Results 231 to 240 of about 1,086,925 (328)

From Droplet to Diagnosis: Spatio‐Temporal Pattern Recognition in Drying Biofluids

Advanced Intelligent Systems, EarlyView.
This article integrates machine learning (ML) with the spatio‐temporal evolution of biofluid droplets to reveal how drying and self‐assembly encode distinctive compositional fingerprints. By leveraging textural features and interpretable ML, it achieves robust classification of blood abnormalities with over 95% accuracy.
Anusuya Pal, Amalesh Gope, Miho Yanagisawa   +2 more
wiley   +1 more source

Evaluation of Hazel-Derived Particleboard as a Substitute for Conventional Wood-Based Composites. [PDF]

Materials (Basel)
Wronka M, Wojnicz D, Wronka A, Kowaluk G.   +3 more
europepmc   +1 more source

SmartDetectAI: An AI‐Powered Web App for Real‐Time Colorimetric Detection of Heavy Metals in Water

Advanced Intelligent Systems, EarlyView.
SmartDetectAI integrates silver nanoparticle‐based colorimetric sensing with an AI‐powered web app for rapid, on‐site detection of toxic heavy metals in water. By combining aggregation‐driven optical changes with machine learning analysis of red ‐ green ‐ blue values, the platform achieves portable, low‐cost, and accurate monitoring of Hg‐ and Cd‐based
Nishat Tasnim, Mirza M. A. Tunur, Fahad Arefin, Shahad Saroar, Md. Wahadoszamen, Taslim Ur Rashid, Aminul I. Talukder, Md. Nurul Amin, Mahmudur Rahman, Ahsan Habib   +9 more
wiley   +1 more source

From Agricultural Residues to Sustainable Boards: Complex Network Analysis of Binderless Composites. [PDF]

Polymers (Basel)
Rossi L, Miccio LA, Ciannamea EM, Stefani PM.   +3 more
europepmc   +1 more source

A Review of Trans‐Dimensional Kirigami: From Compliant Mechanism to Multifunctional Robot

Advanced Intelligent Systems, EarlyView.
This review outlines recent advancements in the geometric design and mechanical properties of kirigami. The kirigami is classified into two categories from a compliant mechanism perspective, highlighting their applications in metamaterials and robotic systems. Finally, the future research directions, is explored focusing on the potential of integrating
Yang Yu, Jinyao Zhang, Dengchen Wang, Yanqi Yin, Yehui Wu, Ruiyu Bai, Jiaqiang Yao, Yupei Zhang, Jingwen Yin, Chao Tang, Alexey S. Fomin, Wenjie Sun, Chen Liu, Bo Li, Guimin Chen   +14 more
wiley   +1 more source

Controlled inoculation provides insight into western redcedar resistance to multiple root- and butt-rot pathogens. [PDF]

Front Plant Sci
Liu JJ, Houston S, Cruickshank M, Zamany A, Leal I, Filipescu C.   +5 more
europepmc   +1 more source

A Generalizable Transformer Framework for Gene Regulatory Network Inference from Single‐Cell Transcriptomes

Advanced Intelligent Systems, EarlyView.
FTGRN introduces an LLM‐enhanced framework for gene regulatory network inference through a two‐stage workflow. It combines a Transformer‐based model, pretrained on GPT‐4 derived gene embeddings and regulatory knowledge, with a fine‐tuning stage utilizing single‐cell RNA‐seq data.
Guangzheng Weng, Hyobin Kim, Patrick Martin, Junil Kim, Tae‐Hyung Kim, Gi‐Hoon Nam, Dongha Kim, Kyoung Jae Won   +7 more
wiley   +1 more source

Mechanical Performance of Plywood TIE Joints Under Tension and Shear in the WikiHouse Skylark Modular System. [PDF]

Materials (Basel)
Sandoval M, Cárdenas-Ramírez JP, Soto-Zúñiga P, Arnett M, Oñate A, Leiva J, Cancino R, Tuninetti V.   +7 more
europepmc   +1 more source

Myoelectric Origami‐Based Soft Robotic Knee Exoskeleton to Enhance Sit‐to‐Stand Assistance in Elderly Populations

Advanced Intelligent Systems, EarlyView.
This work presents a lightweight Miura‐origami soft knee exoskeleton powered by vacuum actuation and integrated with a multimodal physiological intent‐recognition system, providing real‐time assistance during sit‐to‐stand movement to reduce muscle effort and improve user comfort.
Yuchuan Jia, Xingjian Ye, Xuanyi Zhou, Yong Ling, Daniela Rus, Chen‐Hua Yeow   +5 more
wiley   +1 more source

Deep Learning Methods for Assessing Time‐Variant Nonlinear Signatures in Clutter Echoes

Advanced Intelligent Systems, EarlyView.
Motion classification from biosonar echoes in clutter presents a fundamental challenge: extracting structured information from stochastic interference. Deep learning successfully discriminates object speed and direction from bat‐inspired signals, achieving 97% accuracy with frequency‐modulated calls but only 48% with constant‐frequency tones. This work
Ibrahim Eshera, Gaurav Duggal, Rolf Müller   +2 more
wiley   +1 more source