Reconfigurable intelligent surface (RIS) technology is emerging as a promising technique for performance enhancement for next-generation wireless networks. This paper investigates the physical layer security of an RIS-assisted multiple-antenna communication system in the presence of random spatially distributed eavesdroppers. The RIS-to-ground channels are assumed to experience Rician fading. Using stochastic geometry, exact distributions of the received signal-to-noise-ratios (SNRs) at the legitimate user and the eavesdroppers located according to a Poisson point process (PPP) are derived, and closed-form expressions for the secrecy outage probability (SOP) and the ergodic secrecy capacity (ESC) are obtained to provide insightful guidelines for system design. First, the secrecy diversity order is obtained as 2/α 2 , where α 2 denotes the path loss exponent of the RIS-to-ground links. Then, it is revealed that the secrecy performance is mainly affected by the number of RIS reflecting elements, N , and the impact of the number of transmit antennas and transmit power at the base station is marginal. In addition, when the locations of the randomly located eavesdroppers are unknown, deploying the RIS closer to the legitimate user rather than to the base station is shown to be more efficient. Moreover, it is also found that the density of randomly located eavesdroppers, λ e , has an additive effect on the asymptotic ESC performance given by log 2 (1/λ e ). Finally, numerical simulations are conducted to verify the accuracy of these theoretical observations.
Action recognition is essential in security monitoring, home care, and behavior analysis. Traditional solutions usually leverage particular devices, such as smart watches, infrared/visible cameras, etc. These methods may narrow the application areas due to the risk of privacy leakage, high equipment cost, and over/under-exposure. Using wireless signals for motion recognition can effectively avoid the above problems. However, the motion recognition technology based on Wi-Fi signals currently has some defects, such as low resolution caused by narrow signal bandwidth, poor environmental adaptability caused by the multi-path effect, etc., which make it hard for commercial applications. To solve the above problems, we first propose and implement a position adaptive motion recognition method based on Wi-Fi feature enhancement, which is composed of an enhanced Wi-Fi features module and an enhanced convolution Transformer network. Meanwhile, we improve the generalization ability in the signal processing stage to avoid building an extremely complex model and reduce the demand for system hardware. To verify the generalization of the method, we implement real-world experiments using 9300 network cards and the PicoScenes software platform for data acquisition and processing. By contrast with the baseline method using original channel state information(CSI) data, the average accuracy of our algorithm is improved by 14% in different positions and over 16% in different orientations. Meanwhile, our method has best performance with an accuracy of 90.33% compared with the existing models on public datasets WiAR and WiDAR.
Copper (Cu) is a global environmental pollutant that poses a serious threat to humans and ecosystems. Copper induces developmental neurotoxicity, but the underlying molecular mechanisms are unknown. Neurons are nonrenewable, and they are unable to mitigate the excessive accumulation of pathological proteins and organelles in cells, which can be ameliorated by autophagic degradation. In this study, we established an in vitro model of Cu2+-exposed (0, 15, 30, 60 and 120 μM) SH-SY5Y cells to explore the role of autophagy in copper-induced developmental neurotoxicity. The results showed that copper resulted in the reduction and shortening of neural synapses in differentiated cultured SH-SY5Y cells, a downregulated Wnt signaling pathway, and nuclear translocation of β-catenin. Exposure to Cu2+ increased autophagosome accumulation and autophagic flux blockage in terms of increased sequestosome 1 (p62/SQSTM1) and microtubule-associated protein 1 light chain 3B (LC3B) II/LC3BI expressions and inhibition of the phosphatidylinositol 3-kinase (PI3K)/Akt/mTOR pathway. Furthermore, copper induced apoptosis, characterized by increased expressions of Bcl2 X protein (Bax), caspase 3, and Poly (ADP-ribose) polymerase (PARP) and decreased expression of B-cell lymphoma 2 (Bcl2). Compared with the 120 μM Cu2+ exposure group alone, autophagy activator rapamycin pretreatment increased expression of Wnt and β-catenin nuclear translocation, decreased expression of LC3BII/LC3BI and p62, as well as upregulated expression of Bcl2 and downregulated expressions of caspase 3 and PARP. In contrast, after autophagy inhibitor chloroquine pretreatment, expressions of Wnt and β-catenin nuclear translocation were decreased, expression levels of LC3BII/LC3BI and p62 were upregulated, expression of Bcl2 was decreased, while expression levels of caspase 3, Bax, and PARP were increased. In conclusion, the study demonstrated that autophagosome accumulation and autophagic flux blockage were associated with copper-induced developmental neurotoxicity via the Wnt signaling pathway, which might deepen the understanding of the developmental neurotoxicity mechanism of environmental copper exposure.
“Element orders” is one of the most fundamental concepts in group theory. It plays an important role in research in group theory, which can be seen from the famous Burnside problem. Some well-known group theory specialists, such as B.H. Neumann, G. Higman, M. Suzuki and others, have studied the groups whose element orders are of the special values (see [1, 2, 3]). In 1981 Shi investigated the finite groups all of whose elements are of prime order except the identity element, and got the interesting result: The alternating group A5 can be characterized only by its element orders (see [4]). The above work was repeated in [5] since [4] was published in Chinese and not reviewed in “Mathematical Reviews”.
In this paper, we consider high order QAM orthogonal frequency-division multiplexing (OFDM) systems with insufficient cyclic prefix (CP) which will lead to intersymbol interference (ISI) and intercarrier interference (ICI) in the receiver. To cope with the error floor induced by ICI and ISI in one-tap equalization and iterative serial interference cancellation (ISIC), we develop a maximum likelihood (ML) based iterative grouping detection algorithm (ML-IG), which utilizes the correlation among the received signals on adjacent subcarriers to improve the detection accuracy. Since ML-IG for OFDM systems with high order QAM modulation is of significant complexity, detection network (DetNet) based iterative grouping detection algorithm (DetNet-IG) is designed to imitate ML-IG. Simulations show that both ML-IG and DetNet-IG can provide better BER performance than ISIC, and DetNet-IG exhibits distinguished robustness against channel model incongruity.
It has been reported that disordered Cu metabolism is associated with several neurodegenerative diseases, including Alzheimer's disease (AD) and Parkinson's disease (PD). However, the underlying mechanism is still unclear. In this study, 4-week-old male mice were exposed to Cu by free-drinking water for three months. Then, the effects of Cu on cognitive functions in mice were tested by Morris water maze tests, and the potential mechanisms were investigated by the ELISA, immunochemistry, TUNEL, and Western blot tests. It was found that Cu exacerbates learning and memory impairment, and leads to Cu-overload in the brain and urine of mice. The results showed that Cu induces neuronal degeneration and oxidative damage, promotes the expression of apoptosis-related protein Bax, cuproptosis-related proteins FDX1 and DLAT and the proteotoxic stress marker HSP70, and decreases Fe-S cluster proteins. In addition, Cu affects the pre-synaptic and post-synaptic regulatory mechanisms through inhibiting the expression of PSD-95 and SYP. Cu also suppresses phosphorylation levels in CREB and decreases the expression of BDNF and TrkB in the mouse hippocampus. In conclusion, Cu might mediate cuproptosis, damage synaptic plasticity and inhibit the CREB/BDNF pathway to cause cognitive dysfunction in mice.
Over-the-air computation (AirComp) integrates analog communication with task-oriented computation, serving as a key enabling technique for communication-efficient federated learning (FL) over wireless networks. However, owing to its analog characteristics, AirComp-enabled FL (AirFL) is vulnerable to both unintentional and intentional interference. In this paper, we aim to attain robustness in AirComp aggregation against interference via reconfigurable intelligent surface (RIS) technology to artificially reconstruct wireless environments. Concretely, we establish performance objectives tailored for interference suppression in wireless FL systems, aiming to achieve unbiased gradient estimation and reduce its mean square error (MSE). Oriented at these objectives, we introduce the concept of phase-manipulated favorable propagation and channel hardening for AirFL, which relies on the adjustment of RIS phase shifts to realize statistical interference elimination and reduce the error variance of gradient estimation. Building upon this concept, we propose two robust aggregation schemes of power control and RIS phase shifts design, both ensuring unbiased gradient estimation in the presence of interference. Theoretical analysis of the MSE and FL convergence affirms the anti-interference capability of the proposed schemes. It is observed that computation and interference errors diminish by an order of $\mathcal{O}\left(\frac{1}{N}\right)$ where $N$ is the number of RIS elements, and the ideal convergence rate without interference can be asymptotically achieved by increasing $N$. Numerical results confirm the analytical results and validate the superior performance of the proposed schemes over existing baselines.
Abstract Phthalates are a group of neurotoxicants with cognitive-disrupting potentials. Given the structural diversity of phthalates, the corresponding neurotoxicity is dramatically altered. To identify the potential contributions of different phthalates on the process of cognitive impairment, data of 836 elders from the NHANES 2011–2014 cycles were used. Survey-weighted logistic regression and principal component analysis-weighted quantile sum regression (PCA-WQSR) models were applied to estimate the independent and combined associations of 11 urinary phthalate metabolites with cognitive deficit [assessed by 4 tests: Immediate Recall (IR), Delayed Recall (DR), Animal Fluency (AF), and Digit Symbol Substitution test (DSST] and to identify the potential phthalate with high weight. Laboratory mice were further used to examine the effect of phthalates on cognitive function and to explore the potential mechanisms. In logistic regression models, MBzP was the only metabolite positively correlated with four tests, with ORs of 2.53 [quartile 3 (Q3)], 2.26 (Q3), 2.89 (Q4) and 2.45 (Q2), 2.82 (Q4) for IR, DR, AF and DSST respectively. In PCA-WQSR co-exposure models, low-molecular-weight (LMW) phthalates were the only PC positively linked to DSST deficit (OR: 1.93), which was further validated in WQSR analysis (WQS OR 7 − phthalates : 1.56 and WQS OR 8 − phthalates : 1.55); consistent with the results of logistic regression, MBzP was the dominant phthalate. In mice, butyl benzyl phthalate (BBP), the parent phthalate of MBzP, dose-dependently reduced cognitive function and disrupted hippocampal neurons. Additionally, the hippocampal transcriptome analysis identified 431 differential expression genes, among which most were involved in inhibiting the neuroactive ligand‒receptor interaction pathway and activating the cytokine‒cytokine receptor interaction pathway. Our study indicates the critical role of BBP in the association of phthalates and cognitive deficits among elderly individuals, which might be speculated that BBP could disrupt hippocampal neurons, activate neuroinflammation and inhibit neuroactive receptors. Our findings provide new insight into the cognitive-disrupting potential of BBP.
Robots have been increasingly used in production line and real life, such as warehousing, logistics, security, smart home and so on. In most applications, localization is always one of the most basic tasks of the robot. To acquire the object location, existing work mainly relies on computer vision. Such methods encounter many problems in practice, such as high computational complexity, large influence by light conditions, and heavy crafting of pre-training. These problems have become one of the key factors that constrains the precise automation of robots. This paper proposes an RFID-based robot navigation and target localization scheme, which is easy to deploy, low cost, and can work in non-line-of-sight scenarios. The main contributions of this paper are as follows: 1. We collect the phase variation of the tag by a rotating reader antenna, and calculate the azimuth of the tag relative to the antenna by the channel similarity weighted average method. Then, the location of the tag is determined by the AoA method. 2. Based on the theory of tag equivalent circuit, antenna radiation field, and cylindrical symmetry oscillator mutual impedance, the phenomenon of RSS weakening of adjacent tags is analyzed. Based on this phenomenon, we achieve accurate target localization and multi-target relative localization by utilizing region segmentation and dynamic time warping algorithms. 3. The proposed scheme is lightweight and low-cost. We built a prototype system using commercial UHF RFID readers and passive tags, and conduct extensive experiments. The experimental results show that the model can effectively achieve the precise location of the robot and the object with an average error of 27 cm and 2 cm.
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