Results 221 to 230 of about 2,194,170 (284)

Biological Resectability in Colorectal Liver Metastases: A Nomogram‐Based Continuous Risk Model to Define Borderline Disease

Annals of Gastroenterological Surgery, EarlyView.
KRAS mutation demonstrated a prognostic impact according to primary tumor sidedness, independent of tumor morphology, in patients undergoing hepatectomy for colorectal liver metastases. A nomogram integrating biological and clinicopathological factors was developed to provide continuous biological risk assessment.
Yuzo Umeda, Toshiharu Mitsuhashi, Tomokazu Fuji, Toru Kojima, Takeshi Koujima, Tatsuo Matsuda, Daisuke Satoh, Masaru Inagaki, Kosei Takagi, Toshiyoshi Fujiwara   +9 more
wiley   +1 more source

Laparoscopic Surgery Is Associated With Reduced Small Bowel Obstruction Risk After Colorectal Cancer Surgery: A Nationwide Cohort Study of 5458 Patients

Annals of Gastroenterological Surgery, EarlyView.
Abbreviated Abstract This nationwide retrospective cohort study of 5458 colorectal cancer surgeries across 32 Japanese institutions examined the effects of laparoscopic surgery, adhesion prevention materials (APMs), and stoma creation on the 5‐year risk of small bowel obstruction (SBO).
Takeshi Yamada, Fumihiko Fujita, Ken Eto, Kozo Kataoka, Norio Yukawa, Kiichi Sugimoto, Rai Shimoyama, Atsuko Fukazawa, Kensuke Kumamoto, Yuichi Takayama, Akira Komono, Akihisa Matsuda, Ryo Ohta, Hiromichi Sonoda, Koichi Okuya, Keisuke Ihara, Yasuyuki Yokoyama, Takuya Nishino, Yasuki Akiyama, Daisuke Ichikawa   +19 more
wiley   +1 more source

pyDMS: A Python package for the determination of physics‐informed dual‐mode sorption (DMS) parameters

AIChE Journal, EarlyView.
Abstract Sorption in glassy polymer membranes is commonly modeled with the dual‐mode sorption (DMS) model. Fitting the DMS model to sorption isotherms presents challenges, as multiple parameter sets may prove satisfactory. This work presents pyDMS, an open‐source Python package for the computation of DMS parameters obtained via a physics‐informed ...
Brandon C. Tapia, Pablo A. Dean, Jing Ying Yeo, Albert X. Wu, Zachary P. Smith   +4 more
wiley   +1 more source

Machine learning–driven design of catalytic processes for sulfur dioxide oxidation: Lessons from the trenches

AIChE Journal, EarlyView.
Abstract Despite the growing use of ML in chemical engineering, the catalytic conversion of sulfur dioxide (SO2) to sulfur trioxide (SO3) remains underexplored from a data‐driven modeling perspective. This study evaluates an integrated workflow for literature‐derived SO2 oxidation data, combining data curation, preprocessing assessment, machine ...
Farough Agin, Jules Thibault, Clémence Fauteux‐Lefebvre   +2 more
wiley   +1 more source

Toward Knowledge‐Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human–AI Synergy

Advanced Intelligent Discovery, EarlyView.
This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee, Hyeonbin Moon, Junhyeong Lee, Seunghwa Ryu   +3 more
wiley   +1 more source

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

FastCat: Autonomous Discovery of Multielement Layered Double Hydroxide Alloy Catalysts for Alkaline Oxygen Evolution Reaction

Advanced Intelligent Discovery, EarlyView.
A machine learning‐guided self‐driving laboratory screened over 500 nickel‐based layered double‐hydroxide catalysts for alkaline oxygen evolution. Out of the eight metals, the robot uncovered a quaternary Ni–Fe–Cr–Co catalysts requiring only 231 mV overpotential to reach 20 mA cm−2.
Nis Fisker‐Bødker, Enzo Raffaele Moretti, Jin Hyun Chang, Tejs Vegge   +3 more
wiley   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source