We proposed a strategy to address the issue by synthesizing MnO with half-filled 3 d electron orbitals.That is,MnO nanocubes with an edge length of 61.82 nm were successfully prepared through electros-pinning and one-...We proposed a strategy to address the issue by synthesizing MnO with half-filled 3 d electron orbitals.That is,MnO nanocubes with an edge length of 61.82 nm were successfully prepared through electros-pinning and one-step pyrolysis as the cathode electrode for Li-O_(2)batteries.It is observed that the intermediate LiMnO_(4)rather than Li_(2)O_(2)is formed when LiO_(2)interactes with MnO(111)during the discharge process.It is precisely because of LiMnO_(4)that reduces its charge overpotential to 0.29 V.The novel reaction mechanism dominated by LiMnO_(4)further facilitates the lower charge overpotential,thereby enhancing the energy efficiency of the batteries.展开更多
Automated detection of Motor Imagery(MI)tasks is extremely useful for prosthetic arms and legs of stroke patients for their rehabilitation.Prediction of MI tasks can be performed with the help of Electroencephalogram(...Automated detection of Motor Imagery(MI)tasks is extremely useful for prosthetic arms and legs of stroke patients for their rehabilitation.Prediction of MI tasks can be performed with the help of Electroencephalogram(EEG)signals recorded by placing electrodes on the scalp of subjects;however,accurate prediction of MI tasks remains a challenge due to noise that is incurred during the EEG signal recording process,the extraction of a feature vector with high interclass variance,and accurate classification.The proposed method consists of preprocessing,feature extraction,and classification.First,EEG signals are denoised using a bandpass filter followed by Independent Component Analysis(ICA).Multiple channels are combined to form a single surrogate channel.Short Time Fourier Transform(STFT)is then applied to convert time domain EEG signals into the frequency domain.Handcrafted and automated features are extracted from EEG signals and then concatenated to form a single feature vector.We propose a customized two-dimensional Convolutional Neural Network(CNN)for automated feature extraction with high interclass variance.Feature selection is performed using Particle Swarm Optimization(PSO)to obtain optimal features.The final feature vector is passed to three different classifiers:Support Vector Machine(SVM),Random Forest(RF),and Long Short-Term Memory(LSTM).The final decision is made using the Model-Agnostic Meta Learning(MAML).The Proposed method has been tested on two datasets,including PhysioNet and BCI Competition IV-2a,and it achieved better results in terms of accuracy and F1 score than existing state-of-the-art methods.The proposed framework achieved an accuracy and F1 score of 96%on the PhysioNet dataset and 95.5%on the BCI Competition IV,dataset 2a.We also present SHapley Additive exPlanations(SHAP)and Gradient-weighted Class Activation Mapping(Grad-CAM)explainable techniques to enhance model interpretability in a clinical setting.展开更多
基金Funded by the National Natural Science Foundation of China(No.22075035)the Technology Planning Project of Liaoning Province(No.2020JH2/10700008)the Dalian Science and Technology Innovation Fund Project(No.2022JJ11CG005)。
文摘We proposed a strategy to address the issue by synthesizing MnO with half-filled 3 d electron orbitals.That is,MnO nanocubes with an edge length of 61.82 nm were successfully prepared through electros-pinning and one-step pyrolysis as the cathode electrode for Li-O_(2)batteries.It is observed that the intermediate LiMnO_(4)rather than Li_(2)O_(2)is formed when LiO_(2)interactes with MnO(111)during the discharge process.It is precisely because of LiMnO_(4)that reduces its charge overpotential to 0.29 V.The novel reaction mechanism dominated by LiMnO_(4)further facilitates the lower charge overpotential,thereby enhancing the energy efficiency of the batteries.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2601).
文摘Automated detection of Motor Imagery(MI)tasks is extremely useful for prosthetic arms and legs of stroke patients for their rehabilitation.Prediction of MI tasks can be performed with the help of Electroencephalogram(EEG)signals recorded by placing electrodes on the scalp of subjects;however,accurate prediction of MI tasks remains a challenge due to noise that is incurred during the EEG signal recording process,the extraction of a feature vector with high interclass variance,and accurate classification.The proposed method consists of preprocessing,feature extraction,and classification.First,EEG signals are denoised using a bandpass filter followed by Independent Component Analysis(ICA).Multiple channels are combined to form a single surrogate channel.Short Time Fourier Transform(STFT)is then applied to convert time domain EEG signals into the frequency domain.Handcrafted and automated features are extracted from EEG signals and then concatenated to form a single feature vector.We propose a customized two-dimensional Convolutional Neural Network(CNN)for automated feature extraction with high interclass variance.Feature selection is performed using Particle Swarm Optimization(PSO)to obtain optimal features.The final feature vector is passed to three different classifiers:Support Vector Machine(SVM),Random Forest(RF),and Long Short-Term Memory(LSTM).The final decision is made using the Model-Agnostic Meta Learning(MAML).The Proposed method has been tested on two datasets,including PhysioNet and BCI Competition IV-2a,and it achieved better results in terms of accuracy and F1 score than existing state-of-the-art methods.The proposed framework achieved an accuracy and F1 score of 96%on the PhysioNet dataset and 95.5%on the BCI Competition IV,dataset 2a.We also present SHapley Additive exPlanations(SHAP)and Gradient-weighted Class Activation Mapping(Grad-CAM)explainable techniques to enhance model interpretability in a clinical setting.
