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The characteristic of primary carbides in industrial grade M35 high-speed steel was analyzed systematically using an association of scanning electron microscopy (SEM), electron backscattered diffraction (EBSD), and energy dispersive spectrometer (ESD), and the precipitation behavior of various carbides was discussed by combining the thermodynamic calculation and the microsegregation model. The results indicate that the size of network carbides at the center area is larger than that at the edge area in the electroslag remelting (ESR) ingot. The area fraction and average width of carbides increase gradually with the increase of height, and the values located in the top are 6.79% and 4.83 μm, respectively. The lamellar M2C carbide, fishbone M6C carbide and blocky MC carbide compose the network structure, also there are several independently distributed cerebriform carbides. Compared to M6C carbide, the Mo/W ratio and the contents of V and C in M2C carbide are higher, and MC carbide is prominently characterized by the highest V content. Simultaneously, the precipitation of Fe-rich M6C carbide that occurs in the top is closely related to the low cooling rate besides the influence of alloying elements. The current reduction operation can genetically promote a reduction of 0.75% in the average area fraction and 0.04 μm in the average size of carbides in the forged steels, which is beneficial for reducing and homogenizing primary carbides in the ESR ingot.

Following the Paris Agreement, economic development demands energy services while reducing emissions. This study focuses on non-residential natural gas consumption, which plays a crucial role in the energy transition; however, market-based mechanisms are still in the early stages and have not yet been explored. Based on the analysis of data collected from 2000 to 2020 in China, this study constructs a simultaneous equation model to analyze the rebound and induced effects of non-residential natural gas consumption. This paper finds that industrial economic growth has a significant promoting effect on non-residential gas consumption. In addition, implementing and promoting natural gas utilization policies and increases in natural gas price can significantly reduce gas consumption intensity. This article innovatively calculates the rebound and induced effects of non-residential gas consumption, providing a decision-making reference for accelerating the realization of the dual-carbon goal.

The spread of natural gas in the main international natural gas markets shows characteristics of violent fluctuation, frequent mutation, and periodic evolution, which pose a great challenge to the safety and sustainable development of natural gas in China. First, this paper used the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) model to decompose and reconstruct natural gas “Asian premium.” Based on the evolution law of each subsequence, the “Asian premium” was decomposed into four parts: speculative, geographical, energy security, and fundamental. Second, this study adopts the Bayesian model averaging (BMA) method to extract the main factors and uses the TVP-SV-VAR (time varying parameter stochastic volatility vector autoregression) model to characterize the nonlinear relationships. The results showed that the speculative motivation of suppliers and the cross-regional transportation cost of natural gas were the main reasons for the fluctuations, and the influence of oil prices on “Asian premium” was gradually reduced. The impulse response results showed that when the financial crisis hit in 2008, the impact of energy security and economic fundamentals on the “Asian premium” was more obvious. In the case of supply shortages, financial speculation and geographical factors played a more significant role in the “Asian premium” for natural gas. This study provides an important reference for price mechanism reform in China's natural gas market.

Abstract YEATS domain containing 2 (YEATS2), it may function as a proto-oncogene. This study aims to investigate if YEATS2 correlates with prognosis in hepatocellular carcinoma. The prognostic landscape of YEATS2 and its relationship with expression in hepatocellular carcinoma were deciphered with public databases, RT-qPCR and western-blot in tissue samples. The expression profiling and prognostic value of YEATS2 were explored using UALCAN, TIMER, OncoLnc database. Transcription and survival analyses of YEATS2 in hepatocellular carcinoma were investigated with cBioPortal database. The STRING database was explored to identify molecular functions and signaling pathways downstream of YEATS2. YEATS2 expression was significantly higher in hepatocellular carcinoma compared with adjacent non-malignant tissues. Promoter methylation of YEATS2 exhibited different patterns in hepatocellular carcinoma. High expression of YEATS2 was associated with poorer survival. Mechanistically, YEATS2 was involved in mediating multiple biological processes including morphogenesis and migration.

Intact pavement markings organize the utilization of the pavement surface area, increase the road capacity, and improve traffic safety. However, deterioration occurring over the service life will lead to traffic safety hazards. A detailed inspection is regarded as the central tenet of pavement marking management. Conventional manual assessment approaches are time-consuming, qualitative, and argued to be subjective. Thus, cost reduction, quantitative analysis, and automatization have been the focus of research. Unmanned Aerial Vehicle (UAV) platform has been studied wildly in civil tasks due to its low cost and high maneuverability. A UAV-based platform was developed in this research to address the inefficiency, access limitations, and image processing of existing road marking inspection systems. In this research, the resolution of the image is 5472 × 3080 pixels and is captured with a fixed focal length of 8.8 mm. Given intensity differences between pixels of paintings and asphalt backgrounds, the pixels belonging to painting regions are classified using the K-mean clustering algorithm. After detecting all parking lines from images, the Otsu method was employed to determine the threshold of 0.9 as the quantitative indicator of painting quality. The UAV-based platform exhibits expected performance in assessing the pavement markings in a parking area. Future research will explore the deployment in city-scale applications.

Sulfur dioxide (SO2) is a typical pollutant that affects human health, climate, and environmental and ecological conditions. China has been experiencing high concentrations of SO2, particularly in urban areas, since the 1990s. In 2010, a “joint prevention and control” (JPC) policy was issued to address air pollution problems and strengthen the regulation of SO2 emissions. This study aimed to describe the mitigation effects of this policy on SO2 emissions in 116 Chinese cities from 2003 to 2017. We applied global and local Moran's I indices to confirm the presence of significant spatial autocorrelation in SO2 emissions and constructed four spatial models to assess the effect of the JPC policy in reducing SO2 emissions and its transmission mechanism. We found that the policy decreased SO2 emissions by 1.89 × 104 tons, SO2 intensity by 1.70 tons per km2, and SO2 per capita of 158.49 tons per 10,000 people in each city, on average, all of which are significant changes. The empirical results also show that population growth, economic structure, and environmental protection significantly decrease SO2 emissions. Finally, we recommended policies to encourage regional cooperation under the JPC policy, with the aim of promoting further reductions in SO2 emissions.

Bridges are usually located at the junction of land and water, where the surrounding area is open terrain, making them to be easily damaged by lightning strikes. The quantitative analysis of lightning transient characteristics and the impulse effect on bridges can provide scientific data to support the lightning protection design for bridges. In this study, a single-tower cable-stayed bridge is taken as a representative case. The CDEGS (Current Distribution Electromagnetic Interference Grounding and Soil Structure Analysis) software is used to establish a three-dimensional simulation model of the bridge. With this model, the magnetic field, step voltage, and lightning current distribution on the tower top, stay cables, and bridge deck under the most severe direct lightning strike scenario typical for cable-stayed structures are simulated. The results are as follows: (1) when the stay cables are struck by lightning, the peak of the magnetic field intensity is highest at the location of electronic information equipment, followed by that at the top of the tower, and the lowest at the bridge deck. The peak step voltage at the ground below the bridge is the largest when lightning strikes the cable, and that is the smallest when lightning strikes the bridge deck. (2) The magnitude distribution of the lightning current on the stay cables is related to the strike location, the distance between the strike point and the grounding system, and the length of the stay cables. When the stay cables are closer to the grounding system and the length is shorter, the lightning current that flows through them is larger. (3) The magnitude distribution of the lightning current on the grounding system is associated with the location of the strike and the position of the grounding system. The closer the grounding system is to the strike point, the larger the amplitude of the lightning current on the grounding system. The grounding system positioned in the middle significantly reduces the lightning current due to the shielding effect. The grounding system located at the edge shows minimal variation in the time domain characteristics of the lightning current, resulting in less reduction of the initial steepness of the lightning wave. However, the grounding system at the middle position experiences a significant increase in the temporal characteristics of lightning current, reducing the hazards caused by the steepness of the original lightning wave.

Transshipment is an effective method for reducing mismatches between supply and demand among retailers. Consumers attempting to purchase out-of-stock items may wait for transshipment, purchase at another store, or choose not to buy. In this paper, the consumer behavioral heterogeneity is characterized using the transshipment request rate and consumer switching rate. It affects the replenishment and transshipment decisions, as well as the system profits. The inventory replenishment and transshipment decisions are studied in both centralized and decentralized two-location inventory systems with consumer behavioral heterogeneity. We characterize optimal replenishment decisions in a centralized system, prove the existence of a unique Nash equilibrium in a decentralized system with a specific demand distribution, and determine the coordinating transshipment price for some decentralized systems. In numerical studies, the performance of various systems is analyzed for consumers with identical (symmetric) or differing (asymmetric) behavior between retailers. For scenarios with symmetric consumers, a higher transshipment request rate and consumer switching rate resulted in increased total profit in all systems. For scenarios with asymmetric consumers, the retailer with the higher consumer switching rate should reduce ordering in a centralized system but increase ordering in a decentralized system. Moreover, the retailer with the higher transshipment request rate reduces ordering to increase profit, whereas the other retailer increases order quantity yet earns less profit.

Abstract We study the pion electromagnetic form factor in the modulus squared dispersion relation, and do the model independent extraction of the chiral mass embedded in the sub-leading twist light-cone distribution amplitude. The motivation of this work is the recent measurement of timelike form factor in the resonant regions, which makes up the piece lacking solid QCD-based calculation. With the perturbative QCD calculation up to next-to-leading-order of strong coupling and twist four level of meson distribution amplitudes, we obtain the chiral mass of pion meson as $$m_0^\pi (1 \, \textrm{GeV}) = 1.31^{+0.27}_{-0.30} \, \textrm{GeV}$$ m 0 π ( 1 GeV ) = 1 . 31 - 0.30 + 0.27 GeV . Due to the chiral enhancement contribution from the sub-leading twist distribution amplitudes in the perturbative QCD calculation, we can not extract the lowest Gegenbauer moments of pion from the study of electromagnetic form factor. This problem could be settled down with the foresee Belle-II and BESIII measurement of the transition form factor especially in the large momentum transfers regions.

Although the carbon pricing policy is a critical driving factor that will help China achieve economic growth, energy transition, and dual climate change mitigation goals, the kind of carbon pricing policy that will complement the country's current development situation remains controversial. We apply the World Induced Technical Change Hybrid (WITCH) model to explore the heterogeneity and synergy of different carbon pricing policies, and the results indicate that it will be challenging to achieve carbon neutrality before 2060. The study find that the combined policy —a mix of carbon tax and carbon market policies — has the optimal emission reduction effect but comes with the highest economic cost, proving to be unsuitable in the long run. The carbon tax policy is an important transitional means to assist in emission reduction, which can serve as an important supplement to carbon market policy and be phased out after the market mechanism matures.

Abstract Background Esophageal carcinoma (ESCA) is considered to be one of the most common gastrointestinal cancers. Caspase‐8 (CASP8) is a key protein of cross‐talk signaling in a variety of cancers. However, the role of CASP8 expression in the prognosis of patients with ESCA has remained unexplored. Hence, it is needed to explore the clinical significance of CASP8 expression in ESCA. Methods The expression and prognosis of CASP8 were investigated in ESCA using the UALCAN, GEDS, TIMER, and OncoLnc databases. The CASP8 genetic variations in ESCA were assessed using the cBioPortal database. The correlation between CASP8 expression and tumor immune invasion and immune cell surface indicators was examined using the TIMER and TISIDTISIDB datasets. Meanwhile, the abundance of the immunological cells in the tumor and healthy tissues was assessed by the CIBERSORT method. Next, information on the co‐expressed genes of the differentially expressed genes (DEGs) in ESCA‐tumor and ESCA‐healthy tissues was obtained using the cBioPortal, UALCAN, and Coexpedia databases. Subsequently, the PPI network was constructed and the GO and KEGG pathways were analyzed using the SIRING database. Finally, CASP8 mRNA and protein expression in the ESCA tissues and matched adjacent healthy tissues were analyzed using qRT‐PCR, immune‐blotting, and immunohistochemistry. Additionally, the relationship between clinicopathological features and CASP8 expression was assessed. Results ESCA tissues had higher levels of CASP8 mRNA and protein expression compared to healthy tissues. patients with an elevated level of CASP8 expression had poor overall survival (OS). CASP8 expression was significantly correlated with the degree of differentiation (P = 0.004) and lymph node metastasis (P = 0.044). There were diverse patterns of association between immunological cell surface biomarkers and CASP8 expression. Conclusion ESCA showed significant levels of CASP8 expression which may serve as a prognostic biomarker correlated to immune infiltrates in ESCA.

Accurate predictions of hourly PM2.5 concentrations are crucial for preventing the harmful effects of air pollution. In this study, a new decomposition-ensemble framework incorporating the variational mode decomposition method (VMD), econometric forecasting method (autoregressive integrated moving average model, ARIMA), and deep learning techniques (convolutional neural networks (CNN) and temporal convolutional network (TCN)) was developed to model the data characteristics of hourly PM2.5 concentrations. Taking the PM2.5 concentration of Lanzhou, Gansu Province, China as the sample, the empirical results demonstrated that the developed decomposition-ensemble framework is significantly superior to the benchmarks with the econometric model, machine learning models, basic deep learning models, and traditional decomposition-ensemble models, within one-, two-, or three-step-ahead. This study verified the effectiveness of the new prediction framework to capture the data patterns of PM2.5 concentration and can be employed as a meaningful PM2.5 concentrations prediction tool.

Abstract Netrin-4 (NTN4), a member of neurite guidance factor family, can promote neurite growth and elongation. This study aims to investigate if NTN4 correlates with prognosis and immune infiltration in breast cancer. The prognostic landscape of NTN4 and its relationship with immune infiltration in breast cancer were deciphered with public databases and immunohistochemistry (IHC) in tissue samples. The expression profiling and prognostic value of NTN4 were explored using UALCAN, TIMER, Kaplan–Meier Plotter and Prognoscan databases. Based on TIMER, relationships of NTN4 expression with tumor immune invasion and immune cell surface markers were evaluated. Transcription and survival analyses of NTN4 in breast cancer were investigated with cBioPortal database. The STRING database was explored to identify molecular functions and signaling pathways downstream of NTN4. NTN4 expression was significantly lower in invasive breast carcinoma compared with adjacent non-malignant tissues. Promoter methylation of NTN4 exhibited different patterns in breast cancer. Low expression of NTN4 was associated with poorer survival. NTN4 was significantly positively related to infiltration of CD8+ T cells, macrophages and neutrophils, whereas significantly negatively related to B cells and tumor purity. Association patterns varied with different subtypes. Various associations between NTN4 levels and immune cell surface markers were revealed. Different subtypes of breast cancer carried different genetic alterations. Mechanistically, NTN4 was involved in mediating multiple biological processes including morphogenesis and migration.

ObjectiveThe current research aimed to development and validation in signature immune genes for lymphatic metastasis in papillary thyroid cancer (PTC).MethodWeighted correlation network analysis (WGCNA) was performed to identify genes closely correlated with lymphatic metastasis in PTC from TCGA database. Information on immune-related genes (IRGs) was obtained from the ImmPort database. Crossover genes were used with the R package clusterProfiler for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment. Key genes in the protein–protein interaction network of cross-targets were obtained using Cytoscape. Lasso and Random Forest (RF) models were utilized to identify pivotal genes. We constructed a nomogram based on the hub genes. The correlation between hub genes and immune cell infiltration was explored. We collected and assessed clinical samples via immunohistochemistry to detect the expression of hub genes.ResultIn total, 122 IRGs were correlated with lymphatic metastases from PTC. There are 10 key IRGs in the protein–protein interaction network. Then, three hub genes including PTGS2, MET, and ICAM1 were established using the LASSO and RF models. The expression of these hub genes was upregulated in samples collected from patients with lymphatic metastases. The average area under the curve of the model reached 0.83 after a 10-fold and 200-time cross-validation, which had a good prediction ability. Immuno-infiltration analysis showed that the three hub genes were significantly positively correlated with resting dendritic cells and were negatively correlated with activated natural cells, monocytes, and eosinophils. Immunohistochemistry results revealed that lymph node metastasis samples had a higher expression of the three hub genes than non-metastasis samples.ConclusionVia bioinformatics analysis and experimental validation, MET and ICAM1 were found to be upregulated in lymph node metastasis from papillary thyroid carcinoma. Further, the two hub genes were closely correlated with activated natural killer cells, monocytes, resting dendritic cells, and eosinophils. Therefore, these two genes may be novel molecular biomarkers and therapeutic targets in lymph node metastasis from papillary thyroid carcinoma.

Gold has multiple attributes and its price is affected by various factors in the market. This paper studies the dynamic relationship between the gold price returns and its affecting factors. Then we use the STL-ETS, neural network and Bayesian structural time series model to predict the gold price returns, and compare their performance with the benchmark models. The results show that the shocks of crude oil returns and VIX have the positive effect on gold price returns, the shocks of the US dollar index have the negative effect on gold price returns. And the fluctuation of gold price returns mainly depends on crude oil price returns shocks. STL-ETS model can accurately fit the fluctuation trend of the gold price returns and improve prediction accuracy.

Abstract We study the contributions of the resonant states K 0 ∗ $$ {K}_0^{\ast } $$ (1430) and K 0 ∗ $$ {K}_0^{\ast } $$ (1950) in the three-body decays B → Kπh (with h = π, K) in the perturbative QCD approach. The crucial nonperturbative input F Kπ (s) in the distribution amplitudes of the S-wave Kπ system is derived from the matrix element of vacuum to Kπ pair. The C P averaged branching fraction of the quasi-two-body decay process B → K 0 ∗ $$ {K}_0^{\ast } $$ (1950) h → Kπh is about one order smaller than that of the corresponding decay B → K 0 ∗ $$ {K}_0^{\ast } $$ (1430)h → Kπh. In view of the important contribution from the S-wave Kπ system for the B → Kπh decays, it is not appropriate to neglect the K 0 ∗ $$ {K}_0^{\ast } $$ (1950) in the theoretical or experimental studies for the relevant three-body B meson decays. The predictions in this work for the relevant decays are consistent with the existing experimental data.

Abstract:: In view of the abrupt and phased features of natural gas consumption, this paper attempts to predict natural gas consumption in China with a refined forecasting approach. First, we establish a Markov switching (MS) model to identify the phase characteristics after eliminating change points in the natural gas consumption sequence, using the product partition model (PPM). The results show that there are “rapid growth” and “slow growth” regimes in the development process of natural gas consumption in China. Second, the Bayesian model average (BMA) method is employed to determine the core determinants of natural gas consumption under sub-regimes, and it is determined that there are significant differences in the influencing factors under different regimes and periods. Third, this paper establishes the BMA model of the “rapid growth” regime after predicting the state of future natural gas consumption in China. We find that, compared to some other models, the BMA model that fully recognizes the regime without considering change points has the best predictive performance. Finally, the results of static and dynamic scenario analyses show that natural gas consumption continues to rise in 2019 and has obvious seasonal characteristics, while possible ultra-rapid growth of consumption in the future provides a new requirement for the supply of natural gas. Keywords: Natural gas consumption, PPM model, Markov switching model, BMA model, Scenario analysis

We study the quasi-two-body decays B→D⁎h→Dπh with h=(π,K) in the perturbative QCD approach and focus on the virtual contributions from the off-shell D⁎(2007)0 and D⁎(2010)± in the four measured decays B¯0→D0π+π−, B¯0→D0π+K−, B−→D+π−π− and B−→D+π−K−. For the B¯0→D⁎+π−→D0π+π− and B¯0→D⁎+K−→D0π+K− decays, their branching fractions concentrate in a very small region of mD0π+ near D⁎+ pole mass, and the virtual contributions from D⁎+, in the region mD0π+>2.1 GeV, are about 5% of the corresponding quasi-two-body results. We define two ratios RD⁎+ and RD⁎0, from which we conclude that the flavor-SU(3) symmetry will be maintained for the B→D⁎h→Dπh decays with very small breaking at any physical value of the mDπ. The B−→D⁎0π−→D0π0π− and B−→D⁎0K−→D0π0K− decays can be employed as a constraint for the D⁎0 decay width, with preferred values consistent with previous theoretical predictions for this quantity.

Objective To explore the association between socioeconomic status (SES) and vitamin D deficiency/insufficiency of women of childbearing age in rural northern China.Design A population-based, case–control study was conducted.Setting Four counties of Henan Province, China from 2009 to 2010.Participants 1151 non-pregnant healthy women between 18 and 40 years old.Primary and secondary outcome measures Serum 25-hydroxyvitamin D (25(OH)D) levels were measured using high-performance liquid chromatography-tandem mass spectrometry. Vitamin D insufficiency was defined as serum 25(OH)D ≥20 ng/mL and

With growing uncertainty about the evolution of the global landscape, it is of great practical significance to explore the nonlinear dynamic adjustment relationship among the world oil market, the global bulk shipping market, the stock market, and economic growth in China. This paper applied the TVP-SV-VAR model and selected quarterly data from 1998 to 2020 to explore the dynamics. The results indicated that the impact intensity of BDI on China’s economy had a “positive” to “negative” change in different lag periods. This was mainly due to the fact that the negative impact of higher freight prices on China’s economy outweighed the positive impact of higher trade volumes on China’s economy. The impact intensity of BDI on GDP had a distinct medium- to long-term effect. A positive BDI shock had a dampening effect on stock prices in the short and medium term, while a positive BDI shock could promote stock market prosperity in the long-term perspective. The impulse responses of SSE and GDP to BDI showed that the external shipping market shocks to China’s stock market and economic growth gradually became smaller over time. For impulse response at three different time points, the impact intensity of the BDI to GDP varied at different time points, with the largest shock during the financial crisis in 2008, followed by the shock during the oil price crash in 2014, and the smallest during the COVID-19 epidemic. This demonstrated that the external shipping market’s influence on Chinese economic growth and stock market has gradually weakened over time, illustrating the enhancement of Chinese risk-resilience capacity.

The research is based on the 6.8 million cloud-to-ground(CG) lightning data from 2006 to 2012 recorded by the lightning location system(HBLDN: Hubei Lightning Detection Network) which is composed of 12 sensors. The annual lightning intensity, variation of daily and monthly lightning, the distribution of +CG flashes in winter and cumulative probability of lightning current in Hubei province, China are analyzed. The results show that the ratio of negative and + CG flashes is 94.6% and 5.4%, respectively. The peaks of average flashes are in July and August, and the percentage of + CG flashes in winter are higher than other seasons. The distribution of + CG lightning intensity is different in winter from 2006 to 2012 and there is no apparent regularity in the evolution characteristics, and strong + CG flashes mainly occurs at the mountains and plains in the southwest and northwest of Hubei province. The percentage of the +CG flash at 0000–1200 LT is greater than –CG flash and the –CG flash percentage is greater than that of +CG flash at 1300–2100 LT, and the continuous time of diurnal variation is greater than the -CG flash. A probability equation is set up according to the lightning intensity and cumulative frequency, found that less than the 60 kA of lightning intensity is about 94% in Hubei province, and more than 100 kA of +CG flash is showing increasing trend and should be focused on improving the protection and forecasting of +CG flashes. Keywords: Lightning intensity, Cumulative probability, Hubei lightning detection network

In recent years, China’s energy structure has been adjusted unceasingly, where the proportion of natural gas has been increasing year by year, and its external dependence has also been increasing. Therefore, it is necessary to discuss the correlation between China’s natural gas market and the international energy market. This paper studies the dynamic relationship between China’s total natural gas consumption, consumption structure, and the international price of oil from the perspectives of mutation and time-variance, using the cointegration test with regime shifts and a state space model. The results show that during the global financial crisis in 2008, the cointegration relationship between China’s total natural gas consumption and the international oil price has undergone structural changes. January 2012 and March 2015 are potential structural mutation points. After the structural mutation, the impact of the international price of oil on China’s total natural gas consumption has weakened. From a structural point of view, urban gas and power generation gas have both been greatly affected by the change of oil price, while industrial gas and chemical gas are less affected. The conclusion here will provide an important empirical reference for optimizing the structure of natural gas consumption and maintaining energy security in China.

Hubei Province Region (HPR), located in Central China, is a concentrated area of severe convective weather. Three severe convective processes occurred in HPR were selected, namely 14−15 May 2015 (Case 1), 6−7 July 2013 (Case 2), and 11−12 September 2014 (Case 3). In order to investigate the differences between the three cases, the temporal and spatial distribution characteristics of cloud−ground lightning (CG) flashes and precipitation, the distribution of radar parameters, and the evolution of cloud environment characteristics (including water vapor (VD), liquid water content (LWC), relative humidity (RH), and temperature) were compared and analyzed by using the data of lightning locator, S-band Doppler radar, ground-based microwave radiometer (MWR), and automatic weather stations (AWS) in this study. The results showed that 80% of the CG flashes had an inverse correlation with the spatial distribution of heavy rainfall, 28.6% of positive CG (+CG) flashes occurred at the center of precipitation (>30 mm), and the percentage was higher than that of negative CG (−CG) flashes (13%). Moreover, the quantity of thunderstorm cells in Case 1 was more than other cases, the peak time of +CG flashes was prior to that of total CG flashes in Case 2 and Case 3, and the time of +CG flashes’ peak in Case 2 was prior to that of precipitation at about 2 h. Based on the analysis of the cloud environment, there are three main reasons for the differences of CG flashes and precipitation. Firstly, the structure of the LWC vertical profile and the height of the LWC peak are different, and high LWC makes it difficult for the collision of ice particles to generate electricity. Secondly, the differences between convective available potential energy (CAPE), precipitation, and CG flashes is caused by the sudden increase of VD from 1.5 km to 3 km, and thirdly, the production of CG flashes is very sensitive to RH at the surface layer and the total CG flashes increase as the RH increasing.

In this paper, petroleum product (mainly petrol and diesel) consumption in the transportation sector of China is analyzed. This was based on the Bayesian linear regression theory and Markov Chain Monte Carlo method (MCMC), establishing a demand-forecast model of petrol and diesel consumption introduced into the analytical framework with explanatory variables of urbanization level, per capita GDP, turnover of passengers (freight) in aggregate (TPA, TFA), and civilian vehicle number (CVN) and explained variables of petrol and diesel consumption. Furthermore, we forecast the future consumer demand for oil products during “The 12th Five Year Plan” (2011–2015) based on the historical data covering from 1985 to 2009, finding that urbanization is the most sensitive factor, with a strong marginal effect on petrol and diesel consumption in this sector. From the viewpoint of prediction interval value, urbanization expresses the lower limit of the predicted results, and CVN the upper limit of the predicted results. Predicted value from other independent variables is in the range of predicted values which display a validation range and reference standard being much more credible for policy makers. Finally, a comparison between the predicted results from autoregressive integrated moving average models (ARIMA) and others is made to assess our task.

Since natural gas has become a new star in China’s energy mix, a reliable estimation of the price elasticity of natural gas demand is crucial if we are to understand how energy price changes affect natural gas consumption. In this paper, we conduct a Meta-regression analysis to quantitatively synthesize empirical estimates of the price elasticity of natural gas demand reported in previous studies, provide true underlying values, and explain the heterogeneity of the aforementioned estimates. The Fixed-effects model and ordinary least squares (OLS) are applied to estimate the regression models. As a result, this paper reports a mean elasticity of −1.521 and 0.410 for the short- and long-run own-price elasticity, separately; −0.762 and 0.008 for the short- and long-run cross-price elasticity-coal to natural gas, respectively; 2.122 and 1.884 for the short- and long-run cross-price elasticity-electricity to natural gas, separately; and 2.267 and 1.275 for the short- and long-run cross-price elasticity-oil to natural gas, respectively. Our results suggest that natural gas consumption increases with the decrease of its own and coal prices in the short term and rise of electricity and oil prices. It also shows that almost all heterogeneity can be explained by the type of data, sample period, models of analysis, geographical region, and type of consumer.

Presently, the total supply of crude oil is sufficient, but short-term supply and demand imbalances and regional imbalances still exist. The effect of crude oil supply security and price impact cannot be ignored. As the world’s largest oil importer, China is highly dependent on foreign oil. Therefore, the fluctuation of international oil prices may impact the development of China’s various industries in a significant and differential way. However, because the available data have different frequencies, much of the recent research that addresses the effect of oil prices on industry development need to replace, split, or merge the original data, resulting in loss of the information from the original data. Using the mixed data sampling model (MIDAS(m,K,h)-AR(1)) with the first-order lag autoregressive terms of the interpreted variables, this study builds a mixed data model to investigate the effect of oil price volatility on the output of China's industries. This study expands the extant research by financial market fluctuations and macroeconomic analysis, and at the same time makes short-term predictions on the output of China’s seven main industries. The analysis results show that the mixed data regression model brings the original information contained in different frequency data into the model analysis, and utilizes the latest high frequency data of the explanatory variables to perform real-time short-term prediction of low-frequency interpreted variables. This method improves the timeliness of forecasting macroeconomic indicators and the accuracy of short-term forecasts. The empirical results show that the spot price of international crude oil has a significant and differential impact on the outputs of the seven industries in China. Among them, oil price fluctuation has the greatest impact on the output of China’s financial industry.

Considering the speedy growth of industrialization and urbanization in China and the continued rise of coal consumption, this paper identifies factors that have impacted coal consumption in 1985–2011. After extracting the core factors, the Bayesian vector autoregressive forecast model is constructed, with variables that include coal consumption, the gross value of industrial output, and the downstream industry output (cement, crude steel, and thermal power). The impulse response function and variance decomposition are applied to portray the dynamic correlations between coal consumption and economic variables. Then for analyzing structural changes of coal consumption, the exponential smoothing model is also established, based on division of seven sectors. The results show that the structure of coal consumption underwent significant changes during the past 30 years. Consumption of both household sector and transport, storage, and post sectors continues to decline; consumption of wholesale and retail trade and hotels and catering services sectors presents a fluctuating and improving trend; and consumption of industry sector is still high. The gross value of industrial output and the downstream industry output have been promoting coal consumption growth for a long time. In 2015 and 2020, total coal demand is expected to reach 2746.27 and 4041.68 million tons of standard coal in China.