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Objective To investigate the prevalence of mild cognitive impairment (MCI) among the elderly in rural communities in Hangzhou, Zhejiang, and to explore the screening accuracy of Screening Scale for Mild Cognitive Impairment (sMCI) in the elderly with low education. Methods From April 2010 to September 2010, 360 elderly people in Sijiqing street, Jianggan district (now Shangcheng district), Hangzhou, Zhejiang were recruited. Dementia and MCI were judged by Mini⁃Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), sMCI and Clinical Dementia Rating Scale (CDR). Draw receiver operating characteristic (ROC) curve and calculate the area under the curve (AUC), and compare the accuracy of sMCI, MoCA and CDR scores in screening MCI. Results Finally, 171 cases completed all investigations. 1) 55 cases (32.16%) were diagnosed as MCI, including 25 cases (14.62%) of amnestic MCI (aMCI) and 30 cases of non⁃aMCI, 11 cases (6.43%) of dementia, 10 cases (5.85%) of depression, 4 cases (2.34%) of anxiety disorder, one case (0.58%) of bipolar disorder, one case (0.58%) of schizophrenia and one case (0.58%) of mental retardation. Among 154 patients with cognitive impairment, 25 cases (16.23%) were screened for dementia by MMSE, 8 cases (5.19%) were screened for dementia by CDR, and 11 cases (7.14%) were clinically confirmed; 138 cases (89.61%) of MCI were screened by MoCA, 117 cases (75.97%) by sMCI, 70 cases (45.45%) by CDR, and 55 cases (32.16%) were clinically confirmed. 2) Taking clinical diagnosis as reference standard, the ROC curve showed CDR score had the highest accuracy in screening MCI, and the AUC was 0.90 ± 0.03 (95%CI: 0.844-0.957, P=0.000); the AUC of MoCA score was 0.53 ± 0.05 (95%CI: 0.430-0.621, P=0.603); when the cut⁃off value of sMCI score was 23, the AUC was 1.00 ± 0.00 (95%CI: 1.000-1.000, P=0.000). The cut⁃off value of subjects with education level of 0-3 years was adjusted to 22, and the AUC was 0.67 ± 0.05 (95%CI: 0.578-0.756, P=0.001). 3) According to education level, they were divided into 0-3 years group (113 cases) and 4-6 years group (47 cases). Taking CDR score as the reference standard, ROC curve showed the AUC of MoCA score in screening MCI in 4-6 years group was 0.49 ± 0.17 (95%CI: 0.157-0.824, P=0.955), the cut⁃off value of sMCI score was 23, the AUC of sMCI score was 0.50 ± 0.17 (95%CI: 0.161-0.839, P=1.000); the AUC of MoCA score in the 0-3 years group was 0.51 ± 0.06 (95%CI: 0.402-0.617, P=0.858), and the cut⁃off value of sMCI score was adjusted to 22, and the AUC was 0.64 ± 0.05 (95%CI: 0.535-0.744, P=0.011). Conclusions It is more common for the elderly in the rural communities with low education to have MCI, the accuracy of sMCI in screening MCI is higher than MoCA, and the cut⁃off value is 23 (education level 4-6 years) and 22 (education level 0-3 years), which is worthy of clinical application.

Polymeric dielectrics exhibit remarkable dielectric characteristics and wide applicability, rendering them extensively employed within the domain of electrical insulation. Nevertheless, the electrical strength has always been a bottleneck, preventing its further utilization. Nanocomposite materials can effectively improve insulation strength, but uniform doping of nanofillers in engineering applications is a challenge. Consequently, a nanocomposite interfacial coating was meticulously designed to interpose between the electrode and the polymer, which can significantly improve DC breakdown performance. Subsequently, the effects of filler concentration and coating duration on DC breakdown performance, high field conductivity, and trap distribution characteristics were analyzed. The results indicate that the composite coating introduces deep traps between the electrode-polymer interface, which enhances the carrier confinement, resulting in reduced conductivity and enhanced DC breakdown strength. The incorporation of a composite coating at the interface between the electrode and polymer presents novel avenues for enhancing the dielectric insulation of polymers. [ABSTRACT FROM AUTHOR]

With the rapid development of smart health, the privacy problem of medical data has become more prominent. Aiming at the problem of mining the potential value of medical data and realizing secure sharing, a fine‐grained medical data sharing scheme based on federated learning is proposed. The scheme uses collaboration‐oriented attribute‐based encryption technologies to formulate fine‐grained access strategies, allowing medical institutions or doctors to decrypt individually or collaboratively with certain conditions to achieve the purpose of accurately screening the required medical data. In the proposed scheme, the model parameters are shared such that the screened medical data is modeled and analyzed based on federated learning, which allows more people to enjoy top medical resources. In addition, a blockchain‐based incentive mechanism is used to reward medical institutions which are either honest with high‐quality or helpful in decryption. Hence, the enthusiasm of various medical institutions to screen data and participate in federal learning is improved. Finally, security analysis shows that the scheme is secure, and theoretical analysis and simulation test show the practicability of the scheme. [ABSTRACT FROM AUTHOR]

The structural integrity of solid rocket motor(SRM) grain is severely tested owing to the combined action of low temperature and pressure load under the load case of low temperature ignition. The three dimensional finite element model of SRM was created to analyze the structural integrity of the SRM grain subjected to low temperature and ignition pressure based on three dimensional viscoelastic finite element method via MSC.Patran/Marc. Meanwhile, cold pressurization test was applied on certain SRM. The experimental result and numerical result were compared based on uncoupling principal of temperature and pressure. The result show that the safety factor of solid rocket motor grain is 2.46 which can meet the requirement of structural integrity. The experimental results are in good agreement with the simulation results. Relevant research methods and conclusions can provide reference for the design, analysis and test of SRMs.

Electronic textiles present an enticing prospect for personal health assessment and physical monitoring, owing to their strong stretchability, high flexibility, mechanical robustness and high capacity in sensing small deformations in human motions. Herein, a multifunctional robust flexible knitting-shaped strain sensor based on the functional heterostructure composed of the conductive MXene (Ti3C2Tx) nanosheet and the antimicrobial Cu2O nanoparticles is prepared via a solution-processable dip-dry coating approach. The textile-based strain sensor exhibits a highly stable and immediate response over a wide range, which shows great advantages in detecting and monitoring human activities, such as smiling, swallowing, and wrist/finger/joint bending. Significantly, these prepared strain sensors present a promising application in smart wear, which was typically employed as the smart sensing gloves in barrier-free communication for hearing-impaired people. Interestingly, the different resistance evolutions of the knitted sensor under both low and high strain were carried out to study the sensing mechanism for the first time. Notably, the strain sensor displays a reliable antibacterial efficiency of ∼99.1% for Escherichia coli and outstanding breathability as high as 190 mm/s. This developed MXene/Cu2O hybrid materials supplies a new insight for the rational design and synthesis of multifunctional nanomaterials, as well as the achievement of the flexible wearable sensor with high performance. [ABSTRACT FROM AUTHOR]