Adaptive filtering-based multi-innovation gradient algorithm for input nonlinear systems with autoregressive noise

In this paper, by means of the adaptive filtering technique and the multi-innovation identification theory, an adaptive filtering-based multi-innovation stochastic gradient identification algorithm is derived for Hammerstein nonlinear systems with colored noise. The new adaptive filtering configuration consists of a noise whitening filter and a parameter estimator. The simulation results show that the proposed algorithm has higher parameter estimation accuracies and faster convergence rates than the multi-innovation stochastic gradient algorithm for the same innovation length. As the innovation length increases, the filtering-based multi-innovation stochastic gradient algorithm gives smaller parameter estimation errors than the recursive least squares algorithm

A deep error correction network for compressed sensing MRI

Background CS-MRI (compressed sensing for magnetic resonance imaging) exploits image sparsity properties to reconstruct MRI from very few Fourier k-space measurements. Due to imperfect modelings in the inverse imaging, state-of-the-art CS-MRI methods tend to leave structural reconstruction errors. Compensating such errors in the reconstruction could help further improve the reconstruction quality. Results In this work, we propose a DECN (deep error correction network) for CS-MRI. The DECN model consists of three parts, which we refer to as modules: a guide, or template, module, an error correction module, and a data fidelity module. Existing CS-MRI algorithms can serve as the template module for guiding the reconstruction. Using this template as a guide, the error correction module learns a CNN (convolutional neural network) to map the k-space data in a way that adjusts for the reconstruction error of the template image. We propose a deep error correction network. Our experimental results show the proposed DECN CS-MRI reconstruction framework can considerably improve upon existing inversion algorithms by supplementing with an error-correcting CNN. Conclusions In the proposed a deep error correction framework, any off-the-shelf CS-MRI algorithm can be used as template generation. Then a deep neural network is used to compensate reconstruction errors. The promising experimental results validate the effectiveness and utility of the proposed framework

Characterizing the Penumbras of White Matter Hyperintensities and Their Associations With Cognitive Function in Patients With Subcortical Vascular Mild Cognitive Impairment

Normal-appearing white matter (NAWM) surrounding white matter hyperintensities (WMHs), frequently known as the WMH penumbra, is associated with subtle white matter injury and has a high risk for future conversion to WMHs. The goal of this study was to define WMH penumbras and to further explore whether the diffusion and perfusion parameters of these penumbras could better reflect cognitive function alterations than WMHs in subjects with subcortical vascular mild cognitive impairment (svMCI). Seventy-three svMCI subjects underwent neuropsychological assessments and 3T MRI scans, including diffusion tensor imaging (DTI) and arterial spin labeling (ASL). To determine the extent of cerebral blood flow (CBF) and DTI penumbras. A NAWM layer mask was generated for periventricular WMHs (PVWMHs) and deep WMHs (DWMHs) separately. Mean values of CBF, fractional anisotropy (FA), mean diffusivity (MD) within the WMHs and their corresponding NAWM layer masks were computed and compared using paired t-tests. Pearson's partial correlations were used to assess the relations of the mean CBF, FA, and MD values within the corresponding penumbras with composite z-scores of global cognition and four cognitive domains controlling for age, sex, and education. For both PVWMHs and DWMHs, the CBF penumbras were wider than the DTI penumbras. Only the mean FA value of the PVWMH-FA penumbra was correlated with the composite z-scores of global cognition before correction (r = 0.268, p = 0.024), but that correlation did not survive after correcting the p-value for multiple comparisons. Our findings showed extensive white matter perfusion disturbances including white matter tissue, both with and without microstructural alterations. The imaging parameters investigated, however, did not correlate to cognition

Resting-State Activity of Prefrontal-Striatal Circuits in Internet Gaming Disorder: Changes With Cognitive Behavior Therapy and Predictors of Treatment Response

Cognitive behavior therapy (CBT) is effective for the treatment of Internet gaming disorder (IGD). However, the mechanisms by which CBT improves IGD-related clinical symptoms remain unknown. This study aimed to discover the therapeutic mechanism of CBT in IGD subjects using resting-state functional magnetic resonance imaging (rsfMRI). Twenty-six IGD subjects and 30 matched healthy controls (HCs) received rsfMRI scan and clinical assessments; 20 IGD subjects completed CBT and then were scanned again. The amplitude of low-frequency (ALFF) values and the functional connectivity (FC) between the IGD group and the HC group were compared at baseline, as well as the ALFF values and FC before and after the CBT in the IGD group. Prior to treatment, the IGD group exhibited significantly increased ALFF values in the bilateral putamen, the right medial orbitofrontal cortex (OFC), the bilateral supplementary motor area (SMA), the left postcentral gyrus, and the left anterior cingulate (ACC) compared with the HC group. The HC group showed significantly increased FC values between the left medial OFC and the putamen compared with the IGD group, the FC values of IGD group were negatively associated with the BIS-11 scores before treatment. After the CBT, the weekly gaming time was significantly shorter, and the CIAS and BIS-II scores were significantly lower. The ALFF values in the IGD subjects significantly decreased in the left superior OFC and the left putamen, and the FC between them significantly increased after the CBT. The degree of the FC changes (ΔFC/Pre−FC) was positively correlated with the scale of the CIAS scores changes (ΔCIAS/Pre−CIAS) in the IGD subjects. CBT could regulate the abnormal low-frequency fluctuations in prefrontal-striatal regions in IGD subjects and could improve IGD-related symptoms. Resting-state alternations in prefrontal-striatal regions may reveal the therapeutic mechanism of CBT in IGD subjects

Highly Selective Synthesis of Chlorophenols under Microwave Irradiation

Oxychlorination of various phenols is finished in 60 minutes with high efficiency and perfect selectivity under microwave irradiation. These reactions adopt copper(II) chloride (CuCl2) as the catalyst and hydrochloric acid as chlorine source instead of expensive and toxic ones. Oxychlorination of phenols substituted with electron donating groups (methyl, methoxyl, isopropyl, etc.) at ortho- and meta-positions is accomplished with higher conversion rates, lower reaction time, and excellent selectivity. A proposed reaction mechanism is deduced; one electron transfers from CuCl2 to phenol followed by the formation of tautomeric radical that can be rapidly captured by chlorine atom and converts into para-substituted product

Recent Development of Copper-Based Nanozymes for Biomedical Applications

Copper (Cu), an indispensable trace element within the human body, serving as an intrinsic constituent of numerous natural enzymes, carrying out vital biological functions. Furthermore, nanomaterials exhibiting enzyme-mimicking properties, commonly known as nanozymes, possess distinct advantages over their natural enzyme counterparts, including cost-effectiveness, enhanced stability, and adjustable performance. These advantageous attributes have captivated the attention of researchers, inspiring them to devise various Cu-based nanomaterials, such as copper oxide, Cu metal-organic framework, and CuS, and explore their potential in enzymatic catalysis. This comprehensive review encapsulates the most recent advancements in Cu-based nanozymes, illuminating their applications in the realm of biochemistry. Initially, it is delved into the emulation of typical enzyme types achieved by Cu-based nanomaterials. Subsequently, the latest breakthroughs concerning Cu-based nanozymes in biochemical sensing, bacterial inhibition, cancer therapy, and neurodegenerative diseases treatment is discussed. Within this segment, it is also explored the modulation of Cu-based nanozyme activity. Finally, a visionary outlook for the future development of Cu-based nanozymes is presented

Estimation of Real-World Fuel Consumption Rate of Light-Duty Vehicles Based on the Records Reported by Vehicle Owners

Private vehicle travel is the most basic mode of transportation, so that an effective way to control the real-world fuel consumption rate of light-duty vehicles plays a vital role in promoting sustainable economic growth as well as achieving a green low-carbon society. Therefore, the factors impacting individual carbon emissions must be elucidated. This study builds five different models to estimate the real-world fuel consumption rate of light-duty vehicles in China. The results reveal that the light gradient boosting machine (LightGBM) model performs better than the linear regression, naïve Bayes regression, neural network regression, and decision tree regression models, with a mean absolute error of 0.911 L/100 km, a mean absolute percentage error of 10.4%, a mean square error of 1.536, and an R-squared (R2) value of 0.642. This study also assesses a large pool of potential factors affecting real-world fuel consumption, from which the three most important factors are extracted, namely, reference fuel-consumption-rate value, engine power, and light-duty vehicle brand. Furthermore, a comparative analysis reveals that the vehicle factors with the greatest impact are the vehicle brand, engine power, and engine displacement. The average air pressure, average temperature, and sunshine time are the three most important climate factors