Confucio, ética y civilización

My talk is divided into three parts: 1. Confucius concepts about ethics; 2. the suggestions of Chinese civilization for human beings; 3. My thoughts on university ethics taking the instructions of the Qinghua and Beida Universities as examples.Mi charla se divide en tres partes: 1. Conceptos de Confucio sobre ética; 2. las sugerencias de la civilización china para los seres humanos; 3. Mis pensamientos sobre la ética universitaria tomando como ejemplo las instrucciones de las universidades Qin

Synchronization of Chaotic Neural Networks with Leakage Delay and Mixed Time-Varying Delays via Sampled-Data Control

This paper investigates the synchronization problem for neural networks with leakage delay and both discrete and distributed time-varying delays under sampled-data control. By employing the Lyapunov functional method and using the matrix inequality techniques, a delay-dependent LMIs criterion is given to ensure that the master systems and the slave systems are synchronous. An example with simulations is given to show the effectiveness of the proposed criterion

Driving Simulation Study on Speed-change Lanes of the Multi-lane Freeway Interchange

AbstractBecause of the interactions of the multi-lane freeway mainline, upstream, downstream, the diversity of environmental conditions, as well as the complexity of geometric configuration, speed-change lanes of the multi-lane freeway interchange present greatest safety and operational challenges for drivers. Most freeway crashes occur in the vicinity of interchange diverging and merging areas, especially in speed-change lanes. In this paper, the UC-win/Road5 software was used as the technical tool, and a three-dimensional driving scene was built. Multi-lane freeway field data were used for the calibration of model parameters. The geometry configuration of the speed-change lanes as well as the driving behavior characteristics such as speed, acceleration rate, glancing in the diverging and merging areas were studied in this paper. Based on the driving simulation study in the areas, results supply a valuable technical reference for speed-change lane geometry configuration, the length design of speed-change lane, the operational safety evaluation of multi-lane freeway diverging and merging areas, also the operation and management of multi-lane freeways

Effects of particle diameter and inlet flow rate on gas-solid flow patterns of fluidized bed

The complex multiscale characteristics of particle flow are notoriously difficult to predict. In this study, the evolution process of bubbles and the variation of bed height were investigated by conducting high-speed photographic experiments to verify the reliability of numerical simulations. The gas-solid flow characteristics of bubbling fluidized beds with different particle diameters and inlet flow rates were systematically investigated by coupling computational fluid dynamics (CFD) and discrete element method (DEM). The results show that the fluidization in the fluidized bed will change from bubbling fluidization to turbulent fluidization and finally to slugging fluidization, and the conversion process is related to the particle diameter and inlet flow rate. The characteristic peak is positively correlated with the inlet flow rate, but the frequency corresponding to the characteristic peak is constant. The time required for the Lacey mixing index (LMI) to reach 0.75 decreases with increasing inlet flow rate; at the same diameter, the inlet flow rate is positively correlated with the peak of the average transient velocity; and as the diameter increases, the distribution of the average transient velocity curve changes from M to linear. The results of the study can provide theoretical guidance for particle flow characteristics in biomass fluidized beds

MGDTI: Graph Transformer with Meta-Learning for Drug-Target Interaction Prediction

Drug-target interaction (DTI) prediction is of great importance for drug discovery and development. With the rapid development of biological and chemical technologies, computational methods for DTI prediction are becoming a promising strategy. However, there are few methods which explore solving the cold-start problem in DTI prediction scenarios due to most of existing methods require modeling under the existing interaction that can’t effectively capture information from new drugs and new targets which have few interactions in existing literature. In this paper, we propose a graph transformer method based on meta-learning named MGDTI to fill the gap. In particular, we employ drug-drug similarity and target-target similarity as additional information for network to mitigate the scarcity of interactions. Besides, we trained our model via meta-learning to be adaptive to cold-start tasks. Moreover, we introduced graph transformer to prevent over-smoothing by capturing long-range dependencies. Comparison results on the benchmark dataset demonstrate that our proposed MGDTI is effective in the DTI prediction

Fractal pore and its impact on gas adsorption capacity of outburst coal: Geological significance to coalbed gas occurrence and outburst

Pore structure and methane adsorption of coal reservoir are closely correlated to the coalbed gas occurrence and outburst. Full-scale pore structure and its fractal heterogeneity of coal samples were quantitatively characterized using low-pressure N2 gas adsorption (LP-N2GA) and high-pressure mercury intrusion porosimetry (HP-MIP). Fractal pore structure and adsorption capacities between outburst and nonoutburst coals were compared, and their geological significance to gas occurrence and outburst was discussed. The results show that pore volume (PV) is mainly contributed by macropores ( \u3e 1000 nm) and mesopores (100-1000 nm), while specific surface area (SSA) is dominated by micropores ( \u3c 10 nm) and transition pores (10 - 100 nm). On average, the PV and SSA of outburst coal samples are 4.56 times and 5.77 times those of nonoutburst coal samples, respectively, which provide sufficient place for gas adsorption and storage. The pore shape is dominated by semiclosed pores in the nonoutburst coal, whereas open pores and inkbottle pores are prevailing in the outburst coal. The pore size is widely distributed in the outburst coal, in which not only micropores are dominant, but also, transition pores and mesopores are developed to a certain extent. Based on the data from HP-MIP and LP-N2GA, pore spatial structure and surface are of fractal characteristics with fractal dimensions Dm1 (2.81 - 2.97) and Dn (2.50 - 2.73) calculated by Menger model and Frenkel-Halsey-Hill (FHH) model, respectively. The pore structure in the outburst coal is more heterogeneous as its Dn and Dm1 are generally larger than those of the nonoutburst coal. The maximum methane adsorption capacities (VL: 15.34 - 20.86 cm 3 / g) of the outburst coal are larger than those of the nonoutburst coal (VL : 9.97-13.51cm 3 / g). The adsorptivity of coal samples is governed by the micropores, transition pores, and Dn because they are positively correlated with the SSA. The outburst coal belongs to tectonically deformed coal (TDC) characterized by weak strength, rich microporosity, complex pore structure, strong adsorption capacity, but poor pore connectivity because of inkbottle pores. Therefore, the area of TDC is at high risk for gas outburst as there is a high-pressure gas sealing zone with abundant gas enrichment but limited gas migration and extraction

Global μ

The impulsive complex-valued neural networks with three kinds of time delays including leakage delay, discrete delay, and distributed delay are considered. Based on the homeomorphism mapping principle of complex domain, a sufficient condition for the existence and uniqueness of the equilibrium point of the addressed complex-valued neural networks is proposed in terms of linear matrix inequality (LMI). By constructing appropriate Lyapunov-Krasovskii functionals, and employing the free weighting matrix method, several delay-dependent criteria for checking the global μ-stability of the complex-valued neural networks are established in LMIs. As direct applications of these results, several criteria on the exponential stability, power-stability, and log-stability are obtained. Two examples with simulations are provided to demonstrate the effectiveness of the proposed criteria

Estimation of Dry Matter and N Nutrient Status of Choy Sum by Analyzing Canopy Images and Plant Height Information

The estimation accuracy of plant dry matter by spectra- or remote sensing-based methods tends to decline when canopy coverage approaches closure; this is known as the saturation problem. This study aimed to enhance the estimation accuracy of plant dry matter and subsequently use the critical nitrogen dilution curve (CNDC) to diagnose N in Choy Sum by analyzing the combined information of canopy imaging and plant height. A three-year experiment with different N levels (0, 25, 50, 100, 150, and 200 kg center dot ha(-1)) was conducted on Choy Sum. Variables of canopy coverage (CC) and plant height were used to build the dry matter and N estimation model. The results showed that the yields of N-0 and N-25 were significantly lower than those of high-N treatments (N-50, N-100, N-150, and N-200) for all three years. The variables of CC x Height had a significant linear relationship with dry matter, with R-2 values above 0.87. The good performance of the CC x Height-based model implied that the saturation problem of dry matter prediction was well-addressed. By contrast, the relationship between dry matter and CC was best fitted by an exponential function. CNDC models built based on CC x Height information could satisfactorily differentiate groups of N deficiency and N abundance treatments, implying their feasibility in diagnosing N status. N application rates of 50-100 kgN/ha are recommended as optimal for a good yield of Choy Sum production in the study region

Global μ

The complex-valued neural networks with unbounded time-varying delays are considered. By constructing appropriate Lyapunov-Krasovskii functionals, and employing the free weighting matrix method, several delay-dependent criteria for checking the global μ-stability of the addressed complex-valued neural networks are established in linear matrix inequality (LMI), which can be checked numerically using the effective LMI toolbox in MATLAB. Two examples with simulations are given to show the effectiveness and less conservatism of the proposed criteria