Network analysis has been widely used in studying Alzheimer's disease (AD). However, how the white matter network changes in cognitive impaired patients with subjective cognitive decline (SCD) (a symptom emerging during early stage of AD) and amnestic mild cognitive impairment (aMCI) (a pre-dementia stage of AD) is still unclear. Here, structural networks were constructed respectively based on FA and FN for 36 normal controls, 21 SCD patients, and 33 aMCI patients by diffusion tensor imaging and graph theory. Significantly lower efficiency was found in aMCI patients than normal controls (NC). Though not significant, the values in those with SCD were intermediate between aMCI and NC. In addition, our results showed significantly altered betweenness centrality located in right precuneus, calcarine, putamen, and left anterior cingulate in aMCI patients. Furthermore, association was found between network metrics and cognitive impairment. Our study suggests that the structural network properties might be preserved in SCD stage and disrupted in aMCI stage, which may provide novel insights into pathological mechanisms of AD.

This article presents the novel dual−/tri‐band bandpass filter (BPF) by using the multimode rectangular substrate integrated waveguide (SIW) structure. A dual‐band BPF is realized based on a single rectangular SIW structure by using perturbed metallized via‐holes. The perturbations, located at the center of the cavity, are used to shift the resonant frequencies of the TE101 and TE301 modes. This method leads that TE101 and TE201 modes constitute the first passband while TE301 and TE401 modes constitute the second passband. A tri‐band BPF is implemented by vertically coupling two single‐mode SIW cavities to the perturbed rectangular SIW. Due to the cross‐coupling of the high/low order modes, there are two transmission zeros (TZs), which improve the stopband rejection of the filters, in the upper band of each passband. A dual‐band BPF operating at 6.95GHz and 7.95GHz with four TZs and a tri‐band BPF operating at 6.5, 7.35, and 8.15 GHz with six TZs have been designed, fabricated, and measured. The measured results are in good agreement with the simulated ones.

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In this paper, a massively parallel approach of the multilevel fast multipole algorithm (PMLFMA) on graphics processing unit (GPU) heterogeneous platform, noted as GPU-PMLFMA, is presented for solving extremely large electromagnetic scattering problems involving tens of billions of unknowns, In this approach, the flexible and efficient ternary partitioning scheme is employed at first to partition the MLFMA octree among message passing interface (MPI) processes. Then the computationally intensive parts of the PMLFMA on each MPI process, matrix filling, aggregation and disaggregation, etc., are accelerated by using the GPU. Different parallelization strategies in coincidence with the ternary parallel MLFMA approach are designed for GPU to ensures a high computational throughput. Special memory usage strategy is designed to improve the computational efficiency and benefit data re-using. The CPU/GPU asynchronous computing pattern is designed with the OpenMP and CUDA respectively for accelerating the CPU and GPU execution parts and computation time overlapped. GPU architecture-based optimization strategies are implemented to further improve the computational efficiency. Numerical results demonstrate that the proposed GPU-PMLFMA can achieve over 3 times speed-up, compared with the 8-threaded conventional PMLFMA. Solutions of scattering by electrically large and complicated objects with about 24000 wavelengths and over 41.8 billion unknowns, are presented.

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