Rediscover Climate Change during Global Warming Slowdown via Wasserstein Stability Analysis

Climate change is one of the key topics in climate science. However, previous research has predominantly concentrated on changes in mean values, and few research examines changes in Probability Distribution Function (PDF). In this study, a novel method called Wasserstein Stability Analysis (WSA) is developed to identify PDF changes, especially the extreme event shift and non-linear physical value constraint variation in climate change. WSA is applied to 21st-century warming slowdown period and is compared with traditional mean-value trend analysis. The result indicates that despite no significant trend, the central-eastern Pacific experienced a decline in hot extremes and an increase in cold extremes, indicating a La Nina-like temperature shift. Further analysis at two Arctic locations suggests sea ice severely restricts the hot extremes of surface air temperature. This impact is diminishing as sea ice melts. Overall, based on detecting PDF changes, WSA is a useful method for re-discovering climate change.Comment: 12 pages, 4 figures, and 1 Algorith

Leveraging Multi-level Dependency of Relational Sequences for Social Spammer Detection

Much recent research has shed light on the development of the relation-dependent but content-independent framework for social spammer detection. This is largely because the relation among users is difficult to be altered when spammers attempt to conceal their malicious intents. Our study investigates the spammer detection problem in the context of multi-relation social networks, and makes an attempt to fully exploit the sequences of heterogeneous relations for enhancing the detection accuracy. Specifically, we present the Multi-level Dependency Model (MDM). The MDM is able to exploit user's long-term dependency hidden in their relational sequences along with short-term dependency. Moreover, MDM fully considers short-term relational sequences from the perspectives of individual-level and union-level, due to the fact that the type of short-term sequences is multi-folds. Experimental results on a real-world multi-relational social network demonstrate the effectiveness of our proposed MDM on multi-relational social spammer detection

Do Chinese firms benefit from government ownership following cross-border acquisitions?

Chinese firms’ increasing cross-border acquisitions (CBAs) in recent years seem to challenge the explanatory power of received theories of multinational enterprise (MNE) due to their relatively unique characteristics and the active role of the Chinese government. In this study, we seek to revisit and contextualize the OLI paradigm in conjunction with the institution-based view and examine how Chinese firms’ post-CBA long term performance is associated with government ownership. Our study shows that Chinese firms with more government ownership demonstrate better post-CBA long term performance. However, the above relationship is differentially moderated by such firm-level boundary conditions as political connections and financial slack, and the country-level institutional boundary conditions (i.e., the host country formal institutions and the home-host country cultural distance). We discuss our findings in detail and explore theoretical and practical implications for both Chinese firms and other emerging economy (EE) firms

The combined impact of social networks and connectedness on anxiety, stress, and depression during COVID-19 quarantine: a retrospective observational study

IntroductionThe COVID-19 pandemic and associated quarantine measures have precipitated a surge in mental health disorders, particularly depression and anxiety. Government policies and restrictions on physical activity have contributed to this phenomenon, as well as diminished subjective social connectedness and exacerbated objective social isolation. As two dimensions of social isolation, it is worth noting that subjectively perceived social connectedness serves as a protective factor for mental health, whereas the decline in the size of objectively evaluated social networks poses a significant risk. However, research investigating the combined influence of these two dimensions remains limited.MethodsThis study used an online survey to collect data to investigate the effects of objective social connectedness and objective social networks on anxiety, stress, and depression during COVID-19 quarantine. A total of 485 participants were analyzed using statistical methods, including paired t-test, Pearson correlation analysis, linear regression, cluster analysis, ANOVA, and moderated mediated.ResultsThe study found that anxiety and depression scores increased during the quarantine, with age, education, and social connectedness scores associated with the increase. Pre-quarantine anxiety and depression levels were strongly correlated with mental health status during quarantine. Cluster analysis, respectively, revealed three clusters for those without increasing anxiety and depression scores. The study also found that objective social network influences the impact of subjective social connectedness on pre-quarantine mental health, which in turn affects anxiety and depression levels during quarantine.ConclusionThe study identified that quarantine increased anxiety and depression, with age being protective, and education and subjective social connectedness as risk factors. The study also emphasizes the comprehensive impact of objective and subjective social isolation. Although individuals perceive the same degree of social connectedness, those with smaller social networks are more prone to developing symptoms of anxiety and depression, which are also more likely to worsen during quarantine