Results 141 to 150 of about 853,385 (276)

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

Application of an artificial neural network (ANN) simulator to increase the operational efficiency of a roadheader. [PDF]

Sci Rep
Cheluszka P, Głuszek G, Rostami J.
europepmc   +1 more source

Unlocking the Potential of MXene‐Based Electrochemical Biosensors: A Review of Biofunctionalization Strategies and Biosensing Principles

Advanced Materials Technologies, EarlyView.
ABSTRACT Electrochemical biosensors enable the accurate and timely detection of clinical biomarkers, improving healthcare and precision medicine. MXene nanosheets, a class of 2D transition metal carbides, nitrides, and carbonitrides, are promising materials for developing next‐generation electrochemical biosensors due to their unique physicochemical ...
Muhsin Ali, Sharat Chandra Barman, Isobel Steer, Aya H. Alshareef, Dana Alsulaiman   +4 more
wiley   +1 more source

Comparing machine learning and artificial neural network models in psychological research: a ROC-based analysis. [PDF]

Front Psychol
Leitner ML, Arendasy M.
europepmc   +1 more source

Chemically Doped Conductive Polymers for Wearable Health Monitoring

Advanced Materials Technologies, EarlyView.
Among conductive polymers, poly(3,4‐ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS), polyaniline (PANI), and polypyrrole (PPy) are the most studied and applied. Chemical doping significantly boosts intrinsic conductivity and mechanical robustness.
Mengdi Zuo, Jian Song, Hong Hu, Le Zheng, Lei Zhang, Charles H. Lawrie   +5 more
wiley   +1 more source

Artificial neural network analysis of a fractional cyber-epidemic model in wireless sensors under the proportional Hadamard-Caputo operator. [PDF]

Sci Rep
Barakat MA, Hyder AA, Aboelenen T, Iqbal N, Shafee A, Noori N.   +5 more
europepmc   +1 more source

Recent Advances of Slip Sensors for Smart Robotics

Advanced Materials Technologies, EarlyView.
This review summarizes recent progress in robotic slip sensors across mechanical, electrical, thermal, optical, magnetic, and acoustic mechanisms, offering a comprehensive reference for the selection of slip sensors in robotic applications. In addition, current challenges and emerging trends are identified to advance the development of robust, adaptive,
Xingyu Zhang, Liao Wu, Shuai He, Zhao Sha, Yuwan Huang, Shuying Wu, Dewei Chu, Chun H. Wang, Shuhua Peng   +8 more
wiley   +1 more source

Application of Artificial Neural Network and the Monomolecular Model in Describing the Relationship Between Body Weight Gain and Metabolizable Energy Intake in Egg-Type Pullets. [PDF]

Vet Med Sci
Gharajeh ZM, Kuhi HD, Hossein-Zadeh NG.
europepmc   +1 more source

Smart Closed‐Loop Systems in Personalized Healthcare: Advances and Outlook

Advanced Materials Technologies, EarlyView.
A smart closed‐loop e‐textile integrates multimodal sensing, onboard processing, wireless communication, and wearable power to enable real‐time physiological/biochemical monitoring and feedback‐controlled therapy. ABSTRACT Smart textiles represent a revolutionary frontier in healthcare, seamlessly blending fabric and advanced technologies to create ...
Safoora Khosravi, Sang Won Lee, Mohammad Abrar Uddin, Sina Hassanpoor, Neha Suvindran, Katherine Le, Harun Arkaz, Harishkumar Narayana, Saeid Soltanian, Danial Khorsandi, Negar Hosseinzadeh Kouchehbaghi, Taeil Kim, Yangzhi Zhu   +12 more
wiley   +1 more source

Development and verification of a comprehensive diagnostic model for osteosarcoma using random forest and artificial neural network. [PDF]

Discov Oncol
Zou H, Feng X, Yu H, Jiang R, Sun G, Huang L, Lu J, Li B, Shi W, Zhang Y.   +9 more
europepmc   +1 more source