In this paper, a fully integrated low-dropout regulator (LDO) using voltage-to-time conversion (VTC) technique is presented for under-1 V supply voltage application. A synchronous VTC technique is proposed using constant-current (CC) charging and discharging to achieve high loop gain. A high-gain charge pump (CP) is proposed to improve power-supply-rejection (PSR). Furthermore, an asynchronous step detection recovery technique is proposed to achieve fast transient response. A frequency-adaptive oscillator is proposed to remove the noise of the clock signal. The proposed LDO is designed in 28-nm process to achieve a droop voltage of 104 mV at load current transient of 90 mA. The proposed LDO achieves PSR of -77 dB at ILOAD=100 mA and PSR of -65 dB at ILOAD=10 mA for 1-kHz supply ripple frequency. The quiescent current is 32 µA and the peak current efficiency is 99.98%.

MOTIVATION: Protein-protein interaction sites (PPIS) are crucial for deciphering protein action mechanisms and related medical research, which is the key issue in protein action research. Recent studies have shown that graph neural networks have achieved outstanding performance in predicting PPIS. However, these studies often neglect the modeling of information at different scales in the graph and the symmetry of protein molecules within three-dimensional space. RESULTS: In response to this gap, this article proposes the MEG-PPIS approach, a PPIS prediction method based on multi-scale graph information and E(n) equivariant graph neural network (EGNN). There are two channels in MEG-PPIS: the original graph and the subgraph obtained by graph pooling. The model can iteratively update the features of the original graph and subgraph through the weight-sharing EGNN. Subsequently, the max-pooling operation aggregates the updated features of the original graph and subgraph. Ultimately, the model feeds node features into the prediction layer to obtain prediction results. Comparative assessments against other methods on benchmark datasets reveal that MEG-PPIS achieves optimal performance across all evaluation metrics and gets the fastest runtime. Furthermore, specific case studies demonstrate that our method can predict more true positive and true negative sites than the current best method, proving that our model achieves better performance in the PPIS prediction task. AVAILABILITY AND IMPLEMENTATION: The data and code are available at https://github.com/dhz234/MEG-PPIS.git.