Artificial Intelligence (AI) chatbots powered by a large language model (LLM) are entering young children's learning and play, yet little is known about how young children construe these agents or how such construals relate to engagement. We examined anthropomorphism of a social AI chatbot during collaborative storytelling and asked how children's attributions related to their behavior and prefrontal activation. Children at ages 5-6 (N = 23) completed three storytelling sessions: interacting with (1) an AI chatbot only, (2) a parent only, and (3) the AI and a parent together. After the sessions, children completed an interview assessing anthropomorphism toward both the AI chatbot and the parent. Behavioral engagement was indexed by the conversational turn count (CTC) ratio, and concurrent fNIRS measured oxygenated hemoglobin in bilateral vmPFC and dmPFC regions. Children reported higher anthropomorphism for parents than for the AI chatbot overall, although AI ratings were relatively high for perceptive abilities and epistemic states. Anthropomorphism was not associated with CTC. In the right dmPFC, higher perceptive scores were associated with greater activation during the AI-only condition and with lower activation during the AI+Parent condition. Exploratory analyses indicated that higher dmPFC activation during the AI-only condition correlated with higher end-of-session "scared" mood ratings. Findings suggest that stronger perceptive anthropomorphism can be associated with greater brain activation related to interpreting the AI's mental states, whereas parent co-presence may help some children interpret and regulate novel AI interactions. These results may have design implications for encouraging parent-AI co-use in early childhood.

Photon scattering has traditionally limited the ability of near-infrared spectroscopy (NIRS) to extract accurate, layer-specific information from the brain. This limitation restricts its clinical utility for precise neurological monitoring. To address this, we introduce an AI-driven, high-density NIRS system optimized to provide real-time, layer-specific oxygenation data from the brain cortex, specifically targeting acute neuro-emergencies. Our system integrates high-density NIRS reflectance data with a neural network trained on MRI-based synthetic datasets. This approach achieves robust cortical oxygenation accuracy across diverse anatomical variations. In simulations, our AI-assisted NIRS demonstrated a strong correlation (R2=0.913) with actual cortical oxygenation, markedly outperforming conventional methods (R2=0.469). Furthermore, biomimetic phantom experiments confirmed its superior anatomical reliability (R2=0.986) compared to standard commercial devices (R2=0.823). In clinical validation with healthy subjects and ischemic stroke patients, the system distinguished between the two groups with an AUC of 0.943. This highlights its potential as an accessible, high-accuracy diagnostic tool for emergency and point-of-care settings. These results underscore the system's capability to advance neuro-monitoring precision through AI, enabling timely, data-driven decisions in critical care environments.

Soil near-Infrared (NIR) spectral absorbance/reflectance libraries are utilized towards improving agricultural production and analysis of soil properties which are key prerequisite for agro-ecological balance and environmental sustainability. Carbonates in particular, represent a soil property which is mostly affected even by mild, let alone extreme, changes of environmental conditions during climate change. In this study we propose a rapid and efficient way to predict carbonates content in soil by means of Fourier Transform Near-Infrared (FT-NIR) reflectance spectroscopy and by use of deep learning methods. We exploited multiple machine learning methods, such as: 1) a Multi-Layered Perceptron Regressor (MLP) and 2) a Convolutional Neural Network (CNN) and compare their performance with other traditional machine learning algorithms such as Partial Least Squares Regression (PLSR), Cubist and Support Vector Machines (SVM) on the combined dataset of two NIR spectral libraries: Kellogg Soil Survey Laboratory (KSSL) of the United States Department of Agriculture (USDA), a dataset of soil samples reflectance spectra collected nationwide, and Land Use and Coverage Area Frame Survey (LUCAS) TopSoil (European Soil Library) which contains soil sample absorbance spectra from all over the European Union, and use them to predict carbonate content on never-before-seen soil samples. Soil samples in KSSL and in TopSoil spectral libraries were acquired in the spectral region of visible-near infrared (Vis-NIR) (350-2500 nm), however in this study, only the NIR spectral region (1150-2500 nm) was utilized. Quantification of carbonates by means of X-ray-Diffraction is in good agreement with the volumetric method and the MLP prediction. Our work contributes to rapid carbonates content prediction in soil samples in cases where: 1) no volumetric method is available and 2) only NIR spectra absorbance data are available. Up till now and to the best of our knowledge, there exists no other study, that presents a prediction model trained on such an extensive dataset with such promising results on unseen data, undoubtedly supporting the notion that deep learning models present excellent prediction tools for soil carbonates content.

A German research institute has launched a project using artificial intelligence (AI) to determine grape quality and ripeness that it says could also eventually be used in the battle against climate change. The Smart Grape project by the Fraunhofer Institute for Process Engineering and Packaging IVV is using infrared spectroscopy and AI to analysise grapes, and work out if they are . Infrared spectroscopy is a non-destructive optical technique which uses infrared radiation to provide information about the chemical composition of a particular sample. The institure says that this makes it an idea medium to use to work out if the grapes meet the required parameters for a great wine. It is also using mid-infrared wavelenghts (of between 2500 and 50,000 nm to characterize the quality of grapes, rather than the near-infrared wavelengths ( between 780 and 2500 nm) that are more commonly used. This is likely to provide large amounts of information, it said.

Check out their product page … link Get the Chemometrics and Spectroscopy News in real time on Twitter @ CalibModel and follow us. Near-Infrared Spectroscopy (NIRS) "Integrated soluble solid and nitrate content assessment of spinach plants using portable NIRS sensors along the supply chain" LINK "Evaluation of Near Infrared Spectroscopy (NIRS) and Remote Sensing (RS) for Estimating Pasture Quality in Mediterranean Montado Ecosystem" LINK "Evaluation of Homogeneity in Drug Seizures Using Near-Infrared (NIR) Hyperspectral Imaging and Principal Component Analysis (PCA)"LINK "FT-NIRS Coupled with PLS Regression as a Complement to HPLC Routine Analysis of Caffeine in Tea Samples" Foods LINK Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR) "Model based optimization of transflection near infrared spectroscopy as a process analytical tool in a continuous flash pasteurizer" LINK "EXPRESS: Monitoring Polyurethane Foaming Reactions Using Near-Infrared Hyperspectral Imaging" LINK ...

NIR Calibration-Model Services Develop & Optimize NIR chemometric methods for Chemical Analysis with ease LINK Do you want better NIRS prediction results? Check out their product page … link Get the Chemometrics and Spectroscopy News in real time on Twitter @ CalibModel and follow us. Near-Infrared Spectroscopy (NIRS) "Visible and Near-Infrared (VNIR) Hyperspectral Payload Electronics" LINK "A Real-Time Rapid Analysis Method for the Determination of Total Alkaloids in Fritillariae Cirrhosae Bulbus by AOTF-NIR" LINK "Feasibility of NIR spectroscopy detection of moisture content in coco-peat substrate based on the optimization characteristic variables" LINK "Location, year, and tree age impact NIR-based postharvest prediction of dry matter concentration for 58 apple accessions" LINK "FT-NIRS Coupled with PLS Regression as a Complement to HPLC Routine Analysis of Caffeine in Tea Samples." LINK "Vibrational coupling to hydration shell–Mechanism to performance enhancement of qualitative analysis in NIR spectroscopy of carbohydrates in aqueous environment" LINK "Determination of metmyoglobin in cooked tan mutton using Vis/NIR hyperspectral imaging system" LINK Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR) "Deflected Talbot mediated overtone spectroscopy in near-infrared as a label-free sensor on a chip." LINK "Discrimination of Tetrastigma hemsleyanum according to geographical origin by near-infrared spectroscopy combined with a deep learning approach."

LINK "An overview of near-infrared spectroscopy (NIRS) for the detection of insect pests in stored grains" LINK "A high-throughput quantification of resin and rubber contents in Parthenium argentatum using near-infrared (NIR) spectroscopy" LINK The latest generation of near-infrared (NIR) spectroscopy systems designed for on-line measurement of properties opens up new possibilities for measuring product properties. LINK "In situ ripening stages monitoring of Lamuyo pepper using a new generation NIRS sensor" LINK "Detection of aflatoxin B1 on corn kernel surfaces using visible-near infrared spectra" LINK " Estimation of soil phosphorus availability via visible and near-infrared spectroscopy" LINK "Multivariate Classification of Prunus Dulcis Varieties using Leaves of Nursery Plants and Near Infrared Spectroscopy." LINK "Detection of Dibutyl Phthalate (DBP) Content in Liquor Based on Near Infrared Technology" LINK "Analysis of incensole acetate in Boswellia species by near infrared ...

Near Infrared (NIR) Spectroscopy "Fourier-transform near infrared spectroscopy (FT-NIRS) rapidly and non-destructively predicts daily age and growth in otoliths of juvenile red snapper Lutjanus …" LINK "Desarrollo de Modelos NIRS de Predicción para el Análisis de la Finura de Fibras Textiles de Vicuña y Llama" LINK "fNIRS-GANs: Data augmentation using generative adversarial networks for classifying motor tasks from functional near-infrared spectroscopy."

Near Infrared (NIR) Spectroscopy "Fourier-transform near infrared spectroscopy (FT-NIRS) rapidly and non-destructively predicts daily age and growth in otoliths of juvenile red snapper Lutjanus …" LINK "Desarrollo de Modelos NIRS de Predicción para el Análisis de la Finura de Fibras Textiles de Vicuña y Llama" LINK "fNIRS-GANs: Data augmentation using generative adversarial networks for classifying motor tasks from functional near-infrared spectroscopy."

Check out their product page … link Get the Chemometrics and Spectroscopy News in real time on Twitter @ CalibModel and follow us. Near Infrared " Raman Spectroscopy and NIR Spectroscopy as Possible AID in Localisation of Solitary Pulmonary Nodules" LINK NIR spectroscopy has potential for rapid on farm analysis of slurry nutrient content. Wouter Saeys, IFSConf LINK "Modeling for SSC and Firmness Detection of Persimmon Based on NIR Hyperspectral Imaging by Sample Partitioning and Variables Selection" LINK " Application of the NIR Spectroscopy in the Researches of Orthopedics Diseases" LINK "FT-NIR による油脂の迅速な品質管理" LINK "Accuracy improvement of quantitative analysis in VIS-NIR spectroscopy using the GKF-WTEF algorithm." LINK "Rapid determination of the content of digestible energy and metabolizable energy in sorghum fed to growing pigs by near-infrared reflectance spectroscopy." LINK "Characterization of the Processing Conditions upon Textural Profile Analysis (TPA) Parameters of Processed Cheese Using Near-Infrared Hyperspectral Imaging" LINK "Total aromatics of diesel fuels analysis by deep learning and near-infrared spectroscopy" LINK "Rapid Assessment of Soil Quality Indices Using Infrared Reflectance Spectroscopy" LINK "Quantitative Determination of the Fiber Components in Textiles by Near-Infrared Spectroscopy and Extreme Learning Machine" LINK "Non-Destructive Method for Predicting Sapodilla Fruit Quality Using Near Infrared Spectroscopy" LINK "Qualitative analysis for sweetness classification of longan by near infrared hyperspectral imaging" LINK " MENGUKUR BERAT VOLUME TANAH DI LAPANGAN MENGGUNAKAN NEAR INFRARED SPECTROSCOPY MEASUREMENT OF SOIL BULK DENSITY IN …" LINK "Hyperspectral Characteristics of Coastal Saline Soil with Visible/near Infrared Spectroscopy" LINK "Monitoring Soil Surface Mineralogy at Different Moisture Conditions Using Visible Near-Infrared Spectroscopy Data" LINK "Near infrared spectroscopy for assessing mechanical properties of Castanea sativa wood samples" Modulus of elasticity LINK " Development of near-infrared spectroscopic sensing system for online real-time monitoring of milk quality during milking" LINK " Advances in Near-Infrared Spectroscopy and Related Computational Methods" LINK "Morphological, Physicochemical and FTIR Spectroscopic Properties of Bee Pollen Loads from Different Botanical Origin" LINK "Fourier transform infrared imaging and quantitative analysis of pre-treated wood fibers: A comparison between partial least squares and multivariate curve resolution with alternating least squares methods in a case study" LINK "Antioxidant Activity of Blueberry (Vaccinium spp.)