Abstract: A system and method for dynamic pricing of short-term stay accommodations, using machine learning algorithms to set pricing based on a broad set of non-homogenous dynamic data that may affect pricing at the time of booking, including pricing of competitors, market forecasts of pricing, internal factors including room quality, customer behavior related to booking, environmental factors, events, and economic factors.

Abstract: In compliance with the method, the measurement of the characteristic dimensions of the hypothetical pupil are taken on the basis of a sequence of images. The eye is stimulated with the light featuring a pre-defined intensity profile. For each image in this sequence, the characteristic dimensions of the hypothetical pupil are calculated by means of image processing methods. For a sequence of images, the system determines the function ƒ which defines the changes in the characteristic dimensions of the hypothetical pupil within the measurement period, and on the basis of the said changes as well as on the selected mathematical model, the aliveness parameters O of the eye are determined by means of estimation methods. The calculated aliveness parameters are compared with the statistical template by way of classification process.

Abstract: In compliance with the method, the measurement of the characteristic dimensions of the hypothetical pupil are taken on the basis of a sequence of images. The eye is stimulated with the light featuring a pre-defined intensity profile. For each image in this sequence, the characteristic dimensions of the hypothetical pupil are calculated by means of image processing methods. For a sequence of images, the system determines the function ƒ which defines the changes in the characteristic dimensions of the hypothetical pupil within the measurement period, and on the basis of the said changes as well as on the selected mathematical model, the aliveness parameters O of the eye are determined by means of estimation methods. The calculated aliveness parameters are compared with the statistical template by way of classification process.

Abstract: Living cells can be used to identify or quantify bioactive conditions, including without limitation, chemicals, biological pathogens, and environmental conditions, such as pH, in samples based on changes in, for example, cell color, morphology and/or physiology. Such changes can be directly detected or detected with the aid of instrumentation. One embodiment of the method comprises exposing a system to a bioactive condition, such as a chemical agent, a biological pathogen, an environmental condition, such as pH, etc., and combinations of such conditions. The system then exhibits a response to the bioactive condition. The response of the system, or a portion thereof, to the bioactive condition is then represented, such as by digital images. The method then involves attempting to classify a scenario by database comparison. Classification can be in terms of numeric or non-numerical classifiers. Typically, the system comprises living cells.