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From the first principles simulation (using the method of “a priori pseudopotential” and the “quasi-harmonic approximation” method- author’s developments), the basic characteristics of diborides and diborides of multielement transition metals (DMTMs) with an AlB2 type structure were calculated. For both diborides and DMTMs, the linear coefficients of thermal expansion (LCTE) along the axial axes differ little from each other, i.e., transition metal diborides and hexagonal lattice DMTMs are quasi-isotropic. Quasi-isotropy makes it possible to estimate the LCTE using an analytical formula that depends on the melting temperature. In the absence of experimental data on the melting point of DMTMs, a method for calculating it from first principles is presented. The theoretical hardness values of transition metal diborides and DMTMs with averaged parameters were calculated from the first principles. The hardness of both bulk and nano-sized DMTMs was assessed using a hybrid method. There is agreement between the calculated and available experimental data.

To ensure sustainable development and public welfare, the complex impact of demographic factors on macroeconomic dynamics should be investigated properly. Moreover, a few of the Sustainable Development Goals are interconnected robustly with demographic issues. There are some trends (e.g., substantial increase in longevity, rapidly lowered fertility, etc.) common for most advanced and emerging market economies. We examined the impact of selected socio-demographic factors on the real GDP per capita growth rates in a sample of the Baltic states and the Central European countries that have experienced economic transformations in the 1990s and either entered or declared their intentions to join the European Union in the future. We investigated general demographic dynamics in the region from 1960 to 2021. Due to the data availability, our special attention was focused primarily on the highlighted countries’ socio-demographic and macroeconomic variables from 2000 to 2021. We suggested that there was a certain interdependence between the working age population stratum specific weight, the elderly demographic dependency ratio, public healthcare spending, gross capital formation, and the real GDP per capita growth rates. In addition, we propose the main priorities for social and demographic policy in the field of well-being improvement. The potential ways to enhance the model – regarding healthcare, education, and the general profile of fiscal policy – have been disclosed as well.

On the base of a coherent emitting straight segment, the problem of approximate description of the field of an extended source in the far illumination region is considered. It is shown that in regions close to the directions of intensity maximums the source field can be approximated by mean of sphere waves. The sphericity condition in corresponding region is found. The problem of approximation of an extended source field by means of a plane wave is also discusses. As a result of a next approximation of a spherical wave, the condition for the approximation of a field of extended source by means of a plane wave was found.

The exploitation of offshore wind resources is a crucial step towards a clean energy future. It requires an advanced approach for high-resolution wind resource evaluations. We explored the suitability of the Sentinel-1 Level-2 OCN ocean wind field (OWI) product for offshore wind resource assessments. The SAR data were compared to in situ observations and three reanalysis products: the global reanalysis ERA5 and two regional reanalyses CARRA and NORA3. This case study matches 238 scenes from 2022 for the Goliat station, an oil platform located 85 km northwest of Hammerfest in the Barents Sea, where a new offshore wind park has been proposed. The analysis showed that despite their unique limitations in spatial and temporal resolutions, all data sources have similar statistical properties (RMSE, correlation coefficient, and standard deviation). The Weibull parameters characterizing the wind speed distributions showed strong similarities between the Sentinel-1 and all reanalysis data. The Weibull parameters of the in situ measurements showed an underestimation of wind speed compared to all other sources. Comparing the full reanalysis datasets with the subsets matching the SAR scenes, only slight changes in Weibull parameters were found, indicating that, despite its low temporal resolution, the Sentinel-1 Level 2 OWI product can compete with the more commonly used reanalysis products in the estimation of offshore wind resources. Its high spatial resolution, which is unmatched by other methods, renders it especially valuable in offshore areas close to complex coastlines and in resolving weather events at a smaller scale.

Prognosis of patients with juvenile myelomonocytic leukemia not undergoing allogeneic hematopoietic cells transplantation (SCT) is dismal and not affected by any cytotoxic chemotherapy. Differentiating therapy is an attractive approach in JMML, due to ability of JMML cells to differentiate and die under influence of treatment with various agents in vitro. We report results of first-line treatment of 14 patients with JMML with 13-cis retinoic acid (RA) 100 mg/m2 daily peroral intake and low dose Ara-C - 25 mg/m2 daily for 10 days monthly. Complete remission (CR) or hematological improvement (HI) was obtained in 7 patients (50%). Four patients (median age at disease manifestation 5 months, median level of HbF —6,2%) achieved CR after a median of 9 cycles of therapy; in 3 patients the best response was HI (age at disease manifestation - 5; 7 and 46 months, HbF — 4,2; 8,0 and 51,7%) after 2; 3 and 5 cycles. Three out of 4 patients with CR are alive and have been in CR for 10; 17 and 30 months after start of therapy, acute lymphoblastic leukemia developed in one patient after 84 months of CR. Two patients with HI are still receiving treatment and have been in stable condition for 10 months after the initiating therapy, while 1 patient died after 8 months of therapy from catheter-related sepsis. The progression of the disease despite the treatment was registered in 7 patients (median age at disease manifestation 16,3 months, median HbF — 35,4%). We conclude that 13-cis RA in combination with low dose AraC may be useful in the treatment of patients younger than 1 year with low level of HbF, although the general paradigm of treatment of JMML with SCT remains unchanged.

Abstract Collecting labeled data for many important tasks in chemoinformatics is time consuming and requires expensive experiments. In recent years, machine learning has been used to learn rich representations of molecules using large scale unlabeled molecular datasets and transfer the knowledge to solve the more challenging tasks with limited datasets. Variational autoencoders are one of the tools that have been proposed to perform the transfer for both chemical property prediction and molecular generation tasks. In this work we propose a simple method to improve chemical property prediction performance of machine learning models by incorporating additional information on correlated molecular descriptors in the representations learned by variational autoencoders. We verify the method on three property prediction tasks. We explore the impact of the number of incorporated descriptors, correlation between the descriptors and the target properties, sizes of the datasets etc. Finally, we show the relation between the performance of property prediction models and the distance between property prediction dataset and the larger unlabeled dataset in the representation space.

In silico (quantitative) structure–activity relationship modeling is an approach that provides a fast and cost-effective alternative to assess the genotoxic potential of chemicals. However, one of the limiting factors for model development is the availability of consolidated experimental datasets. In the present study, we collected experimental data on micronuclei in vitro and in vivo, utilizing databases and conducting a PubMed search, aided by text mining using the BioBERT large language model. Chemotype enrichment analysis on the updated datasets was performed to identify enriched substructures. Additionally, chemotypes common for both endpoints were found. Five machine learning models in combination with molecular descriptors, twelve fingerprints and two data balancing techniques were applied to construct individual models. The best-performing individual models were selected for the ensemble construction. The curated final dataset consists of 981 chemicals for micronuclei in vitro and 1309 for mouse micronuclei in vivo, respectively. Out of 18 chemotypes enriched in micronuclei in vitro, only 7 were found to be relevant for in vivo prediction. The ensemble model exhibited high accuracy and sensitivity when applied to an external test set of in vitro data. A good balanced predictive performance was also achieved for the micronucleus in vivo endpoint.

Abstract The most common source of information about sea ice conditions is remote sensing data, especially images obtained from synthetic aperture radar (SAR) and passive microwave radiometers (PMR). Here we introduce an adaptive fusion scheme based on Graph Laplacians that allows us to retrieve the most relevant information from satellite images. In a first test case, we explore the potential of sea ice classification employing SAR and PMR separately and simultaneously, in order to evaluate the complementarity of both sensors and to assess the result of a combined use. Our test case illustrates the flexibility and efficiency of the proposed scheme and indicates an advantage of combining AMSR‐2 89 GHz and Sentinel‐1 data for sea ice mapping.

Semi-supervised learning is a branch of machine learning focused on improving the performance of models when the labeled data is scarce, but there is access to large number of unlabeled examples. Over the past five years there has been a remarkable progress in designing algorithms which are able to get reasonable image classification accuracy having access to the labels for only 0.1% of the samples. In this survey, we describe most of the recently proposed deep semi-supervised learning algorithms for image classification and identify the main trends of research in the field. Next, we compare several components of the algorithms, discuss the challenges of reproducing the results in this area, and highlight recently proposed applications of the methods originally developed for semi-supervised learning.

The automatic detection and analysis of ocean eddies in the marginal ice zone via remote sensing is a very challenging task but of critical importance for scientific applications and anthropogenic activities. Therefore, as one of the first steps toward the automation of the eddy detection process, we investigated the potential of applying YOLOv5, a deep convolutional neural network architecture, to specifically collected and labeled high-resolution synthetic aperture radar data for a very dynamic area over the Fram Strait. Our approach involved fine-tuning pre-trained YOLOv5 models on a sparse dataset and achieved accurate results with minimal training data. The performances of the models were evaluated using several metrics, and the best model was selected by visual examination. The experimental results obtained from the validation and test datasets consistently demonstrated the robustness and effectiveness of the chosen model to identify submesoscale and mesoscale eddies with different structures. Moreover, our work provides a foundation for automated eddy detection in the marginal ice zone using synthetic aperture radar imagery and contributes to advancing oceanography research.

The paper reports the results of investigation intermolecular interactions between alanine and sarcosine anhydride in organic solvents. The absorption frequencies of cyclic dipeptide in solvents were measured by IR spectroscopy. The effect of Van der Waals interactions and the hydrogen bonding of solvents on absorption frequencies of the C=O group of anhydrides was discussed. The spectroscopic parameters for C=O∙∙∙H-O hydrogen bonding complexes of anhydride with methanol in aprotic and proton donor solvent are obtained. In multi-particle complexes of dipeptides with aliphatic alcohols, the hydrogen bond enhancement was between 10 and 16%, which is significantly lower than it is for amide complexes.