Performance of Spatial Diversity DCO-OFDM in a Weak Turbulence Underwater Visible Light Communication Channel

The performance of underwater visible light communication (UVLC) system is severely affected by absorption, scattering and turbulence. In this article, we study the performance of spectral efficient DC-biased optical orthogonal frequency division multiplexing (DCO-OFDM) in combination with the transceiver spatial diversity in turbulence channel. Based on the approximation of the weighted sum of lognormal random variables (RVs), we derived a theoretical exact bit error rate (BER) for DCO-OFDM systems with spatial diversity. The simulation results are compared with the analytical prediction, confirming the validity of the analysis. It is shown that spatial diversity can effectively reduce the turbulence-induced channel fading. The obtained results can be useful for designing, predicting, and evaluating the DCO-OFDM UVLC system in a weak oceanic turbulence condition

Rigidity theorems of spacelike entire self-shrinking graphs in the pseudo-Euclidean space

In this paper, we firstly establish a new volume growth estimate for spacelike entire graphs in the pseudo-Euclidean space $\mathbb{R}^{m+n}_n$. Then by using this volume growth estimate and the Co-Area formula, we prove various rigidity results for spacelike entire self-shrinking graphs

Ergodic Capacity and Error Performance of Spatial Diversity UWOC Systems over Generalized Gamma Turbulence Channels

In this paper, we study the ergodic capacity (EC) and average bit error rate (BER) of spatial diversity underwater wireless optical communications (UWOC) over the generalized gamma (GG) fading channels using quadrature amplitude modulation (QAM) direct current-biased optical orthogonal frequency division multiplexing (DCO-OFDM). We derive closed-form expressions of the EC and BER for the spatial diversity UWOC with the equal gain combining (EGC) at receivers based on the approximation of the sum of independent identical distributed (i.i.d) GG random variables (RVs). Numerical results of EC and BER for QAM DCO-OFDM spatial diversity systems over GG fading channels are presented. The numerical results are shown to be closely matched by the Monte Carlo simulations, verifying the analysis. The study clearly shows the adverse effect of turbulence on the EC & BER and advantage of EGC to overcome the turbulence effect

A Novel Self-Supervised Learning-Based Anomaly Node Detection Method Based on an Autoencoder in Wireless Sensor Networks

Due to the issue that existing wireless sensor network (WSN)-based anomaly detection methods only consider and analyze temporal features, in this paper, a self-supervised learning-based anomaly node detection method based on an autoencoder is designed. This method integrates temporal WSN data flow feature extraction, spatial position feature extraction and intermodal WSN correlation feature extraction into the design of the autoencoder to make full use of the spatial and temporal information of the WSN for anomaly detection. First, a fully connected network is used to extract the temporal features of nodes by considering a single mode from a local spatial perspective. Second, a graph neural network (GNN) is used to introduce the WSN topology from a global spatial perspective for anomaly detection and extract the spatial and temporal features of the data flows of nodes and their neighbors by considering a single mode. Then, the adaptive fusion method involving weighted summation is used to extract the relevant features between different models. In addition, this paper introduces a gated recurrent unit (GRU) to solve the long-term dependence problem of the time dimension. Eventually, the reconstructed output of the decoder and the hidden layer representation of the autoencoder are fed into a fully connected network to calculate the anomaly probability of the current system. Since the spatial feature extraction operation is advanced, the designed method can be applied to the task of large-scale network anomaly detection by adding a clustering operation. Experiments show that the designed method outperforms the baselines, and the F1 score reaches 90.6%, which is 5.2% higher than those of the existing anomaly detection methods based on unsupervised reconstruction and prediction. Code and model are available at https://github.com/GuetYe/anomaly_detection/GLS

LDPC-Coded CAP with Spatial Diversity for UVLC Systems over Generalized-Gamma Fading Channel

In this paper, low-density parity-check (LDPC)-coded carrierless amplitude and phase (CAP) modulation with spatial diversity is proposed to mitigate turbulence-induced fading in an underwater visible-light communication (UVLC) channel. Generalized-gamma (GG) distribution was used to model the fading, as this model is valid for weak- and strong-turbulence regimes. On the basis of the characteristic function (CHF) of GG random variables, we derived an approximated bit-error rate (BER) for the CAP modulation scheme with spatial diversity and equal-gain combining (EGC). Furthermore, we simulated the performance of the CAP system with diversity and LDPC for various turbulence conditions and validated the analysis. Obtained results showed that the combination of LDPC and spatial diversity is effective in mitigating turbulence-induced fading, especially when turbulence strength is strong

Nonlinear Gossip Algorithms for Wireless Sensor Networks

We study some nonlinear gossip algorithms for wireless sensor networks. Firstly, two types of nonlinear single gossip algorithms are proposed. By using Lyapunov theory, Lagrange mean value theorem, and stochastic Lasalle’s invariance principle, we prove that the nonlinear single gossip algorithms can converge to the average of initial states with probability one. Secondly, two types of nonlinear multigossip algorithms are also presented and the convergence is proved by the same methods. Finally, computer simulation is also given to show the validity of the theoretical results

A novel long non-coding RNA, AC012456.4, as a valuable and independent prognostic biomarker of survival in oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) is a major malignant cancer of the head and neck. Long non-coding RNAs (lncRNAs) have emerged as critical regulators during the development and progression of cancers. This study aimed to identify a lncRNA-related signature with prognostic value for evaluating survival outcomes and to explore the underlying molecular mechanisms of OSCC. Associations between overall survival (OS), disease-free survival (DFS) and candidate lncRNAs were evaluated by Kaplan–Meier survival analysis and univariate and multivariate Cox proportional hazards regression analyses. The robustness of the prognostic significance was shown via the Gene Expression Omnibus (GEO) database. A total of 2,493 lncRNAs were differentially expressed between OSCC and control samples (fold change >2, p < 0.05). We used Kaplan–Meier survival analysis to identify 21 lncRNAs for which the expression levels were associated with OS and DFS of OSCC patients (p < 0.05) and found that down-expression of lncRNA AC012456.4 especially contributed to poor DFS (p = 0.00828) and OS (p = 0.00987). Furthermore, decreased expression of AC012456.4 was identified as an independent prognostic risk factor through multivariate Cox proportional hazards regression analyses (DFS: p = 0.004, hazard ratio (HR) = 0.600, 95% confidence interval(CI) [0.423–0.851]; OS: p = 0.002, HR = 0.672, 95% CI [0.523–0.863). Gene Set Enrichment Analysis (GSEA) indicated that lncRNA AC012456.4 were significantly enriched in critical biological functions and pathways and was correlated with tumorigenesis, such as regulation of cell activation, and the JAK-STAT and MAPK signal pathway. Overall, these findings were the first to evidence that AC012456.4 may be an important novel molecular target with great clinical value as a diagnostic, therapeutic and prognostic biomarker for OSCC patients

Research Progress in the Effect of Glycosylation on Functional Properties and Allergenicity of Dairy Products

In recent years, milk allergy has attracted more and more attention because of its increasing incidence and in-depth research on it. The glycosylation modification of milk proteins not only can improve the functional properties of dairy products, but also reduce the allergenicity of dairy products by destroying or shielding the epitope of allergen proteins. This modification has found applications in various fields such as food flavor, antioxidants and carriers. This paper introduces common glycosylation reactions in the dairy industry, discusses their advantages and disadvantages and the pattern of changes in functional properties and allergenicity of milk proteins after glycosylation, and summarizes the methods to avoid the negative effects of glycosylation reactions. This review provides a reference for the research, development and application of glycosylation modification in milk allergy and dairy processing