Study of the Prediction of Development Stage and Population Size of Soybean Aphid in Northern Liaoning, China

A model for predicting the days for aphids to reach 10 thousand/100 plants in Northern Liaoling province was established by stepwise regression. In order to include enough parameters in the stepwise regression model to predict the aphid population, parameters were carefully selected by path analysis at first, and then a multiple regression model was established (F=0). At the same time, a stepwise discriminant model was established too. Integrated prediction was conducted by the combination of these two methods. Prediction fed with historical data had 100% fitness. Forecasting tests in the past two years were all correct.Originating text in Chinese.Citation: Tian, Zhengreng, Zhao, Shuyan, Hu, Chenxiao. (1990). Study of the Prediction of Development Stage and Population Size of Soybean Aphid in Northern Liaoning, China. Plant Protection (Institute of Plant Protection, CAAS, China), 16(6), 19-21

Learning-based Intelligent Surface Configuration, User Selection, Channel Allocation, and Modulation Adaptation for Jamming-resisting Multiuser OFDMA Systems

Reconfigurable intelligent surfaces (RISs) can potentially combat jamming attacks by diffusing jamming signals. This paper jointly optimizes user selection, channel allocation, modulation-coding, and RIS configuration in a multiuser OFDMA system under a jamming attack. This problem is non-trivial and has never been addressed, because of its mixed-integer programming nature and difficulties in acquiring channel state information (CSI) involving the RIS and jammer. We propose a new deep reinforcement learning (DRL)-based approach, which learns only through changes in the received data rates of the users to reject the jamming signals and maximize the sum rate of the system. The key idea is that we decouple the discrete selection of users, channels, and modulation-coding from the continuous RIS configuration, hence facilitating the RIS configuration with the latest twin delayed deep deterministic policy gradient (TD3) model. Another important aspect is that we show a winner-takes-all strategy is almost surely optimal for selecting the users, channels, and modulation-coding, given a learned RIS configuration. Simulations show that the new approach converges fast to fulfill the benefit of the RIS, due to its substantially small state and action spaces. Without the need of the CSI, the approach is promising and offers practical value.Comment: accepted by IEEE TCOM in Jan. 202

Isolation and Characteristics of a Bacterial Strain for Deodorization of Dimethyl Sulfide

AbstractThe removal characteristics of dimethyl sulfide (DMS) with a peat packed tower were studied. The peat itself did not remove DMS. The peat inoculated with activated sludge as a source of microorganisms showed an efficient removal of DMS. Dominant microorganisms for degradation of DMS in the peat packed tower were some chemolithotrophic and non-acidophilic sulfur-oxidizing microorganisms originating from sludge. A dominant DMS-oxidizing strain Au7 was isolated and identified as chemolithotrophic Thiobacilli. Product of DMS oxidation by strain Au7 was sulfate. The optimum pH of DMS removal by strain Au7 was 7-5.45

Detection and Mitigation of Position Spoofing Attacks on Cooperative UAV Swarm Formations

Detecting spoofing attacks on the positions of unmanned aerial vehicles (UAVs) within a swarm is challenging. Traditional methods relying solely on individually reported positions and pairwise distance measurements are ineffective in identifying the misbehavior of malicious UAVs. This paper presents a novel systematic structure designed to detect and mitigate spoofing attacks in UAV swarms. We formulate the problem of detecting malicious UAVs as a localization feasibility problem, leveraging the reported positions and distance measurements. To address this problem, we develop a semidefinite relaxation (SDR) approach, which reformulates the non-convex localization problem into a convex and tractable semidefinite program (SDP). Additionally, we propose two innovative algorithms that leverage the proximity of neighboring UAVs to identify malicious UAVs effectively. Simulations demonstrate the superior performance of our proposed approaches compared to existing benchmarks. Our methods exhibit robustness across various swarm networks, showcasing their effectiveness in detecting and mitigating spoofing attacks. {\blue Specifically, the detection success rate is improved by up to 65\%, 55\%, and 51\% against distributed, collusion, and mixed attacks, respectively, compared to the benchmarks.Comment: accepted by IEEE TIFS in Dec. 202

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Detecting spoofing attacks on the positions of unmanned aerial vehicles (UAVs) within a swarm is challenging. Traditional methods relying solely on individually reported positions and pairwise distance measurements are ineffective in identifying the misbehavior of malicious UAVs. This paper presents a novel systematic structure designed to detect and mitigate spoofing attacks in UAV swarms. We formulate the problem of detecting malicious UAVs as a localization feasibility problem, leveraging the reported positions and distance measurements. To address this problem, we develop a semidefinite relaxation (SDR) approach, which reformulates the non-convex localization problem into a convex and tractable semidefinite program (SDP). Additionally, we propose two innovative algorithms that leverage the proximity of neighboring UAVs to identify malicious UAVs effectively. Simulations demonstrate the superior performance of our proposed approaches compared to existing benchmarks. Our methods exhibit robustness across various swarm networks, showcasing their effectiveness in detecting and mitigating spoofing attacks. Specifically, the detection success rate is improved by up to 65%, 55%, and 51% against distributed, collusion, and mixed attacks, respectively, compared to the benchmarks.info:eu-repo/semantics/publishedVersio

OFDMA-F2^2L: Federated Learning With Flexible Aggregation Over an OFDMA Air Interface

Federated learning (FL) can suffer from a communication bottleneck when deployed in mobile networks, limiting participating clients and deterring FL convergence. The impact of practical air interfaces with discrete modulations on FL has not previously been studied in depth. This paper proposes a new paradigm of flexible aggregation-based FL (F$^2$L) over orthogonal frequency division multiple-access (OFDMA) air interface, termed as ``OFDMA-F$^2$L'', allowing selected clients to train local models for various numbers of iterations before uploading the models in each aggregation round. We optimize the selections of clients, subchannels and modulations, adapting to channel conditions and computing powers. Specifically, we derive an upper bound on the optimality gap of OFDMA-F$^2$L capturing the impact of the selections, and show that the upper bound is minimized by maximizing the weighted sum rate of the clients per aggregation round. A Lagrange-dual based method is developed to solve this challenging mixed integer program of weighted sum rate maximization, revealing that a ``winner-takes-all'' policy provides the almost surely optimal client, subchannel, and modulation selections. Experiments on multilayer perceptrons and convolutional neural networks show that OFDMA-F$^2$L with optimal selections can significantly improve the training convergence and accuracy, e.g., by about 18\% and 5\%, compared to potential alternatives.Comment: Accepted by IEEE TWC in Nov. 202

Failure Analysis in Next-Generation Critical Cellular Communication Infrastructures

The advent of communication technologies marks a transformative phase in critical infrastructure construction, where the meticulous analysis of failures becomes paramount in achieving the fundamental objectives of continuity, security, and availability. This survey enriches the discourse on failures, failure analysis, and countermeasures in the context of the next-generation critical communication infrastructures. Through an exhaustive examination of existing literature, we discern and categorize prominent research orientations with focuses on, namely resource depletion, security vulnerabilities, and system availability concerns. We also analyze constructive countermeasures tailored to address identified failure scenarios and their prevention. Furthermore, the survey emphasizes the imperative for standardization in addressing failures related to Artificial Intelligence (AI) within the ambit of the sixth-generation (6G) networks, accounting for the forward-looking perspective for the envisioned intelligence of 6G network architecture. By identifying new challenges and delineating future research directions, this survey can help guide stakeholders toward unexplored territories, fostering innovation and resilience in critical communication infrastructure development and failure prevention

Patients with IBD have a more cautious attitude towards COVID-19 vaccination

BackgroundTo understand the awareness of COVID-19 vaccine, the willingness to vaccinate and the influencing factors of willingness to vaccinate in inflammatory bowel disease (IBD) patients.MethodsThe online questionnaire was distributed to conduct a survey to analyze and evaluate the willingness, awareness and trust in vaccines of IBD patients. Bivariate analyses and logistic regression models were used to analysis influencing factors.ResultsWe sent the questionnaire to the WeChat group for patient management and 304 patients responded, out of which 16 respondents had to be excluded and 288 respondents were included for the analysis. Among them, 209 patients vaccinated with COVID-19 vaccine. Among the non-vaccinated 79 patients, the main reasons for their concerns were afraid of vaccination aggravating IBD and fear of adverse effects. Our results showed that IBD patients with long disease duration were more willing to receive COVID-19 vaccination (P<0.05). We also observed that a high perception of benefits and cues to action to receive the vaccine were the two most important constructs affecting a definite intention for COVID-19 vaccination (P<0.05).ConclusionsPatients with IBD have a more cautious attitude towards COVID-19 vaccination, which may lead to a higher rate of vaccine hesitancy. Further efforts should be made to protect patients with IBD from COVID-19 infections and achieve adequate vaccination coverage