The analysis and solution for intercity travel behaviors during holidays in the post-epidemic era based on big data.

The COVID-19 had a huge impact on the transportation industry. In the post-epidemic stage, intercity transportation will face great challenges as places are unsealed, tourism and other service industries begin to recover, and residents' travel demand gradually increases. An in-depth study of residents' intercity travel behavior during holidays in the post-epidemic era will help restore public trust in public transportation and improve the quality of public transportation services. Based on traditional research on ways of travelling, the study adopted the Complex Network Analysis Theory. The city clusters of Shandong Peninsula were taken as the research region. The research studied the impact of the differences in regional attributes of the cities in Shandong Peninsula on residents' intercity travel in the post-epidemic times. A dynamic evolution model of how residents choose to travel was built to simulate the changes to their ways of traveling in the post-epidemic era under two conditions, which are: traveling under the government's supervision of intercity travel and traveling under the government's optimization of intercity travel conditions. The conclusions drawn from the analyses of Complex Network Theory and Evolutionary Game Theory are as follows. First, in the holiday intercity travel in the post-epidemic times, the neighboring cities of Shandong Peninsula are closely connected, thus traveling between neighboring cities dominates intercity travel. Second, the travel network concentration of residents on long-term holidays is lower than that on short-term holidays, and the migration intensity of residents is higher than that on short-term holidays, while the willingness of residents' migration on short-term holidays is higher than that on long-term holidays. The willingness to migrate on holidays is generally lower than that before the epidemic. Third, in a normal intercity travel network, the travel between two cities with medium and long distances is mainly by public transport. However, the dominance of public transport will be affected under the impact of the epidemic. In short-distance travel between two cities, private transport is in an advantageous position, and under the impact of the epidemic, this advantage will become more significant. The government can improve the position of public transport in short-distance travel by making optimizations.

Distinct response patterns of bacterial communities in Ag- and ZnO-rGO nanocomposite-amended silt loam soils.

The widespread application of metal-based nanoparticle (MNPs)/reduced graphene oxide (rGO) composites inevitably leads to their release into soils. However, we lack a detailed understanding of the bacterial community response to MNPs-rGO exposure in farmland soils. Here, we conducted a soil microcosm experiment to analyze the potential impact of MNPs-rGO on bacterial communities in two field soils via high-throughput sequencing. The change in alpha diversity of bacterial communities was more susceptible to Ag-rGO and ZnO-rGO treatments than CuO-rGO. In both soils, MNPs-rGO significantly changed the bacterial community structure even at a low dose (1 mg kg-1). The bacterial community structure was most strongly affected by Ag-rGO at 30 days, but the greatest changes occurred in ZnO-rGO at 60 days. The differences in soil properties could shape bacterial communities to MNPs-rGO exposure. Distance-based redundancy analysis and functional annotation of prokaryotic taxa showed that some bacterial species associated with nitrogen cycling were greatly influenced by Ag-rGO and ZnO-rGO exposure. In sum, Ag-rGO and ZnO-rGO may potentially affect bacterial communities and nitrogen turnover under long-term realistic field exposure. These findings present a perspective on the response of bacterial communities to MNPs-rGO and provide a fundamental basis for estimating the ecological behavior of MNPs-rGO.

A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition.

Daily land surface temperature (LST) forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD) coupled with Machine Learning (ML) algorithms are useful for achieving these purposes because they can reduce the difficulty of modeling, require less history data, are easy to develop, and are less complex than physical models. In this article, a computationally simple, less data-intensive, fast and efficient novel hybrid data-driven model called the EEMD Long Short-Term Memory (LSTM) neural network, namely EEMD-LSTM, is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The daily LST data series from the Mapoling and Zhijaing stations in the Dongting Lake basin, central south China, from 1 January 2014 to 31 December 2016 is used as a case study. The EEMD is firstly employed to decompose the original daily LST data series into many Intrinsic Mode Functions (IMFs) and a single residue item. Then, the Partial Autocorrelation Function (PACF) is used to obtain the number of input data sample points for LSTM models. Next, the LSTM models are constructed to predict the decompositions. All the predicted results of the decompositions are aggregated as the final daily LST. Finally, the prediction performance of the hybrid EEMD-LSTM model is assessed in terms of the Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Pearson Correlation Coefficient (CC) and Nash-Sutcliffe Coefficient of Efficiency (NSCE). To validate the hybrid data-driven model, the hybrid EEMD-LSTM model is compared with the Recurrent Neural Network (RNN), LSTM and Empirical Mode Decomposition (EMD) coupled with RNN, EMD-LSTM and EEMD-RNN models, and their comparison results demonstrate that the hybrid EEMD-LSTM model performs better than the other five models. The scatterplots of the predicted results of the six models versus the original daily LST data series show that the hybrid EEMD-LSTM model is superior to the other five models. It is concluded that the proposed hybrid EEMD-LSTM model in this study is a suitable tool for temperature forecasting.

Reduction behavior induced by HL010183, a metformin derivative against the growth of cutaneous squamous cell carcinoma.

Metformin is a biguanide widely prescribed as a first-line antidiabetic drug in type 2 diabetes mellitus patients. Animal and cellular studies support that metformin has a strong anti-proliferative effect on various cancers. Herein, we report that metformin derivative, HL010183 significantly inhibited human epidermoid A431 tumor xenograft growth in nu/nu mice, which in turn is associated with a significant reduction in proliferative biomarkers PCNA and cyclins D1/B1. Enhanced apoptotic cell death and an increase in Bax: Bcl2 ratio supported the tumor growth reduction. The mechanism of the drug effects appears to be dependent on the inhibition of nuclear factor kappa B (NFkB) and mTOR signaling pathways. Reduced enhancement of NFkB transcriptional target proteins, iNOS/COX-2 together with decreased phosphorylation of NFkB inhibitory protein IKBa were also observed. Further, AKT signaling activation was evaluated by the reduced phosphorylation at Ser473. In addition, a concomitant decrease in mTOR signaling pathway was also estimated from the reduced phosphorylation at mTOR regulatory proteins p70S6K and 4E-BP-1. Along with this, decreased phosphorylation of GSK3b, which is carried out by AKT kinases was also observed. Overall results suggested that HL010183 interrupt SCC growth via NFkB and mTOR signaling pathways.