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Multi-source data is widely used in the field of energy future prices forecasting, the improvement of forecast ability and data screening are becoming the focus of current research. In this paper, two tree-based models (namely, Random Forest and XGBoost model) are employed to predict China's crude oil future prices. The empirical analysis confirms that Random Forest and XGBoost model have superior prediction performances than benchmark and the XGBoost model performs best. An important finding is that there is a time gap between investor information search and processing because the prediction performance within the time lags is obviously superior than that of the current period. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has radically expanded the role of algorithmic governance in everyday mobility. In China, urban and provincial governments have introduced health codes app as a national contract tracing and quarantine enforcement method to restrict the movements of "risky" individuals through malls, subways, railways, as well as between regions. Yet the health codes have been implemented with uneven efficacy and unexpected consequences. Drawing on glitch politics, we read these unintended consequences as "bugs" emerging from the introduction of platform-based management into everyday life. These bugs mediated individuals' lived experiences of the digital app and the hybrid space constituted by population governance, individual digital navigation, and technology. Drawing on a database of posts scraped from Zhihu, a popular Chinese question-and-answer site, we examine three dimensions of the bug: the algorithmic bug, the territorial bug, and the corporeal bug. This paper sheds light on the significance of end-user experiences in digital infrastructure and contributes to our understanding of the digital geographies of bugs in algorithmic governance and platform urbanism. [ABSTRACT FROM AUTHOR]

Experiences of childhood trauma (CT) would lead to serious mental problems, especially depression. Therefore, it becomes crucial to identify influential factors related to depression and explore their associations. The objectives were to 1) identify critical depression-related factors using the extreme gradient boosting (XGBoost) method from a large-scale survey data; 2) explore associations between these factors for targeted interventions and treatments. A large-scale epidemiological study covering 63 universities was conducted in Jilin Province, China. The XGBoost model was trained and tested to classify young adults with CT experiences who had or did not have depression (N = 27,671). The essential factors were selected by SHapley Additive exPlanations (SHAP) value. Multiple logistic regression analyses were conducted for validation. The associations between these depression-related factors were further explored using network analysis. The XGBoost model selected the top 10 features associated with depression with satisfactory performance (AUC = 0.91; sensitivity = 0.88 and specificity = 0.76). These factors significantly differed between depression and non-depression groups (p < 0.001). There are strong positive associations between anxiety and obsessive-compulsive disorder (OCD), anxiety and post-traumatic stress disorder (PTSD), social anxiety disorder (SAD) and appearance anxiety, and negative associations between sleep quality and anxiety, sleep quality and PTSD among CT participants with depression. The cross-sectional design cannot draw causality, and biases in self-report measurements cannot be ignored. XGBoost model and network analysis were useful methods for discovering and understanding depression-related factors in this epidemiological study. Moreover, these essential factors could offer insights into future interventions and treatments for depressed young adults with CT experiences. • The XGBoost model identified the top 10 depression-related factors, including anxiety, sleep quality, PTSD, loneliness, and OCD, among others. • These selected factors exhibited variations between the depression and non-depression groups. • Specifically, anxiety emerged as the leading risk factor for depression. • Furthermore, among CT participants with depression, strong positive associations were observed between anxiety, OCD, PTSD, and SAD. [ABSTRACT FROM AUTHOR]

[Display omitted] • Fertilization F72 kg N/ha increased fungi diversity in maize experimental field. • Maize recruit different associated fungi than soil, by altering network complexity. • Core genera play an important role for nitrogen utilization in maize plants. • Endophytic fungi are valuable natural based solutions to improve crop fertilizer management. Endophytic mycobiomes are an integral part of crops in agricultural systems. Fertilization is a conventional practice to improve agricultural crops and may alter the mycobiomes of soil and plant systems. In agroecosystems, Nature Based Solutions (NBS) are key strategies for agricultural suitability. Investigating mycobiomes under N flux can provide fundamental data for constructive NBS strategies. In this study, the effects of nitrogen (N) fertilizer treatments on fungal compositions of the soil-maize systems were systematically investigated in field experiments at six N rates (F0, F72, F126, F180, F234, F280 kg N/ha) in northern China. Meta-omics amplicon sequencing of the internal transcribed spacer (ITS) region was employed to study community dynamics, network correlation, metabolic functional assemblies, abundance patterns and core fungi. This study found that fungal communities were significantly higher at 72 kg N/ha compared with control and other treatments. Network topology increased in maize fungi (R2 = 0.69) but decreased in the soil (R2 = 0.51). Abundances of carbon–nitrogen metabolizing functional genes (Ure , AMT, gdhA and GDH) were significantly correlated with maize fungal communities and were assembled by stochastic processes. Additionally, the relative abundance of Ascomycota (67.26 %) was dominant in the soil, whereas Basidiomycota (61. 73 %) prevailed in maize samples at 72 kg N/ha. Moreover, Sporidiobolus , Alternaria , Fusarium , and Penicillium served as conserved core genera and were positively correlated with NH 4 +-N mg/kg, NO 2 –-N mg/kg and NO 3 –-N mg/kg, and pH components. Furthermore, selective genera were tested and confirmed to increase the growth rate and nitrogen utilization in maize. The study highlights the promising role of associated endophytes as Nature-Based Solution for optimizing fertilization to improve crop production in maize-agroecosystem. [ABSTRACT FROM AUTHOR]