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Terrestrial gross primary productivity (GPP) plays an important role in terrestrial-atmosphere system carbon cycles and contributes to human welfare, as it is the basis for food, fiber, and wood production (Beer et al., 2010). Although rising atmospheric CO 2 has increased GPP in many regions of the world during the last several decades (Schimel et al., 2015;Tharammal et al., 2019), factors such as drought, heat stress, and nutrient limitations decrease GPP, and their effects are poorly understood at regional scale due to

The ways people use words online can furnish psychological processes about their beliefs, fears, thinking patterns, and so on. Extracting from online employees’ reviews on the workplace community websites, we can quantify the psychological effects of employees during the phase of the COVID-19 pandemic. We collect the anonymous employees’ reviews of Top 100 digital companies from the Glassdoor website which allows people to evaluate and review the companies they have worked for or are working for. Here, based on the data of numerical evaluations and textual reviews, we firstly use Z-score to investigate the psychological effects of employees in digital companies during the phase of COVID-19 pandemic. Next, we use a text analysis application called Linguistic Inquiry and Word Count (LIWC), which provides an efficient and effective method for studying the various emotional, cognitive, and structural components existing in individuals’ verbal and written speech samples, to mine these reviews to obtain changes in personal pronouns and 10 dimensions of psychological processes. Finally, we use Z-score to count on all aspects of drives and personal concerns in psychological processes.

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The personal description of a company associated with job satisfaction, company culture, and opinions of senior leadership is available on workplace community websites. However, it is almost impossible to read all of the different and possibly even contradictory reviews and make an accurate overall rating. Therefore, extracting aspects or sentiments from online reviews and the corresponding ratings is an important challenge. We collect online anonymous employees’ reviews from Glassdoor.com which allows people to evaluate and review the companies they have worked for or are working for. Here, we propose a joint rules-based model which combines the numerical evaluation reflected in the form of 1–5 stars, and the reviewed context to extract aspects. The model first inputs the five aspects with the initial word sets that are manually screened, and expands the aspect keyword sets through bootstrapping semi-supervised learning, and then uses latent rating regression to obtain the aspect score and aspect weight to update the corresponding score. Our experimental evaluation has shown better results as compared with an unsupervised learning of the latent Dirichlet allocation. The results could not only help companies understand their strengths and weaknesses, but also help job seekers apply for companies.

The growing interest in the study of economic complexity has been motivated by the intrinsic features of statistical physics. Entropy is one of essential concepts in statistical physics and a particularly active concept for characterizing the complexity of a system. Motived by this, we propose a new method based on a function of the entropy to quantify economic complexity of China trade flows. We focus on regional economy activities, and collect China trade flows according to the database of national export and import items. Compared with classic economic complexity measurements including diversity and ubiquity, eigenvector-based complexity index, fitness and complexity index, Hirschman-Herfindahl index, the proposed method based on entropy can generate a proper ranking list when we take USA as a benchmark. Furthermore, the comparison of the main economic complexity statics also exposes that several of which performance moderate correlations. The stability of the ranking positions from the static evolution of the economic complexity over 8 years discloses that the index of economic complexity performs extremely sensitive depending on the eigenvector of the product network space. The results reveal that we should pay attention to the relationships between economic complexity methods when analysing the structural characteristics of economic entity activities.