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A personal comfort model is an approach to thermal comfort modeling, for thermal environmental design and control, that predicts an individual's thermal comfort response, instead of the average response of a large population. We developed personal thermal comfort models using lab grade wearable in normal daily activities. We collected physiological signals (e.g., skin temperature, heart rate) of 14 subjects (6 female and 8 male adults) and environmental parameters (e.g., air temperature, relative humidity) for 2-4 weeks (at least 20 hours per day). Then we trained 14 models for each subject with different machine-learning algorithms to predict their thermal preference. The results show that the median prediction power could be up to 24% /78% /0.79 (Cohen's kappa/accuracy/AUC) with all features considered. The median prediction power reaches 21% /71% /0.7 after 200 subjective votes. We explored the importance of different features on the prediction performance by considering all subjects in one dataset. When all features included for the entire dataset, personal comfort models can generate the highest performance of 35% /76% /0.80 by the most predictive algorithm. Personal comfort models display the highest prediction power when occupants' thermal sensations is outside thermal neutrality. Skin temperature measured at the ankle is more predictive than measured at the wrist. We suggest that Cohen's kappa or AUC should be employed to assess the performance of personal thermal comfort models for imbalanced datasets due to the capacity to exclude random success.

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Principal components present in fruits are low molecular weight sugars and some organic acids. They have low glass transition temperature (T g ) and are very hygroscopic in their amorphous state, so the dry product becomes sticky. Water acts as a plasticizer and decreases the glass transition temperature of the product with the increase in moisture content and water activity. To overcome this problem, ingredients having high T g value, such as maltodextrin, and food grade anti-caking agents were added to prepare vacuum dried fruit powders. The relationship between T g and a w provides a simple method for prediction of safe storage temperature at different relative humidities environment. Food powders namely, mango, pineapple, and tomato (3-4% w.b moisture content) were produced by mixing with maltodextrin and tri calcium phosphate at predetermined levels before drying. The relationship among glass transition temperature (T g ), sticky point temperature (T s ), moisture content and water activity of the three powders was represented in a stability/mobility diagram to find out safe storage conditions. Glass transition temperature of the fruit powders were interpreted in terms of the Gordon-Taylor model for verification. Glass transition and sticky point temperatures were compared by plotting them in a graph against moisture content.