Lattice Boltzmann model for combustion and detonation

In this paper we present a lattice Boltzmann model for combustion and detonation. In this model the fluid behavior is described by a finite-difference lattice Boltzmann model by Gan et al. [Physica A, 2008, 387: 1721]. The chemical reaction is described by the Lee-Tarver model [Phys. Fluids, 1980, 23: 2362]. The reaction heat is naturally coupled with the flow behavior. Due to the separation of time scales in the chemical and thermodynamic processes, a key technique for a successful simulation is to use the operator-splitting scheme. The new model is verified and validated by well-known benchmark tests. As a specific application of the new model, we studied the simple steady detonation phenomenon. To show the merit of LB model over the traditional ones, we focus on the reaction zone to study the non-equilibrium effects. It is interesting to find that, at the von Neumann peak, the system is nearly in its thermodynamic equilibrium. At the two sides of the von Neumann peak, the system deviates from its equilibrium in opposite directions. In the front of von Neumann peak, due to the strong compression from the reaction product behind the von Neumann peak, the system experiences a sudden deviation from thermodynamic equilibrium. Behind the von Neumann peak, the release of chemical energy results in thermal expansion of the matter within the reaction zone, which drives the system to deviate the thermodynamic equilibrium in the opposite direction. From the deviation from thermodynamic equilibrium, defined in this paper, one can understand more on the macroscopic effects of the system due to the deviation from its thermodynamic equilibrium

(RS/SR)-2-Oxo-4-phenyl­azetidin-3-yl acetate

In the title compound, C11H11NO3, a modified synthetic acetate derivative, the four memebered β-lactam ring is roughly planar, with a maximum deviation of 0.21 (3) Å, and makes a dihedral angle of 81.46 (14)° with the phenyl ring. In the crystal, a single N—H⋯O hydrogen bond links mol­ecules into a chain parallel to the a axis and thus stabilizes the structure. Although the absolute configuration could not be reliably determined, the compound corresponds to the diasteroisomer (RS/SR

Knowledge Contribution in Customer-Centric Brand Community: A Person-Environment-Fit Model

Recently, we witness a shift from product-centric to customer-centric brand community. The customer-centric approach allows value co-creation in brand community by involving customers in various activities that bring a product to the market. It is thus interesting and necessary to examine customers\u27 motivations in helping brand and community grow and succeed. Based on the person-environment fit framework, this study presents an attempt to investigate consumer contribution in one of the largest brand communities (i.e. XiaoMi Community) in Mainland China. The results demonstrate that both complementary fit and supplementary fit significantly predict consumers\u27 satisfaction with and their commitment to the community, which in turn leads to contribution intention. The findings further suggest the importance of person-environment fit in promoting knowledge sharing in customer-centric brand community, and contribute to both research and practice on facilitating consumer participation

The impact of planetary boundary layer parameterisation scheme over the Yangtze River Delta region, China, part I: seasonal and diurnal sensitivity studies.

The planetary boundary layer (PBL) is the main region for the exchange of matter, momentum and energy between land and atmosphere. The transport processes in the PBL determine the distribution of temperature, water vapour, wind speed and other physical quantities within the PBL and are very important for the simulation of the physical characteristics of the meteorology. Based on the two non-local closure PBL schemes (YSU, ACM2) and two local closure PBL schemes (MYJ, MYNN) in the Weather Research and Forecasting (WRF) model, seasonal and daily cycles of meteorological variables over the Yangtze River Delta (YRD) region are investigated. It is shown that all the four PBL schemes overestimate 10-m wind speed and 2-m temperature, while underestimate relative humidity. The MYJ scheme produces the largest biases on 10-m wind speed and the smallest biases on humidity, while the ACM2 scheme show WRF-simulated 2-m temperature and 10-m wind speed are closer to surface meteorological observations in summer. The ACM2 scheme performs well with daytime PBL height, the MYNN scheme performs the lowest mean bias of 0.04 km and the ACM2 scheme shows the highest correlation coefficient of 0.59 compared with observational data. It is found that there is a varying degree of sensitivity of the respective PBL in winter and summer and a best-performing PBL scheme should be chosen to predict various meteorological conditions under different seasons over a complicated region like the YRD

The impact of planetary boundary layer parameterisation over the Yangtze River Delta region, China, part 1: meteorological simulation.

The planetary boundary layer (PBL) is the main region for the exchange of matter, momentum, and energy between land and atmosphere. The transport processes in the PBL determine the distribution of temperature, water vapour, wind speed and other physical quantities and are very important for the simulation of the physical characteristics of the meteorology. Based on the two non-local (YSU, ACM2) and two local closure PBL schemes (MYJ, MYNN) in the Weather Research and Forecasting (WRF) model, seasonal and daily cycles of meteorological variables over the Yangtze River Delta (YRD) region are investigated. It is shown that all four PBL schemes overestimate 10-m wind speed and 2-m temperature, while underestimate relative humidity. Inter-comparisons among the different PBL schemes show that the MYNN scheme results in closer match of 2-m temperature and 10-m wind speed to surface observations in summer, while the MYJ scheme shows the smallest bias of 2-m temperature and relative humidity in winter. Compared to the observed PBL height obtained from a micro-pulse lidar system, the MYNN scheme exhibits lowest mean bias while the ACM2 scheme shows the highest correlation. It is also found that there is a varying degree of sensitivity of the PBL height in winter and summer, respectively; a best-performing PBL scheme should be chosen under different seasons to predict various meteorological conditions over complicated topography like the YRD region

Impact of digital transformation on renewable energy companies’ performance: Evidence from China

The rapid growth of the renewable energy industry provides essential opportunities for China to achieve the goal of carbon peaking and carbon neutrality. A rising number of renewable energy companies are positively embracing digital transformation in the digital age. However, the relationship between digital transformation and the performance of renewable energy companies remains unclear. To fill this gap, leveraging the latest advances in textual analysis, we quantify the extent of a renewable energy enterprise’s digital transformation. Meanwhile, based on fixed effect model and mediating effect model, we investigate the influence of digital transformation on firm performance using a panel data of Chinese A-share listed renewable energy companies. The results indicate that digital transformation enhances a renewable energy enterprise’s performance. Further, the promotion effect of digital transformation is greater among state-owned enterprises and large firms and is only helpful for firms in the eastern area. Moreover, we document that when a renewable energy enterprise adopts digital transformation, it has higher operating efficiency, lower cost, and better innovation success resulting in better performance. This research elucidates the role of digital transformation in forwarding the development of renewable energy companies and bears significant policy implications

APPL: A Prompt Programming Language for Harmonious Integration of Programs and Large Language Model Prompts

Large Language Models (LLMs) have become increasingly capable of handling diverse tasks with the aid of well-crafted prompts and integration of external tools, but as task complexity rises, the workflow involving LLMs can be complicated and thus challenging to implement and maintain. To address this challenge, we propose APPL, A Prompt Programming Language that acts as a bridge between computer programs and LLMs, allowing seamless embedding of prompts into Python functions, and vice versa. APPL provides an intuitive and Python-native syntax, an efficient parallelized runtime with asynchronous semantics, and a tracing module supporting effective failure diagnosis and replaying without extra costs. We demonstrate that APPL programs are intuitive, concise, and efficient through three representative scenarios: Chain-of-Thought with self-consistency (CoT-SC), ReAct tool use agent, and multi-agent chat. Experiments on three parallelizable workflows further show that APPL can effectively parallelize independent LLM calls, with a significant speedup ratio that almost matches the estimation

The silver lining of COVID‐19: estimation of short‐term health impacts due to lockdown in the Yangtze River Delta region, China.

The outbreak of COVID-19 in China has led to massive lockdowns in order to reduce the spread of the epidemic and control human-to-human transmission. Subsequent reductions in various anthropogenic activities have led to improved air quality during the lockdown. In this study, we apply a widely used exposure-response function to estimate the short-term health impacts associated with PM2.5 changes over the Yangtze River Delta (YRD) region due to COVID-19 lockdown. Concentrations of PM2.5 during lockdown period reduced by 22.9% to 54.0% compared to pre-lockdown level. Estimated PM2.5-related daily premature mortality during lockdown period is 895 (95% confidential interval: 637–1,081), which is 43.3% lower than pre-lockdown period and 46.5% lower compared with averages of 2017–2019. According to our calculation, total number of avoided premature death aassociated with PM2.5 reduction during the lockdown is estimated to be 42.4 thousand over the YRD region, with Shanghai, Wenzhou, Suzhou (Jiangsu province), Nanjing, and Nantong being the top five cities with largest health benefits. Avoided premature mortality is mostly contributed by reduced death associated with stroke (16.9 thousand, accounting for 40.0%), ischemic heart disease (14.0 thousand, 33.2%), and chronic obstructive pulmonary disease (7.6 thousand, 18.0%). Our calculations do not support or advocate any idea that pandemics produce a positive note to community health. We simply present health benefits from air pollution improvement due to large emission reductions from lowered human and industrial activities. Our results show that continuous efforts to improve air quality are essential to protect public health, especially over city-clusters with dense population