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Alzheimer's disease (AD) is a degenerative disease with insidious onset and chronic nervous system, which can be roughly classified into three stages: preclinical AD, mild cognitive impairment (MCI), and clinical AD. Given that timely treatment of MCI can delay the course of AD, identifying what stage of the disease is at the time of early diagnosis is critical in clinical practice. Based on the survey data of elderly residents in Changsha community, this paper constructs the cognitive impairment data set and explores the application of the emerging classification algorithm Convolutional Support Vector Machine(CSVM) in identifying elderly cognitive impairment, a small data classification problem. CSVM is an improved Support Vector Machine classification(SVC) algorithm, which uses the convolution filtering idea of the Convolutional Neural Networks(CNN) algorithm to preprocess the nonlinearly separable sample set to improve the classification ability of SVM. In addition, the optimal convolution filter (solution) is found by the Simplified Swarm Optimization(SSO) algorithm and orthogonal array experimental design. A variable update amplitude method is also proposed to optimize the SSO update strategy further. Aiming at the problem of an unbalanced sample set, a stratified sampling method is used to divide and cross-validate the sample data, and a variety of evaluation indicators are used to evaluate the performance of the model. Experimental results show that the data set processed by a specific convolution filter can significantly improve the classification performance of linear SVM. The accurate differentiation of normal cognition(NC), MCI, and AD helps intervene in adjuvant treatment for potential patients timely. • In the data preprocessing stage, justified features in the original data are selected by the mutual information method, and several experimental data sets with different feature numbers are established. • In the model building stage, the optimal variable updating amplitude of the filter is studied and analyzed, and a new amplitude changing method is proposed. • In the model evaluation stage, the performance was evaluated through stratified sampling cross-validation and sample point distribution research to test the classification performance of the model better. [ABSTRACT FROM AUTHOR]

Although the composition-driven multi-phase coexistence strategy has been widely used to modify the piezoelectric properties of potassium sodium niobate ((K, Na)NbO 3 , KNN) based ceramics, the trade-off between long-range ferroelectric ordering and chemical additives-induced disorder hinders the further improvement of piezoelectricity and makes the electric field-induced strain temperature-dependent. Herein, to resolve two issues, we proposed a new concept, that is, manipulating defect dipoles and local stress of a pre-constructed multi-phase coexistence through controlling zirconium (Zr) content. We validated the new concept by designing 0.96(K 0.4 Na 0.6)Nb 0.955 Sb 0.045 O 3 -0.04(Bi 0.5 Na 0.5)Zr (1+ x) O 3 ceramics. In samples with Zr deficiency (i.e., x =-0.1), we obtained high retention of 91% in normalized unipolar strain (S uni) over the temperature range of 30-160 °C, even under low electric fields of 10-20 kV/cm, superior to those of other representative KNN-based ceramics. In samples with slight Zr excess (i.e., x =0.07), we achieved an increase in direct piezoelectric coefficient (d 33) and converse piezoelectric coefficient (d 33 *) by 12% and 25%, respectively, in comparison with that at x =0. The enhanced temperature stability stems from the released domain walls that are pinned by defect dipoles, and the increased d 33 (and d 33 *) originates from the synergetic contributions of the multi-phase coexistence, increased grain size, and stabilization of the ZrO 2 secondary phase. Therefore, our new concept would benefit the composition design and performance improvement of KNN-based ceramics in the future. [Display omitted] [ABSTRACT FROM AUTHOR]

Different types of defect dipoles form, F e A.. − V A ′ for A-site and F e N b ″ − V O.. for B-site according to the dopant sites [Fig. 1(a)]. Both of the temperature stable miniatured domain [Fig. 1(b)] and active defect dipole F e N b ″ − V O.. [Fig. 1(c)] in the B-site 0.05 leads to the preferable temperature stability, that is, the remnant polarization (P r) and unipolar strain (S) vary less than 10% and 15% in the temperature range of 20-140 °C, which is superior to other systems [Fig. 1(d)]. We believe that the strategy of designed defect dipoles is helpful for improving electrical properties and further promoting the development of KNN-based ceramics. [Display omitted] The designed defect-engineering has been proved to be an effective way to promote properties in potassium sodium niobate (KNN)-based ceramics. Here, we focus on the introduction of Fe element into different sites of KNN-based ceramics to form defects and optimize properties. Two types of defect dipoles can be formed, F e A.. − V A ′ for A-site and F e N b ″ − V O.. for B-site. Compared with the defect dipoles of A-site, the defect dipoles of B-site are sensitive to both electric field and temperature. So that, the B-site defect dipoles switch easily under the thermo-electric treatment, and share the same behavior with spontaneous polarization after removing the electric field, which compensates the remnant polarization. In addition, the domain miniaturizes as the Fe ions doping. Both of the defect dipole behavior and nanodomain lead to the preferable temperature stability: remnant polarization (P r) and unipolar strain (S) of the B-site engineered ceramics vary less than 10% and 15% in the temperature range of 20-140 °C. We hope that the strategy of designed defect dipoles is helpful for improving electrical properties and further promoting the development of KNN-based ceramics. [ABSTRACT FROM AUTHOR]