Electromagnetic form factors of the baryon decuplet with flavor SU(3) symmetry breaking

We investigate the electromagnetic form factors of the baryon decuplet within the framework of the $\mathrm{SU(3)}$ self-consistent chiral quark-soliton model, taking into account the $1/N_c$ rotational corrections and the effects of flavor $\mathrm{SU(3)}$ symmetry breaking. We first examine the valence- and sea-quark contributions to each electromagnetic form factor of the baryon decuplet and then the effects of the flavor SU(3) symmetry breaking. The present results are in good agreement with the recent lattice data. We also compute the charge radii, the magnetic radii, the magnetic dipole moments and the electric quadrupole moments, comparing their results with those from other theoretical works. We also make a chiral extrapolation to compare the present results with the lattice data in a more quantitative manner. The results show in general similar tendency to the lattice results. In particular, the results of the $M1$ and $E2$ form factors are in good agreement with those of lattice QCD.Comment: 29 pages, 15 figures. Comparison with the lattice data was elaborate

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

Online home appliance control using EEG-Based brain-computer interfaces

Brain???computer interfaces (BCIs) allow patients with paralysis to control external devices by mental commands. Recent advances in home automation and the Internet of things may extend the horizon of BCI applications into daily living environments at home. In this study, we developed an online BCI based on scalp electroencephalography (EEG) to control home appliances. The BCI users controlled TV channels, a digital door-lock system, and an electric light system in an unshielded environment. The BCI was designed to harness P300 andN200 components of event-related potentials (ERPs). On average, the BCI users could control TV channels with an accuracy of 83.0% ?? 17.9%, the digital door-lock with 78.7% ?? 16.2% accuracy, and the light with 80.0% ?? 15.6% accuracy, respectively. Our study demonstrates a feasibility to control multiple home appliances using EEG-based BCIs

Modulation of A375 human melanoma cell proliferation and apoptosis by nitric oxide

The present study aimed to assess the effect of NO• on melanoma A375 cell growth and apoptotic cell death. Trypan blue exclusion assay was employed to detect the cytotoxicity induced by controlled steady-state concentrations (given in µM • min) of NO•. The characteristics of the cellular cell cycle and apoptosis in NO•-treated A375 cells were also analyzed by Annexin V/PI and DNA fragmentation assays. Western blotting was applied to detect the expression of apoptosis-related proteins (p53, Bax, Fas, DR5, caspase-3 and -9, and PARP). When exposed to preformed 100% NO• for 8 h reactor system, a cumulative dose of 3360 μM • min reduced the viability by 22% 24 h after treatment and promoted apoptosis, 2.9- and 12.2-folds 24 and 48 h after treatment higher than the argon control, respectively. Cell cycle analysis 48 h after treatment revealed S-phase arrest in cells treated with 3360 μM • min NO•. It was also observed that the expression of p53, DR5, caspase 9 and PARP increased significantly upon NO• treatment. In addition, the present study assessed the inhibitory effects of endogenous NO• on the proliferation of human melanoma cells by employing specific (AMG, 1400W and/or SMTC) and nonspecific (NMA) NO• synthase (NOS) inhibitors resulting in melanoma cell growth inhibition; the highest cytotoxic effect was seen when inducible NOS inhibition by 1 mM 1400W treatment. Collectively, the present data suggest that NO• is involved in a key mechanism limiting melanoma proliferation and apoptosis, which may play in improving the efficacy of melanoma treatment