Suchergebnisse

The Levinsohn-Petrin consistent semi-parametric estimation method was used to empirically analyze input-output panel data on rice yields in 30 Chinese provinces from 1990 to 2019 and to predict the level of rice yields at the end of the 21st century. The results show that, in addition to natural disasters and objective natural conditions, rice yields depend mainly on pesticide application, that China's current rice production and operation model is insufficient to match the rapidly growing level of agricultural mechanization, and that promoting large-scale production in rice fields is an effective way to address the waste of agricultural machinery resources. From the prediction results, the model has stronger prediction ability for the central-eastern and southern production areas of China, and very low prediction ability for the northern and western areas. The prediction of rice yield levels in China shows that rice yields in China at the end of the 21st century increase by 9.81% under the RCP8.5 climate scenario. This paper puts forward targeted suggestions for improving rice yields. The government should strengthen the promotion and training of agricultural technology and increase the channels for farmers to learn rice cultivation techniques; promote large-scale rice production and operation; and improve the education level of rice growers. ; Máster Universitario en Análisis Económico Aplicado (M148)

Human mobility datasets, such as traffic flow data, reveal the connections between urban spaces. A novel framework is proposed to explore the spatial association between urban commercial and residential spaces via consumption travel flows in Shanghai. A social network analysis and a community detection method are employed using taxi trajectory data during the daytime to validate the framework. The machine learning-based approach, such as the community detection method, can overcome the limitation regarding spatial uncertainty and spatial effects. The empirical findings suggest that people's commercial activities are sensitive to the power of accessible commercial centers and travel distances. The high-level commercial centers would contribute to the monocentric structure in the outer urban region based on consumption flows. In the central urban region, increasing the number of high-level commercial centers and making the powers of commercial centers hierarchical can contribute to a polycentric mobility pattern of people's consumption. This research contributes to the literature by providing a novel framework to model, analyze and visualize people's mobility based on the trajectory big data, which is promising in future urban research.

In contemporary society, the escalating anxiety among college students has emerged as a pressing social concern, impacting both their mental well-being and academic performance by influencing levels of learning motivation. This research posits a negative correlation between the burgeoning learning anxiety and the motivation to learn among college students. Employing a quantitative research approach, data is gathered through structured questionnaires, and subsequent analysis is conducted using SPSS statistical software to derive meaningful insights. The study primarily focuses on Chinese college students, aiming to unveil the intricate relationship between learning anxiety and motivation. Through this investigation, the researchers seek to formulate strategies that mitigate learning anxiety and concurrently bolster intrinsic motivation for sustained learning among Chinese college students. This research serves as a key step in understanding and addressing the contemporary challenges associated with the mental health and academic performance of college students, paving the way for interventions that foster a positive learning environment.

The number of samples in biological experiments is continuously increasing, but complex protocols and human error in many cases lead to suboptimal data quality and hence difficulties in reproducing scientific findings. Laboratory automation can alleviate many of these problems by precisely reproducing machine-readable protocols. These instruments generally require high up-front investments, and due to the lack of open application programming interfaces (APIs), they are notoriously difficult for scientists to customize and control outside of the vendor-supplied software. Here, automated, high-throughput experiments are demonstrated for interdisciplinary research in life science that can be replicated on a modest budget, using open tools to ensure reproducibility by combining the tools OpenFlexure, Opentrons, ImJoy, and UC2. This automated sample preparation and imaging pipeline can easily be replicated and established in many laboratories as well as in educational contexts through easy-to-understand algorithms and easy-to-build microscopes. Additionally, the creation of feedback loops, with later pipetting or imaging steps depending on the analysis of previously acquired images, enables the realization of fully autonomous "smart" microscopy experiments. All documents and source files are publicly available to prove the concept of smart lab automation using inexpensive, open tools. It is believed this democratizes access to the power and repeatability of automated experiments.