Material,design,and fabrication of custom prosthetic liners for lower-extremity amputees:A review

As a physical interface,a prosthetic liner is commonly used as a transition material between the residual limb and the stiff socket.Typically made from a compliant material such as silicone,the main function of a prosthetic liner is to protect the residual limb from injuries induced by load-bearing normal and shear stresses.Compared to conventional liners,custom prosthetic lower-extremity(LE)liners have been shown to better relieve stress concentrations in painful and sensitive regions of the residual limb.Although custom LE liners have been shown to offer clinical benefits,no review article on their design and efficacy has yet been written.To address this shortcoming in the literature,this paper provides a comprehensive survey of custom LE liner materials,design,and fabrication methods.First,custom LE liner materials and components are summarized,including a description of commercial liners and their efficacy.Subsequently,digital methods used to design and fabricate custom LE liners are addressed,including residual limb biomechanical modeling,finite element-based design methods,and 3-D printing techniques.Finally,current evaluation methods of custom/commercial LE liners are presented and discussed.We hope that this review article will inspire further research and development into the design and manufacture of custom LE liners.

A mutli-scale spatial-temporal convolutional neural network with contrastive learning for motor imagery EEG classification

Motor imagery(MI)based Brain-computer interfaces(BCIs)have a wide range of applications in the stroke rehabilitation field.However,due to the low signal-to-noise ratio and high cross-subject variation of the electroencephalogram(EEG)signals generated by motor imagery,the classification performance of the existing methods still needs to be improved to meet the need of real practice.To overcome this problem,we propose a multi-scale spatial-temporal convolutional neural network called MSCNet.We introduce the contrastive learning into a multi-temporal convolution scale backbone to further improve the robustness and discrimination of embedding vectors.Experimental results of binary classification show that MSCNet outperforms the state-of-theart methods,achieving accuracy improvement of 6.04%,3.98%,and 8.15%on BCIC IV 2a,SMR-BCI,and OpenBMI datasets in subject-dependent manner,respectively.The results show that the contrastive learning method can significantly improve the classification accuracy of motor imagery EEG signals,which provides an important reference for the design of motor imagery classification algorithms.

Minimal-active-space multistate density functional theory for excitation energy involving local and charge transfer states

Multistate density functional theory(MSDFT)employing a minimum active space(MAS)is presented to determine charge transfer(CT)and local excited states of bimolecular complexes.MSDFT is a hybrid wave function theory(WFT)and density functional theory,in which dynamic correlation is first incorporated in individual determinant configurations using a Kohn–Sham exchangecorrelation functional.Then,nonorthogonal configuration-state interaction is performed to treat static correlation.Because molecular orbitals are optimized separately for each determinant by including Kohn–Sham dynamic correlation,a minimal number of configurations in the active space,essential to representing low-lying excited and CT states of interest,is sufficient to yield the adiabatic states.We found that the present MAS-MSDFT method provides a good description of covalent and CT excited states in comparison with experiments and high-level computational results.Because of the simplicity and interpretive capability through diabatic configuration weights,the method may be useful in dynamic simulations of CT and nonadiabatic processes.