Notes

Higher stress of cerebral whitened make any difference lesion in Being unfaithful Asian urban centers.
COVID-19-related information on social media is overabundant and sometimes questionable, resulting in an "infodemic" during the pandemic. While previous studies suggest social media usage increases the risk of developing anxiety symptoms, how induced anxiety affects attitudes and behaviors is less discussed, let alone during a global pandemic. Little is known about the relationship between older adults using social media during a pandemic and their anxiety, their attitudes toward social trust in information, and behaviors to avoid contracting COVID-19.

The goal of this study was to investigate the associations between using social media for COVID-19-related information and anxiety symptoms as well as the mediation effect of anxiety symptoms on social trust in information and COVID-safe behaviors among older adults.

A cross-sectional telephone survey was conducted in Hong Kong between May and August 2020. A rapid warm-call protocol was developed to train social workers and volunteers from participant nonon online. The negligible impact on COVID-safe behaviors suggested that social media may have caused more confusion than consolidating a consistent effort against the pandemic. Media literacy education is recommended to promote critical evaluation of COVID-19-related information and responsible sharing among older adults.[This corrects the article DOI 10.2196/24175.].
Over the past decade, there has been an increasing secular trend in the number of studies on social media and health.

The purpose of this cross-sectional study was to examine the content and characteristics of TikTok videos that are related to an important aspect of community mitigation-the use of masks as a method for interrupting the transmission of SARS-CoV-2.

In total, 100 trending videos with the hashtag #WearAMask (ie, a campaign on TikTok), along with 32 videos that were posted by the World Health Organization (WHO) and involved masks in any way (ie, all related WHO videos at the time of this study), were included in our sample. We collected the metadata of each post, and created content categories based on fact sheets that were provided by the WHO and the US Centers for Disease Control and Prevention. We used these fact sheets to code the characteristics of mask use.

Videos that were posted on TikTok and had the hashtag #WearAMask garnered almost 500 million views, and videos that were posted he platform's incredible reach, TikTok has great potential in conveying important public health messages to various segments of the population.In this article, we apply a high-order control to a dynamical system with uncertainty. There are two characteristics. NSC697923 First, the uncertain part, which is time-varying but bounded, is described in a fuzzy aspect. Specifically, the uncertainty lies within a fuzzy set and the bound is regarded as a fuzzy number. Second, the systems are uniformly bounded and uniformly ultimately bounded with a deterministic controller based on the Lyapunov theory. To obtain better system performance and lower control input, we apply the noncooperative game theory to optimize the parameters by establishing a Nash game. Then, the D-operation is proposed for the uncertainty related to fuzzy numbers. Finally, we perform the numerical simulations of the steering-by-wire system for verification.Cross-manifold clustering is an extreme challenge learning problem. Since the low-density hypothesis is not satisfied in cross-manifold problems, many traditional clustering methods failed to discover the cross-manifold structures. In this article, we propose multiple flat projections clustering (MFPC) for cross-manifold clustering. In our MFPC, the given samples are projected into multiple localized flats to discover the global structures of implicit manifolds. Thus, the intersected clusters are distinguished in various projection flats. In MFPC, a series of nonconvex matrix optimization problems is solved by a proposed recursive algorithm. Furthermore, a nonlinear version of MFPC is extended via kernel tricks to deal with a more complex cross-manifold learning situation. The synthetic tests show that our MFPC works on the cross-manifold structures well. Moreover, experimental results on the benchmark datasets and object tracking videos show excellent performance of our MFPC compared with some state-of-the-art manifold clustering methods.This article addresses the problem of fault-tolerant consensus control of a general nonlinear multiagent system subject to actuator faults and disturbed and faulty networks. By using neural network (NN) and adaptive control techniques, estimations of unknown state-dependent boundaries of nonlinear dynamics and actuator faults, which can reflect the worst impacts on the system, are first developed. A novel NN-based adaptive observer is designed for the observation of faulty transformation signals in networks. On the basis of the NN-based observer and adaptive control strategies, fault-tolerant consensus control schemes are designed to guarantee the bounded consensus of the closed-loop multiagent system with disturbed and faulty networks and actuator faults. The validity of the proposed adaptively distributed consensus control schemes is demonstrated by a multiagent system composed of five nonlinear forced pendulums.Object detection has made enormous progress and has been widely used in many applications. However, it performs poorly when only limited training data is available for novel classes that the model has never seen before. Most existing approaches solve few-shot detection tasks implicitly without directly modeling the detectors for novel classes. In this article, we propose GenDet, a new meta-learning-based framework that can effectively generate object detectors for novel classes from few shots and, thus, conducts few-shot detection tasks explicitly. The detector generator is trained by numerous few-shot detection tasks sampled from base classes each with sufficient samples, and thus, it is expected to generalize well on novel classes. An adaptive pooling module is further introduced to suppress distracting samples and aggregate the detectors generated from multiple shots. Moreover, we propose to train a reference detector for each base class in the conventional way, with which to guide the training of the detector generator. The reference detectors and the detector generator can be trained simultaneously. Finally, the generated detectors of different classes are encouraged to be orthogonal to each other for better generalization. The proposed approach is extensively evaluated on the ImageNet, VOC, and COCO data sets under various few-shot detection settings, and it achieves new state-of-the-art results.Second-order pooling has proved to be more effective than its first-order counterpart in visual classification tasks. However, second-order pooling suffers from the high demand for a computational resource, limiting its use in practical applications. In this work, we present a novel architecture, namely a detachable second-order pooling network, to leverage the advantage of second-order pooling by first-order networks while keeping the model complexity unchanged during inference. Specifically, we introduce second-order pooling at the end of a few auxiliary branches and plug them into different stages of a convolutional neural network. During the training stage, the auxiliary second-order pooling networks assist the backbone first-order network to learn more discriminative feature representations. When training is completed, all auxiliary branches can be removed, and only the backbone first-order network is used for inference. Experiments conducted on CIFAR-10, CIFAR-100, and ImageNet data sets clearly demonstrated the leading performance of our network, which achieves even higher accuracy than second-order networks but keeps the low inference complexity of first-order networks.The neurophysiological characteristics of sustained attention states are unclear in discrete multi-finger force control tasks. In this study, we developed an immersive visuo-haptic task for conducting stimulus-response measurements. Visual cues were randomly provided to signify the required amplitude and tolerance of fingertip force. Participants were required to respond to the visual cues by pressing force transducers using their fingertips. Response time variation was taken as a behavioral measure of sustained attention states during the task. 50% low-variability trials were classified as the optimal state and the other high-variability trials were classified as the suboptimal state using z-scoring over time. A 64-channel electroencephalogram (EEG) acquisition system was used to collect brain activities during the tasks. The haptics-elicited potential amplitude at 20 ~ 40 ms in latency and over the frontal-central region significantly decreased in the optimal state. Furthermore, the alpha-band power in the spectra of 8 ~ 13 Hz was significantly suppressed in the frontal-central, right temporal, and parietal regions in the optimal state. Taken together, we have identified neuroelectrophysiological features that were associated with sustained attention during multi-finger force control tasks, which would be potentially used in the development of closed-loop attention detection and training systems exploiting haptic interaction.Epilepsy is one of the most chronic brain disorder recorded from since 2000 BC. Almost one-third of epileptic patients experience seizures attack even with medicated treatment. The menace of SUDEP (Sudden unexpected death in epilepsy) in an adult epileptic patient is approximately 8-17% more and 34% in a children epileptic patient. The expert neurologist manually analyses the Electroencephalogram (EEG) signals for epilepsy diagnosis. The non-stationary and complex nature of EEG signals this task more error-prone, time-consuming and even expensive. Hence, it is essential to develop automatic epilepsy detection techniques to ensure an appropriate identification and treatment of this disease. Nowadays, graph-theory has been considered as a prominent approach in the neuroscience field. The network-based approach characterizes a hidden sight of brain activity and brain-behavior mapping. The graph-theory not even helps to understand the underlying dynamics of EEG signals at microscopic, mesoscopic, and macroscopic level but also provide the correlation among them. This paper provides a review report about graph-theory based automated epilepsy detection methods. Furthermore, it will assist the expert's neurologist and researchers with the information of complex network-based epilepsy detection and aid the technician for developing an intelligent system that improving the diagnosis of epilepsy disorder.Phytopathogens are responsible for huge losses in the agriculture sector. Amongst them, fungal phytopathogen is quite difficult to control. Many chemicals are available in the market, claiming the high activity against them. However, the development of resistance by the fungal pathogen is the main concern to overcome their menace. Nanotechnology-based products can be a potential alternative to conventional fungicides. Amongst various nanoparticles, Copper nanoparticles (CuNPs) are appearing to be a promising antifungal candidate. It can be synthesized by various methods, but the myco-fabrication appears to be an environmental-friendly approach. Hence, the present study is an attempt to synthesize CuNPs using Aspergillus flavus. The myco-fabricated CuNPs were characterized by UV spectrophotometer, Fourier transform infrared spectroscopy (FTIR), Nanoparticles tracking and analysis system (NTA), Transmission Electron Microscopy (TEM), X-ray diffraction (XRD) and Zeta potential measurement. Myco-fabricated CuNPs showed maximum absorbance at 602 nm and particle size ranging 5-12 nm with the least average size of 8 nm with spherical shape and moderate stability.
My Website: https://www.selleckchem.com/products/nsc697923.html