In a generation method according to one embodiment, a master curve of a relationship between an extent of reaction and a heat treatment condition is generated using a thermal analysis result of a member. Also, in the generation method, first data that is related to a first heat treatment condition of a relationship between time and temperature is generated using the first heat treatment condition and the master curve. Also, in the generation method, a second heat treatment condition of a relationship between time and temperature is calculated using the master curve and a target condition for heat treatment.

In a generation method according to one embodiment, a master curve of a relationship between an extent of reaction and a heat treatment condition is generated using a thermal analysis result of a member. Also, in the generation method, first data that is related to a first heat treatment condition of a relationship between time and temperature is generated using the first heat treatment condition and the master curve. Also, in the generation method, a second heat treatment condition of a relationship between time and temperature is calculated using the master curve and a target condition for heat treatment.

A system for calorimetry includes a well having a volume for receiving a sample, an input feature to facilitate reception of the sample in the well, a light source to irradiate the well and the sample with incident light, and a photonic sensor chip disposed at the bottom of the well. The photonic sensor chip includes plural nanohole array (NHA) sensors. A light detector is configured to measure transmission of light through the NHA sensors to obtain a series of extraordinary optical transmission (EOT) measurements. Frame elements secure and mutually couple the light source, the photonic sensor chip, the light detector, and the input feature to form a calorimetry unit. A processor is configured to calculate a calorimetry measurement as a function of the series of EOT measurements, the calorimetry measurement being indicative of energy released as a result of the sample in the well undergoing a change.

A system for calorimetry includes a well having a volume for receiving a sample, an input feature to facilitate reception of the sample in the well, a light source to irradiate the well and the sample with incident light, and a photonic sensor chip disposed at the bottom of the well. The photonic sensor chip includes plural nanohole array (NHA) sensors. A light detector is configured to measure transmission of light through the NHA sensors to obtain a series of extraordinary optical transmission (EOT) measurements. Frame elements secure and mutually couple the light source, the photonic sensor chip, the light detector, and the input feature to form a calorimetry unit. A processor is configured to calculate a calorimetry measurement as a function of the series of EOT measurements, the calorimetry measurement being indicative of energy released as a result of the sample in the well undergoing a change.

The invention discloses a method for evaluating a thermal protection duration of a fabric. A persistent thermal exposure duration for skin to reach a second-degree burn when heat storage release of a fabric is not considered and a corresponding first safe cooling duration are determined, a minimum thermal exposure duration for skin to reach a second-degree burn when heat storage release of a fabric is completely considered and a corresponding second safe cooling duration are determined, and a plurality of safe thermal exposure durations between the minimum and persistent thermal exposure durations and a plurality of third safe cooling durations are determined. The plurality of thermal exposure durations are used as x data and the plurality of cooling durations are used as y data to obtain a safe duration curve. The curve, x-axis, and y-axis further form a closed area to obtain a safe duration zone of the fabric.

The invention discloses a method for evaluating a thermal protection duration of a fabric. A persistent thermal exposure duration for skin to reach a second-degree burn when heat storage release of a fabric is not considered and a corresponding first safe cooling duration are determined, a minimum thermal exposure duration for skin to reach a second-degree burn when heat storage release of a fabric is completely considered and a corresponding second safe cooling duration are determined, and a plurality of safe thermal exposure durations between the minimum and persistent thermal exposure durations and a plurality of third safe cooling durations are determined. The plurality of thermal exposure durations are used as x data and the plurality of cooling durations are used as y data to obtain a safe duration curve. The curve, x-axis, and y-axis further form a closed area to obtain a safe duration zone of the fabric.

Provided herein is technology relating to measuring temperature and particularly, but not exclusively, to devices, methods, systems, and kits for doing measuring temperature at high resolution, e.g., in living organisms.

Provided herein is technology relating to measuring temperature and particularly, but not exclusively, to devices, methods, systems, and kits for doing measuring temperature at high resolution, e.g., in living organisms.

The present invention relates to a method for identifying fruit shelf life automatically based on thermal imaging Identifying every day the pattern of change in temperature of fruit's thermal image, we can predict the shelf life of fruit which is how many days the fruit will remain edible in an accurate manner without destructing the fruit. In this system, thermal dataset is created comprising of samples of thermal images (206) of fruit taken on every day after harvesting where fruit may be from cold storage or room temperature using thermal imaging device (204). Transfer learning a deep learning technique is applied on this thermal dataset to compute the threshold weights (210) which are then used to classify or predict the fruit shelf life by comparing these pre-trained weights with the features of fruit's thermal image extracted from convolution and poling layer (212) of deep learning model

The present invention relates to a method for identifying fruit shelf life automatically based on thermal imaging Identifying every day the pattern of change in temperature of fruit's thermal image, we can predict the shelf life of fruit which is how many days the fruit will remain edible in an accurate manner without destructing the fruit. In this system, thermal dataset is created comprising of samples of thermal images (206) of fruit taken on every day after harvesting where fruit may be from cold storage or room temperature using thermal imaging device (204). Transfer learning a deep learning technique is applied on this thermal dataset to compute the threshold weights (210) which are then used to classify or predict the fruit shelf life by comparing these pre-trained weights with the features of fruit's thermal image extracted from convolution and poling layer (212) of deep learning model