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This paper investigates the split-step theta (SST) method to approximate a class of time-changed stochastic differential equations, whose drift coefficient can grow super-linearly and diffusion coefficient obeys the global Lipschitz condition. The strong convergence of the SST method is proved, and the SST method attains the classical 1 of convergence. In addition, the mean square stability of the time-changed stochastic differential equations is investigated. Two examples are presented to show the consistency of the theoretical results. [ABSTRACT FROM AUTHOR]

The plant cuticle is essential in plant defense against biotic and abiotic stresses. To systematically elucidate the genetic architecture of maize (Zea mays L.) cuticular wax metabolism, 2 cuticular wax–related traits, the chlorophyll extraction rate (CER) and water loss rate (WLR) of 389 maize inbred lines, were investigated and a genome-wide association study (GWAS) was performed using 1.25 million single nucleotide polymorphisms (SNPs). In total, 57 nonredundant quantitative trait loci (QTL) explaining 5.57% to 15.07% of the phenotypic variation for each QTL were identified. These QTLs contained 183 genes, among which 21 strong candidates were identified based on functional annotations and previous publications. Remarkably, 3 candidate genes that express differentially during cuticle development encode β-ketoacyl-CoA synthase (KCS). While ZmKCS19 was known to be involved in cuticle wax metabolism, ZmKCS12 and ZmKCS3 functions were not reported. The association between ZmKCS12 and WLR was confirmed by resequencing 106 inbred lines, and the variation of WLR was significant between different haplotypes of ZmKCS12. In this study, the loss-of-function mutant of ZmKCS12 exhibited wrinkled leaf morphology, altered wax crystal morphology, and decreased C32 wax monomer levels, causing an increased WLR and sensitivity to drought. These results confirm that ZmKCS12 plays a vital role in maize C32 wax monomer synthesis and is critical for drought tolerance. In sum, through GWAS of 2 cuticular wax–associated traits, this study reveals comprehensively the genetic architecture in maize cuticular wax metabolism and provides a valuable reference for the genetic improvement of stress tolerance in maize. Genome-wide association study of 2 cuticular wax–associated traits in maize reveals a β-ketoacyl-CoA synthase gene critical for drought tolerance. [ABSTRACT FROM AUTHOR]

Prediction of fine particulate matter with particle size less than 2.5 µm (PM2.5) is an important component of atmospheric pollution warning and control management. In this study, we propose a deep learning model, namely, a spatiotemporal weighted neural network (STWNN), to address the challenge of poor long-term PM2.5 prediction in areas with sparse and uneven stations. The model, which is based on convolutional neural network–bidirectional long short-term memory (CNN–Bi-LSTM) and attention mechanisms and uses a geospatial data-driven approach, considers the spatiotemporal heterogeneity effec It is correct.ts of PM2.5. This approach effectively overcomes instability caused by sparse station data in forecasting daily average PM2.5 concentrations over the next week. The effectiveness of the STWNN model was evaluated using the Xinjiang Uygur Autonomous Region as the study area. Experimental results demonstrate that the STWNN exhibits higher performance (RMSE = 10.29, MAE = 6.4, R2 = 0.96, and IA = 0.81) than other models in overall prediction and seasonal clustering. Furthermore, the SHapley Additive exPlanations (SHAP) method was introduced to calculate the contribution and spatiotemporal variation of feature variables after the STWNN prediction model. The SHAP results indicate that the STWNN has significant potential in improving the performance of long-term PM2.5 prediction at the regional station level. Analyzing spatiotemporal differences in key feature variables that influence PM2.5 provides a scientific foundation for long-term pollution control and supports emergency response planning for heavy pollution events. [ABSTRACT FROM AUTHOR]

The fundamental component of the ecological compensation system, as well as the crucial basis for its efficient functioning, is calculating the ecological compensation amount and establishing the ecological compensation standard. This study integrates the ecological footprint with natural capital monetization and other methods by introducing a natural capital accounting system. From the standpoint of natural capital supply and demand, it also builds an accounting framework for ecological compensation standards that is standardized, dynamic, and regionally differentiated while taking local socioeconomic aspects into account. We determined the amount of ecological compensation by using Xinjiang as the research object and calculating and analyzing the features of regional and temporal changes in the monetary and physical quantities of natural capital in Xinjiang from 2010 to 2020. The findings show that from 2010 to 2020, Xinjiang's ecological footprint increased by 1.26 times in physical terms and 1.21 times in monetary terms and that its ecological carrying capacity increased by 4.13% in physical terms and 9.42% in monetary terms. The ecological deficit continues to grow in physical and monetary terms, with a per capita ecological deficit in 2020 of 19.92 s-nha/cap and 70,100 CNY/cap in physical and monetary terms, respectively. The amount of ecological compensation required to be paid in Xinjiang increased from CNY 5659 million to CNY 10,259 million, and the per capita ecological compensation payment standard increased from 259.42 CNY/cap/yr to 396.11 CNY/cap/yr. In summary, Xinjiang's natural capital supply is insufficient to meet the demand for consumption, and the ecological deficit is growing with time, necessitating the payment of ecological compensation. The study's results lay the foundation for formulating and implementing ecological compensation policies in Xinjiang and provide theoretical support for constructing ecological civilization in Xinjiang. In addition, the ecological compensation accounting framework constructed in this study organically integrates natural capital theory, ecosystem services, and socioeconomic influencing factors, which enriches the methodology of accounting for ecological compensation standards, and, at the same time, can be used as a paradigm of a dynamic and equitable ecological compensation accounting framework to further promote its use at different scales and regions. [ABSTRACT FROM AUTHOR]

In public–private partnership (PPP) projects, governments often need to provide guarantees to investors because of the risks created by the large scale of the investments and the long tenures of the projects. Yet, research on the assessment of fiscal risk in PPP projects remains scarce. Based on the theory of project life-cycle management, this study constructed a multidimensional identification and assessment framework for PPP fiscal risk. A fiscal risk matrix model was developed, and feasibility, standardization, and sustainability were identified. These were categorized into three dimensions of PPP fiscal risk, and their indicators' hierarchical levels were evaluated and ranked. A risk model, called the fuzzy evaluation model of PPP fiscal risk, which shows the value of the comprehensive evaluation of PPP fiscal risk based on a fuzzy analytic hierarchy process (FAHP) approach, is proposed. The results demonstrate the significance of this valuation for both governments and investors, providing a clear reference when they face pressure from the fiscal risks of PPP projects. [ABSTRACT FROM AUTHOR]

The development of high‐performance organic blue light‐emitting emitters is in urgent to act as an excitation source to contribute the white light generation. On the other hand, the investigation on optical waveguides have been received increasing attentions because they can manipulate the light propagation accurately in the microscale to boost the optoelectronic and energy conversion applications. In this work, we facilely prepared a deep‐blue aggregation‐induced emission (AIE) dye, namely TPP‐4OMe, which shows high luminescent efficiency, narrow emission band and good stability in the aggregate state. TPP‐4OMe can be fabricated as deep‐blue AIE microfibers readily with definite morphology and composition. Based on the AIE microfibers, the active waveguide to transmit deep‐blue emission signals can be achieved with a very low optical loss coefficient (α) of 6.7 × 10−3 dB μm−1. Meanwhile, the full‐visible broadband low‐loss passive waveguide can be well performed with these AIE microfibers, which has never been observed in the pure organic crystals. More interestingly, the excellent properties of AIE microfibers enable them to act as a wave‐guiding excitation source, resulting in a distinct and pure white light emission. The present work not only provides excellent blue light‐emitting materials but also bridges the waveguide to realize the efficient white light emission to accelerate the practical applications. [ABSTRACT FROM AUTHOR]

The random inspection of the China Securities Regulatory Commission (CSRC) is an important policy for practicing "standardizing regulatory behavior and innovating management modes". This study investigates how CSRC's random affect capital market information efficiency from the perspective of stock price synchronicity. Using a sample of China's non-financial A-share firms from 2013 to 2019, we find that random inspections significantly reduced the stock price synchronicity of inspected firms. Random inspections could increase the probability of releasing management earnings forecasts, media coverage and investor attention, and thereby improve information efficiency. Further study finds that the effect of random inspections on stock price synchronicity is stronger for non-state controlled firms, districts with more listed firms, and districts with more transparent government. This study enriches the literature on the consequences of random inspections and extends our knowledge of the relationship between regulatory innovations and the information efficiency of the capital market. [ABSTRACT FROM AUTHOR]

Abstract Background Accumulated evidences have demonstrated that long non-coding RNAs (lncRNAs) are dysregulated and correlate with the pathophysiological basis of malignant tumors. The objective of this research is to uncover the possible molecular mechanism of MACC1-AS1 regarding the regulation of pancreatic carcinoma (PC) metastasis. Methods lncRNA microarray and qRT-PCR were applied to identify differentially expressed lncRNA profile in PC. The function and role of MACC1-AS1 in PC were assessed via in vitro as well as in vivo assays. Luciferase analyses, RNA immunoprecipitation, and RNA pull-down were performed to determined the underlying MACC1-AS1 mechanisms. Results Numbers of differentially expressed lncRNAs in PC were identified via lncRNA microarrays, among which MACC1-AS1 was revealed as the most abundant lncRNA. The upregulation of MACC1-AS1 in PC was further confirmed in two expanded PC cohorts, which showed that MACC1-AS1 expression was upregulated in those PC patients with poor survival. Functionally, knockdown of MACC1-AS1 inhibited the proliferation as well as metastasis of PC cells. Meanwhile, MACC1-AS1 upregulated the expression of PAX8 protein, which promoted aerobic glycolysis and activated NOTCH1 signaling. Additionally, PAX8 was upregulated in PC tissues, which was correlated with the expression of MACC1-AS1 and the overall survival of PC patients. Conclusions Together, our findings indicate a critical role of MACC1-AS1/PAX8/NOTCH1 signaling, which may be an alternative treatment target in PC therapy.