Digitizing molecular structure information of diverse compounds is essential for AI-driven drug discovery. While a significant amount of molecular structure data is already stored and managed in digital formats for in silico predictions of drug stability and side effects, a significant portion remains in analog documents. To address this, deep learning-based optical chemical structure recognition (OCSR) technologies have been developed. Although these models excel with abundant digitally rendered chemical structure images, they underperform on hand-drawn images due to lack of sufficient training data for such images. This study proposes OCSAug, a diffusion model-based data augmentation technique to improve OCSR performance on hand-drawn molecular images. Using a denoising diffusion probabilistic model to capture irregularities and distortions, and the RePaint algorithm to generate augmented data, OCSAug significantly enhances recognition accuracy. Evaluation on the DECIMER dataset shows an improvement of 1.918–3.820 times over results without augmentation.

A high-speed, low-power, and highly reliable frequency multiplier is proposed for a delay-locked loop-based clock generator to generate a multiplied clock with a high frequency and wide frequency range. The proposed edge combiner achieves a high-speed and highly reliable operation using a hierarchical structure and an overlap canceller. In addition, by applying the logical effort to the pulse generator and multiplication-ratio control logic design, the proposed frequency multiplier minimizes the delay difference between positive- and negative-edge generation paths, which causes a deterministic jitter. Finally, a numerical analysis is performed to analyze and compare the performance of the proposed frequency multiplier with that of previous frequency multipliers. The proposed frequency multiplier is fabricated using a 0.13-μm CMOS process technology, and has the multiplication ratios of 1, 2, 4, 8, and 16, and an output range of 100 MHz-3.3 GHz. The frequency multiplier achieves a power consumption to a frequency ratio of 2.9 μW/MHz.

The use of NAND flash memory for building permanent storage has been increasing in many embedded systems due to properties such as non-volatility and low energy consumption. The persistent requirements for high storage capacity have given rise to the increase of bit density per cell as in multi-level cells but this has come at the expense of performance and has resulted in degradation of durability. In this paper, we introduce a complementary approach to boost the performance and durability of MLC-based storage systems by employing a non-volatile buffer that temporarily holds the data heading to MLCs. We also propose algorithms to efficiently eliminate unnecessary write and erase operations in MLCs by performing a pre-merge in the buffer. Our experiments show that the proposed approach can increase performance by up to 4 times and durability by 4 times by adding only a small hardware cost.

BACKGROUND: The quality of cDNA microarray data is crucial for expanding its application to other research areas, such as the study of gene regulatory networks. Despite the fact that a number of algorithms have been suggested to increase the accuracy of microarray gene expression data, it is necessary to obtain reliable microarray images by improving wet-lab experiments. As the first step of a cDNA microarray experiment, spotting cDNA probes is critical to determining the quality of spot images. RESULTS: We developed a governing equation of cDNA deposition during evaporation of a drop in the microarray spotting process. The governing equation included four parameters: the surface site density on the support, the extrapolated equilibrium constant for the binding of cDNA molecules with surface sites on glass slides, the macromolecular interaction factor, and the volume constant of a drop of cDNA solution. We simulated cDNA deposition from the single model equation by varying the value of the parameters. The morphology of the resulting cDNA deposit can be classified into three types: a doughnut shape, a peak shape, and a volcano shape. The spot morphology can be changed into a flat shape by varying the experimental conditions while considering the parameters of the governing equation of cDNA deposition. The four parameters were estimated by fitting the governing equation to the real microarray images. With the results of the simulation and the parameter estimation, the phenomenon of the formation of cDNA deposits in each type was investigated. CONCLUSION: This study explains how various spot shapes can exist and suggests which parameters are to be adjusted for obtaining a good spot. This system is able to explore the cDNA microarray spotting process in a predictable, manageable and descriptive manner. We hope it can provide a way to predict the incidents that can occur during a real cDNA microarray experiment, and produce useful data for several research applications involving cDNA microarrays.