Results 211 to 220 of about 862,981 (299)

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

A multiscale, Bayesian inference approach to augment mechanistic models of cell signaling with machine-learning predictions of binding affinity. [PDF]

PLoS Comput Biol
Huber HA, Finley SD.
europepmc   +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

Approximate Bayesian inference of directed acyclic graphs in biology with flexible priors on edge states. [PDF]

PLoS Comput Biol
Martin EA, Patchigolla V, Fu AQ.
europepmc   +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

Bayesian Inference for Spatially-Temporally Misaligned Data Using Predictive Stacking. [PDF]

Environmetrics
Pan S, Banerjee S.
europepmc   +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

Revisiting the relationship between impulsivity, apathy, and action control: Bayesian inference from a stop-signal task study. [PDF]

PLoS One
Michel E, Garnier-Allain A, Kaliuzhna M, Bennabi D, Servant M, Béreau M.   +5 more
europepmc   +1 more source

Factorization Machine‐Based Active Learning for Functional Materials Design with Optimal Initial Data

Advanced Intelligent Discovery, EarlyView.
This work investigates the optimal initial data size for surrogate‐based active learning in functional material optimization. Using factorization machine (FM)‐based quadratic unconstrained binary optimization (QUBO) surrogates and averaged piecewise linear regression, we show that adequate initial data accelerates convergence, enhances efficiency, and ...
Seongmin Kim, In‐Saeng Suh
wiley   +1 more source

Automatic forward model parameterization with Bayesian inference of conformational populations. [PDF]

APL Mach Learn
Raddi RM, Marshall T, Voelz VA.
europepmc   +1 more source