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BACKGROUND: Succinate dehydrogenase is an important target of fungicides. Succinate dehydrogenase inhibitors (SDHIs) have widely been used to combat destructive plant pathogenic fungi because they possess efficient and broad‐spectrum antifungal activities and as well as unique mode of action. The research and development of novel SDHIs have been ongoing. RESULTS: Thirty‐six novel quinolin‐2(1H)‐one derivatives were designed, synthesized and characterized. The single crystal structure of compound 3c was determined through the X‐ray diffraction of single crystals. The bioassay results displayed that most compounds had good antifungal activities at 16 μg mL−1 against Rhizoctonia cerealis, Erysiphe graminis, Botrytis cinerea, Penicillium italicum and Phytophthora infestans. Compounds 6o, 6p and 6r had better antifungal activities than the commercialized fungicide pyraziflumid against Botrytis cinerea. Their half maximal effective concentration (EC50) values were 0.398, 0.513, 0.205 and 0.706 μg mL−1, respectively. Moreover, the inhibiting activities of the bioactive compounds were tested against succinate dehydrogenase. The results indicated that they possessed outstanding activities. Compounds 6o, 6p and 6r also exhibited better inhibiting activities than pyraziflumid against succinate dehydrogenase. Their half maximal inhibitory concentration (IC50) values were 0.450, 0.672, 0.232 and 0.858 μg mL−1, respectively. The results of molecular dynamic (MD) simulations indicated that compound 6r displayed stronger affinity to succinate dehydrogenase than pyraziflumid. CONCLUSION: The results of the present study displayed that quinolin‐2(1H)‐one derivative could be one scaffold of potential SDHIs and will provide some valuable information for the research and development of new SDHIs. © 2022 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

In this paper, we explore whether the activity of SARS-CoV-2 was associated with seasonality. MF-DFA model is utilized to calculate multifractal strength and multifractal complexity to evaluate the change state of SARS-CoV-2 activity. We select 10 countries with serious epidemic in the world, which are distributed in different latitudes of the northern and southern hemispheres. The study utilized the time series data of daily new cases and daily new deaths recorded in these countries. We regard May to October as the "high temperature season" for countries in the northern hemisphere, November to April as the "low temperature season", and the southern hemisphere is just the opposite. By comparing the multifractal intensity Δ H and multifractal complexity Δ α of the two time series in the two seasons, we draw a conclusion that, for both the sequence of the daily newly diagnosed persons and the daily newly increased number of deaths, in the countries of both the northern and southern hemispheres, Δ H and Δ α are weaker in the "low temperature season". That is, in the low temperature environment, SARS-CoV-2 can survive for a long time and be more infectious. In addition, we also observe that in the northern hemisphere, Iran is at a lower latitude, and although the SARS-CoV-2 activity in the low temperature season is higher than that in the high temperature season, the difference is not significant. Therefore, the lower latitude may resist this phenomenon. However, most of the countries in the southern hemisphere are within 30° of south latitude, with low latitude, and other meteorological characteristics such as humidity in the countries in the southern hemisphere are also relatively unique. Although SARS-CoV-2 is characterized by high activity in low temperature seasons, no direct evidence related to the characteristics of latitude distribution has been found. [ABSTRACT FROM AUTHOR]

Carbon price fluctuations significantly impact the development of industries, energy, agriculture, and stock investments. The carbon price possesses the features of nonlinearity, non-stationarity, and high complexity as a time series. To overcome the negative impact of these characteristics on prediction and to improve the prediction accuracy of carbon price series, a combination prediction model named Lp-CNN-LSTM, which utilizes both convolutional neural networks and long short-term memory networks, has been proposed. Strategy one involved establishing distinct models of CNN-LSTM and LSTM to analyze high-frequency and low-frequency carbon price sequences; the combination of output was integrated to predict carbon prices more precisely. Strategy two comprehensively considered the economic and technical indicators of carbon price sequences based on the Pearson correlation coefficient, while the Multi-CNN-LSTM model selected explanatory variables that strongly correlated with carbon prices. Finally, a predictive model for a combination of carbon prices was developed using Lp-norm. The empirical study focused on China's major carbon markets, including Hubei, Guangdong, and Shanghai. According to the error indicators, the performance of the Lp-CNN-LSTM model was superior to individual strategy prediction models. The Lp-CNN-LSTM model has excellent accuracy, superiority, and robustness in predicting carbon prices, which can provide a necessary basis for revising carbon pricing strategies, regulating carbon trading markets, and making investment decisions. [ABSTRACT FROM AUTHOR]

Node importance is a key factor affecting the overall operation efficiency of a railway freight transportation network (RFTN) that can be measured with the indicators of Hub and PageRank. Based on complex network theory and the national railway cargo exchange data of China's provinces, this study constructs an RFTN model with the 31 provinces as the nodes and measures the values of Hub and PageRank for the 31 provinces. Then, the time evolution law of the importance of the provincial nodes is analyzed comprehensively, and, using a regression model, the influencing factors of the importance of the provincial nodes are identified. The results show the following. (1) The uneven distribution of natural resources will affect the spatial changes in the importance of RFTN nodes. The Hub values tended to cluster around the average, and the economic structure of the output-oriented provinces improved as a whole. At the same time, the PageRank values of many provinces in the central and western regions significantly increased, and those provinces exhibited more frequent exchanges of goods with other provinces and closer economic ties with other regions. (2) The traffic fixed asset investments and the population density have the most obvious influences on the importance of the provincial nodes with a positive effect. In contrast, the railway freight concentration (RFC) coefficient, geographical location (longitude and latitude), and coastal region all have negative effects on the importance of provincial nodes. The results of this study provide scientific decision-making support for the reasonable establishment and distribution of RFTN hubs in China., 1. Introduction The transportation network, which constitutes one of the three major networks (transportation, energy, and information) created by human society, lays an important foundation for the development of modern [...]