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Temperature

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Light-Induced Topological Patterning toward 3D Shape-Reconfigurable Origami.

Small (Weinheim an der Bergstrasse, Germany)
Shape-reconfigurable materials are crucial in many engineering applications. However, because of their isotropic deformability, they often require complex molding equipment for shaping. A polymeric origami structure that follows predetermined deforme...

Modeling Soil Temperature for Different Days Using Novel Quadruplet Loss-Guided LSTM.

Computational intelligence and neuroscience
Soil temperature ( ), a key variable in geosciences study, has generated growing interest among researchers. There are many factors affecting the spatiotemporal variation of , which poses immense challenges for the estimation. To enrich processi...

Rapid Temperature-Dependent Rheological Measurements of Non-Newtonian Solutions Using a Machine-Learning Aided Microfluidic Rheometer.

Analytical chemistry
Biofluids such as synovial fluid, blood plasma, and saliva contain several proteins which impart non-Newtonian properties to the biofluids. The concentration of such protein macromolecules in biofluids is regarded as an important biomarker for the di...

Attention-Based Deep Recurrent Neural Network to Forecast the Temperature Behavior of an Electric Arc Furnace Side-Wall.

Sensors (Basel, Switzerland)
Structural health monitoring (SHM) in an electric arc furnace is performed in several ways. It depends on the kind of element or variable to monitor. For instance, the lining of these furnaces is made of refractory materials that can be worn out over...

Investigation of Three-Dimensional Condensation Film Problem over an Inclined Rotating Disk Using a Nonlinear Autoregressive Exogenous Model.

Computational intelligence and neuroscience
This paper analyzed the three-dimensional (3D) condensation film problem over an inclined rotating disk. The mathematical model of the problem is governed by nonlinear partial differential equations (NPDE's), which are reduced to the system of nonlin...

Deep learning-based optimization of a microfluidic membraneless fuel cell for maximum power density via data-driven three-dimensional multiphysics simulation.

Bioresource technology
A deep learning-based method for optimizing a membraneless microfluidic fuel cell (MMFC)performance by combining the artificial neural network (ANN) and genetic algorithm (GA) was for the first time introduced. A three-dimensional multiphysics model ...

Interpretable Short-Term Electrical Load Forecasting Scheme Using Cubist.

Computational intelligence and neuroscience
Daily peak load forecasting (DPLF) and total daily load forecasting (TDLF) are essential for optimal power system operation from one day to one week later. This study develops a Cubist-based incremental learning model to perform accurate and interpre...

Modelling the reference crop evapotranspiration in the Beas-Sutlej basin (India): an artificial neural network approach based on different combinations of meteorological data.

Environmental monitoring and assessment
Accurate prediction of the reference evapotranspiration (ET) is vital for estimating the crop water requirements precisely. In this study, we developed multi-layer perceptron artificial neural network (MLP-ANN) models considering different combinatio...

The effects of temperature on the dynamics of the biological neural network.

Journal of biological physics
The nerve cells are responsible for transmitting messages through the action potential, which generates electrical stimulation. One of the methods and tools of electrical stimulation is infrared neural stimulation (INS). Since the mechanism of INS is...

Bimodal Tactile Sensor without Signal Fusion for User-Interactive Applications.

ACS nano
Tactile sensors with multimode sensing ability are cornerstones of artificial skin for applications in humanoid robotics and smart prosthetics. However, the intuitive and interference-free reading of multiple tactile signals without involving complex...