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Machine learning-based analysis and prediction of meteorological factors and urban heatstroke diseases.

Frontiers in public health
INTRODUCTION: Heatstroke is a serious clinical condition caused by exposure to high temperature and high humidity environment, which leads to a rapid increase of the core temperature of the body to more than 40°C, accompanied by skin burning, conscio...

Numerical investigation of two-dimensional fuzzy fractional heat problem with an external source variable.

PloS one
This study suggests a strategy for calculating the fuzzy analytical solutions to a two-dimensional fuzzy fractional-order heat problem including a diffusion variable connected externally. We propose Sawi residual power series scheme (SRPSS) which is ...

Artificial Intelligence-Powered Construction of a Microbial Optimal Growth Temperature Database and Its Impact on Enzyme Optimal Temperature Prediction.

The journal of physical chemistry. B
Accurate prediction of enzyme optimal temperature (Topt) is crucial for identifying enzymes suitable for catalytic functions under extreme bioprocessing conditions. The optimal growth temperature (OGT) of microorganisms serves as a key indicator for ...

Leveraging data science and machine learning for urban climate adaptation in two major African cities: a HEAT Center study protocol.

BMJ open
INTRODUCTION: African cities, particularly Abidjan and Johannesburg, face challenges of rapid urban growth, informality and strained health services, compounded by increasing temperatures due to climate change. This study aims to understand the compl...

Design of Co-Cured Multi-Component Thermosets with Enhanced Heat Resistance, Toughness, and Processability via a Machine Learning Approach.

Macromolecular rapid communications
Designing heat-resistant thermosets with excellent comprehensive performance has been a long-standing challenge. Co-curing of various high-performance thermosets is an effective strategy, however, the traditional trial-and-error experiments have long...

Deep Learning Models for Health-Driven Forecasting of Indoor Temperatures in Heat Waves in Canada: An Exploratory Study Using Smart Thermostats.

Studies in health technology and informatics
In Canada, extreme heat occurrences present significant risks to public health, particularly for vulnerable groups like older individuals and those with pre-existing health conditions. Accurately predicting indoor temperatures during these events is ...

Investigation of heat-induced pork batter quality detection and change mechanisms using Raman spectroscopy coupled with deep learning algorithms.

Food chemistry
Pork batter quality significantly affects its product. Herein, this study explored the use of Raman spectroscopy combined with deep learning algorithms for rapidly detecting pork batter quality and revealing the mechanisms of quality changes during h...

Exploring molecular mechanisms underlying changes in lipid fingerprinting of salmon (Salmo salar) during air frying integrating machine learning-guided REIMS and lipidomics analysis.

Food chemistry
Lipid oxidation in air-fried seafood poses a risk to human health. However, the effect of a prooxidant environment on lipid oxidation in seafood at different air frying (AF) temperatures remains unknown. An integrated machine learning (ML) - guided R...

Achieving sustainability in heat drying processing: Leveraging artificial intelligence to maintain food quality and minimize carbon footprint.

Comprehensive reviews in food science and food safety
The food industry is a significant contributor to carbon emissions, impacting carbon footprint (CF), specifically during the heat drying process. Conventional heat drying processes need high energy and diminish the nutritional value and sensory quali...