Hasil untuk "Engineering"

H. Kim, Jin-Soo Kim

G. Desiraju

In-Yong Kim, S. Seo, H. Moon et al.

D. Voiry, A. Mohite, M. Chhowalla

Alberto Di Martino, M. Sittinger, M. Risbud

Dd Wang, Js Chen, 王东东

G. Desiraju

Attila Becskei, L. Serrano

K. Burg, Scott E. Porter, J. Kellam

Robert L. Wears

Guoqiang Chen, Qiong Wu

Lingfeng Wang, Kay Chen Tan

M. Nakaniwa, T. Duerig, K. Melton et al.

K. Nguyen, J. West

S. Levenberg, J. Rouwkema, Mara L Macdonald et al.

S. Khorshidi, A. Solouk, H. Mirzadeh et al.

Christian A. Cancino, J. Merigó, Freddy Coronado et al.

J. Lian, Shekhar Mishra, Huimin Zhao

Metabolic engineering aims to develop efficient cell factories by rewiring cellular metabolism. As one of the most commonly used cell factories, Saccharomyces cerevisiae has been extensively engineered to produce a wide variety of products at high levels from various feedstocks. In this review, we summarize the recent development of metabolic engineering approaches to modulate yeast metabolism with representative examples. Particularly, we highlight new tools for biosynthetic pathway optimization (i.e. combinatorial transcriptional engineering and dynamic metabolic flux control) and genome engineering (i.e. clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR associated (Cas) system based genome engineering and RNA interference assisted genome evolution) to advance metabolic engineering in yeast. We also discuss the challenges and perspectives for high throughput metabolic engineering.

Klaas-Jan Stol, Brian Fitzgerald

A variety of research methods and techniques are available to SE researchers, and while several overviews exist, there is consistency neither in the research methods covered nor in the terminology used. Furthermore, research is sometimes critically reviewed for characteristics inherent to the methods. We adopt a taxonomy from the social sciences, termed here the ABC framework for SE research, which offers a holistic view of eight archetypal research strategies. ABC refers to the research goal that strives for generalizability over Actors (A) and precise measurement of their Behavior (B), in a realistic Context (C). The ABC framework uses two dimensions widely considered to be key in research design: the level of obtrusiveness of the research and the generalizability of research findings. We discuss metaphors for each strategy and their inherent limitations and potential strengths. We illustrate these research strategies in two key SE domains, global software engineering and requirements engineering, and apply the framework on a sample of 75 articles. Finally, we discuss six ways in which the framework can advance SE research.

Junwei Yu, Mufeng Yang, Yepeng Ding et al.

The proliferation of AI-powered search engines has shifted information discovery from traditional link-based retrieval to direct answer generation with selective source citation, creating new challenges for content visibility. While existing Generative Engine Optimization (GEO) approaches focus primarily on semantic content modification, the role of structural features in influencing citation behavior remains underexplored. In this paper, we propose GEO-SFE, a systematic framework for structural feature engineering in generative engine optimization. Our approach decomposes content structure into three hierarchical levels: macro-structure (document architecture), meso-structure (information chunking), and micro-structure (visual emphasis), and models their impact on citation probability across different generative engine architectures. We develop architecture-aware optimization strategies and predictive models that preserve semantic integrity while improving structural effectiveness. Experimental evaluation across six mainstream generative engines demonstrates consistent improvements in citation rate (17.3 percent) and subjective quality (18.5 percent), validating the effectiveness and generalizability of the proposed framework. This work establishes structural optimization as a foundational component of GEO, providing a data-driven methodology for enhancing content visibility in LLM-powered information ecosystems.