This paper presents a scalable hybrid boost converter (SHBC) with adjusted number of stages (N) to acquire high conversion ratio (CR) and high power efficiency. Firstly, unlike most converters whereCRis related only to duty cycle (D), theCRof SHBC is related to bothDand N. This allows the converter to provide a highCRwhile keepingDat a reasonable value for easy loop control. Secondly, N flying capacitors in series with the inductor can divide output voltage ($V_{\mathrm {OUT}}$) to use low voltage devices with small figure of metric (FOM) for high power efficiency. Moreover, N flying capacitors in parallel with the inductor can shunt the inductor current to reduce the inductor DCR loss, which also helps improve power efficiency. Thirdly, when many N values meet the requirements of a specific application scenario, an optimal number of stages (NOPT) is proposed to minimize the total loss and optimize the power efficiency further. The prototype design of SHBC with$\mathrm{N}_{\mathrm {OPT}} =2$has been discussed and fabricated by$0.18~\mu $m BCD process. The measurement results demonstrate its operation is normal in theCRrange from 4 to 8. Besides, a 93.34% peak efficiency is also achieved with$I_{\mathrm {OUT}} =0.125$A andCR= 4.

This paper presents an enhanced three-level buck converter (E3LBC) with configurable power stage (CPS) and configurable control stage (CCS) for fast load transient response. Compared with the conventional 3LBC and existing solutions, the proposed CPS can raise the inductor current slope significantly by changing the switching node voltage to 3/2 times or -1/2 times the input voltage (VIN) during the load-increasing or load-decreasing transient, which will improve the load transient response. Moreover, the CPS can realize the series or parallel operations of flying capacitors (CF1,CF2) and make their voltages (VCF1,VCF2) eventually converge toVIN/2. On the other hand, the proposed CCS can eliminate the minimum off or on time by using hysteresis control and keep the inductor charging or discharging all the time during the load-increasing or load-decreasing transient for further improving load transient response. Besides, the CCS can provide an adaptive-on-time control in the steady state to acquire a pseudo-constant frequency for small output voltage ripple. The prototype design has been fabricated with the 0.18-μm CMOS process. According to the measurement results, the E3LBC exhibits undershoot/overshoot voltages and settling time of -19/+30 mV and 1.2/1.1 μs, when the load current is changed between 100 mA and 500 mA. The other measurements and comparisons also verify the effectiveness of CPS and CCS for the E3LBC.

MOTIVATION: Lipid nanoparticles (LNPs) are the most widely used vehicles for mRNA vaccine delivery. The structure of the lipids composing the LNPs can have a major impact on the effectiveness of the mRNA payload. Several properties should be optimized to improve delivery and expression including biodegradability, synthetic accessibility, and transfection efficiency. RESULTS: To optimize LNPs, we developed and tested models that enable the virtual screening of LNPs with high transfection efficiency. Our best method uses the lipid Simplified Molecular-Input Line-Entry System (SMILES) as inputs to a large language model. Large language model-generated embeddings are then used by a downstream gradient-boosting classifier. As we show, our method can more accurately predict lipid properties, which could lead to higher efficiency and reduced experimental time and costs. AVAILABILITY AND IMPLEMENTATION: Code and data links available at: https://github.com/Sanofi-Public/LipoBART.

MOTIVATION: RNA design is the search for a sequence or set of sequences that will fold to desired structure, also known as the inverse problem of RNA folding. However, the sequences designed by existing algorithms often suffer from low ensemble stability, which worsens for long sequence design. Additionally, for many methods only a small number of sequences satisfying the MFE criterion can be found by each run of design. These drawbacks limit their use cases. RESULTS: We propose an innovative optimization paradigm, SAMFEO, which optimizes ensemble objectives (equilibrium probability or ensemble defect) by iterative search and yields a very large number of successfully designed RNA sequences as byproducts. We develop a search method which leverages structure level and ensemble level information at different stages of the optimization: initialization, sampling, mutation, and updating. Our work, while being less complicated than others, is the first algorithm that is able to design thousands of RNA sequences for the puzzles from the Eterna100 benchmark. In addition, our algorithm solves the most Eterna100 puzzles among all the general optimization based methods in our study. The only baseline solving more puzzles than our work is dependent on handcrafted heuristics designed for a specific folding model. Surprisingly, our approach shows superiority on designing long sequences for structures adapted from the database of 16S Ribosomal RNAs. AVAILABILITY AND IMPLEMENTATION: Our source code and data used in this article is available at https://github.com/shanry/SAMFEO.