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Smart Specialisation Strategy and the Role of Strong Clusters: As a Development Leverage in Asia
Anastasopoulos, Despina,Brochler, Raimund,Kalentzis, Arion Louis World Technopolis Association 2017 World Technopolis Review Vol.6 No.2
In this increasingly globalised and rapidly-changing world, the various challenges that can arise are also increasingly globalised and complex. These may range from economic, environmental, societal or even demographic challenges. Solutions should therefore be applicable world-wide, but they need to be properly adapted to the specifications and needs at the regional and country level. This implies that past models of centralised innovation can be progressively substituted by new approaches based on openness and strategic collaboration between the various players involved. There are various models of openness and collaboration in research, development and innovation creating scientific networks at different levels. This paper is designed in a way to present the concept of smart specialisation and clusters and how they are linked and contribute to the support of Smart Specialisation Strategy in the Asian countries. The following paragraphs describe how smart specialisation is applied and the importance of clusters in developing a S3 strategy. In addition, the status of cluster policies in Asia as well as the steps towards S3 are also presented. The status of cluster policies and their steps towards S3 policies in Asia are described. The approach of China to adopt S3 in their R&I policy is also presented. The scope of this paper, is to demonstrate the policy framework of cluster and S3 policies in the region of Asia and how they are applied. China has been further analysed as a case, since they are more active in applying such policies.
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Weikert Thomas,Rapaka Saikiran,Grbic Sasa,Re Thomas,Chaganti Shikha,Winkel David J.,Anastasopoulos Constantin,Niemann Tilo,Wiggli Benedikt J.,Bremerich Jens,Twerenbold Raphael,Sommer Gregor,Comaniciu 대한영상의학회 2021 Korean Journal of Radiology Vol.22 No.6
Objective: To extract pulmonary and cardiovascular metrics from chest CTs of patients with coronavirus disease 2019 (COVID-19) using a fully automated deep learning-based approach and assess their potential to predict patient management. Materials and Methods: All initial chest CTs of patients who tested positive for severe acute respiratory syndrome coronavirus 2 at our emergency department between March 25 and April 25, 2020, were identified (n = 120). Three patient management groups were defined: group 1 (outpatient), group 2 (general ward), and group 3 (intensive care unit [ICU]). Multiple pulmonary and cardiovascular metrics were extracted from the chest CT images using deep learning. Additionally, six laboratory findings indicating inflammation and cellular damage were considered. Differences in CT metrics, laboratory findings, and demographics between the patient management groups were assessed. The potential of these parameters to predict patients’ needs for intensive care (yes/no) was analyzed using logistic regression and receiver operating characteristic curves. Internal and external validity were assessed using 109 independent chest CT scans. Results: While demographic parameters alone (sex and age) were not sufficient to predict ICU management status, both CT metrics alone (including both pulmonary and cardiovascular metrics; area under the curve [AUC] = 0.88; 95% confidence interval [CI] = 0.79–0.97) and laboratory findings alone (C-reactive protein, lactate dehydrogenase, white blood cell count, and albumin; AUC = 0.86; 95% CI = 0.77–0.94) were good classifiers. Excellent performance was achieved by a combination of demographic parameters, CT metrics, and laboratory findings (AUC = 0.91; 95% CI = 0.85–0.98). Application of a model that combined both pulmonary CT metrics and demographic parameters on a dataset from another hospital indicated its external validity (AUC = 0.77; 95% CI = 0.66–0.88). Conclusion: Chest CT of patients with COVID-19 contains valuable information that can be accessed using automated image analysis. These metrics are useful for the prediction of patient management.
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