Unveiling predictive factors for household-level stunting in India: A machine learning approach using NFHS-5 and satellite-driven data.

Objectives: Childhood stunting remains a significant public health issue in India, affecting approximately 35% of children under 5. Despite extensive research, existing prediction models often fail to incorporate diverse data sources and address the complex interplay of socioeconomic, demographic, and environmental factors. This study bridges this gap by employing machine learning methods to predict stunting at the household level, using data from the National Family Health Survey combined with satellite-driven datasets.

Monitoring defects in plates using topological acoustic sensing and sideband peak counting.

We demonstrate an integrated non-destructive inspection methodology that employs the nonlinear ultrasonics-based sideband peak counting (SPC) technique in conjunction with topological acoustics (TA) sensing to comprehensively characterize the acoustic response of steel plates that contain differing levels of damage. By combining the SPC technique and TA, increased sensitivity to defect/damage detection as well as the ability to spatially resolve the presence of defects was successfully established. Towards this end, using a Rockwell hardness indenter, steel plates were subject to one, three and five centrally located indentations respectively.

Development of a smartphone integrated 3D-printed point of care platform for sensitive detection of bilirubin.

Strategic design and development of nanomaterials-based detection platforms specific to critical biomarkers like bilirubin holds immense promise for revolutionizing early disease detection. Bilirubin (BR) plays a pivotal role as a biomarker for liver function, making accurate and timely detection of BR crucial for diagnosing and monitoring of liver diseases. In this work, we synthesized blue light emitting graphene quantum dots (GQDs) via a single step pyrolysis method, which exhibited excellent photostability and biocompatibility.

A signal energy approach of acoustic source localization in plate structures using a discrete sensor array.

In the field of engineering structural health monitoring, acoustic source localization (ASL) is a common method to monitor early damage. Most of the existing ASL techniques have high requirements for accurate acquisition of time of arrival, and require complex iterative algorithms or signal processing techniques, which are not conducive to real-time monitoring. In this paper, a signal energy approach of acoustic source localization in plate structures using a discrete sensor array is proposed.

Online detection and evaluation of early fatigue cracks using sideband peak intensity technique with frequency-mismatched excitation pulse-echo method.

This work presents a nonlinear ultrasonic (NLU) technique called sideband peak intensity (SPI) combining an improved pulse-echo (PE) experimental method for online detection and evaluation of fatigue cracks at their early stages. Advantages of the proposed technique are that it enjoys the high sensitivity and ease of application of NLU SPI technique and easy implementation of the PE experimental method. The PE experimental method is improved by adopting frequency-mismatched excitations to enhance the sensitivity and robustness of the SPI technique.