Results 171 to 180 of about 29,068 (302)

A Worm‐Inspired Origami Robot with Multimodal Locomotion for Adaptive Mobility in Complex Pipeline Environments

Advanced Science, EarlyView.
An origami worm‐inspired robot achieves multimodal locomotion in confined pipelines through mechatronic integration that embeds actuation, control, and communication within each origami module. Large, reversible configuration and dimensional changes enable 25 gaits synthesized by a unified framework across peristaltic, inchworm, and wheel‐rolling modes
Qiwei Zhang, Kangning Tan, Zihan He, Hongsen Pang, Yanjie Wang, Hongbin Fang, Jian Xu   +6 more
wiley   +1 more source

Research on joint vehicle routing optimization considering multiple distribution centers. [PDF]

PLoS One
Liu D, Li M, Lei Y, Yu P, Li B, Tang S.
europepmc   +1 more source

A compromise based fuzzy goal programming approach with satisfaction function for multi-objective portfolio selection [PDF]

, 2015
He, F., Qu, R., John, R.
core  

Bionic Solar‐Powered Heavy Metal Trap for Eco‐Friendly Sludge Drying and Simultaneous Electricity Generation

Advanced Science, EarlyView.
Herein, we propose a solar‐powered heavy metal trap (SPHT) for sludge disposal. The indoor experiments demonstrated that the sludge water content decreased from 90 % to 31 %, coupled with a decrease in heavy metal content to 6 % at most. Furthermore, the designed SPHT generated an initial potential of 0.10 V, and monitoring this hydrovoltaic potential ...
Yanlin Li, Jinglan Wang, Hailin Gu, Zhen Yu, Fawei Lin, Cunku Dong, Ningning Cao, Miao Yu   +7 more
wiley   +1 more source

Fuzzy-based multi-objective scheduling for human-robot manufacturing systems. [PDF]

Sci Rep
Deng Y, Huang B, Lai S.
europepmc   +1 more source

Combining Spatial Multi‐Omics Data to Decipher Spatial Domains and Elucidate Cell Heterogeneity Based on Self‐Supervised Graph Learning

Advanced Science, EarlyView.
A self‐supervised multi‐view graph fusion framework integrates spatial multi‐omics, excelling in domain identification and denoising. It reconstructs spatial pseudo‐expression, jointly analyzes multi‐omics data, infers RNA velocity, predicts spatial omics features from single‐cell multi‐omics, and detects spatially dark genes and transcription factors,
Yuejing Lu, Rui Qiao, Ying Li, Junhong Li, Nuoya Yue, Jing Ge, Jiao Wang, Luonan Chen, Peiluan Li   +8 more
wiley   +1 more source

Teaching artificial intelligence through drug-drug interaction clustering analysis: Integrating project-based learning and large language models. [PDF]

PLoS Comput Biol
Lv J, Liu G, Zhou C.
europepmc   +1 more source

STAID: A Self‐Refining Deep Learning Framework for Spatial Cell‐Type Deconvolution with Biologically Informed Modeling

Advanced Science, EarlyView.
STAID is a unified deep learning framework that couples iterative pseudo‐spot refinement with neural network training through a feedback loop and exploits gene co‐expression information to model higher‐order interactions, achieving accurate and robust cell‐type deconvolution in spatial transcriptomics.
Jixin Liu, Shuli Sun, Zhengliang Lv, Xinyu Liu, Yihua Wang, Bingqiang Liu   +5 more
wiley   +1 more source

A multi-objective multi-period mathematical programming model for integrated project portfolio optimization and contractor selection. [PDF]

MethodsX
Zahedirad M, Khalili-Damghani K, Ghezavati V, Komijan AR.   +3 more
europepmc   +1 more source

Planning and Scheduling Staff Duties by Goal Programming [PDF]

, 2003
Yuen, CSY, Chu, SCK
core