An estimated 10.5% of the world’s population aged 20–79 years are currently living with diabetes in 2021. An urgent task is to develop a non-invasive express-diagnostics of diabetes with high accuracy. Type 1 diabetes mellitus (T1DM) diagnostic method based on infrared laser spectroscopy of human exhaled breath is described. A quantum cascade laser emitting in a pulsed mode with a peak power of up to 150 mW in the spectral range of 5.3–12.8 μm and Herriot multipass gas cell with an optical path length of 76 m were used. We propose a method for collecting and drying an exhaled human air sample and have measured 1200 infrared exhaled breath spectra from 60 healthy volunteers (the control group) and 60 volunteers with confirmed T1DM (the target group). A 1-D convolutional neural network for the classification of healthy and T1DM volunteers with an accuracy of 99.7%, recall 99.6% and AUC score 99.9% was used. The demonstrated results require clarification on a larger dataset and series of clinical studies and, further, the method can be implemented in routine medical practice.

The main problems of remote sensing of the Earth’s surface within the frequency range 1.2–1.6 GHz are discussed. They are related to the resonant quantum properties of the radio wave propagation medium in the lower ionosphere. It is shown that, for the passive remote sensing, the main source is incoherent microwave radiation of the D and E ionospheric layers in the decimeter range. For the first time, a theoretically grounded principally new scheme of measurements is suggested. The scheme assumes that the radiation source exists below the satellite orbit and accounts for the fact that two types of radiation (direct and reflected) reach the satellite sensor. The separation of the respective fluxes is a serious problem that should be solved for the correct interpretation of the measurements. The question is raised regarding the correct calibration of measuring equipment, depending on the current state of the ionosphere.

Greenhouse gases absorb the Earth’s thermal radiation and partially return it to the Earth’s surface. When accumulated in the atmosphere, greenhouse gases lead to an increase in the average global air temperature and, as a result, climate change. In this paper, an approach to measuring CO${}_2$ and CH${}_4$ concentrations using Fourier transform infrared spectroscopy (FTIR) is proposed. An FTIR spectrometer mockup, operating in the wavelength range from 1.0 to 1.7 $\mathsf{\mu }$m with a spectral resolution of 10 cm${}^{-1}$, is described. The results of CO${}_2$ and CH${}_4$ observations throughout a day in urban conditions are presented. A low-resolution FTIR spectrometer for the 16U CubeSat spacecraft is described. The FTIR spectrometer has a 2.0–2.4 $\mathsf{\mu }$m spectral range for CO${}_2$ and CH${}_4$ bands, a 0.75–0.80 $\mathsf{\mu }$m range for reference O${}_2$ bands, an input field of view of 10${}^{-2}$ rad and a spectral resolution of 2 cm${}^{-1}$. The capabilities of the 16U CubeSat spacecraft for remote sensing of greenhouse gas emissions using a developed FTIR spectrometer are discussed. The design of a 16U CubeSat spacecraft equipped with a compact, low-resolution FTIR spectrometer is presented.

In this work, we consider the models of cosmological inflation based on generalized scalar–tensor theories of gravity with quadratic connection between the Hubble parameter and coupling function. For such a class of the models, we discuss the correspondence between well-known versions of the scalar–tensor gravity theories and physically motivated potentials of a scalar field. It is shown that this class of models corresponds to the Planck observational constraints on the cosmological perturbation parameters for an arbitrary potential of a scalar field and arbitrary version of a scalar–tensor gravity theory. The spectrum of relict gravitational waves is analyzed, and the frequency range corresponding to maximal energy density is determined. The possibility of direct detection of the relic gravitational waves, predicted in such a class of models, by satellite and ground-based detectors is discussed as well.

In this paper, the application of machine learning and deep learning in the spectral analysis of multicomponent gas mixtures is considered. The experimental setup consists of a quantum cascade laser with a tuning range of 5.3–12.8 µm, a peak power of up to 150 mW, and an astigmatic Herriott gas cell with an optical path length of up to 76 m. Acetone, ethanol, methanol, and their mixtures are used as test substances. For the detection and clustering of substances, including molecular biomarkers, methods of machine learning, such as stochastic embedding of neighbors with a t-distribution, principal component analysis and classification methods, such as random forest, gradient boosting, and logistic regression, are proposed. A shallow convolutional neural network based on TensorFlow (Google) and Keras is used for the spectral analysis of gas mixtures. Model spectra of substances are used as a training sample, and model and experimental spectra are used as a test sample. It is shown that neural networks trained on model spectra (NIST database) can recognize substances in experimental gas mixtures. We propose using machine learning methods for clustering and classification of pure substances and gas mixtures and neural networks for the identification of gas mixture components. Using the experimental setup described, the experimentally obtained concentration limits are 80 ppb for acetone and 100–120 ppb for ethanol and methanol. The possibility of using the proposed methods for analyzing spectra of human exhaled air is shown, which is significant for biomedical applications.

We consider the exact inflationary solutions for a single scalar field with an arbitrary potential and an arbitrary nonminimal coupling to the Gauss-Bonnet term in the flat Friedmann-Robertson-Walker universe on the basis of connection with standard inflation. The possibility of the registration of relic gravitational waves by using the phenomenon of a low-frequency optical resonance in Fabri-Perot interferometers are also received.