During and after any disaster, a situation report (SITREP) is prepared, based on the Daily Incident Updates (DIU), as an initial decision support information base. It is observed that the decision support system and best practices are not optimized through the available formal reporting on disaster incidents. The rapidly evolving situation, misunderstood terms, inaccurate data and delivery delays of DIU are challenges to the daily SITREP. Multiple stakeholders stipulated with different tasks should be properly understood for the SITREP to initiate relevant response tasks. To fill this research gap, this paper identifies the weaknesses of the current practice and discusses the upgrading of the incident-reporting process using a freely available software tool, enabling further visualization, and producing a comprehensive timely output to share among the stakeholders. In this case, “Power-BI” (a data visualization software) is used as a 360-degree view of useful metrics—in a single place, with real-time updates while being available on all devices for operational decision-making. When a dataset is transformed into several analytical reports and dashboards, it can be easily shared with the target users and action groups. This article analyzed two sources of data, namely the Disaster Management Center (DMC) and the National Disaster Relief Service Center (NDRSC) of Sri Lanka. Senior managers of disaster emergencies were interviewed and explored social media to develop a scheme of best practices for disaster reporting, starting from just before the occurrence, and following the unfolding sequence of the disasters. Using a variety of remotely acquired imageries, rapid mapping, grading, and delineating impacts of natural disasters, were made available to concerned users.

With fresh bitter melon and green tea as main ingredients, xylitol, sucrose and citric acid as auxiliary ingredients, a new cool health tea beverage was developed. The optimum formula of low sugar bitter melon green tea compound beverage was developed by single factor experiment and orthogonal test based on sensory evaluation. The results showed that the optimum formula was as follows: Bitter melon juice was added at 7%, green tea extract was added at 20%, total xylitol and sucrose was 6% (mass ratio 1:1), citric acid was added at 0.2%, and the volume was fixed to 100% with deionized water. The product has light green color, harmonious aroma, moderate acidity and sweetness, and clear texture. The aftertaste is long, with tea polyphenol content of 342 mg/kg, soluble solids of 5.2% and pH 5.8.

Background: Through the development of robust techniques and their comprehensive validation, cardiac magnetic resonance imaging (CMR) has developed a wide range of indications in its almost 25 years of clinical use. The recording of cardiac volumes and systolic ventricular function as well as the characterization of focal myocardial scars are now part of standard CMR imaging. Recently, the introduction of accelerated image acquisition technologies, the new imaging methods of myocardial T1 and T2 mapping and 4-D flow measurements, and the new post-processing technique of myocardial feature tracking have gained relevance. Method: This overview is based on a comprehensive literature search in the PubMed database on new CMR techniques and their clinical application. Results and conclusion: This article provides an overview of the latest technical developments in the field of CMR and their possible applications based on the most important clinical questions.

Universities play a key role in university-industry-government interactions and are important in innovation ecosystem studies. Universities are also expected to engage with industries and governments and contribute to economic development. In the age of artificial intelligence (AI), governments have introduced relevant policies regarding the AI-enabled innovation ecosystem in universities. Previous studies have not focused on the provision of a dynamic capabilities perspective on such an ecosystem based on policy analysis. This research work takes China as a case and provides a framework of AI-enabled dynamic capabilities to guide how universities should manage this based on China’s AI policy analysis. Drawing on two main concepts, which are the innovation ecosystem and dynamic capabilities, we analyzed the importance of the AI-enabled innovation ecosystem in universities with governance regulations, shedding light on the theoretical framework that is simultaneously analytical and normative, practical, and policy-relevant. We conducted a text analysis of policy instruments to illustrate the specificities of the AI innovation ecosystem in China’s universities. This allowed us to address the complexity of emerging environments of innovation and draw meaningful conclusions. The results show the broad adoption of AI in a favorable context, where talents and governance are boosting the advance of such an ecosystem in China’s universities.