Abstract: Methods and systems for analyzing the carbon number distribution of distillation fractions may utilize a fast gas chromatograph. For example, methods may comprise: distilling a hydrocarbon feed to provide a plurality of distillation fractions using a distillation column; obtaining a draw stream from one or more of the plurality of distillation fractions; and analyzing the draw stream with a fast gas chromatograph to directly determine a carbon number distribution of the draw stream, the fast gas chromatograph having a cycle time that is less than a residence time of a specified component of the draw stream within the distillation column, and the fast gas chromatograph being in-line with or in parallel with the draw stream.

Abstract: A computer-implemented method of assessing a mental state of a subject (106) includes receiving (302), as input, a heartbeat record (200) of the subject. The heartbeat record comprises a sequence of heartbeat data samples obtained over a time span which includes a pre-sleep period (208), a sleep period (209) having a sleep onset time (224) and a sleep conclusion time (226), and a post-sleep period (210). At least the sleep onset time and the sleep conclusion time are identified (304) within the heartbeat record. A knowledge base (124) is then accessed (306), which comprises data obtained via expert evaluation of a training set of subjects and which embodies a computational model of a relationship between mental state and heart rate characteristics. Using information in the knowledge base, the computational model is applied (308) to compute at least one metric associated with the mental state of the subject, and to generate an indication of mental state based upon the metric.

Abstract: A computer-implemented method of assessing a stress condition of a subject (106) includes receiving (302), as input, a heartbeat record (200) of the subject. The heartbeat record comprises a sequence of heartbeat data samples obtained over a time span which includes a pre-sleep period (208), a sleep period (209) having a sleep onset time (224) and a sleep conclusion time (226), and a post-sleep period (210). At least the sleep onset time and the sleep conclusion time are identified (304) within the heartbeat record. A knowledge base (124) is then accessed (306), which comprises data obtained via expert evaluation of a training set of subjects and which embodies a computational model of a relationship between stress condition and heart rate characteristics. Using information in the knowledge base, the computational model is applied (308) to compute at least one metric associated with the stress condition of the subject, and to generate an indication of stress condition based upon the metric.

Abstract: A computer-implemented method of assessing a mental state of a subject (106) includes receiving (302), as input, a heartbeat record (200) of the subject. The heartbeat record comprises a sequence of heartbeat data samples obtained over a time span which includes a pre-sleep period (208), a sleep period (209) having a sleep onset time (224) and a sleep conclusion time (226), and a post-sleep period (210). At least the sleep onset time and the sleep conclusion time are identified (304) within the heartbeat record. A knowledge base (124) is then accessed (306), which comprises data obtained via expert evaluation of a training set of subjects and which embodies a computational model of a relationship between mental state and heart rate characteristics. Using information in the knowledge base, the computational model is applied (308) to compute at least one metric associated with the mental state of the subject, and to generate an indication of mental state based upon the metric.

Abstract: A computer-implemented method of assessing a mental state of a subject (106) includes receiving (302), as input, a heartbeat record (200) of the subject. The heartbeat record comprises a sequence of heartbeat data samples obtained over a time span which includes a pre-sleep period (208), a sleep period (209) having a sleep onset time (224) and a sleep conclusion time (226), and a post-sleep period (210). At least the sleep onset time and the sleep conclusion time are identified (304) within the heartbeat record. A knowledge base (124) is then accessed (306), which comprises data obtained via expert evaluation of a training set of subjects and which embodies a computational model of a relationship between mental state and heart rate characteristics. Using information in the knowledge base, the computational model is applied (308) to compute at least one metric associated with the mental state of the subject, and to generate an indication of mental state based upon the metric.

Abstract: A computer-implemented method of assessing a stress condition of a subject (106) includes receiving (302), as input, a heartbeat record (200) of the subject. The heartbeat record comprises a sequence of heartbeat data samples obtained over a time span which includes a pre-sleep period (208), a sleep period (209) having a sleep onset time (224) and a sleep conclusion time (226), and a post-sleep period (210). At least the sleep onset time and the sleep conclusion time are identified (304) within the heartbeat record. A knowledge base (124) is then accessed (306), which comprises data obtained via expert evaluation of a training set of subjects and which embodies a computational model of a relationship between stress condition and heart rate characteristics. Using information in the knowledge base, the computational model is applied (308) to compute at least one metric associated with the stress condition of the subject, and to generate an indication of stress condition based upon the metric.

Abstract: A computer-implemented method of assessing a stress condition of a subject (106) includes receiving (302), as input, a heartbeat record (200) of the subject. The heartbeat record comprises a sequence of heartbeat data samples obtained over a time span which includes a pre-sleep period (208), a sleep period (209) having a sleep onset time (224) and a sleep conclusion time (226), and a post-sleep period (210). At least the sleep onset time and the sleep conclusion time are identified (304) within the heartbeat record. A knowledge base (124) is then accessed (306), which comprises data obtained via expert evaluation of a training set of subjects and which embodies a computational model of a relationship between stress condition and heart rate characteristics. Using information in the knowledge base, the computational model is applied (308) to compute at least one metric associated with the stress condition of the subject, and to generate an indication of stress condition based upon the metric.

Abstract: A computer-implemented method of assessing a stress condition of a subject (106) includes receiving (302), as input, a heartbeat record (200) of the subject. The heartbeat record comprises a sequence of heartbeat data samples obtained over a time span which includes a pre-sleep period (208), a sleep period (209) having a sleep onset time (224) and a sleep conclusion time (226), and a post-sleep period (210). At least the sleep onset time and the sleep conclusion time are identified (304) within the heartbeat record. A knowledge base (124) is then accessed (306), which comprises data obtained via expert evaluation of a training set of subjects and which embodies a computational model of a relationship between stress condition and heart rate characteristics. Using information in the knowledge base, the computational model is applied (308) to compute at least one metric associated with the stress condition of the subject, and to generate an indication of stress condition based upon the metric.

Abstract: A computer-implemented method of assessing a mental state of a subject (106) includes receiving (302), as input, a heartbeat record (200) of the subject. The heartbeat record comprises a sequence of heartbeat data samples obtained over a time span which includes a pre-sleep period (208), a sleep period (209) having a sleep onset time (224) and a sleep conclusion time (226), and a post-sleep period (210). At least the sleep onset time and the sleep conclusion time are identified (304) within the heartbeat record. A knowledge base (124) is then accessed (306), which comprises data obtained via expert evaluation of a training set of subjects and which embodies a computational model of a relationship between mental state and heart rate characteristics. Using information in the knowledge base, the computational model is applied (308) to compute at least one metric associated with the mental state of the subject, and to generate an indication of mental state based upon the metric.

Abstract: The present invention relates to a buoyancy control system, and in particular to a buoyancy control system for controlling the buoyancy of an underwater submersible. The buoyancy control system comprises a buoyancy chamber (5) having a seawater inlet (5A) and a seawater outlet (11A), a power supply(31) used to power at least one electrical component of the system, and a hydraulic system for pumping seawater from the chamber through the outlet. The hydraulic system comprises a hydraulic pump (28) and a pressure multiplier (34), the hydraulic pump for applying pressure to the pressure multiplier, and the pressure multiplier for increasing the pressure applied thereto by the hydraulic pump, and for applying the increased pressure to seawater from the chamber to thereby pump out the seawater.

Abstract: The present invention relates to a method and device for ascertaining characteristics relating to porosity and grain size in rock sample. Four illuminators A, B, C and D direct collimated illuminating beams to a point O at the centre of a sample 40 and have projections onto the plane of the surface of the sample 40 at points A1, B1, C1, and D1. Angles <AOA1, <BOB1, <COC1, and <DOD1 have the same value of 45°. A camera (41) located directly above the point (O) acquires four images as the illuminators are individually illuminated. A processing section receives the acquired image data on line (42) and, after filtering, a common point distribution is evaluated based on the common points in the light intensity distribution data of two pairs of images with opposite illumination directions is found (A & C, B & D). At the same time, an edge enhancing distribution is evaluated based on the opposing value points in the light intensity distribution data for the opposing directions.