The separation of racemic molecules is of substantial significance not only for basic science but also for technical applications, such as fine chemicals and drug development. Here we report two ...isostructural chiral metal-organic frameworks decorated with chiral dihydroxy or -methoxy auxiliares from enantiopure tetracarboxylate-bridging ligands of 1,1'-biphenol and a manganese carboxylate chain. The framework bearing dihydroxy groups functions as a solid-state host capable of adsorbing and separating mixtures of a range of chiral aromatic and aliphatic amines, with high enantioselectivity. The host material can be readily recycled and reused without any apparent loss of performance. The utility of the present adsorption separation is demonstrated in the large-scale resolution of racemic 1-phenylethylamine. Control experiments and molecular simulations suggest that the chiral recognition and separation are attributed to the different orientations and specific binding energies of the enantiomers in the microenvironment of the framework.
Heterogeneous catalysts typically lack the specific steric control and rational electronic tuning required for precise asymmetric catalysis. Here we demonstrate that a phosphonate metal-organic ...framework (MOF) platform that is robust enough to accommodate up to 16 different metal clusters, allowing for systematic tuning of Lewis acidity, catalytic activity and enantioselectivity. A total of 16 chiral porous MOFs, with the framework formula M
L
(solvent)
that have the same channel structures but different surface-isolated Lewis acid metal sites, are prepared from a single phosphono-carboxylate ligand of 1,1'-biphenol and 16 different metal ions. The phosphonate MOFs possessing tert-butyl-coated channels exhibited high thermal stability and good tolerances to boiling water, weak acid and base. The MOFs provide a versatile family of heterogeneous catalysts for asymmetric allylboration, propargylation, Friedel-Crafts alkylation and sulfoxidation with good to high enantioselectivity. In contrast, the homogeneous catalyst systems cannot catalyze the test reactions enantioselectively.
As a new brain-inspired computational model of the artificial neural network, a spiking neural network encodes and processes neural information through precisely timed spike trains. Spiking neural ...networks are composed of biologically plausible spiking neurons, which have become suitable tools for processing complex temporal or spatiotemporal information. However, because of their intricately discontinuous and implicit nonlinear mechanisms, the formulation of efficient supervised learning algorithms for spiking neural networks is difficult, and has become an important problem in this research field. This article presents a comprehensive review of supervised learning algorithms for spiking neural networks and evaluates them qualitatively and quantitatively. First, a comparison between spiking neural networks and traditional artificial neural networks is provided. The general framework and some related theories of supervised learning for spiking neural networks are then introduced. Furthermore, the state-of-the-art supervised learning algorithms in recent years are reviewed from the perspectives of applicability to spiking neural network architecture and the inherent mechanisms of supervised learning algorithms. A performance comparison of spike train learning of some representative algorithms is also made. In addition, we provide five qualitative performance evaluation criteria for supervised learning algorithms for spiking neural networks and further present a new taxonomy for supervised learning algorithms depending on these five performance evaluation criteria. Finally, some future research directions in this research field are outlined.
In this paper, a local spatiotemporal descriptor, namely, the volume local binary count (VLBC), is proposed for the representation and recognition of dynamic texture. This descriptor, which is ...similar in spirit to the volume local binary pattern (VLBP), extracts histograms of thresholded local spatiotemporal volumes using both appearance and motion features to describe dynamic texture. Unlike VLBP using binary encoding, VLBC does not exploit the local structure information and only counts the number of 1s in the thresholded codes. Thus, VLBC can include more neighboring pixels without exponentially increasing the feature dimension as VLBP does. Furthermore, a completed version of VLBC (CVLBC) is also proposed to enhance the performance of dynamic texture recognition with additional information about local contrast and central pixel intensities. The proposed method is not only efficient to compute but also effective for dynamic texture representation. In experiments with three dynamic texture databases, namely, UCLA, DynTex, and DynTex++, the proposed method produces classification rates that are comparable to those produced by the state-of-the-art approaches. In addition to dynamic texture recognition, we propose utilizing CVLBC for 2-D face spoofing detection. As an effective spatiotemporal descriptor, CVLBC can well describe the differences between facial videos of valid users and impostors, thus achieving good performance for face spoofing detection. For comparison with other methods, the proposed method is evaluated on three face antispoofing databases: Print-Attack, Replay-Attack, and CAS Face Antispoofing. The experimental results demonstrate the effectiveness of CVLBC for 2-D face spoofing detection.
•The gravitational coefficient plays a positive role on the diffusion outcome.•Hub nodes can moderate the negative effect of consumption inertia.•Clustering coefficient can enhance the positive ...effect of the trust coefficient.•The negative impact of information noise is aggravated by network heterogeneity.
The popularity of mobile social media has promoted the process of consumers accepting new products online, that is, online new product diffusion (ONPD). However, previous research has seldom considered the dynamic social networks (varying-weight) resulting from the gravitational field generated by consumer interactions. To explore the effect of varying-weight social networks on ONPD, we propose a Brownian agent simulation model integrating the gravitational field theory and evolutionary game. Specifically, the evolutionary game theory is utilized to describe the interactions between consumers. Then the gravitational field theory is introduced to investigate the gravitational field generated by consumer interactions, which serves as a formation mechanism of a varying-weight social network. Then, a Brownian agent ONPD model is realized under different network structures. The effectiveness of the simulation model proposed is validated by comparison with real-world cases. The results show that: (1) The gravitational coefficient (represents the attraction of members in an online community) positively affects the diffusion outcome in the social network, but a moderate gravitational coefficient can accelerate the diffusion in the early stage. (2) Hub nodes can effectively moderate the negative effect of consumption inertia on product diffusion. (3) The network clustering coefficient can enhance the positive effect of the trust coefficient on product diffusion. (4) The negative impact of information noise (e.g., fake news, false information in online social media) on product diffusion is aggravated by network heterogeneity. This integrated study provides a new perspective to explore ONPD by applying the gravitational field theory to the diffusion model.
•The novel metal-free BNQDs/BPS-CN photocatalysts are successfully synthesized.•The photocatalysts show superior photocatalytic activities and stability in photodegradation of ...sulfamethazine.•Enhanced visible light absorption and charge transfer from BNQDs/BPS-CN are recorded.•A pathway for photocatalytic degradation of sulfamethazine is proposed.
Antibiotics may pose a great risk to ecosystem and human health. Photocatalysis, as a low-cost and environmentally friendly technology is widely used for the removal of antibiotics from wastewater. Graphitic carbon nitride (g-C3N4) has shown promising prospects in visible light photocatalysis, while its photocatalytic performance is greatly limited because of the sluggish charge separation and transfer. In this work, metal-free boron nitride quantum dots (BNQDs) modified bisphenol S (BPS)-doped g-C3N4 nanosheets (BNQDs/BPS-CN) heterojunction was synthesized to overcome these defects and applied in photodegradation of sulfamethazine (SMZ, a typical antibiotic) under the visible light. Multifarious characterization methods were used to explore the structure, porosity, elemental composition, optical performances, photo-electrochemical properties and photocatalytic performances of as-prepared BNQDs/BPS-CN composites. The degradation efficiency of SMZ with BNQDs/BPS-CN-4 composite reaches 100% within 60 min, and the rate constant is 13.7 times higher than that of pure g-C3N4. This phenomenon is because of the shrinking band gap width and the introduction of electronegative BNQDs, which is conducive to the absorption of visible light and high-efficiency separation of photoexcited charge carriers. Meanwhile, the results of free radical trapping experiments and electron spin resonance characterization prove that the photogenerated holes and superoxide radicals play predominant roles in the photodegradation of SMZ. This study proposes an effective mechanism for the construction of novel visible-light-driven photocatalysts using metal-free two-dimensional materials and quantum dots, which can be applied in the treatment of organic contaminants.
Neuroscience research confirms that the synaptic delays are not constant, but can be modulated. This paper proposes a supervised delay learning algorithm for spiking neurons with temporal encoding, ...in which both the weight and delay of a synaptic connection can be adjusted to enhance the learning performance. The proposed algorithm firstly defines spike train kernels to transform discrete spike trains during the learning phase into continuous analog signals so that common mathematical operations can be performed on them, and then deduces the supervised learning rules of synaptic weights and delays by gradient descent method. The proposed algorithm is successfully applied to various spike train learning tasks, and the effects of parameters of synaptic delays are analyzed in detail. Experimental results show that the network with dynamic delays achieves higher learning accuracy and less learning epochs than the network with static delays. The delay learning algorithm is further validated on a practical example of an image classification problem. The results again show that it can achieve a good classification performance with a proper receptive field. Therefore, the synaptic delay learning is significant for practical applications and theoretical researches of spiking neural networks.
The temperature distribution of electrolyte in ECM affects the distribution of machining gap and the precision of forming. Considering the influence of hydrogen bubbles and electrolyte with low ...velocity near the wall on heat transfer, the Euler–Euler two-fluid model was used to calculate the gas volume fraction distribution and the turbulent shear stress transport (SST) model was used to calculate the electrolyte velocity. The multi-field coupling model of electric field, flow field, and temperature field was established to solve the temperature of electrolyte. The temperature detection device is set up to detect the inlet, outlet temperature of the electrolyte, and the temperature of the sampling points on the workpiece. The experimental results show that the results of the coupled model considering the hydrogen bubbles and the low flow velocity electrolyte are more accurate than those of the turbulent SST and
k
−
ε
models ignoring the influence of the bubbles.
Local binary descriptors, such as local binary pattern (LBP) and its various variants, have been studied extensively in texture and dynamic texture analysis due to their outstanding characteristics, ...such as grayscale invariance, low computational complexity and good discriminability. Most existing local binary feature extraction methods extract spatio-temporal features from three orthogonal planes of a spatio-temporal volume by viewing a dynamic texture in 3D space. For a given pixel in a video, only a proportion of its surrounding pixels is incorporated in the local binary feature extraction process. We argue that the ignored pixels contain discriminative information that should be explored. To fully utilize the information conveyed by all the pixels in a local neighborhood, we propose extracting local binary features from the spatio-temporal domain with 3D filters that are learned in an unsupervised manner so that the discriminative features along both the spatial and temporal dimensions are captured simultaneously. The proposed approach consists of three components: 1) 3D filtering; 2) binary hashing; and 3) joint histogramming. Densely sampled 3D blocks of a dynamic texture are first normalized to have zero mean and are then filtered by 3D filters that are learned in advance. To preserve more of the structure information, the filter response vectors are decomposed into two complementary components, namely, the signs and the magnitudes, which are further encoded separately into binary codes. The local mean pixels of the 3D blocks are also converted into binary codes. Finally, three types of binary codes are combined via joint or hybrid histograms for the final feature representation. Extensive experiments are conducted on three commonly used dynamic texture databases: 1) UCLA; 2) DynTex; and 3) YUVL. The proposed method provides comparable results to, and even outperforms, many state-of-the-art methods.
In this work, two Ni-based superalloys with 13 wt.% and 35 wt.% Co were prepared via selective laser melting (SLM), and the effects of Co on the microstructure and mechanical properties of the ...additively manufactured superalloys were investigated. As the Co fraction increased from 13 wt.% to 35 wt.%, the average grain size decreased from 25.69 μm to 17.57 μm, and the size of the nano-phases significantly increased from 80.54 nm to 230 nm. Moreover, the morphology of the γ' phase changed from that of a cuboid to a sphere, since Co decreased the γ/γ' lattice mismatch from 0.64% to 0.19%. At room temperature, the yield strength and ultimate tensile strength of the 13Co alloy reached 1379 MPa and 1487.34 MPa, and those of the 35Co alloy were reduced to 1231 MPa and 1350 MPa, while the elongation increased by 52%. The theoretical calculation indicated that the precipitation strengthening derived from the γ' precipitates made the greatest contribution to the strength.
PDF
