Research on application of a hybrid heuristic algorithm in transportation carbon emission.

China's transportation industry is entering a stage of high-quality development. Carbon emissions and environmental protection issues have put pressure on the construction of a green and low-carbon transportation system, and the transportation industry has become one of the industries with the fastest growth in carbon emissions. Therefore, it is of great significance to study the influencing factors of carbon dioxide emissions in the transportation industry and predict its carbon emissions. This article first thoroughly analyzes the main sources of carbon emissions in the transportation industry, including nine major energy consumption sources such as coal, gasoline, and diesel, and obtains the carbon emission values ​​from 2000 to 2017. Secondly, a linear regression analysis was performed on 13 pre-selected influencing factors and CO2 emissions in the transportation industry. In order to obtain the potential similarities between the factors, factor 13 is divided into four categories: economic scale, population size, transportation structure, and energy consumption. Each category and factor analysis is divided into four potential factors. Third, a training model was established based on the data from 2000 to 2012. Four algorithms, neural network (BP), extreme learning machine (ELM), genetic algorithm optimized neural network (GA-BP), and genetic algorithm optimized extreme learning machine (GA-ELM) are used to predict 2013 to 2017 and compare the predicted value of its respective algorithm with the actual value. Finally, the results show that the genetic algorithm optimized extreme learning machine (GA-ELM) hybrid heuristic algorithm has the highest degree of fit between the predicted value and the true value, which further illustrates the carbon emissions of the hybrid heuristic algorithm in the transportation industry. For the superiority of the prediction, the study also shows that the four influencing factors seriously affect the carbon emissions of the transportation industry. Therefore, accelerating the upgrading of the transportation structure and changing the proportion of energy consumption will be important measures for the transportation sector to control carbon emissions in the next step, so as to promote the sustainable development of the transportation system.

Male Patients With Dilated Cardiomyopathy Exhibiting a Higher Heart Rate Acceleration Capacity or a Lower Deceleration Capacity Are at Higher Risk of Cardiac Death.

The effects of dilated cardiomyopathy (DCM) on cardiac autonomic regulation and electrophysiology, and the consequences of such changes, remain unclear. We evaluated the associations between heart rate acceleration capacity (AC) and deceleration capacity (DC), heart structural and functional changes, and cardiac death in 202 healthy controls and 100 DCM patients. The DC was lower and the AC was higher in DCM patients (both males and females). Multivariable, linear, logistic regression analyses revealed that in males, age was positively associated with AC in healthy controls (N = 85); the left atrial diameter (LAD) was positively and the left ventricular ejection fraction (LVEF) was negatively associated with AC in DCM patients (N = 65); age was negatively associated with DC in healthy controls (N = 85); and the LAD was negatively and the LVEF was positively associated with DC in DCM patients (N = 65). In females, only age was associated with either AC or DC in healthy controls (N = 117). Kaplan-Meier analysis revealed that male DCM patients with greater LADs (≥46.5 mm) (long-rank chi-squared value = 11.1, P = 0.001), an elevated AC (≥-4.75 ms) (log-rank chi-squared value = 6.8, P = 0.009), and a lower DC (≤4.72 ms) (log-rank chi-squared value = 9.1, P = 0.003) were at higher risk of cardiac death within 60 months of follow-up. In conclusion, in males, DCM significantly affected both the AC and DC; a higher AC or a lower DC increased the risk of cardiac death.

Heart acceleration and deceleration capacities associated with dilated cardiomyopathy.

BACKGROUND: Heart rate deceleration capacity and acceleration capacity are novel autonomic nervous system indicators of cardiac neural regulation. Dilated cardiomyopathy (DCM) changes cardiac electrophysiology; however, how deceleration capacity and acceleration capacity associated with DCM remain unclear. MATERIALS AND METHODS: To evaluate the association between heart rate acceleration capacity, deceleration capacity and DCM, 66 DCM patients with DCM and 209 controls were enrolled in the study. Demographic data, echocardiographic data, heart rate variability, deceleration capacity and acceleration capacity were collected. The association pattern between DCM and these indexes were studied by multiple logistic regression analysis. RESULTS: Deceleration capacity and acceleration capacity were independent risk factors for DCM with an odds ratio (OR) and 95% confidence interval (CI), determined by multiple logistic regression analysis, of 7·97 (3·87-16·42) and 0·09 (0·05-0·19), respectively. Univariate ordinal logistic regression analysis showed that acceleration capacity, fastest heart rate, standard deviation of normal-to-normal RR intervals (SDNN) and left ventricular ejection fraction (LEVF) associated with heart failure grade. The OR for each covariate was further adjusted for the effects of other significant covariates in multivariate ordinal logistic regression analysis. Acceleration capacity, fastest heart rate and LVEF were still independent risk factors in the final equation with ORs of 1·32 (1·03-1·79), 1·04 (0·01-1·07) and 0·46 (0·23-0·93), respectively. CONCLUSION: Heart rate acceleration capacity and deceleration capacity are independent risk factors for DCM, and acceleration capacity is a predictive factor for heart failure exacerbation in patients with DCM.