Results 91 to 100 of about 921,622 (228)

High‐Performance Transparent, Deformable, and Recoverable Biomimetic Stevia–PVA Hydrogel Triboelectric Nanogenerator with Machine Learning‐Assisted Motion Recognition

Advanced Materials, EarlyView.
A transparent, deformable stevia–PVA hydrogel triboelectric nanogenerator delivers significantly enhanced mechanical strength and electrical output through biomimetic hydrogen‐bonded networks. Coupled with machine learning–assisted signal recognition, the self‐powered hydrogel enables accurate human‐motion sensing for intelligent wearable and IoT ...
Thien Trung Luu, Bui Minh Quang, Toan Minh Pham, Jinsoo Kim, Kyungwho Choi, Dukhyun Choi   +5 more
wiley   +1 more source

A hybrid machine learning approach for detecting DDoS attacks in software-defined networks. [PDF]

Sci Rep
Mahar IA, Aziz K, Chakrabarti P, Ahmed N, Ladan M, Javed Y.   +5 more
europepmc   +1 more source

Understanding Operando Water Management in Hydroxide‐Exchange‐Membrane Fuel Cells

Advanced Materials Interfaces, EarlyView.
Effective water management is vital for high‐performance hydroxide‐exchange‐membrane fuel cells. Using a custom water‐flux station, this study quantifies how membrane thickness, microporous layers, and operating conditions dictate internal water transport.
Catherine M. Weiss, Shiyi Wang, Brian P. Setzler, Yushan Yan, Adam Z. Weber   +4 more
wiley   +1 more source

Comparing regular expression and machine learning approaches to predict immigrant status from primary care electronic medical record data in Ontario, Canada. [PDF]

PLOS Digit Health
Garies S, Meaney C, Weyman K, Bloch G, Gronsbell J, Vahidi-Williams N, Wang R, Crampton N, Tu K, Pinto AD.   +9 more
europepmc   +1 more source

Hydrogel‐Based 3D‐Printable Stretchable Pressure Sensor

Advanced Materials Technologies, EarlyView.
We present a carbon‐black‐functionalized double‐network granular hydrogel (DNGH) pressure sensor capable of detecting pressures from 200 Pa, equivalent to a light finger touch, up to 500 kPa. The sensor exhibits signal drifts below 3.5% after 800 cycles and response times around 80 ms. Leveraging this broad sensing range, we 3D print this material into
Tianyu Yuan, Chenzhuo Li, Antonia Georgopoulou, John M. Kolinski, Esther Amstad   +4 more
wiley   +1 more source

Machine learning improves SNP microarray performance in challenged samples. [PDF]

Bioinform Adv
Chiao A, Crysup B, King JL, Coble MD, Woerner AE.   +4 more
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

Using Machine Learning to Analyze the Predictors of Life Satisfaction: Focus on Lifestyle Attitudes and Psychological Factors. [PDF]

Int J Methods Psychiatr Res
Alptekin FB, Torlak E, Asik Ö, Karaaslan B, Turgal E, Burhan HS, Aytac HM, Guclu O.   +7 more
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

Machine learning for evolutionary genetics and molecular evolution. [PDF]

Trends Genet
Svetec N, Lee U, Zhao L.
europepmc   +1 more source