•BPNN has good prediction accuracy for UCS, while RF performs better in predicting slump.•PSO is efficient in tuning hyperparameters of machine learning models.•The Pareto front of the mixture ...optimization problem is obtained by MOPSO.
For the optimization of concrete mixture proportions, multiple objectives (e.g., strength, cost, slump) with many variables (e.g., concrete components) under highly nonlinear constraints need to be optimized simultaneously. The current single-objective optimization models are not applicable to multi-objective optimization (MOO). This study proposes an MOO method based on machine learning (ML) and metaheuristic algorithms to optimize concrete mixture proportions. First, the performances of different ML models in the prediction of concrete objectives are compared on data sets collected from the published literature. The winner is selected as the objective function for the optimization procedure. In the optimization step, a multi-objective particle swarm optimization algorithm is used to optimize mixture proportions to achieve optimal objectives. The results show that the backpropagation neural network has better performance on continuous data (e.g., strength), whereas the random forest algorithm has higher prediction accuracy on more discrete data (e.g., slump). The Pareto fronts of a bi-objective mixture optimization problem for high-performance concrete and a tri-objective mixture optimization problem for plastic concrete are successfully obtained by the MOO model. The MOO model can serve as a design guide to facilitate decision-making before the construction phase.
Depletion of river sand due to large-scale concrete production has caused many environmental problems. To address this issue, river sand can be replaced with sand manufactured from waste deposits. To ...facilitate manufactured-sand concrete production, this study proposes three tree-based models: one individual model (regression tree (RT)), and two ensemble models (random forest (RF) and gradient boosted regression tree (GBRT)) to predict its mechanical properties, such as uniaxial compressive strength (UCS), and splitting tensile strength (STS). These tree-based models were trained and tested on a dataset collected from previous literature. In addition, to understand the importance of each input variable on the mechanical properties of manufactured-sand concrete, the variable importance is calculated using the RF algorithm. The results show that the highest correlation coefficients are achieved by GBRT in predicting UCS (0.9887) and STS (0.9666), which respectively increase by 3.0%–10.8% and 16.0%–21.6% in comparison with the models in previous literature. The mechanical properties UCS and STS are highly sensitive to the curing age with relative importance of 36.8% and 40.3%, respectively. To facilitate the application of the tree-based models in predicting mechanical properties of manufactured-sand concrete, a graphical user interface has been designed in this study.
•The mechanical properties of manufactured-sand concrete were predicted by tree-based models.•Firefly algorithm was used to tune the hyperparameters of the tree-based models.•Importance of input variables was calculated using random forest algorithm.•A GUI was developed to facilitate the application of the proposed models.
The Internet of Vehicles (IoV) aims to provide a new convenient, comfortable, and safe driving way, and in turn enables intelligent transportation through wireless communications among road-side ...units, on-board units (OBUs), phones, and other devices inside a vehicle. However, significantly increasing reliance on wireless communication, control, and computing technology makes IoV more vulnerable to potential attacks, such as remote intrusion, control, and trajectory tracking. Therefore, efficient authentication solutions preventing unauthorized visitors need to be addressed to cope with these issues. Hence, in this paper we focus on the security and privacy-preserving by developing a dual authentication scheme for IoV according to its different scenarios. First, the OBU self-generates an anonymous identity and temporary encryption key to open an authentication session. Second, the legitimacy of the vehicle's real and anonymous identity can be verified by trust authority (TA). After that, the vehicle's reputation is evaluated according to its history interactive behavior and the session key for V2V can be finally established. There are three major advantages, including privacy-preserving and security enhancement without a burden of key management in the condition of acceptable time delay range, introducing trust evaluation into authentication protocol, as well as considering the vehicle behavior attributes in the new reputation evaluation method. In addition, we also prove the correctness of this scheme using the Burrows-Abadi-Needham (BAN) logic, and the performance comparison against the existing schemes is given as well.
AMPA receptors mediate fast excitatory neurotransmission in the mammalian brain and transduce the binding of presynaptically released glutamate to the opening of a transmembrane cation channel. ...Within the postsynaptic density, however, AMPA receptors coassemble with transmembrane AMPA receptor regulatory proteins (TARPs), yielding a receptor complex with altered gating kinetics, pharmacology, and pore properties. Here, we elucidate structures of the GluA2-TARP γ2 complex in the presence of the partial agonist kainate or the full agonist quisqualate together with a positive allosteric modulator or with quisqualate alone. We show how TARPs sculpt the ligand-binding domain gating ring, enhancing kainate potency and diminishing the ensemble of desensitized states. TARPs encircle the receptor ion channel, stabilizing M2 helices and pore loops, illustrating how TARPs alter receptor pore properties. Structural and computational analysis suggests the full agonist and modulator complex harbors an ion-permeable channel gate, providing the first view of an activated AMPA receptor.
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•Agonist-bound complexes illuminate mechanisms of activation and desensitization•Partially open channel gate and complete pore structure defined in active states•TARPs act as molecular “buoys” and prevent LBD compression toward membrane•TARPs encircle LBDs and reduce conformational heterogeneity upon desensitization
Cryo-EM reconstructions of an AMPA receptor-TARP complex illuminate receptor activation and desensitization.
With the rapid development of vehicular networks, there is an increasing demand for extensive networking, computting, and caching resources. How to allocate multiple resources effectively and ...efficiently for dynamic vehicular networks is extremely important. Most existing works on resource management in vehicular networks assume static network conditions. In this paper, we propose a general framework that can enable fast-adaptive resource allocation for dynamic vehicular environments. Specifically, we model the dynamics of the vehicular environment as a series of related Markov Decision Processes (MDPs), and we combine hierarchical reinforcement learning with meta-learning, which makes our proposed framework quickly adapt to a new environment by only fine-tuning the top-level master network, and meanwhile the low-level sub-networks can make the right resource allocation policy. Extensive simulation results show the effectiveness of our proposed framework, which can quickly adapt to different scenarios, and significantly improve the performance of resource management in dynamic vehicular networks.
•CAZ-AVI maintains good activity against Gram-negative bacteria, especially Enterobacteriaceae.•The higher resistance rate of CAZ-AVI is associated with carbapenem resistance.•β-Lactamase-related ...mutations are represent the main mechanism leading to CAZ-AVI resistance.•For CAZ-AVI-resistant strains, other effective antibacterial agents or CAZ-AVI in combination with other antibacterial agents should be considered.
Ceftazidime–avibactam (CAZ-AVI) is a novel synthetic β-lactamase inhibitor combination. Although the combination has been available clinically for only a few years, cases of resistance to CAZ-AVI have already been reported.
In the present review, we summarize the distribution of CAZ-AVI-resistant strains and the possible resistance mechanisms.
There are no significant differences in CAZ-AVI resistance rates across different regions. CAZ-AVI maintains good activity against Gram-negative bacteria, especially Enterobacteriaceae. Pseudomonas aeruginosa is less susceptible to CAZ-AVI compared with Enterobacteriaceae, with a resistance rate ranging from 2.9 to 18%. The resistance to CAZ-AVI exceeds 50% in Acinetobacter baumannii. A higher resistance rate to CAZ-AVI is associated with carbapenem resistance. Moreover, β-lactamase-related amino acid substitutions are the main mechanisms that lead to CAZ-AVI resistance. Membrane protein amino acid substitutions and efflux pumps also play important roles in CAZ-AVI resistance.
To maintain its efficacy, CAZ-AVI should not be used for pathogens that are naturally resistant to it. For CAZ-AVI-resistant strains, other effective antibacterial agents or CAZ-AVI in combination with other antibacterial agents should be considered.
The development of efficient hydrogen evolution reaction electrocatalysts is critical to the realization of clean hydrogen fuel production, while the sluggish kinetics of the Volmer‐step ...substantially restricts the catalyst performances in alkali electrolyzers, even for noble metal catalysts such as Pt. Here, a Pt‐decorated Ni3N nanosheet electrocatalyst is developed to achieve a top performance of hydrogen evolution in alkaline conditions. Possessing a high metallic conductivity and an atomic‐thin semiconducting hydroxide surface, the Ni3N nanosheets serve as not only an efficient electron pathway without the hindrance of Schottky barriers, but also provide abundant active sites for water dissociation and generation of hydrogen intermediates, which are further adsorbed on the Pt surface to recombine to H2. The Pt‐decorated Ni3N nanosheet catalyst exhibits a hydrogen evolution current density of 200 mA cm−2 at an overpotential of 160 mV versus reversible hydrogen electrode, a Tafel slope of ≈36.5 mV dec−1, and excellent stability of 82.5% current retention after 24 h of operation. Moreover, a hybrid cell consisting of a Pt‐decorated Ni3N nanosheet cathode and a Li‐metal anode is assembled to achieve simultaneous hydrogen evolution and electricity generation, exhibiting >60 h long‐term hydrogen evolution reaction stability and an output voltage ranging from 1.3 to 2.2 V.
A Pt‐decorated Ni3N nanosheet electrocatalyst with a top performance of hydrogen evolution in alkaline conditions is developed. The high metallic conductivity and an atomic‐thin Ni(OH)2 surface of Ni3N nanosheets provide an efficient electron pathway, and accelerate the water dissociation and generation of hydrogen intermediates. Moreover, an integrated hydrogen evolution and electricity generation cell based on the Ni3N/Pt electrocatalysts is also demonstrated.
Recent progress in semantic segmentation has been driven by improving the spatial resolution under Fully Convolutional Networks (FCNs). To address this problem, we propose a Stacked Deconvolutional ...Network (SDN) for semantic segmentation. In SDN, multiple shallow deconvolutional networks, which are called as SDN units, are stacked one by one to integrate contextual information and bring the fine recovery of localization information. Meanwhile, inter-unit and intra-unit connections are designed to assist network training and enhance feature fusion since the connections improve the flow of information and gradient propagation throughout the network. Besides, hierarchical supervision is applied during the upsampling process of each SDN unit, which enhances the discrimination of feature representations and benefits the network optimization. We carry out comprehensive experiments and achieve the new state-ofthe- art results on four datasets, including PASCAL VOC 2012, CamVid, GATECH, COCO Stuff. In particular, our best model without CRF post-processing achieves an intersection-over-union score of 86.6% in the test set.
Abstract
Solar-driven electrochemical carbon dioxide (CO
2
) reduction is capable of producing value-added chemicals and represents a potential route to alleviate carbon footprint in the global ...environment. However, the ever-changing sunlight illumination presents a substantial impediment of maintaining high electrocatalytic efficiency and stability for practical applications. Inspired by green plant photosynthesis with separate light reaction and (dark) carbon fixation steps, herein, we developed a redox-medium-assisted system that proceeds water oxidation with a nickel-iron hydroxide electrode under light illumination and stores the reduction energy using a zinc/zincate redox, which can be controllably released to spontaneously reduce CO
2
into carbon monoxide (CO) with a gold nanocatalyst in dark condition. This redox-medium-assisted system enables a record-high solar-to-CO photoconversion efficiency of 15.6% under 1-sun intensity, and an outstanding electric energy efficiency of 63%. Furthermore, it allows a unique tuning capability of the solar-to-CO efficiency and selectivity by the current density applied during the carbon fixation.
Recently, convolutional neural networks (CNN) have demonstrated impressive performance in various computer vision tasks. However, high performance hardware is typically indispensable for the ...application of CNN models due to the high computation complexity, which prohibits their further extensions. In this paper, we propose an efficient framework, namely Quantized CNN, to simultaneously speed-up the computation and reduce the storage and memory overhead of CNN models. Both filter kernels in convolutional layers and weighting matrices in fully-connected layers are quantized, aiming at minimizing the estimation error of each layer's response. Extensive experiments on the ILSVRC-12 benchmark demonstrate 4 ~ 6× speed-up and 15 ~ 20× compression with merely one percentage loss of classification accuracy. With our quantized CNN model, even mobile devices can accurately classify images within one second.
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