Multi-Armed Bandit-Based User Network Node Selection.

In the scenario of an integrated space-air-ground emergency communication network, users encounter the challenge of rapidly identifying the optimal network node amidst the uncertainty and stochastic fluctuations of network states. This study introduces a Multi-Armed Bandit (MAB) model and proposes an optimization algorithm leveraging dynamic variance sampling (DVS). The algorithm posits that the prior distribution of each node's network state conforms to a normal distribution, and by constructing the distribution's expected value and variance, it maximizes the utilization of sample data, thereby maintaining an equilibrium between data exploitation and the exploration of the unknown. Theoretical substantiation is provided to illustrate that the Bayesian regret associated with the algorithm exhibits sublinear growth. Empirical simulations corroborate that the algorithm in question outperforms traditional ε-greedy, Upper Confidence Bound (UCB), and Thompson sampling algorithms in terms of higher cumulative rewards, diminished total regret, accelerated convergence rates, and enhanced system throughput.

GA-Dueling DQN Jamming Decision-Making Method for Intra-Pulse Frequency Agile Radar.

Optimizing jamming strategies is crucial for enhancing the performance of cognitive jamming systems in dynamic electromagnetic environments. The emergence of frequency-agile radars, capable of changing the carrier frequency within or between pulses, poses significant challenges for the jammer to make intelligent decisions and adapt to the dynamic environment. This paper focuses on researching intelligent jamming decision-making algorithms for Intra-Pulse Frequency Agile Radar using deep reinforcement learning. Intra-Pulse Frequency Agile Radar achieves frequency agility at the sub-pulse level, creating a significant frequency agility space. This presents challenges for traditional jamming decision-making methods to rapidly learn its changing patterns through interactions. By employing Gated Recurrent Units (GRU) to capture long-term dependencies in sequence data, together with the attention mechanism, this paper proposes a GA-Dueling DQN (GRU-Attention-based Dueling Deep Q Network) method for jamming frequency selection. Simulation results indicate that the proposed method outperforms traditional Q-learning, DQN, and Dueling DQN methods in terms of jamming effectiveness. It exhibits the fastest convergence speed and reduced reliance on prior knowledge, highlighting its significant advantages in jamming the subpulse-level frequency-agile radar.

Engineering of Cry3Bb1 provides mechanistic insights toward countering western corn rootworm resistance.

The western corn rootworm (WCR), Diabrotica virgifera virgifera LeConte (Coleoptera: Chrysomelidae), is an economically important pest of corn (maize) in North America and Europe. Current management practices for WCR involve transgenic expression of insecticidal proteins to minimize larval feeding damage to corn roots. The evolution of resistant WCR populations to transgenic corn expressing insecticidal proteins (e.g. Cry3Bb1, Gpp34Ab1/Tpp35Ab1) necessitates efforts to discover and deploy new modes of action for WCR control. Here, we tested the hypothesis that the addition of short peptides selected for binding to the WCR gut would restore insecticidal activity of Cry3Bb1 to resistant insects. Phage display technology coupled with deep sequencing was used to identify peptides selected for binding to WCR brush border membrane vesicles and to recombinant putative receptors aminopeptidase and cadherin. The binding and specificity of selected peptides was confirmed by ELISA and pull-down assays, and candidate gut surface binding partners were identified. Although production of 284 novel Cry3Bb1 variants with these peptides did not restore activity against resistant WCR in artificial diet bioassays, 112 variants were active against susceptible insects. These results provided insights for the mechanism of Cry3Bb1 activity and toward engineering a new mode-of-action via receptor re-targeting in the context of protein structure and function.

Computation Offloading in UAV-Enabled Edge Computing: A Stackelberg Game Approach.

This paper studies an efficient computing resource offloading mechanism for UAV-enabled edge computing. According to the interests of three different roles: base station, UAV, and user, we comprehensively consider the factors such as time delay, operation, and transmission energy consumption in a multi-layer game to improve the overall system performance. Firstly, we construct a Stackelberg multi-layer game model to get the appropriate resource pricing and computing offload allocation strategies through iterations. Base stations and UAVs are the leaders, and users are the followers. Then, we analyze the equilibrium states of the Stackelberg game and prove that the equilibrium state of the game exists and is unique. Finally, the algorithm's feasibility is verified by simulation, and compared with the benchmark strategy, the Stackelberg game algorithm (SGA) has certain superiority and robustness.

A QoE-Oriented Uplink Allocation for Multi-UAV Video Streaming.

Video streaming has become a kind of main information carried by Unmanned Aerial Vehicles (UAVs). Unlike single transmission, when a cluster of UAVs execute the real-time video shooting and uploading mission, the insufficiency of wireless channel resources will lead to bandwidth competition among them and the competition will bring bad watching experience to the audience. Therefore, how to allocate uplink bandwidth reasonably in the cluster has become a crucial problem. In this paper, an intelligent and distributed allocation mechanism is designed for improving users' video viewing satisfication. Each UAV in a cluster can independently adjust and select its video encoding rate so as to achieve flexible uplink allocation. This choice relies neither on the existence of the central node, nor on the large amount of information interaction between UAVs. Firstly, in order to distinguish video service from ordinary data, a utility function for the overall Quality of Experience (QoE) is proposed. Then, a potential game model is built around the problem. By a distributed self-learning algorithm with low complexity, all UAVs can iteratively update their own bandwidth strategy in a short time until equilibria, thus achieving the total quality optimization of all videos. Numeric simulation results indicate, after a few iterations, that the algorithm converges to a set of correlation equilibria. This mechanism not only solves the uplink allocation problem of video streaming in UAV cluster, but also guarantees the wireless resource providers in distinguishing and ensuring network service quality.

The relationship between serum uric acid and metabolic syndrome in premenopausal and postmenopausal women in the Jinchang Cohort.

The aim of the study was to explore the association between serum uric acid (SUA) and metabolic syndrome (MetS) in premenopausal and postmenopausal women in the Jinchang Cohort. We studied 3808 female Jinchuan Nonferrous Metals Corporation workers aged 40-60 years. Cohort data from epidemiological surveys and medical exams were used. MetS was defined using the 2009 Joint Interim Society criteria. The relationship between SUA and MetS was evaluated using multiple logistic regression after adjusting for potential confounders. MetS and hyperuricemia were more prevalent in postmenopausal women than premenopausal ones (35.3% versus 15.2% and 9.2% versus 4.2%, respectively). Premenopausal and postmenopausal women with hyperuricemia had 2.81 (95% CI: 1.72-4.61) and 2.10 (95% CI: 1.44-3.08), respectively, times the odds of having MetS than their counterparts without hyperuricemia. Even within normal SUA quartiles, only premenopausal women in the highest and second-highest quartile had 3.57 (95% CI: 2.24-5.68) and 2.78 (95% CI: 1.71-4.50), respectively, times the odds of having MetS than those in the lowest quartile. Even in the normal range, the odds ratios for MetS increased gradually according to SUA levels in all women (Ptrend < 0.001). In conclusion, there was a significant correlation between SUA levels and MetS, and the association was stronger in premenopausal women than postmenopausal ones.

[Disease burden of colorectal cancer in Jinchang cohort].

OBJECTIVE: To evaluate the disease burden of colorectal cancer in Jinchang cohort, and provide evidence for preventing colorectal cancer and reducing the disease burden of colorectal cancer in the cohort. METHODS: The colorectal cancer mortality data from 2001 to 2013 and the medical records of colorectal cancer patients from 2001 to 2010 were collected for this retrospective cohort study. The colorectal cancer disease burden was described by using mortality rate, standardized mortality rate, medical expenditure, potential years of life lost (PYLL), average potential years of life lost (APYLL), working potential years of life lost (WPYLL), and average working potential years of life lost (AWPYLL). The development trend in disease burden of colorectal cancer was analyzed by using Spearman correlation and the average growth rate. RESULTS: The crude mortality rate of colorectal cancer from 2001 to 2013 was 9.53/100,000 with the average annual growth rate of 12.89%. The PYLL, APYLL, WPYLL and AWPYLL of colorectal cancer were 485.00 person-years, 9.15 years, 253.00 person-years, and 4.77 years, respectively. The direct medical expenditure due to colorectal cancer was 7064.38 Yuan per case and 408.43 Yuan per day. There was no increasing trend in the direct medical expenditure due to colorectal cancer. CONCLUSION: Colorectal cancer mortolity rate was on the rise and it caused heavy disease burden in Jinchang cohort.

Capacity Model and Constraints Analysis for Integrated Remote Wireless Sensor and Satellite Network in Emergency Scenarios.

This article investigates the capacity problem of an integrated remote wireless sensor and satellite network (IWSSN) in emergency scenarios. We formulate a general model to evaluate the remote sensor and satellite network capacity. Compared to most existing works for ground networks, the proposed model is time varying and space oriented. To capture the characteristics of a practical network, we sift through major capacity-impacting constraints and analyze the influence of these constraints. Specifically, we combine the geometric satellite orbit model and satellite tool kit (STK) engineering software to quantify the trends of the capacity constraints. Our objective in analyzing these trends is to provide insights and design guidelines for optimizing the integrated remote wireless sensor and satellite network schedules. Simulation results validate the theoretical analysis of capacity trends and show the optimization opportunities of the IWSSN.

Channel estimation in DCT-based OFDM.

This paper derives the channel estimation of a discrete cosine transform-(DCT-) based orthogonal frequency-division multiplexing (OFDM) system over a frequency-selective multipath fading channel. Channel estimation has been proved to improve system throughput and performance by allowing for coherent demodulation. Pilot-aided methods are traditionally used to learn the channel response. Least square (LS) and mean square error estimators (MMSE) are investigated. We also study a compressed sensing (CS) based channel estimation, which takes the sparse property of wireless channel into account. Simulation results have shown that the CS based channel estimation is expected to have better performance than LS. However MMSE can achieve optimal performance because of prior knowledge of the channel statistic.

Arabidopsis E2Fa plays a bimodal role in regulating cell division and cell growth.

The onset of cell cycle in mammalian systems is primarily controlled by E2F-like transcription factors. Recent evidence shows that plant E2F homologs and their associated proteins likely play similar roles in higher plant development. We studied the function of plant E2F in gene regulation and morphogenesis using transgenic Arabidopsis plants over-expressing AtE2Fa. Examination of rosettes showed that AtE2Fa over-expression resulted in increased expression of both cell cycle promoters and cell cycle inhibitors. The positive factors up-regulated by AtE2Fa emcompassed genes for G1/S transition, DNA synthesis and mitosis, and the negative factors up-regulated by AtE2Fa included RB1 , encoding the E2F binding protein, as well as KRP3 and KRP5 , encoding the plant CDK inhibitors. Moreover, AtE2Fa over-expression in rosettes led to elevated expression of ATPK19 , the homolog of the highly conserved S6 kinase that is known to enhance cell growth. The transgenic plants exhibited narrower rosette leaves when compared to wild-type control. Consistent with elevated expression of cell cycle inhibitors and ATPK19 , the mature rosette leaves displayed reduced cell number but increased cell size. These results demonstrate that AtE2Fa controls cell division and plant development by assuming a bimodal function in balancing the expression of both positive and negative regulators involved in cell division and growth.