AIMC Topic: Temperature

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Modeling risk of Sclerotinia sclerotiorum-induced disease development on canola and dry bean using machine learning algorithms.

Scientific reports
Diseases caused by the fungus Sclerotinia sclerotiorum are managed mainly through fungicide applications in canola and dry bean. Accurate estimation of the risk of disease development on these crops could help farmers make spraying decisions. Five ma...

Novel Model Based on Artificial Neural Networks to Predict Short-Term Temperature Evolution in Museum Environment.

Sensors (Basel, Switzerland)
The environmental microclimatic characteristics are often subject to fluctuations of considerable importance, which can cause irreparable damage to art works. We explored the applicability of Artificial Intelligence (AI) techniques to the Cultural He...

Temporal Prediction of Paralytic Shellfish Toxins in the Mussel Using a LSTM Neural Network Model from Environmental Data.

Toxins
Paralytic shellfish toxins (PSTs) are produced mainly by (formerly ). Since 2000, the National Institute of Fisheries Science (NIFS) has been providing information on PST outbreaks in Korean coastal waters at one- or two-week intervals. However, a d...

An Artificial Intelligence Approach Toward Food Spoilage Detection and Analysis.

Frontiers in public health
Aiming to increase the shelf life of food, researchers are moving toward new methodologies to maintain the quality of food as food grains are susceptible to spoilage due to precipitation, humidity, temperature, and a variety of other influences. As a...

Advanced Recovery and High-Sensitive Properties of Memristor-Based Gas Sensor Devices Operated at Room Temperature.

ACS sensors
Fast recovery, high sensitivity, high selectivity, and room temperature (RT) sensing characteristics of NO gas sensors are essential for environmental monitoring, artificial intelligence, and inflammatory diagnosis of asthma patients. However, the co...

Application of Artificial Neural Network Based on Traditional Detection and GC-MS in Prediction of Free Radicals in Thermal Oxidation of Vegetable Oil.

Molecules (Basel, Switzerland)
In this study, electron paramagnetic resonance (EPR) and gas chromatography-mass spectrometry (GC-MS) techniques were applied to reveal the variation of lipid free radicals and oxidized volatile products of four oils in the thermal process. The EPR r...

ColGen: An end-to-end deep learning model to predict thermal stability of de novo collagen sequences.

Journal of the mechanical behavior of biomedical materials
Collagen is the most abundant structural protein in humans, with dozens of sequence variants accounting for over 30% of the protein in an animal body. The fibrillar and hierarchical arrangements of collagen are critical in providing mechanical proper...

Enhanced precision of real-time control photothermal therapy using cost-effective infrared sensor array and artificial neural network.

Computers in biology and medicine
Photothermal therapy (PTT) requires tight thermal dose control to achieve tumor ablation with minimal thermal injury on surrounding healthy tissues. In this study, we proposed a real-time closed-loop system for monitoring and controlling the temperat...

Data-driven method based on deep learning algorithm for detecting fat, oil, and grease (FOG) of sewer networks in urban commercial areas.

Water research
The content of fat, oil and grease (FOG) in the sewer network sediments is the key indicator for diagnosing sewer blockage and overflow. However, the traditional FOG detection is time-consuming and costly, and the establishment of mathematical models...

Random Forest Predictor for Diblock Copolymer Phase Behavior.

ACS macro letters
Physics-based models are the primary approach for modeling the phase behavior of block copolymers. However, the successful use of self-consistent field theory (SCFT) for designing new materials relies on the correct chemistry- and temperature-depende...