Search Results (5)

A novel tool utilizing machine learning techniques was designed to forecast ap index values for the next three consecutive days (24 values). The tool employs time series data from the 3 h ap index of solar cycles 23 and 24 to train the Long Short-Term Memory (LSTM) model, predicting ap index values for the next 72 h at three-hour intervals. During periods of quiet geomagnetic activity, the LSTM model’s performance is sufficient to yield favorable outcomes. Nevertheless, during geomagnetically disturbed conditions, such as geomagnetic storms of different levels, the model needs to be adapted in order to provide accurate ap index results. In particular, when coronal mass ejections occur, the ap Prediction tool is modulated by inserting predominant features of coronal mass ejections such as the date of the event, the estimated time of arrival and the linear speed. In the present work, this tool is described thoroughly; moreover, results for G2 and G3 geomagnetic storms are presented. Full article

We investigate the differences between the effects of geomagnetic storms due to Interplanetary Coronal Mass Ejections (ICME) and due to Stream Interaction Regions or Corotating Interaction Regions (SIR/CIR) on the ionospheric F2-layer during the maximum of solar cycle 24. We have created a unique list of the ICME- and SIR/CIR-driven geomagnetic storm events for the time interval between November 2012 and October 2014. Finally, 42 clear ICME and 34 clear SIR/CIR events were selected for this analysis. The individual geomagnetic storm periods were grouped by seasons, time of day, and local time of Dst_min and were analyzed using three different methods: linear correlation analysis using 4-h averages of foF2 parameters and the geomagnetic indices (1st), daily variation of deltafoF2 (2nd), and 3D plotting: geomagnetic indices vs. time vs. deltafoF2 (3rd). The main phase day of the ICME- and SIR/CIR-induced geomagnetic storms was our main focus. We used manually evaluated ionospheric foF2 parameters measured at the Sopron ionosonde station and the geomagnetic indices (Kp, Dst, and AE) for this analysis. We have found that in most cases, the variation of the Dst index is the best indicator of the impact caused in the F2 layer. We conclude as well that the representation of the data by the third method gives a better description of the ICME and SIR/CIR-triggered storm behavior. In addition, our investigation shows that the SIR/CIR-related perturbations can be predicted with greater accuracy with the second method. Full article

Electrohydrodynamic jet (E-jet) printing has broad application prospects in the preparation of flexible electronics and optical devices. Ejection cycle time and droplet size are two key factors affecting E-jet-printing quality, but due to the complex process of E-jet printing, it remains a challenge to establish accurate relationships among ejection cycle time and droplet diameter and printing parameters. This paper develops a model based on random forest regression (RFR) for E-jet-printing prediction. Trained with 72 groups of experimental data obtained under four printing parameters (voltage, nozzle-to-substrate distance, liquid viscosity, and liquid conductivity), the RFR model achieved a MAPE (mean absolute percent error) of 4.35% and an RMSE (root mean square error) of 0.04 ms for eject cycle prediction, as well as a MAPE of 2.89% and an RMSE of 0.96 μm for droplet diameter prediction. With limited training data, the RFR model achieved the best prediction accuracy among several machine-learning models (RFR, CART, SVR, and ANN). The proposed prediction model provides an efficient and effective way to simultaneously predict the ejection cycle time and droplet diameter, advancing E-jet printing toward the goal of accurate, drop-on-demand printing. Full article

The cardiac function is influenced by respiration. In particular, various parameters such as cardiac time intervals and the stroke volume are modulated by respiratory activity. It has long been recognized that cardio-respiratory interactions modify the morphology of cardio-mechanical signals, e.g., phonocardiogram, seismocardiogram (SCG), and ballistocardiogram. Forcecardiography (FCG) records the weak forces induced on the chest wall by the mechanical activity of the heart and lungs and relies on specific force sensors that are capable of monitoring respiration, infrasonic cardiac vibrations, and heart sounds, all simultaneously from a single site on the chest. This study addressed the changes in FCG heartbeat morphology caused by respiration. Two respiratory-modulated parameters were considered, namely the left ventricular ejection time (LVET) and a morphological similarity index (MSi) between heartbeats. The time trends of these parameters were extracted from FCG signals and further analyzed to evaluate their consistency within the respiratory cycle in order to assess their relationship with the breathing activity. The respiratory acts were localized in the time trends of the LVET and MSi and compared with a reference respiratory signal by computing the sensitivity and positive predictive value (PPV). In addition, the agreement between the inter-breath intervals estimated from the LVET and MSi and those estimated from the reference respiratory signal was assessed via linear regression and Bland–Altman analyses. The results of this study clearly showed a tight relationship between the respiratory activity and the considered respiratory-modulated parameters. Both the LVET and MSi exhibited cyclic time trends that remarkably matched the reference respiratory signal. In addition, they achieved a very high sensitivity and PPV (LVET: 94.7% and 95.7%, respectively; MSi: 99.3% and 95.3%, respectively). The linear regression analysis reported almost unit slopes for both the LVET (R² = 0.86) and MSi (R² = 0.97); the Bland–Altman analysis reported a non-significant bias for both the LVET and MSi as well as limits of agreement of ±1.68 s and ±0.771 s, respectively. In summary, the results obtained were substantially in line with previous findings on SCG signals, adding to the evidence that FCG and SCG signals share a similar information content. Full article

Background: Acute myocardial infarction (AMI) is one of the leading causes of death in the world. How to simply predict mortality for AMI patients is important because the appropriate treatment should be done for the patients with higher risk. Recently, a novel parameter of upstroke time per cardiac cycle (UTCC) in lower extremities was reported to be a good predictor of peripheral artery disease and mortality in elderly. However, there was no literature discussing the usefulness of UTCC for prediction of cardiovascular (CV) and overall mortality in AMI patients. Methods: 184 AMI patients admitted to the cardiac care unit were enrolled. Ankle-brachial index (ABI) and UTCC were measured by an ABI-form device in the same day of admission. Results: The median follow-up to mortality was 71 months. There were 36 CV and 124 overall mortality. Higher UTCC was associated with increased CV and overall mortality after multivariable analysis (P = 0.033 and P < 0.001, respectively). However, ABI was only associated with CV mortality and overall mortality in the univariable analysis but became insignificant after the multivariable analysis. In addition, after adding UTCC into a basic model including important clinical parameters, left ventricular ejection fraction, Charlson comorbidity index, and ABI, we found the basic model + UTCC had a better predictive value for overall mortality than the basic model itself (P < 0.001). Conclusions: Our study is the first one to evaluate the usefulness of UTCC in AMI patients for prediction of long-term mortality. Our study showed UTCC was an independent predictor of long-term CV and overall mortality and had an additive predictive value for overall mortality beyond conventional parameters. Therefore, screening AMI patients by UTCC might help physicians to identify the high-risk group with increased mortality. Full article