Optimization of Covert Communication in Multi-Sensor Asymmetric Noise Systems.

This work investigates wireless covert communication in a multi-sensor asymmetric noise scenario. We adopt KL (Kullback-Leibler) divergence as the covertness constraint metric and mutual information as the transmission rate metric. To accurately approximate KL divergence and mutual information in covert communication, we employ the Taylor series expansion technique. Analytical expressions for KL divergence and mutual information in covert communication are derived, and we optimize the amplitude gain and phase angles based on these analytical expressions. Our findings underscore the importance of phase angle selection in covert communication within asymmetric noise systems. We propose an effective method for optimizing the transmission amplitude gain and phase angles in scenarios with asymmetric noise. Numerical results validate the effectiveness and superiority of our proposed method.

A Fast Anti-Jamming Algorithm Based on Imitation Learning for WSN.

Wireless sensor networks (WSNs), integral components underpinning the infrastructure of the internet of things (IoT), confront escalating threats originating from attempts at malicious jamming. Nevertheless, the limited nature of the hardware resources in distributed, low-cost WSNs, such as those for computing power and storage, poses a challenge when implementing complex and intelligent anti-jamming algorithms like deep reinforcement learning (DRL). Hence, in this paper a rapid anti-jamming method is proposed based on imitation learning in order to address this issue. First, on-network nodes obtain expert anti-jamming trajectories using heuristic algorithms, taking historical experiences into account. Second, an RNN neural network that can be used for anti-jamming decision making is trained by mimicking these expert trajectories. Finally, the late-access network nodes receive anti-jamming network parameters from the existing nodes, allowing them to obtain a policy network directly applicable to anti-jamming decision making and thus avoiding redundant learning. Experimental results demonstrate that, compared with traditional Q-learning and random frequency-hopping (RFH) algorithms, the imitation learning-based algorithm empowers late-access network nodes to swiftly acquire anti-jamming strategies that perform on par with expert strategies.

Phylogeny and character evolution of the fern genus Tectaria (Tectariaceae) in the Old World inferred from chloroplast DNA sequences.

In this study we provide a phylogeny for the pantropical fern genus Tectaria, with emphasis on the Old World species, based on sequences of five plastid regions (atpB, ndhF plus ndhF-trnL, rbcL, rps16-matK plus matK, and trnL-F). Maximum parsimony, maximum likelihood, and Bayesian inference are used to analyze 115 individuals, representing ca. 56 species of Tectaria s.l. and 36 species of ten related genera. The results strongly support the monophyly of Tectaria in a broad sense, in which Ctenitopsis, Hemigramma, Heterogonium, Psomiocarpa, Quercifilix, Stenosemia, and Tectaridium should be submerged. Such broadly circumscribed Tectaria is supported by the arising pattern of veinlets and the base chromosome number (x=40). Four primary clades are well resolved within Tectaria, one from the Neotropic (T. trifoliata clade) and three from the Old World (T. subtriphylla clade, Ctenitopsis clade, and T. crenata clade). Tectaria crenata clade is the largest one including six subclades. Of the genera previously recognized as tectarioid ferns, Ctenitis, Lastreopsis, and Pleocnemia, are confirmed to be members in Dryopteridaceae; while Pteridrys and Triplophyllum are supported in Tectariaceae. To infer morphological evolution, 13 commonly used characters are optimized on the resulting phylogenetic trees and in result, are all homoplastic in Tectaria.

Simulating the effect of climate change on soil microbial community in an Abies georgei var. smithii forest.

Qinghai-Tibet Plateau is considered a region vulnerable to the effects of climate change. Studying the effects of climate change on the structure and function of soil microbial communities will provide insight into the carbon cycle under climate change. However, to date, changes in the successional dynamics and stability of microbial communities under the combined effects of climate change (warming or cooling) remain unknown, which limits our ability to predict the consequences of future climate change. In this study, in situ soil columns of an Abies georgei var. smithii forest at 4,300 and 3,500 m elevation in the Sygera Mountains were incubated in pairs for 1 year using the PVC tube method to simulate climate warming and cooling, corresponding to a temperature change of ±4.7°C. Illumina HiSeq sequencing was applied to study alterations in soil bacterial and fungal communities of different soil layers. Results showed that warming did not significantly affect the fungal and bacterial diversity of the 0-10 cm soil layer, but the fungal and bacterial diversity of the 20-30 cm soil layer increased significantly after warming. Warming changed the structure of fungal and bacterial communities in all soil layers (0-10 cm, 10-20 cm, and 20-30 cm), and the effect increased with the increase of soil layers. Cooling had almost no significant effect on fungal and bacterial diversity in all soil layers. Cooling changed the structure of fungal communities in all soil layers, but it showed no significant effect on the structure of bacterial communities in all soil layers because fungi are more adapted than bacteria to environments with high soil water content (SWC) and low temperatures. Redundancy analysis (RDA) and hierarchical analysis showed that changes in soil bacterial community structure were primarily related to soil physical and chemical properties, whereas changes in soil fungal community structure primarily affected SWC and soil temperature (Soil Temp). The specialization ratio of fungi and bacteria increased with soil depth, and fungi were significantly higher than bacteria, indicating that climate change has a greater impact on microorganisms in deeper soil layers, and fungi are more sensitive to climate change. Furthermore, a warmer climate could create more ecological niches for microbial species to coexist and increase the strength of microbial interactions, whereas a cooler climate could have the opposite effect. However, we found differences in the intensity of microbial interactions in response to climate change in different soil layers. This study provides new insights to understand and predict future effects of climate change on soil microbes in alpine forest ecosystems.

Voter conformism and inefficient policies.

A reelection-seeking politician makes a policy decision that can reveal her private information. This information bears on whether her political orientation and capabilities will be a good fit to future circumstances. We study how she may choose inappropriate policies to hide her information, even in the absence of specific conflicts of interests, and how voters' conformism affects her incentives to do so. Conformism is independent from policies and from voters' perceptions. Yet we identify a 'conformism advantage' for the incumbent that exists only when there is also an incumbency advantage. Conformism changes the incentives of the incumbent and favors the emergence of an efficient, separating equilibrium. It may even eliminate the pooling equilibrium (that can consist in inefficient persistence). Conformism has a mixed impact on social welfare however: it improves policy choices and the information available to independent voters, but fosters inefficient reelection in the face of a stronger opponent. When the incumbent is 'altruistic' and values social welfare even when not in power, she partly internalizes this latter effect. The impact of conformism is then non monotonous.

CeO2 Structure Adjustment by H2O via the Microwave-Ultrasonic Method and Its Application in Imine Catalysis.

CeO2 with fusiform structures were prepared by the combined microwave-ultrasonic method, and their morphologies and surface structure were changed by simply adding different amounts of H2O (1-5 ml) to the precursor system. The addition of H2O changed the PVP micelle structure and the surface state, resulting in CeO2 with a different specific surface area (64-111 m2 g-1) and Ce3+ defects (16.5%-28.1%). The sample with 2 ml H2O exhibited a high surface area (111.3 m2∙g-1) and relatively more surface defects (Ce3+%: 28.1%), resulting in excellent catalytic activity (4.34 mmol g-1 h-1).

Role of interferon-gamma (IFN-γ) and IFN-γ receptor 1/2 (IFNγR1/2) in regulation of immunity, infection, and cancer development: IFN-γ-dependent or independent pathway.

IFN-γ, a soluble cytokine being produced by T lymphocytes, macrophages, mucosal epithelial cells, or natural killer cells, is able to bind to the IFN-γ receptor (IFNγR) and in turn activate the Janus kinase (JAK)-signal transducer and transcription protein (STAT) pathway and induce expression of IFN-γ-stimulated genes. IFN-γ is critical for innate and adaptive immunity and aberrant IFN-γ expression and functions have been associated with different human diseases. However, the IFN-γ/IFNγR signaling could be a double-edged sword in cancer development because the tissue microenvironments could determine its anti- or pro-tumorigenic activities. The IFNγR protein consists of two IFNγR1 and IFNγR2 chains, subunits of which play different roles under certain conditions. This review assessed IFNγR polymorphisms, expression and functions in development and progression of various human diseases in an IFN-γ-dependent or independent manner. This review also discussed tumor microenvironment, microbial infection, and vital molecules in the IFN-γ upstream signaling that might regulate IFNγR expression, drug resistance, and druggable strategy, to provide evidence for further application of IFNγR.