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Based on data from the European Centre for Medium-Range Weather Forecasts (ECMWF) ECWMF ERA5 Reanalysis (ERA-5) and the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) datasets, the diurnal variation of the evaporation duct height (EDH) in the South China Sea (SCS) and its relationship with environmental variables using the Naval Postgraduate School (NPS) model are analyzed, discussed, and presented. The findings are given as follows. First, the EDH diurnal variations are primarily influenced by the sea-land thermal contrast, with the major influencing variables being the relative humidity (RH) and the near-surface wind speed (WS), followed by the air-sea temperature difference (ASTD). Secondly, the areas where the EDH and the RH are highly correlated are mostly in northern SCS, and the two variables are negatively correlated. Besides, in southeastern SCS, the EDH has a greater and positive correlation with the WS. Seasonal changes can be found in areas where the EDH is highly correlated with the ASTD, primarily to the northwest of Luzon Island in winter and the east coast of Vietnam in summer. Finally, the spatial patterns of the EDH first mode in the SCS present a monopolar structure with the strong loading in northern SCS. [ABSTRACT FROM AUTHOR]

Solder interconnects are essential to the reliability of electronic devices. The addition of alloying elements was reported to provide beneficial effects to the microstructure and mechanical reliability of lead-free solder alloys. In this work, the effects of Sb addition on the mechanical behavior of SAC305 solder alloy were investigated. Based on differential thermal analysis, it was observed that melting point of SAC-Sb solder was slightly higher than that of SAC305. The mechanical behavior was studied at different temperatures (25 °C–125 °C) and strain rates (1 × 10−4 – 1 × 10−2 1/s) for SAC-Sb and SAC 305 solders as a comparison. From the results, ultimate tensile strength of SAC-Sb (10–72 MPa) was generally higher than that of SAC305 (4.54–61.5 MPa) at the aforementioned test conditions. A linear relation between the UTS and yield strength and temperature was identified for test results of SAC305, but the relation was found to be nonlinear for SAC-Sb solder because of solution strengthening effect of Sb in the Sn matrix. Parameters of Anand model were estimated based on mechanical behavior of SAC-Sb and SAC305 solders. Next, the model was used to evaluate thermal fatigue life of an insulated gate bipolar transistor (IGBT) module with SAC-Sb and SAC305 as chip-to-directed bonding copper (DBC) and DBC-to-baseplate attachment materials by finite element modeling. The increase of maximum inelastic strain in one thermal cycle in SAC-Sb solder joints was lower than that in SAC305 solder joints. This result indicates that SAC-Sb solder can provide a considerably better fatigue life under harsh service conditions. [ABSTRACT FROM AUTHOR]

The functional magnetic resonance imaging (fMRI) is a noninvasive technique for studying brain activity, such as brain network analysis, neural disease automated diagnosis and so on. However, many existing methods have some drawbacks, such as limitations of graph theory, lack of global topology characteristic, local sensitivity of functional connectivity, and absence of temporal or context information. In addition to many numerical features, fMRI time series data also cover specific contextual knowledge and global fluctuation information. Here, we propose multi-scale time-series kernel-based learning model for brain disease diagnosis, based on Jensen-Shannon divergence. First, we calculate correlation value within and between brain regions over time. In addition, we extract multi-scale synergy expression probability distribution (interactional relation) between brain regions. Also, we produce state transition probability distribution (sequential relation) on single brain regions. Then, we build time-series kernel-based learning model based on Jensen-Shannon divergence to measure similarity of brain functional connectivity. Finally, we provide an efficient system to deal with brain network analysis and neural disease automated diagnosis. On Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, our proposed method achieves accuracy of 0.8994 and AUC of 0.8623. On Major Depressive Disorder (MDD) dataset, our proposed method achieves accuracy of 0.9166 and AUC of 0.9263. Experiments show that our proposed method outperforms other existing excellent neural disease automated diagnosis approaches. It shows that our novel prediction method performs great accurate for identification of brain diseases as well as existing outstanding prediction tools. [ABSTRACT FROM AUTHOR]