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In this study, based on the data of the Chinese listed firms, the effect of digital transformation on capital mismatch was examined. And the potential mechanism was also further discussed. It was found that digital transformation can significantly suppress capital mismatch, especially for non-state-owned enterprises, mature enterprises, and regions with high marketization and financial technology level. In addition, management capability and information environment are potential influencing mechanisms of digital transformation to suppress capital mismatch. These findings have important implications for revealing the relationship between enterprise digital transformation and capital mismatch, provides new ideas for improving the efficiency of capital allocation, and also provides important insights for enterprises to accelerate digital transformation and promote the high-quality development of enterprises.

Russian trolls generally supported the Trump campaign and were particularly active on Twitter 2015-2017. We find that trolling fell 35% on Russian holidays and to a lesser extent, when temperatures were cold in St. Petersburg. Exogenous variation in trolling by day allows us to consider indirectly -affected political behaviors in the US—outcomes that are less traceable via tweet sharing but potentially more important to policymakers than the direct dissemination previously studied. As a case in point, we describe reduced form evidence that Russian holidays affected daily trading prices in 2016 election betting markets. This response is consistent with successful Russian interference in support of Trump.

This study proposes a battery thermal management system based on L-shaped heat pipes coupled with liquid cooling. Experimental and computational fluid dynamics (CFD) numerical simulation studies have been conducted on the performance of the thermal management system. The thermal performance of three heat dissipation methods including forced air cooling, bottom liquid cooling and heat pipe coupled liquid cooling were compared. The results demonstrate that the coupling system can control the maximum temperature and temperature difference of the module at 30.12°C and 2.02°C at a 3C discharge rate. Compared with forced air cooling and bottom liquid cooling, the maximum temperature was decreased by 30.16% and 17.01% and the temperature difference was decreased by 72.14% and 77.20%, respectively. Studied the impact of factors such as coolant flow rate, the number of liquid-cooled plate channels, and the coolant inlet temperature under different ambient temperatures on the thermal management performance of the coupled system. By monitoring the maximum temperature of the module and the ambient temperature, a method for controlling the flow rate and the inlet temperature of the cooling water has been developed to implement an intermittent liquid cooling strategy for the battery module. Intermittent liquid cooling at various ambient temperatures can obtain similar thermal management performance to continuous liquid cooling, while significantly reducing liquid cooling energy consumption. Compared to continuous liquid cooling, intermittent liquid cooling can reduce energy consumption by a maximum of 97.05% and a minimum of 30.00%.

High-speed fuel solenoid valves (HFSVs) are the key control elements of aero-engine vane regulators. A strong electromagnetic force generated from the HFSVs is essential to achieve precise control over timing and quantification for fuel supply. In this paper, the Taguchi method is adopted to improve the HFSV’s static electromagnetic characteristics. First, an electromagnetic model of the HFSV was established and experiments were conducted to modify and validate the model. Effects of key structural factors on the static electromagnetic characteristics of the HFSV are then investigated via the finite element method (FEM). Based on the optimization, an HFSV prototype is finally manufactured and tested. The experiment results are in good agreement with those of the simulations. It provides a significant guideline for the manufacturing process of such HFSVs.

The Discrete Element Method (DEM) has been widely employed to investigate the behavior of particle systems at a macroscopic scale. However, effectively simulating the gradual filling of bulk cereal grains within silos using the DEM remains a formidable challenge due to time constraints. Thus, there is a critical need to develop a simplified model capable of substantially reducing the computational time required for simulating cereal grain accumulation. This study introduces a Layered Filling Method (LFM) designed to expedite the computational process for cereal grain piles within silos. By utilizing particle kinetic energy as a specific criterion, this model identifies particles as stable situations when their kinetic energy drops below a designated threshold. Throughout the filling process, lower particles that were judged to satisfy the condition of stability are isolated, forming sub-heaps that are exempt from persistent detection. The whole particle heap is subsequently segregated into multiple sub-piles and a main pile till the process’s culmination, and these divisions are merged back together. In order to validate the model’s feasibility and accuracy, a comparative analysis was performed on the characteristics of the porosity and airflow patterns of grain piles generated using the LFM and the progressive filling method (PFM), respectively. The research results indicate that there is a marginally higher porosity value in the grain pile simulated by the LFM in comparison to the PFM. However, the average relative error remains below 5.00%. Both the LFM and PFM exhibit a similar spiral upward trend in the simulated airflow paths. Notably, the LFM demonstrates a substantial reduction in the time required to construct grain piles.

Rice agriculture is a major source of atmospheric methane, but current emission inventories are highly uncertain, mostly due to poor rice‐specific inundation data. Inversions of atmospheric methane observations can help to better quantify rice emissions but require high‐resolution prior information on the location and timing of emissions. Here we use Landsat satellite data at 30 m resolution to map the global monthly distribution of rice paddy fractional areas on a 0.1° × 0.1° (∼10 × 10 km) grid by optimizing an algorithm for flooded vegetation and combining it with a 30 m global cropland database and rice‐specific data. We validate this global rice paddy map with an independent US rice database and with seasonal flux measurements from the FLUXNET CH4 network, estimating errors on rice area fraction of 31% on the 0.1° × 0.1° grid and 10% regionally. We combine the rice paddy map with an extensive global data set of emission factors (EFs) per unit of rice paddy area. The resulting Global Rice Paddy Inventory (GRPI) provides methane emission estimates at 0.1° × 0.1° (∼10 × 10 km) spatial resolution and monthly resolution. Our global emission of 39.3 ± 4.7 Tg a−1 for 2022 (best estimate and error standard deviation) is higher than previous inventories that use outdated rice maps and IPCC‐recommended EFs now considered to be too low. China is the largest rice emitter in GRPI (8.2 ± 1.0 Tg a−1), followed by India (6.5 ± 1.0 Tg a−1), Bangladesh (5.7 ± 1.2 Tg a−1), Vietnam (5.7 ± 1.0 Tg a−1), and Thailand (4.4 ± 0.9 Tg a−1). These five countries together account for 78% of global total rice emissions. Seasonality of emissions varies considerably between and within individual countries reflecting differences in climate and crop practices. We define a rice methane intensity (methane emission per unit of rice produced) to assess the potential of mitigating methane emission without compromising food security. We find national methane intensities ranging from 10 to 120 kg methane per ton of rice produced (global mean 51) for major rice‐growing countries. Countries can achieve low intensities with high‐yield cultivars, upland rice agriculture, water management, and organic matter management. Plain Language Summary Rice agriculture is a major source of atmospheric methane, a potent greenhouse gas with strong warming potential. Current emission estimates for rice agriculture are highly uncertain because of poor inundation data. Here we use Landsat satellite data to develop a new Global Rice Paddy Inventory (GRPI) of methane emissions at 10 km resolution for each month of 2022. We find that global rice methane emissions are higher than previously thought, at 39.3 million metric tons in 2022. Five countries (China, India, Bangladesh, Vietnam, and Thailand) account for 78% of these emissions. We introduce a metric of methane intensity ‐ methane emitted per ton of rice produced ‐ to assess the potential to reduce methane emission without compromising food security. We find that methane intensities vary widely between countries. Key Points We developed a new Global Rice Paddy Inventory of methane emissions at 0.1° × 0.1° monthly resolution using Landsat satellite inundation data Our global emission of 39.3 ± 4.7 Tg a−1 is higher than previous inventories that use outdated rice maps and IPCC‐recommended emission factors now considered too low Countries can mitigate methane without compromising food security by developing high‐yield cultivars, upland rice agriculture, water management, and organic matter management

Methane is 28 to 86 times more potent as a driver of global warming than CO2. Global methane concentrations have increased at an accelerating rate since 2004, yet the role of fossil fuels and revitalized natural gas extraction and distribution in accelerating methane concentrations is poorly recognized. Here we examine the policy positioning of university-based energy centres towards natural gas, given their growing influence on climate discourse. We conducted sentiment analysis using a lexicon- and rule-based sentiment scoring tool on 1,168,194 sentences in 1,706 reports from 26 universities, some of which receive their primary funding from the natural gas industry. We found that fossil-funded centres are more favourable in their reports towards natural gas than towards renewable energy, and tweets are more favourable when they mention funders by name. Centres less dependent on fossil funding show a reversed pattern with more neutral sentiment towards gas, and favour solar and hydro power.University-based energy centres play an important role in climate discourse but many are funded by fossil fuel businesses. This study shows that fossil-fuel-funded centres express more positive sentiment towards natural gas, compared to renewable energy, than those not funded by the fossil industry.

Lysophosphatidylcholine acyltransferase 3 (LPCAT3) is an important enzyme in phospholipid remodeling, a process that influences the biophysical properties of cell membranes and thus cell function. Multiple lines of evidence suggest that LPCAT3 is involved in several diseases, including atherosclerosis, non‐alcoholic steatohepatitis, and carcinoma. Thus, LPCAT3 may have potential as a therapeutic target for these diseases. In the present study, we devised an assay based on reversed‐phase HPLC to measure LPCAT3 activity, which may facilitate the identification of LPCAT3 inhibitors and activators. We found that optimal pH and temperature of recombinant human LPCAT3 are 6.0 and 30 °C, respectively. The enzyme Km values for substrates NBD‐labelled lysophosphatidylcholine and arachidonoyl CoA were 266.84 ± 3.65 and 11.03 ± 0.51 μmol·L−1, respectively, and the Vmax was 39.76 ± 1.86 pmol·min−1·U−1. Moreover, we used our new method to determine the IC50 of a known LPCAT inhibitor, TSI‐10. In conclusion, this novel assay can be used to measure the effects of compounds on LPCAT3 activity. Lysophosphatidylcholine acyltransferase 3 (LPCAT3) is an important enzyme involved in phospholipid remodeling. We describe a new reversed‐phase HPLC method to measure the activity of purified recombinant human LPCAT3 using NBD‐labeled substrates. Furthermore, we have determined the kinetic parameters of LPCAT3 using this method.

This paper gives an overview of the performance of China’s seven regional carbon market pilots and the range of approaches they have used. We assessed the outcomes of these pilots using publicly available secondary market trading data. The differences in market performance are explained by the design of key market elements such as emission allowances, covered sectors, allowance allocation, monitoring, reporting and verification, compliance and penalties, and offset market. The lessons learned from the regional carbon market pilots are used to provide insights that can aid in the design of the upcoming national carbon market.

Chemotherapy is an important treatment modality for gastric cancer, and multidrug resistance (MDR) represents a major obstacle for successful cancer chemotherapy. There is a lack of research on whether microRNA (miR)-30a regulation affects the chemosensitivity of resistant gastric cancer cells, and mechanisms underlying the effects of miR-30a on drug resistance and cell autophagy require further investigation. In the present study, the expression of miR-30a and its effects in cisplatin (CDDP)-resistant human gastric cancer cells were investigated. A CDDP-resistant variant of the SGC-7901 cell line (SGC-7901/CDDP) was established by exposing the cells to gradually increasing drug concentrations, and miR-30a expression was detected by reverse transcription-semi quantitative polymerase chain reaction (RT-sqPCR). To examine the effect of miR-30a expression in the SGC-7901/CDDP cells, miR30a mimics or negative control miRNA were transfected into the cells, and a Cell Counting Kit-8 assay was performed to analyze the chemosensitivity of the different cell groups. RT-sqPCR and western blot analysis were also used to measure MDR1 mRNA and P-glycoprotein expression, and the light chain (LC)3-II/LC3-I ratio. Furthermore, apoptosis induced by the chemotherapeutic CDDP in the different groups was assessed using flow cytometry. The results demonstrated that low expression of miR-30a was associated with chemoresistance in gastric cancer cells, and in the chemoresistant cell line SGC7901/CDDP, CDDP-induced apoptosis was weakened. Additionally, it was demonstrated that the LC3-II/LC3-I ratio was elevated in SGC7901/CDDP cells compared with chemosensitive SGC7901 cells (P<0.001), which could be attenuated by upregulating miR-30a expression (P<0.001 vs. SGC7901/CDDP control cells). These results suggested that autophagy may contribute to drug resistance in gastric cancer cells, and that the reduction of LC3-II in response to miR-30a overexpression may inhibit chemoresistance-associated autophagy in gastric cancer cells.