Driving behavior-guided battery health monitoring for electric vehicles using machine learning

An accurate estimation of the state of health (SOH) of batteries is critical to ensuring the safe and reliable operation of electric vehicles (EVs). Feature-based machine learning methods have exhibited enormous potential for rapidly and precisely monitoring battery health status. However, simultaneously using various health indicators (HIs) may weaken estimation performance due to feature redundancy. Furthermore, ignoring real-world driving behaviors can lead to inaccurate estimation results as some features are rarely accessible in practical scenarios. To address these issues, we proposed a feature-based machine learning pipeline for reliable battery health monitoring, enabled by evaluating the acquisition probability of features under real-world driving conditions. We first summarized and analyzed various individual HIs with mechanism-related interpretations, which provide insightful guidance on how these features relate to battery degradation modes. Moreover, all features were carefully evaluated and screened based on estimation accuracy and correlation analysis on three public battery degradation datasets. Finally, the scenario-based feature fusion and acquisition probability-based practicality evaluation method construct a useful tool for feature extraction with consideration of driving behaviors. This work highlights the importance of balancing the performance and practicality of HIs during the development of feature-based battery health monitoring algorithms

Key steps in the structure-based optimization of the hepatitis C virus NS3/4A protease inhibitor SCH503034

Crystal structures of protease/inhibitor complexes guided optimization of the buried nonpolar surface area thereby maximizing hydrophobic binding. The resulting potent tripeptide inhibitor is in clinical trials

Crystal Structure of Complete Rhinovirus RNA Polymerase Suggests Front Loading of Protein Primer

Picornaviruses utilize virally encoded RNA polymerase and a uridylylated protein primer to ensure replication of the entire viral genome. The molecular details of this mechanism are not well understood due to the lack of structural information. We report the crystal structure of human rhinovirus 16 3D RNA-dependent RNA polymerase (HRV16 3D(pol)) at a 2.4-Å resolution, representing the first complete polymerase structure from the Picornaviridae family. HRV16 3D(pol) shares the canonical features of other known polymerase structures and contains an N-terminal region that tethers the fingers and thumb subdomains, forming a completely encircled active site cavity which is accessible through a small tunnel on the backside of the molecule. The small thumb subdomain contributes to the formation of a large cleft on the front face of the polymerase which also leads to the active site. The cleft appears large enough to accommodate a template:primer duplex during RNA elongation or a protein primer during the uridylylation stage of replication initiation. Based on the structural features of HRV16 3D(po1) and the catalytic mechanism known for all polymerases, a front-loading model for uridylylation is proposed

Lattice Compressive Strain of Co₃O₄ Induced by Synthetic Solvents Promotes Efficient Oxidation of Benzene at Low Temperature

A series of Co3O4 with different surface defective structures were prepared by the solvothermal method and tested for the activity of benzene oxidation. The characterizations revealed that the synthetic solvent had a dramatic effect on the composition of Co3O4 precursors as well as the physicochemical properties of Co3O4. Although all Co3O4 exhibited a cubic spinel structure, Co3O4 prepared with triethylene glycol (Co-TEG) had the highest compressive strain due to the nature of high viscosity of triethylene glycol. These in turn affected the surface chemical structure and the low-temperature redox properties. Co-TEG exhibited the best benzene oxidation activity and showed excellent stability and good water resistance. In situ diffuse reflectance infrared Fourier transform spectroscopy was used to study the oxidation process of benzene. It was found that Co-TEG with more defective structures had abundant surface adsorbed oxygen and active lattice oxygen, which promoted the conversion of benzene and the corresponding intermediates at low temperature