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Identification of key functional sites for ATPase activity in the OBC protein complex using artificial intelligence approaches 认领 引用
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作者 Lei Hua Yuhang He +2 位作者 Peng Zhao Jiayao Liu Kun Shang 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第4期682-692,共11页
Microorganisms evolve diverse immune defense systems to protect against phage and viral invasion.The bacteriophage exclusion(BREX)system represents a common bacterial genomic defense mechanism,and preliminary studies ... Microorganisms evolve diverse immune defense systems to protect against phage and viral invasion.The bacteriophage exclusion(BREX)system represents a common bacterial genomic defense mechanism,and preliminary studies indicate that the interaction between BrxC and OrbA proteins(forming the OBC complex)can alter the aggregation pattern of defense system components and modulate immune function.Notably,the adenosine triphosphatase(ATPase)domain of this system plays a pivotal role.This study employs optimized network screening(ONS),an artificial intelligence-based integrated analytical approach combining machine learning and deep learning,to identify critical enzymatic active sites in target macromolecular complexes and to provide crucial insights for potential drug target discovery.Through comprehensive ONS analysis of multiple sites,we identified lysine(K73)as a key residue governing the ATPase activity of this protein complex.Following plasmid construction and expression,we successfully purified the OrbA-BrxC(OBC)complex,along with newly engineered OBCK(K73A mutant of the OBC complex)and the negative control OBCE(E255A mutant of the OBC complex).ATPase activity assays demonstrated complete loss of enzymatic function in the OBCK mutant,confirming K73 as the essential catalytic site for BREX system ATPase activity.Comparative analysis revealed minimal differences between OBC and OBCE complexes,but significant divergence from OBCK.The single K73 mutation alone was sufficient to abolish critical enzymatic activity and disrupt BREX system function,achieving efficient complex engineering without requiring multiple mutations.Building on this artificial intelligence methodology,we further analyzed potential functional sites and antigenic epitopes in OBC and OBCK complexes,thereby establishing a research foundation for developing anti-drug-resistant pathogen therapeutics. 展开更多
关键词 artificial intelligence methods BREX defense system co-expression and purification ATPase activity construction of protein complexes
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Survey on Collaborative Task Assignment for Heterogeneous UAVs Based on Artificial Intelligence Methods 认领 引用 被引量：1
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作者 Mengzhen Li Na Li +2 位作者 Xiaoyu Shao Jiahe Wang Dachuan Xu 《CAAI Artificial Intelligence Research》 2024年第1期153-163,共11页
Heterogeneous unmanned aerial vehicle(UAV)swarms have garnered significant attention from researchers worldwide due to their remarkable flexibility,diverse mission capabilities,and wide-ranging potential applications.... Heterogeneous unmanned aerial vehicle(UAV)swarms have garnered significant attention from researchers worldwide due to their remarkable flexibility,diverse mission capabilities,and wide-ranging potential applications.Mission planning stands at the core of UAV swarm operations,requiring consideration of various factors including mission environment,requirements,and inherent characteristics.In this paper,we investigate the model of the cooperative tasking problem in heterogeneous UAV swarms.We provide a comprehensive review of artificial intelligence algorithms applied in UAV swarm mission planning,analyzing their strengths and weaknesses in multi-UAV cooperative environments.By discussing these key techniques and their practical applications,the article highlights future research trends and challenges.This review serves as a valuable reference for understanding the current state of AI algorithm applications in heterogeneous UAV swarm task assignments. 展开更多
关键词 heterogeneous unmanned aerial vehicles(UAVs) collaborative task assignment artificial intelligence methods
A generative artificial intelligence approach to modular skeletal framework modeling:Bamboo stilt houses as a case study 认领 引用
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作者 Xianchuan Meng Jiadong Liang Ximing Zhong 《Frontiers of Architectural Research》 EI CSCD 2025年第6期1621-1635,共15页
This paper presents a new generative artificial intelligence(AI)approach for creating modular skeletal frameworks,using vernacular bamboo stilt houses as examples to investigate an innovative methodological perspectiv... This paper presents a new generative artificial intelligence(AI)approach for creating modular skeletal frameworks,using vernacular bamboo stilt houses as examples to investigate an innovative methodological perspective.By transforming building skeletons to connected graphs,our method uses Variational Graph Autoencoders(VGAE)and Graph Sample and Aggregate(GraphSAGE)to generate 3D modular components based on spatial constraints set by users,such as axis grids and chosen room areas.The graph representation encodes structural elements as edges and their connections as nodes,maintaining critical dimensional constraints and spatial relationships.Using data from bamboo stilt houses built without architects,we make a specialized dataset of geometric skeletons for model training.Experimental results demonstrate the effectiveness of our approach in capturing the distribution of featured elements in building frameworks and in generating structurally sound designs,with GraphSAGE showing better performance compared to alternative methods.The probabilistic edge prediction approach allows for a collaborative human-AI design process,empowering designers while utilizing computational capabilities.The inherent flexibility of the graph-based representation makes it adaptable to a wide range of materials and scales. 展开更多
关键词 Graph neural networks Modular components Bamboo architecture Computational design Generative artificial intelligence method
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Prediction of landslide displacement with dynamic features using intelligent approaches 认领 引用 被引量：25
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作者 Yonggang Zhang Jun Tang +4 位作者 Yungming Cheng Lei Huang Fei Guo Xiangjie Yin Na Li 《International Journal of Mining Science and Technology》 SCIE EI CAS CSCD 2022年第3期539-549,共11页
Landslide displacement prediction can enhance the efficacy of landslide monitoring system,and the prediction of the periodic displacement is particularly challenging.In the previous studies,static regression models(e.... Landslide displacement prediction can enhance the efficacy of landslide monitoring system,and the prediction of the periodic displacement is particularly challenging.In the previous studies,static regression models(e.g.,support vector machine(SVM))were mostly used for predicting the periodic displacement.These models may have bad performances,when the dynamic features of landslide triggers are incorporated.This paper proposes a method for predicting the landslide displacement in a dynamic manner,based on the gated recurrent unit(GRU)neural network and complete ensemble empirical decomposition with adaptive noise(CEEMDAN).The CEEMDAN is used to decompose the training data,and the GRU is subsequently used for predicting the periodic displacement.Implementation procedures of the proposed method were illustrated by a case study in the Caojiatuo landslide area,and SVM was also adopted for the periodic displacement prediction.This case study shows that the predictors obtained by SVM are inaccurate,as the landslide displacement is in a pronouncedly step-wise manner.By contrast,the accuracy can be significantly improved using the dynamic predictive method.This paper reveals the significance of capturing the dynamic features of the inputs in the training process,when the machine learning models are adopted to predict the landslide displacement. 展开更多
关键词 Landslide displacement prediction Artificial intelligent methods Gated recurrent unit neural network CEEMDAN Landslide monitoring
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AI-powered oriented synthesis of naphthalenediimide-based MOFs for photochromic encryption and ammonia sensing 认领 引用
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作者 Jing Wang Chao Zhang +3 位作者 Ming-Rui Zhang Yun-Huan Xue Zi-Xin You Ming-Dong Zhou 《Inorganic Chemistry Frontiers》 SCIE EI CAS CSCD 2026年第6期2439-2449,共11页
The oriented synthesis of metal-organic frameworks(MOFs)has been a goal that scientists strive to achieve.Exhaustive methods and orthogonal experiments are generally the most labor-intensive but also the most effectiv... The oriented synthesis of metal-organic frameworks(MOFs)has been a goal that scientists strive to achieve.Exhaustive methods and orthogonal experiments are generally the most labor-intensive but also the most effective synthetic strategies.Herein,an artificial intelligence synthetic method powered by the generative large language model DeepSeek is adopted.Based on N,N′-bis(carboxymethyl)-1,4,5,8-naphthalenediimide(H2CMNDI)and the transition metals ions Zn(Ⅱ)and Cd(Ⅱ),we synthesized a series of novel MOFs.Conventional microanalysis methods were employed to analyze the structure and properties of the MOFs.The synthesized MOFs achieve the expected performance level and exhibit stimulus-responsive activity.They possess recoverable photochromic properties,rapidly darkening significantly under ultraviolet light and then regaining their original color upon heating and being kept away from light in a static state,making them suitable for encryption applications.Additionally,the Zn-MOF can detect ammonia water sensitively,with a limit of detection as low as 3.28μM.The synthesis and application of this series of MOFs are an excellent exploration and a good example of the oriented synthesis of MOFs. 展开更多
关键词 transition metals ions orthogonal experiments artificial intelligence synthetic method synthetic strategieshereinan Naphthalenediimide generative large language model deepseek Artificial Intelligence Metal Organic Frameworks
An intelligent approach to prediction of tailings dam displacement safety using dynamic preventive control modeling 认领 引用
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作者 Di Liu Hui Yang +3 位作者 Caiwu Lu Wenci Wang Qinghua Gu Shunling Ruan 《Green and Smart Mining Engineering》 CAS CSCD 2026年第1期93-106,共14页
Tailings dams are a critical infrastructure for mining enterprises,and their safety directly affects production security and environmental protection.However,owing to the loose nature of dam materials and their unique... Tailings dams are a critical infrastructure for mining enterprises,and their safety directly affects production security and environmental protection.However,owing to the loose nature of dam materials and their unique geological structures,traditional slope stability assessment models have limited applicability to tailings dams.Dam displacement serves as a key indicator for evaluating the stability and identifying potential developmental issues during operation,making it essential for safety monitoring.Therefore,developing reliable displacement prediction methods is crucial for early warning and mitigation of disasters.This study proposes a“feature derivation-decomposition forecasting-model optimization”approach for predicting displacements in tailings dams.First,the IDBO-VMD(Improved Dung Beetle Optimizer-Variational Mode Decomposition)decomposition algorithm is employed to separate dam displacement into the trend and periodic components.Subsequently,the trend and periodic displacements are predicted using the DBN(Deep Belief Network)and IDBO-TCN(Temporal Convolutional Network)-BiGRU(Bidirectional Gated Recurrent Unit)-self-attention models,respectively,with linear weighting applied to enhance feature representation.The final displacement prediction is obtained by superimposing the predicted components.The method was validated using the tailings reservoir of the Dayi Company in Lueyang County.The results showed that the predicted cumulative landslide displacement closely matched the measured values,achieving a correlation coefficient of 0.995 and a mean absolute error(MAE)of 0.092 mm.Specifically,the trend component prediction yielded an R value of 0.996 and MAE of 0.065 mm,whereas the multi-algorithm coupled IDBO-TCN-BiGRU-self-attention model achieved higher overall precision for the periodic component,with an MAE of 0.132 mm and R of 0.984.These results demonstrate that the proposed model provides a novel framework for intelligent early warning of tailings dams and can accurately predict stagewise variations in displacement. 展开更多
关键词 Tailings dam Artificial intelligent methods Displacement Multi-algorithm coupling Mine safety Intelligent early warning
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