Rehabilitation assistive control system

Rehabilitation assistive control syste

Replication-based innovation view of the internationalization process

The traditional literature on internationalization process has emphasized on the knowledge accumulation and commitments, which, however, becomes challenging in explaining how digital platforms achieve globalization nowadays. To address this gap, in this study, we build the replication-based innovation theory by analyzing business model as the unit of analysis through a multi-case method. Our inductive, comparative study of ByteDance and Kuaishou shows that these digital platforms achieved globalization, based on replication strategy, through a dynamic process of reproduction, adaption, variation, and innovation. Our findings reveal the importance of the replication strategy for business model innovation during the internationalization process, and then highlight two distinct patterns of replication-based innovation: gradual, as demonstrated by ByteDance’s great improvements of its replicated business model in host countries and positive revision in home country; and radical, as exemplified by Kuaishou’s slight adjustments in host countries and negative reconstruction in the home country. We further combine these distinct patterns together as the replication-based innovation theory, which is helpful to explain why some digital platforms are more successful in globalization, while others are not. Our study provides new insights into the internationalization process of digital platforms and offers guidance to managers on navigating the replication-innovation game.</p

Information Seeking Regarding Tobacco and Lung Cancer: Effects of Seasonality

<div><p>This paper conducted one of the first comprehensive international Internet analyses of seasonal patterns in information seeking concerning tobacco and lung cancer. Search query data for the terms “tobacco” and “lung cancer” from January 2004 to January 2014 was collected from Google Trends. The relevant countries included the USA, Canada, the UK, Australia, and China. Two statistical approaches including periodogram and cross-correlation were applied to analyze seasonal patterns in the collected search trends and their associations. For these countries except China, four out of six cross-correlations of seasonal components of the search trends regarding tobacco were above 0.600. For these English-speaking countries, similar patterns existed in the data concerning lung cancer, and all cross-correlations between seasonal components of the search trends regarding tobacco and that regarding lung cancer were also above 0.700. Seasonal patterns widely exist in information seeking concerning tobacco and lung cancer on an international scale. The findings provide a piece of novel Internet-based evidence for the seasonality and health effects of tobacco use.</p></div

The cross-correlations between seasonal components of search trends regarding tobacco and those regarding lung cancer.

<p>The cross-correlations between seasonal components of search trends regarding tobacco and those regarding lung cancer.</p

Raw search trends regarding tobacco from Google Trends.

<p>It contains the trends of the USA, Canada, the UK, Australia, and China. The time interval contains 522 weeks from January 4, 2004 to January 4, 2014.</p

Exploring Entrainment Patterns of Human Emotion in Social Media

<div><p>Emotion entrainment, which is generally defined as the synchronous convergence of human emotions, performs many important social functions. However, what the specific mechanisms of emotion entrainment are beyond in-person interactions, and how human emotions evolve under different entrainment patterns in large-scale social communities, are still unknown. In this paper, we aim to examine the massive emotion entrainment patterns and understand the underlying mechanisms in the context of social media. As modeling emotion dynamics on a large scale is often challenging, we elaborate a pragmatic framework to characterize and quantify the entrainment phenomenon. By applying this framework on the datasets from two large-scale social media platforms, we find that the emotions of online users entrain through social networks. We further uncover that online users often form their relations via dual entrainment, while maintain it through single entrainment. Remarkably, the emotions of online users are more convergent in nonreciprocal entrainment. Building on these findings, we develop an entrainment augmented model for emotion prediction. Experimental results suggest that entrainment patterns inform emotion proximity in dyads, and encoding their associations promotes emotion prediction. This work can further help us to understand the underlying dynamic process of large-scale online interactions and make more reasonable decisions regarding emergency situations, epidemic diseases, and political campaigns in cyberspace.</p></div

Seasonal components of the tobacco-related search trends of the USA, Canada, the UK, Australia, and China.

<p>These seasonal components are extracted by using ideal pass filter on the raw search trends.</p

Emotional distance at different peer levels (Top figures).

<p>Bottom figures correspond to the varying ratio of the number of Dual entrainment over the number of Single entrainment; purple dashed lines separate the two stages of relationship establishment: ‘Develop’ stage and ‘Maintain’ stage.</p

Change in posting number.

<p>Breakdown of posts each year. From bottom up: posts with positive emotion tags (POS), neutral emotion tags (NEU), negative emotion tags (NEG), unknown emotion tags within our emotion classification scheme (UNKNONWN), and no emotion tags (NOTAG). Blue curve corresponds to fitted exponential function: y = a*exp(b*(t-t₀)).</p

Change in user base.

<p>Breakdown of active users each year. From bottom up: users that joined the community that year (and did not abandon the same year), users that joined and abandoned the community that year, users that abandoned the community that year (and did not join the same year) and other active users. Blue curve corresponds to fitted exponential function: y = a*exp(b*(t-t₀)). ‘*’ means only partial data samples are used in curve fitting.</p