Multi-component transition group metal borides(MMB2)have become a research hotspot due to their new composition design concepts and superior properties compared with conventional *** of the current methods,however,are...
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Multi-component transition group metal borides(MMB2)have become a research hotspot due to their new composition design concepts and superior properties compared with conventional *** of the current methods,however,are complicated and time-consuming,the mass production remains a ***,we proposed a new high-efficiency strategy for synthesis of MMB2 using molten aluminum as the medium for the first *** prepared Al-containing multi-component borides(TiZrHfNbTa)B2 microcrystals had a homogeneous composition with a hexagonal AIB2 structure and ultra-high hardness value of～35.3 GPa,which was much higher than data reported in the literature and the rule of mix-ture ***,combined with the First-principles calculation results,we found that the Poisson's ratio(v)values exhibit a clearly ascending trend from 0.17 at VEC=3.5 to 0.18 at VEC=3.4,then to 0.201 at VEC=3.2 with the increasing of Al *** indicates that the intrinsic toughness of multi-component boride microcrystals is obviously enhanced by the trace-doped Al ***,the fabricated Al-containing multi-component boride microcrystals have superior oxidation activation en-ergy and structural *** enhanced oxidation resistance is mainly attributed to the formation of a protective Al2O3 oxide layer and the lattice distortion,both of which lead to sluggish diffusion of *** findings propose a new unexplored avenue for the fabrication of MMB2 materials with supe-rior comprehensive performance including ultra-hardness and intrinsically improved thermo-mechanical properties.
Metal-organic framework(MOF) has been widely applied in photocatalysis, which is significant for addressing energy crises and environmental issues. Based on density functional theory calculations,the performances of C...
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Metal-organic framework(MOF) has been widely applied in photocatalysis, which is significant for addressing energy crises and environmental issues. Based on density functional theory calculations,the performances of Cu-BTC, a copper-based MOF, and its derivatives Cu TM-BTC via the substitution of transition metal(TM) elements at the Cu site for photocatalytic overall water splitting(POWS) have been studied. POWS of Cu-BTC suffers from the sluggish hydrogen evolution reaction due to the large overpotential of 2.02 V and limited solar utilization due to a wide HOMO-LUMO gap of 4.11 e V. Via TM substitution, the HOMO-LUMO gap narrows but still satisfies the redox potentials when taken 3d-TM of Cr, Fe, Co or Ni, 4d-TM of Rh or Pd, or 5d-TM of Re or Pt into consideration, benefiting for the light absorption. Furthermore, Cr and Re could serve as active sites for hydrogen evolution with remarkably lowered overpotentials of 0.79 V and 0.28 V, respectively;similarly, oxygen evolution activities could be enhanced by Fe, Co and Rh because of their reduced overpotentials which are less than 0.5 V. Therefore,our findings pave guidance for designing Cu-BTC derivatives in overall water splitting.
Dear Editor,This letter investigates predefined-time optimization problems(OPs) of multi-agent systems(MASs), where the agent of MASs is subject to inequality constraints, and the team objective function accounts for ...
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Dear Editor,This letter investigates predefined-time optimization problems(OPs) of multi-agent systems(MASs), where the agent of MASs is subject to inequality constraints, and the team objective function accounts for impulse effects. Firstly, to address the inequality constraints,the penalty method is introduced. Then, a novel optimization strategy is developed, which only requires that the team objective function be strongly convex.
Mounds of spatter are generated in laser powder-bed fusion(L-PBF)additive manufacturing,which reduces build quality and laser *** to the lack of supplemental airflow above the chamber,the conventional build chamber wi...
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Mounds of spatter are generated in laser powder-bed fusion(L-PBF)additive manufacturing,which reduces build quality and laser *** to the lack of supplemental airflow above the chamber,the conventional build chamber with a single gas inlet exhibits a pronounced tendency for gas to flow upward near the *** phenomenon results in the formation of a large vortex within the build *** vortex leads to the chaotic motion trajectory of the spatter in the build *** design defects of the existing build chamber based on dual gas inlets are shown in this *** established a coupled computational fluid dynamics-discrete phase model(CFD-DPM)model to optimize the build chamber by adjusting the position and structure of the second gas *** homogeneity of the flow is increased with a distance of 379 mm between the two inlets and a wider-reaching second *** Coanda effect is also crucial in the spatter-removal *** Coanda effect is reduced by modifying the right sidewall of the build chamber and increasing the pressure difference between the inlet and ***,we found that the spatter-removal rate rose from 8.9%to 76.1%between the conventional build chamber with a single gas inlet and the optimized build chamber with two gas inlets.
This study considers the issue of nonfragile state estimation (SE) for memristor-based bidirectional associative memory (MBAM) neural networks with probabilistic time-varying delays. The primary objective is to develo...
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All-reduce is a widely used communication technique for distributed and parallel applications typically implemented using either a tree-based or ring-based scheme. Each of these approaches has its own limitations: tre...
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All-reduce is a widely used communication technique for distributed and parallel applications typically implemented using either a tree-based or ring-based scheme. Each of these approaches has its own limitations: tree-based schemes struggle with efficiently exchanging large messages, while ring-based solutions assume constant communication throughput,an unrealistic expectation in modern network communication infrastructures. We present FMCC-RT, an all-reduce approach that combines the advantages of tree-and ring-based implementations while mitigating their drawbacks. FMCC-RT dynamically switches between tree and ring-based implementations depending on the size of the message being processed. It utilizes an analytical model to assess the impact of message sizes on the achieved throughput, enabling the derivation of optimal work partitioning parameters. Furthermore, FMCC-RT is designed with an Open MPI-compatible API, requiring no modification to user code. We evaluated FMCC-RT through micro-benchmarks and real-world application tests. Experimental results show that FMCC-RT outperforms state-of-the-art tree-and ring-based methods, achieving speedups of up to 5.6×.
Rapid and accurate identification of high-quality patents can accelerate the transformation process of scientific and technological achievements, optimize the management of intellectual property rights and enhance the...
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Rapid and accurate identification of high-quality patents can accelerate the transformation process of scientific and technological achievements, optimize the management of intellectual property rights and enhance the vitality of innovation. Aiming at the shortcomings of the traditional high-value patent assessment method, which is relatively simple and seldom considers the influence of patentees, this paper proposes a high-quality patent method HMFM (High-Value Patent Multi-Feature Fusion Method) that fuses multi-dimensional features. A weighted node importance assessment method in complex network called GLE (Glob-Local-struEntropy) based on improved structural entropy is designed to calculate the influence of the patentee to form the patentee’s features, and the patent text features are extracted by BERT-DPCNN deep learning model, which is supplemented to the basic patent indicator system. Finally a machine learning algorithm is used to assess the value of patents. Experiment results show that our method can identify high-value patents more effectively and accurately.
Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address ...
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Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks.
Sign Language Production (SLP) aims to convert text or audio sentences into sign language videos corresponding to their semantics, which is challenging due to the diversity and complexity of sign languages, and cross-...
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