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Temperature

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Spatiotemporal characteristics of carbon emissions in Shaanxi, China, during 2012-2019: a machine learning method with multiple variables.

Environmental science and pollution research international
Global warming attributed to the emission of greenhouse gases has caused unprecedented extreme weather events, such as excessive heatwave and rainfall, posing enormous threats to human life and sustainable development. China, as the toppest CO emitte...

ABTCN: an efficient hybrid deep learning approach for atmospheric temperature prediction.

Environmental science and pollution research international
Temperature prediction is an important and significant step for monitoring global warming and the environment to save and protect human lives. The climatology parameters such as temperature, pressure, and wind speed are time-series data and are well ...

Physics-Guided Generative Adversarial Networks for Sea Subsurface Temperature Prediction.

IEEE transactions on neural networks and learning systems
Sea subsurface temperature, an essential component of aquatic wildlife, underwater dynamics, and heat transfer with the sea surface, is affected by global warming in climate change. Existing research is commonly based on either physics-based numerica...

Techno-economic optimization of a new waste-to-energy plant for electricity, cooling, and desalinated water using various biomass for emission reduction.

Chemosphere
A newly developed waste-to-energy system using a biomass combined energy system designed and taken into account for electricity generation, cooling, and freshwater production has been investigated and modeled in this project. The investigated system ...

Thermally trainable dual network hydrogels.

Nature communications
Inspired by biological systems, trainable responsive materials have received burgeoning research interests for future adaptive and intelligent material systems. However, the trainable materials to date typically cannot perform active work, and the tr...

Rapid Prediction of a Liquid Structure from a Single Molecular Configuration Using Deep Learning.

Journal of chemical information and modeling
Molecular dynamics simulation is an indispensable tool for understanding the collective behavior of atoms and molecules and the phases they form. Statistical mechanics provides accurate routes for predicting macroscopic properties as time-averages ov...

Study of the Feasibility of Decoupling Temperature and Strain from a -PA-OFDR over an SMF Using Neural Networks.

Sensors (Basel, Switzerland)
Despite several existing techniques for distributed sensing (temperature and strain) using standard Single-Mode optical Fiber (SMF), compensating or decoupling both effects is mandatory for many applications. Currently, most decoupling techniques req...

Machine and deep learning for modelling heat-health relationships.

The Science of the total environment
Extreme heat events pose a significant threat to population health that is amplified by climate change. Traditionally, statistical models have been used to model heat-health relationships, but they do not consider potential interactions between tempe...

A CMOS Temperature Sensor with a Smart Calibrated Inaccuracy of ±0.11 (3σ).

Sensors (Basel, Switzerland)
This paper presents a BJT-based smart CMOS temperature sensor. The analog front-end circuit contains a bias circuit and a bipolar core; the data conversion interface features an incremental delta-sigma analog-to-digital converter. The circuit utilize...

Recurrent neural network modeling of multivariate time series and its application in temperature forecasting.

PloS one
Temperature forecasting plays an important role in human production and operational activities. Traditional temperature forecasting mainly relies on numerical forecasting models to operate, which takes a long time and has higher requirements for the ...