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A machine learning dataset on winter roads of Krasnoyarsk Krai, Russia for the forestry and infrastructural projects
European Journal of Forest Engineering. 2026. Vol. 12. No. 1. P. 7–22.
Ekaterina S. Podolskaia, Sinitsina A.
Machine learning in transport modeling has become a trend in science and industry. In this paper, we observe its main directions and focus on a dataset of seasonal road creation. Seasonality as a parameter in transport modeling has a significant impact on transport scenarios but is underestimated worldwide and in Russia, despite modern data challenges. A major challenge in seasonality studies is the absence of systematically collected data across multiple spatial scales, ranging from national to regional levels. This study presents the first machine learning–oriented dataset of winter roads for Krasnoyarsk Krai, the Siberian federal district, Russia, organized by 60 municipalities and integrating open geodata, government datasets, raster-based environmental variables, and socio-economic indicators. We propose a reproducible workflow for harmonizing heterogeneous sources, extracting geospatial predictors, and modeling winter road operational duration. Results indicate that forest area (correlation coefficient ρ=0.78), permafrost coverage (ρ=0.74), and snow depth (ρ=0.61) are the strongest predictors of winter road exploitation. Random Forest shows the best performance (R²=0.84; average exploitation forecast MAE=6.3 days) among other ML methods while linear models performed well with only two predictors. The study highlights the dominant role of natural and climatic factors in shaping seasonal road operability. It also outlines methodological directions for future regional datasets under increasing climate variability. The dataset will be extended to the entire Siberian Federal District and maintained to collect statistics for dual-season (winter and summer) accessibility modeling. This data has practical use in the forestry, road development, regional planning, and petroleum, and could interest researchers studying seasonal roads.
Aleksei Samarin, Nazarenko A., Nazarenko D. et al., Machine Learning and Knowledge Extraction 2026 Vol. 8 No. 10 Article 330
Automated microscopic image analysis is essential for quantitative microbiology, yet manual acquisition and generic recognition models struggle with morphological variability and scene artifacts. We address both ends of this pipeline. First, we present a low-cost, open-source acquisition system, based on an Arduino UNO R3 microcontroller and NEMA 17 stepper motors, that automates focusing, magnification, and ...
Added: October 14, 2026
Mamedov I., Fofanov M., Lukianchenko P. et al., IEEE Access 2026 No. 14 P. 26662–26671
DreamBooth-style personalization, which often relies on one or a few images, can memorize context (background, pose, lighting) rather than learning the subject’s identity, thereby reducing fidelity and limiting prompt-driven edits. P-LoRA (Probability LoRA) is introduced as a training-time modification that biases diffusion timestep sampling and utilizes importance weights, ensuring that the overall objective remains unchanged. ...
Added: October 14, 2026
Springer, 2026.
This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students and practitioners a unique update on the latest advances in theory and a multitude of applications. It presents the peer-reviewed proceedings of the XIV International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2025). The carefully selected papers cover ...
Added: October 13, 2026
Springer, 2026.
This book offers a comprehensive overview of the latest research, innovations, and practical applications in the field of sustainability, with a focus on construction materials and their use in building and civil engineering structures. As an outcome of the 3rd International Conference on Sustainability: Developments and Innovations (ICSDI 2026), this book presents a selection of ...
Added: October 13, 2026
Dmitriev A., Kornilov V., Batienkov R., Lecture Notes in Civil Engineering 2026 Vol. 886 P. 249–255
Numerous studies have provided compelling evidence that the probability distribution of avalanche sizes follows a power law, and that 1/f noise is present in the stochastic dynamics of landslides. We present a theoretical explanation of these phenomena within the framework of self-organized criticality theory, proposing a discrete-time model in the form of a sandpile cellular ...
Added: October 13, 2026
Dmitriev A., Shulman I., Lebedev A., Studies in Computational Intelligence 2026 Vol. 1265 P. 266–273
A discrete-time model is presented for the self-organization of the X/Twitter social network to the edge of a phase transition, characterized by avalanche-like propagation of information in the network. The model is based on the analogy between the critical dynamics of the X social network and sandpile cellular automata with Manna rule on the Chung-Lu ...
Added: October 13, 2026
Stepanov S., Вестник гражданского права 2026 Т. 26 № 4 С. 7–78
This article examines the prospects of using Russian tort law to compensate for harm caused by climate change. Drawing on a comparative analysis of case law from Germany, the Netherlands, and the United States, it demonstrates that climate-related disputes are increasingly falling within the scope of tort law. Particular attention is paid to the transformation ...
Added: October 13, 2026
Makarov D. M., Kalikin N., Fashchevsky K. A. et al., Electrochimica Acta 2026 Vol. 579 Article 150159
Machine learning (ML) has become a powerful tool for accelerating the development of carbon-based supercapacitors;
however, most published models assume that a single predictive framework can describe both conventional
porous carbons and hybrid carbon electrodes. Here, we test this assumption using a comprehensive
benchmark dataset and an extended literature-curated dataset enriched with synthesis- and electrolyte-related
descriptors. Three ML algorithms ...
Added: October 13, 2026
Zhang Z., Ilvovsky D., Information Processing and Management 2026 Vol. 63 No. 7
Transformer-based Large Language Models (LLMs) rely on Key-Value (KV) cache to accelerate autoregressive decoding, but memory and compute requirements still grow with context length, limiting deployment on resource-constrained devices. Existing training-free KV cache compression methods mainly rely on attention-driven retention heuristics, which may under preserve structurally informative tokens under tight cache budgets, especially when such ...
Added: October 13, 2026
Батова О. Н., Савинецкая Л. Е., Проявка С. В. et al., Nature Conservation Research. Заповедная наука 2026 Т. 11 № 4 С. 25–39
Spermophilus suslicus is a threatened species listed in Red Data Books at all levels (from regional Red Data
Books to the IUCN Red List of Threatened Species), and it currently requires special conservation activity.
Over the past 30–50 years, its range has changed from continuous to mosaic, and its population has declined
everywhere. In 2015, in Russia experimental ...
Added: October 13, 2026
No Species Is an Island, Entire of Itself: Genetic Speciation Algorithm with Interplay among Species
Gromov V., Sohrabi M., Fathollahi-Fard A., International Journal of Engineering, Transactions B: Applications 2027 Vol. 40 No. 5 P. 1437–1460
This paper, with a particular emphasis on Genetic Algorithm (GA), proposes a novel approach that integrates clustering mechanisms, predator-prey dynamics, and a distinctive selection strategy for generating subsequent populations. The proposed evolutionary algorithm, referred to as Genetic Speciation Algorithm with Interplay Among Species (GSAIS), is comprehensively evaluated against several variants of GAs as well as ...
Added: October 12, 2026
Alexandrov D., Vasilevsky V., Rezunik L. et al., Труды Института системного программирования РАН 2026 Т. 38 № 5 С. 73–90
Large language models demonstrate limitations in tasks that require precise information retrieval from very long input sequences. This limitation affects code generation in large object-oriented systems, where correctness depends on complex inter-class dependencies and the analysis of long execution traces that exceed standard context window sizes. This paper presents JavaCapsule, a technology for automated generation ...
Added: October 12, 2026
Singapore: Springer, 2026.
This volume presents the Proceedings of the 34th IUPAP Conference on Computational Physics (CCP2023), held in Kobe, Japan on August 4–8, 2023. It offers a collection of peer-reviewed articles that highlight recent developments in computational and simulation research across various areas of physics. Key topics include the use of data science, quantum computing, and machine ...
Added: October 12, 2026
Ari G., Liu X., Jiang L. et al., Ecological Indicators 2026 Vol. 191 Article 115592
Snow drought has become a research hotspot due to its widespread impacts on ecosystems and water resources, yet its effects on vegetation phenology remain relatively understudied. Focusing on the Mongolian Plateau, this study used the Snow Water Equivalent Index and the start of growing season (SOS) to assess the associations between snow drought and vegetation ...
Added: October 12, 2026
Nikulina Y., / Высшая школа экономики. Серия EC "Economics". 2026. № 1.
This paper estimates the impact of the international sanctions of 2014 and 2022, as well as the food embargo, on the technical efficiency of agricultural enterprises. The assessment was carried out using panel data from agricultural enterprises specializing in dairy production in the Leningrad Region for the period 2010–2023, employing stochastic frontier analysis. It is ...
Added: October 11, 2026
Zykov S. V., Ермаков С. Р., Современные наукоемкие технологии 2026 № 9 С. 43–53
При федеративном обучении с неоднородными клиентскими данными и переменными задержками запоздавшие обновления могут ухудшать качество модели, а обмен параметрами с глобальным сервером увеличивает сетевую нагрузку. Целью исследования является разработка и теоретико-экспериментальное обоснование метода и алгоритмов адаптивного федеративного обучения с учетом распределенного запаздывания клиентских обновлений. Для обоснования разрабатываемых решений использованы событийная и непрерывная модели градиентных обновлений, ...
Added: October 11, 2026
Брычков М. Е., Незнанов А.А., Программирование 2026 № 4 С. 50–66
The matrix profile (MP) quickly became one of the most important time series preprocessing methods when it was introduced in 2016, facilitating the solution of a wide range of time series analysis problems, in particular anomaly and pattern detection problems. The high significance has led to the emergence of various tools for MP calculating, but ...
Added: October 9, 2026
Aleskerov F. T., Вайншток А. П., Делахова А. М. et al., Информационные процессы 2026 Т. 26 № 3 С. 895–913
The development and effective management of territorial entities are priority issues for all states, particularly in the context of the digital transformation of public administration. Over the past decade, the integration of information technologies into municipal and regional planning has significantly altered approaches to strategic development, service delivery, and public engagement. This paper describes the ...
Added: October 9, 2026
Stepanyants V., Andrey V. Fizulin, Chibirov A. et al., FUTURE TRANSPORTATION 2026 Vol. 6 No. 5 Article 225
Reliable Vehicle-to-Everything (V2X) evaluation requires propagation models that represent terrain and multilevel infrastructure. Most integrated vehicular simulators still rely on planar models, but the magnitude of the resulting bias is unclear. This study quantitatively compares flattened two-dimensional (2D) and terrain-aware three-dimensional (3D) variants of three scenarios using identical Sionna RT settings. The pipeline combines OpenStreetMap ...
Added: October 8, 2026
Chistikov M., Makarov I. A., Вестник международных организаций: образование, наука, новая экономика 2026 Т. 21 № 3
As the negative socio-economic consequences of climate change increase, attention to adaptation issues is growing worldwide. Currently, most of the adaptation measures are reactive and not very large-scale. The more ambitious approach to climate change adaptation is evident in the idea of "transformational adaptation," which is gaining growing interest in both academic discussions and international ...
Added: October 8, 2026
Didenkulova E., Chugunov V., Didenkulova I., Wave Motion 2026 Vol. 147 Article 103787
This work presents a new analytical solution of the shallow water theory for cylindrical propagation of long tsunami-like waves in the basin of variable depth. Considered model cases are related to tsunami wave propagation from an asteroid fallen into the sea, or from underwater volcanic eruptions. The corresponding bottom geometries represent radially symmetric obstacles such ...
Added: October 8, 2026
Flamarion M., Didenkulova E., Pelinovsky E., Physics Letters A 2026 Vol. 598 Article 132218
In this letter, bipolar solitary wave interactions in the Schamel equation are revisited numerically. Two simulation settings are considered: repeated collisions in a periodic domain, and the interaction of a positive solitary wave with a train of negative polarity solitary waves of equal amplitude. In the first case, when the amplitudes are comparable, the larger ...
Added: October 8, 2026
Люксембург А. А., УРСС, 2005.
Изучается возможность автоматизированного построения математических теорий. Рассматривается дедуктивная система, основанная на языке логики предикатов первого порядка, объектами системы являются математические выражения или формулы, которые описывают математические объекты или их свойства. В дедуктивной системе выводятся математические определения и теоремы. Для доказательства теорем используются методы автоматического доказательства. Разработан алгоритм, выводящий часть формул системы. Для решения задачи используется аппарат математической ...
Added: October 7, 2026
Konstantinov A., Elizarova Anastasiya P., Utkin L., Computing, Telecommunications and Control 2026 Vol. 19 No. 1 P. 16–25
Decision trees and their ensemble extensions, such as random forests, are widely used as classification models due to their simplicity and interpretability. However, in many real-world tasks where class labels overlap in the feature space, standard decision trees rely on hard splits that create fragile decision boundaries. In these regions, small perturbations in the input ...
Added: October 2, 2026