Research of Gear Meshing Stiffness Identification Algorithm based on Exponential Window Interception Recursive Least Square Method by Maohui Wang, Haixiang Li, Ping Yang, Jiao Chen, Wei Xia

“…To calculate speed curve of the input and output gears，the empirical mode decomposition （EMD） method is used to decompose vibration signal into intrinsic mode function （IMF） of different frequency，the IMFs are used to reconstruct character signals of input and output gears basing on the mean frequency of IMFs. …”
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Quantifying Uncertainties in the Quiet‐Time Ionosphere‐Thermosphere Using WAM‐IPE by Weijia Zhan, Alireza Doostan, Eric Sutton, Tzu‐Wei Fang

“…A variance‐based global sensitivity analysis suggests that the IMF Bz plays a dominant role in the uncertainty of electron density. …”
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Magnetopause Standoff Position Changes and Geosynchronous Orbit Crossings: Models and Observations by Y. M. Collado‐Vega, P. Dredger, R. E. Lopez, S. Khurana, L. Rastaetter, D. Sibeck, M. Anastopulos

“…Results show that there are discrepancies between the MHD models' standoff positions of the dayside magnetopause for the same solar wind conditions on events that included (a) an increase in solar wind dynamic pressure and a step function in the Interplanetary Magnetic Field Bz component; (b) nominal solar wind conditions (values of approximated 400 km/s for solar wind speed and 5 nT for the magnetic field magnitude) with a northward IMF; and (c) compression caused by several coronal mass ejections impacting the near Earth environment. …”
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Estimating the costs of Israel’s four COVID-19 waves by Panagiotis TSIGARIS, Jaime A. TEIXEIRA DA SILVA, Francesco CHIRICO

“…Methods: Four costs of the pandemic in Israel were assessed: economic losses, costs of premature mortality, mental health, and health impairment, estimated using IMF forecasts of GDP with COVID- 19 relative to GDP without COVID-19 (i.e., the counterfactual) from 2019 until 2030, estimated number of deaths based on IHME data multiplied by VSL values, a Cutler and Summers method that assessed disutility using HRQoL, and the loss in VSL due to the disutility from suffering, respectively. …”
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A novel hybrid model by integrating TCN with TVFEMD and permutation entropy for monthly non-stationary runoff prediction by Huifang Wang, Xuehua Zhao, Qiucen Guo, Xixi Wu

“…Then, the temporal convolutional network (TCN) model is built for runoff prediction for each high-frequency IMFs and the reconstructed low-frequency IMF respectively. …”
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Noise Source Identification Method for a Carpet Tufting Machine Based on CEEMDAN-AIC by Wang Haohui, Sheng Xiaowei, Xu Yang

“…Next, the number of effective IMFs is estimated based on the AIC criterion, and the effective IMFs are selected by combining the energy characteristic index and the Pearson correlation coefficient method. …”
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Field‐Aligned Currents Induced by Magnetopause Motions Under Pressure Perturbations by Boyi Wang, Xinyu Xu, Yukitoshi Nishimura, Yen‐Jung Wu, Zhonghua Xu, Primož Kajdic, De‐Sheng Han, Yi Wang, Xueshang Feng

“…The results show that the magnetopause motions induced by pressure enhancements associated with IMF discontinuities or foreshock cavities likely generate upward FACs in the closed field lines near the magnetopause. …”
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Global Ionospheric TEC Forecasting for Geomagnetic Storm Time Using a Deep Learning‐Based Multi‐Model Ensemble Method by Xiaodong Ren, Pengxin Yang, Dengkui Mei, Hang Liu, Guozhen Xu, Yue Dong

“…Seven features in 170 geomagnetic storm events, including the three components Bx, By and Bz of interplanetary magnetic field (IMF), the Kp and Dst indices of geomagnetic activity data, the F10.7 index of solar activity data and global TEC data, were used for modeling. …”
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Nighttime Geomagnetic Response to Jumps of Solar Wind Dynamic Pressure: A Possible Cause of Québec Blackout in March 1989 by T. Zhang, Y. Ebihara, T. Tanaka

“…The nighttime geomagnetic perturbations associated with the MI occur regardless of the magnitude of the solar wind dynamic pressure and IMF orientation. The amplitude of the geoelectric field, which is closely related to the geomagnetically induced currents (GICs), reaches the maximum value just before and around the maximum of the southward magnetic disturbance. …”
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A Multivariate and Multistage Medium- and Long-Term Streamflow Prediction Based on an Ensemble of Signal Decomposition Techniques with a Deep Learning Network by Muhammad Sibtain, Xianshan Li, Snoober Saleem

“…ICEEMDAN was employed as a first decomposition stage, to decompose the three data series into intrinsic mode functions (IMFs) and a residual component. In the second decomposition stage, the component of high frequency (IMF1) was decomposed by VMD, as the second decomposition. …”
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Drivers for Geostationary 2–200 keV Electron Fluxes as Observed at GOES Satellites by M. van deKamp, N. Ganushkina, L. Simms, M. Liemohn

“…For this purpose, a range of solar wind parameters, IMF parameters and geomagnetic indices are examined, to look for the parameters which most significantly affect the electron flux. …”
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Public Debt of Japan: Analysis of Features and Assessment of Prospects by R. B. Nozdreva

“…Despite the pessimistic mood of the IMF, investors continue to actively acquire debt obligations of the Japanese government. …”
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What Do We Know of Childhood Exposures to Metals (Arsenic, Cadmium, Lead, and Mercury) in Emerging Market Countries? by Lindsey M. Horton, Mary E. Mortensen, Yulia Iossifova, Marlena M. Wald, Paula Burgess

“…A literature review was conducted for English language articles from the 21st century on pediatric exposures to arsenic, cadmium, lead, and mercury in the International Monetary Fund's (IMF) top 10 Emerging Market countries: Brazil, China, India, Indonesia, Mexico, Poland, Russia, South Korea, Taiwan, and Turkey. …”
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Phasic and periodic change of drought under greenhouse effect by Yang Li, Zhicheng Zheng, Yaochen Qin, Haifeng Tian, Zhixiang Xie, Peijun Rong

“…Drought variation was predominantly influenced by interannual oscillations, with the periods of the components of intrinsic mode functions 1 (IMF1) and 2 (IMF2) being 3.1 and 7.3 years, respectively. …”
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How geomagnetic storms affect the loss of Starlink satellites in February 2022? by Nizam Ahmad, La Ode Muhammad Musafar Kilowasid, Hanif Fakhrurroja, Neflia, Abdul Rachman, Asnawi Husin, Haries Fathoni

“…We employed the solar wind and IMF Bz to see their impact on geomagnetic activity. …”
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Modeling Radiation Belt Electrons With Information Theory Informed Neural Networks by Simon Wing, Drew L. Turner, Aleksandr Y. Ukhorskiy, Jay R. Johnson, Thomas Sotirelis, Romina Nikoukar, Giuseppe Romeo

“…The model inputs solar wind parameters (velocity, density, interplanetary magnetic field (IMF) |B|, Bz, and By), magnetospheric state parameters (SYM‐H and AL), and L*. …”
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Storm‐Time Ring Current Plasma Pressure Prediction Based on the Multi‐Output Convolutional Neural Network Model by Yun Yan, Zi‐Kang Xie, Chao Yue, Jiu‐Tong Zhao, Fan Yang, Lun Xie, Qiu‐Gang Zong, Xu‐Zhi Zhou, Shan Wang

“…Taking solar wind parameters, interplanetary magnetic field (IMF) data, and geomagnetic indices with a time history of 3 days as input parameters, the model shows good performances for electron plasma pressure, H+ plasma pressure, He+ plasma pressure, and O+ plasma pressure in both quiet‐time and storm‐time periods, with high correlation coefficients and small root mean square errors between the measured and the predicted values. …”
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Interplanetary Influence on Thermospheric Mass Density: Insights From Deep Learning Analyses by Wenbo Li, Libo Liu, Yiding Chen, Yi‐Jia Zhou, Huijun Le, Ruilong Zhang

“…The DL model indicates that the east‐west component of the interplanetary magnetic field (IMF By) has a great impact on TMD variations, and its modulation is different from the typical energy injection process during storms. …”
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Thermospheric Wind Response to March 2023 Storm: Largest Wind Ever Observed With a Fabry‐Perot Interferometer in Tromsø, Norway Since 2009 by S. Oyama, H. Vanhamäki, L. Cai, A. Shinbori, K. Hosokawa, T. Sakanoi, K. Shiokawa, A. Aikio, I. I. Virtanen, Y. Ogawa, Y. Miyoshi, S. Kurita, N. Nishitani

“…The positive Y‐component of the IMF for 6 days before the storm caused a successive westward component of the FPI‐measured wind during the storm main phase. …”
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Geomagnetic Storm‐Induced Plasma Density Enhancements in the Southern Polar Ionospheric Region: A Comparative Study Using St. Patrick's Day Storms of 2013 and 2015 by P. R. Shreedevi, R. K. Choudhary, Smitha V. Thampi, Sneha Yadav, T. K. Pant, Yiqun Yu, Ryan McGranaghan, Evan G. Thomas, Anil Bhardwaj, A. K. Sinha

“…The same was weaker during the 17 March 2013 storm due to the fast fluctuating nature of interplanetary magnetic field (IMF) Bz. This study shows that the duration and extent of magnetopause erosion play an important role in the spatiotemporal evolution of the plasma density distribution in the high‐midlatitude ionosphere.…”
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