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DC link capacitors (DCLCs) are key devices in converters. The relative humidity affects the performance of the elements of a DCLC. Currently, there are relatively few studies on the effect of relative humidity on the electrical characteristics of the elements of DCLCs during the production process. This study describes the effects and the control of the relative humidity in the production process. For this purpose, a DCLC component withstand voltage test platform and a DCLC component aging test platform were established. The voltage withstand tests were conducted during different processes and at different relative humidities, and life aging tests were conducted at different relative humidities and different storage times. The results show that after the winding and metal-spraying processes, the voltage withstand levels of the components stored at 30% RH, 60% RH, and 80% RH were very close to that of the components at 3000 VDC; after the heat-setting process, the voltage withstand levels of the components stored at 30% RH, 60% RH, and 80% RH showed a decreasing tendency compared with the level of the components at 3000 VDC. Regarding the samples, after the heat-setting process, stored at 30% RH and 60% RH for 24 h, 96 h, 168 h, 240 h, and 336 h and 2000 h of the aging tests, the capacitance decreases monotonically as the storage time is increased but never exceeds 3%, and the capacitance change decreases as the relative humidity increases. This study can provide important guidance for the humidity control of various processes in the production of DCLCs.

Insulation resistance is a vital factor in dry DC link capacitors (DCLCs), and crucially influences their voltage equalization and energy storage performance. However, at present, there is a lack of experimental observation on the insulation resistance characteristics of DCLCs in the presence of high temperatures and operating voltages. In the present study, the insulation resistance and conductivity of DCLCs are methodically analyzed. For this purpose, the corresponding test platform is appropriately fabricated, the insulation resistance measurement experiments are performed at various temperatures and operating voltages, and the factors affecting the insulation resistance and conductivity of the DCLC are carefully examined. The results reveal that the insulation resistance of the DCLC reduces exponentially with the growth in voltage and operating temperature. When the operating voltage becomes greater than 4480 V or the operating temperature reaches higher than 70 °C, the decline in insulation resistance slows down. The conductivity of metalized polypropylene film in the DCLC increases exponentially with increasing voltage and temperature. By increasing the operating voltage from 1960 V to 5600 V and the temperature from 20 °C to 90 °C, the DCLC’s insulation resistance exhibits a descending trend from 891.30 MΩ to 2.14 MΩ, while its conductivity grows from 3.49 × 10−16 S/m to 1.47 × 10−13 S/m. The results reveal that the key factors affecting the insulation resistance of the DCLC are the polypropylene film and the metal evaporated from the metal layer. This research is anticipated to provide a valuable reference for the further development of science and technology pertinent to the insulation resistance of DCLCs.

Mechanical stress is a crucial factor affecting the life and insulation performance of DC-link capacitors (DCLCs). However, at present, there is a lack of long-term experimental observations on the effects of mechanical stress on the life and insulation performance of DCLCs. The element-winding process for DCLCs is carried out by winding metalized film on a reel and adjusting the various winding tensions and pressures according to performance requirements, usually with a winding tension coefficient (WTC) of kT = 1.5. The pull pressure of the winding machine on the film produces tension during the elements’ winding process, and the tension in the film grows after the heat-setting process. In this study, by adjusting the four tension coefficients of the elements in the winding process, which were 1.4, 1.5, 1.6, and 1.7, various winding tensions of the DCLC components were changed. Additionally, various heat-setting shrinkage tensions were appropriately generated by setting different heat-setting temperatures (HSTs). Relevant test platforms were established, and a life aging test, insulation resistance measurement, and withstand voltage test were performed on these DCLCs at different tension coefficients and HSTs. The obtained results reveal that the mechanical stress of DCLCs is affected by the parameters of the material itself, including the tension coefficient during the winding process and the HST. The winding tension affects the life of DCLCs, such that those with the highest tension (kT = 1.7) demonstrate the longest life at an HST of 105 °C, whereas samples with the lowest tension (kT = 1.4) exhibit the longest life at an HST of 110 °C. HSTs are capable of improving the lifetime of DCLCs. HSTs are also able to improve the withstand voltage capability of DCLCs, but the tension is not proportional to the withstand voltage capability of DCLCs. This research provides a suitable basis for further explorations of the life and insulation performance of DCLCs.

The accurate diagnosis and prediction for individuals is crucial in computer-aided diagnosis of Alzheimer's disease (AD). The existing structural magnetic resonance imaging based classification methods of AD diagnosis mainly focus on the voxel level, region level and patch level morphological pattern analysis. However, most of these methods extract features with high dimension which may lead to overfitting problem. Besides, the interaction of different patches is not considered in the classifier ensemble. In this article, we propose a novel anatomical landmarks and directed acyclic graph (DAG) network feature learning based classification algorithm for the diagnosis of AD individuals. First, the anatomical feature patches of gray matter image are identified by the morphological and statistical analysis. Second, a simple and efficient DAG convolutional neural network is proposed to extract the discriminative deep features of image representation. Especially, the deep features are obtained by fusing feature maps of different network levels which contain semantic high-level and high-resolution low-level features. Finally, support vector machine and deep features are utilized to construct the classification model and predict the individual of AD. Experiments on three public datasets including ADNI-1, ADNI-2 and MIRIAD demonstrate that the proposed method can effectively improve the classification performance compared with the state-of-the-art methods for AD diagnosis.

Objective: The purpose of this study was to examine the neural substrates and mechanisms that generate memory deficits, seizures and neuropsychiatric abnormalities in encephalitis with LGI1 antibodies using a data-driven, multimodal magnetic resonance imaging (MRI) approach.Methods: Functional MRI data were acquired from 14 anti-LGI1 encephalitis patients and 14 age and gender matched normal controls. Independent component analysis with hierarchical partner matching (HPM-ICA) was used to assess the whole-brain intrinsic functional connectivity. Granger causality (GC) was applied to investigate the effective connectivity among the brain regions that identified by HPM-ICA. Diffusion tensor imaging (DTI) was utilized to investigate white matter microstructural changes of the patients.Results: Participants with LGI1 antibodies encephalitis presented reduced functional connectivity in the brain areas associated with memory, cognition and motion circuits, while increased functional connectivity in putamen and caudate in comparison to the normal controls. Moreover, the effective connectivity in patients was decreased from the frontal cortex to supplementary motor area. Finally, patients had significant reductions in fractional anisotropy (FA) for the corpus callosum, internal capsule, corona radiata and superior longitudinal fasciculus, accompanied by increases in mean diffusivity (MD) for these regions as compared to controls.Conclusion: Our findings suggest that the neural disorder and behavioral deficits of anti-LGI1 encephalitis may be associated with extensive changes in brain connectivity and microstructure. These pathological alterations affect the basal ganglia and limbic system besides the temporal and frontal lobe.

Machine learning and pattern recognition have been widely investigated in order to look for the biomarkers of Alzheimer’s disease (AD). However, most existing methods extract features by seed-based correlation, which not only requires prior information but also ignores the relationship between resting state functional magnetic resonance imaging (rs-fMRI) voxels. In this study, we proposed a deep learning classification framework with multivariate data-driven based feature extraction for automatic diagnosis of AD. Specifically, a three-level hierarchical partner matching independent components analysis (3LHPM-ICA) approach was proposed first in order to address the issues in spatial individual ICA, including the uncertainty of the numbers of components, the randomness of initial values, and the correspondence of ICs of multiple subjects, resulting in stable and reliable ICs which were applied as the intrinsic brain functional connectivity (FC) features. Second, Granger causality (GC) was utilized to infer directional interaction between the ICs that were identified by the 3LHPM-ICA method and extract the effective connectivity features. Finally, a deep learning classification framework was developed to distinguish AD from controls by fusing the functional and effective connectivities. A resting state fMRI dataset containing 34 AD patients and 34 normal controls (NCs) was applied to the multivariate deep learning platform, leading to a classification accuracy of 95.59%, with a sensitivity of 97.06% and a specificity of 94.12% with leave-one-out cross validation (LOOCV). The experimental results demonstrated that the measures of neural connectivities of ICA and GC followed by deep learning classification represented the most powerful methods of distinguishing AD clinical data from NCs, and these aberrant brain connectivities might serve as robust brain biomarkers for AD. This approach also allows for expansion of the methodology to classify other psychiatric disorders.

Neural disruptions during emotion regulation are common of generalized anxiety disorder (GAD). Identifying distinct functional and effective connectivity patterns in GAD may provide biomarkers for their diagnoses. This study aims to investigate the differences of features of brain network connectivity between GAD patients and healthy controls (HC), and to assess whether those differences can serve as biomarkers to distinguish GAD from controls. Independent component analysis (ICA) with hierarchical partner matching (HPM-ICA) was conducted on resting-state functional magnetic resonance imaging data collected from 20 GAD patients with medicine-free and 20 matched HC, identifying nine highly reproducible and significantly different functional brain connectivity patterns across diagnostic groups. We then utilized Granger causality (GC) to study the effective connectivity between the regions that identified by HPM-ICA. The linear discriminant analysis was finally used to distinguish GAD from controls with these measures of neural connectivity. The GAD patients showed stronger functional connectivity in amygdala, insula, putamen, thalamus, and posterior cingulate cortex, but weaker in frontal and temporal cortex compared with controls. Besides, the effective connectivity in GAD was decreased from the cortex to amygdala and basal ganglia. Applying the ICA and GC features to the classifier led to a classification accuracy of 87.5%, with a sensitivity of 90.0% and a specificity of 85.0%. These findings suggest that the presence of emotion dysregulation circuits may contribute to the pathophysiology of GAD, and these aberrant brain features may serve as robust brain biomarkers for GAD.

Background ‒ Heart failure (HF) is a serious and advanced stage of various cardiac diseases with high mortality and rehospitalization rates. Phosphoglycerate mutase 2 (PGAM2) overexpression was identified in the serum of patients with HF. Material/methods ‒ One hundred and fifty-three cases of HF were included in the present work. According to New York Heart Association (NYHA) classification, 22 were grade II, 84 were grade III, and 47 were grade IV. Serum PGAM2, NT-proBNP, B-type natriuretic peptide (BNP), troponin T (TNT), and Cys-C of HF patients were detected using ELISA assay. Left ventricular ejection fraction, left ventricular end-diastolic inner diameter, and left atrium (LA) inner diameter of the included cases were also detected by the cardiac color Doppler. Results ‒ The number of patients with atrial fibrillation was significantly higher in NYHA IV group than in groups II and III with statistical difference (p < 0.05). The serum PGAM2, NT-proBNP, and Cys-C were significantly higher in NYHA IV group than in NYHA II and NYHA III groups (pall < 0.05). NT-proBNP had the highest prediction efficacy of HF severity and PGAM2 was also a potential biomarker for HF severity evaluation with relatively high sensitivity, specificity, and area under the ROC. The overall survival among NYHA II, III, and IV groups were statistically different (p = 0.04) with the median survival time of 25 months for NYHA III and IV groups. Conclusion ‒ PGAM2 is a new promising biomarker for evaluation of the severity of HF. Combination detection using multiple serum factors such as PGAM2, NT-proBNP, BNP, TNT, and Cys-C can improve the HF severity differential diagnosis performance. [ABSTRACT FROM AUTHOR]