Power Allocation Strategies for Secure Spatial Modulation

In this paper, power allocation (PA) strategies in secure spatial modulation networks, are investigated under the total power constraint. Considering that there is no closed-form expression for secrecy rate (SR), an approximate closed-form expression of SR is derived as an efficient metric to optimize PA factor, which can greatly reduce the computation complexity. Based on this expression, a convex optimization (CO) method of maximizing SR (Max-SR) is proposed accordingly. Furthermore, a method of maximizing the product of signal-to-leakage and noise ratio (SLNR) and artificial noise-to-leakage and noise ratio (Max-P-SAN) is proposed to provide an analytic solution for PA factor with extremely low complexity. Simulation results demonstrate that the SR performance of the proposed CO method is close to that of the optimal PA strategy with exhaustive search, and is better than that of Max-P-SAN in the high signal-to-noise ratio (SNR) region. However, in the low and medium SNR regions, the proposed Max-P-SAN slightly outperforms the proposed CO scheme in terms of SR performance

Personalized Federated Deep Reinforcement Learning-based Trajectory Optimization for Multi-UAV Assisted Edge Computing

In the era of 5G mobile communication, there has been a significant surge in research focused on unmanned aerial vehicles (UAVs) and mobile edge computing technology. UAVs can serve as intelligent servers in edge computing environments, optimizing their flight trajectories to maximize communication system throughput. Deep reinforcement learning (DRL)-based trajectory optimization algorithms may suffer from poor training performance due to intricate terrain features and inadequate training data. To overcome this limitation, some studies have proposed leveraging federated learning (FL) to mitigate the data isolation problem and expedite convergence. Nevertheless, the efficacy of global FL models can be negatively impacted by the high heterogeneity of local data, which could potentially impede the training process and even compromise the performance of local agents. This work proposes a novel solution to address these challenges, namely personalized federated deep reinforcement learning (PF-DRL), for multi-UAV trajectory optimization. PF-DRL aims to develop individualized models for each agent to address the data scarcity issue and mitigate the negative impact of data heterogeneity. Simulation results demonstrate that the proposed algorithm achieves superior training performance with faster convergence rates, and improves service quality compared to other DRL-based approaches

On IRS-assisted covert communication with a friendly UAV

Driven by the rapidly growing demand for information security, covert wireless communication has become an essential technology and attracted tremendous attention. However, traditional wireless covert communication is continuously exposing the inherent limitations, creating challenges around deployment in environments with a large number of obstacles, such as cities with high-rise buildings. In this paper, we propose an intelligent reflecting surface (IRS)-assisted covert communication system (CCS) for communicating with a friendly unmanned aerial vehicle (UAV) in which the UAV generates artificial noise (AN) to interfere with monitoring. Furthermore, we model the power of AN emitted by the UAV using an uncertainty model, through which the closed-form detection error probability (DEP) of the covert wireless communication for monitoring is derived. Under the derived DEP, we formulate the optimization problem to maximize the covert rate, then design an iterative algorithm to solve the optimization problem and obtain the optimal covert rate using Dinkelbach method. Simulation results show that the proposed system achieves the maximum covert rate when the phase of the IRS units and the trajectory and transmit power of the UAV are optimized jointly

Design of Two-Level Incentive Mechanisms for Hierarchical Federated Learning

Hierarchical Federated Learning (HFL) is a distributed machine learning paradigm tailored for multi-tiered computation architectures, which supports massive access of devices' models simultaneously. To enable efficient HFL, it is crucial to design suitable incentive mechanisms to ensure that devices actively participate in local training. However, there are few studies on incentive mechanism design for HFL. In this paper, we design two-level incentive mechanisms for the HFL with a two-tiered computing structure to encourage the participation of entities in each tier in the HFL training. In the lower-level game, we propose a coalition formation game to joint optimize the edge association and bandwidth allocation problem, and obtain efficient coalition partitions by the proposed preference rule, which can be proven to be stable by exact potential game. In the upper-level game, we design the Stackelberg game algorithm, which not only determines the optimal number of edge aggregations for edge servers to maximize their utility, but also optimize the unit reward provided for the edge aggregation performance to ensure the interests of cloud servers. Furthermore, numerical results indicate that the proposed algorithms can achieve better performance than the benchmark schemes

Prediction of patient choice tendency in medical decision-making based on machine learning algorithm

ObjectiveMachine learning (ML) algorithms, as an early branch of artificial intelligence technology, can effectively simulate human behavior by training on data from the training set. Machine learning algorithms were used in this study to predict patient choice tendencies in medical decision-making. Its goal was to help physicians understand patient preferences and to serve as a resource for the development of decision-making schemes in clinical treatment. As a result, physicians and patients can have better conversations at lower expenses, leading to better medical decisions.MethodPatient medical decision-making tendencies were predicted by primary survey data obtained from 248 participants at third-level grade-A hospitals in China. Specifically, 12 predictor variables were set according to the literature review, and four types of outcome variables were set based on the optimization principle of clinical diagnosis and treatment. That is, the patient's medical decision-making tendency, which is classified as treatment effect, treatment cost, treatment side effect, and treatment experience. In conjunction with the study's data characteristics, three ML classification algorithms, decision tree (DT), k-nearest neighbor (KNN), and support vector machine (SVM), were used to predict patients' medical decision-making tendency, and the performance of the three types of algorithms was compared.ResultsThe accuracy of the DT algorithm for predicting patients' choice tendency in medical decision making is 80% for treatment effect, 60% for treatment cost, 56% for treatment side effects, and 60% for treatment experience, followed by the KNN algorithm at 78%, 66%, 74%, 84%, and the SVM algorithm at 82%, 76%, 80%, 94%. At the same time, the comprehensive evaluation index F1-score of the DT algorithm are 0.80, 0.61, 0.58, 0.60, the KNN algorithm are 0.75, 0.65, 0.71, 0.84, and the SVM algorithm are 0.81, 0.74, 0.73, 0.94.ConclusionAmong the three ML classification algorithms, SVM has the highest accuracy and the best performance. Therefore, the prediction results have certain reference values and guiding significance for physicians to formulate clinical treatment plans. The research results are helpful to promote the development and application of a patient-centered medical decision assistance system, to resolve the conflict of interests between physicians and patients and assist them to realize scientific decision-making

Concentrations and gas-particle partitioning of PCDD/Fs in the urban air of Dalian, China

PCDD/Fs in the urban air of Dalian, China were monitored with high-volume active sampler from November 2009 to October 2010. The concentration of Cl4-8DD/Fs ranged from 3065 to 49538 fg m(-3), with an average of 10249 fg m(-3). The international toxic equivalents (I-TEQ) value of that was 61.8-1182 fg m(-3), with an average of 235 fg m(-3), which was comparable to those in the other urban locations around the world. It was found that the Cl4-8DD/Fs appeared to be present mainly in the particle phase during winter, spring and autumn, while during summer which were dominantly in gas phase. The ratio of Cl4-8DD/Fs present in particle phase increased with the increasing level of chlorination. The concentrations of PCDFs and PCDDs decreased with the increase of chlorinated level, while the concentrations of 2,3,7,8-PCDDs congeners increased with the increase of chlorination level. The homolog profiles of the concentrations of PCDFs presented were higher than those of the PCDDs, which indicated the PCDD/Fs pollution source of the air in Dalian was characteristic for thermal source pollution. The correlation analysis of meteorological parameters with the concentrations of Cl4-8CDD/Fs was conducted using SPSS packages, and it was found that the ambient temperature and atmospheric pressure were important factors influence the concentration of PCDD/Fs in the air. The respiratory risk and intake dioxins of the residents around the sampling sites were studied in the paper. It was found that Junge-Pankow model was much more accurate in predicting the gas-particle partitioning behavior of PCDD/Fs homologues during winter, while the Harner-Bidleman model shows better agreement with the measured data during winter and summer

Knockdown of Notch1 inhibits nasopharyngeal carcinoma cell growth and metastasis via downregulation of CCL2, CXCL16, and uPA

Notch pathway is a highly conserved cell signaling system that plays very important roles in controlling multiple cell differentiation processes during embryonic and adult life. Multiple lines of evidence support the oncogenic role of Notch signaling in several human solid cancers; however, the pleiotropic effects and molecular mechanisms of Notch signaling inhibition on nasopharyngeal carcinoma (NPC) remain unclear. In this study, we evaluated Notch1 expression in NPC cell lines (CNE1, CNE2, SUNE1, HONE1, and HK1) by real-time quantitative PCR and Western blot analysis, and we found that CNE1 and CNE2 cells expressed a higher level of Notch1 compared with HONE1, SUNE1, and HK1 cells. Then Notch1 expression was specifically knocked down in CNE1 and CNE2 cells by Notch1 short hairpin RNA (shRNA). In Notch1 knockdown cells, cell proliferation, migration, and invasion were significantly inhibited. The epithelial-mesenchymal transition of tumor cells was reversed in Notch1-shRNA-transfected cells, accompanied by epithelioid-like morphology changes, increased protein levels of E-cadherin, and decreased expression of vimentin. In addition, knockdown of Notch1 markedly inhibited the expression of urokinase plasminogen activator (uPA) and its receptor uPAR, and chemokines C-C motif chemokine ligand 2 and C-X-C motif chemokine ligand 16, indicating that these factors are downstream targets of Notch1. Furthermore, deleting uPA expression had similar effects as Notch1. Finally, knockdown of Notch1 significantly diminished CNE1 cell growth in a murine model concomitant with inhibition of cell proliferation and induction of apoptosis. These results suggest that Notch1 may become a novel therapeutic target for the clinical treatment of NPC.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151248/1/mc23082_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151248/2/mc23082.pd