Scientific Research Institute of the Rubber Industry

Russian-language resources for medical natural language processing (NLP) are expanding rapidly; however, their fragmentation, uneven curation, and limited clinical reliability hinder the development of safe machine learning systems for prognosis, prevention, and precision medicine. We provide the first systematic survey of Russian medical NLP datasets and analyze their suitability for clinically meaningful tasks as defined by the MedHELM taxonomy. We additionally perform expert clinical validation of three representative public corpora—RuMedPrimeData (real outpatient notes), MedSyn (synthetic clinical notes), and RuMedNLI (translated natural language inference)—assessing clinical plausibility, diagnosis accuracy, and logical consistency. Experts identified substantial reliability issues: across randomly sampled subsets of each corpus, only approximately 20% of RuMedPrimeData records, fewer than 15% of MedSyn records, and approximately 55% of RuMedNLI pairs met essential quality criteria, which can hinder downstream ML systems built on these data. To support robust applications—ranging from medical chatbots and triage assistants to predictive and preventive models—we outline practical requirements for high-quality datasets: coordinated, expert-validated, machine-readable corpora aligned with clinical guidelines and insurance logic, standardized de-identification, and transparent provenance. Strengthening these data foundations will enable the development of reliable, reproducible, and clinically relevant AI systems suitable for real-world healthcare applications.

Abstract We empirically study internal cluster validity indices for attributed networks by evaluating feature- and network-space criteria under controlled generators that decouple attributes from topology while sharing cluster cardinalities. Using unified notation, Gaussian blobs for features, and a stochastic block model for graphs, we assess Silhouette Width (SW), Calinski–Harabasz (CH), Davies–Bouldin (DBI), $${{S}_{{{\text{Dbw}}}}}$$ , Average Isolability (AVI), Average Unifiability (AVU), and ANUI on both ground-truth and random partitions. SW is stable and saturates once enough features are present; CH grows strongly with sample size (suggesting reporting CH/N); DBI and $${{S}_{{{\text{Dbw}}}}}$$ separate ground truth from random partitions but have K -dependent random baselines, motivating baseline normalization. In network space, AVI increases with assortativity and decreases roughly as 1/ K , AVU drops with K toward a floor, and ANUI follows these trends; all indices approach random baselines as overlap/mixing increases, while confidence intervals narrow with more samples or informative features. We provide an empirical benchmark, simple scaling heuristics, and practical guidance for applying CVIs in attributed networks.

Abstract Polylactic acid (PLA) composites with multifunctional properties and minimal filler content are increasingly in demand across various industries. However, achieving a balance between high mechanical strength, electrical conductivity, thermal conductivity, and electromagnetic interference (EMI) shielding remains challenging. In this study, a dual‐processing strategy combining Pickering emulsion templating and melt blending is presented to hybridize 1D carbon nanotubes (CNTs) and 2D graphene nanoplatelets (GNPs) within a PLA matrix. This approach successfully forms a stable dual‐filler network, ensuring uniform dispersion of the fillers. The results show that this method significantly enhances the performance of the resulting PM‐PGxCy composites (P: Pickering emulsion; M: melt blending; x and y: mass fractions of GNPs and CNTs, respectively). Specifically, the PM‐PG1.43C1.43 composite exhibits remarkable improvements in mechanical strength (56.2 MPa), electrical conductivity (43.5 S m−1), EMI shielding effectiveness (20.1 dB), and thermal conductivity (0.34 W m·K−1), outperforming composites prepared using either method alone. These findings indicate that the dual‐processing strategy effectively combines 1D and 2D fillers, facilitating superior interfacial interactions and enhancing the multifunctional properties of PLA‐based composites. This study offers a new approach to achieving high‐performance PLA composites with low filler content, offering significant potential for applications in electronics, packaging, and EMI shielding technologies.

The natural rubber has good resistance for some acids such as hydrochloric acid.Therefore, natural rubber composites are used in the lining of acid containers.Acid resistance of the natural rubber can be improved by types and levels of fillers.This research studies the effect of silica nanoparticles on acid resistance of natural rubber composites for (37% w/w) hydrochloric acid.Six rubber compounds were prepared with various levels of nano silica of 0, 1, 2, 3, 4, 5 (parts per hundred parts of rubber weight).Tensile properties, hardness (Shore A), compression set, tear resistance and abrasion resistance were tested.Test specimens of tensile properties were immersed in hydrochloric acid for 70 h at room temperature.After immersion, they were dried and tested for determining acid resistance.The acid resistance of rubber composites improved by the addition of nano silica and the best acid resistance of rubber composite was gotten at 3.

Unsteady heat transfer in agarose gels of various concentrations was studied in order to make a breakthrough in the technology of 3-D additive bioprinting. Data on the kinetics of the phase transformation was obtained using spectroscopy as a function of temperature during the formation of agarose hydrogel. The dynamics of aging was investigated for gels of different densities. The time dependence of the structural changes was obtained. Particular attention was paid to the changes in the structure of the gel due to the processes of evaporation of the liquid during the gel formation and during long-term storage. Experiments were performed to determine the dynamics of the temperature fields simultaneously with heat flux measurements during the formation of agarose gels from different initial concentrations. A technique based on experimental data for the computations of the thermophysical coefficients of agarose gels was developed.

Researchers at FGUP GNTs RF NIIAR performed a series of experiments on the lithium reduction of the spent nuclear fuel (SNF) of a VVER-1000 reactor to a metal in a lithium chloride melt. The depletion of the nuclear fuel taken before experiments is about 30000 (MW day)/t, and the cooling time is 5 years. The experiments are performed on 5.8-kg samples of a prepared SNF powder. Data are obtained on a decrease in the heat release, the specific activity of the processed powder, and the distribution of actinides and the main fission products between a salt phase and a reduced SNF powder.