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In highly dynamic vehicular networking scenarios, when Vehicle-to-Infrastructure links and Vehicle-to-Vehicle links share spectrum resources, the traditional distributed resource allocation method lacks global optimization and fails to respond to environmental changes in a timely manner, which leads to low spectral efficiency of the system. A resource allocation method based on federated multi-agent deep reinforcement learning is proposed for Vehicular Networking communication, by fusing Asynchronous Federated Learning (AFL) and Multi-Agent Deep Deterministic Policy Gradient (MADDPG). Synergistic optimization of resource allocation. First, vehicles as agent dynamically optimize spectrum access, power control, and bandwidth allocation based on local channel states through the collaborative policy of MADDPG to reduce cross-link interference. Second, the asynchronous federation architecture is designed, where vehicles independently upload local model parameters to the global server, dynamically adjust the aggregation weights according to the real-time channel quality, and optimize the update of global model parameters. Finally, the global model parameters are fed back to the vehicles to further optimize the local resource allocation strategy, thus improving the system spectrum efficiency. The simulation results show that the system spectrum efficiency is improved by 19.1% on average compared with the centralized DDPG, MADDPG, MAPPO and FL-DuelingDQN algorithms in the Vehicle Networking scenario, while the transmission success rate of the V2V link is improved by 9.3% on average, and the total capacity of the V2I link is increased by 16.1% on average.

Landslide displacement prediction is essential to establish the early warning system (EWS). According to the dynamic characteristics of landslide evolution and the shortcomings of the traditional static prediction model, a dynamic prediction model of landslide displacement based on long short-term memory (LSTM) neural networks was proposed. Meanwhile, the Variational mode decomposition (VMD) theory was used to decompose the cumulative displacement and triggering factors, which not only give clear physical meaning to each displacement subsequence, but also closely connect the rock and soil conditions with the influence of external factors. Besides, the maximum information coefficient (MIC) was used to sort the redundant features. The LSTM is a dynamic model that can remember historical information and apply it to the current output. The hyperparameters of the LSTM model was optimized by the Grey wolf optimizer (GWO), and the dynamic one-step prediction was carried out for each displacement. All the predicted values were superimposed to complete the displacement prediction based on the time series model. The Tangjiao landslide in the Three Gorges Reservoir area (TGRA), China, was taken as a case study. The displacement data of monitoring sites GPS03 and GPS06 had step-like characteristics. Measured data from March 2007 to December 2016 were selected for analysis. The results indicate that the displacement prediction model based on MIC-GWO-LSTM model effectively improves the prediction accuracy and generalization ability, and is better than other prediction models. This model provides a new idea and exploration for the displacement prediction of step-like characteristics landslide in the Three Gorges Reservoir area.

Aiming at the problem of low estimation accuracy under a low signal-to-noise ratio due to the failure to consider the “beam squint” effect in millimeter-wave broadband systems, this paper proposes a model-driven channel estimation method for millimeter-wave massive MIMO broadband systems. This method considers the “beam squint” effect and applies the iterative shrinkage threshold algorithm to the deep iterative network. First, the millimeter-wave channel matrix is transformed into a transform domain with sparse features through training data learning to obtain a sparse matrix. Secondly, a contraction threshold network based on an attention mechanism is proposed in the phase of beam domain denoising. The network selects a set of optimal thresholds according to feature adaptation, which can be applied to different signal-to-noise ratios to achieve a better denoising effect. Finally, the residual network and the shrinkage threshold network are jointly optimized to accelerate the convergence speed of the network. The simulation results show that the convergence speed is increased by 10% and the channel estimation accuracy is increased by 17.28% on average under different signal-to-noise ratios.

Aiming at the problem of poor prediction accuracy of Channel State Information (CSI) caused by fast time-varying channels in wireless communication systems, this paper proposes a gated recurrent network based on experience replay and Snake Optimizer for real-time prediction in real-world non-stationary channels. Firstly, a two-channel prediction model is constructed by gated recurrent unit, which adapts to the real and imaginary parts of CSI. Secondly, we use the Snake Optimizer to find the optimal learning rate and the number of hidden layer elements to build the model. Finally, we utilize the experience pool to store recent historical CSI data for fast learning and complete learning. The simulation results show that, compared with LSTM, BiLSTM, and BiGRU, the gated recurrent network based on experience replay and Snake Optimizer has better performance in the optimization ability and convergence speed. The prediction accuracy of the model is also significantly improved under the dynamic non-stationary environment.

Environmental music, or ambient music, refers to music played in certain conditions in order to achieve a specific function or effect. In China, environmental music is rarely used in factory plants. The environment for physical work is filled with noises and less tranquil than the mental work environment. Most employees in a factory plant are physical laborers. The present study first selects environmental music with certain characteristics and apply computerized waveform analysis to the pieces to identify their similarities and differences. Next, music is played, in two different experiments, in the assembly plant of a certain fabrication factory. Its impact on the emotion and productivity of employees is measured and discussed. Further, this work will explore the practical impact of environmental music on human factors in the factory plant through the analysis of experimental data. Experiment results demonstrate that music tempo has the most prominent effect on productivity in the factory plant being studied. Music with a moderato tempo can significantly reduce fatigue experienced during work, create a pleasant work ambiance and enhance the average productivity by 2.92%.

Despite green building and BIM technology being hot spots in the construction industry, most research remains at the technical level. Leading to exploring the fundamental driving reason and mechanism of BIM for green buildings is still lacking. This paper explored BIM’s impact mechanism and driving path on green buildings from the management’s perspective to fill this gap. Based on a literature review, 18 expert interviews, and three case studies of green buildings, the influence mechanism was analysed via a qualitative method (ISM). Then, the importance of driving factors was evaluated via quantitative analysis (ANP). Specifically, this study probed the driving path by combining qualitative and quantitative analysis (ISM-ANP). The research findings show that the driving force of BIM for green buildings comes from the fundamental factor layer and is transferred to the intermediate and direct factors layer. The critical driving path of BIM for green building is to promote the visualization of building information, collaborative management, and expand real estate investment through the guidance of policies and standards. Based on research results, this paper puts forward five suggestions: 1) Improving the policy and standard system; 2) Striving to research native software; 3) Adopting an informatization project management mode; 4) Accelerating the construction and improvement of the green building industry chain; 5) Promoting government enterprise cooperation. These results may benefit not only the coupling and coordination of the two but also the construction industry’s green transformation and high-quality development.

Detection of pathogens by plants is mediated by intracellular nucleotide-binding site leucine-rich repeat (NLR) receptor proteins. NLR proteins are defined by their stereotypical multidomain structure: an N-terminal Toll–interleukin receptor (TIR) or coiled-coil (CC) domain, a central nucleotide-binding (NB) domain, and a C-terminal leucine-rich repeat (LRR). The plant innate immune system contains a limited NLR repertoire that functions to recognize all potential pathogens. We isolated Response to the bacterial type III effector protein HopBA1 (RBA1), a gene that encodes a TIR-only protein lacking all other canonical NLR domains. RBA1 is sufficient to trigger cell death in response to HopBA1. We generated a crystal structure for HopBA1 and found that it has similarity to a class of proteins that includes esterases, the heme-binding protein ChaN, and an uncharacterized domain of Pasteurella multocida toxin. Self-association, coimmunoprecipitationwith HopBA1, and function of RBA1 require two previously identified TIR–TIR dimerization interfaces. Although previously described as distinct in other TIR proteins, in RBA1 neither of these interfaces is sufficient when the other is disrupted. These data suggest that oligomerization of RBA1 is required for function. Our identification of RBA1 demonstrates that “truncated” NLRs can function as pathogen sensors, expanding our understanding of both receptor architecture and the mechanism of activation in the plant immune system.

In order to solve the problem wherein too many base station antennas are deployed in a massive multiple-input–multiple-output system, resulting in high overhead for downlink channel state information feedback, this paper proposes an uplink-assisted channel feedback method based on deep learning. The method applies the reciprocity of the uplink and downlink, uses uplink channel state information in the base station to help users give feedback on unknown downlink information, and compresses and restores the channel state information. First, an encoder–decoder structure is established. The encoder reduces the network depth and uses multi-resolution convolution to increase the accuracy of channel state information extraction while reducing the number of computations relating to user equipment. Afterward, the channel state information is compressed to reduce feedback overhead in the channel. At the decoder, with the help of the reciprocity of the uplink and downlink, the feature extraction of the uplink’s magnitudes is carried out, and the downlink channel state information is integrated into a channel state information feature matrix, which is restored to its original size. The simulation results show that compared with CSINet, CRNet, CLNet, and DCRNet, indoor reconstruction precision was improved by an average of 16.4%, and outside reconstruction accuracy was improved by an average of 21.2% under all compressions.

Peach fruit was treated with 5 mM γ-aminobutyric acid (GABA) to further investigate the mechanism by which GABA induced chilling tolerance. Here, we found that GABA not only inhibited the occurrence of chilling injury in peach fruit during cold storage but also maintained fruit quality. Most of the ascorbic acid (AsA) and glutathione (GSH) biosynthetic genes were up-regulated by GABA treatment, and their levels were increased accordingly, thus reducing chilling damage in treated peaches. Meanwhile, the increased transcript of genes in the AsA-GSH cycle by GABA treatment was also related to the induced tolerance against chilling. GABA treatment also increased the expression levels of several candidate ERF transcription factors involved in AsA and GSH biosynthesis. In conclusion, our study found that GABA reduced chilling injury in peach fruit during cold storage due to the higher AsA and GSH contents by positively regulating their modifying genes and candidate transcription factors.

Citrate is essential to biomineralization of the bone especially as an integral part of apatite nanocomposite. Citrate precipitate of apatite is hypothesized to be derived from mesenchymal stem/stromal cells (MSCs) upon differentiation into mature osteoblasts. Based on 13C‐labeled signals identified by solid‐state multinuclear magnetic resonance analysis, boosted mitochondrial activity and carbon‐source replenishment of tricarboxylic acid cycle intermediates coordinate to feed forward mitochondrial anabolism and deposition of citrate. Moreover, zinc (Zn2+) is identified playing dual functions: (i) Zn2+ influx is influenced by ZIP1 which is regulated by Runx2 and Osterix to form a zinc‐Runx2/Osterix‐ZIP1 regulation axis promoting osteogenic differentiation; (ii) Zn2+ enhances citrate accumulation and deposition in bone apatite. Furthermore, age‐related bone loss is associated with Zn2+ and citrate homeostasis; whereas, restoration of Zn2+ uptake alleviates age‐associated declining osteogenic capacity and amount of citrate deposition. Together, these results indicate that citrate is not only a key metabolic intermediate meeting the emerging energy demand of differentiating MSCs but also participates in extracellular matrix mineralization, providing mechanistic insight into Zn2+ homeostasis and bone formation. Citrate precipitate of bone apatite is found to be derived from mesenchymal stem/stromal cells upon differentiation into mature osteoblasts. Runx2/Osterix and Zn2+ uptake regulate this process, contributing to osteogenic differentiation, citrate accumulation, and deposition. Aging negatively impacts this process leading to loss of bone mass and structural integrity.