How the market driving approach can create brand value through a digital platform.

This paper examines how the value of a brand is enhanced through a learning process that incorporates customer dialogue and results in co-creation and the incorporation of a consumer oriented relationship marketing approach. We adopted the market driving concept and related it to how companies can strengthen their competitive standing by increasing the knowledge transfer capability by utilizing data collected through social media technology (SMT). A qualitative research strategy was deployed involving a critical friendship group and a group interview, and a number of constructs were identified and then grouped according to a number of underlying themes. Our findings, a Digital Stakeholder Communication Platform (DSCP) framework, supports and explains how the concept of market driving enhances a pro-active strategic capability. It highlights the importance of learning and managing a value system that reflects on societal values, and the need for companies to consider how they contribute to societal well being

Increased Risk of Ischemic Stroke during Sleep in Apneic Patients.

BACKGROUND AND PURPOSE:The literature indicates that obstructive sleep apnea (OSA) increases the risk of ischemic stroke. However, the causal relationship between OSA and ischemic stroke is not well established. This study examined whether preexisting OSA symptoms affect the onset of acute ischemic stroke. METHODS:We investigated consecutive patients who were admitted with acute ischemic stroke, using a standardized protocol including the Berlin Questionnaire on symptoms of OSA prior to stroke. The collected stroke data included the time of the stroke onset, risk factors, and etiologic subtypes. The association between preceding OSA symptoms and wake-up stroke (WUS) was assessed using multivariate logistic regression analysis. RESULTS:We identified 260 subjects with acute ischemic strokes with a definite onset time, of which 25.8% were WUS. The presence of preexisting witnessed or self-recognized sleep apnea was the only risk factor for WUS (adjusted odds ratio=2.055, 95% confidence interval=1.035-4.083, p=0.040). CONCLUSIONS:Preexisting symptoms suggestive of OSA were associated with the occurrence of WUS. This suggests that OSA contributes to ischemic stroke not only as a predisposing risk factor but also as a triggering factor. Treating OSA might therefore be beneficial in preventing stroke, particularly that occurring during sleep

LM Unit Root Test with Panel Data: A Test Robust To Structural Changes

This paper proposes an LM test for the unit root hypothesis using panel data. The LM statistic based on the pooled likelihood function is obtained by standardizing the average of the LM statistic for individual time series. Under the null hypothesis, the statistic follows the standard normal distribution in the limit as N, T goes to infinity as long as N/T approaches any finite number, regardless of whether structural breaks are present. According to the Monte Carlo simulation results, the LM test is robust to the presence of structural breaks, and is more powerful than the popular test proposed by Im, Pesaran and Shin (1997) in the benchmark case where no structural breaks are involved.

Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks

Changes in Arctic sea ice affect atmospheric circulation, ocean current, and polar ecosystems. There have been unprecedented decreases in the amount of Arctic sea ice due to global warming. In this study, a novel 1-month sea ice concentration (SIC) prediction model is proposed, with eight predictors using a deep-learning approach, convolutional neural networks (CNNs). This monthly SIC prediction model based on CNNs is shown to perform better predictions (mean absolute error - MAE - of 2.28 %, anomaly correlation coefficient - ACC - of 0.98, root-mean-square error - RMSE - of 5.76 %, normalized RMSE - nRMSE - of 16.15 %, and NSE - Nash-Sutcliffe efficiency - of 0.97) than a random-forest-based (RF-based) model (MAE of 2.45 %, ACC of 0.98, RMSE of 6.61 %, nRMSE of 18.64 %, and NSE of 0.96) and the persistence model based on the monthly trend (MAE of 4.31 %, ACC of 0.95, RMSE of 10.54 %, nRMSE of 29.17 %, and NSE of 0.89) through hindcast validations. The spatio-temporal analysis also confirmed the superiority of the CNN model. The CNN model showed good SIC prediction results in extreme cases that recorded unforeseen sea ice plummets in 2007 and 2012 with RMSEs of less than 5.0 %. This study also examined the importance of the input variables through a sensitivity analysis. In both the CNN and RF models, the variables of past SICs were identified as the most sensitive factor in predicting SICs. For both models, the SIC-related variables generally contributed more to predict SICs over ice-covered areas, while other meteorological and oceanographic variables were more sensitive to the prediction of SICs in marginal ice zones. The proposed 1-month SIC prediction model provides valuable information which can be used in various applications, such as Arctic shipping-route planning, management of the fishing industry, and long-term sea ice forecasting and dynamics

Delineation of high resolution climate regions over the Korean Peninsula using machine learning approaches

In this research, climate classification maps over the Korean Peninsula at 1 km resolution were generated using the satellite-based climatic variables of monthly temperature and precipitation based on machine learning approaches. Random forest (RF), artificial neural networks (ANN), k-nearest neighbor (KNN), logistic regression (LR), and support vector machines (SVM) were used to develop models. Training and validation of these models were conducted using in-situ observations from the Korea Meteorological Administration (KMA) from 2001 to 2016. The rule of the traditional Koppen-Geiger (K-G) climate classification was used to classify climate regions. The input variables were land surface temperature (LST) of the Moderate Resolution Imaging Spectroradiometer (MODIS), monthly precipitation data from the Tropical Rainfall Measuring Mission (TRMM) 3B43 product, and the Digital Elevation Map (DEM) from the Shuttle Radar Topography Mission (SRTM). The overall accuracy (OA) based on validation data from 2001 to 2016 for all models was high over 95%. DEM and minimum winter temperature were two distinct variables over the study area with particularly high relative importance. ANN produced more realistic spatial distribution of the classified climates despite having a slightly lower OA than the others. The accuracy of the models using high altitudinal in-situ data of the Mountain Meteorology Observation System (MMOS) was also assessed. Although the data length of the MMOS data was relatively short (2013 to 2017), it proved that the snowy, dry and cold winter and cool summer class (Dwc) is widely located in the eastern coastal region of South Korea. Temporal shifting of climate was examined through a comparison of climate maps produced by period: from 1950 to 2000, from 1983 to 2000, and from 2001 to 2013. A shrinking trend of snow classes (D) over the Korean Peninsula was clearly observed from the ANN-based climate classification results. Shifting trends of climate with the decrease/increase of snow (D)/temperate (C) classes were clearly shown in the maps produced using the proposed approaches, consistent with the results from the reanalysis data of the Climatic Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC)