Results 91 to 100 of about 197,316 (278)
Biomass Native Structure Into Functional Carbon‐Based Catalysts for Fenton‐Like Reactions
This study indicates that eight biomasses with 2D flaky and 1D acicular structures influence surface O types, morphology, defects, N doping, sp2 C, and Co nanoparticles loading in three series of carbon, N‐doped carbon, and cobalt/graphitic carbon. This work identifies how these structural factors impact catalytic pathways, enhancing selective electron
Wenjie Tian +7 more
wiley +1 more source
Electroactive Metal–Organic Frameworks for Electrocatalysis
Electrocatalysis is crucial in sustainable energy conversion as it enables efficient chemical transformations. The review discusses how metal–organic frameworks can revolutionize this field by offering tailorable structures and active site tunability, enabling efficient and selective electrocatalytic processes.
Irena Senkovska +7 more
wiley +1 more source
Boosting approach to brine viscosity estimation: Binary system development
Accurate prediction of brine viscosity is essential for the design and optimisation of desalination, hydrometallurgical, and energy-storage systems. In this work, machine-learning-based regression models were developed to predict the viscosity of binary ...
Vinita Sangwan +3 more
doaj +1 more source
Soft Gradient Boosting Machine
Gradient Boosting Machine has proven to be one successful function approximator and has been widely used in a variety of areas. However, since the training procedure of each base learner has to take the sequential order, it is infeasible to parallelize the training process among base learners for speed-up.
Feng, Ji +3 more
openaire +2 more sources
We introduce a nucleic acid nanoparticle (NANP) platform designed to be rrecognized by the human innate immune system in a regulated manner. By changing chemical composition while maintaining constant architectural parameters, we identify key determinants of immunorecognition enabling the rational design of NANPs with tunable immune activation profiles
Martin Panigaj +21 more
wiley +1 more source
Tiivistelmä. Tässä tutkielmassa käsitellään Gradient boosting algoritmia. Algoritmi käsittelee ohjatun oppimisen menetelmin läpi dataa ja pyrkii tekemään tämän avulla luotettavia ennusteita. Tutkielmassa käydään läpi yleisesti ohjattua oppimista ja päätöspuita sekä verrataan mahdollisia muita samankaltaisia menetelmiä.
openaire +1 more source
Rumboost: Gradient Boosted Random Utility Models
This paper introduces the RUMBoost model, a novel discrete choice modelling approach that combines the interpretability and behavioural robustness of Random Utility Models (RUMs) with the generalisation and predictive ability of deep learning methods.
Nicolas Salvadé, Tim Hillel
openaire +2 more sources
Accelerating Gradient Boosting Machine
Gradient Boosting Machine (GBM) is an extremely powerful supervised learning algorithm that is widely used in practice. GBM routinely features as a leading algorithm in machine learning competitions such as Kaggle and the KDDCup. In this work, we propose Accelerated Gradient Boosting Machine (AGBM) by incorporating Nesterov's acceleration techniques ...
Lu, Haihao +3 more
openaire +2 more sources
Gradient Boosted Normalizing Flows
Appearing in the 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver ...
Giaquinto, Robert, Banerjee, Arindam
openaire +2 more sources
Peptide Sequencing With Single Acid Resolution Using a Sub‐Nanometer Diameter Pore
To sequence a single molecule of Aβ1−42–sodium dodecyl sulfate (SDS), the aggregate is forced through a sub‐nanopore 0.4 nm in diameter spanning a 4.0 nm thick membrane. The figure is a visual molecular dynamics (VMD) snapshot depicting the translocation of Aβ1−42–SDS through the pore; only the peptide, the SDS, the Na+ (yellow/green) and Cl− (cyan ...
Apurba Paul +8 more
wiley +1 more source

