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Energy poverty is a global challenge and the scarcity has been emerging as a global issue. Therefore, the relationship among energy scarcity, sustainable finance, and carbon emissions is analyzed with the help of global data from 40 developing countries until the beginning of the COVID-19 era. For empirical results’ estimation, the study analyzed a panel data ranging from 2000 to 2019. To measure the energy poverty, some part of population that has no access to energy is considered, and empirical analysis based on augmented mean group (AMG) regression method was carried out. The findings of the study suggest the inverse relation among energy poverty and carbon emissions. Moreover, a negative relationship was also observed between sustainable finance and carbon emissions. These findings highlight that alleviation of energy poverty can intensify environmental pollution. While improvement in access to clean energy will benefit society by alleviating energy poverty and controlling carbon emissions. Moreover, improvement in the share of sustainable finance in total investment may improve the environment quality by reducing carbon emissions. Therefore, it is suggested that regional plans along with sustainable finance are required on a priority basis for the promotion of clean energy to control carbon emissions and alleviate energy poverty at the household level.

The construction of China’s Free Trade Zone (FTZ) is an important strategy for China’s thorough deepening of opening up and achievement of long-term high-quality development. Based on the panel data of 283 prefecture-level and above cities in China from 2008–2019, this paper adopts the methods of Spatial Differences-in-Differences (SDID), Spatial Differences-in-Differences-in-Differences (SDDD), and spatial intermediary effect test to empirically examine the impact and the spatial spillover of China’s free trade zone on the environmental pollution of the pilot areas and its influencing mechanism. According to the findings of the study, the establishment of pilot free trade zones may suppress urban PM2.5 emissions by around 2.9 percent, and FTZs can also greatly enhance the air quality of neighboring cities. Further examination of the influencing mechanism reveals that the establishment of a FTZ inhibits PM2.5 pollution and has a significant positive spillover on PM2.5 reduction in surrounding cities by the following means: attracting more foreign direct investment; improving the industrial structure through increasing the proportion of tertiary industry; prompting the local government to strengthen environmental regulation as part of the FTZ’s supporting policies; increasing the investment in science and technology innovation, developing scientific and technological level to achieve green production. The empirical results of this paper are still robust after a series of robustness tests when the explained variable is replaced by the traditional air pollution indicator industrial nitrogen oxide emissions, another sort of spatial matrix is introduced, the propensity score matching SDID (PSM-SDID) and placebo tests as well as winsorize method are carried out. Furthermore, the inhibitory effect of FTZs on air pollution is modified by changes in city size, geographic location and city type, according to heterogeneity analysis. Finally, this paper proposes feasible policy recommendations.

Owing to the uneven distribution of key features in Chinese texts, key features play different roles in text recognition in Chinese text classification tasks. We propose a feature-enhanced fusion model based on attention mechanism for Chinese text classification, a long short-term memory (LSTM) network, a convolutional neural network (CNN), and a feature-difference enhancement attention algorithm model. The Chinese text is digitized into a vector form containing certain semantic context information into the embedding layer to train and test the neural network by preprocessing. The feature-enhanced fusion model is implemented by double-layer LSTM and CNN modules to enhance the fusion of text features extracted from the attention mechanism for classifying the classifiers. The feature-difference enhancement attention algorithm model not only adds more weight to important text features but also strengthens the differences between them and other text features. This operation can further improves the effect of important features on Chinese text recognition. The two models are classified by the softmax function. The text classification experiments are conducted based on the Chinese text corpus. The experimental results show that compared with the contrast model, the proposed algorithm can significantly improve the recognition ability of Chinese text features.

Background Long non-coding RNAs (lncRNAs) are essential modulators in tumor progression. While fatty acid (FA) metabolism can potentiate tumorigenesis, colonization, and metastasis, the roles of lncRNAs in reprograming FA metabolism and regulating gastric cancer (GC) metastasis remain elusive. Methods Whole RNA-sequencing and in silico analyses were conducted to identify clinically significant lncRNAs involved in GC metastasis. Among the identified lncRNAs, we focused on the novel lncRNA TENM3-AS1. RT-qPCR and FISH analyses revealed an increased expression of TENM3-AS1 in GC cell lines and patients. In vitro and in vivo functional experiments validated the effects of TENM3-AS1 to GC metastasis and the reprogramming of FA metabolism. ChIP, Biotinylated RNA pull-down, RIP, CHX-chase assay, ubiquitination assay, and RNA stabilization assay were employed to perceive the mechanisms underlying the effects of TENM3-AS1 in GC cells. Results TENM3-AS1 expression was significantly elevated in metastatic tumors and advanced primary tumors of GC patients. This increased expression was also associated with a worsened overall survival and progression-free survival. Functionally, TENM3-AS1 enhanced the migration and invasiveness of GC cells in vitro, promoted tumorigenesis and liver metastasis in vivo, and increased FA biosynthesis in GC cells. Mechanistically, our studies showed that the transcription factor EGR1 activated TENM3-AS1, which in turn upregulated the expression of FASN and hnRNPK. Furthermore, TENM3-AS1 interacted with and stabilized hnRNPK by increasing its deubiquitination. This interaction reprogrammed FA metabolism and promoted GC progression by increasing FASN mRNA stability through hnRNPK. Conclusions In this study, by comparing lncRNA sequencing data from paired primary and peritoneal metastatic tumors and public transcriptome data from non-metastatic and metastatic samples, we clarified a novel lncRNA, TENM3-AS1. It was found that TENM3-AS1 was aberrantly activated in metastatic and advanced primary tumors, and was strongly correlated with a shorter survival in GC patients. Our study reveals the EGR1/TENM3-AS1/ hnRNPK/FASN axis as a novel curative target in metastatic GC.

Moldy corn produces aflatoxin and gibberellin, which can have adverse effects on human health if consumed. Mold is a significant factor that affects the safe storage of corn. If not detected and controlled in a timely manner, it will result in substantial food losses. Understanding the infection patterns of mold on corn kernels and the changing characteristics of the internal structure of corn kernels after infection is crucial for guiding innovation and optimizing detection methods for moldy corn. This knowledge also helps maintain corn storage and ensure food safety. This study was based on X-ray tomography technology to non-destructively detect changes in the structural characteristics of moldy corn kernels. It used image processing technology and model reconstruction algorithms to obtain the 3D model of the embryo, pores and cracks, endosperm and seed coat, and kernels of moldy corn kernels; qualitative analysis of the characteristic changes of two-dimensional slice grayscale images and 3D models of moldy corn kernels; and quantitative analysis of changes in the volume parameters of corn kernels, embryos, endosperm, and seed coats as a whole. It explored the detection method of moldy corn kernels based on a combination of X-ray tomography technology and deep learning algorithms. The analysis concluded that mold infection in maize begins in the embryo and gradually spreads and that mold damage to the tissue structure of maize kernels is irregular in nature. The overall volume parameter changes of corn kernels, embryos, endosperm, and seed coats in the four stages of 0 d, 5 d, 10 d, and 15 d showed a trend of first increasing and then decreasing. The ResNet50 model was enhanced for detecting mold on maize kernels, achieving an accuracy of over 93% in identifying mold features in sliced images of maize kernels. This advancement enabled the non-destructive detection and classification of the degree of mold in maize kernel samples. This article studies the characterization of the characteristic changes of moldy corn kernels and the detection of mildew, which will provide certain help for optimizing the monitoring of corn kernel mildew and the development of rapid detection equipment.