Results 131 to 140 of about 93,522 (265)

Integrating Artificial Intelligence With Droplet‐Based Microfluidics: Advances, Challenges, and Emerging Opportunities

Advanced Intelligent Systems, EarlyView.
Droplet‐based microfluidics enables precise, high‐throughput microscale reactions but continues to face challenges in scalability, reproducibility, and data complexity. This review examines how artificial intelligence enhances droplet generation, detection, sorting, and adaptive control and discusses emerging opportunities for clinical and industrial ...
Junyan Lai, Sukrit Gaira, Rongfeng Wang, Qingtian Zhang, Ming Li, Liao Wu, Diangeng Li, Yi Li, Chengchen Zhang, Jinhong Guo, Shi‐Yang Tang   +10 more
wiley   +1 more source

ConvCGP: A convolutional neural network to predict genetic values of agronomic traits from compressed genome-wide polymorphisms. [PDF]

Plant Genome
Raihan T, Kim CH, Shimono H, Kimura A, Iwata H.   +4 more
europepmc   +1 more source

Bayesian Optimisation for the Experimental Sciences: A Practical Guide to Data‐Efficient Optimisation of Laboratory Workflows

Advanced Intelligent Systems, EarlyView.
This study provides an introduction to Bayesian optimisation targeted for experimentalists. It explains core concepts, surrogate modelling, and acquisition strategies, and addresses common real‐world challenges such as noise, constraints, mixed variables, scalability, and automation.
Chuan He, Martin Singull, T. Jesper Jacobsson   +2 more
wiley   +1 more source

TinyAct: A framework for real-time action recognition in the cloud through distillation learning. [PDF]

PLoS One
Wanna Y, Wiratchawa K, Intharah T.
europepmc   +1 more source

Artificial Intelligence for Multiscale Modeling in Solid‐State Physics and Chemistry: A Comprehensive Review

Advanced Intelligent Systems, EarlyView.
This review explores the transformative impact of artificial intelligence on multiscale modeling in materials research. It highlights advancements such as machine learning force fields and graph neural networks, which enhance predictive capabilities while reducing computational costs in various applications.
Artem Maevskiy, Vitalii Kapitan, Andrey Ustyuzhanin   +2 more
wiley   +1 more source

Rise of the Machine: Detecting Aberrant Response Patterns in Survey Instruments Using Autoencoder. [PDF]

Appl Psychol Meas
Ding C.
europepmc   +1 more source

SCP‐Pose: Leveraging Structural Consistency Prior Knowledge for Real‐Time Category‐Level 6D Pose Estimation

Advanced Intelligent Systems, EarlyView.
This paper presents a high‐speed object pose estimation method that deconstructs objects into geometric components. Inspired by human cognitive generalization, it detects these primitives and infers the 6D pose from their stable spatial configuration.
Xuyang Li, Mingxuan Yu, Xuemei Xie, Zhaozhuo An, Qiyue Li, Xiaodan Song, Guangming Shi   +6 more
wiley   +1 more source

Reinforcing smart grid resilience through blockchain-supported deep learning models for theft detection. [PDF]

Sci Rep
Bibi F, Rehman SU, Bibi S, Aziz K, Alshammari A, Karovič V.   +5 more
europepmc   +1 more source

Fibroblast Transcriptomics in Molecular Diagnostics of a Comprehensive Dystonia Cohort

Annals of Neurology, EarlyView.
Objective Genomic sequencing leaves >50% of dystonia‐affected individuals without a diagnosis. Where DNA‐oriented approaches remain insufficient, integrating multiomics is essential to advance genome interpretation. Herein, we incorporated RNA sequencing (RNA‐seq) data from 167 patients with dystonia across a range of ages and presentations. Methods We
Alice Saparov, Ivana Dzinovic, Theresa Brunet, Vicente A. Yépez, Florian Hölzlwimmer, Elisabetta Indelicato, Birgit Assmann, Susann Badmann, Diana Ballhausen, Steffen Berweck, Felix Brechtmann, Melanie Brugger, Kevork Derderian, Felix Distelmaier, Philip Harrer, Denisa Harvanova, Petra Havrankova, Ann‐Kathrin Jaroszynski, Miriam Kolnikova, Robert Kopajtich, Anne Koy, Magdalena Krygier, Lukas Kunc, Katarina Kusikova, Oliver Maier, Maria Mazurkiewicz‐Bełdzińska, Christian Mertes, Ava Oberlack, Timo Roser, Alexandra Sitzberger, Ugo Sorrentino, Antonia M. Stehr, Katharina Vill, Matias Wagner, Holger Prokisch, Sylvia Boesch, Jan Necpal, Robert Jech, Juliane Winkelmann, Elisabeth Graf, Julien Gagneur, Matej Skorvanek, Michael Zech   +42 more
wiley   +1 more source

Contrasting Global and Patient-Specific Regression Models via a Neural Network Representation. [PDF]

Biom J
Behrens M, Stolz D, Papakonstantinou E, Nolde JM, Bellerino G, Rohde A, Hess M, Binder H.   +7 more
europepmc   +1 more source