Results 161 to 170 of about 60,981 (309)

Efficiency and Technology Gap in European Apple Production—A Metafrontier Model for Germany, Italy, and Poland

Agribusiness, EarlyView.
ABSTRACT EU member states have exhibited varying rates of apple production growth. Technical efficiency (TE) estimation is suitable for identifying best‐practice farm performance. This study examined whether the development of the apple sector in Germany, Italy, and Poland was influenced by production efficiency, access to technology, as well as ...
Anika Muder, Jakub Staniszewski
wiley   +1 more source

Tertiary lymphoid structures in breast cancer: formation, immune functions and clinical implications. [PDF]

Front Immunol
Dong W, Ai S, Shen X, Yao Y.
europepmc   +1 more source

A Physics Constrained Machine Learning Pipeline for Young's Modulus Prediction in Multimaterial Hyperelastic Cylinders Guided by Contact Mechanics

Advanced Intelligent Discovery, EarlyView.
A physics‐guided machine learning framework estimates Young's modulus in multilayered multimaterial hyperelastic cylinders using contact mechanics. A semiempirical stiffness law is embedded into a custom neural network, ensuring physically consistent predictions. Validation against experimental and numerical data on C.
Christoforos Rekatsinas, Panagiotis Krokidas, Vasileios Vavourakis, Clara Essmann, George Giannakopoulos   +4 more
wiley   +1 more source

Gaussian Process Regression–Neural Network Hybrid with Optimized Redundant Coordinates: A New Simple Yet Potent Tool for Scientist's Machine Learning Toolbox

Advanced Intelligent Discovery, EarlyView.
A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley   +1 more source

The life cycle of tertiary lymphoid structures in pancreatic cancer-a window of opportunity for immunotherapy. [PDF]

Front Immunol
Chen Y, Xiao Y, Zhang T, Wang X.
europepmc   +1 more source

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source

Predicting survival in oral squamous cell carcinoma via integrated analysis of tumor budding and tertiary lymphoid structures. [PDF]

Front Oncol
Xiang Z, Huang Y, Yun B, Li D, Liu F, Liu J, Xie N, Zhuang Z, Hou J, Wang C.   +9 more
europepmc   +1 more source

TLS Library for Isolated Enclaves : Optimizing the performance of TLS libraries for SGX

, 2019
Nowadays cloud computing systems handle large amounts of data and process this data across different systems. It is essential to considering data security vulnerabilities and data protection.
Li, Jiatong
core  

AI‐BioMech: Deep Learning Prediction of Mechanical Behavior in Aperiodic Biological Cellular Materials

Advanced Intelligent Discovery, EarlyView.
AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia, Marcelo A. Dias, Parvez Alam   +2 more
wiley   +1 more source

Tumor immune microenvironment states inferred from TLS-associated immune-cell composition stratify prognosis in hepatocellular carcinoma. [PDF]

Front Immunol
Cheng C, Chen G, Zhang J, Qiu L, Li Z, Dong X, Lu C, Wu Q, Peng X, Cai Z, Zeng Y.   +10 more
europepmc   +1 more source