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Abstract Hyperspectral imaging has broad applications and impacts in areas including environmental science, weather, and geo/space exploration. The intrinsic spectral–spatial structures and potential multi-level features in different frequency bands make multilayer graph an intuitive model for hyperspectral images (HSI). To study the underlying characteristics of HSI and to take the advantage of graph signal processing (GSP) tools, this work proposes a multilayer graph spectral analysis for hyperspectral images based on multilayer graph signal processing (M-GSP). More specifically, we present multilayer graph (MLG) models and tensor representations for HSI. By exploring multilayer graph spectral space, we develop MLG-based methods for HSI applications, including unsupervised segmentation and supervised classification. Our experimental results demonstrate the strength of M-GSP in HSI processing and spectral–spatial information extraction.

Human papillomavirus (HPV) is one of the most common sexually transmitted infections (STIs). However, despite widespread under-vaccination amongst men and the importance of vaccinating both sexes to curb the spread of HPV, research has focused on promoting HPV vaccination predominantly amongst women. Therefore, the current study examines the effectiveness of different informational interventions in promoting vaccination intentions amongst heterosexual men. In a preregistered study of 583 unvaccinated adult men, we randomly assigned participants to one of four informational interventions aimed at promoting awareness of HPV risks and vaccine uptake: (1) risks to oneself (n = 145), (2) risks to their female partner (n = 144), (3) risks to oneself and their female partner (n = 153), and (4) general vaccine information (n = 153). Amongst participants reporting a sexual history (67%), intentions to get vaccinated significantly increased by 10.75 points on a 100-point scale (p < 0.01) after they received information about the risks of HPV for both themselves and their female partner, compared to receiving information about only their own HPV risk. These findings provide valuable guidance for public health officials and policymakers into the effectiveness of different messaging strategies in promoting HPV vaccination amongst adult male populations to increase vaccination rates.

For being the world’s largest low voltage direct current (LVDC) microgrid (MG) in space, the power generation and distribution systems aboard the International Space Station (ISS) employ a hierarchical assortment of electric power sources, energy storage, control devices, power electronics, and loads operating cooperatively at multifarious system dispositions and multi-stage configurations. At the early phase of design, for such time-critical systems, the trade-off between reliability and convergence rate of device modeling, varying accuracy requirements of control flows, and especially the implementation for real-time performance have brought new challenges and problems for testing and validation of the MG. One of the solutions presented by this paper is to use the hardware-in-the-loop (HIL) emulation, where the MG is emulated using the field-programmable gate array (FPGA) hardware platform. In parallel with the emulation effort, comprehensive modeling solutions for both large-scale photovoltaic (PV) solar array wings (SAWs) and nonlinear behavior model (NBM) of insulated-gate bipolar transistors (IGBTs) have been utilized based on machine learning (ML) concepts of artificial neural network (ANN) and recurrent neural network (RNN). Both system-level (validated by Matlab/Simulink) and device-level (validated by SaberRD) transient simulations are carried out, and the results exhibit high accuracy and fidelity of the models and significant improvements in execution speed and hardware resource consumption.

How ROS levels are regulated in cancer stem cells and their contribution to cancer resistance is currently not clear. Here, the authors show that prohibitin regulates mitochondrial ROS production stabilizing the peroxidase PRDX3 and this accounts for radiotherapy resistance in glioma stem-like cells.

A systematic comparison of fiber-based gas pressure sensors using different types of hollow-core photonic crystal fibers (PCFs) is conducted. The sensor was fabricated by splicing a segment of hollow-core silica capillary between a single mode fiber (SMF) and a hollow-core PCF. A Fabry-Perot (FP) cavity configuration is thus formed with the capillary tube as the sensing cavity, while the PCF acts as gas passage to the external environment. External pressure change would lead to the variation in the refractive index of air in the sensing cavity, resulting in a wavelength shift of the interference dips. The pressure sensitivity is measured to be around 4 nm/MPa, with a high linearity of 99.7% or above, regardless of the type of the PCFs we used as gas inlet. Among the four different PCFs, the large-mode-area (LMA) PCF shows the highest fringe contrast in the reflection spectrum. Modal analysis reveals that this is due to the high reflectivity caused by the solid core of LMA-PCF. Our experimental results also indicate that the length of the sensing cavity, as well as the offset fusion splice will influence the fringe contrast. The sensor has potential application in gas pressure sensing for advantages of high sensitivity, compact size and ease of fabrication.