Results 81 to 90 of about 14,298 (230)

A Review on Recent Trends of Bioinspired Soft Robotics: Actuators, Control Methods, Materials Selection, Sensors, Challenges, and Future Prospects

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This article reviews the current state of bioinspired soft robotics. The article discusses soft actuators, soft sensors, materials selection, and control methods used in bioinspired soft robotics. It also highlights the challenges and future prospects of this field.
Abhirup Sarker, Tamzid Ul Islam, Md. Robiul Islam   +2 more
wiley   +1 more source

Refining Coarse-grained Spatial Data using Auxiliary Spatial Data Sets with Various Granularities

, 2019
We propose a probabilistic model for refining coarse-grained spatial data by utilizing auxiliary spatial data sets. Existing methods require that the spatial granularities of the auxiliary data sets are the same as the desired granularity of target data.
Iwata, Tomoharu, Kurashima, Takeshi, Okawa, Maya, Tanaka, Toshiyuki, Tanaka, Yusuke, Toda, Hiroyuki   +5 more
core   +1 more source

Adaptive Macroscopic Ensemble Allocation for Robot Teams Monitoring Spatiotemporal Processes

Advanced Intelligent Systems, EarlyView.
We propose an online, environment feedback‐driven macroscopic ensemble approach to adapt robot team task allocation in spatiotemporal environments by controlling robot populations rather than assigning individual robots, all while maintaining robust team performance even for small teams. Our simulation and experimental results show better or comparable
Victoria Edwards, Thales C. Silva, M. Ani Hsieh   +2 more
wiley   +1 more source

Multimodal Chromatography in the Downstream Processing of mAb‐Based Products: Mechanisms, Strategies, and Applications

Biotechnology and Bioengineering, EarlyView.
ABSTRACT Multimodal chromatography has emerged as a powerful tool for the purification of monoclonal antibodies (mAbs) and their derivatives—including antibody fragments (Fabs), Fc‐fusions, bispecific (BsAb), and antibody–drug conjugates (ADCs)—offering enhanced selectivity through the integration of ionic, hydrophobic, hydrogen‐bonding, and π–π ...
Amin Javidanbardan, Marco P․ C․ Marques, Ana M. Azevedo, Daniel G. Bracewell, Stephen Goldrick   +4 more
wiley   +1 more source

Implementation of Machine Learning Models to Predict Functionality of Pea Flour From Its Composition

Cereal Chemistry, EarlyView.
ABSTRACT Background and Objectives The goal of this research was to examine the relationship between the composition and functionality of pea flour using the following machine learning algorithms: linear regression, partial least squares regression (PLSR), Gaussian process regression (GPR), support vector regression, gradient‐boosted decision trees ...
Colten N. Nickerson, Sarah Hogarth, Andrea K. Stone, Gene C. Arganosa, Pankaj Bhowmik, Tom D. Warkentin, Jordan Ubbens, Michael T. Nickerson   +7 more
wiley   +1 more source

Radiation Mapping: A Gaussian Multi-Kernel Weighting Method for Source Investigation in Disaster Scenarios

Sensors
Structural collapses caused by accidents or disasters could create unexpected radiation shielding, resulting in sharp gradients within the radiation field.
Songbai Zhang, Qi Liu, Jie Chen, Yujin Cao, Guoqing Wang   +4 more
doaj   +1 more source

Machine Learning Paradigm for Advanced Battery Electrolyte Development

Carbon Energy, EarlyView.
Electrolyte materials determine ion transport kinetics within the bulk and interphases, ultimately influencing the performance of battery systems. As data‐driven paradigms increasingly reshape materials discovery, this review provides an application‐oriented exploration of the intersection between machine learning and electrolyte science. By evaluating
Chang Su, Mengfan Pei, Keyu Chen, Xigao Jian, Fangyuan Hu   +4 more
wiley   +1 more source

Barrier distribution extraction via Gaussian process regression [PDF]

EPJ Web of Conferences
This work presents a novel method for extracting potential barrier distributions from experimental fusion cross sections. We utilize a simple Gaussian process regression (GPR) framework to model the observed cross sections as a function of energy for ...
Godbey Kyle
doaj   +1 more source

Electricity Price Prediction Using Multikernel Gaussian Process Regression Combined With Kernel‐Based Support Vector Regression

Journal of Forecasting, EarlyView.
ABSTRACT This paper presents a new hybrid model for predicting German electricity prices. The algorithm is based on a combination of Gaussian process regression (GPR) and support vector regression (SVR). Although GPR is a competent model for learning stochastic patterns within data and for interpolation, its performance for out‐of‐sample data is not ...
Abhinav Das, Stephan Schlüter, Lorenz Schneider   +2 more
wiley   +1 more source

Benchmarking Large Language Models for Polymer Property Predictions

Macromolecular Rapid Communications, EarlyView.
Large language models (LLMs) are fine‐tuned on polymer thermal property datasets to directly predict glass transition, melting, and decomposition temperatures from SMILES inputs. Compared to state‐of‐the‐art models such as Polymer Genome, polyGNN, and polyBERT, LLMs achieve competitive yet lower accuracy.
Sonakshi Gupta, Akhlak Mahmood, Shivank Shukla, Rampi Ramprasad   +3 more
wiley   +1 more source