Results 171 to 180 of about 1,208,864 (273)

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

Machine Learning and SHapley Additive exPlanation-Based Interpretation for Predicting Mastitis in Dairy Cows. [PDF]

Animals (Basel)
Zhou X, Qu Y, Xu C, Wang H, Lang D, Jia B, Jiang N.   +6 more
europepmc   +1 more source

Unraveling the Mg Loss Mechanism and Degradation Kinetics in Thermoelectric n‐Type Mg2Si‐Mg2Sn Solid Solutions

Advanced Functional Materials, EarlyView.
Mg‐based thermoelectrics are among the most promising candidates for power generation applications but their performance is compromised by Mg loss at device operation temperatures due to the higher chemical potential of Mg (μMg${\mu}_{\mathrm{Mg}}$) inside the material compared to the environment.
Aryan Sankhla, Eckhard Mueller, Johannes de Boor   +2 more
wiley   +1 more source

Generating synthetic CEM from low-energy images using deep learning: A future without contrast media? A proof-of-concept study. [PDF]

Eur Radiol Exp
Zormpas-Petridis K, Kalantar R, Iaccarino L, Mancino M, Franceschini G, Clauser P, Longo V, Sala E, Belli P, D'Angelo A.   +9 more
europepmc   +1 more source

Spectrally Tunable 2D Material‐Based Infrared Photodetectors for Intelligent Optoelectronics

Advanced Functional Materials, EarlyView.
Intelligent optoelectronics through spectral engineering of 2D material‐based infrared photodetectors. Abstract The evolution of intelligent optoelectronic systems is driven by artificial intelligence (AI). However, their practical realization hinges on the ability to dynamically capture and process optical signals across a broad infrared (IR) spectrum.
Junheon Ha, Yingshan Ma, Yong Nam An, Sung‐Un An, Hyeon Hak Jung, Suvi‐Tuuli Varjamo, Jiwon Yoo, Junho Min, Hanvit Kim, Faisal Ahmed, Sang Hoon Chae, Young Min Song, Weiwei Cai, Tawfique Hasan, Zhipei Sun, Dong‐Ho Kang, Hyeon‐Jin Shin, Yunyun Dai, Hoon Hahn Yoon   +18 more
wiley   +1 more source

Spatial Cognition in the Field: A New Approach Using the Smartphone's Compass Sensors and Navigation Apps. [PDF]

J Intell
Stieger S, Volsa S, Lewetz D, Willinger D.   +3 more
europepmc   +1 more source

Smarter Sensors Through Machine Learning: Historical Insights and Emerging Trends across Sensor Technologies

Advanced Functional Materials, EarlyView.
This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
Kichul Lee, Osman Gul, Yeongjae Kwon, Jaeseok Jeong, Seokjoo Cho, Jihyeon Ahn, Jehee Yu, Cheolmin Kim, Donho Lee, Hyeonseok Han, Byeongju Lee, Jungrak Choi, Ji‐Hwan Ha, Yongrok Jeong, Kyungnam Kang, Ali Javey, Junseong Ahn, Inkyu Park   +17 more
wiley   +1 more source

Extraction of robust functional connectivity patterns across psychiatric disorders using principal component analysis-based feature selection. [PDF]

Imaging Neurosci (Camb)
Yamashita A, Itahashi T, Sakai Y, Takamura M, Togo H, Yoshihara Y, Okada T, Yamagata H, Harada K, Takagishi H, Hosomi K, Okada N, Abe O, Okada G, Okamoto Y, Hashimoto R, Hanakawa T, Murai T, Matsuo K, Takahashi H, Kasai K, Hayashi T, Koike S, Tanaka SC, Kawato M, Imamizu H, Yamashita O.   +26 more
europepmc   +1 more source

From Waste to Value: Conversion of Calcium Sulfate to Vaterite via Carbon Capture and Storage

Advanced Functional Materials, EarlyView.
This study introduces a new concept for carbon management that relies on the carbonation of industrial gypsum waste and yields phase‐pure vaterite at ambient conditions without any additives. The obtained vaterite is further shown to be a reactive material that develops compressive strength in aqueous suspensions like conventional cements.
Carlos Pimentel, German Montes‐Hernández, Pablo Almarza‐Cifuentes, Matthias Kellermeier, Alexander E.S. Van Driessche   +4 more
wiley   +1 more source

Predictors of one-year adverse outcomes after laparoscopic resection for hepatocellular carcinoma: Development and validation of an early-warning model. [PDF]

World J Gastroenterol
Feng W, Ye QW, Wang QL, Chen SY, Ma Y, Meng FL.   +5 more
europepmc   +1 more source