Chinese cities show different trend toward carbon peak.

Understanding the disparities in carbon emission trend among cities is critical for achieving carbon peak goal. However, the status and trends of carbon peaking and reduction in various city types are still unclear. Therefore, this study classified 315 Chinese cities according to their economic and industrial structure by SOM-K-means, aiming to evaluate the trends and dynamic drivers of carbon peaking progress in different city types. The findings reveal a decline in carbon emissions in 110 cities (34.9 %) since 2020. Notably, all city types show potential for carbon reduction and achieving carbon peaking. Specifically, resource-based cities and high-end service cities have the most effect on reducing emissions, with 48.4 % and 42.1 % of the cities declining in carbon emissions. Energy-based and heavy industrial cities face heightened pressure to reduce carbon emissions. Additionally, in high-end service cities, energy efficiency and investment intensity contribute to emission reduction, while industrial structure adjustment decrease carbon emissions in resource-based cities. Furthermore, enhancing energy efficiency effects and R&D intensity are effective ways to significantly reduce carbon emissions in heavy industrial cities. We conclude that differentiating carbon reduction pathways for different cities should constitute be a breakthrough in achieving the goal of carbon peaking. These insights provide recommendations for cities that have yet to reach their carbon peak for both China and other developing countries.

Antidepressant effect of 4-Butyl-alpha-agarofuran via HPA axis and serotonin system.

Depression is a leading cause of disability worldwide and the psychiatric diagnosis most commonly associated with suicide. 4-Butyl-alpha-agarofuran (AF-5), a derivative of agarwood furan, is currently in phase III clinical trials for generalized anxiety disorder. Herein, we explored the antidepressant effect and its possible neurobiological mechanisms in animal models. In present study, AF-5 administration markedly decreased the immobility time in mouse forced swim test and tail suspension test. In the sub-chronic reserpine-induced depressive rats, AF-5 treatment markedly increased the rectal temperature and decreased the immobility time of model rats. In addition, chronic AF-5 treatment markedly reversed the depressive-like behaviors in chronic unpredictable mild stress (CUMS) rats by reducing immobility time of forced swim test. Single treatment with AF-5 also potentiated the mouse head-twitch response induced by 5-hydroxytryptophan (5-HTP, a metabolic precursor to serotonin), and antagonized the ptosis and motor ability triggered by reserpine. However, AF-5 had no effect on yohimbine toxicity in mice. These results indicated that acute treatment with AF-5 produced serotonergic, but not noradrenergic activation. Furthermore, AF-5 reduced adrenocorticotropic hormone (ACTH) level in serum and normalized the neurotransmitter changes, including the decreased serotonin (5-HT) in hippocampus of CUMS rats. Moreover, AF-5 affected the expressions of CRFR1 and 5-HT2C receptor in CUMS rats. These findings confirm the antidepressant effect of AF-5 in animal models, which may be primarily related to CRFR1 and 5-HT2C receptor. AF-5 appears to be promising as a novel dual target drug for depression treatment.

Generating Road Networks for Old Downtown Areas Based on Crowd-Sourced Vehicle Trajectories.

With the popularity of portable positioning devices, crowd-sourced trajectory data have attracted widespread attention, and led to many research breakthroughs in the field of road network extraction. However, it is still a challenging task to detect the road networks of old downtown areas with complex network layouts from high noise, low frequency, and uneven distribution trajectories. Therefore, this paper focuses on the old downtown area and provides a novel intersection-first approach to generate road networks based on low quality, crowd-sourced vehicle trajectories. For intersection detection, virtual representative points with distance constraints are detected, and the clustering by fast search and find of density peaks (CFDP) algorithm is introduced to overcome low frequency features of trajectories, and improve the positioning accuracy of intersections. For link extraction, an identification strategy based on the Delaunay triangulation network is developed to quickly filter out false links between large-scale intersections. In order to alleviate the curse of sparse and uneven data distribution, an adaptive link-fitting scheme, considering feature differences, is further designed to derive link centerlines. The experiment results show that the method proposed in this paper preforms remarkably better in both intersection detection and road network generation for old downtown areas.

An Improved HMM-Based Approach for Planning Individual Routes Using Crowd Sourcing Spatiotemporal Data.

With the rapid development of LBSs (location-based services) in recent years, researchers have increasingly taken an interest in trying to make travel routes more practicable and individualized. Despite the fact that many studies have been conducted on routes using LBS data, the specific routes are deficient in dynamic scalability and the correlations between environmental constraints and personal choices have not been investigated. This paper proposes an improved HMM-based (hidden Markov model) method for planning personalized routes with crowd sourcing spatiotemporal data. It tries to integrate the dynamic public preferences, the individual interests and the physical road network space in the same spatiotemporal framework, ensuring that reasonable routes will be generated. A novel dual-layer mapping structure has been proposed to bridge the gap from brief individual preferences to specific entries of POIs (points-of-interest) inside realistic road networks. A case study on Changsha city has proven that the proposed method can not only flexibly plan people's travel routes under different spatiotemporal backgrounds but also is close to people's natural selection by the perception of the group.

City-Wide Traffic Flow Forecasting Using a Deep Convolutional Neural Network.

City-wide traffic flow forecasting is a significant function of the Intelligent Transport System (ITS), which plays an important role in city traffic management and public travel safety. However, this remains a very challenging task that is affected by many complex factors, such as road network distribution and external factors (e.g., weather, accidents, and holidays). In this paper, we propose a deep-learning-based multi-branch model called TFFNet (Traffic Flow Forecasting Network) to forecast the short-term traffic status (flow) throughout a city. The model uses spatiotemporal traffic flow matrices and external factors as its input and then infers and outputs the future short-term traffic status (flow) of the whole road network. For modelling the spatial correlations of the traffic flows between current and adjacent road segments, we employ a multi-layer fully convolutional framework to perform cross-correlation calculation and extract the hierarchical spatial dependencies from local to global scales. Also, we extract the temporal closeness and periodicity of traffic flow from historical observations by constructing a high-dimensional tensor comprised of traffic flow matrices from three fragments of the time axis: recent time, near history, and distant history. External factors are also considered and trained with a fully connected neural network and then fused with the output of the main component of TFFNet. The multi-branch model is automatically trained to fit complex patterns hidden in the traffic flow matrices until reaching pre-defined convergent criteria via the back-propagation method. By constructing a rational model input and network architecture, TFFNet can capture spatial and temporal dependencies simultaneously from traffic flow matrices during model training and outperforms other typical traffic flow forecasting methods in the experimental dataset.