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Alzheimer's disease (AD), a neuropsychiatric disorder, continually arises in the elderly. To date, no targeted medications have been developed for AD. Early and fast diagnosis of AD plays a pivotal role in identifying potential AD patients, enabling timely medical interventions, and mitigating disease progression. Computer-aided diagnosis (CAD) becomes possible with the burgeoning of deep learning. However, the existing CAD models for processing 3D Alzheimer's disease images usually have the problems of slow convergence, disappearance of gradient, and falling into local optimum. This makes the training of 3D diagnosis models need a lot of time, and the accuracy is often poor. In this paper, a novel 3D aggregated residual network with accelerated mirror descent optimization is proposed for diagnosing AD. First, a novel unbiased subgradient accelerated mirror descent (SAMD) optimization algorithm is proposed to speed up diagnosis network training. By optimizing the nonlinear projection process, our proposed algorithm can avoid the occurrence of the local optimum in the non-Euclidean distance metric. The most notable aspect is that, to the best of our knowledge, this is the pioneering attempt to optimize the AD diagnosis training process by improving the optimization algorithm. Then, we provide a rigorous proof of the SAMD's convergence, and the convergence of SAMD is better than any existing gradient descent algorithms. Finally, we use our proposed SAMD algorithm to train our proposed 3D aggregated residual network architecture (ARCNN). We employed the ADNI dataset to train ARCNN diagnostic models separately for the AD vs. NC task and the sMCI vs. pMCI task, followed by testing to evaluate the disease diagnostic outcomes. The results reveal that the accuracy can be improved in diagnosing AD, and the training speed can be accelerated. Our proposed method achieves 95.4% accuracy in AD diagnosis and 79.9% accuracy in MCI diagnosis; the best results contrasted with several state-of-the-art diagnosis methods. In addition, our proposed SAMD algorithm can save about 19% of the convergence time on average in the AD diagnosis model compared with the gradient descent algorithms, which is very momentous in clinic.

The reaction wheel is a typical actuator and a crucial weak link in the spacecraft attitude control system, and much attention has been paid to its reliability problem. Due to limited samples and high cost for reaction wheel life tests, a simulation method by introducing attitude coupling dynamics and multiplicative fault concept is developed to analyze the logic of electric current as a performance indicator and verify its accuracy for reliability modeling. Furthermore, a new and intrinsic performance indicator of multiplicative fault is proposed for more application scenarios of reliability modeling and an adaptive sliding mode observer is designed for fault estimation. An illustrative example shows that the performance indicator of multiplicative fault can be used for various mission scenarios but requires certain persistent excitation, while electric current is the opposite., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 ISA. Published by Elsevier Ltd. All rights reserved.)

Alzheimer's disease (AD) is the most common neurodegenerative disorder in the elderly. Early diagnosis of AD plays a vital role in slowing down the progress of AD because there is no effective drug to treat the disease. Some deep learning models have recently been presented for AD diagnosis and have more satisfactory performance than classic machine learning methods. Nevertheless, most of the existing computer-aided diagnostic models used neuroimaging features for diagnosis, ignoring patients' clinical and biological information. This makes the AD diagnosis inaccurate. In this study, we propose a novel multimodal feature transformation and fusion model for AD diagnosis. The feature transformation aims to avoid the difference in feature dimensions between different modal data and further mine the significant features for AD diagnosis. A geometric algebra-based feature extension method is proposed to obtain different levels of high-dimensional features from patients' clinical and personal biological data. Then, an influence degree-based feature filtration algorithm is proposed to filtrate those features that have no apparent guiding significance for AD diagnosis. Finally, an ANN (Artificial Neural Network)-based framework is designed to fuse transformed features with neuroimaging features extracted by CNN (Convolutional Neural Network) for AD diagnosis. The more in-depth feature mining of patients' clinical information and biological information can significantly improve the performance of computer-aided AD diagnosis. The experiments are obtained on the ADNI dataset. Our proposed model can converge faster and achieves 96.2% accuracy in AD diagnostic task and 87.4% accuracy in MCI (Mild Cognitive Impairment) diagnostic task. Compared with other methods, our proposed approach has an excellent performance in AD diagnosis and surpasses SOTA (state-of-the-art) methods. Therefore, our model can provide more reasonable suggestions for clinicians to diagnose and treat disease., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

This article is concerned with a quasiperiodic disturbance estimation problem for dynamic control systems without prior knowledge on frequency. As a major challenge of our work, the quasiperiodic disturbance to be treated is always submerged by untargeted waves, leading to complicated coupling between disturbance separation and frequency identification. Existing approaches on quasiperiodic disturbance rejection have circumvented, rather than overcome, this challenge by assuming either a known frequency or a measurable disturbance signal. In this work, an expectation-maximization (EM) framework is proposed where disturbance signal separation and frequency identification are carried out in an iterative manner. In the E-step, the expected log-likelihood function is evaluated via reconstruction of the quasiperiodic signal based on the latest frequency estimate; and in the M-step, the frequency estimate is updated by maximizing the log-likelihood function obtained in the E-step. To facilitate recursive frequency estimation, an online EM algorithm is also developed based on the forward-only smoothing techniques. Furthermore, we show that the proposed method can be easily extended to deal with nonlinear system models and time-varying frequencies.

To solve the problems existing in the research of scene recognition, this paper studies a new convolutional neural network target detection model to achieve a better balance between the accuracy and speed of high-speed scene image recognition. First, aiming at the problem that the image is easy to be disturbed by impurities and poor quality in fine-grained image recognition, a preprocessing method based on the Canny edge detection is designed and the Canny operator is introduced to process the gray image. Second, the L2 regularization algorithm is used to optimize the basic network framework of the convolutional neural network, enhance the stability of the model in a complex environment, improve the generalization ability of the model, and improve the recognition accuracy of the algorithm to a certain extent. Finally, by collecting the campus environment datasets under different environmental conditions, the location recognition experiment and heat map visualization experiment are carried out. Experiments show that compared with the basic convolution neural network algorithm, the algorithm has better recognition performance and good generalization ability. The research of this study realizes the effective combination of multiframe convolution neural network and batch normalization algorithm and has a good practical effect on scene image recognition., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Pinhe Wang et al.)

Alzheimer's disease (AD), the most common type of dementia, is a progressive disease beginning with mild memory loss, possibly leading to loss of the ability to carry on a conversation and respond to environments. It can seriously affect a person's ability to carry out daily activities. Therefore, early diagnosis of AD is conducive to better treatment and avoiding further deterioration of the disease. Magnetic resonance imaging (MRI) has become the main tool for humans to study brain tissues. It can clearly reflect the internal structure of a brain and plays an important role in the diagnosis of Alzheimer's disease. MRI data is widely used for disease diagnosis. In this paper, based on MRI data, a method combining a 3D convolutional neural network and ensemble learning is proposed to improve the diagnosis accuracy. Then, a data denoising module is proposed to reduce boundary noise. The experimental results on ADNI dataset demonstrate that the model proposed in this paper improves the training speed of the neural network and achieves 95.2% accuracy in AD vs. NC (normal control) task and 77.8% accuracy in sMCI (stable mild cognitive impairment) vs. pMCI (progressive mild cognitive impairment) task in the diagnosis of Alzheimer's disease.

Objective: A number of studies have evaluated the efficacy and safety of fondaparinux versus low-molecular-weight heparin (LMWH) in patients with acute coronary syndrome (ACS), but the findings were not consistent across these studies., Methods: Electronic databases and article references were searched for studies that assessed fondaparinux versus LMWH in ACS patients., Results: Six studies met the inclusion criteria. There was a lower risk of major adverse cardiac events (MACE) with fondaparinux-based regimens both in randomized controlled trials (RCT; risk ratio, RR: 0.91, p = 0.04) and observational studies (RR: 0.85, p < 0.0001). Mortality decreased in fondaparinux-treated patients in RCT (RR: 0.84, p = 0.02), but not in observational studies (RR: 1.44, p = 0.64). For the analysis of myocardial infarction (MI), recurrent ischemia and stroke, none of the studies showed significant results. In addition, fondaparinux lowered the risk of major bleeding in RCT (RR: 0.62, p < 0.0001) and observational studies (RR: 0.65, p < 0.0001). The net clinical outcome also favored fondaparinux over LMWH in RCT (RR: 0.82, p < 0.0001) and observational studies (RR: 0.84, p < 0.0001)., Conclusions: Among ACS patients, a fondaparinux-based regimen presented advantages regarding MACE and major bleeding, and a net clinical benefit compared with LMWH, although the benefit is minimal regarding MACE. For death, MI, recurrent ischemia and stroke, fondaparinux has not shown significant benefits., (© 2015 S. Karger AG, Basel.)

A sensitive, simple and specific high performance liquid chromatography-electro spray ionization mass spectrometry method was developed for the determination of ilaprazole in the plasma of beagles administered via i.v. bolus doses of ilaprazole. The procedure employed buspirone as the internal standard and a simple protein precipitation step. The separation was achieved using a C18 column (100 mm x 2.1 mm, 5 microm) with a mobile phase consisting of water-methanol-acetonitrile (69:8:23, v/v/v) containing 0.1% (v/v) formic acid at a flow rate of 0.2 mL/min. The detection was accomplished by a mass spectrometer using selected ion monitoring (SIM) in positive mode. The linearity was from 5 microg/L to 10,000 microg/L with a sensitivity of 5 microg/L as the lower limit of quantification. The inter- and intra- day precisions were within 9.00%. The mean recoveries at three spiked levels were about 106% and the matrix effects were less than 142.0%. The method described above was successfully applied to analyze the beagle plasma samples of ilaprazole in a pharmacokinetic study. The area under the plasma concentration-time curve (AUC(0-infinity) of ilaprazole after i.v. doses of 0.2, 0.8 and 3.2 mg/kg were (2.4 x 10(4) +/- 3 x 10(3)), (8.8 x 10(4) +/- 1.6 x 10(4)) and (5.4 x 10(5) +/- 8 x 10(4)) microg/L x min, respectively. On the basis of AUC, the pharmacokinetic property of ilaprazole was proposed to be linear dynamics.