In July 2023,the center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 *** report summarizes the rich discussions that occ...
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In July 2023,the center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 *** report summarizes the rich discussions that occurred during the *** workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data,social media,and wastewater *** advancements were noted in the development of predictive models,with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease *** role of open collaboration between various stakeholders in modelling was stressed,advocating for the continuation of such partnerships beyond the pandemic.A major gap identified was the absence of a common international framework for data sharing,which is crucial for global pandemic ***,the workshop underscored the need for robust,adaptable modelling frameworks and the integration of different data sources and collaboration across sectors,as key elements in enhancing future pandemic response and preparedness.
In this paper, we introduced an ARIMA-CNN-LSTM model to forecast the carbon futures price. The ARIMA-CNN-LSTM model employs the ARIMA model and the deep neural network structure that combines the CNN and LSTM layers t...
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In this paper, we introduced an ARIMA-CNN-LSTM model to forecast the carbon futures price. The ARIMA-CNN-LSTM model employs the ARIMA model and the deep neural network structure that combines the CNN and LSTM layers to capture linear and nonlinear data features. In ARIMA-CNN-LSTM model structure, the ARIMA is used to capture the linear features. The Convolutional Neural network (CNN) is used to capture the hierarchical data structure while the Long Short Term Memory network (LSTM) is used to capture the long-term dependencies in the data. Comprehensive performance evaluation has been conducted using weekly carbon futures price. Results have confirmed that ARIMA-CNN-LSTM model can achieve better prediction accuracy than the benchmark models, in terms of Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) performance measures.
In this paper, we propose a new Value at Risk estimate based on the Deep Belief network ensemble model with Empirical Mode Decomposition (EMD) technique. It attempts to capture the multi-scale data features with the E...
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In this paper, we propose a new Value at Risk estimate based on the Deep Belief network ensemble model with Empirical Mode Decomposition (EMD) technique. It attempts to capture the multi-scale data features with the EMD-DBN ensemble model and predict the risk movement more accurately. Individual data components are extracted using EMD model while individual forecasts can be calculated at different scales using ARMA-GARCH model. The DBN model is introduced to search for the optimal nonlinear ensemble weights to combine the individual forecasts at different scales into the ensembled exchange rate VaR forecasts. Empirical studies using major exchange rates confirm that the proposed model demonstrates the superior performance compared to the benchmark models.
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Millard, William B.The new 8th edition of the Advanced Trauma Life Support (ATLS) course manual contains a small but significant change. The phrase
“trauma is a surgical disease” long a point of contention with other specialties caring for trauma patients has been removed.
Now used in over 50 countries as the basis for training in the initial assessment and management of trauma this publication reflects the research and clinical experience of the American College of Surgeons (ACS) Committee on Trauma and expresses that organization's philosophy toward triage diagnosis and clinical care. Astute readers of the ATLS materials have noticed that a certain message is conspicuous by its absence. The preface to the 7th edition of the ATLS describes the ACS's role as follows:
In accordance with that role and recognizing that trauma is a surgical disease the ACS Committee on Trauma (COT) has worked to establish guidelines for the care of the trauma patient.
The 8th edition includes a substantially similar sentence minus the crucial phrase on trauma as a “surgical disease.” John B. Kortbeek MD FACS professor of surgery and critical care at the University of Alberta and a member of the COT who was instrumental in the revision process for the manual confirms that the deletion is intentional.
Dr. Kortbeek explains the change in historical terms. “The intent of making that statement” he says “was to emphasize that to have a successful trauma system and a successful trauma hospital surgeons needed to be included in the management team and the care of the trauma patient. That remains true today. What changed over time is that that statement became a focal point and could be interpreted in varying ways including in a negative exclusive way suggesting that only surgeons should be managing trauma patients which is not correct and never was the intent of the statement.” The ATLS he says presents a “common language” for a safe and effective response to trauma not a mandatory formula.
Harmonious relations among the various spe
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