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

A new palladium alloy with near-ideal hydrogen storage performance

Hydrogen-based fuels demand high-density storage that can operate under ambient temperatures. Pd and its alloys are the most investigated metal hydrides for hydrogen fuel cell applications. This study presented an alternative Pd alloy for hydrogen storage that can store and release hydrogen at room temperature. The surface of the most studied Pd (110) was modified with Au and Rh so that the hydrogen adsorption energy was 0.49 eV and the release temperature was 365 K. Both values are quite near to the optimum values for the adsorption energy and release temperature of a hydrogen fuel cell in real-world usage

Human error control in the collaborative workflow modeling tool based on GEMS model

Business process should support the execution of collaboration process with agility and flexibility through the integration of enterprise inner or outer application and human resources from the collaborative workflow view.Although the dependency of enterprise activities to the automated system has been increasing, human role is as important as ever.In the workflow modelling this human role is emphasized and the structure to control human error by analysing decision-making itself is needed.Also, through the collaboration of activities agile and effective communication should be constructed, eventually by the combination and coordination of activities to the aimed process the product quality should be improved.This paper classifies human errors can be occurred in collaborative workflow by applying GEMS(Generic Error Modelling System) to control them, and suggests human error control method through hybrid based modelling as well.On this base collaborative workflow modeling tool is designed and implemented. Using this modelling methodology it is possible to workflow modeling could be supported considering human characteristics has a tendency of human error to be controlled

Antiasthmatic Effects of Hesperidin, a Potential Th2 Cytokine Antagonist, in a Mouse Model of Allergic Asthma

Background and Objective. The features of asthma are airway inflammation, reversible airflow obstruction, and an increased sensitivity to bronchoconstricting agents, termed airway hyperresponsiveness (AHR), excess production of Th2 cytokines, and eosinophil accumulation in the lungs. To investigate the antiasthmatic potential of hesperidin as well as the underlying mechanism involved, we studied the inhibitory effect and anti-inflammatory effect of hesperidin (HPN) on the production of Th2 cytokines, eotaxin, IL-17, -OVA-specific IgE in vivo asthma model mice. Methods. In this paper, BALB/c mice were systemically sensitized to ovalbumin (OVA) followed intratracheally, intraperitoneally, and by aerosol allergen challenges. We investigated the effect of HPN on airway hyperresponsiveness, pulmonary eosinophilic infiltration, various immune cell phenotypes, Th2 cytokine production and OVA-specific IgE production in a mouse model of asthma. Results. In BALB/c mice, we found that HPN-treated groups had suppressed eosinophil infiltration, allergic airway inflammation, and AHR, and these occurred by suppressing the production of IL-5, IL-17, and OVA-specific IgE. Conclusions. Our data suggest that the therapeutic mechanism by which HPN effectively treats asthma is based on reductions of Th2 cytokines (IL-5), eotaxin, OVA-specific IgE production, and eosinophil infiltration via inhibition of GATA-3 transcription factor

Effects of Radix Adenophorae and Cyclosporine A on an OVA-Induced Murine Model of Asthma by Suppressing to T Cells Activity, Eosinophilia, and Bronchial Hyperresponsiveness

The present study is performed to investigate the inhibitory effects of Radix Adenophorae extract (RAE) on ovalbumin-induced asthma murine model. To study the anti-inflammatory and antiasthmatic effects of RAE, we examined the development of pulmonary eosinophilic inflammation and inhibitory effects of T cells in murine by RAE and cyclosporine A (CsA). We examined determination of airway hyperresponsiveness, flow cytometric analysis (FACS), enzyme-linked immunosorbent assay (ELISA), quantitative real time (PCR), hematoxylin-eosin staining, and Masson trichrome staining in lung tissue, lung weight, total cells, and eosinophil numbers in lung tissue. We demonstrated how RAE suppressed development on inflammation and decreased airway damage

Effects of Corni fructus on ovalbumin-induced airway inflammation and airway hyper-responsiveness in a mouse model of allergic asthma

<p>Abstract</p> <p>Background</p> <p>Allergic asthma is a chronic inflammatory lung disease that is characterized by airway hyperresponsiveness (AHR) to allergens, airway oedema, increased mucus secretion, excess production of T helper-2 (Th2) cytokines, and eosinophil accumulation in the lungs. Corni fructus (CF) is a fruit of <it>Cornus officinalis </it>Sieb. Et. Zucc. (Cornaceae) and has been used in traditional Korean medicine as an anti-inflammatory, analgesic, and diuretic agent. To investigate the anti-asthmatic effects of CF and their underlying mechanism, we examined the influence of CF on the development of pulmonary eosinophilic inflammation and airway hyperresponsiveness in a mouse model of allergic asthma.</p> <p>Methods</p> <p>In this study, BALB/c mice were systemically sensitized to ovalbumin (OVA) by intraperitoneal (i.p.), intratracheal (i.t.) injections and intranasal (i.n.) inhalation of OVA. We investigated the effect of CF on airway hyperresponsiveness, pulmonary eosinophilic infiltration, various immune cell phenotypes, Th2 cytokine production, and OVA-specific immunoglobulin E (IgE) production.</p> <p>Results</p> <p>The CF-treated groups showed suppressed eosinophil infiltration, allergic airway inflammation, and AHR via reduced production of interleuin (IL) -5, IL-13, and OVA-specific IgE.</p> <p>Conclusions</p> <p>Our data suggest that the therapeutic effects of CF in asthma are mediated by reduced production of Th2 cytokines (IL-5), eotaxin, and OVA-specific IgE and reduced eosinophil infiltration.</p

Competition between B-Z and B-L transitions in a single DNA molecule: Computational studies

Under negative torsion, DNA adopts left-handed helical forms, such as Z-DNA and L-DNA. Using the random copolymer model developed for a wormlike chain, we represent a single DNA molecule with structural heterogeneity as a helical chain consisting of monomers which can be characterized by different helical senses and pitches. By Monte Carlo simulation, where we take into account bending and twist fluctuations explicitly, we study sequence dependence of B-Z transitions under torsional stress and tension focusing on the interaction with B-L transitions. We consider core sequences, (GC)(n) repeats or (TG)(n) repeats, which can interconvert between the right-handed B form and the left-handed Z form, imbedded in a random sequence, which can convert to left-handed L form with different (tension dependent) helical pitch. We show that Z-DNA formation from the (GC)(n) sequence is always supported by unwinding torsional stress but Z-DNA formation from the (TG)(n) sequence, which are more costly to convert but numerous, can be strongly influenced by the quenched disorder in the surrounding random sequence.National Research Foundation NRF-2012 R1A1A3013044 NRF-2014R1A1A2055681NRF-2012R1A1A2021736IBS-R023-D1NRF-2015R1A2A2A01005916Chemistr