Background: Surgical mortality data are collected routinely in high-income countries, yet virtually no low-or middle-income countries have outcome surveillance in place. The aim was prospectively to collect worldwide mortality data following emergency abdominal surgery, comparing findings across countries with a low, middle or high Human Development Index (HDI).Methods: This was a prospective, multicentre, cohort study. Self-selected hospitals performing emergency surgery submitted prespecified data for consecutive patients from at least one 2-week interval during July to December 2014. Postoperative mortality was analysed by hierarchical multivariable logistic regression.
Experience and big amount of data are generated and used in risk and crisis management. Structuring the volume of data and learning from them are still big challenges to be faced to help actors either in decision-making or in operations. Data collection, for instance, is an important aspect, and sometimes, there can be overemphasis on using raw social media data for crisis informatics without adopting appropriate methodologies for cleaning the data and ensuring it is applicable to the situation at hand (i.e. assessing topical relevance). In recent years, this has become even more important with growing recognition that bots can often wield undue influence in social media, especially Twitter. Several techniques have been developed in the last years in Artificial Intelligence (AI) study and Computer Supported Cooperative Work (CSCW) that can be applied to face these challenges. The combination of these tools and methods continue to show promising results in improving sharing of information in crisis and emergency contexts.There are many approaches of AI, such as neural networks and ontologies that can be used to support risk and crisis management.Machine learning, in particular, is an approach that gives "computers the ability to learn without being explicitly programmed" by learning from and making predictions from data. Also, the use of symbolic AI approaches, like ontologies as a knowledge representation mechanism, offers many advantages in information retrieval and analysis.In addition, semantic models of knowledge allow users as well as systems to clearly understand what is happening in a crisis situation and can provide support to decision makers. This special issue mainly addresses the application of semantic models and AI methods and tools trying to answer to users' needs in the scope of risk management, crisis response, prediction, modelling and mitigation. According to a policy forum article in Science in 2016, (Palen & Anderson, 2016) describe crisis informatics as a "multidisciplinary field combining computing and social science knowledge of disasters." The special issue covers a broad range of topics that fall within, and even expand the scope of, crisis informatics. The articles are particularly timely today, as the world grapples with the COVID-19 pandemic that has resulted in hundreds of thousands of deaths, and millions of infections. Although this issue was prepared before the COVID-19 crisis struck, many of the individual topics covered by an international set of authors are highly relevant to the situation unfolding before our eyes.
Laboratory Experiment were carried out to study the effect of different temperature's degrees, (15, 20, 25, 27 and 30 °C) on the developmental stages of the American bollworm (ABW) Helicoverpa armigera (Hübner) reared on artificial diet. As temperature increased from 15 to 30 °C the life span decreased. The lower thermal threshold for the development of ABW eggs is 13.01 °C, for larvae is 11.98 °C, for pupal stage is 9.79 °C and for pre-ovipostion period is 12.83 °C. The thermal constant for the development of eggs is 36.69 day degree (DD's), for larvae is 245.17 DD's, for the pupal stage is 181.64 DD's and 34.99 DD's for the pre-ovipostion period. The lower thermal threshold for generation of ABW is 11.54 °C and the thermal constant is 494.39 DD's. Obtained results are essential information for predicting the field population of ABW.
This research aimed to study the effect of addition carrot powder and pumpkin powder on some properties of flat bread. Carrot powder and pumpkin powder were added with the ratio of 10 % and 20% with bread flour in order to prepare flat bread. Chemical composition, caloric value, mineral content, dietary fiber, texture profile and staling rate of flat bread samples were studied. Obtained results indicated that addition of carrot powder and pumpkin powder increased the amount of protein, ash, crude fibers, minerals and dietary fiber in prepared flat bread samples. While, the carbohydrates and caloric value content decreased in compared with those of control samples. Texture properties also resulted that addition of carrot and pumpkin powder positively influence on some texture parameters. Staling rate results showed that there was gradual decrease in all fortified flat bread samples for freshness up to 72 hours of storage in compared with those of control sample, also, an observed decrease in staling rate after 72 hours of storage of all flat bread samples. Results of sensory evaluation showed that flat bread samples with 10% carrot and pumpkin powder were more acceptable than those of other sample with 20% carrot and pumpkin powder.
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