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A novel lightweight computerized ECG interpretation approach based on clinical 12-lead data

Science China(Technological Sciences) 2024年第2期67卷 449-463页

作者： liu YunQing QIN chengjin liu JinLei JIN YanRui LI ZhiYuan ZHAO LiQun liu ChengLiang School of Mechanical Engineering Shanghai Jiao Tong UniversityShanghai 200240China Key Labof Artificial Intelligence(Ministryof Education) AI InstituteShanghai Jiao Tong UniversityShanghai 200240China Department of Cardiology Shanghai First Peoples Hospital Affiliated to Shanghai Jiao Tong UniversityShanghai 200080China

Although 12-lead electrocardiograms(ECGs)provide a wide range of spatiotemporal characteristics,interpreting them for arrhythmia detection is difficult due to a lack of reliable large-scale clinical datasets.Herein,we proposed an innovative lightweight computerized ECG interpretation approach based on 12-lead data.Our model was trained,validated,and tested on 53845 standard 12-lead ECG records collected at Shanghai First People’s Hospital in affiliation with Shanghai Jiao Tong University.The experiments revealed that our approach had a classification accuracy of 94.41%in the classification task of seven types of rhythms,which was markedly superior to related single-lead and 12-lead ECG classification methods.Moreover,the average receiver operating characteristic area under the curve reached a value of 0.940,and the precision values for sinus tachycardia and sinus bradycardia were 0.945 and 0.91,respectively,with specificity values of 0.996 and 0.994.By employing our boosting method,we were able to improve the accuracy to 94.85%.To investigate the performance degradation of the proposed neural network in some classes,an ECG cardiologist was enlisted to review questionable ECGs;this process provides a promising direction for network performance improvement.Therefore,the proposed computerized ECG interpretation approach has practical significance because it could help professional physicians analyze patients’heart conditions based on real-time 12-lead ECG or grade their disease severity in advance.

关键词： computerized ECG interpretation large-scale 12-lead clinical ECG database lightweight neural network

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Strategies for improving extraction capacity through preorganization structure:A novel 5,6-bicyclicmalonamide extractant(THPPD)

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Journal of Rare Earths 2024年第4期42卷 768-774页

作者： Miaomiao Zhang Zhen Yang Yuhua Cui Jianhui Su Heng Zhao chengjin Xu Xiaolei liu Guoxin Sun Xiujing Peng Yu Cui School of Chemistry and Chemical Engineering University of JinanJinan250022China Institute for Smart Materials&Engineering University of JinanJinan250022China Shandong Institute for Food and Drug Control Jinan250101China

Rational design of extractant structure to improve the extraction and separation capacity of rare earth elements(REEs)is a long-standing challenge.Herein,a new strategy was proposed to improve the stability of the chelating structure formed by malonamide and REEs.The stability of the chelating structure is greatly improved by using a double-ring framework structure which makes the two carbonyl groups of malonamide unable to rotate freely and both of them point to the metal ion position.Three benzyl groups were used to construct the hydrophobicity of the extractant and the organic shell of the extracted species.Tribenzylhexahydro-pyrrolo-pyridine-dione(THPPD)was designed and synthesized.The structure reduces the energy consumed by rotating carbonyl group in the coordination with metal ions and then improves the extraction ability of extractant.The crystal structure was preorganized as expected,with the two carbonyls pointing in a favorable coordination direction to the structural complement of the metal ion.The extraction behaviors of REEs with THPPD in a nitric acid medium were studied.Compared with N,N’-dibenzyl-N,N’-dimethylmalonamide(DBDM-MA)with a chain structure,the extraction capacity of THPPD is 360 times higher than that of DBDM-MA at 5.0 mol/L sodium nitrate.Furthermore,the binding energy and Gibbs free energy were investigated by density functional theory(DFT)in conjunction with the B3LYP.The theoretical results show that THPPD has more effective interaction with Pr(NO_(3))3 than DBDM-MA.The construction of chelating groups conformation is a worthy direction to improve the coordination ability and even selectivity of extractant.

关键词： Rare earth elements Extraction separation 5,6-bicyclicdiamide extractant

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A residual denoising and multiscale attention-based weighted domain adaptation network for tunnel boring machine main bearing fault diagnosis

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Science China Technological Sciences 2024年

作者： ZHONG Tao QIN chengjin SHI Gang ZHANG ZhiNan TAO JianFeng liu ChengLiang State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University

As a critical component of a tunnel boring machine (TBM), the precise condition monitoring and fault analysis of the main bearing is essential to guarantee the safety and efficiency of the TBM cutter drive. Currently, under conditions of strong noise and complex working environments, traditional signal decomposition and machine learning methods struggle to extract weak fault features and achieve high fault classification accuracy. To address these issues, we propose a novel residual denoising and multiscale attention-based weighted domain adaptation network (RDMA-WDAN) for TBM main bearing fault diagnosis. Our approach skillfully designs a deep feature extractor incorporating residual denoising and multiscale attention modules, achieving better domain adaptation despite significant domain interference. The residual denoising component utilizes a convolutional block to extract noise features, removing them via residual connections. Meanwhile, the multiscale attention module uses a 4-branch convolution and 3 pooling strategy-based channel–spatial attention mechanism to extract multiscale features, concentrating on deep fault features. During training, a weighting mechanism is introduced to prioritize domain samples with clear fault features. This optimizes the deep feature extractor to obtain common features, enhancing domain adaptation. A low-speed and heavy-loaded bearing testbed was built, and fault data sets were established to validate the proposed method. Comparative experiments show that in noise domain adaptation tasks, proposed the RDMA–WDAN significantly improves target domain classification accuracy by 42.544%, 23.088%, 43.133%, 16.344%, 5.022%, and 9.233%over dense connection network(DenseNet), squeeze–excitation residual network (SE-ResNet), antinoise multiscale convolutional neural network(ANMSCNN), multiscale attention module-based convolutional neural network (MSAMCNN), domain adaptation network, and hybrid weighted domain adaptation (HWDA). In combined noise and working condition domain adaptation

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Three-dimensional structural Cu^(6)Sn_(5)/carbon nanotubes alloy thin-film electrodes fabricated by in situ electrodeposition from the leaching solution of waste-printed circuit boards

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International Journal of Minerals,Metallurgy and Materials 2023年第6期30卷 1171-1180页

作者： Shuqing Nie Yu Xin Qiuyun Wang chengjin liu Chang Miao Limin Yu Wei Xiao College of Chemistry and Environmental Engineering Yangtze UniversityJingzhou 434023China

Tin-based materials are very attractive anodes because of their high theoretical capacity,but their rapid capacity fading from volume expansions limits their practical applications during alloying and dealloying processes.Herein,the improved binder-free tin-copper intermetallic/carbon nanotubes(Cu6Sn5/CNTs)alloy thin-film electrodes are directly fabricated through efficient in situ electrodeposition from the leaching solution of treated waste-printed circuit boards(WPCBs).The characterization results show that the easily agglomerated Cu6Sn5 alloy nanoparticles are uniformly dispersed across the three-dimensional network when the CNTs concentration in the electrodeposition solution is maintained at 0.2 g·L−1.Moreover,the optimal Cu6Sn5/CNTs-0.2 alloy thin-film electrode can not only provide a decent discharge specific capacity of 458.35 mAh·g^(−1)after 50 cycles at 100 mA·g^(−1)within capacity retention of 82.58%but also deliver a relatively high reversible specific capacity of 518.24,445.52,418.18,345.33,and 278.05 mAh·g^(−1)at step-increased current density of 0.1,0.2,0.5,1.0,and 2.0 A·g^(−1),respectively.Therefore,the preparation process of the Cu6Sn5/CNTs-0.2 alloy thin-film electrode with improved electrochemical performance may provide a cost-effective strategy for the resource utilization of WPCBs to fabricate anode materials for lithium-ion batteries.

关键词： tin-copper intermetallic in situ electrodeposition carbon nanotubes anode material lithium-ion battery

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贝莱斯芽胞杆菌GZ8-6对马铃薯根际土壤酶活性的影响及促生作用

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植物保护 2024年第1期50卷 48-55,84页

作者：陈雨薇王喜刚郭成瑾沈瑞清刘东川宁夏大学农学院银川750021 宁夏农林科学院植物保护研究所银川750002 宁夏西吉县马铃薯产业服务中心固原756206

为探究生防菌对马铃薯根系土壤酶活性及马铃薯生长的影响。分别以马铃薯疮痂病Streptomyces scabies X-1菌液、LB液体培养基以及生防菌贝莱斯芽胞杆菌Bacillus velezensis GZ8-6发酵液对马铃薯苗进行灌根处理,测定和比较不同时间段的土... 详细信息

为探究生防菌对马铃薯根系土壤酶活性及马铃薯生长的影响。分别以马铃薯疮痂病Streptomyces scabies X-1菌液、LB液体培养基以及生防菌贝莱斯芽胞杆菌Bacillus velezensis GZ8-6发酵液对马铃薯苗进行灌根处理,测定和比较不同时间段的土壤酶活性和马铃薯生长相关指标。结果表明:X-1和贝莱斯芽胞杆菌GZ8-6处理后,马铃薯根际土壤中脲酶、蔗糖酶、过氧化氢酶和纤维素酶的活性明显高于病原菌处理和LB液体培养基处理。X-1和GZ8-6处理后第30、60天,土壤蔗糖酶活性达到高峰,分别比病原菌处理高1.70倍和2.71倍,土壤脲酶活性在施用后第10、20天较病原菌处理分别提升了52.53%和59.48%;土壤纤维素酶和土壤过氧化氢酶活性在马铃薯生育期内呈上升-下降-上升-下降的趋势。经X-1和GZ8-6处理后马铃薯的侧根数、茎粗、地下部鲜重等各项生长指标都优于病原菌处理和培养基处理,处理后60 d株高和地下部鲜重分别较病原菌处理提高了34.65%和124.79%,茎粗较LB液体培养基处理平均增加0.53 cm。因此,生防菌处理不仅对马铃薯有促生作用,同时还能提高土壤关键酶活性。

关键词：马铃薯贝莱斯芽胞杆菌土壤酶活性促生作用

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A multi-scale convolutional neural network for bearing compound fault diagnosis under various noise conditions

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Science China(Technological Sciences) 2022年第11期65卷 2551-2563页

作者： JIN YanRui QIN chengjin ZHANG ZhiNan TAO JianFeng liu ChengLiang School of Mechanical Engineering Shanghai Jiao TongUniversityShanghai 200240China

Recently,with the urgent demand for data-driven approaches in practical industrial scenarios,the deep learning diagnosis model in noise environments has attracted increasing attention.However,the existing research has two limitations:(1)the complex and changeable environmental noise,which cannot ensure the high-performance diagnosis of the model in different noise domains and(2)the possibility of multiple faults occurring simultaneously,which brings challenges to the model diagnosis.This paper presents a novel anti-noise multi-scale convolutional neural network(AM-CNN)for solving the issue of compound fault diagnosis under different intensity noises.First,we propose a residual pre-processing block according to the principle of noise superposition to process the input information and present the residual loss to construct a new loss function.Additionally,considering the strong coupling of input information,we design a multi-scale convolution block to realize multi-scale feature extraction for enhancing the proposed model’s robustness and effectiveness.Finally,a multi-label classifier is utilized to simultaneously distinguish multiple bearing faults.The proposed AM-CNN is verified under our collected compound fault dataset.On average,AM-CNN improves 39.93%accuracy and 25.84%F1-macro under the no-noise working condition and 45.67%accuracy and 27.72%F1-macro under different intensity noise working conditions compared with the existing methods.Furthermore,the experimental results show that AM-CNN can achieve good cross-domain performance with 100%accuracy and 100%F1-macro.Thus,AM-CNN has the potential to be an accurate and stable fault diagnosis tool.

关键词： anti-noise residual pre-processing block bearing compound fault multi-label classifier multi-scale convolution feature extraction

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Multi-class 12-lead ECG automatic diagnosis based on a novel subdomain adaptive deep network

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Science China(Technological Sciences) 2022年第11期65卷 2617-2630页

作者： JIN YanRui LI ZhiYuan liu YunQing liu JinLei QIN chengjin ZHAO LiQun liu ChengLiang State Key Laboratory of Mechanical System and Vibration School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Department of Cardiology Shanghai First People's Hospital Afiliated to Shanghai Jiao Tong University Shanghai 200080China

Arrhythmia is a common type of cardiovascular disease,which has become the leading cause of global deaths.Recently,the automatic 12-lead ECG diagnosis system based on numerous labelled data has attracted increasing attention.However,labelling 12-lead ECG recordings is a complex and time-consuming task for clinicians.And then,the existence of data distribution differences limits the direct cross domain use of the trained model.Enlighted by subdomain adaptation methods,this paper designs a novel subdomain adaptative deep network(SADN)for excavating diagnosis knowledge from source domain datasets.Firstly,the convolutional layer,residual blocks and SE-Residual blocks are utilized for extracting meaningful deep features automatically.Additionally,the feature classifier uses these deep features for obtaining the final diagnosis predictions.Further,designing a novel loss function with local maximum mean discrepancy is utilized for restricting data distribution discrepancy from different datasets.Finally,the Clinical Outcomes in Digital ECG and 1st China Physiological Signal Challenge datasets are utilized for evaluating the superiority of SADN,which presents that SADN enhances algorithm performance on the unlabelled target domain dataset.Further,compared with the existing methods,the proposed network structure acquires better performance with a F1-macro of 89.43%and a F1-macro1 of 87.09%.Besides,among the 4 kinds of ECG abnormalities,the diagnostic effect of the SADN is better than that of cardiology residents.Thus,SADN has promising potential as an auxiliary diagnostic tool for the clinical environment.

关键词： arrhythmia detection subdomainadaptation deep network 12-lead ECG

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A novel LSTM-autoencoder and enhanced transformer-based detection method for shield machine cutterhead clogging

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Science China(Technological Sciences) 2023年第2期66卷 512-527页

作者： QIN chengjin WU RuiHong HUANG GuoQiang TAO JianFeng liu ChengLiang State Key Laboratory of Mechanical System and Vibration School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghai 200240China

Shield tunneling machines are paramount underground engineering equipment and play a key role in tunnel construction.During the shield construction process,the“mud cake”formed by the difficult-to-remove clay attached to the cutterhead severely affects the shield construction efficiency and is harmful to the healthy operation of a shield tunneling machine.In this study,we propose an enhanced transformer-based detection model for detecting the cutterhead clogging status of shield tunneling machines.First,the working state data of shield machines are selected from historical excavation data,and a long short-term memory-autoencoder neural network module is constructed to remove outliers.Next,variational mode decomposition and wavelet transform are employed to denoise the data.After the preprocessing,nonoverlapping rectangular windows are used to intercept the working state data to obtain the time slices used for analysis,and several time-domain features of these periods are extracted.Owing to the data imbalance in the original dataset,the k-means-synthetic minority oversampling technique algorithm is adopted to oversample the extracted time-domain features of the clogging data in the training set to balance the dataset and improve the model performance.Finally,an enhanced transformer-based neural network is constructed to extract essential implicit features and detect cutterhead clogging status.Data collected from actual tunnel construction projects are used to verify the proposed model.The results show that the proposed model achieves accurate detection of shield machine cutterhead clogging status,with 98.85%accuracy and a 0.9786 F1 score.Moreover,the proposed model significantly outperforms the comparison models.

关键词： shield tunneling machine cutterhead clogging fault diagnosis autoencoder multihead self-attention mechanism imbalanced data

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山黧豆AAE超家族基因鉴定及LsAAE3的酶活检测

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西北植物学报 2024年第3期44卷 443-450页

作者：贾海燕张颖陈红刘嘉怡焦成瑾徐全乐陇东学院生命科学与技术学院甘肃省高校陇东生物资源保护与利用省级重点实验室甘肃庆阳745000 西北农林科技大学生命科学学院陕西杨凌712100 天水师范学院生物工程与技术学院甘肃天水74100 甘肃省功能基因组与分子诊断重点实验室兰州730000

【目的】三七素(β-N-草酰-L-α,β-二氨基丙酸,β-ODAP)是山黧豆、三七等物种中的神经活性物质,其生物合成与草酰辅酶A密切相关。研究基于全基因组鉴定山黧豆草酰辅酶A合成酶的编码基因,为β-ODAP生物合成的调控研究奠定基础。【方法】... 详细信息

【目的】三七素(β-N-草酰-L-α,β-二氨基丙酸,β-ODAP)是山黧豆、三七等物种中的神经活性物质,其生物合成与草酰辅酶A密切相关。研究基于全基因组鉴定山黧豆草酰辅酶A合成酶的编码基因,为β-ODAP生物合成的调控研究奠定基础。【方法】用AMP结合结构域隐马尔科夫模型在山黧豆基因组中鉴定酰基激活酶超家族(acyl-activating enzyme superfamily,AAE)基因,在此基础上,克隆山黧豆草酰辅酶A合成酶基因LsAAE3,用多序列比对、原核表达及酶活检测等方法分析LsAAE3基因功能。【结果】山黧豆中至少存在22条AAE家族基因;系统发育分析表明:LsAAE3与拟南芥AtAAE3等聚为一支,隶属于酰基辅酶A合成酶家族。基因克隆及序列分析表明:LsAAE3基因cDNA全长为1566 bp;LsAAE3和AtAAE3的保守结构域类似,均含有AMP结合位点、CoA结合位点及AAE保守结构域。SDS-PAGE检测表明所获融合蛋白条带单一,大小在56 kD左右;用His标签抗体进行Western blot分析发现,诱导后和纯化的融合蛋白均能检测到特征条带,说明所获融合蛋白为LsAAE3。体外酶活检测表明:LsAAE3具有草酰辅酶A合成酶活性,其活性发挥依赖于ATP、Mg^(2+)。【结论】研究在全基因组水平鉴定山黧豆AAE超家族基因,并验证了LsAAE3具有草酰辅酶A合成酶活性,为进一步分析山黧豆LsAAE3在β-ODAP生物合成过程中的功能奠定基础。

关键词：山黧豆三七素 AAE超家族草酰辅酶A合成酶酶活测定

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Data-driven Reliability Assessment of an Electric Vehicle Penetrated Grid Utilizing the Diffusion Estimator and Slice Sampling

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CSEE Journal of Power and Energy Systems 2023年第5期9卷 1845-1853页

作者： Songyu Huang chengjin Ye Si liu Wei Zhang Yi Ding Ruoyun Hu Jianbai Li College of Electrical Engineering Zhejiang UniversityHangzhou 310027China State Grid Zhejiang Electric Power Research Institute Hangzhou 310014China

Due to the stochasticity of charging behaviors of electric vehicles(EVs),it is difficult to anticipate when charging load demand will be densely concentrated.If massive charging loads and the system peak profile appear at the same time,it may pose a risk to the reliable operation of power grids.For a system integrated with renewable energies,this risk can be much higher because of their unsteady power output.With load measurements more widely collected,this paper presents a data-driven framework to assess the reliability of a power grid considering charging EVs.Specifically,the diffusion estimator is firstly applied to estimate the probability density function of EV charging loads,which possesses both regional adaptivity and good boundary estimation performance.Then,charging load samples are produced through slice sampling.It is capable of sampling from irregularly-shaped distributions with high accuracy.The proposed approach is verified by the numerical results from the simulations on a modified IEEE 30-bus test system based on real measurement data.

关键词： Charging loads data-driven diffusion estimator electric vehicles slice sampling system reliability

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