Results 111 to 120 of about 100,988 (265)

Increase in Pancreatoduodenectomy Volume at Mid‐Volume Facilities in the United States: A National Cancer Database Analysis

Annals of Gastroenterological Surgery, EarlyView.
ABSTRACT Background The annual number of pancreatoduodenectomies (PDs) in the United States has increased over the past decade, but the facility types driving this growth and whether outcomes have improved remain unclear. We aimed to identify characteristics of facilities with increasing PD volumes and assess national trends in centralization ...
Koichi Tomita, Naruhiko Ikoma
wiley   +1 more source

Annual Report of the 2022 National Clinical Database: Decade‐Long Trends and Current Status of Gastroenterological Surgery in Japan

Annals of Gastroenterological Surgery, EarlyView.
ABSTRACT Aim The National Clinical Database (NCD) of Japan is the largest nationwide registry, covering over 95% of surgeries in the country. This 2022 annual report summarizes the short‐term outcomes of gastroenterological surgeries and discusses trends and insights over the past decade.
Koshi Kumagai, Arata Takahashi, Hideki Ueno, Naoki Hiki, Yoshiki Kajiwara, Tomoki Ebata, Takao Otsuka, Taro Oshikiri, Yoshihiro Kakeji, Takanori Goi, Akio Saiura, Hiroshi Saeki, Chie Tanaka, Masanori Tokunaga, Tsutomu Fujii, Satoru Matsuda, Tsunekazu Mizushima, Hiroyuki Yamamoto, Yuko Kitagawa, Ken Shirabe   +19 more
wiley   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Efficient Multi-Cluster Scheduling for Heterogeneous Workloads

IEEE Access
As heterogeneous multi-cluster environments become increasingly prevalent in large-scale AI and data-intensive workloads, traditional scheduling strategies struggle to balance efficiency, scalability, and adaptability. This paper proposes a unified cross-
Xuemin Wang, Xinchi Li, Xiaoqing Xia, Mingchuan Yang   +3 more
doaj   +1 more source

Flexible Memory: Progress, Challenges, and Opportunities

Advanced Intelligent Discovery, EarlyView.
Flexible memory technology is crucial for flexible electronics integration. This review covers its historical evolution, evaluates rigid systems, proposes a flexible memory framework based on multiple mechanisms, stresses material design's role, presents a coupling model for performance optimization, and points out future directions.
Ruizhi Yuan, Sen Chen, Minghui Guo, Ziliang Cui, Yujie Du, Jing Liu   +5 more
wiley   +1 more source

A New Randomized Algorithm for Handling Scheduling Conflicts in Grids

Advances in Electrical and Computer Engineering, 2009
As the scale of Grid platforms grows, the idea of a centralized scheduler loses its efficiency, and it is replaced with the scheme of decentralized schedulers.
VAHDAT-NEJAD, H., ZAMANIFAR, K.
doaj   +1 more source

The Necessity of Dynamic Workflow Managers for Advancing Self‐Driving Labs and Optimizers

Advanced Intelligent Discovery, EarlyView.
We assess the maturity and integration readiness of key methodologies for Materials Acceleration Platforms, highlighting the need for dynamic workflow managers. Demonstrating this, we integrate PerQueue into a color‐mixing robot, showing how flexible orchestration improves coordination and optimization.
Simon K. Steensen, Patrick W. V. Butler, Jesper R. Pedersen, Nis Fisker‐Bødker, Tejs Vegge, Jin Hyun Chang, Ivano E. Castelli   +6 more
wiley   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

Artificial Intelligence (AI) and Agribusiness: From Automation to Augmentation in a Global Context

Agribusiness, EarlyView.
Alexis H. Villacis
wiley   +1 more source

Self‐Driving Laboratory Optimizes the Lower Critical Solution Temperature of Thermoresponsive Polymers

Advanced Intelligent Discovery, EarlyView.
A low‐cost, self‐driving laboratory is developed to democratize autonomous materials discovery. Using this "frugal twin" hardware architecture with Bayesian optimization, the platform rapidly converges to target lower critical solution temperature (LCST) values while self‐correcting from off‐target experiments, demonstrating an accessible route to data‐
Guoyue Xu, Renzheng Zhang, Tengfei Luo
wiley   +1 more source