Clustering Cities over Features Extracted from Multiple Virtual Sensors Measuring Micro-Level Activity Patterns Allows One to Discriminate Large-Scale City Characteristics.

The impact of micro-level people's activities on urban macro-level indicators is a complex question that has been the subject of much interest among researchers and policymakers. Transportation preferences, consumption habits, communication patterns and other individual-level activities can significantly impact large-scale urban characteristics, such as the potential for innovation generation of the city. Conversely, large-scale urban characteristics can also constrain and determine the activities of their inhabitants. Therefore, understanding the interdependence and mutual reinforcement between micro- and macro-level factors is critical to defining effective public policies. The increasing availability of digital data sources, such as social media and mobile phones, has opened up new opportunities for the quantitative study of this interdependency. This paper aims to detect meaningful city clusters on the basis of a detailed analysis of the spatiotemporal activity patterns for each city. The study is carried out on a worldwide city dataset of spatiotemporal activity patterns obtained from geotagged social media data. Clustering features are obtained from unsupervised topic analyses of activity patterns. Our study compares state-of-the-art clustering models, selecting the model achieving a 2.7% greater Silhouette Score than the next-best model. Three well-separated city clusters are identified. Additionally, the study of the distribution of the City Innovation Index over these three city clusters shows discrimination of low performing from high performing cities relative to innovation. Low performing cities are identified in one well-separated cluster. Therefore, it is possible to correlate micro-scale individual-level activities to large-scale urban characteristics.

The impact of international technical cooperation in new energy industry on carbon emissions: evidence from the top 30 countries in the global innovation index.

International cooperation has become a global consensus to solve environmental problems. This paper selects the top 30 countries in the global innovation index as the research sample, based on Patent Cooperation Treaties (PCT) data jointly applied in the field of new energy, constructs the international technical cooperation network, and uses the fixed effect panel regression model to verify the influence of international technical cooperation of new energy industry on carbon emission intensity. The results show that the USA, Germany, the UK, France, and the Netherlands are at the center of the network. International technological cooperation in new energy industry has a significant negative impact on carbon emission intensity. The convening of the United Nations Climate Change Conference in Copenhagen has accelerated global industrial upgrading, and the effect of international technical cooperation in new energy on carbon emission reduction has been strengthened. In addition, the level of economic development, international trade, and research and development (R&D) are also important factors affecting carbon emission intensity. Countries with high network centrality should give full play to their network influence to promote global cooperation in the field of new energy and achieve carbon mitigation targets by signing more environmental agreements.

Digital Pandemic Stress in Higher Education in Venezuela.

The COVID-19 pandemic had a great impact on the process of integrating digital technologies in higher education and caused digital stress among professors, mainly in countries with a lower level of digitalization. In this work, quantitative research was carried out on the stress of professors in Venezuela due to the digitalization of their teaching activities caused by the pandemic, and gender gaps were identified in this regard. This digital stress was compared with that of professors in other countries with a low level of digitalization. For this purpose, a questionnaire designed by the authors was used. The questionnaire was distributed to a sample of 129 Venezuelan professors and 132 professors from countries with low digitalization levels. As a result, it was found that Venezuelan professors have lower digital competence and lower digital stress than their colleagues in weakly digitized countries, and that digital stress decreases as digital competence increases. Moreover, among Venezuelan professors, there was a strong gender gap in digital stress, which was higher among females in all subject areas, except for Health Sciences. This gender gap is specific to Venezuela since it differs from that in countries with low digital levels. According to the results, we urgently recommend investing resources in the digital training of faculty members, especially in regards to the integration of female professors.

Identifying critical drivers of innovation in pharmaceutical industry using TOPSIS method.

Insights in understanding critical growth drivers of innovation are essential for industrial development and economic growth for competitive advantage. This study identifies indicators from the world's major indexes and industry-level concerns. The indicators are mapped to these concerns using expert opinions and then treated mathematically using technique of order preference by similarity to an ideal solution (TOPSIS). This mapping ranks and identifies the most favorable indicators for several concerns. It, thus, identifies the critical role the indicators play for the drivers to the most effective advantage using the TOPSIS method as a comprehensive ranking of indicators effectively facilitates decision-making for estimated levels. The method highlights are as follows: • The most prevailing indicators for innovation are considered from the major innovation indexes for industries for mapping with concerns within the industry. • Industry-specific concerns for the pharmaceutical industry are selected for the study. • The mapping of indicators to concerns using expert opinion and using the TOPSIS method generated a matrix of the ranked indicators, aids in prioritizing resources and existing knowledge to resolve the concerns.

The internal and external effects of air pollution on innovation in China.

China is now the world's largest energy consumer, but severe air pollution problems have brought greater pressure to the production and development of its domestic economy. As an unavoidable result of air pollution, PM2.5 emissions are increasing. Previous literature has focused more on the impact of PM2.5 on the micro-level such as resident health and company location, yet macro-pattern studies between PM2.5 and innovation are inadequate. To bridge this gap, our research uses a spatial dynamic panel data model to systematically investigate the internal and external effects of PM2.5 concentration on innovation in China during the period 2001-2016. After forming a dataset of real-time PM2.5 concentration from satellite detection and using an innovation index instead of patents, we find a stronger spatial linkage between PM2.5 concentration and innovation. Thus, PM2.5 inhibits regional innovation significantly, and this result still exists after using the air mobility index as an instrument variable to alleviate endogenous problems. Lastly, PM2.5 concentration in neighboring regions also impedes local innovation considerably, indicating a spatial ripple effect of PM2.5.

Preliminary construction of evaluation system of scientific and technological innovation index in Beijing municipal hospitals / 中华医学科研管理杂志

Objective:Construct a set of evaluation system of scientific and technological innovation index of Beijing municipal hospitals, so as to lay a foundation for the systematic evaluation of scientific and technological innovation capacities of such hospitals.Methods:By using the methods of literature review and expert consultation, the composition and weight of scientific and technological innovation index of Beijing municipal hospitals are determined.Results:The evaluation system includes 5 first-level indexes, 13 second-level indexes and 17 third-level indexes, which including innovation resource index, talent development index, academic contribution index, development vitality index and industry support index.Conclusions:The evaluation system of scientific and technological innovation index of Beijing municipal hospitals pays attention to the talent development and high-quality output, innovation introduces development vitality index and industrial support index, which is a useful exploration for the " collectivization" horizontal quantitative evaluation of scientific and technological innovation capacity.

New indicators and indexes for benchmarking university-industry-government innovation in medical and life science clusters: results from the European FP7 Regions of Knowledge HealthTIES project.

BACKGROUND: While the European Union is striving to become the 'Innovation Union', there remains a lack of quantifiable indicators to compare and benchmark regional innovation clusters. To address this issue, a HealthTIES (Healthcare, Technology and Innovation for Economic Success) consortium was funded by the European Union's Regions of Knowledge initiative, research and innovation funding programme FP7. HealthTIES examined whether the health technology innovation cycle was functioning differently in five European regional innovation clusters and proposed regional and joint actions to improve their performance. The clusters included BioCat (Barcelona, Catalonia, Spain), Medical Delta (Leiden, Rotterdam and Delft, South Holland, Netherlands), Oxford and Thames Valley (United Kingdom), Life Science Zürich (Switzerland), and Innova Észak-Alföld (Debrecen, Hungary). METHODS: Appreciation of the 'triple helix' of university-industry-government innovation provided the impetus for the development of two quantifiable innovation indexes and related indicators. The HealthTIES H-index is calculated for disease and technology platforms based on the h-index proposed by Hirsch. The HealthTIES Innovation Index is calculated for regions based on 32 relevant quantitative and discriminative indicators grouped into 12 categories and 3 innovation phases, namely 'Input' (n = 12), 'Innovation System' (n = 9) and 'Output' (n = 11). RESULTS: The HealthTIES regions had developed relatively similar disease and technology platform profiles, yet with distinctive strengths and weaknesses. The regional profiles of the innovation cycle in each of the three phases were surprisingly divergent. Comparative assessments based on the indicators and indexes helped identify and share best practice and inform regional and joint action plans to strengthen the competitiveness of the HealthTIES regions. CONCLUSION: The HealthTIES indicators and indexes provide useful practical tools for the measurement and benchmarking of university-industry-government innovation in European medical and life science clusters. They are validated internally within the HealthTIES consortium and appear to have a degree of external prima facie validity. Potentially, the tools and accompanying analyses can be used beyond the HealthTIES consortium to inform other regional governments, researchers and, possibly, large companies searching for their next location, analyse and benchmark 'triple helix' dynamics within their own networks over time, and to develop integrated public-private and cross-regional research and innovation strategies in Europe and beyond.