In Quantum Information Theory(QIT) the classical measures of information content in probability distributions are replaced by the corresponding resultant entropic descriptors containing the nonclassical terms generate...In Quantum Information Theory(QIT) the classical measures of information content in probability distributions are replaced by the corresponding resultant entropic descriptors containing the nonclassical terms generated by the state phase or its gradient(electronic current). The classical Shannon(S[p]) and Fisher(I[p]) information terms probe the entropic content of incoherent local events of the particle localization, embodied in the probability distribution p, while their nonclassical phase-companions, S[ Φ ] and I[ Φ ], provide relevant coherence information supplements.Thermodynamic-like couplings between the entropic and energetic descriptors of molecular states are shown to be precluded by the principles of quantum mechanics. The maximum of resultant entropy determines the phase-equilibrium state, defined by "thermodynamic" phase related to electronic density,which can be used to describe reactants in hypothetical stages of a bimolecular chemical reaction.Information channels of molecular systems and their entropic bond indices are summarized, the complete-bridge propagations are examined, and sequential cascades involving the complete sets of the atomic-orbital intermediates are interpreted as Markov chains. The QIT description is applied to reactive systems R = A―B, composed of the Acidic(A) and Basic(B) reactants. The electronegativity equalization processes are investigated and implications of the concerted patterns of electronic flows in equilibrium states of the complementarily arranged substrates are investigated. Quantum communications between reactants are explored and the QIT descriptors of the A―B bond multiplicity/composition are extracted.展开更多
BACKGROUND：Neuro-rehabilitative training has been shown to promote motor function recovery in stroke patients,although the underlying mechanisms have not been fully clarified.OBJECTIVE：To investigate the effects of ...BACKGROUND：Neuro-rehabilitative training has been shown to promote motor function recovery in stroke patients,although the underlying mechanisms have not been fully clarified.OBJECTIVE：To investigate the effects of finger movement training on functional connectivity and information flow direction in cerebral motor areas of healthy people using electroencephalogram （EEG）.DESIGN,TIME AND SETTING：A self-controlled,observational study was performed at the College of Life Science and Bioengineering,Beijing University of Technology between December 2008 and April 2009.PARTICIPANTS：Nineteen healthy adults,who seldom played musical instruments or keyboards,were included in the present study.METHODS：Specific finger movement training was performed,and all subjects were asked to separately press keys with their left or right hand fingers,according to instructions.The task comprised five sessions of test train test train-test.Thirty-six channel EEG signals were recorded in different test sessions prior to and after training.Data were statistically analyzed using one-way analysis of variance.MAIN OUTCOME MEASURES：The number of effective performances,correct ratio,average response time,average movement time,correlation coefficient between pairs of EEG channels,and information flow direction in motor regions were analyzed and compared between different training sessions.RESULTS：Motor function of all subjects was significantly improved in the third test comparedwith the first test （P〈 0.01）.More than 80% of connections were strengthened in the motor-related areas following two training sessions,in particular the primary motor regions under the C4 electrode.Compared to the first test,a greater amount of information flowed from the Cz and Fcz electrodes （corresponding to supplementary motor area） to the C4 electrode in the third test.CONCLUSION：Finger task training increased motor ability in subjects by strengthening connections and changing information flow in the motor areas.These results provided a greater understanding of the mechanisms involved in motor rehabilitation.展开更多
目的探讨呼吸困难指数气流受限程度指数(dyspnea index air flow restriction degree,ADO)在慢性阻塞性肺疾病(chronic obstructive pulmonary disease,COPD)患者近期预后评估中的价值。方法选取新疆医科大学第二附属医院呼吸内科自2021...目的探讨呼吸困难指数气流受限程度指数(dyspnea index air flow restriction degree,ADO)在慢性阻塞性肺疾病(chronic obstructive pulmonary disease,COPD)患者近期预后评估中的价值。方法选取新疆医科大学第二附属医院呼吸内科自2021年3月—2023年3月的COPD患者120例,并依照患者最终转归情况将其分为存活组(n=95)与死亡组(n=25)。观察2组患者的基础病情况及患者性别、年龄、第1秒用力呼气容积(first second forced expiratory volume,FEV1)占预计值的百分比和ADO指数等相关指标。比较ADO指数不同分数患者病死率。比较ADO指数预测180 d死亡的受试者工作特征(receiver operating characteristic,ROC)曲线面积。结果2组患者的高血压、冠心病、心律失常、糖尿病、慢性肝病、慢性肾病、亚临床甲减发生情况对比,差异无统计学意义(P>0.05)。死亡组患者的FEV1占预计值的百分比、FEV1占预计值的百分比评分、呼吸困难分[英国医学研究委员会(the Medical Research Council,MRC)]评分以及ADO指数均高于存活组患者(P<0.05)。ADO指数<5分者的死亡率高于ADO指数≥5分者(P<0.05)。ADO指数预测180 d死亡的ROC曲线面积为0.851(95%CI:0.767~0.928,P<0.001),ADO指数为5.5时,约登指数最大,为0.565。结论ADO可有效反映COPD病情严重程度,对于患者而言可准确反映其病情进展情况,帮助其获得良好的疾病治疗效果,对于患者近期预后而言也具有积极意义,临床应用效果良好。展开更多
针对无人机图像中由于目标微小且相互遮挡、特征信息少导致检测精度低的问题,提出一种基于改进YOLOv7的无人机图像目标检测算法。在颈部和检测头中加入了坐标卷积,能更好地感受特征图中目标的位置信息;增加P2检测层,减少小目标特征丢失...针对无人机图像中由于目标微小且相互遮挡、特征信息少导致检测精度低的问题,提出一种基于改进YOLOv7的无人机图像目标检测算法。在颈部和检测头中加入了坐标卷积,能更好地感受特征图中目标的位置信息;增加P2检测层,减少小目标特征丢失、提高小目标检测能力;提出多信息流融合注意力机制——Spatial and Channel Attention Mechanism(SCA),动态调整注意力对空间信息流和语义信息流的关注,获得更丰富的特征信息以提高捕获目标的能力;更换损失函数为SIoU,加快模型收敛速度。在公开数据集VisDrone2019上进行对比实验,改进后算法的mAP50值相比YOLOv7提高了4%,达到了52.4%,FPS为37,消融实验验证了每个模块均提升了检测精度。实验表明,改进后的算法能较好地检测无人机图像中的目标。展开更多
Predicting the external flow field with limited data or limited measurements has attracted long-time interests of researchers in many industrial applications.Physics informed neural network(PINN)provides a seamless fr...Predicting the external flow field with limited data or limited measurements has attracted long-time interests of researchers in many industrial applications.Physics informed neural network(PINN)provides a seamless framework for combining the measured data with the deep neural network,making the neural network capable of executing certain physical constraints.Unlike the data-driven model to learn the end-to-end mapping between the sensor data and high-dimensional flow field,PINN need no prior high-dimensional field as the training dataset and can construct the mapping from sensor data to high dimensional flow field directly.However,the extrapolation of the flow field in the temporal direction is limited due to the lack of training data.Therefore,we apply the long short-term memory(LSTM)network and physics-informed neural network(PINN)to predict the flow field and hydrodynamic force in the future temporal domain with limited data measured in the spatial domain.The physical constraints(conservation laws of fluid flow,e.g.,Navier-Stokes equations)are embedded into the loss function to enforce the trained neural network to capture some latent physical relation between the output fluid parameters and input tempo-spatial parameters.The sparsely measured points in this work are obtained from computational fluid dynamics(CFD)solver based on the local radial basis function(RBF)method.Different numbers of spatial measured points(4–35)downstream the cylinder are trained with/without the prior knowledge of Reynolds number to validate the availability and accuracy of the proposed approach.More practical applications of flow field prediction can compute the drag and lift force along with the cylinder,while different geometry shapes are taken into account.By comparing the flow field reconstruction and force prediction with CFD results,the proposed approach produces a comparable level of accuracy while significantly fewer data in the spatial domain is needed.The numerical results demonstrate that the proposed approach with a specific deep neural network configuration is of great potential for emerging cases where the measured data are often limited.展开更多
文摘In Quantum Information Theory(QIT) the classical measures of information content in probability distributions are replaced by the corresponding resultant entropic descriptors containing the nonclassical terms generated by the state phase or its gradient(electronic current). The classical Shannon(S[p]) and Fisher(I[p]) information terms probe the entropic content of incoherent local events of the particle localization, embodied in the probability distribution p, while their nonclassical phase-companions, S[ Φ ] and I[ Φ ], provide relevant coherence information supplements.Thermodynamic-like couplings between the entropic and energetic descriptors of molecular states are shown to be precluded by the principles of quantum mechanics. The maximum of resultant entropy determines the phase-equilibrium state, defined by "thermodynamic" phase related to electronic density,which can be used to describe reactants in hypothetical stages of a bimolecular chemical reaction.Information channels of molecular systems and their entropic bond indices are summarized, the complete-bridge propagations are examined, and sequential cascades involving the complete sets of the atomic-orbital intermediates are interpreted as Markov chains. The QIT description is applied to reactive systems R = A―B, composed of the Acidic(A) and Basic(B) reactants. The electronegativity equalization processes are investigated and implications of the concerted patterns of electronic flows in equilibrium states of the complementarily arranged substrates are investigated. Quantum communications between reactants are explored and the QIT descriptors of the A―B bond multiplicity/composition are extracted.
基金the National Natural Science Foundation of China,No. 30670543
文摘BACKGROUND：Neuro-rehabilitative training has been shown to promote motor function recovery in stroke patients,although the underlying mechanisms have not been fully clarified.OBJECTIVE：To investigate the effects of finger movement training on functional connectivity and information flow direction in cerebral motor areas of healthy people using electroencephalogram （EEG）.DESIGN,TIME AND SETTING：A self-controlled,observational study was performed at the College of Life Science and Bioengineering,Beijing University of Technology between December 2008 and April 2009.PARTICIPANTS：Nineteen healthy adults,who seldom played musical instruments or keyboards,were included in the present study.METHODS：Specific finger movement training was performed,and all subjects were asked to separately press keys with their left or right hand fingers,according to instructions.The task comprised five sessions of test train test train-test.Thirty-six channel EEG signals were recorded in different test sessions prior to and after training.Data were statistically analyzed using one-way analysis of variance.MAIN OUTCOME MEASURES：The number of effective performances,correct ratio,average response time,average movement time,correlation coefficient between pairs of EEG channels,and information flow direction in motor regions were analyzed and compared between different training sessions.RESULTS：Motor function of all subjects was significantly improved in the third test comparedwith the first test （P〈 0.01）.More than 80% of connections were strengthened in the motor-related areas following two training sessions,in particular the primary motor regions under the C4 electrode.Compared to the first test,a greater amount of information flowed from the Cz and Fcz electrodes （corresponding to supplementary motor area） to the C4 electrode in the third test.CONCLUSION：Finger task training increased motor ability in subjects by strengthening connections and changing information flow in the motor areas.These results provided a greater understanding of the mechanisms involved in motor rehabilitation.
基金This work is supported by National Natural Science Foundation of China （Grant No.60773093, 60873209, and 60970107）, the Key Program for Basic Research of Shanghai （Grant No. 09JC1407900, 09510701600, 10511500100）, IBM SUR Funding and IBM Research-China JP Funding, and Key Lab of Information Network Security, Ministry of Public Security.
文摘目的探讨呼吸困难指数气流受限程度指数(dyspnea index air flow restriction degree,ADO)在慢性阻塞性肺疾病(chronic obstructive pulmonary disease,COPD)患者近期预后评估中的价值。方法选取新疆医科大学第二附属医院呼吸内科自2021年3月—2023年3月的COPD患者120例,并依照患者最终转归情况将其分为存活组(n=95)与死亡组(n=25)。观察2组患者的基础病情况及患者性别、年龄、第1秒用力呼气容积(first second forced expiratory volume,FEV1)占预计值的百分比和ADO指数等相关指标。比较ADO指数不同分数患者病死率。比较ADO指数预测180 d死亡的受试者工作特征(receiver operating characteristic,ROC)曲线面积。结果2组患者的高血压、冠心病、心律失常、糖尿病、慢性肝病、慢性肾病、亚临床甲减发生情况对比,差异无统计学意义(P>0.05)。死亡组患者的FEV1占预计值的百分比、FEV1占预计值的百分比评分、呼吸困难分[英国医学研究委员会(the Medical Research Council,MRC)]评分以及ADO指数均高于存活组患者(P<0.05)。ADO指数<5分者的死亡率高于ADO指数≥5分者(P<0.05)。ADO指数预测180 d死亡的ROC曲线面积为0.851(95%CI:0.767~0.928,P<0.001),ADO指数为5.5时,约登指数最大,为0.565。结论ADO可有效反映COPD病情严重程度,对于患者而言可准确反映其病情进展情况,帮助其获得良好的疾病治疗效果,对于患者近期预后而言也具有积极意义,临床应用效果良好。
文摘目的 探讨血流限制下低强度增强式跳跃训练(LI-PJT+BFR)对功能性踝关节不稳(FAI)大学生的下肢动态姿势控制的影响。方法 2023年3月至5月,招募西安体育学院FAI大学生40例,随机分为高强度增强式跳跃训练(HI-PJT, n=14)组、低强度增强式跳跃训练(LI-PJT, n=13)组和LI-PJT+BFR组(n=13),各组完成相应的干预训练,共6周。干预前后,采用无线遥感表面肌电测试仪测量胫骨前肌、腓骨长肌、腓肠肌外侧头、臀大肌、股外侧肌、股二头肌和半腱肌最大自主等长收缩(MVIC)和单腿下落(SLL)时肌电均方根值(RMS),采用Y平衡和坎伯兰踝关节不稳问卷(CAIT)进行评定。结果 干预后,除LI-PJT组腓骨长肌、臀大肌、股二头肌和半腱肌MVIC和RMS,LI-PJT+BFR组腓骨长肌RMS外,各组其余肌肉MVIC和RMS均较干预前提高(t> 2.218, P <0.05);3组中,除腓骨长肌外,LIPJT组各肌肉MVIC和RMS均最低(F> 3.262, P <0.05);各组Y平衡各方向评分和综合分均提高(t> 2.485,P <0.05),3组中LI-PJT组最低(F> 5.042, P <0.05);各组CAIT评分显著改善(t> 5.227, P <0.001),3组中LI-PJT组最低(F=4.640, P <0.05)。结论 LI-PJT+BFR可改善功能恢复期FAI大学生下肢动态姿势控制能力,效果与HI-PJT相似。
文摘针对无人机图像中由于目标微小且相互遮挡、特征信息少导致检测精度低的问题,提出一种基于改进YOLOv7的无人机图像目标检测算法。在颈部和检测头中加入了坐标卷积,能更好地感受特征图中目标的位置信息;增加P2检测层,减少小目标特征丢失、提高小目标检测能力;提出多信息流融合注意力机制——Spatial and Channel Attention Mechanism(SCA),动态调整注意力对空间信息流和语义信息流的关注,获得更丰富的特征信息以提高捕获目标的能力;更换损失函数为SIoU,加快模型收敛速度。在公开数据集VisDrone2019上进行对比实验,改进后算法的mAP50值相比YOLOv7提高了4%,达到了52.4%,FPS为37,消融实验验证了每个模块均提升了检测精度。实验表明,改进后的算法能较好地检测无人机图像中的目标。
基金supported by the National Natural Science Foundation of China(Grant Nos.52206053,52130603)。
文摘Predicting the external flow field with limited data or limited measurements has attracted long-time interests of researchers in many industrial applications.Physics informed neural network(PINN)provides a seamless framework for combining the measured data with the deep neural network,making the neural network capable of executing certain physical constraints.Unlike the data-driven model to learn the end-to-end mapping between the sensor data and high-dimensional flow field,PINN need no prior high-dimensional field as the training dataset and can construct the mapping from sensor data to high dimensional flow field directly.However,the extrapolation of the flow field in the temporal direction is limited due to the lack of training data.Therefore,we apply the long short-term memory(LSTM)network and physics-informed neural network(PINN)to predict the flow field and hydrodynamic force in the future temporal domain with limited data measured in the spatial domain.The physical constraints(conservation laws of fluid flow,e.g.,Navier-Stokes equations)are embedded into the loss function to enforce the trained neural network to capture some latent physical relation between the output fluid parameters and input tempo-spatial parameters.The sparsely measured points in this work are obtained from computational fluid dynamics(CFD)solver based on the local radial basis function(RBF)method.Different numbers of spatial measured points(4–35)downstream the cylinder are trained with/without the prior knowledge of Reynolds number to validate the availability and accuracy of the proposed approach.More practical applications of flow field prediction can compute the drag and lift force along with the cylinder,while different geometry shapes are taken into account.By comparing the flow field reconstruction and force prediction with CFD results,the proposed approach produces a comparable level of accuracy while significantly fewer data in the spatial domain is needed.The numerical results demonstrate that the proposed approach with a specific deep neural network configuration is of great potential for emerging cases where the measured data are often limited.
