Search

In this paper, a new meshing criterion for the equivalent thermal analysis of GaAs PHEMT MMICs (Monolithic microwave integrated circuit) is proposed. Based on the meshing criterion, an equivalent thermal model of GaAs PHEMTs with remarkably reduced mesh complexity is established, and the simplification of both layout pattern and vias of MMICs are performed. Theoretical analysis is applied for the calibration of the equivalent thermal model. Assisted by the meshing criterion, chip-level simulators are capable to obtain the peak temperature of MMICs without using averaging approximations, and achieve considerably high simulation accuracy. As examples, two MMIC power amplifiers are designed and implemented using GaAs PHEMT process. Thermal simulation and measurement results obtained with ANSYS ICEPAK and infrared thermography, respectively, show high consistency. The proposed meshing criterion can be applied to improve the accuracy of thermal analysis of MMICs, and the obtained precise peak temperature can be used to effectively assess the power threshold of the designed amplifiers in reliability tests. [ABSTRACT FROM AUTHOR]

Objective: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths. Cell proliferativity and hypoxia have important impact on the response to radiotherapy or chemotherapy. The purpose of this study was to investigate the association of apparent diffusion coefficient (ADC) values and the molecular markers Ki-67 and hypoxia inducible factor-α (HIF-α) in hepatocellular carcinoma (HCC).Materials and Methods: Forty-seven patients diagnosed with HCC were included in this study. All patients performed diffusion-weighted magnetic resonance imaging (DW-MRI) before any anticancer treatment. The ADC maps were automatically calculated on a Syngo workstation. The Ki-67 and HIF-1α expression were assessed by immunohistochemistry. The Pearson correlation test was used to assess the correlation between ADC values and Ki-67 and HIF-1α expression.Results: Ki-67 staining was clearly identified based on the brown nuclear staining in tumor cells. High Ki-67 expression was correlated with low differentiation (p=0.028). A significant correlation was observed between HIF-1α expression and maximum diameter (p=0.014). The mean ADC value was (0.983±0.21)×10(-3) mm(2)/s. The level of Ki-67 expression was correlated inversely with the ADC values (r=-0.371, p=0.01). There was a significant positive correlation between the ADC values and HIF-1α expression (r=0.389, p=0.007).Conclusion: The ADC values were observed to correlate significantly with the molecular markers Ki-67 and HIF-1α. Our results suggest that the ADC values on DW-MRI may be used as a measure of cell proliferativity and hypoxia in hepatocellular carcinoma. [ABSTRACT FROM AUTHOR]

The probabilistic forecasting of electricity loads is crucial for effective scheduling and decision-making in volatile and competitive energy markets with ever-growing uncertainties. We propose a novel approach to construct the probabilistic predictors for curves (PPC) of electricity loads, which leads to properly defined predictive bands and quantiles in the context of curve-to-curve regression. The proposed predictive model provides not only accurate hourly load point forecasts, but also generates well-defined probabilistic bands and day-long trajectories of the loads at any probability level, pre-specified by managers. We also define the predictive quantile curves that exhibit future loads in extreme scenarios and provide insights for hedging risks in the supply management of electricity. When applied to the day-ahead forecasting for French half-hourly electricity loads, the PPC outperform several state-of-the-art time series and machine learning predictive methods with more accurate point forecasts (mean absolute percentage error of 1.10%, compared to 1.36%–4.88% for the alternatives), a higher coverage rate of the day-long trajectory of loads (coverage rate of 95.5%, against 31.9%–90.7% for the alternatives) and a narrower average length of the predictive bands. In a series of numerical experiments, the PPC further demonstrate robust performance and general applicability, achieving accurate coverage probabilities under a variety of data-generating mechanisms. • A novel predictive methodology for electricity loads with probability interpretation. • Good accuracy in both point and interval forecasts for French electricity loads. • Cross-sectional dependence among multiple loads is successfully modeled and estimated. • The predictive methodology is robust and applicable in various scenarios [ABSTRACT FROM AUTHOR]

• We provide an innovative hybrid model for German natural gas forecasting. • The model handles complex dependence with state-of-the-art statistical and deep learning. • We achieve accurate and robust day-ahead forecasts for 92 nodes with various types. • Compared to alternatives, the short-term forecast accuracy improves up to fourfold. As the natural gas market is moving towards short-term planning, accurate and robust short-term forecasts of the demand and supply of natural gas is of fundamental importance for a stable energy supply, a natural gas control schedule, and transport operation on a daily basis. We propose a hybrid forecast model, Functional AutoRegressive and Convolutional Neural Network model, based on state-of-the-art statistical modeling and artificial neural networks. We conduct short-term forecasting of the hourly natural gas flows of 92 distribution nodes in the German high-pressure gas pipeline network, showing that the proposed model provides nice and stable accuracy for different types of nodes. It outperforms all the alternative models, with an improved relative accuracy up to twofold for plant nodes and up to fourfold for municipal nodes. For the border nodes with rather flat gas flows, it has an accuracy that is comparable to the best performing alternative model. [ABSTRACT FROM AUTHOR]