Results 131 to 140 of about 93,551 (286)

Impact of Prophylactic Cefepime on Surgical Site Infection and Severe Morbidity in Patients Who Underwent Pancreaticoduodenectomy With Preoperative Biliary Drainage

Annals of Gastroenterological Surgery, EarlyView.
ABSTRACT Background Although prophylactic broad‐spectrum antibiotics can reduce postoperative complications after pancreaticoduodenectomy (PD), the optimal regimen remains uncertain. This study evaluated the impact of prophylactic cefepime (CFPM) on surgical site infection (SSI) and severe morbidity after PD with preoperative biliary drainage.
Genki Watanabe, Akihiro Murata, Yu Kasamatsu, Atsushi Ishihara, Daisuke Nagashima, Tetsuzo Tashima, Shintaro Kodai, Akishige Kanazawa, Sadatoshi Shimizu   +8 more
wiley   +1 more source

Possibilities of constructing design load diagrams of a helical cable shock damper when the direction of inertia forces changes

RUDN Journal of Engineering Research
Currently, experimental load diagrams in several main directions of load perception are used to describe the stiffness characteristics of cable shock dampers.
Elena M. Reizmunt, Sergey V. Doronin
doaj   +1 more source

Preoperative Gemcitabine/Nab‐Paclitaxel With Concurrent Radiation Therapy for Borderline Resectable Pancreatic Cancer: A Comparative Analysis With Preoperative Chemoradiation Therapy Using Gemcitabine Alone

Annals of Gastroenterological Surgery, EarlyView.
ABSTRACT Background Although NCCN guidelines recommend preoperative therapy for borderline resectable pancreatic cancer (BR‐PC), it is still unclear which regimen is better. The study objective was to elucidate the prognostic significance of neoadjuvant chemoradiotherapy (NACRT) with gemcitabine/nab‐paclitaxel (GnP‐RT) compared to gemcitabine alone ...
Hirofumi Akita, Hidenori Takahashi, Yousuke Mukai, Masahiko Kubo, Shinichiro Hasegawa, Masayoshi Yasui, Takeshi Omori, Hiroshi Miyata, Hiroshi Wada, Kunihito Gotoh   +9 more
wiley   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 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

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

Advanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +1 more source

Memory-Efficient Imagification for Light-Weight Prediction Model of Multivariate Time-Series Data

IEEE Access
This paper addresses the challenge of memory-efficient time-series forecasting in resource-constrained environments. To this end, an imagification method is proposed that enables lightweight convolutional neural network CNN-based prediction by ...
Seungwoo Kang, Ohyun Jo
doaj   +1 more source

An Autonomous Large Language Model‐Agent Framework for Transparent and Local Time Series Forecasting

Advanced Intelligent Discovery, EarlyView.
Architecture of the proposed large language model (LLM)‐based agent framework for autonomous time series forecasting in thermal power generation systems. The framework operates through a vertical pipeline initiated by natural language queries from users, which are processed by the LLM Agent Core powered by Llama.cpp and a ReAct loop with persistent ...
William Gouvêa Buratto, Gabriel Villarrubia Gonzalez, Ademir Nied, Laio Oriel Seman, Anne Carolina Rodrigues Klaar, Stefano Frizzo Stefenon   +5 more
wiley   +1 more source

Bayesian Optimisation for the Experimental Sciences: A Practical Guide to Data‐Efficient Optimisation of Laboratory Workflows

Advanced Intelligent Systems, EarlyView.
This study provides an introduction to Bayesian optimisation targeted for experimentalists. It explains core concepts, surrogate modelling, and acquisition strategies, and addresses common real‐world challenges such as noise, constraints, mixed variables, scalability, and automation.
Chuan He, Martin Singull, T. Jesper Jacobsson   +2 more
wiley   +1 more source

Application of multivariate statistics and geostatistical techniques to identify the spatial variability of heavy metals in groundwater resources

Caspian Journal of Environmental Sciences, 2015
The performance of geostatistical and spatial interpolation techniques were investigated for estimation of spatial variability of heavy metals and water quality mapping of groundwater resources in Ramiyan district (Golestan province, Iran).
Khanduzi, F., Parizanganeh, A., Zamani, A.   +2 more
doaj