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Screening and pilot-scale evaluation of a highly efficient pesticide-degrading Pseudomonas *** BL5

Chinese Journal of Chemical engineering 2024年第10期74卷 287-294页

作者： Rui Chen Xinyu Zuo He Bai Ruolin Qin Zhiguo Chen Yiyun Liu Wenqing Cao Jingpeng Song Xiaoqiang Jia Department of Biochemical Engineering School of Chemical Engineering and TechnologyTianjin UniversityTianjin 300072China Tianjin Huakan Environmental Protection Technology Co. Ltd.Tianjin 300170China Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering(Ministry of Education) School of Chemical Engineering and TechnologyTianjin UniversityTianjin 300350China

The widespread use of pesticides has caused serious harm to ecosystems,necessitating effective and environmentally friendly treatment *** stands out as a promising approach for pollutant treatment,wherein the metabolic activities of microorganisms can transform toxic pesticides into compounds with lower or no *** this study,we obtained eight pesticide-degrading strains from pesticide-contaminated sites through continuous enrichment and *** highly efficient pesticide-degrading strains(degradation ratios exceeding 80%)were *** them,Pseudomonas ***5 exhibited the strongest growth(exceeding 10^(9) CFU·ml^(-1))and outstanding degradation of benzene derivatives and chlorinated hydrocarbons at both laboratory and pilot scales,with degradation ratios exceeding 98%and 99.6%,*** research provides new tools and insights for the bioremediation of pesticide-related pollutants.

关键词： Pesticides Pilot-scale Degradation Remediation Pseudomonas sp. Benzene

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Reducing the Risk of Completeness Loss by Subgraph Reasoning in engineering Applications 22

Reducing the Risk of Completeness Loss by Subgraph Reasoning...

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6th International Conference on Computer science and Application engineering, CSAE 2022

作者： Zhu, Xixi Liu, Bin Ding, Zhaoyun Yao, Li Zhu, Cheng Zhu, Xianqiang Science and Technology on Information Systems and Engineering Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781450396004

The ontology knowledge base is divided into TBox and ABox, the former is the schema-level information of the knowledge base, which is used to describe the relationship between the recognized concepts and attributes, and the latter is a collection of instance assertions or fact statements in the domain. Using TBox, the process of reasoning more implicit assertions in ABox is called ABox materialization, which plays an important role in knowledge base applications. Some ontology reasoners for OWL DL and DL-safe SWRL, the reasoning results are considered to be correct and complete. However, in practical applications, this paper finds that in some cases, the reasoning results are not complete when reasoning about the ontology knowledge base constructed by OWL DL and DL-sale SWRL. In those application scenarios that require completeness, blindly trusting these reasoners will bring risks. In response to this problem, this paper proposes a method based on subgraph reasoning, which can identify whether the result of ontology reasoning is complete in engineering, and at the same time, when it is incomplete, new assertions that have not been found before can be inferred. The comparative experimental results on two open-source ontologies verify the method in this paper. © 2022 Association for Computing Machinery.

关键词： Ontology

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Autonomous sortie scheduling for carrier aircraft fleet under towing mode

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Defence technology(防务技术) 2025年第1期43卷 1-12页

作者： Zhilong Deng Xuanbo Liu Yuqi Dou Xichao Su Haixu Li Lei Wang Xinwei Wang Department of Engineering Mechanics State Key Laboratory of Structural AnalysisOptimization and CAE Software for Industrial EquipmentDalian University of TechnologyDalian116024China School of Mathematical Sciences Dalian University of TechnologyDalian 116024China Naval Aviation University Yantai 264001China China State Shipbuilding Corporation Systems Engineering Research Institute Beijing 100094China Department of Automation School of Information Science and TechnologyTsinghua UniversityBeijing 100084China

Safe and efficient sortie scheduling on the confined flight deck is crucial for maintaining high combat effectiveness of the aircraft *** primary difficulty exactly lies in the spatiotemporal coordination,i.e.,allocation of limited supporting resources and collision-avoidance between heterogeneous dispatch *** this paper,the problem is investigated in the perspective of hybrid flow-shop scheduling problem by synthesizing the precedence,space and resource ***,eight processing procedures are abstracted,where tractors,preparing spots,catapults,and launching are virtualized as *** analyzing the constraints in sortie scheduling,a mixed-integer planning model is *** particular,the constraint on preparing spot occupancy is improved to further enhance the sortie *** basic trajectory library for each dispatch entity is generated and a delayed strategy is integrated to address the collision-avoidance *** efficiently solve the formulated HFSP,which is essentially a combinatorial problem with tightly coupled constraints,a chaos-initialized genetic algorithm is *** solution framework is validated by the simulation environment referring to the Fort-class carrier,exhibiting higher sortie efficiency when compared to existing *** animation of the simulation results is available at ***/video/BV14t421A7Tt/.The study presents a promising supporting technique for autonomous flight deck operation in the foreseeable future,and can be easily extended to other supporting scenarios,e.g.,ammunition delivery and aircraft maintenance.

关键词： Carrier aircraft Autonomous sortie scheduling Resource allocation Collision-avoidance Hybrid flow-shop scheduling problem

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Smart Surveillance Object Detection System 3

Smart Surveillance Object Detection System

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Chaudhary, Gaurav Pathak, Shreya Choudhary, Vikas Chauhan, Love Tripathi, Abhishek N School of Electronics Engineering Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350372120

This paper presents an integrated Internet of Things based approach to smart surveillance and urban parking management systems, emphasizing robust object detection and precise parking occupancy monitoring. Our solution adopts cost-effective hardware like the ESP32 CAM module, leveraging its built-in camera and powerful processor for real-time image processing. For surveillance applications, we implement a custom object detection model built with OpenCV, a renowned Python library. This model, finely-tuned for efficient person detection, runs directly on the ESP32 CAM, eliminating the need for external processing units. Additionally, the ESP32 CAM's Wi-Fi connectivity enables seamless integration with existing networks, simplifying deployment. This integrated approach, combining the cost-effectiveness of ESP32 CAM modules with the image processing capabilities of OpenCV, offers a comprehensive solution for smart surveillance and urban parking management. By utilizing readily available hardware and open-source software, the system promotes widespread adoption and affordability, making it a valuable tool for creating smarter and more sustainable cities. © 2024 IEEE.

关键词： Computer vision

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Stability and performance analysis of the compressed Kalman filter algorithm for sparse stochastic systems

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science China(Technological sciences) 2024年第2期67卷 380-394页

作者： LI RongJiang GAN Die XIE SiYu LüJinHu Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China Zhongguancun Laboratory Beijing 100049China School of Aeronautics and Astronautics University of Electronic Science and Technology of ChinaChengdu 611731China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China

This paper considers the problem of estimating unknown sparse time-varying signals for stochastic dynamic *** deal with the challenges of extensive sparsity,we resort to the compressed sensing method and propose a compressed Kalman filter(KF)*** algorithm first compresses the original high-dimensional sparse regression vector via the sensing matrix and then obtains a KF estimate in the compressed low-dimensional ***,the original high-dimensional sparse signals can be well recovered by a reconstruction *** ensure stability and establish upper bounds on the estimation errors,we introduce a compressed excitation condition without imposing independence or stationarity on the system signal,and therefore suitable for feedback *** further present the performance of the compressed KF ***,we show that the mean square compressed tracking error matrix can be approximately calculated by a linear deterministic difference matrix equation,which can be readily evaluated,analyzed,and ***,a numerical example demonstrates that our algorithm outperforms the standard uncompressed KF algorithm and other compressed algorithms for estimating high-dimensional sparse signals.

关键词： sparse signal compressed sensing Kalman filter algorithm compressed excitation condition stochastic stability tracking performance

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Debris cloud structure and hazardous fragments distribution under hypervelocity yaw impact

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Defence technology（防务技术） 2023年第9期27卷 169-183页

作者： Chenyang Wu Qi-Guang He Xiaowei Chen Chunbo Zhang Zebin Shen State Key Laboratory of Explosion Science and Technology Beijing Institute of TechnologyHaidian DistrictBeijing100081China School of Mechatronical Engineering Beijing Institute of TechnologyHaidian DistrictBeijing100081China Institute of Systems Engineering China Academy of Engineering PhysicsMianyangSichuan621999China

This study investigates how the debris cloud structure and hazardous fragment distribution vary with attack angle by simulating a circular cylinder projectile hypervelocity impinging on a thin plate using the finite element-smoothed particle hydrodynamics(FE-SPH)adaptive *** on the comparison and analysis of the experimental and simulation results,the FE-SPH adaptive method was applied to address the hypervelocity yaw impact problem,and the variation law of the debris cloud structure with the attack angle was *** screening criterion of the hazardous fragment at yaw impact is given by analyzing the debris formation obtained by the FE-SPH adaptive method,and the distribution characteristics of hazardous fragments and their relationship with the attack angle are ***,the velocity space was used to evaluate the distribution range and damage capability of asymmetric hazardous *** maximum velocity angle was extended from fully symmetrical working conditions to asymmetrical cases to describe the asymmetrical debris cloud distribution *** this range,the energy density was calculated to quantitatively analyze how much damage hazardous fragments inflict on the rear *** results showed that the number of hazardous fragments generated by the case near the 35°attack angle was the largest,the distribution range was the smallest,and the energy density was the *** results suggest that in this case,debris cloud generated by the impact had the strongest damage to the rear plate.

关键词： Debris cloud Hypervelocity yaw impact FE-SPH adaptive method Velocity space Hazardous fragments distribution

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Joint user profiling with hierarchical attention networks

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Frontiers of Computer science 2023年第3期17卷 133-143页

作者： Xiaojian LIU Yi ZHU Xindong WU Key Laboratory of Knowledge Engineering with Big Data(Hefei University of Technology) Ministry of EducationHefei 230009China School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China School of Information Engineering Yangzhou UniversityYangzhou 225009China Mininglamp Academy of Sciences MininglampBeijing 100193China

User profiling by inferring user personality traits,such as age and gender,plays an increasingly important role in many real-world *** existing methods for user profiling either use only one type of data or ignore handling the noisy information of ***,they usually consider this problem from only one *** this paper,we propose a joint user profiling model with hierarchical attention networks(JUHA)to learn informative user representations for user *** JUHA method does user profiling based on both inner-user and inter-user *** explore inner-user features from user behaviors(e.g.,purchased items and posted blogs),and inter-user features from a user-user graph(where similar users could be connected to each other).JUHA learns basic sentence and bag representations from multiple separate sources of data(user behaviors)as the first round of data *** this module,convolutional neural networks(CNNs)are introduced to capture word and sentence features of age and gender while the self-attention mechanism is exploited to weaken the noisy *** this,we build another bag which contains a user-user ***-user features are learned from this bag using propagation information between linked users in the *** acquire more robust data,inter-user features and other inner-user bag representations are joined into each sentence in the current bag to learn the final bag ***,all of the bag representations are integrated to lean comprehensive user representation by the self-attention *** experimental results demonstrate that our approach outperforms several state-of-the-art methods and improves prediction performance.

关键词： user profiling hierarchical attention joint learning inner-user feature inter-user feature

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Incremental Reasoning for Dynamic ABox Materialization Based on Subgraph 8

Incremental Reasoning for Dynamic ABox Materialization Based...

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8th International Conference on Big Data and information Analytics, BigDIA 2022

作者： Zhu, Xixi Liu, Bin Ding, Zhaoyun Yao, Li Zhu, Cheng Zhu, Xianqiang National University of Defense Technology Science and Technology on Information Systems and Engineering Laboratory Changsha China

ISBN: (数字)9781665487962

ISBN: (纸本)9781665487962

The ontology knowledge base mainly includes: TBox and ABox, the former models schema-level knowledge in the domain, and the latter is a statement of a set of instance assertions or facts. ABox materialization is the process of discovering the implicit knowledge in ABox by reasoning based on the existing knowledge, which has important value in the application of knowledge base. In practice, TBox has stability and durability, and is suitable for one-time construction and repeated use, while ABox is usually in a state of continuous growth and change. Even with efficient reasoning techniques, recomputing all possible results whenever the ABox changes is a time-and-resource-intensive operation, which can cause a significant delay, and is unacceptable for some application scenarios. In response to this problem, this paper proposes a subgraph-based incremental reasoning method for dynamic ABox materialization, which first divides the ABox into instance-centric subgraphs, and uses the collection of extended subgraphs of each instance that reasoning under the complete TBox as the materialized result. When the assertion is changed, the impact on the materialization result can be regarded as a limited step of information propagation between subgraphs, and only a small number of relevant subgraphs need to be re-derived, thus realizing the incremental reasoning of ABox materialization. The experimental results on two open-source ontology knowledge bases show that the method in this paper has high reasoning quality, and can effectively reduce the response time for obtaining materialized results when ABox changes dynamically. © 2022 IEEE.

关键词： Knowledge based systems

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Resilience-Centric Trust Evaluation in Cloud Computing: A Comparative Assessment of Cloud Trust Evaluation Methods Using the RAA Metric

Resilience-Centric Trust Evaluation in Cloud Computing: A Co...

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2024 International Conference on Smart Electronics and Communication systems, ISENSE 2024

作者： John, Jomina John Singh, K. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (数字)9798331528126

ISBN: (纸本)9798331528126

In cloud computing environments, trust evaluation models are critical for assessing the reliability and security of cloud service providers. The accuracy of trust evaluations may be impacted by the failure of traditional trust measurements to take resilience under dynamic settings into consideration. In order to assess the flexibility and resilience of trust assessment models in cloud computing, this research presents a new metric called the Resilience-Aware Assessment. The Risk Assessment offers a thorough assessment of trust models in a variety of cloud environments by taking into account elements including system performance during high stress, reaction to malevolent activity, and recovery time following interruptions. In order to compare various trust evaluation models already in use, this study evaluates them using the RAA in addition to other traditional criteria like accuracy, scalability, and convergence time. The findings show that the RAA metric, especially in situations with uncertainty and volatility, successfully draws attention to trust model strengths and shortcomings that are missed by traditional metrics. The integration of the RAA metric into multi-criteria decision-making frameworks for cloud service selection is also covered in the article, offering a more adaptable and robust method for evaluating trust. Future research will examine how the RAA measure can be used to improve autonomous trust management systems in edge-cloud contexts as well as how it can be applied in real-time cloud orchestration. © 2024 IEEE.

关键词： Cloud security

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Graph Convolutional Neural Network Based Emotion Recognition with Brain Functional Connectivity Network

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International Journal of Crowd science 2024年第4期8卷 195-204页

作者： Gao, Pengzhi Zheng, Xiangwei Wang, Tao Zhang, Yuang School of Information Science and Engineering Shandong Normal University Jinan250300 China State Key Laboratory of High-End Server & Storage Technology Jinan250300 China

Emotion recognition plays an important role in Human Computer Interaction (HCI) and the evaluation of human behavior based on emotional state is an important research topic. The purpose of emotion recognition is to automatically identify human’s emotional states by analyzing physiological or non-physiological signals. The conventional emotion classification methods cannot comprehensively leverage global and local features which are extracted from Electroencephalogram (EEG) signal generated after being stimulated. Therefore, we propose the graph convolutional neural network based emotion recognition with brain functional connectivity network (GERBN). Firstly, raw EEG data of the public DEAP and SEED datasets is preprocessed and adopted in this study. Secondly, emotion-related brain functional connection pattern is constructed using Phase-Locking Value (PLV) adjacency matrix to measure connectivity between the signals of different EEG channels according to phase synchronization. A novel graph structure is constructed where the EEG electrode channels are defined as the vertex, and the edge is strong connection of the binary brain network. Thirdly, the GERBN model that includes six layers is designed to classify and recognize emotional states on the two-dimensional emotional models of valence and arousal. Finally, extensive experiments are conducted on DEAP and SEED datasets. Experimental results demonstrate that the proposed method can improve classification accuracies, in which average accuracies of 80.43% and 88.47% on DEAP are attained on valence and arousal dimensions, respectively. On the SEED dataset, the accuracy reaches 92.37% higher than some of the other methods. © The author(s) 2024.

关键词： Electroencephalography

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