Modeling Paying Behavior in Game Social Networks

Online gaming is one of the largest industries on the Internet, generating tens of billions of dollars in revenues annually. One core problem in online game is to find and convert free users into paying customers, which is of great importance for the sustainable development of almost all online games. Although much research has been conducted, there are still several challenges that remain largely unsolved: What are the fundamental factors that trigger the users to pay? How does users? paying behavior influence each other in the game social network? How to design a prediction model to recognize those potential users who are likely to pay? In this paper, employing two large online games as the basis, we study how a user becomes a new paying user in the games. In particular, we examine how users' paying behavior influences each other in the game social network. We study this problem from various sociological perspectives including strong/weak ties, social structural diversity and social influence. Based on the discovered patterns, we propose a learning framework to predict potential new payers. The framework can learn a model using features associated with users and then use the social relationships between users to refine the learned model. We test the proposed framework using nearly 50 billion user activities from two real games. Our experiments show that the proposed framework significantly improves the prediction accuracy by up to 3-11% compared to several alternative methods. The study also unveils several intriguing social phenomena from the data. For example, influence indeed exists among users for the paying behavior. The likelihood of a user becoming a new paying user is 5 times higher than chance when he has 5 paying neighbors of strong tie. We have deployed the proposed algorithm into the game, and the Lift_Ratio has been improved up to 196% compared to the prior strategy

Protective Effect of Edaravone in Primary Cerebellar Granule Neurons against Iodoacetic Acid-Induced Cell Injury

Edaravone (EDA) is clinically used for treatment of acute ischemic stroke in Japan and China due to its potent free radical-scavenging effect. However, it has yet to be determined whether EDA can attenuate iodoacetic acid- (IAA-) induced neuronal death in vitro. In the present study, we investigated the effect of EDA on damage of IAA-induced primary cerebellar granule neurons (CGNs) and its possible underlying mechanisms. We found that EDA attenuated IAA-induced cell injury in CGNs. Moreover, EDA significantly reduced intracellular reactive oxidative stress production, loss of mitochondrial membrane potential, and caspase 3 activity induced by IAA. Taken together, EDA protected CGNs against IAA-induced neuronal damage, which may be attributed to its antiapoptotic and antioxidative activities

The spatiotemporal variation and control mechanism of surface pCO2 in winter in Jiaozhou Bay, China

In many mid-latitude coastal waters during winter months, in addition to temperature, the large change in biogeochemical processes often influence and complicate the surface partial pressure of CO2 (pCO2). Based on the hydrological and carbonate parameters in seven cruises, this study analysed the evolution process and explored the control mechanism of the surface pCO2 in Jiaozhou Bay, China, from December to March. The results showed that the pCO2 ranged from 157 μatm to 647 μatm, and the bay represented a sink for atmospheric CO2 (-3.8 mmol m-2 d-1) in the whole winter. The non-temperature processes were the dominant factors affecting intra-winter pCO2 variation. In December, the bay was dominated by aerobic respiration and acted as a CO2 source (3.0 mmol m-2 d-1). From early January to late February, however, the vigorous growth of cold algae caused strong primary production, and the bay presented as a CO2 sink (from -6.4 mmol m-2 d-1 in early January to -15.5 mmol m-2 d-1 in late February). In March, primary production weakened and the effects of the CaCO3 precipitation appeared, and the strength of the CO2 sink was obviously weakened (-1.1 mmol m-2 d-1). Meanwhile, the water temperature decreased gradually from December to late January and then increased until March, and it further expanded the variation range of pCO2. Our results highlight the obvious source/sink change in mid-latitude seawater CO2 in winter, while more field observations are still needed to further understand the complicated biogeochemical processes and its influence on seawater pCO2

Theoretical and Case Studies of Interval Nonprobabilistic Reliability for Tailing Dam Stability

The stability of the operation of a tailing dam is affected by reservoir water level, phreatic line, and mechanical parameters of tailings. The values of these factors are not a definite value in different situations. Meanwhile, the existence of the phreatic line makes it a more complex issue to analyze the stability of the tailing dam. Additionally, it is very hard to give a definite limit to the state of tailing dam from security to failure. To consider the uncertainty when calculating the stability of the tailing dams, interval values are used to indicate the physical and mechanical parameters of tailings. An interval nonprobabilistic reliability model of the tailing dam, which can be used when the data is scarce, is developed to evaluate the stability of the tailing dam. The interval nonprobabilistic reliability analysis model of tailing dam is established in two cases, including with and without considering phreatic line conditions. The proposed model was applied to analyze the stability of two tailing dams in China and the calculation results of the interval nonprobabilistic reliability are found to be in agreement with actual situations. Thus, the interval nonprobabilistic reliability is a beneficial complement to the traditional analysis method of random reliability

Machine Learning Based Identification of Microseismic Signals Using Characteristic Parameters

Microseismic monitoring system is one of the effective means to monitor ground stress in deep mines. The accuracy and speed of microseismic signal identification directly affect the stability analysis in rock engineering. At present, manual identification, which heavily relies on manual experience, is widely used to classify microseismic events and blasts in the mines. To realize intelligent and accurate identification of microseismic events and blasts, a microseismic signal identification system based on machine learning was established in this work. The discrimination of microseismic events and blasts was established based on the machine learning framework. The microseismic monitoring data was used to optimize the parameters and validate the classification methods. Subsequently, ten machine learning algorithms were used as the preliminary algorithms of the learning layer, including the Decision Tree, Random Forest, Logistic Regression, SVM, KNN, GBDT, Naive Bayes, Bagging, AdaBoost, and MLP. Then, training set and test set, accounting for 50% of each data set, were prospectively examined, and the ACC, PPV, SEN, NPV, SPE, FAR and ROC curves were used as evaluation indexes. Finally, the performances of these machine learning algorithms in microseismic signal identification were evaluated with cross-validation methods. The results showed that the Logistic Regression classifier had the best performance in parameter identification, and the accuracy of cross-validation can reach more than 0.95. Random Forest, Decision Tree, and Naive Bayes also performed well in this data set. There were some differences in the accuracy of different classifiers in the training set, test set, and all data sets. To improve the accuracy of signal identification, the database of microseismic events and blasts should be expanded, to avoid the inaccurate data distribution caused by the small training set. Artificial intelligence identification methods, including Random Forest, Logistic Regression, Decision Tree, Naive Bayes, and AdaBoost algorithms, were applied to signal identification of the microseismic monitoring system in mines, and the identification results were consistent with the actual situation. In this way, the confusion caused by manual classification between microseismic events and blasts based on the characteristics of waveform signals is solved, and the required source parameters are easily obtained, which can ensure the accuracy and timeliness of microseismic events and blasts identification

Closed-Form Solutions for Locating Heat-Concentrated Sources Using Temperature Difference

The closed-form solution, one of the effective and sufficient optimization methods, is usually less computationally burdensome than iterative and nonlinear minimization in optimization problems of heat source localization. This work presents two-dimensional, closed-form solutions for locating heat-concentrated sources using temperature differences for known and unknown temperature gradient systems. The nonlinear location equations for heat-concentrated source location are simplified to linear equations, and they are solved directly to obtain the analytical solution. To validate the accuracy of the proposed analytical solutions, three numerical examples of heat source localization were conducted. Results show that the proposed analytical solutions have a higher accuracy than iterative results by Levenberg–Marquardt. The locating accuracy for the three sources using AS-KTG improved by 94.82%, 90.40%, and 92.77%, while the locating accuracy for the three sources using AS-UTG improved by 68.94%, 16.72%, and 46.86%, respectively. It is concluded that the proposed method can locate the heat sources using temperatures and coordinates of sensors without the need for a heat transfer coefficient, a heat transfer rate, and thermal conductivity. These proposed analytical solutions can provide a new approach to locating heat sources for more complicated conditions using temperature differences, such as the localization of geothermal sources and nuclear waste leak points

Genetic Mapping and Identification of the Gibberellin 3-Oxidase Gene GA3ox Leading to a GA-Deficient Dwarf Phenotype in Pumpkin (Cucurbita moschata D.)

Plant height is an important indicator in the ideal plant model and contributes to optimizing yield and lodging resistance. The emergence of a dwarfing phenotype provides an opportunity for plant height improvement. In a previous study, we identified a dwarf mutant Si1 in pumpkin (Cucurbita moschata D.) obtained by ethyl methane sulfonate (EMS) mutagenesis of the inbred line N87. Phenotype identification for Si1 revealed a decrease in cell size and shorter internodes than those of wild type. Genetic analysis revealed that the dwarf mutant trait was controlled by a single recessive gene, CmaSI1. By bulked segregant analysis (BSA) and subsequent fine mapping, we mapped the CmaSI1 locus to a 463 kb region on chromosome 8 that contained 28 annotated genes in the F2 population. Only one nonsynonymous single nucleotide polymorphism (SNP) in CmoCh08G006170 was obtained according to whole-genome resequencing of the two parents. CmoCh08G006170, a homolog of Arabidopsis gibberellin 3-beta hydroxylase (GA3ox), is a key enzyme in the regulation of bioactive gibberellins (GAs). RNA-seq analysis and qRT-PCR showed that the expression level of CmoCh08G006170 in stems of Si1 was changed compared with that of wild type. The dwarf phenotype could be restored by exogenous GA3 treatment, suggesting that Si1 is a GA-deficient mutant. The above results demonstrated that CmoCh08G006170 may be the candidate gene controlling the dwarf phenotype. This study provides an important theoretical basis for the genetic regulation of vine length and crop breeding in pumpkin

Optimization for U-Shaped Steel Support in Deep Tunnels under Coupled Static-Dynamic Loading

With the effects of high geostress and intensive dynamic disturbances in deep mining, the stability and safety of tunnels are seriously affected. The optimization for U-shaped steel support is of vital significance and can solve the problems of cost reduction and tunnel instability. Based on the perturbation equation, a coupled formula for U-shaped steel and the surrounding rock mass was proposed to evaluate the practical stability of a U-shaped steel support. Through a numerical simulation method, the characteristics of U-shaped steel support can be obtained under coupled static-dynamic loading. Furthermore, the field test was carried out and compared with the numerical simulation, which was discussed. The results show that there will be a stress concentration when the contact area is small. In addition, the concentrated stress will release with the increase in contact area. With the increase in the lateral stress coefficient, the deformation exhibits a downward trend under static loading, indicating that the lateral stress is the dominant force driving the deep geostress activity. The support requirement of this section of surrounding rock can be satisfied by a U-shaped steel group with 1.5 m spacing under dynamic disturbance