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A container shipping network connects coastal countries with each other and facilitates most of the world merchandise trade. Reliable maritime connectivity ensures the availability of commodities and economic growth. The global spread of COVID-19 has led to port failures and service cancellations, resulting in decreased connectivity level of container ports. To mitigate the impact of the pandemic, a graph theory approach is proposed to strength the container shipping network connectivity by considering topology and the possibility of opening new shipping links between ports. It is designed to maximize network connectivity with limited addable routes. The network connectivity is measured by algebraic connectivity, and the possibility of opening new shipping links is estimated by an extended gravity model. A heuristic algorithm based on Fiedler vector is introduced to obtain the optimal solutions. The performance of the proposed model and algorithm are verified by testing on a real-world container shipping network based on the Alphaliner database. Experimental results illustrate that the presented model is efficient and effective for strengthening the connectivity. Policy makers can refer to the suggested optimal shipping links to facilitate better shipping network connectivity in the context of the COVID-19 pandemic. [Display omitted] [ABSTRACT FROM AUTHOR]

Japan's plan to dump nuclear wastewater into the sea has generated a tremendous amount of discussion on social media due to the potential wide-ranging impact. To our knowledge, few studies have mined social media platforms to assess similar pollution concerns. We use the Octopus Collector to collect online textual data regarding "Japan's plan to dump nuclear wastewater into the sea" from Sina Weibo since April 13, 2021. After the posts from Sina Weibo were preprocessed, user opinions were analyzed using natural language processing. We used a naive Bayes classifier for sentiment analysis and latent dirichlet allocation (LDA) to extract and cluster topics from the posts, allowing for users' related opinions to be mined and analyzed. The study found that there were three major themes in terms of public concern: nuclear pollution and marine ecology, seafood imports and food safety, and international responsibility and public ethics. In our emotional analysis, we found that most people expressed negative emotions about the plan. However, there was also a positive emotional aspect because, with the release of relevant information and the popularization of knowledge, the public has been able to have a rational discussion about the consequences of this event, and the topic includes a focus on positive factors such as environmental protection and sustainable development. For this reason, the government and relevant agencies should keep up-to-date with the latest news of the incident to further raise public awareness and lead the public to a rational discussion to avoid excessive negative emotions. [Display omitted] • This study explored the public opinion towards the Japan's dumping nuclear wastewater event from social media. • The sentiment analysis and LDA were employed to perform the quantitative analysis for public opinion. • Public concerns about nuclear pollution and marine ecology, seafood imports and food safety, and international responsibility and public ethics. • The emotional analysis found that most people expressed negative emotions about the plan. • The government should keep up-to-date with the latest news of the incident to further raise public awareness. [ABSTRACT FROM AUTHOR]

Individuals' attention and sentiment are the keys to adopting electric vehicles (EVs). Traditional questionnaires and interviews cannot fully and accurately reflect the attention and sentiments. Social media interactions can provide a new data-driven perspective to explore the sentiment toward EVs. This study uses data from public posts on Weibo to investigate intersectionality in EV - sentiment and attention as per user, gender and region. On a 1,149,243-text corpus extracted from the Weibo posts, a computational social science methodology was employed with a mixed-method of deep learning and topic modeling through Latent Dirichlet Allocation algorithm. Results showed that attention toward EVs mainly comes from official users rather than individual users (IUs), and IUs' attention is closely linked with EV policy change. Additionally, the attention level and growth rate toward EVs vary across regions and men pay more attention to EVs. There exist significant differences in both positive and negative sentiment driving factors across genders. This study facilitates to EVs' policy-making and strategy in China and other countries. [ABSTRACT FROM AUTHOR]

The present study investigated how pain appraisals from other individuals modulated self-pain anticipation and perception. Appraisals of pain intensity from 10 other individuals were presented before the participants received identical electrical pain stimulation themselves. In reality, the presented other's pain appraisals, with either low or high in mean and variance, were generated by the experimenter, and were randomly paired with the subsequent electrical stimulation at either low or high intensity. Specifically, the mean and variance of others' pain appraisals were manipulated to induce participants' expectation and certainty to the upcoming pain. Subjective ratings of pain intensity and electroencephalographic (EEG) responses to the electrical stimulation, as well as anticipatory EEG activities measured prior to the onset of electrical stimulation, were compared. Results showed that the mean and variance of others' pain appraisal modulated the subjective pain ratings and the affective-motivational P2 responses elicited by the electrical stimulation, as well as anticipatory sensorimotor α-oscillation measured before the onset of pain stimulation. When the mean of others' pain appraisal was low, higher variance suppressed the sensorimotor α-oscillations and enhanced subsequent pain perception. In contrast, when the mean was high, the higher variance enhanced sensorimotor α-oscillations and suppressed subsequent pain perception. These results demonstrated that others' pain appraisals can modulate both of the anticipation and perception of first-hand pain. It also suggested that the top-down modulation of others' pain appraisals on pain perception could be partially driven by the different brain states during the anticipation stage, as captured by the prestimulus sensorimotor α-oscillations. • Others' pain appraisals modulated the first-hand pain anticipation and perception • Mean and variance of others' appraisals modulated self-reported pain ratings • Mean and variance of others' appraisals modulated pain elicited brain responses • Others' appraisals influenced anticipatory stage captured by α-oscillations [ABSTRACT FROM AUTHOR]