The development of supply chain finance in China

Fast economic growth inChina, as one of the largest manufacturing bases and markets in global economy, has stimulated innovations in financial service for facilitating supply chain operations.  We have surveyed the supply chain finance industry inChina, and provide an introduction of commonly used financial products.  We point out the obstacles and shortfalls in the development of the supply chain finance inChinaand provide recommendations for both private companies and policy makers on how to facilitate the growth of supply chain finance

Global enhancement of cortical excitability following coactivation of large neuronal populations

Correlated activation of cortical neurons often occurs in the brain and repetitive correlated neuronal firing could cause long-term modifications of synaptic efficacy and intrinsic excitability. We found that repetitive optogenetic activation of neuronal populations in the mouse cortex caused enhancement of optogenetically evoked firing of local coactivated neurons as well as distant cortical neurons in both ipsilateral and contralateral hemispheres. This global enhancement of evoked responses required coactivation of a sufficiently large population of neurons either within one cortical area or distributed in several areas. Enhancement of neuronal firing was saturable after repeated episodes of coactivation, diminished by inhibition of N-methyl-D-aspartic acid receptors, and accompanied by elevated excitatory postsynaptic potentials, all consistent with activity-induced synaptic potentiation. Chemogenetic inhibition of neuronal activity of the thalamus decreased the enhancement effect, suggesting thalamic involvement. Thus, correlated excitation of large neuronal populations leads to global enhancement of neuronal excitability

Global enhancement of cortical excitability following coactivation of large neuronal populations

Correlated activation of cortical neurons often occurs in the brain and repetitive correlated neuronal firing could cause long-term modifications of synaptic efficacy and intrinsic excitability. We found that repetitive optogenetic activation of neuronal populations in the mouse cortex caused enhancement of optogenetically evoked firing of local coactivated neurons as well as distant cortical neurons in both ipsilateral and contralateral hemispheres. This global enhancement of evoked responses required coactivation of a sufficiently large population of neurons either within one cortical area or distributed in several areas. Enhancement of neuronal firing was saturable after repeated episodes of coactivation, diminished by inhibition of N-methyl-D-aspartic acid receptors, and accompanied by elevated excitatory postsynaptic potentials, all consistent with activity-induced synaptic potentiation. Chemogenetic inhibition of neuronal activity of the thalamus decreased the enhancement effect, suggesting thalamic involvement. Thus, correlated excitation of large neuronal populations leads to global enhancement of neuronal excitability

Research on the Selective Grinding of Zn and Sn in Cassiterite Polymetallic Sulfide Ore

When cassiterite polymetallic sulfide ore is being ground in the ball mill, the contradiction between over grinding of cassiterite and under grinding of sulfide ore is inevitable due to their mechanical property differences. In this paper, a selective grinding characterization method is proposed to optimize the grinding of cassiterite polymetallic sulfide ore based on the respective selective grinding indexes, namely, the changes in the cumulative grade and cumulative quantities of metal. The preferred grinding characteristics were studied by varying three grinding operation factors, the grinding time, grinding concentration, and mill speed, as these all affect the selective grinding behavior of the ball mill. In the proposed method, the breaking process preferentially begins with the Zn minerals in the cassiterite polymetallic sulfide ore; however, Sn minerals are found to break first when the specific energy of the grinding media is large. The differences in the crushing characteristics of Zn and Sn minerals narrow down as the grinding time and concentration increase. When the grinding concentration is lower than 50%, the two types of minerals are broken with little difference. However, when the grinding concentration is higher than 50%, the Zn minerals are broken prior to the Sn minerals

Research on the Selective Grinding of Zn and Sn in Cassiterite Polymetallic Sulfide Ore

When cassiterite polymetallic sulfide ore is being ground in the ball mill, the contradiction between over grinding of cassiterite and under grinding of sulfide ore is inevitable due to their mechanical property differences. In this paper, a selective grinding characterization method is proposed to optimize the grinding of cassiterite polymetallic sulfide ore based on the respective selective grinding indexes, namely, the changes in the cumulative grade and cumulative quantities of metal. The preferred grinding characteristics were studied by varying three grinding operation factors, the grinding time, grinding concentration, and mill speed, as these all affect the selective grinding behavior of the ball mill. In the proposed method, the breaking process preferentially begins with the Zn minerals in the cassiterite polymetallic sulfide ore; however, Sn minerals are found to break first when the specific energy of the grinding media is large. The differences in the crushing characteristics of Zn and Sn minerals narrow down as the grinding time and concentration increase. When the grinding concentration is lower than 50%, the two types of minerals are broken with little difference. However, when the grinding concentration is higher than 50%, the Zn minerals are broken prior to the Sn minerals

Study on the Grinding Law of Ball Media for Cassiterite–Polymetallic Sulfide Ore

To solve the problem involved in the grinding of cassiterite–polymetallic sulfide ore in which fine grinding causes the cassiterite to be overground or coarse grinding leads to inadequate liberation of sulfide minerals, the influences of the ball grinding medium on the size distribution of the grinding product were investigated. Two types of ball filling patterns, namely, single-sized and multi-sized ball grinding media, were adopted in wet batch grinding tests. The results show that increasing the grinding time resulted in a rapid increase in minus 0.038 mm particles and a slight increase in the Sn grade in this fine size fraction. The smaller the ball filling fraction was, the more obviously the ball size affected the size distribution of the grinding product, the variation of which with the ball size became complicated with the increase in the ball filling fraction. Obvious jumping phenomena in the plotting of the percentages of the discussed size fractions against the ball size were observed when the balling filling fraction was larger than 30%; the most obvious jumping phenomena took place at a 35% ball filling fraction. The results of the grinding tests with the multi-sized media show that the size distribution of the grinding product was closely related to that of the mixed ball sizes and their composition percentages

Research on Grinding Characteristics and Comparison of Particle-Size-Composition Prediction of Rich and Poor Ores

The particle size composition of grinding products will significantly affect the technical and economic indexes of subsequent separation operations. The polymetallic complex ores from Tongkeng and Gaofeng are selected as the research object in this paper. Through the JK drop-weight test, the batch grinding test, and the population-balance kinetic model of grinding with the Simulink platform, the grinding characteristics of the two types of ores and the particle-size-composition prediction methods of grinding products are studied. The results show that the impact-crushing capacity of Tongkeng ore and Gaofeng ore are “medium” grade and “soft” grade, respectively. The crushing resistance of Tongkeng ore increases with the decrease in particle size, and the crushing effect is more easily affected by particle size than that of Gaofeng ore. For the same ore, the accuracy order of the three methods is: PSO–BP method > JK drop-weight method > BIII method. For the same method, only the BIII method has higher accuracy in predicting Gaofeng ore than Tongkeng ore, and other methods have better accuracy in predicting Tongkeng ore than Gaofeng ore. The prediction accuracy of the BIII method is inferior to that of the JK drop-weight method and the PSO–BP method and is easily affected by the difference in mineral properties. The PSO–BP method has a high prediction accuracy and fast model operation speed, but the accuracy and speed of the iterative results are easily affected by parameters such as algorithm program weight and threshold. The parameter-solving process of each prediction method is based on different simplifications and assumptions. Therefore, appropriate hypothetical theoretical models should be selected according to different ore properties for practical application

Research on Grinding Law and Grinding Parameters Optimization of Polymetallic Complex Ores

Grinding plays an important role in mining, construction, metallurgy, chemical, coal and other basic industries. In terms of beneficiation, grinding is the most energy consuming operation. So, reasonable grinding conditions according to the properties of ores is the key to obtain good grinding results and reduce energy consumption and resource waste. In this paper, Tongkeng and Gaofeng polymetallic complex ores are taken as research objects, and the effects of grinding law based on single factor condition test and the grinding parameters optimization based on response surface method were studied for two kinds of ores. The results show that grinding time is a significant factor affecting the particle size composition. The suitable grinding concentration of Tongkeng ore and Gaofeng ore is 70% and 75%, respectively. The effect of mill filling ratio on Gaofeng ore is not obvious. The rotational rate has little effect on the grinding technical efficiency. The regression model equations obtained by response surface method are extremely significant, and the relative errors of prediction are all within 1%, indicating high reliability of fitting equations. The order of influencing factors of the two ores is as follows: grinding time > filling ratio > grinding concentration. For Tongkeng ore, the optimized grinding conditions are grinding time 5.4 min, grinding concentration 67% and filling ratio 35%. For Gaofeng ore, the optimized grinding conditions are grinding time 3.8 min, grinding concentration 73% and filling ratio 34%

Research on Grinding Law and Grinding Parameters Optimization of Polymetallic Complex Ores

Grinding plays an important role in mining, construction, metallurgy, chemical, coal and other basic industries. In terms of beneficiation, grinding is the most energy consuming operation. So, reasonable grinding conditions according to the properties of ores is the key to obtain good grinding results and reduce energy consumption and resource waste. In this paper, Tongkeng and Gaofeng polymetallic complex ores are taken as research objects, and the effects of grinding law based on single factor condition test and the grinding parameters optimization based on response surface method were studied for two kinds of ores. The results show that grinding time is a significant factor affecting the particle size composition. The suitable grinding concentration of Tongkeng ore and Gaofeng ore is 70% and 75%, respectively. The effect of mill filling ratio on Gaofeng ore is not obvious. The rotational rate has little effect on the grinding technical efficiency. The regression model equations obtained by response surface method are extremely significant, and the relative errors of prediction are all within 1%, indicating high reliability of fitting equations. The order of influencing factors of the two ores is as follows: grinding time > filling ratio > grinding concentration. For Tongkeng ore, the optimized grinding conditions are grinding time 5.4 min, grinding concentration 67% and filling ratio 35%. For Gaofeng ore, the optimized grinding conditions are grinding time 3.8 min, grinding concentration 73% and filling ratio 34%

Inadequately Pre-trained Models are Better Feature Extractors

Pre-training has been a popular learning paradigm in deep learning era, especially in annotation-insufficient scenario. Better ImageNet pre-trained models have been demonstrated, from the perspective of architecture, by previous research to have better transferability to downstream tasks. However, in this paper, we found that during the same pre-training process, models at middle epochs, which is inadequately pre-trained, can outperform fully trained models when used as feature extractors (FE), while the fine-tuning (FT) performance still grows with the source performance. This reveals that there is not a solid positive correlation between top-1 accuracy on ImageNet and the transferring result on target data. Based on the contradictory phenomenon between FE and FT that better feature extractor fails to be fine-tuned better accordingly, we conduct comprehensive analyses on features before softmax layer to provide insightful explanations. Our discoveries suggest that, during pre-training, models tend to first learn spectral components corresponding to large singular values and the residual components contribute more when fine-tuning