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Temperature

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DLTKcat: deep learning-based prediction of temperature-dependent enzyme turnover rates.

Briefings in bioinformatics
The enzyme turnover rate, ${k}_{cat}$, quantifies enzyme kinetics by indicating the maximum efficiency of enzyme catalysis. Despite its importance, ${k}_{cat}$ values remain scarce in databases for most organisms, primarily because of the cost of exp...

Three-dimensional force detection and decoupling of a fiber grating sensor for a humanoid prosthetic hand.

Optics express
A fiber Bragg grating (FBG) based three-dimensional (3D) force sensor for a humanoid prosthetic hand is designed, which can precisely detect 3D force and compensate for ambient temperature. FBG was encapsulated in polydimethylsiloxane (PDMS) for forc...

Machine learning approach for the estimation of missing precipitation data: a case study of South Korea.

Water science and technology : a journal of the International Association on Water Pollution Research
Precipitation is one of the driving forces in water cycles, and it is vital for understanding the water cycle, such as surface runoff, soil moisture, and evapotranspiration. However, missing precipitation data at the observatory becomes an obstacle t...

Exhaustive state-specific dissociation study of the N2(Σg+1)+N(S4) system using QCT combined with a neural network method.

The Journal of chemical physics
This work studies the exhaustive rovibrational state-specific collision-induced dissociation properties of the N2+N system by QCT (quasi-classical trajectory) combined with a neural network method based on the ab initio PES recently published by Varg...

Predicting Dynamic Heterogeneity in Glass-Forming Liquids by Physics-Inspired Machine Learning.

Physical review letters
We introduce GlassMLP, a machine learning framework using physics-inspired structural input to predict the long-time dynamics in deeply supercooled liquids. We apply this deep neural network to atomistic models in 2D and 3D. Its performance is better...

Application of Supervised and Unsupervised Learning Approaches for Mapping Storage Conditions of Biopharmaceutical Product-A Case Study of Human Serum Albumin.

Journal of chromatographic science
The stability of biopharmaceutical therapeutics over the storage period/shelf life has been a challenging concern for manufacturers. A noble strategy for mapping best and suitable storage conditions for recombinant human serum albumin (rHSA) in labor...

A review of enzyme design in catalytic stability by artificial intelligence.

Briefings in bioinformatics
The design of enzyme catalytic stability is of great significance in medicine and industry. However, traditional methods are time-consuming and costly. Hence, a growing number of complementary computational tools have been developed, e.g. ESMFold, Al...

Analysis of the influence of surgical robot drilling parameters on the temperature of skull drilling based on Box-Behnken design.

Science progress
It is easy to cause thermal damage to the bone tissue when the surgical robot performs skull drilling to remove bone flaps, due to the large diameter of the drill bit, the large heat-generating area, and the long drilling time. Therefore, in order to...

Perspective on optimal strategies of building cluster expansion models for configurationally disordered materials.

The Journal of chemical physics
Cluster expansion (CE) provides a general framework for first-principles-based theoretical modeling of multicomponent materials with configurational disorder, which has achieved remarkable success in the theoretical study of a variety of material pro...

Facilitating ab initio configurational sampling of multicomponent solids using an on-lattice neural network model and active learning.

The Journal of chemical physics
We propose a scheme for ab initio configurational sampling in multicomponent crystalline solids using Behler-Parinello type neural network potentials (NNPs) in an unconventional way: the NNPs are trained to predict the energies of relaxed structures ...