What motivates academics toward entrepreneurship? Examining the formation of academic entrepreneurial intention from the push-pull perspective

IntroductionAlthough academic entrepreneurship has received considerable attention over the last decades, little attention has been devoted to discussing the formation process of academic entrepreneurial intention underlying the push-pull perspective. This study attempts to explore the mechanism of how academic entrepreneurial intention is shaped, with job-related negative elements as push factors, and entrepreneurship-related positive attractors as pull factors.MethodsIn this paper, regression analysis and Bootstrap were conducted using SPSS 26.0 and MPLUS 7.0, whose applicability has been widely demonstrated in research.ResultsFindings were derived from 1042 academics from Chinese universities. Results showed that both push and pull factors do contribute to boosting academic entrepreneurial intention. Particularly, the entrepreneurship-related pull factors including entrepreneurial opportunity identification and expected entrepreneurial benefits play a dominant role in trigging academic intention to engaging entrepreneurship. Moreover, our findings further confirmed the moderating role of social network in the process of academics pushed by negative job-related factors to emerge entrepreneurial intention.DiscussionThis study extends the research perspective on the factors influencing academic entrepreneurial intention by examines the impact of push and pull factors on academic entrepreneurial intention simultaneously. This deepens the formation mechanism of academic entrepreneurial intention. Besides, the current study identifies a new look at the role that social network plays in academic entrepreneurship

Technological Innovation Research: A Structural Equation Modelling Approach

The paper explores the relationship among technological innovation, technological trajectory transition, and firms’ innovation performance. Technological innovation is studied from the perspectives of innovation novelty and innovation openness. Technological trajectory transition is categorized into creative cumulative technological trajectory transition and creative disruptive technological trajectory transition. A structural equation model is developed and tested with data collected by surveying 366 Chinese firms. The results indicate that both innovation novelty and innovation openness positively affects creative cumulative technological trajectory transition as well as creative disruptive technological trajectory transition. Innovation openness and creative disruptive technological trajectory transition both positively affect firms’ innovation performance. However, neither innovation novelty nor creative cumulative technological trajectory transition positively affects firms’ innovation performance. Implications for managers and directions for future studies are discussed

IMPUS: Image Morphing with Perceptually-Uniform Sampling Using Diffusion Models

We present a diffusion-based image morphing approach with perceptually-uniform sampling (IMPUS) that produces smooth, direct, and realistic interpolations given an image pair. A latent diffusion model has distinct conditional distributions and data embeddings for each of the two images, especially when they are from different classes. To bridge this gap, we interpolate in the locally linear and continuous text embedding space and Gaussian latent space. We first optimize the endpoint text embeddings and then map the images to the latent space using a probability flow ODE. Unlike existing work that takes an indirect morphing path, we show that the model adaptation yields a direct path and suppresses ghosting artifacts in the interpolated images. To achieve this, we propose an adaptive bottleneck constraint based on a novel relative perceptual path diversity score that automatically controls the bottleneck size and balances the diversity along the path with its directness. We also propose a perceptually-uniform sampling technique that enables visually smooth changes between the interpolated images. Extensive experiments validate that our IMPUS can achieve smooth, direct, and realistic image morphing and be applied to other image generation tasks

Anthropogenic Activities Generate High-Refractory Black Carbon along the Yangtze River Continuum

12 pages, 7 figuresCombustion-driven particulate black carbon (PBC) is a crucial slow-cycling pool in the organic carbon flux from rivers to oceans. Since the refractoriness of PBC stems from the association of non-homologous char and soot, the composition and source of char and soot must be considered when investigating riverine PBC. Samples along the Yangtze River continuum during different hydrological periods were collected in this study to investigate the association and asynchronous combustion drive of char and soot in PBC. The results revealed that PBC in the Yangtze River, with higher refractory nature, accounts for 13.73 ± 6.89% of particulate organic carbon, and soot occupies 37.53 ± 11.00% of PBC. The preponderant contribution of fossil fuel combustion to soot (92.57 ± 3.20%) compared to char (27.55 ± 5.92%), suggested that fossil fuel combustion is a crucial driver for PBC with high soot percentage. Redundancy analysis and structural equation modeling confirmed that the fossil fuel energy used by anthropogenic activities promoting soot is the crucial reason for high-refractory PBC. We estimated that the Yangtze River transported 0.15–0.23 Tg of soot and 0.15–0.25 Tg of char to the ocean annually, and the export of large higher refractory PBC to the ocean can form a long-term sink and prolong the residence time of terrigenous carbonThis study was supported by grants from the National Natural Science Foundation of China (nos. 42277214, 42207256, and 41971286), major programs of the National Social Science Foundation of China (grant nos. 22&ZD136), the Special Science and Technology Innovation Program for Carbon Peak and Carbon Neutralization of Jiangsu Province (grant no. BE2022612)Peer reviewe

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Uncovering Spatial Structures of Regional City Networks from Expressway Traffic Flow Data: A Case Study from Jiangsu Province, China

On the basis of the “space of flows” theory, city networks emphasizing factor connectivity and spatial connection have become a core perspective for regional spatial relationships. They provide a context for discussing the spatial structures of city networks and a scientific basis for making regional development policies. Relying on expressway traffic flow data from Jiangsu Province in 2014, this study describes macro-spatial patterns and hierarchical structures of city networks, and uses the Walktrap algorithm of community detection to execute space divisions and elucidate potential spatial connection structures in the city networks. In comparison with other algorithms, the Walktrap algorithm demonstrates significant adaptation and stability to the short expressway traffic flows with the characteristics of strong network density and sparse node distribution. The results indicate that the macro-spatial patterns of Jiangsu’s city networks have clear regional differences. The cities with relatively dense spatial linkages are distributed along the Yangtze River banks, and many different sub-network systems have also developed internally. Cities and linkage axles have clear hierarchical structures. City hierarchies have certain spatial couplings with the cities’ intrinsic social economic attributes and geographical locations. The linkage axles hierarchy has clear spatial interactions and superposition with geographical distance. The community detection algorithm identified six spatially connected “city communities”, the Suzhou-Wuxi-Changzhou Community (SWCC), the Nanjing-Zhenjiang-Yangzhou-Taizhou Community (NZYTC), the Nantong-Yancheng Community (NYC), the Lianyungang Community (LYGC), the Huai’an-Suqian Community (HSC), and the Xuzhou Community (XZC). The community spatial metaphors had four aspects. First, trans-prefectural linkages were formed through spatially integrated effects of metropolis regions. Second, some communities share the same boundaries with their prefecture level administrative units; this reveals that significant administrative regional economies still exist in contemporary Jiangsu. Third, several cities located in the marginal areas of prefectures and captured by the powerful center cities in neighboring prefectures have been absorbed into the communities of neighboring prefectures. Fourth, the two cities Jiangyin and Jingjiang, divided into different administrative districts, have switched status opposite city community divisions