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Thermal performance enhancement of a two-phase closed thermosyphon via dropwise condensation
No full textPyridine-to-Pyridazine Skeletal Editing
No full textNitrogen-containing heterocycles underpin many pharmaceuticals, where subtle atomic rearrangements can markedly alter efficacy and safety. Pyridines are ubiquitous scaffolds in pharmaceuticals, yet their close analogues, pyridazines with two adjacent ring nitrogens, remain underexplored owing to limited synthetic access. Here, we report a skeletal editing strategy that converts pyridines into pyridazines by replacing one ring carbon with nitrogen while preserving aromaticity. The sequence comprises N-amine assembly, followed by an m-chloroperoxybenzoic acid (mCPBA)-mediated ring-remodeling sequence proceeding via a 1,2-diazatriene intermediate to effect carbon-to-nitrogen substitution. The two-step process is operationally simple, runs at ambient temperature in air, and requires no UV irradiation or preinstalled groups. The method shows broad functional-group tolerance, including complex, drug-derived molecules, providing rapid, scalable access to pyridazines. This platform expands heterocyclic chemical space and enables late-stage diversification for drug discovery.
Selection of the most probable best
No full textWe consider an expected-value ranking and selection (R&S) problem where all k solutions' simulation outputs depend on a common parameter whose uncertainty can be modeled by a distribution. We define the most probable best (MPB) to be the solution that has the largest probability of being optimal with respect to the distribution and design an efficient sequential sampling algorithm to learn the MPB when the parameter has a finite support. We derive the large deviations rate of the probability of falsely selecting the MPB and formulate an optimal computing budget allocation problem to find the rate-maximizing static sampling ratios. The problem is then relaxed to obtain a set of optimality conditions that are interpretable and computationally efficient to verify. We devise a series of algorithms that replace the unknown means in the optimality conditions with their estimates and prove the algorithms' sampling ratios achieve the conditions as the simulation budget increases. Furthermore, we show that the empirical performances of the algorithms can be significantly improved by adopting the kernel ridge regression for mean estimation while achieving the same asymptotic convergence results. The algorithms are benchmarked against a state-of-the-art contextual R&S algorithm and demonstrated to have superior empirical performances.
A Dynamic Framework for Cloud Migration
No full text디지털전환을 통한 기업의 경쟁력 확보를 위해서는 클라우드(Cloud) 환경으로의 전환이 필수적이다. 그러나 클라우드 전환의 본질은 단순한 기술적 변화가 아니라, 기업의 비즈니스 모델을 근본적으로혁신하고, 시장에서의 독보적 리더십을 확립하는 기회라는 점이다. 이러한 클라우드 전환 과정에서기업은 기술적 복잡성, 불확실성, 비용, 고려해야 할 수많은 변수 등 다양한 어려움을 겪게 된다. 본연구는 이러한 어려움 속에서 기업의 클라우드 전환과정을 효율적으로 실행할 수 있는 동적 프레임워크를제시한다. 이 프레임워크를 통해 기업은 조직의 준비도와 애플리케이션의 민첩성 요구수준을 기준으로네 가지 기본 전환전략(Swift, Renovation, Takeout, Evolution)을 도출하고 이를 바탕으로 상황에 따라동적으로 변화하는 전략적 선택을 내릴 수 있다. 이 프레임워크에서 활용되는 시나리오 플래닝 기법은외부 불확실성에 대응하고 이를 통해 클라우드 전환 과정에서 직면할 수 있는 기업의 다양한 리스크를효과적으로 관리할 수 있도록 도와준다. 이 클라우드 전환 프레임워크는 온라인 유통 및 바이오 CDMO 산업의 적용 사례를 통해 매우 유용한 접근방법임을 실증하였다. 본 연구에서 제시된 클라우드 전환을위한 동적 프레임워크는 디지털전환의 시대에 단순한 기술적 변화를 넘어 조직의 민첩성, 경쟁력, 시장 리더십을 극대화하는 강력한 도구로 활용될 수 있다.
Temperature Field Prediction of a Li-ion Battery using Deep Operator Network under Various Operating Conditions
No full textBandwidth-efficient Signal Acquisition for 21mm FOV Retinal OCTA
No full text<jats:p>We present a 21 mm FOV retinal SPML-OCTA acquired within a 1 GHz signal bandwidth. A 4.73 MHz SPML-OCT system with concentric-circular scanning and automatic reference arm length adjustment enabled high-quality retinal OCTA with 21 mm FOV acquisition in five seconds.</jats:p>
MDSGEN: FAST AND EFFICIENT MASKED DIFFUSION TEMPORAL-AWARE TRANSFORMERS FOR OPEN-DOMAIN SOUND GENERATION
No full textWe introduce MDSGen, a novel framework for vision-guided open-domain sound generation optimized for model parameter size, memory consumption, and inference speed. This framework incorporates two key innovations: (1) a redundant video feature removal module that filters out unnecessary visual information, and (2) a temporal-aware masking strategy that leverages temporal context for enhanced audio generation accuracy. In contrast to existing resource-heavy Unet-based models, MDSGen employs denoising masked diffusion transformers, facilitating efficient generation without reliance on pre-trained diffusion models. Evaluated on the benchmark VGGSound dataset, our smallest model (5M parameters) achieves 97.9% alignment accuracy, using 172× fewer parameters, 371% less memory, and offering 36× faster inference than the current 860M-parameter state-of-the-art model (93.9% accuracy). The larger model (131M parameters) reaches nearly 99% accuracy while requiring 6.5× fewer parameters. These results highlight the scalability and effectiveness of our approach. The code is available at https://bit.ly/mdsgen
