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Background Diabetes is frequently associated with poor prognosis among acute myocardial infarction (AMI) patients. Patients with these comorbidities often have atypical symptoms and subsequent delay in treatment. Few studies have reported detailed AMI symptoms in patients with diabetes. This study compared AMI symptoms and presentation characteristics between diabetics and non-diabetics. Methods We included patients from the China AMI registry diagnosed with AMI between January 2013 and September 2014. Baseline characteristics, symptomology, and delay in treatment were compared between diabetics and non-diabetics. Multivariable logistic regression analysis was used to explore independent predictors of atypical symptoms. Results A total of 4450 (20.2%) patients had diabetes. They were older, more often women, higher in body mass index, and more likely to have non-ST segment elevation myocardial infarction. Fewer diabetic patients presented with persistent precordial chest pain (63.1% vs. 68%, P < 0.0001), diaphoresis (60.1% vs. 65.6%, P < 0.0001), fatigue (16.7% vs. 18.3%, P = 0.0123), and incontinence (0.4% vs. 0.7%, P = 0.0093). Time to hospital presentation was longer among patients with diabetes than those without. In multivariable analysis, diabetes was identified as an independent predictor of atypical symptoms (OR: 1.112, 95% CI: 1.034–1.196). Conclusions Our study is the first large-scale study providing evidence that diabetics are less likely to present with typical chest pain and more likely to experience treatment delay when suffering from an AMI. Our results may increase clinician awareness of recognizing AMI patients rapidly to reduce diagnosis and treatment delay, particularly in the context of diabetes.

Objectives To evaluate the feasibility and safety of a second generation robotic percutaneous coronary intervention (R-PCI) system in China. Background Robotic PCI has been shown to be an effective method for conducting coronary interventions. It has further benefits of more accurate lesion measurement, improved stent deployment, reduced incidence of geographic miss and reduction of operator radiation exposure. Methods This single center evaluation enrolled 10 consecutive patients who had been selected for PCI. Clinical success was defined as residual stenosis < 30% and no in-hospital major adverse cardiovascular events. Learning curve effect was assessed by comparing efficiency metrics of early vs. later cases. Results Eleven lesions were treated all successfully without manual interruption or MACE events. Most lesions (63%) were ACC/AHA class B2 and C. Mean procedure time was 57.7 ± 26.4 min, however two procedures were part of live demonstrations. Excluding the two live cases, the mean procedure time was 51.8 ± 23.7 min. Procedural efficiency tended to improve from early cases to later cases based on PCI time (48.3 ± 32.9 vs. 25.5 ± 13.0 min, P = 0.27), fluoroscopy time (20.3 ± 8.2 vs. 12.5 ± 4.6 min, P = 0.16), contrast volume (145.0 ± 28.9 vs. 102.5 ± 17.1 mL, P = 0.05) and Air Kerma dose (1932 ± 978 vs. 1007 ± 70 mGy, P = 0.31). Conclusions Second generation robotic PCI was safe, effective and there were trends toward improvements in procedural efficiency during this early experience in China.

A new hybrid methodology is introduced which is a combination of multiple regression model and generalised autoregressive conditional heteroskedasticity (GARCH) model. Comparing the new approach and the vector auto-regression (VAR) model, this paper analyses the short-term dynamics of the energy efficiency index (EEI) in response to change in the five indicator variables for thermal power plants in China. The result indicates that: (i) The new hybrid model can directly calculate the EEIs of thermal power plants without artificial intervention. (ii) It can eliminate the disturbance of residual superposition. (iii) The new method will offer more direct information on the degree of volatility among determinants and operating inefficiency.

In this article, the non-parametric data envelopment analysis method (DEA) is employed to evaluate energy efficiency (EE) of 34 coal-fired power units in China. Input-oriented CCR (Charnes, Cooper and Rhodes) model is used for EE analysis. Two efficiency indices, generalized EE and special EE are defined and analyzed. The generalized EE is calculated based on four input parameters: coal consumption, oil consumption, water consumption and auxiliary power consumption by power units. The special EE is only based on two input parameters: coal consumption and auxiliary power consumption. Relations between these two EE indices and non-comparable factors including quality of coal, load factor, capacity factor, parameters of main steam and cooling method are studied. Comparison between EE evaluation results of the two indices is conducted. Results show that these two kinds of EE are more sensitive to the load factor than the capacity factor. The influence of the cooling method on EE is larger than that of main steam parameter. The influence of non-comparable factors on the special EE is stronger than that on the generalized EE.

In this paper, both energy efficiency and environmental efficiency of coal-fired power units are studied based on the non-parametric data envelopment analysis method (DEA). Compared with energy efficiency, the emission of SO 2 and NO x are taken into consideration for the environmental efficiency. The ST model is employed to calculate pure technical efficiency. And the strong disposability model is used to calculate the environmental efficiency. Analysis and comparison for energy and environmental efficiency are conducted. By comparing the influence of ownership factor on these two kinds of efficiency, it can be found that the China Guodian Corporation ranks the first in both the energy and environmental efficiency. China Datang Corporation ranks the third in energy efficiency while ranks the last one in environmental efficiency.

This study proposes a new hybrid forecasting methodology for short-term energy efficiency prediction, this new method composes stochastic frontier analysis-generalized autoregressive conditional heteroskedasticity (SFA-GARCH) model and radial basis function neural (RBFN) model. A three-step procedure is implemented. First, the selected independent variables are analysed via SFA-GARCH model, to present their casual relations. Second, regional energy efficiency level is evaluated based upon the time series data obtained from past ten years. Finally, the proposed hybrid model considers a 6-years ahead prediction of regional energy efficiency level. The result demonstrates good performance according to tail loss test when compared with normal SFA method, it proves that the hybrid methodology should be an appropriate measure.

The so-called Hierarchical–Indicator Comparison (HIC) method is introduced in this paper. It mainly serves for industrial energy conservation programs in China. A chemical industry named purified terephthalic acid (PTA) is used to outline this method. Two key points of the HIC method are the construction of energy efficiency indicators (EEI) system and the way to utilize indicators appropriately. After a brief review of EE evaluation methods in literature, the construction procedure of energy efficiency indicators (EEI) system for PTA industry is presented firstly. How to correct reference values for indicators according to non-comparable factors is discussed. Then, how to implement the HIC method based on EEI system is presented. Every indicator has its own advantages and disadvantages. Disadvantages of an indicator can be conquered by other indicators. With multiple indicators used together, more objective EE evaluation result can be obtained. Finally, some proposals for further work of this method are also presented.

In this article, the non-parametric data envelopment analysis method (DEA) is employed to evaluate energy efficiency (EE) of coal-fired power units in China. The data set contains inputs and outputs of 34 coal-fired power units with the capacity of 600MW. Input-oriented CCR (Charnes-Cooper-Rhodes) is employed. The value of CCR efficiency calculated in this paper is based on two input parameters: fuel consumption and auxiliary power consumption. The relations between EE and factors including, parameter of main steam, cooling method, capacity utilization rate and annual utilization hours are analysed. Some relation curves are fitted.

Energy consumption by industrial sectors has possessed a large proportion of total energy use in many countries especially China. For the purposes of energy conservation, the government of China has implemented long-term policies to continuously assist thermal power industries in improving the energy efficiency. This paper investigates the short- and long-term causal relationship implied by the standard coal consumption per unit product of power (SCC), the rate of electricity consumption of power plant (EC), the total water consumption per unit product of power generation (WC) and the total oil consumption per year (OC). The result indicates that there is mutual influence among variables. The SCC is significantly correlated with the EC, a large amount of EC may impact SCC heavily. The OC is proved to be a new driving factor, and it has long-run equilibrium relationship with other three variables.

In this paper, evaluation index system with two levels is established to evaluate energy efficiency of fossil-fuel power plant in a comprehensive and objective way, in order to provide detailed methods for energy-saving and emission reduction. The index system includes five indicators at the first level and two indicators at the second level. Principal component analysis (PCA) model is adopted to comprehensively evaluate energy efficiency of fossil-fuel power units. PCA is a relatively objective evaluation method, which can not only determine weights objectively, but also roundly reflect most information from multiple indicators. The method presented in this paper is implemented on seventeen 1000MW thermal power units. The results obtained agree well with the actual situation and those in the references.

A multi-scale modeling framework combining finite volume method (FVM) and lattice Boltzmann method (LBM) previously developed by our group is used to predict electrochemical transport reaction in proton exchange membrane fuel cell (PEMFC) cathode with a parallel gas channel (GC), a gas diffusion layer (GDL) with porous structures and a catalyst layer (CL) with idealized microstructures. In this framework, the PEMFC cathode is divided into two sub-domains, one is GC and the other contains GDL and CL. The FVM is used to simulate transport phenomena in the GC sub-domain, while the LBM is employed for pore-scale transport phenomena in the GDL and CL as well as proton conduction in the CL in the other sub-domain. Two reconstruction operators are adopted to transfer macro density, velocities and concentration in the FVM to density distribution functions and concentration distribution functions in the LBM at the interface between the two sub-domains. Simulation results show that the coupled (hybrid) simulation strategy developed is able to predict transport phenomena in the GC and to capture the pore-scale transport processes in porous GDL and CL. In addition, some techniques to save the computational resources and to improve the efficiency of the coupled (hybrid) simulation strategy are discussed.

In this paper, a concentration distribution function reconstruction operator is derived to lift macroscopic parameter concentration to concentration distribution function in lattice Boltzmann method (LBM). Combined with a density–velocity distribution function reconstruction operator previously derived by the author’s group, the coupled finite volume method and LBM scheme (CFVLBM), previously proposed by the authors’ group is extended to simulate both fluid flow and mass transport processes. The accuracy of concentration distribution function reconstruction operator and the feasibility of CFVLBM are validated by two numerical examples, diffusion–convection–reaction problem and natural convection in a square cavity induced by concentration gradient. Finally, the CFVLBM is further adopted to simulate fluid flow and mass transport in the gas channel (GC) and gas diffusion layer (GDL) of a proton exchange membrane fuel cell (PEMFC). It is found that the CFVLBM can capture the pore-scale information of fluid flow and species transport in porous GDL and can save the computational resources.

When forest stock volume is quantitative estimated using SPOT-5, QuickBird and ALOS optical data with linear regression model, the optimal ratio of remote sensing band is chosen from the above three types of optical remote sensing data respectively, which is a significant part. In this study, the experiments are taken in Zhazuo Forest of Xiuwen County of Guizhou Province. Comprehensive utilization of the three optical data, the selected ratio of band is confirmed according with characteristics of the forest region. Optimization of the ratio of band remote sensing method used is the criteria of mean residual sum of square called RMSq. In this paper the multicollinearity which commonly exsits between ratio of the original band is analyzed and studied to get rid of its unfavorable influence in this paper. By means of the criteria of mean residual sum of square, the ratio of remote sensing band which determines the impact of forest stock volume estimation is confirmed finally. Conclusions are as follows: Compared with the selected band, multiple-correlation has been greatly reduced. The optimal ratio of remote sensing band such as SP4, SP2-3/2+3, SP 1-4/1+4, SP1*3/2 has an important role on the interpretation of forest stock volume estimation.