Results 161 to 170 of about 917,026 (367)

Deep Learning Model for Predicting Operative Mortality After Total Gastrectomy: Analysis of the Japanese National Clinical Database (NCD)

open access: yesAnnals of Gastroenterological Surgery, EarlyView.
Deep learning‐based prediction model for operative mortality using the National Clinical Database (NCD). The model achieved a C‐statistic of 0.74. ABSTRACT Background Radical gastrectomy with lymph node dissection is the primary treatment for gastric cancer.
Ryosuke Fukuyo   +5 more
wiley   +1 more source

Virtual Classroom Management and Communicative Writing Pedagogy [PDF]

open access: yes, 1996
Writing, essentially a social act, is concerned with cognition and is alliedto context. Most writing takes the form of dialogue and it is out of dialogic processes that language acquisition takes place.
Mills, Jon
core  

Prolonged Prophylactic Antibiotics Based on Preoperative Bile Culture Reduce Surgical Site Infections After Pancreaticoduodenectomy Following Preoperative Biliary Drainage: A Propensity‐Matched Analysis

open access: yesAnnals of Gastroenterological Surgery, EarlyView.
ABSTRACT Objective The optimum duration of prophylactic antibiotics after pancreaticoduodenectomy following preoperative biliary drainage to prevent surgical site infections remains controversial. We evaluate whether a prolonged course of prophylactic antibiotics reduces surgical site infection after pancreaticoduodenectomy following biliary drainage ...
Kyohei Matsumoto   +9 more
wiley   +1 more source

In Situ Graph Reasoning and Knowledge Expansion Using Graph‐PRefLexOR

open access: yesAdvanced Intelligent Discovery, EarlyView.
Graph‐PRefLexOR is a novel framework that enhances language models with in situ graph reasoning, symbolic abstraction, and recursive refinement. By integrating graph‐based representations into generative tasks, the approach enables interpretable, multistep reasoning.
Markus J. Buehler
wiley   +1 more source

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

open access: yesAdvanced 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

A Review on Recent Trends of Bioinspired Soft Robotics: Actuators, Control Methods, Materials Selection, Sensors, Challenges, and Future Prospects

open access: yesAdvanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This article reviews the current state of bioinspired soft robotics. The article discusses soft actuators, soft sensors, materials selection, and control methods used in bioinspired soft robotics. It also highlights the challenges and future prospects of this field.
Abhirup Sarker   +2 more
wiley   +1 more source

Tiling Robotics: A New Paradigm of Shape‐Morphing Reconfigurable Robots

open access: yesAdvanced Intelligent Systems, EarlyView.
Tiling robotics is a novel paradigm of shape‐morphing reconfigurable robots, defining them as polyform‐inspired machines capable of transforming between at least two polymorphic shapes. Various reconfiguration‐enabling and locomotion mechanisms of tiling robots are comparatively analyzed, with the electromechanical developments, along with a proposed ...
S. M. Bhagya P. Samarakoon   +2 more
wiley   +1 more source

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