Abstract: A method may include providing a query and context associated with the query to a generative artificial intelligence (Gen AI) model, the Gen AI model trained to generate a response to the query based on the context. The method may further include performing analysis of the Gen AI model based on a first relevancy between the query and the context, a second relevancy between the query and the response, and a third relevancy between the response and the context and refining the response based on the analysis.

Abstract: A method may include providing a query and context associated with the query to a generative artificial intelligence model, in which the generative artificial intelligence model may be trained to generate a response to the query based on the context. The method may further include obtaining one or more policies, in which at least one of the one or more policies are specific to the user. An analysis of the response may be performed based on the one or more policies. Based on the analysis, alignment issues in the response may be identified. The response may be refined to improve the alignment issues.

Abstract: A method may include providing a query and context associated with the query to a generative artificial intelligence model, in which the generative artificial intelligence model may be trained to generate a response to the query based on the context. The method may further include obtaining one or more policies, in which at least one of the one or more policies are specific to the user. An analysis of the response may be performed based on the one or more policies. Based on the analysis, alignment issues in the response may be identified. The response may be refined to improve the alignment issues.

Abstract: According to an aspect of an embodiment, a method may include obtaining a blueprint associated with a retrieval-augmented generation (RAG) pipeline. The blueprint may define one or more objectives associated with the RAG pipeline. The method may further include performing an analysis of the RAG pipeline with respect to the one or more objectives. In some embodiments, one or more hyperparameters of the RAG pipeline may be adjusted based at least on the analysis.

Abstract: A method may include providing a query and context associated with the query to a generative artificial intelligence (Gen AI) model, the Gen AI model trained to generate a response to the query based on the context. The method may further include performing analysis of the Gen AI model based on a first relevancy between the query and the context, a second relevancy between the query and the response, and a third relevancy between the response and the context and refining the response based on the analysis.

Abstract: Methods and systems for collecting and analyzing sensor data to predict water fixture failure and water consumption are provided. In one embodiment, a method is provided that includes receiving sensor data regarding a water fixture. Changepoints may then be calculated within the sensor data and the sensor data may be split into intervals at the changepoints. A machine learning model may then be used to classify the intervals and a status of the water fixture and water consumption may be identified based on the classified intervals.

Abstract: In-situ fluorimeters and methods and systems for collecting and analyzing sensor data to predict water source contamination are provided. In one embodiment, a method is provided that includes receiving sensor data regarding a water source. Changepoints may then be calculated within the sensor data and the sensor data may be split into intervals at the changepoints. A machine learning model may then be used to classify the intervals and a predicted contamination event for the water source may be identified based on the classified intervals. In another embodiment, an in-situ fluorimeter is provided. The in-situ fluorimeter comprises one or more UV LEDs centered around a pre-set excitation wavelength (e.g., a TLF excitation wavelength), a bandpass filter, a lens, a photodiode system, a machine learning platform; and an alarm triggered by contamination events, wherein the alarm is calibrated through the machine learning system.

Abstract: In-situ fluorimeters and methods and systems for collecting and analyzing sensor data to predict water source contamination are provided. In one embodiment, a method is provided that includes receiving sensor data regarding a water source. Changepoints may then be calculated within the sensor data and the sensor data may be split into intervals at the changepoints. A machine learning model may then be used to classify the intervals and a predicted contamination event for the water source may be identified based on the classified intervals. In another embodiment, an in-situ fluorimeter is provided. The in-situ fluorimeter comprises one or more UV LEDs centered around a pre-set excitation wavelength (e.g., a TLF excitation wavelength), a bandpass filter, a lens, a photodiode system, a machine learning platform; and an alarm triggered by contamination events, wherein the alarm is calibrated through the machine learning system.

Abstract: Methods and systems for collecting and analyzing sensor data to predict water fixture failure and water consumption are provided. In one embodiment, a method is provided that includes receiving sensor data regarding a water fixture. Changepoints may then be calculated within the sensor data and the sensor data may be split into intervals at the changepoints. A machine learning model may then be used to classify the intervals and a status of the water fixture and water consumption may be identified based on the classified intervals.

Abstract: Microcomputer with a sleep mode system uses a non-volatile memory stack for saving system variables, then switches the microcomputer completely off to go into sleep-mode. In sleep-mode, the microcomputer sleep mode system uses a low powered clock and sensors to trigger time events and device events that power up the microcomputer when needed. When in awake-mode, the microcomputer logs any data from connected device(s). The microcomputer turns on an internet radio and transmits data logged, updates its clock time and receives any configuration updates before powering back down into sleep-mode.

Abstract: Green emitting phosphors have the empirical composition RE1?wAwMxEy, where RE may be one or more Rare Earth elements (for example, Eu or Gd), A may be one or more elements selected from the group Mg, Ca, Sr, or Ba, M may be one or more elements selected from the group Al, Ga, B, In, Sc, Lu or Y, E may be one or more elements selected from the group S, Se, O, or Te, w is greater than or equal to zero, or greater than or equal to 0.01, or greater than or equal to 0.05, and less than or equal to about 0.8, 2?x?4, and 4?y?7.

Abstract: A gaming event is executed on electronic gaming media. A wager is accepted by a processor and a random selection of both virtual symbols and moves is provided to a player by a display screen on a wagering device. The virtual symbols are randomly arranged on a grid having columns and rows. The player inputs commands to the processor to switch individual pairs of virtual symbols, one pair switched in each available move, until moves are exhausted. A final arrangement of virtual symbols on the grid is evaluated according to paylines and paytables to resolve the wager.

Abstract: Phosphors emitting green light over a narrow wavelength range may be used to broaden the gamut of display devices. In one aspect, a light emitting device comprises a light emitting solid state device emitting blue or violet light, a first phosphor that absorbs blue or violet light emitted by the light emitting solid state device and in response emits green light in a spectral band having a peak at wavelength ?G and a height of one half its peak on a long wavelength edge of the band at wavelength ?Ghalf; and a second phosphor that absorbs blue or violet light emitted by the light emitting solid state device and in response emits red light in a spectral band having a peak at wavelength ?R and a height of one half its peak on a short wavelength edge of the band at wavelength ?Rhalf. The ratio (?Rhalf??Ghalf)/(?R??G) is greater than 0.70.

Abstract: In one aspect, a phosphor converted white light LED comprising a narrow green phosphor rather than a conventional broad green phosphor may simultaneously exhibit high R9, and high Luminance Efficacy of Radiation, optionally without use of a deep red phosphor to maintain desired red color rendering. In another aspect, a phosphor converted white light LED comprising a narrow green phosphor rather than a conventional broad green phosphor may provide an emission spectrum exhibiting a significant dip in the yellow region of the spectrum and thereby provide high red-green contrast without use of a filter. The yellow dip may be shallower than in conventional devices, and the device may therefore be brighter, while maintaining desired CRI and R9.

Abstract: A luminescent composition of matter is characterized by the formula REM2+xEy, where RE may be one or more Rare Earth elements (for example, Eu or Gd), M may be one or more elements selected from the group Al, Ga, B, In, Sc, Lu, and Y; E is one or more elements selected from the group S, Se, O, and Te; x is greater than zero; and y has the value that achieves charge balance in the formula assuming that E has a charge of ?2.

Abstract: Microcomputer with a sleep mode system uses a non-volatile memory stack for saving system variables, then switches the microcomputer completely off to go into sleep-mode. In sleep-mode, the micro-computer sleep mode system uses a low powered clock and sensors to trigger time events and device events that power up the microcomputer when needed. When in awake-mode, the microcomputer logs any data from connected device(s). The micro-computer turns on an internet radio and transmits data logged, updates its clock time and receives any configuration updates before powering back down into sleep-mode.

Abstract: A gaming event is executed on electronic gaming media. A wager is accepted by a processor and a random selection of both virtual symbols and moves is provided to a player by a display screen on a wagering device. The virtual symbols are randomly arranged on a grid having columns and rows. The player inputs commands to the processor to switch individual pairs of virtual symbols, one pair switched in each available move, until moves are exhausted. A final arrangement of virtual symbols on the grid is evaluated according to paylines and paytables to resolve the wager.