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Assessment of skeletal maturity is important for a clinician to make a decision of the most appropriate treatment on various skeletal disorders. This task is very challenging when using machine learning method due to the limited data and large anatomical variations among different subjects. In this article, we propose an ensemble-based deep learning pipeline to automatically assess the distal radius and ulna (DRU) maturity from left-hand radiographs. At the same time, we adapted the concept of densely connected mechanism in the proposed network architecture to reuse features and prevent gradient disappearance. Therefore, the model acquires two convincing advantages: first, our model preserves the maximum information flow and has a much faster convergence rate. Second, our model avoids overfitting even if training with limited data. The experimental dataset contains 1189 left-hand $X$ -ray scans of children and teenagers. The proposed method achieves 85.27% and 91.68% for radius and ulna classification, respectively. Extensive experiments prove that our model performs better than using other network structures. [ABSTRACT FROM AUTHOR]

Transient recovery characteristics of DC voltage are critical to analyze the stability criterion and the fault ride-through capability of voltage source converter (VSC)-based DC power systems. In this paper, the dynamic model of a flexible DC power grid was initially established, and then, the dynamic operation mechanism of the system was presented by analyzing the movement locus of the operating point when disturbances occur in the DC grid. Subsequently, the DC transient voltage limit and the DC fault clearing time were calculated in order to obtain the transient stability criterion of the DC power system. In addition, a novel voltage transient recovery control strategy was proposed by improving the droop control of the DC voltage to extend the transient stability margin of the power system. Finally, a typical multi-terminal flexible DC power grid was developed on a hardware-in-the-loop platform. The results demonstrated that the voltage recovery ability of the DC power system can be quantitatively estimated by the stability criterion, while the system transient stability can be improved by the proposed control strategy. [ABSTRACT FROM AUTHOR]

Increasing power delivery and performance requirements for next-generation Intel microprocessors has led to the need for a high-capacitance low-inductance decoupling option very close to the die. An embedded array capacitor (EAC) is a large array capacitor embedded in the high-density interconnect (HDI) substrate core and provides a low-inductance path to the die. This paper describes technology development challenges encountered while enabling EACs on Intel's advanced microprocessors. Various package interfaces resulting from the EAC embedding showed very high stress and propensity to delaminate. These defects were addressed by tailoring suitable materials and process modifications during the manufacturing process. Expansion of the embedding material in the HDI substrate resulted in high C4 area warpage. This led to issues such as solder bump bridging, underfill voids, and reliability fails. The technology development challenges and the solution paths described in this paper are very useful to industry in understanding the integration of advanced decoupling options in ultrahigh-performance microprocessor packages. [ABSTRACT FROM PUBLISHER]