199 China Laboratory of Green & Smart Chemical Engineering in Universities of Shandong College of Chemical Engineering Qingdao University of Science and Technology Qingdao Shandong 266042 China Sichuan Provincial Key Lab of Process Equipment and Control Sichuan University of Science & Engineering Zigong 643000 China

The mild reaction conditions in chemical looping ammonia synthesis (CLAS) offer the potential for green ammonia technology. This study developed W 2 N nitrogen carrier(NC), characterized by a high lattice nitrogen conversion rate and an increased ammonia yield. The results demonstrate that reaction temperature is a key factor in regulating the lattice nitrogen conversion rate of the NC. At 600 °C, the conversion of lattice nitrogen is incomplete. However, as the reaction temperature increases from 600 °C to 850 °C, a notable transition occurs in the crystal phase of the NC, changing from W 2 N (at 600 °C) → W 2 N and W (at 750 °C) → W (at 850 °C). The lattice nitrogen conversion rate increases significantly from 12.5 % to 73.3 %. At 800 °C, the ammonia yield reached 594 μmol g −1 h −1 . This work lays a foundation for the design of efficient transition metal NC.

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Analysis of reservoir dynamic evolution and well production with CO2-enhanced coalbed methane recovery using a multi-lateral horizontal well

Geoenergy Science and Engineering

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Geoenergy science and engineering 2025年 254卷

作者： Wang, Zhonghui Peng, Shoujian Xu, Jiang Jia, Li Cheng, Liang Xu, Qingfeng Chang, Chenyang State Key Laboratory of Coal Mine Disaster Dynamics and Control Chongqing University Chongqing400030 China National Engineering Research Center for Coal Gas Control China University of Mining and Technology Xuzhou221116 China College of Safety Science and Engineering Xi'an University of Science and Technology Xi'an710054 China Warwick Manufacturing Group University of Warwick CoventryCV4 7AL United Kingdom

CO2-enhanced coalbed methane (CO2-ECBM) can improve CBM recovery efficiency while realizing CO2 sequestration capacity. However, the outputs of different CBM production wells and the main control factors are unknown. CO2-ECBM engineering with multi-lateral horizontal wells was used to construct large-scale physical experiments under conventional CBM recovery and different CO2 injection pressures. The reservoir pressure during conventional CBM recovery decreased with time, whereas with the CO2-ECBM process it decreased, then increased, and finally achieved a dynamic balance. In the early stage of displacement, CH4 desorption caused reservoir temperature to decrease, the continuous injection of CO2 into reservoir increased the temperature. The recovery rate of CH4 is 93.18 % under the 2.5 MPa injection pressure, which is higher than that of 89.60 % under the 2.0 MPa injection pressure and 75.17 % under conventional CBM recovery. During the CO2-ECBM process, CO2 from production well Ⅰ, closest to injection well, breaks through first, making its CH4 recovery less than that of production wells II and III. The higher the CO2 injection pressure, the more CH4 was extracted from production well Ⅰ, and the less CH4 was extracted from production well Ⅲ, farthest from injection well. Thus, to optimize the CO2-ECBM injection process, the closest production well should be closed first, the greater the CO2 injection pressure, the later the shut-in time for production well I, whereas for production well III, the shut-in time will be earlier. © 2025 Elsevier B.V.

关键词： Gas injection (Enhanced recovery)

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Triboelectric–Electromagnetic Nanogenerator for Energy Harvesting of Power Transmission Lines Galloping and Self-Powered Galloping state Monitoring

SSRN

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SSRN 2022年

作者： Gao, Sihang Zeng, Xisong Zhang, Guowen Zhang, Jiajia Chen, Yiduo Feng, Shaoxuan Lan, Wenyu Zhou, Jie Wang, Zhong Lin Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China Beijing Institute of Nanoenergy and Nanosystems Chinese Academy of Sciences Beijing101400 China School of Nanoscience and Technology University of Chinese Academy of Sciences Beijing100049 China School of Materials Science and Engineering Georgia Institute of Technology AtlantaGA30332-0245 United States

With the vigorous advancement of Ubiquitous Electric Internet of Things (UEIOT), online monitoring is essential for a stable operation of the transmission system. The energy supply that drives the monitoring system has become one of the bottlenecks restricting the development of distributed sensing systems. In view of the vibration with the characteristics of low-frequency and large-amplitude generated in the conductor galloping, a self-powered sensor has been proposed. This sensor consists of an electromagnetic generator (EMG), a horizontal TENG (H-TENG), and a vertical TENG (V-TENG) to provide an alternative approach for capturing mechanical energy derived from conductor galloping. Based on triboelectric–electromagnetic working principal, a sophisticated-designed stator and rotor structure oriented to the application scenarios of transmission lines successfully achieved self-power through the designed EMG and galloping state sensing through the designed H-TENG and V-TENG, respectively. The output performance of the hybridized nanogenerator was well validated on the linear motor platform and the conductor galloping test platform, and the output current was used to realize the characterization of the conductor galloping amplitude and galloping trajectory. This work not only provides an effective and sustainable solution to the self-powered intelligent sensor nodes in the complex surroundings of power transmission system, but also reveals TENG promising potential on the sensing applications in low-power monitoring sensors for conductor galloping. © 2022, The Authors. All rights reserved.

关键词： Nanogenerators

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Process Monitoring with Sparse Bayesian Model for industrial Methanol Distillation

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IFAC-PapersOnLine 2020年第2期53卷 424-430页

作者： Lin Luo Lei Xie Hongye Su Jiusun Zeng School of Information and Control Engineering Liaoning Shihua University Fushun 113001 Liaoning PR China State Key Laboratory of Industrial Control Technology Institute of Cyber-systems and Control Zhejiang University Hangzhou 310027 Zhejiang PR China College of Metrology & Measurement Engineering China Jiliang University Hangzhou 310018 Zhejiang PR China

Following the intuition that not all latent variables in probabilistic principal component analysis method shifts simultaneously, this paper proposes a spike-and-slab regularization technique for nonlinear fault detection and isolation. Different from the existing probabilistic latent variable models, a spike-and-slab prior is introduced to downweight the irrelevant information of latent variables for the discriminative model. The resulting latent subspace supported by regularization parameters is not only sensitive to the informative variables, but it also eliminates the influence of the non-informative ones. The feasibility and efficiency of the proposed approach will be tested on an industrial methanol distillation dataset. Moreover, the performance will be compared with conventional probabilistic latent variables methods.

关键词： Process monitoring fault detection isolation Bayesian variable selection spike-and-slab prior industrial methanol distillation

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Correction:Direct Regeneration of Spent Lithium‑Ion Battery Cathodes:From Theoretical Study to Production Practice

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Nano-Micro Letters 2024年第11期16卷 70-70页

作者： Meiting Huang Mei Wang Liming Yang Zhihao Wang Haoxuan Yu Kechun Chen Fei Han Liang Chen Chenxi Xu Lihua Wang Penghui Shao Xubiao Luo National–Local Joint Engineering Research Center of Heavy Metals Pollutants Control and Resource Utilization Nanchang Hangkong UniversityNanchang 330063People’s Republic of China Key Laboratory of Hunan Province for Advanced Carbon–based Functional Materials School of Chemistry and Chemical EngineeringHunan Institute of Science and TechnologyYueyang 414006People’s Republic of China College of Materials Science and Engineering Central South University of Forestry and TechnologyChangsha 410004People’s Republic of China School of Life Science Jinggangshan UniversityJi’an 343009People’s Republic of China

The Highlights session of the article unfortunately was taken falsely from another *** correct Highlights session is now in *** correct is:Analyze the primary causes of cathode failure in three representative batterie... 详细信息

关键词： Battery mechanism Regeneration

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Emission of marine microbes via sea-spray aerosol generation: Enrichment, influencing factors and the potential role in marine carbon cycle

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Atmospheric Environment 2025年

作者： Linyao Wu Ting Liu Yingyi Zhang Yuhan Shen Yinxi Yao Zhaokang Ruan Yee Jun Tham Wei Song Shan Huang Jun Li Senchao Lai The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters Ministry of Education School of Environment and Energy South China University of Technology Guangzhou 510006 China School of Environmental Science and Engineering Hubei Polytechnic University Hubei Key Laboratory of Mine Environmental Pollution Control and Remediation Huangshi 435003 China School of Marine Sciences State Key Laboratory of Environmental Adaptability for Industrial Products Sun Yat-sen University Zhuhai 519082 China State Key Laboratory of Advanced Environmental Technology Guangzhou Institute of Geochemistry Chinese Academy of Sciences Guangzhou 510640 China College of Environment and Climate Jinan University Guangzhou 511443 China

Marine microorganisms, widely distributed in seawater, can be transferred into the atmosphere through sea spray aerosols (SSA) generated by the bursting of bubbles. Considering the vast oceanic coverage on Earth, this process has significant implications on regional and global scales, particularly under the influence of global warming. Using a self-constructed sea spray aerosol generation tank (SAGT), we simulated SSA production with seawater of varying salinities to investigate the transfer and enrichment of microorganisms in SSA. Our results confirm that microorganisms can be enriched in SSA. The microbial enrichment was size-dependent, with elevated enrichment factors (EFs) observed in small particles. Chlorophyll a, a proxy for marine primary production, demonstrated a significant positive correlation with microbial enrichment in submicron SSA, indicating the influence of marine biological activities. Additionally, salinity was found to play a critical role, with high salinity promoting the enrichment of microbes in SSA. Microbial cell growth in seawater should also be considered when assessing marine microbial contributions. We estimated the emission fluxes of microbial carbon via SSA production from low and high salinity seawater, and a global annual emission of marine microbes was estimated in the range of 0.68 - 2.50 Gg yr -1 . This study underscores the importance of understanding microbial transfer through SSA production and highlights its relevance in the context of global environmental changes driven by warming, emphasizing the intricate connections between marine ecosystems and atmospheric processes.

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