Evaluating the Accuracy of Generative AI in Producing Culturally Relevant Recipe Recommendations for Health Applications

Culturally relevant meal recommendations have been shown to aid in long-term diet adherence and positive attitudes towards healthy eating. However, as interest in AI-driven food and health recommendations grows, the cultural accuracy of popular large language models (LLMs) is unknown. This study evaluates the accuracy of culturally relevant recipes generated by Google's Gemini LLM by comparing them to real recipes obtained from a culturally diverse, reputable online resource, AllRecipes.com. We applied various lexical and semantic feature extraction methods in combination with diverse classification methods to determine which pairing is most appropriate when looking at recipe data. Our findings align with previous research that simpler methods, like word frequency-based embeddings and Naïve Bayes classification, yield the highest accuracy in classifying a recipe's cultural origin. We further improved the usability of our classifier by implementing hierarchical clustering to group culturally similar cuisines and reduce classification complexity. Overall, our findings demonstrate the viability of using generative AI in culturally sensitive, health-conscious recipe recommendations and add to the larger body of work verifying generative AI output to ensure human safety

The Lunar Polar Hydrogen Mapper (LunaH-Map) Mission

The Lunar Polar Hydrogen Mapper (LunaH-Map) mission will map hydrogen enrichments within permanently shadowed regions at the lunar south pole using a miniature neutron spectrometer. While hydrogen enrichments have been identified regionally from previous orbital missions, the spatial extent of these regions are often below the resolution of the neutron instruments that have flown on lunar missions. LunaH-Map will enter into an elliptical, low altitude perseline orbit which will enable the mission to spatially isolate and constrain the hydrogen enrichments within permanently shadowed regions. LunaH-Map will use a solid iodine ion propulsion system, X-Band radio communications through the NASA Deep Space Network, star tracker, C&DH and EPS systems from Blue Canyon Technologies, solar arrays from MMA Designs, LLC, mission design and navigation by KinetX. Spacecraft systems design, integration, qualification, test and mission operations are performed by Arizona State University

LunaH-Map: Revealing Lunar Water with a New Radiation Sensor Array

A new type of neutron and gamma-ray spectrometer called the Miniature Neutron Spectrometer (Mini-NS) has been developed, assembled, qualified and delivered as part of the Lunar Polar Hydrogen Mapper (LunaH-Map) cubesat mission. The LunaH-Map spacecraft is currently manifested as a secondary payload on the Space Launch System (SLS) Artemis-1 rocket. LunaH-Map will deploy from Artemis-1 and enter a low altitude perilune elliptical orbit around the Moon. The Mini-NS will measure the lunar epithermal neutron albedo, and measurements around perilune will be used to produce maps of hydrogen enrichments and depletions across the lunar South Pole region including both within and outside of permanently shadowed regions (PSRs). The Min-NS was designed to achieve twice the epithermal neutron count rate of the Lunar Prospector Neutron Spectrometer (LP-NS). The instrument response was characterized through the collection of pre-flight neutron counting data with a Cf-252 neutron source at Arizona State University across hundreds of power cycles, as well as across the expected temperature range. The instrument spatial response was characterized at the Los Alamos National Laboratories (LANL) Neutron Free In-Air Facility. The LunaH-Map Mini-NS was designed to fit within the cubesat form-factor and uses two detectors with eight sensor heads that can be operated independently. For future missions with different science goals that can be achieved with epithermal neutron detection, the number of Mini-NS sensor heads can easily be modified without requiring a complete re-design and re-qualification

A Search for Technosignatures Around 11,680 Stars with the Green Bank Telescope at 1.15-1.73 GHz

We conducted a search for narrowband radio signals over four observing sessions in 2020-2023 with the L-band receiver (1.15-1.73 GHz) of the 100 m diameter Green Bank Telescope. We pointed the telescope in the directions of 62 TESS Objects of Interest, capturing radio emissions from a total of ~11,680 stars and planetary systems in the ~9 arcminute beam of the telescope. All detections were either automatically rejected or visually inspected and confirmed to be of anthropogenic nature. In this work, we also quantified the end-to-end efficiency of radio SETI pipelines with a signal injection and recovery analysis. The UCLA SETI pipeline recovers 94.0% of the injected signals over the usable frequency range of the receiver and 98.7% of the injections when regions of dense RFI are excluded. In another pipeline that uses incoherent sums of 51 consecutive spectra, the recovery rate is ~15 times smaller at ~6%. The pipeline efficiency affects calculations of transmitter prevalence and SETI search volume. Accordingly, we developed an improved Drake Figure of Merit and a formalism to place upper limits on transmitter prevalence that take the pipeline efficiency and transmitter duty cycle into account. Based on our observations, we can state at the 95% confidence level that fewer than 6.6% of stars within 100 pc host a transmitter that is detectable in our search (EIRP > 1e13 W). For stars within 20,000 ly, the fraction of stars with detectable transmitters (EIRP > 5e16 W) is at most 3e-4. Finally, we showed that the UCLA SETI pipeline natively detects the signals detected with AI techniques by Ma et al. (2023).Comment: 22 pages, 9 figures, submitted to AJ, revise