Fabrication of Ag2O/Ag decorated ZnAl-layered double hydroxide with enhanced visible light photocatalytic activity for tetracycline degradation.

The photocatalytic performance of layered double hydroxides (LDH) is usually confined to the slow interface mobility and high recombination rate of photogenerated electron-hole pairs in material. To overcome the low photocatalytic efficiency, novel Ag2O/Ag decorated LDH (LDH-Ag2O/Ag) was successfully synthesized by depositing Ag2O on the surface of LDH and then converted to Ag° nanoparticles in the right position after heat treatment. The as-synthesized LDH-Ag2O/Ag composites were characterized by Powder X-ray diffraction (XRD), Scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), UV-visible diffuse reflectance spectra (UV-vis DRS), photoluminescence spectra (PL) and transient photocurrent (TPC) analysis. Compared with virgin LDH, the photocatalytic activities of LDH-Ag2O/Ag composites were enhanced significantly. The optimum photocatalytic efficiency of LDH-Ag10 (0.0184 min-1) was nearly 46 times higher than that of virgin LDH (0.0004 min-1). The result of active species trapping experiments indicated that •OH, h+, and •O2- have an effect on the TC degradation, where •OH played the predominant role during the photocatalytic process. The possible photocatalytic mechanisms involving the charge transfer pathway and reactive species generation during the process of TC degradation were also discussed. The improved photocatalytic activity of LDH-Ag2O/Ag could be attributed to the synergetic effect between LDH and Ag2O/Ag that extended visible light range and reduced photogenerated charge carriers recombination.

Reliability Evaluation for Clustered WSNs under Malware Propagation.

We consider a clustered wireless sensor network (WSN) under epidemic-malware propagation conditions and solve the problem of how to evaluate its reliability so as to ensure efficient, continuous, and dependable transmission of sensed data from sensor nodes to the sink. Facing the contradiction between malware intention and continuous-time Markov chain (CTMC) randomness, we introduce a strategic game that can predict malware infection in order to model a successful infection as a CTMC state transition. Next, we devise a novel measure to compute the Mean Time to Failure (MTTF) of a sensor node, which represents the reliability of a sensor node continuously performing tasks such as sensing, transmitting, and fusing data. Since clustered WSNs can be regarded as parallel-serial-parallel systems, the reliability of a clustered WSN can be evaluated via classical reliability theory. Numerical results show the influence of parameters such as the true positive rate and the false positive rate on a sensor node's MTTF. Furthermore, we validate the method of reliability evaluation for a clustered WSN according to the number of sensor nodes in a cluster, the number of clusters in a route, and the number of routes in the WSN.

A novel BA complex network model on color template matching.

A novel BA complex network model of color space is proposed based on two fundamental rules of BA scale-free network model: growth and preferential attachment. The scale-free characteristic of color space is discovered by analyzing evolving process of template's color distribution. And then the template's BA complex network model can be used to select important color pixels which have much larger effects than other color pixels in matching process. The proposed BA complex network model of color space can be easily integrated into many traditional template matching algorithms, such as SSD based matching and SAD based matching. Experiments show the performance of color template matching results can be improved based on the proposed algorithm. To the best of our knowledge, this is the first study about how to model the color space of images using a proper complex network model and apply the complex network model to template matching.

A game-theoretic response strategy for coordinator attack in wireless sensor networks.

The coordinator is a specific node that controls the whole network and has a significant impact on the performance in cooperative multihop ZigBee wireless sensor networks (ZWSNs). However, the malicious node attacks coordinator nodes in an effort to waste the resources and disrupt the operation of the network. Attacking leads to a failure of one round of communication between the source nodes and destination nodes. Coordinator selection is a technique that can considerably defend against attack and reduce the data delivery delay, and increase network performance of cooperative communications. In this paper, we propose an adaptive coordinator selection algorithm using game and fuzzy logic aiming at both minimizing the average number of hops and maximizing network lifetime. The proposed game model consists of two interrelated formulations: a stochastic game for dynamic defense and a best response policy using evolutionary game formulation for coordinator selection. The stable equilibrium best policy to response defense is obtained from this game model. It is shown that the proposed scheme can improve reliability and save energy during the network lifetime with respect to security.

On Secrecy Performance in D-MoSK-Based 3-D Diffusive Molecular Communication System.

This paper studies the secrecy performance in a 3-D diffusive molecular communication system with the general depleted molecule shift keying (D-MoSK) modulation, where a point transmitter Alice transmits through diffusion multiple types of molecules modulation to a legitimate absorbing receiver Bob, suffering the eavesdropping from an absorbing eavesdropper Eve. We first develop a solid theoretical framework to determine the probabilistic distributions for the number of molecules absorbed by Bob and Eve, respectively. Based on the results, we then derive the average symbol error rate (SER) as well as the mutual information of Alice-Bob and Alice-Eve, and further apply the Shannon theory to determine the secrecy capacity of Alice-Bob transmission. We also develop the closed-form results for the optimal detection threshold at Bob to achieve the secrecy capacity, and thus devise a complete algorithm for secrecy capacity maximization. Finally, we provide numerical results to illustrate the secrecy performance in the concerned system.

Select Your Own Counterparts: Self-Supervised Graph Contrastive Learning With Positive Sampling.

Contrastive learning (CL) has emerged as a powerful approach for self-supervised learning. However, it suffers from sampling bias, which hinders its performance. While the mainstream solutions, hard negative mining (HNM) and supervised CL (SCL), have been proposed to mitigate this critical issue, they do not effectively address graph CL (GCL). To address it, we propose graph positive sampling (GPS) and three contrastive objectives. The former is a novel learning paradigm designed to leverage the inherent properties of graphs for improved GCL models, which utilizes four complementary similarity measurements, including node centrality, topological distance, neighborhood overlapping, and semantic distance, to select positive counterparts for each node. Notably, GPS operates without relying on true labels and enables preprocessing applications. The latter aims to fuse positive samples and enhance representative selection in the semantic space. We release three node-level models with GPS and conduct extensive experiments on public datasets. The results demonstrate the superiority of GPS over state-of-the-art (SOTA) baselines and debiasing methods. In addition, the GPS has also been proven to be versatile, adaptive, and flexible.

Event-based consensus control for multi-agent systems against joint sensor and actuator attacks.

The paper focuses on the observer-based consensus control issue for a class of discrete-time multi-agent systems suffering from the joint actuator and sensor attacks. An event-triggered communication rule with the adaptive threshold update is introduced to reduce the communication burden. A PI-type controller with a finite-time window integral loop is constructed to achieve bounded consensus in the mean-square sense (BCMS). With the help of common properties of Laplace matrices, the closed-loop multi-agent system is converted into an easy-to-analyze pattern. In light of such a pattern, a sufficient condition is derived to realize the desired consensus by the stochastic analysis. Furthermore, the expected gains of the controller and the observer are determined by resorting to matrix inequalities in combination with the cone complementarity linearization algorithm. Finally, a numerical example and a simulation of the platooning vehicle are proposed to illustrate the effectiveness of the proposed control scheme.

Time-Aware Missing Healthcare Data Prediction Based on ARIMA Model.

Healthcare uses state-of-the-art technologies (such as wearable devices, blood glucose meters, electrocardiographs), which results in the generation of large amounts of data. Healthcare data is essential in patient management and plays a critical role in transforming healthcare services, medical scheme design, and scientific research. Missing data is a challenging problem in healthcare due to system failure and untimely filing, resulting in inaccurate diagnosis treatment anomalies. Therefore, there is a need to accurately predict and impute missing data as only complete data could provide a scientific and comprehensive basis for patients, doctors, and researchers. However, traditional approaches in this paradigm often neglect the effect of the time factor on forecasting results. This paper proposes a time-aware missing healthcare data prediction approach based on the autoregressive integrated moving average (ARIMA) model. We combine a truncated singular value decomposition (SVD) with the ARIMA model to improve the prediction efficiency of the ARIMA model and remove data redundancy and noise. Through the improved ARIMA model, our proposed approach (named MHDP SVD_ARIMA) can capture underlying pattern of healthcare data changes with time and accurately predict missing data. The experiments conducted on the WISDM dataset show that MHDP SVD_ARIMA approach is effective and efficient in predicting missing healthcare data.

Non-Isothermal Decomposition Kinetic of Polypropylene/Hydrotalcite Composite.

P3O5-10 pillared Mg/Al hydrotalcite (HTs) as a functional fire-retarding filler was successfully prepared by impregnation-reconstruction, where the HTs was used to prepare polypropylene (PP) and HTs composite (PP/HTs). Thermal decomposition was crucial for correctly identifying the thermal behavior for the PP/HTs, and studied using thermogravimetry (TG) at different heating rates. Based on single TG curves and Málek method, as well as 41 mechanism functions, the thermal decompositions of the PP/HTs composite and PP in nitrogen atmosphere were studied under non-isothermal conditions. The mechanism functions of the thermal decomposition reactions for the PP/HTs composite and PP were separately "chemical reaction F3" and "phase boundary reaction R2," which were also in good agreement with corresponding experimental data. It was found that the addition of the HTs increased the apparent activation energy Ea of the PP/HTs comparing to the PP, which improved the thermal stability of the polypropylene. A difference in the set of kinetic and thermodynamic parameters was also observed between the PP/HTs and PP, particularly with respect to lower ΔS≠ value assigned to higher thermal stability of the PP/HTs composite.