Abstract: In some embodiments, data from multiple vehicle-based natural gas leak detection survey runs are used by computer-implemented machine learning systems to generate a list of natural gas leaks ranked by hazard level. A risk model embodies training data having known hazard levels, and is used to classify newly-discovered leaks. Hazard levels may be expressed by continuous variables, and/or probabilities that a given leak fits within a predefined category of hazard (e.g. Grades 1-3). Each leak is represented by a cluster of leak indications (peaks) originating from a common leak source. Hazard-predictive features may include maximum, minimum, mean, and/or median CH4/amplitude of aggregated leak indications; estimated leak flow rate, determined from an average of leak indications in a cluster; likelihood of leak being natural gas based on other indicator data (e.g. ethane concentration); probability the leak was detected on a given pass; and estimated distance to leak source.

Abstract: Various implementations disclosed herein include devices, systems, and methods that detect surfaces and reflections in such surfaces. Some implementations involve providing a CGR environment that includes virtual content that replaces the appearance of a user or the user's device in a mirror or other surface providing a reflection. For example, a CGR environment may be modified to include a reflection of the user that does not include the device that the user is holding or wearing. In another example, the CGR environment is modified so that virtual content, such as a newer version of the electronic device or a virtual wand, replaces the electronic device in the reflection. In another example, the CGR environment is modified so that virtual content, such as a user avatar, replaces the user in the reflection.

Abstract: Described herein is a process for increasing mass flow of an exhaust gas through a catalytic converter system for a vehicle. The process may comprise determining a centerline and corresponding cumulative centerline bend angle of a first catalytic converter system spanning from an inlet point at a first end of the catalytic converter systems exhaust pipe to an outlet point at a second end of the catalytic converter systems extension pipe. Once determined, the cumulative centerline bend angle may be increased by increasing an individual bend radius of at least one bend within the exhaust pipe and/or within the extension pipe.

Abstract: Various implementations disclosed herein include devices, systems, and methods that detect surfaces and reflections in such surfaces. Some implementations involve providing a CGR environment that includes virtual content that replaces the appearance of a user or the user's device in a mirror or other surface providing a reflection. For example, a CGR environment may be modified to include a reflection of the user that does not include the device that the user is holding or wearing. In another example, the CGR environment is modified so that virtual content, such as a newer version of the electronic device or a virtual wand, replaces the electronic device in the reflection. In another example, the CGR environment is modified so that virtual content, such as a user avatar, replaces the user in the reflection.

Abstract: Described herein is a process for increasing mass flow of an exhaust gas through a catalytic converter system for a vehicle. The process may comprise determining a centerline and corresponding cumulative centerline bend angle of a first catalytic converter system spanning from an inlet point at a first end of the catalytic converter systems exhaust pipe to an outlet point at a second end of the catalytic converter systems extension pipe. Once determined, the cumulative centerline bend angle may be increased by increasing an individual bend radius of at least one bend within the exhaust pipe and/or within the extension pipe.

Abstract: Various implementations disclosed herein include devices, systems, and methods that detect surfaces and reflections in such surfaces. Some implementations involve providing a CGR environment that includes virtual content that replaces the appearance of a user or the user's device in a mirror or other surface providing a reflection. For example, a CGR environment may be modified to include a reflection of the user that does not include the device that the user is holding or wearing. In another example, the CGR environment is modified so that virtual content, such as a newer version of the electronic device or a virtual wand, replaces the electronic device in the reflection. In another example, the CGR environment is modified so that virtual content, such as a user avatar, replaces the user in the reflection.

Abstract: Described herein is a process for increasing mass flow of an exhaust gas through a catalytic converter system for a vehicle. The process may comprise determining a centerline and corresponding cumulative centerline bend angle of a first catalytic converter system spanning from an inlet point at a first end of the catalytic converter systems exhaust pipe to an outlet point at a second end of the catalytic converter systems extension pipe. Once determined, the cumulative centerline bend angle may be increased by increasing an individual bend radius of at least one bend within the exhaust pipe and/or within the extension pipe.

Abstract: Described herein is a process for increasing mass flow of an exhaust gas through a catalytic converter system for a vehicle. The process may comprise determining a centerline and corresponding cumulative centerline bend angle of a first catalytic converter system spanning from an inlet point at a first end of the catalytic converter systems exhaust pipe to an outlet point at a second end of the catalytic converter systems extension pipe. Once determined, the cumulative centerline bend angle may be increased by increasing an individual bend radius of at least one bend within the exhaust pipe and/or within the extension pipe.

Abstract: Various implementations disclosed herein include devices, systems, and methods that detect surfaces and reflections in such surfaces. Some implementations involve providing a CGR environment that includes virtual content that replaces the appearance of a user or the user's device in a mirror or other surface providing a reflection. For example, a CGR environment may be modified to include a reflection of the user that does not include the device that the user is holding or wearing. In another example, the CGR environment is modified so that virtual content, such as a newer version of the electronic device or a virtual wand, replaces the electronic device in the reflection. In another example, the CGR environment is modified so that virtual content, such as a user avatar, replaces the user in the reflection.

Abstract: Described herein is a process for increasing mass flow of an exhaust gas through a catalytic converter system for a vehicle. The process may comprise determining a centerline and corresponding cumulative centerline bend angle of a first catalytic converter system spanning from an inlet point at a first end of the catalytic converter systems exhaust pipe to an outlet point at a second end of the catalytic converter systems extension pipe. Once determined, the cumulative centerline bend angle may be increased by increasing an individual bend radius of at least one bend within the exhaust pipe and/or within the extension pipe.

Abstract: A method of building a machine learning pipeline for predicting the efficacy of anti-epilepsy drug treatment regimens is provided.

Abstract: Various implementations disclosed herein include devices, systems, and methods that detect surfaces and reflections in such surfaces. Some implementations involve providing a CGR environment that includes virtual content that replaces the appearance of a user or the user's device in a mirror or other surface providing a reflection. For example, a CGR environment may be modified to include a reflection of the user that does not include the device that the user is holding or wearing. In another example, the CGR environment is modified so that virtual content, such as a newer version of the electronic device or a virtual wand, replaces the electronic device in the reflection. In another example, the CGR environment is modified so that virtual content, such as a user avatar, replaces the user in the reflection.

Abstract: A water treatment system is provided, including a raw water tank connected to a water supply, a reverse osmosis unit arranged to produce purified water from water input from the raw water tank via a raw water supply line, at least one water treatment facility alongside the raw water supply line downstream of the raw water tank and upstream of the reverse osmosis unit, and a reuse water feedback line arranged to feed waste water and/or grey water collected from the reverse osmosis unit and/or the at least one water treatment facility back to the raw water tank for reuse. In a water treatment method in such a water treatment system, waste water and/or grey water collected from the reverse osmosis unit and/or the at least one water treatment facility is fed back to the raw water tank for reuse.

Abstract: Systems and methods are provided for manipulating objects in a framework software application that embeds another software application that does not natively support object manipulation controls of the framework software application. To overcome this difficulty, a user interface of the embedded software application is provided in an embedded window disposed within a framework window. Moreover, the user interface of the framework software application is provided in the framework window. Next, a transparent interface element, configured to detect events generated by the object manipulation controls of the framework software application, is generated, and is positioned over the embedded window.

Abstract: Systems and methods are provided for manipulating objects in a framework software application that embeds another software application that does not natively support object manipulation controls of the framework software application. To overcome this difficulty, a user interface of the embedded software application is provided in an embedded window disposed within a framework window. Moreover, the user interface of the framework software application is provided in the framework window. Next, a transparent interface element, configured to detect events generated by the object manipulation controls of the framework software application, is generated, and is positioned over the embedded window.

Abstract: A water treatment system is provided, including a raw water tank connected to a water supply, a reverse osmosis unit arranged to produce purified water from water input from the raw water tank via a raw water supply line, at least one water treatment facility alongside the raw water supply line downstream of the raw water tank and upstream of the reverse osmosis unit, and a reuse water feedback line arranged to feed waste water and/or grey water collected from the reverse osmosis unit and/or the at least one water treatment facility back to the raw water tank for reuse. In a water treatment method in such a water treatment system, waste water and/or grey water collected from the reverse osmosis unit and/or the at least one water treatment facility is fed back to the raw water tank for reuse.

Abstract: A method of building a machine learning pipeline for predicting the efficacy of anti-epilepsy drug treatment regimens is provided.

Abstract: A method of building a machine learning pipeline for predicting refractoriness of epilepsy patients is provided. The method includes providing electronic health records data; constructing a patient cohort from the electronic health records data by selecting patients based on failure of at least one anti-epilepsy drug; constructing a set features found in or derived from the electronic health records data; electronically processing the patient cohort to identify a subset of the features that are predictive for refractoriness for inclusion in a predictive model configured for classifying patients as refractory or non-refractory; and training the predictive computerized model to classify the patients having at least one anti-epilepsy drug failure based on likelihood of becoming refractory.