Do the government subsidies inhibit the entity over-financialization? Fresh evidence from China

In order to verify effect of the industrial policies on solving the problem of market failure, we collect the data from China A-share listed companies among 2008-2019, and analyze the effect of government subsidies on the entity over-financialization. The results show that government subsidies significantly inhibit the entity over-financialization. Because the government subsidies could increase the performance of enterprise’s main business and level of the enterprise’s profitability. Subsequently, the enterprise’s arbitrage from cross-industries and the managers’ composition could be decreased. Consequently, government subsidies could reduce the entity over-financialization by the reduce of enterprise’s arbitrage from multi-industries and increase of the managers’ composition which is related to the enterprise’s performance. The results also indicate that the entity financialization is mainly motivated by enterprise arbitrage rather than ‘preventive reserve’ in China. Moreover, the inhibitory effect of government subsidies on the entity over-financialization is only significant in the enterprises with non-state-owned, high-tech, and higher level of demand of innovation. Thus, the government should accurately implement subsidy policies for the enterprises and increase the supports for enterprises with high-tech and higher level of demand of innovation, which could promote economy high-quality development

Spherical Transformer: Adapting Spherical Signal to CNNs

Convolutional neural networks (CNNs) have been widely used in various vision tasks, e.g. image classification, semantic segmentation, etc. Unfortunately, standard 2D CNNs are not well suited for spherical signals such as panorama images or spherical projections, as the sphere is an unstructured grid. In this paper, we present Spherical Transformer which can transform spherical signals into vectors that can be directly processed by standard CNNs such that many well-designed CNNs architectures can be reused across tasks and datasets by pretraining. To this end, the proposed method first uses locally structured sampling methods such as HEALPix to construct a transformer grid by using the information of spherical points and its adjacent points, and then transforms the spherical signals to the vectors through the grid. By building the Spherical Transformer module, we can use multiple CNN architectures directly. We evaluate our approach on the tasks of spherical MNIST recognition, 3D object classification and omnidirectional image semantic segmentation. For 3D object classification, we further propose a rendering-based projection method to improve the performance and a rotational-equivariant model to improve the anti-rotation ability. Experimental results on three tasks show that our approach achieves superior performance over state-of-the-art methods

Pročišćavanje i karakterizacija fibrinolitičkog enzima iz plijesni Rhizopus microsporus var. tuberosus

Extracellular fibrinolytic enzyme from Rhizopus microsporus var. tuberosus was purified and characterised. The microorganism was isolated in a distillery from daqu, a fermentative agent used in the production of Chinese liquor and vinegar at diff erent temperatures. The fibrinolytic enzyme was partially purifi ed by ammonium sulphate precipitation, dialysis, DEAE Sepharose® Fast Flow ion exchange chromatography and Sephadex G-75 gel filtration chromatography. The molecular mass of the fi brinolytic enzyme was estimated to be 24.5 kDa by SDS-PAGE. The purified enzyme showed optimal activity at pH=7.0 and 37 °C by fibrin plate method. It showed stronger resistance to the inhibition by trypsin and was stable at 37 °C retaining 96.1 % residual activity aft er 4 h of incubation. The fibrinolytic activity of the enzyme was enhanced by Na+, Ca2+, Mg2+ and Mn2+. Conversely, Zn2+ and Cu2+ partly inhibited enzymatic activity. Using fibrin plate method, we found that the enzyme not only degrades fibrin directly, but also activates plasminogen into plasmin to degrade fibrin. The results indicate that the pure enzyme has a potential in dissolving blood clot, and the possibility for application in the treatment of thrombosis.U radu je pročišćen i ispitan izvanstanični fibrinolitički enzim iz plijesni Rhizopus microsporus var. tuberosus. Mikroorganizam je izoliran u distileriji iz starter kulture koja se koristi za fermentaciju tradicionalnog kineskog likera i octa pri različitim temperaturama, tzv. daqu. Fibrinolitički je enzim djelomično pročišćen taloženjem pomoću amonijevog sulfata, dijalizom, ionskom kromatografijom na koloni DEAE Sepharose Fast Flow i gel-filtracijskom kromatografijom na koloni Sephadex G-7. Molekularna masa fibrinolitičkog enzima, određena pomoću SDS-PAGE, iznosila je 24,5 kDa. Optimalni uvjeti za aktivnost pročišćenog enzima bili su pH=7,0 i 37 °C. Enzim je bio otporan na inhibiciju tripsinom, stabilan pri 37 °C, te je zadržao 96,1 % aktivnosti nakon 4 sata inkubacije. Fibrinolitička se aktivnost enzima pojačala u prisutnosti iona Na+, Ca2+, Mg2+ i Mn2+, dok su ioni Zn2+ i Cu2+ djelomično inhibirali njegovu aktivnost. Utvrđeno je da enzim izravno razgrađuje fibrin, i aktivira plazminogen, pri čemu nastali plazmin razgrađuje fibrin. Rezultati pokazuju da se pročišćeni enzim može primijeniti u liječenju tromboze, jer ima sposobnost razgradnje krvnih ugrušaka

Stability analysis of token-based wireless networked control systems under deception attacks

Currently, cyber-security has attracted a lot of attention, in particular in wireless industrial control networks (WICNs). In this paper, the stability of wireless networked control systems (WNCSs) under deception, attacks is studied with a token-based protocol applied to the data link layer (DLL) of WICNS. Since deception attacks cause the stability problem of WNCSs by changing the data transmitted over a wireless network, it is important to detect deception attacks, discard the injected false data and compensate for the missing data (i.e., the discarded original data with the injected false data). The main contributions of this paper are: 1) With respect to the character of the token-based protocol, a switched system model is developed. Different from the traditional switched system where the number of subsystems is fixed, in our new model this number will be changed under deception attacks. 2) For this model, a new Kalman filter (KF) is developed for the purpose of attack detection and the missing data reconstruction. 3) For the given linear feedback WNCSs, when the noise level is below a threshold derived in this paper, the maximum allowable duration of deception attacks is obtained to maintain the exponential stability of the system. Finally, a numerical example based on a linearized model of an inverted pendulum is provided to demonstrate the proposed design

Lattice Boltzmann Phase Field Simulations of Droplet Slicing

ACKNOWLEDGEMENT This research was sponsored by Shanghai Sailing Program (No. 20YF1416000) and SUES Distinguished Overseas Professor Program.Peer reviewedPostprin

Production and Characterization of Antifungal Compounds Produced by Lactobacillus plantarum IMAU10014

Lactobacillus plantarum IMAU10014 was isolated from koumiss that produces a broad spectrum of antifungal compounds, all of which were active against plant pathogenic fungi in an agar plate assay. Two major antifungal compounds were extracted from the cell-free supernatant broth of L. plantarum IMAU10014. 3-phenyllactic acid and Benzeneacetic acid, 2-propenyl ester were carried out by HPLC, LC-MS, GC-MS, NMR analysis. It is the first report that lactic acid bacteria produce antifungal Benzeneacetic acid, 2-propenyl ester. Of these, the antifungal products also have a broad spectrum of antifungal activity, namely against Botrytis cinerea, Glomerella cingulate, Phytophthora drechsleri Tucker, Penicillium citrinum, Penicillium digitatum and Fusarium oxysporum, which was identified by the overlay and well-diffusion assay. F. oxysporum, P. citrinum and P. drechsleri Tucker were the most sensitive among molds

A CNN-GRU rock burst risk analysis model considering micro-seismic precursor characteristics

Micro-seismic signals are important for the analysis of rock burst risk in coal mines. However, microseismic events generally cannot determine whether rock bursts have occurred, and the established models rarely consider the characteristics of micro-seismic, resulting in insufficient model performance. In this study, a microseismic dataset with rock burst risk labels is obtained by tracking and calibrating the characteristics of microseismic events in coal mines, and a micro-seismic risk analysis method based on CNN-GRU model is proposed. This method considers the characteristics of micro-seismic precursors, and employs the time, location and energy of micro-seismic signals to establish the characteristic values. The established initial data set is divided into training set, verification set and test set in time scale, and the problem of imbalance between dangerous and non-dangerous samples is dealt with. Finally, the CNN-GRU model is trained by the training set, and the model with the best effect in the verification set is used for testing, which strictly regulates the generalization ability of the model. This method has achieved good results in rock burst risk analysis of micro-seismic monitoring events in a coal mine, which proves that it is reliable to use deep learning method to analyze the rock burst risk on the basis of selecting appropriate analysis features

Categorization of Factors Affecting the Resistance and Parameters Optimization of Ultra-Fine Cemented Paste Backfill Pipeline Transport

Ultra-fine cemented paste backfill (UCPB) is prepared using tailings, binder and water. The factors affecting the resistance of UCPB pipe transport are numerous and complex, and the factor interactions restrict the rational development of the filling pipe transport design, which is not conducive to reducing the resistance. This paper categorizes and integrates the factors of pipe transport resistance by theoretical analysis and uses response surface methodology (RSM) to study the influence of different types of factors on the UCPB pipe transport resistance. The results show that the pipe transport resistance factors are classified into endogenous and exogenous factors. According to the classification, the reduction rate of the optimized pipe transport resistance is as high as 25.31% and 15.81%. This shows that the categorization of factors affecting the pipe transport resistance is important for investigating UCPB pipe flow. The single-factor terms with the highest significance under the effect of endogenous and exogenous factors are mass concentration and pipe diameter, respectively. The two interaction terms with highest significance are mass concentration and slurry temperature, pipe diameter and flow velocity, respectively. The results provide new ideas to reduce the resistance of mine pipeline and improve the filling benefit and convenience of pipeline design