An Epidemiological Modeling and Data Integration Framework

2010 ◽  
Vol 49 (03) ◽  
pp. 290-293 ◽  
Author(s):  
F. Hanser ◽  
M. Seger ◽  
M. Netzer ◽  
M. Osl ◽  
R. Modre-Osprian ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Cellular Automaton ◽  
Data Warehouse ◽  
Software Package ◽  
Prediction Models ◽  
Winter Season ◽  
Modeling Framework ◽  
Federal States ◽  
Contingency Plans ◽  
Simulation Results

Summary Objectives: In this work, a cellular automaton software package for simulating different infectious diseases, storing the simulation results in a data warehouse system and analyzing the obtained results to generate prediction models as well as contingency plans, is proposed. The Brisbane H3N2 flu virus, which has been spreading during the winter season 2009, was used for simulation in the federal state of Tyrol, Austria. Methods: The simulation-modeling framework consists of an underlying cellular automaton. The cellular automaton model is parameterized by known disease parameters and geographical as well as demographical conditions are included for simulating the spreading. The data generated by simulation are stored in the back room of the data warehouse using the Talend Open Studio software package, and subsequent statistical and data mining tasks are performed using the tool, termed Knowledge Discovery in Database Designer (KD3). Results: The obtained simulation results were used for generating prediction models for all nine federal states of Austria. Conclusion: The proposed framework provides a powerful and easy to handle interface for parameterizing and simulating different infectious diseases in order to generate prediction models and improve contingency plans for future events.

Applying Deep Learning to Audit Procedures: An Illustrative Framework

2019 ◽  
Vol 33 (3) ◽  
pp. 89-109 ◽  
Author(s):  
Ting (Sophia) Sun
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Data Warehouse ◽  
Visual Recognition ◽  
Prediction Models ◽  
Historical Data ◽  
Structured Data ◽  
Text Understanding ◽  
Audit Data ◽  
Learning Functions

SYNOPSIS This paper aims to promote the application of deep learning to audit procedures by illustrating how the capabilities of deep learning for text understanding, speech recognition, visual recognition, and structured data analysis fit into the audit environment. Based on these four capabilities, deep learning serves two major functions in supporting audit decision making: information identification and judgment support. The paper proposes a framework for applying these two deep learning functions to a variety of audit procedures in different audit phases. An audit data warehouse of historical data can be used to construct prediction models, providing suggested actions for various audit procedures. The data warehouse will be updated and enriched with new data instances through the application of deep learning and a human auditor's corrections. Finally, the paper discusses the challenges faced by the accounting profession, regulators, and educators when it comes to applying deep learning.

scholarly journals Prediction and Stability Assessment of Soft Foundation Settlement of the Fishbone-Shaped Dike Near the Estuary of the Yangtze River Using Machine Learning Methods

2021 ◽  
Vol 13 (7) ◽  
pp. 3744
Author(s):  
Mingcheng Zhu ◽  
Shouqian Li ◽  
Xianglong Wei ◽  
Peng Wang
Keyword(s):  
Extreme Learning Machine ◽  
Prediction Models ◽  
Good Prediction ◽  
Foundation Settlement ◽  
Machine Method ◽  
Settlement Prediction ◽  
Soft Foundation ◽  
Simulation Results ◽  
Learning Machine ◽  
The Stability

Fishbone-shaped dikes are always built on the soft soil submerged in the water, and the soft foundation settlement plays a key role in the stability of these dikes. In this paper, a novel and simple approach was proposed to predict the soft foundation settlement of fishbone dikes by using the extreme learning machine. The extreme learning machine is a single-hidden-layer feedforward network with high regression and classification prediction accuracy. The data-driven settlement prediction models were built based on a small training sample size with a fast learning speed. The simulation results showed that the proposed methods had good prediction performances by facilitating comparisons of the measured data and the predicted data. Furthermore, the final settlement of the dike was predicted by using the models, and the stability of the soft foundation of the fishbone-shaped dikes was assessed based on the simulation results of the proposed model. The findings in this paper suggested that the extreme learning machine method could be an effective tool for the soft foundation settlement prediction and assessment of the fishbone-shaped dikes.

scholarly journals STRESSING ISSUE OF A PIEZOCERAMIC CANTILEVER WITH LONGITUDINAL POLARISATION AND ELECTRODE COATINGS

2020 ◽  
Vol 19 (2) ◽  
pp. 113
Author(s):  
Igor Jovanović ◽  
Ljubiša Perić ◽  
Uglješa Jovanović ◽  
Dragan Mančić
Keyword(s):  
General Solution ◽  
Free Vibration ◽  
Electric Potential ◽  
Software Package ◽  
Longitudinal Polarization ◽  
Main Subject ◽  
Piezoceramic Material ◽  
Coupled Equations ◽  
Specific Strain ◽  
Simulation Results

The main subject of this study is the investigation of the free vibration of a rectangular prismatic piezoceramic cantilever with longitudinal polarization and electrode coatings. Based on the general solution of coupled equations for piezoceramic material, applying the equations of electro-elasticity and satisfying electrical and mechanical conditions for the stress of a cantilever made from PZT4 piezoceramic material, componential displacements, electric potential, specific strain, electric field, and piezoelectric displacement, are determined and numerically obtained with Matlab software package. Based on the obtained equations and simulation results, it is possible to optimize the dimensions of the cantilever and determine the type of piezoceramic.

scholarly journals Googling for Ticks and Borreliosis in Germany: Nationwide Google Search Analysis From 2015 to 2018 (Preprint)

2020 ◽  
Author(s):  
Cora Scheerer ◽  
Melvin Rüth ◽  
Linda Tizek ◽  
Martin Köberle ◽  
Tilo Biedermann ◽  
...  
Keyword(s):  
Public Health ◽  
Infectious Diseases ◽  
Tick Bite ◽  
German Population ◽  
Internet Search ◽  
Tick Bites ◽  
Federal States ◽  
Search Terms ◽  
Health Studies ◽  
Very High

BACKGROUND Borreliosis is the most frequently transmitted tick-borne disease in Europe. It is difficult to estimate the incidence of tick bites and associated diseases in the German population due to the lack of an obligation to register across all 16 federal states of Germany. OBJECTIVE The aim of this study is to show that Google data can be used to generate general trends of infectious diseases on the basis of borreliosis and tick bites. In addition, the possibility of using Google AdWord data to estimate incidences of infectious diseases, where there is inconsistency in the obligation to notify authorities, is investigated with the perspective to facilitate public health studies. METHODS Google AdWords Keyword Planner was used to identify search terms related to ticks and borreliosis in Germany from January 2015 to December 2018. The search volume data from the identified search terms was assessed using Excel version 15.23. In addition, SPSS version 24.0 was used to calculate the correlation between search volumes, registered cases, and temperature. RESULTS A total of 1999 tick-related and 542 borreliosis-related search terms were identified, with a total of 209,679,640 Google searches in all 16 German federal states in the period under review. The analysis showed a high correlation between temperature and borreliosis (<i>r</i>=0.88), and temperature and tick bite (<i>r</i>=0.83), and a very high correlation between borreliosis and tick bite (<i>r</i>=0.94). Furthermore, a high to very high correlation between Google searches and registered cases in each federal state was observed (Brandenburg <i>r</i>=0.80, Mecklenburg-West Pomerania <i>r</i>= 0.77, Saxony <i>r</i>= 0.74, and Saxony-Anhalt <i>r</i>=0.90; all <i>P</i>&lt;.001). CONCLUSIONS Our study provides insight into annual trends concerning interest in ticks and borreliosis that are relevant to the German population exemplary in the data of a large internet search engine. Public health studies collecting incidence data may benefit from the results indicating a significant correlation between internet search data and incidences of infectious diseases.

scholarly journals Exploring Plant Sesquiterpene Diversity by Generating Chemical Networks

2019 ◽  
Author(s):  
Waldeyr Silva ◽  
Jakob Andersen ◽  
Maristela Holanda ◽  
Maria Emília Walter ◽  
Marcelo Brigido ◽  
...  
Keyword(s):  
Reaction Mechanisms ◽  
Metabolic Pathways ◽  
Software Package ◽  
Farnesyl Diphosphate ◽  
Graph Database ◽  
Simulation Results ◽  
Biological Evidence

Plants produce a diverse portfolio of sesquiterpenes that are important in their response to herbivores and the interaction with other plants. Their biosynthesis from farnesyl diphosphate depends on the sesquiterpene synthases. Here, we investigate to what extent metabolic pathways can be reconstructed just from knowledge of the final product and the reaction mechanisms catalyzed by sesquiterpene synthases. We use the software package Med&Oslash;lDatschgerl (M&Oslash;D) to generate chemical networks and elucidate pathways contained in them. As examples, we successfully consider the reachability of the important plant sesquiterpenes &beta;-caryophyllene, &alpha;-humulene, and &beta;-farnesene. We also introduce a graph database to integrate simulation results with experimental biological evidence for selected predicted sesquiterpenes biosynthesis.

scholarly journals Forecasting of Daily PM10 Concentrations in Brno and Graz by Different Regression Approaches

2016 ◽  
Vol 41 (4) ◽  
Author(s):  
Ernst Stadlober ◽  
Zuzana Hübnerová ◽  
Jaroslav Michálek ◽  
Miroslav Kolář
Keyword(s):  
Linear Regression ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Prediction Models ◽  
Winter Season ◽  
Ambient Air ◽  
Quality Measures ◽  
Limit Value ◽  
Two Cities ◽  
Daily Pm10

Brno and Graz, the second largest cities of their countries, observe in each winter season PM10 concentrations of daily means which regularly exceed the limit value of 50 ?g/m3. This is mainly caused by unfavorable dissemination conditions of the ambient air. Hence, partial regulation measureshave to be taken in Brno and Graz where specific decisions for certain regulations may be based on the average PM10 concentration of the next day provided that reliable forecasts of these values are available. For several sites in the two cities we establish forecasts of daily PM10 concentrations based onmultiple linear regression and generalized linear models utilizing both measured covariates of the present day and meteorological forecasts of the next day. The comparisons, based on different quality measures demonstrate the usefulness of both model approaches as they yield results of similar quality.Our prediction models may support future decisions concerning possible traffic restrictions or other regulations.

scholarly journals Impact of Traffic Sign on Pedestrians’ Walking Behavior

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Hui Xiong ◽  
Pingfu Yao ◽  
Xuedong Guo ◽  
Chenglong Chu ◽  
Wuhong Wang
Keyword(s):  
Cellular Automaton ◽  
Critical Level ◽  
Critical Distance ◽  
Pedestrian Flow ◽  
Traffic Sign ◽  
Walking Behavior ◽  
Simulation Results ◽  
Walking Environment ◽  
The Impact

To study the impact of traffic sign on pedestrian walking behavior, the paper applies cellular automaton to simulate one-way pedestrian flow. The channel is defined as a rectangle with one open entrance and two exits of equal width. Traffic sign showing that exit is placed with some distance in the middle front of the two exits. In the simulation, walking environment is set with various input density, width of exit, width and length of the channel, and distance of the traffic sign to exit. Simulation results indicate that there exists a critical distance from the traffic sign to exit for a given channel layout. At the critical distance, pedestrian flow fluctuates. Below such critical distance, flow is getting larger with the increase of input density. However, the flow drops sharply when the input density is over a critical level. If the distance is a little bit further than the critical distance, the largest flow occurs and the flow can remain steady no matter what input density will be.

scholarly journals Size-Resolved Evaluation of Simulated Deep Tropical Convection

2018 ◽  
Vol 146 (7) ◽  
pp. 2161-2182 ◽  
Author(s):  
Fabian Senf ◽  
Daniel Klocke ◽  
Matthias Brueck
Keyword(s):  
Deep Convection ◽  
Winter Season ◽  
Power Laws ◽  
Moist Convection ◽  
Modeling Framework ◽  
Western Atlantic ◽  
Spatial Coupling ◽  
Wide Range ◽  
Realistic Representation ◽  
Deep Moist Convection

Abstract Deep moist convection is an inherently multiscale phenomenon with organization processes coupling convective elements to larger-scale structures. A realistic representation of the tropical dynamics demands a simulation framework that is capable of representing physical processes across a wide range of scales. Therefore, storm-resolving numerical simulations at 2.4 km have been performed covering the tropical Atlantic and neighboring parts for 2 months. The simulated cloud fields are combined with infrared geostationary satellite observations, and their realism is assessed with the help of object-based evaluation methods. It is shown that the simulations are able to develop a well-defined intertropical convergence zone. However, marine convective activity measured by the cold cloud coverage is considerably underestimated, especially for the winter season and the western Atlantic. The spatial coupling across the resolved scales leads to simulated cloud number size distributions that follow power laws similar to the observations, with slopes steeper in winter than summer and slopes steeper over ocean than over land. The simulated slopes are, however, too steep, indicating too many small and too few large tropical cloud cells. It is also discussed that the number of larger cells is less influenced by multiday variability of environmental conditions. Despite the identified deficits, the analyzed simulations highlight the great potential of this modeling framework for process-based studies of tropical deep convection.

Infect-DB—A Data Warehouse Approach for Integrating Genomic Data of Infectious Diseases

2019 ◽  
pp. 305-314
Author(s):  
Shakuntala Baichoo ◽  
Zahra Mungloo-Dilmohamud ◽  
Parinita Ujoodha ◽  
Veeresh Ramphull ◽  
Yasmina Jaufeerally-Fakim
Keyword(s):  
Infectious Diseases ◽  
Data Warehouse ◽  
Genomic Data
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