Shen Wang

Jan 8, 2025 | nature.com | Shen Wang |Yang Chen |Shixuan Wang |Cong Ma

AbstractAutophagy, a conserved catabolic process implicated in a diverse array of human diseases, requires efficient fusion between autophagosomes and lysosomes to function effectively. Recently, SNAP47 has been identified as a key component of the dual-purpose SNARE complex mediating autophagosome-lysosome fusion in both bulk and selective autophagy. However, the spatiotemporal regulatory mechanisms of this SNARE complex remain unknown.

Aug 16, 2024 | datacenterknowledge.com | Shen Wang

By Shen Wang, principal analyst, data center power & cooling systems, OmdiaIn today's hyper-connected digital age, data centers are the cornerstone of our digital ecosystem, powering a vast array of networks, cloud computing and many other digital applications. However, the continuous operation of high-performance IT equipment generates substantial heat. This presents challenges that necessitate effective cooling solutions to ensure optimal operating conditions.

Jun 18, 2024 | morningstar.com | Shen Wang

Omdia research predicts data center cooling market to reach $16.87 billion in 2028PR NewswireLONDON, June 18, 2024LONDON, June 18, 2024 /PRNewswire/ -- In a groundbreaking development, the data center thermal management market has surged to a staggering $7.67bn, outpacing previous forecasts according to new research from Omdia. This unprecedented growth is poised to continue with a robust CAGR of 18.4% until 2028.

Jun 18, 2024 | finanzen.net | Byd Aktie |Shen Wang

, /PRNewswire/ -- In a groundbreaking development, the data center thermal management market has surged to a staggering $7.67bn, outpacing previous forecasts according to new research from Omdia. This unprecedented growth is poised to continue with a robust CAGR of 18.4% until 2028. This surge will largely be fueled by AI-driven demands and innovations in high-density infrastructure marking a pivotal moment for the industry.

Sep 22, 2023 | amazon.science | Shen Wang |Xiaofei Ma |Henry Zhu |Danilo Neves Ribeiro

We introduce STREET, a unified multi-task and multi-domain natural language reasoning and explanation benchmark. Unlike most existing question-answering (QA) datasets, we expect models to not only answer questions, but also produce step-by-step structured explanations describing how premises in the question are used to produce intermediate conclusions that can prove the correctness of a certain answer.