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Objective. Nonalcoholic fatty liver disease (NAFLD) and type 2 diabetes (T2DM) commonly coexist and act synergistically to drive adverse clinical outcomes. This study is aimed at investigating the effects of exercise intervention and oral hypoglycaemic drug of metformin (MET) alone or combined on hepatic lipid accumulation. To investigate if oxidative stress and endoplasmic reticulum stress (ERS) are involved in lipotoxicity-induced hepatocyte apoptosis in diabetic mice and whether exercise and/or MET alleviated oxidative stress or ERS-apoptosis by AMPK-Nrf2-HO-1 signaling pathway. Methods. Forty db/db mice with diabetes ( random blood glucose ≥ 250 mg / dL ) were randomly allocated into four groups: control (CON), exercise training alone (EX), metformin treatment alone (MET), and exercise combined with metformin (EM) groups. Hematoxylin-eosin and oil red O staining were carried out to observe hepatic lipid accumulation. Immunohistochemical and TUNEL methods were used to detect the protein expression of the binding immunoglobulin protein (BiP) and superoxide dismutase-1 (SOD1) and the apoptosis level of hepatocytes. ERS-related gene expression and the AMPK-Nrf2-HO-1 signaling pathway were tested by western blotting. Results. Our data showed that db/db mice exhibited increased liver lipid accumulation, which induced oxidative and ER stress of the PERK-eIF2α-ATF4 pathway, and hepatocyte apoptosis. MET combined with exercise training significantly alleviated hepatic lipid accumulation by suppressing BiP expression, the central regulator of ER homeostasis, and its downstream PERK-eIF2α-ATF4 pathway, as well as upregulated the AMPK-Nrf2-HO-1 signaling pathway. Moreover, the combination of exercise and MET displayed protective effects on hepatocyte apoptosis by downregulating Bax expression and TUNEL-positive staining, restoring the balance of cleaved-caspase-3 and caspase-3, and improving the antioxidant defense system to prevent oxidative damage in db/db mice. Conclusion. Compared to MET or exercise intervention alone, the combined exercise and metformin exhibited significant effect on ameliorating hepatic steatosis, inhibiting oxidative and ER stress-induced hepatocyte apoptosis via improving the capacity of the antioxidant defense system and suppression of the PERK-eIF2α-ATF4 pathway. Furthermore, upregulation of AMPK-Nrf2-HO-1 signaling pathway might be a key crosstalk between MET and exercise, which may have additive effects on alleviating hepatic lipid accumulation.

Shared transportation is widely used in current urban traffic. As a representative mode of transport, shared bikes have strong mobility and timeliness, so it is particularly critical to accurately predict the number of bikes used in an area every hour. In this paper, London bike-sharing data are selected as a data set to primarily analyze the impact of meteorological elements and time factors on bike-sharing demand. At the same time, it is important to use LSTM neural network models and popular machine learning models to predict demand for shared bikes at an hourly level. Through data analysis and visualization, the major elements affecting the bike-sharing demand are found to include humidity, peak hours, temperature, and other elements. The root mean squared error of the LSTM model is 314.17, the R2 score is as high as 0.922, and the error is small in comparison to other machine learning models. Through the evaluation indicators, it can be seen that the LSTM model has the smallest error between the prediction results and the true values of the compared machine learning methods, and the change trend of the model prediction result curve is basically consistent with the actual result curve.

Persistent hyperglycemia increases inflammation response, promoting the development of myocardial fibrosis. Based on our previous research that exercise and metformin alone or their combination intervention could attenuate myocardial fibrosis in db/db mice, this study aimed to further explore the underlying mechanisms by which these interventions attenuate myocardial fibrosis in early diabetic cardiomyopathy. Forty BKS db/db mice were randomly divided into four groups. Diabetic db/db mice without intervention were in the C group. Aerobic exercise (7-12 m/min, 30-40 min/day, 5 days/week) was performed in the E group. Metformin (300 mg·kg

The aim of the study was to explore different effects of exercise, metformin alone, or exercise combined with metformin on cardiovascular morphological and functional changes in early stage of type 2 diabetes mellitus. Eight-week-old diabetic db/db mice and BKS mice were recruited and exposed to three different treatments (exercise, metformin alone, or their combination) for 8 weeks. Metformin was administered intragastrically, and aerobic exercise was performed using treadmill with 7–12 m/min, 30–40 min/day, 5 days/week. In the combination group, aerobic exercise was carried out for 30 min after intragastric administration of metformin. The results showed that all three treatments improved cardiac fibrosis and aortic lipid deposition. Exercise intervention failed to alleviate myocardial hypertrophy, but it improved the declined heart rate and diastolic blood pressure in diabetic db/db mice. In contrast, metformin caused opposite effects in these mice. The combination of exercise and metformin had additive effects on glucose intolerance and insulin sensitivity rather than on the improvement of myocardial and aortic structure. In conclusion, metformin improved changes in the morphology and structure of the heart and aorta, while exercise alone or in combination with metformin demonstrated more advantages in cardiac functional reserve through the physiological hypertrophy of myocardium in diabetic db/db mice.

Research on dangerous driving behavior recognition is beneficial to regulate the driving behavior of drivers. As the existing algorithms are sensitive to noise, and abnormal data often affects the process of identifying dangerous driving behaviors. This paper proposes a novel driving behavior research method. Such method establishes a driving behavior recognition model based on Support Vector Machine (SVM) and oversampling. The experimental results show that the proposed model demonstrates a higher recognition rate.

The continuous integration of computer technology and art has promoted the development of digital music. In recent years, the massive music data has promoted the problems of music inquiry, music classification, music content understanding and so on, making a new subject develop continuously, that is music information retrieval. The current mainstream feature extraction of music is based on acoustics, such as pitch, timbre, loudness, zero-crossing rate, etc. Direct symbolic music feature extraction and music analysis are relatively rare. This paper aims to present an empirical study on text clustering ideas in natural language processing into the field of symbolic music style analysis. Firstly, Symbolic music is textualized and proposed by us as an inspiration. To be precise, textualization of symbolic music is converted into weighted structured data through tf-idf algorithm. In the following step, three different types of mainstream clustering algorithms, K-Means, OPTICS, and Birch are used to perform cluster analysis and comparison on the traditional Chinese folk music dataset we crawled, T-SNE algorithm is used to visualize the dimensionality reduction of high-dimensional data. Finally, a series of objective evaluation indicators of clustering are used to evaluate the three clustering algorithms. Through comprehensive evaluation of indicators, it is proved that the clustering algorithm has achieved an excellent clustering effect on the midi note dataset we extracted. As a result of the clustering, the professional music theory knowledge and the historical development characteristics of traditional Chinese folk music are comprehensively integrated, which reversely verifies that the 1300 midi music data sets have distinct modal characteristics of traditional Chinese folk music.

At present, oil recovery is generally carried out by pumping unit in oilfield development. Monitoring working status of pumping unit in real time is vital to avoid inefficient production, prevent and control hidden dangers. The condition diagnosis is one of the important contents which often rely on analyzing and classifying the dynamometer card. The traditional method of dynamometer card identification is insufficient to process the huge dynamometer card data in real time. Moreover, the experience and knowledge of the onsite engineers will affect the accuracy of identification. Therefore, in this paper, Freeman chain code and differential code are adopted to extract the characteristics of the dynamometer card data of the pumping unit. Then BP neural network based diagnosis model is proposed to realize the automatic identification of the fault type of the pumping unit according to the dynamometer card. The experimental result shows that the proposed model achieves an accuracy of 94.79%, thus can be used to assist decision making in actual production.

Haze pollution is a serious weather condition which occurs frequently in mainland China. As there has been an increasing worldwide research interest around topics in environment protection and human health, PM2.5 concentration is regarded as a vital index to reflect the air quality. The current PM2.5 prediction algorithm has the problems of single index and insufficient features. In this paper, based on XGBoost, a PM2.5 prediction model is proposed. Such model adopts the random forest algorithm for feature selection. Then, the optimal feature subset that affects PM2.5 concentration is selected. The experiment is conducted on real dataset of daily air quality and weather observations in Beijing from 2017 to 2019. The results show that the daily average concentration of PM2.5 can be accurately predicted with root mean square error at 8.63, and correlation at 95.41%.

Larvae of Chalcophora japonica chinensis Schaufuss, 1879 were collected from within dead trunks in Hubei Province, China, in February 2019. These specimens created an opportunity to provide the first description of the larval stage of this subspecies; The larva is described and illustrated based on morphological characters and DNA barcoding.

The construction and promotion of emission trading information platform makes it possible for enterprises to collect and use emission rights and other data. How to conduct game analysis for industrial enterprises' emission trading under data driven has become an effective basis and inevitable trend to assist enterprises to achieve emission reduction and optimal decision-making. However, existing game methods are not used for comprehensive optimal decision for enterprises based on these data. Therefore, this paper integrates dynamic game and data to effectively solve optimal choice in the process of emission trading among industrial enterprises. The bargaining dynamic game model and forward reasoning method are proposed to realize the game analysis of emission trading among enterprises in the secondary market based on the data mining or evaluation of pollutant emissions, market price and marginal revenue of emission rights and initial emission rights by Support Vector Regression (SVR), Linear Regression (LR) and Analytical Hierarchy Process (AHP). Taking six industrial enterprises in Tianjin as an example, this paper analyzes the optimal trading price, trading volume and object of emission trading among different enterprises under different loss factors.

In this study, an analytical hierarchy process based model is proposed to perform the decision-making for virtual machine migration towards green cloud computing. The virtual machine migration evaluation index system is established based on the process of constructing hierarchies for evaluation of virtual machine migration, and selection of task usage. A comparative judgment of two hierarchies has been conducted. In the experimental study, five-point rating scale has been adopted to map the raw data to the scaled rating score; this rating method is used to analyze the performance of each virtual machine and its task usage data. The results show a significant improvement in the decision-making process for the virtual machine migration. The deduced results are useful for the system administrators to migrate the exact virtual machine, and then switch on the power of physical machine that the migrated virtual machine resides on. Thus the proposed method contributes to the green cloud computing environment.

This paper uses the ID3 algorithm to analyze and extract the classification rules hidden in the original data of the physical fitness test of junior college students in mobile app platform database of “Running Shida”. These classification rules are highly consistent with the actual data in the database and are highly consistent with the results of individual survey of students. The forecasting conclusion of these classification rules is of great significance for quickly and scientifically determining students’ physique, putting forward reasonable suggestions for sports training and promoting the reform of “integration in-and-outside class” teaching mode of physical education in colleges and universities.

A virtual machine placement optimization model based on optimized ant colony algorithm is proposed. The model is able to determine the physical machines suitable for hosting migrated virtual machines. Thus, it solves the problem of redundant power consumption resulting from idle resource waste of physical machines. First, based on the utilization parameters of the virtual machine, idle resources and energy consumption models are proposed. The models are dedicated to quantifying the features of virtual resource utilization and energy consumption of physical machines. Next, a multi-objective optimization strategy is derived for virtual machine placement in cloud environments. Finally, an optimal virtual machines placement scheme is determined based on feature metrics, multi-objective optimization, and the ant colony algorithm. Experimental results indicate that compared with the traditional genetic algorithms-based MGGA model, the convergence rate is increased by 16%, and the optimized highest average energy consumption is reduced by 18%. The model exhibits advantages in terms of algorithm efficiency and efficacy.

Botnet has become a popular technique for deploying Internet crimes. The command of botnet has evolved into a major way for attackers to launch Distributed Denial of Service attacks on network servers. Modelized analysis methods need to be studied for botnet attacks implements, defense, and prediction. In this paper, we propose a novel game theory-based model to describe the scenario, in which the botmaster launching Distributed Denial of Service attacks using a botnet while the defender equipped a firewall defending. In our model, we consider the following: firstly, the botmaster and the defender can be rational or irrational; secondly, the interaction between the botmaster and the defender is modeled as a dynamic game; thirdly, their supporting or not self-learning databases. We detail the analysis of eight sub-scenarios for the assumptions and give an easy-to-use algorithm for adjustment of offensive and defensive strategy. We use the OPNET to validate our model and its effectiveness. The experiment result shows that our strategy can improve the firewall abilities to lower false alarm rate FR and improve the botmaster lower exposure rate of botnet to avoid detection. Furthermore, the model is helpful to evaluate defense ability of the defender towards current botmaster attacks by analyzing attack log in sandbox. Copyright © 2016 John Wiley & Sons, Ltd.

Increasingly complex urban traffic conditions often challenge the express services with long delivery path and much more time as consumed. In this paper, a Traffic Impact Factor (TIF) is introduced to model the impact of urban traffic conditions on express services. Based on the TIF, an optimization model is constructed to minimize the delivery distance and the time consumed.In the solution , the objective function is defined by incorporating TIF into the probability transfer formula of the Ant Colony Optimization (ACO), which has thereby improved the update rule of pheromone. The improved ACO is suitable for the optimization of the express delivery path. In the experiment, 30 cities express delivery experimental data are used to compare the distribution distance, the delivery time and the objective function value between the ACO and the improved ACO. The result shows that the improved ACO may reduce the distribution cost related to the distribution distance and the time factor

The security improvement of service provided by peer-to-peer (P2P) network has been widely studied. Se- curity issue of P2P is mainly related to the users of P2P services and threat of malicious software. Especially, some malicious software could threaten other P2P users and even threaten the security of the entire P2P network. In this paper, based on the analytical hierarchy process, a degenerated node evaluation model has been proposed to evaluate the node resource usage of the P2P. Moreover, an attribute clustering based collaborative filtering algorithm is de- picted for calculating the similarities between the identified degenerated node and all other nodes within a hybrid autonomous cluster. The algorithm utilizes similarity characteristics of node task resource attributes, especially CPU related attributes, to filter redundant data by feature selection. Experiments show that, the proposed model and method is feasible and has the vital significance for providing decision support and recommendation to the search node. It is practical for secure computing of P2P network.

Virtual machines (VM) migration can improve availability, manageability, performance and fault tolerance of systems. Current migration researches mainly focus on the promotion of the efficiency by using shared storage, priority-based policy etc.. But the effect of migration is not well concerned. In fact, once physical servers are overloaded from denial-of-service attack (DDoS) attack, a hasty migration operation not only unable to alleviate the harm of the attack, but also increases the harmfulness. In this paper, a novel DDoS attack, Cloud-Droplet-Freezing (CDF) attack, is described according to the characteristics of cloud computing cluster. Our experiments show that such attack is able to congest internal network communication of cloud server cluster, whilst consume resources of physical server. Base on the analysis of CDF attack, we highlight the method of evaluating potential threats hidden behind the normal VM migration and analyze the flaws of existing intrusion detection systems/prevention system for defensing the CDF attack.

By combining the modified Random-SMOTE oversampling algorithm with the Biased-SVM classification method, this paper has proposed an improved classification approach for the imbalanced data sets. This algorithm is able to cluster the minority samples, and ensures the support vectors as the parent samples according to the distances between the cluster centers in the minority class and the majority class center. It then could generate the new samples for the minority class. The experiment is conducted upon the five imbalanced data sets from UCI data set and the proposed algorithm is compared with other algorithms. The experimental results show that the improved algorithm has significant classification effect for the imbalanced data sets.

This paper analyzes the system of OLAP physical examination data mining system, the concept of classification, and the nature of decision tree to define the implementation process of ID3 algorithm. Transaction data of the physical examination information management system of Xi’an Shiyou University Hospital from 2015 to 2018 are selected and prepared for cleaning and preprocessing, then the data is dimensionally reduced and transformed, to design a direct data model suitable for classification analysis. The sampling data set is classified and analyzed by ID3 algorithm, and the classification rules are extracted. Using the prediction conclusions of these classification rules, the medical examiner can quickly and scientifically predict the individual physique of each college teacher, so as to classify college teachers of different physiques, and specifically guide the teacher’s further treatment plan of exercise, diet and work. It has high reference value and promotion towards teachers’ health.

In this paper, we review the classical secure communication issues, which is always described as a set of interactive rules following a specified sequence, in the perspective of game theory. By introducing rational communication participants, we model the secure communication process in the manner of game theory to capture the interactions of distrusted communication parties. More specifically, we propose a formal framework to provide a precise description of the computation and communication rules in a secure communication game. Each player tends to behave in a way that maximizes their profits in this framework. Following the framework, the fairness of a protocol is presented according to Nash equilibrium of the communication game, as well as the equivalence condition towards a fair protocol. To verify the effectiveness of this framework, we design and implement a series of experiments. The experimental results show that our rational framework is more secure and closer to the practice compared with traditional cryptographic models, which can be a promising analysis solution for future cryptographic protocols.