Results 131 to 140 of about 162,584 (247)

Lattice Structures for Bone Replacement: The Intersection of Bone Biomechanics, Lattice Design, and Additive Manufacturing

Advanced Materials Technologies, EarlyView.
This review outlines how understanding bone's biology, hierarchical architecture, and mechanical anisotropy informs the design of lattice structures that replicate bone morphology and mechanical behavior. Additive manufacturing enables the fabrication of orthopedic implants that incorporate such structures using a range of engineering materials ...
Stylianos Kechagias, Maxwell J. Munford, Frederik C.H. Masure, Richard J. van Arkel, Reece N Oosterbeek   +4 more
wiley   +1 more source

Artificial Neural Networks for Predicting Emissions from the Livestock Sector: A Review. [PDF]

Animals (Basel)
Santoro LM, D'Urso PR, Arcidiacono C, Cascone G, Coco S.   +4 more
europepmc   +1 more source

Record High Polarization at 2 V and Imprint‐Free Operation in Superlattice HfO2‐ZrO2 by Proper Tuning of Ferro and Antiferroelectricity

Advanced Materials Technologies, EarlyView.
Low‐power ferroelectric capacitors, based on superlattice HfO2‐ZrO2 is reported. With proper tuning of ferro and antiferroelectricity, an imprint‐free high switchable polarization charge (2Pr of 76 µC/cm2) is obtained with 2MV/cm leading to linear analog weight update and non‐volatile retention, providing a design guideline for emerging non‐volatile ...
Xinye Li, Padma Srivari, Mari Honkanen, Turkka Salminen, Sayani Majumdar   +4 more
wiley   +1 more source

Development and optimization of a morphodiversity model for mountainous areas using supervised classification and artificial neural networks. [PDF]

Sci Rep
Bartuś T.
europepmc   +1 more source

Flow‐Adaptive Gas Sensing Enabled Using a Uniform Au Nanosheet Sensor Array and a Neural Network Inference

Advanced Materials Technologies, EarlyView.
Integrated Au nanosheet sensor array enables simultaneous inference of gas concentration and flow rate via deep neural network analysis, without external flow control. ABSTRACT Gas sensor responses are considerably affected by gas flow rates, thereby inhibiting the accurate detection of target gas concentrations in variable‐flow applications such as ...
Taro Kato, Takahisa Tanaka, Yusuke Hamanaka, Ryo Toyoshima, Ken Uchida   +4 more
wiley   +1 more source

Optimizing intervertebral disc cell metabolic phenotyping with machine learning and artificial neural networks. [PDF]

Sci Rep
Bahar ME, Maulidi RF, Ngo QN, Kim DR.
europepmc   +1 more source

End‐to‐End Sensing Systems for Breast Cancer: From Wearables for Early Detection to Lab‐Based Diagnosis Chips

Advanced Materials Technologies, EarlyView.
This review explores advances in wearable and lab‐on‐chip technologies for breast cancer detection. Covering tactile, thermal, ultrasound, microwave, electrical impedance tomography, electrochemical, microelectromechanical, and optical systems, it highlights innovations in flexible electronics, nanomaterials, and machine learning.
Neshika Wijewardhane, Lyn I. Jones, Richard M. Martin, Helen Winter, Faezeh Arab Hassani   +4 more
wiley   +1 more source

Integration of geospatial techniques and artificial neural networks for groundwater potential zonation in India. [PDF]

Sci Rep
Badapalli PK, Nakkala AB, Pujari PS, Gugulothu S, Kottala RB, Mannala P, Alkhuraiji WS, Thamaga KH, Zhran M.   +8 more
europepmc   +1 more source

Transducers Across Scales and Frequencies: A System‐Level Framework for Multiphysics Integration and Co‐Design

Advanced Materials Technologies, EarlyView.
Transducers convert physical signals into electrical and optical representations, yet each mechanism is bounded by intrinsic trade‐offs across bandwidth, sensitivity, speed, and energy. This review maps transduction mechanisms across physical scale and frequency, showing how heterogeneous integration and multiphysics co‐design transform isolated ...
Aolei Xu, Mengyao Xiao, Zhouli Sui, Yinpeng Wang, Daoye Zheng, Yuxin Liu, Luwei Wang, Huajun Liu, Chengkuo Lee   +8 more
wiley   +1 more source

The cost of unmodeled biological complexity in artificial neural networks. [PDF]

Patterns (N Y)
Bikić A, Kaspar C, Pernice WHP.
europepmc   +1 more source