Abstract
Soil penetration resistance is a key indicator used to evaluate soil physical conditions, root growth environments, and the suitability of tillage practices. However, measurements obtained with penetrometers equipped with conical and cylindrical tips are not directly comparable because the mechanisms of soil deformation generated by these deformers differ substantially. This study aimed to develop and validate a methodology for harmonizing penetration-resistance measurements obtained using cylindrical and standardized conical penetrometers. A theoretical framework describing soil deformation during cylindrical and conical penetration was developed using soil bearing capacity, volumetric compression, and soil–steel friction relationships. Analytical equations were derived to determine the permissible penetration depth of a cylindrical deformer and to establish an equivalence relationship between the radii of cylindrical and conical penetrometer tips. The proposed methodology was validated through field experiments using a standardized LAN-M penetrometer fitted with interchangeable conical and cylindrical tips. The results demonstrated that reliable penetration-resistance measurements obtained with a cylindrical tip require limiting penetration depth to 0.15 m. An experimental comparison of deformers with identical diameters showed that measurements remain statistically comparable only within the upper 0.05 m of soil depth. At greater depths, significant discrepancies arise because of different soil-penetration mechanisms. A new analytical relationship between cylindrical and conical tip radii was developed and experimentally verified. The proposed equation enables the selection of geometrically equivalent deformers, yielding statistically comparable penetration resistance values across the investigated 0–0.15 m soil layer. The findings support the standardization and cross-comparison of soil-strength datasets obtained using different penetrometer designs. Future research should validate the proposed methodology across wider ranges of soil textures, moisture conditions, bulk densities, and penetration rates, and integrate the developed relationships into digital soil-monitoring and precision-agriculture systems.

Cite this article
Nadykto, V., Glowacki, S., Golub, G. et al. Comparative evaluation of penetrometers with conical and cylindrical tips. Sci Rep 16, 35416 (2026). https://doi.org/10.1038/s41598-026-64837-6

Abstract
Assessing the spatial proximity of rural settlements to rescue-service locations remains an important task for sustainable community safety planning, particularly in regions characterized by dispersed settlements, limited infrastructure, and incomplete spatial data. This study proposes a data-driven framework for classifying rural rescue-service proximity-based coverage using OpenStreetMap-derived geospatial data and explainable machine learning methods. A spatially structured dataset containing 304 rural settlements was constructed using infrastructural and settlement-related indicators, including schools, medical facilities, places of worship, industrial facilities, buildings, and major-road access. The target variable represented binary proximity-based coverage defined using a 5 km Euclidean buffer around rescue-service locations and was intended solely for comparative territorial screening; it does not represent observed emergency-response time or operational performance. To ensure methodological validity, leakage-prone variables directly related to target construction were excluded from model training. Several classification approaches were comparatively evaluated using stratified cross-validation, including Logistic Regression, Random Forest, Gradient Boosting, MLP, and XGBoost models. The obtained results demonstrated that ensemble machine learning methods provided the most stable and interpretable performance for spatial tabular data. The Gradient Boosting model achieved the highest mean F1-score of 0.77, with a balanced accuracy of 0.85, ROC–AUC of 0.95, and PR–AUC of 0.82 under stratified five-fold cross-validation. Model interpretability was additionally assessed using permutation feature importance, SHAP analysis, and multicollinearity diagnostics, which identified model-specific associations between infrastructural characteristics and the proximity-based coverage class. Spatial decision-support maps were developed to identify settlements with lower model-based proximity-coverage probabilities that may warrant further detailed assessment. The proposed framework demonstrates the feasibility of integrating OpenStreetMap data and explainable machine learning into practical decision-support systems for sustainable rural safety management and infrastructure planning under conditions of limited and fragmented spatial information.

Keywords
OpenStreetMap
Proximity-based coverage
Rural communities
Explainable machine learning
Spatial decision support
Sustainable community safety
Geospatial analysis
Gradient boosting
SHAP analysis
Emergency planning

Cite this article
Tryhuba, A., Koval, L., Rudynets, M. et al. Explainable machine learning classification of rural rescue service proximity coverage using OpenStreetMap data. Sci Rep (2026). https://doi.org/10.1038/s41598-026-72396-z

The 2nd International Conference on Thermochemical Conversion and Bioenergy – TCCB 2026 was held in Łódź, Poland, on 24–26 September 2026.

The conference brought together representatives of the scientific and business communities to discuss current developments in energy, bioenergy and sustainable development.

The main thematic areas of the conference included:

• thermochemical conversion technologies;
• hydrogen technologies;
• biomass conditioning;
• renewable energy sources and the circular economy.

Particular attention was given to the development of international scientific cooperation, researcher mobility, innovation in renewable energy, and the creation of joint research and applied projects.

TCCB 2026 provided a platform for exchanging scientific results, presenting innovative technological solutions and establishing international cooperation in the fields of green energy and the circular economy.

W dniach 9–12 września 2026 r. w Guzowym Piecu odbyła się X Szkoła Inżynierii Systemów BioTechnicznych – interdyscyplinarne wydarzenie naukowe poświęcone nowoczesnym metodom badawczym, modelowaniu systemów oraz wykorzystaniu narzędzi matematycznych, statystycznych i informatycznych.

W wydarzeniu uczestniczyli przedstawiciele różnych dziedzin nauki, którzy mieli okazję wymienić doświadczenia, zaprezentować wyniki badań oraz nawiązać nowe kontakty i współpracę. Program obejmował wykłady interdyscyplinarne, warsztaty, sesje referatowe oraz dyskusje dotyczące m.in. modelowania matematycznego i statystycznego, metod optymalizacji, wspomagania decyzji oraz zastosowania informatyki w inżynierii systemów.

Istotnym elementem Szkoły była również prezentacja innowacyjnych rozwiązań inżynierskich i problemów praktycznych wymagających interdyscyplinarnego podejścia. Swoje wyniki badań prezentowali także młodzi naukowcy i doktoranci, uczestnicząc w merytorycznej dyskusji z przedstawicielami różnych środowisk naukowych.

Organizatorami X Szkoły były wspólnie: Polskie Towarzystwo Inżynierii Rolniczej, Wydział Nauk Technicznych Uniwersytetu Warmińsko-Mazurskiego w Olsztynie, Wydział Elektrotechniki, Elektroniki, Informatyki i Automatyki Politechniki Łódzkiej, Instytut Agrofizyki im. Bohdana Dobrzańskiego PAN, Instytut Inżynierii Mechanicznej Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie oraz Wydział Inżynierii Produkcji i Energetyki Uniwersytetu Rolniczego w Krakowie.

X Szkoła stworzyła przestrzeń do integracji środowisk naukowych, wymiany wiedzy i doświadczeń oraz rozwijania interdyscyplinarnej współpracy nad nowoczesnymi rozwiązaniami dla nauki i praktyki.

This study presents a deterministic multi-objective framework for the energy-efficient design of a diaphragm spring for automotive clutch applications. The proposed methodology is based on the direct feasible-set approach and similarity theory, enabling synthesis-level optimal-rational design within a closed dimensionless parameter space. Unlike stochastic search-based optimization methods, the developed framework constructs a complete information set of admissible design solutions and identifies rational configurations through adaptive constraint management. The multi-objective analysis demonstrates the absence of a globally optimal solution and justifies the concept of optimal-rational design. The synthesized configurations achieve up to + 22% increase in compressive force, − 12% reduction in material volume, and improved stiffness characteristics compared to the reference configuration. To ensure mechanical admissibility, the analytical synthesis model is validated through nonlinear finite element analysis incorporating geometric nonlinearity and localized elastoplastic behavior. The peak equivalent stress reaches 2244 MPa but remains confined to highly localized regions with equivalent plastic strain below 0.004. Experimental validation under full actuator displacement (17 mm) confirms stable nonlinear behavior, ± 2% force variation over 5000 cycles, and moderate thermal rise (10–12 °C). Energy efficiency is quantitatively assessed through hysteresis analysis, revealing a 25% reduction in mechanical energy dissipation per cycle and improved mechanical efficiency. The strong agreement between analytical predictions, FEM simulations, and experimental measurements confirms the predictive reliability of the proposed deterministic optimization framework. The developed approach provides a transparent and computationally efficient methodology for energy-efficient diaphragm spring synthesis and can be extended to other nonlinear elastic components in transmission systems.

Cite this article
Baranovskyi, D., Sergienko, A., Kalinin, P. et al. Multi-objective optimization of an energy-efficient diaphragm spring for clutch applications. Sci Rep (2026). https://doi.org/10.1038/s41598-026-56129-w

This review analyses and logically structures modern intelligent sensor technologies in the context of animal husbandry, feed production, and veterinary medicine. The main research discussed in the article focuses on machine learning based on modern neural network models, computer vision, and sensor systems that are transforming the methods for assessing the health, behavior, and nutrition of farm animals. The first part examines modern approaches to quality control and optimization of mineral and vitamin premixes, including visual inspection using visual sensors and neural networks. Key roles are played by precise dosing, component stability (minerals, vitamins), and the transition to more bioefficient organic forms of micronutrients to reduce environmental impact. Improvements in feed and premix production are analyzed, including automation, energy management, and the use of machine learning for non-destructive quality control, defect detection, mixing homogeneity assessment, and vitamin stability prediction. The second part analyzes methods for animal location and behavior detection. This article presents computer vision-based systems, including modifications of YOLO, for automatically tracking and classifying key behavioral patterns (lying down, standing, feeding, and aggression) in cattle and pigs, even in crowded conditions. It also discusses the use of ultra-wideband (UWB) systems and accelerometers combined with machine learning for high-precision positioning and detection of specific behavioral anomalies, such as lameness and playfulness. The third section focuses on the application of machine learning in veterinary diagnostics, including the automated interpretation of medical images (X-ray, ultrasound, and MRI) as sensor data streams for the diagnosis of cardiovascular, oncological, and orthopedic diseases in farm and small animals. Furthermore, the article examines the use of machine learning models for proactive disease diagnosis in farm animals and poultry based on multimodal data and image analysis. Considerable attention is given to methods and tools for radiometric diagnosis of animal diseases at an early stage using microwave sensors, as well as laser therapy and surgery in veterinary medicine. The review concludes that the integration of intelligent systems enables a transition to data-driven livestock management, significantly improving animal welfare and, consequently, the efficiency and sustainability of agricultural production.

Keywords
accelerometry; spectrometry; tensometry; biosensorics; videomonitoring; telemetry; datastream; CNN; ML; DL; animals; diagnostics; premix

Kiktev, N.; Hradoboiev, D.; Pravilov, M.; Antypov, I.; Meish, Y.; Stroianovska, L.; Kielbasa, P.; Hutsol, T. Precision Livestock Farming and Biomedical Engineering: Assessing Feed Quality, Animal Health, and Behavior Using Machine Learning for Sensor Data. Sensors 2026, 26, 4015. https://doi.org/10.3390/s26134015

Ukrainian University in Europe – Foundation (UUE-F) acted as a co-organizer of the 11th International Scientific and Business Conference “Renewable Energy Sources”, which brought together representatives of academia, business, local governments, and the energy sector to discuss current challenges and future perspectives in renewable energy development.

The conference participants included representatives of municipalities, businesses, and scientific institutions from Poland, Ukraine, Slovakia, Germany, France, Lithuania, and Turkey. The event served as an international platform for knowledge exchange, the presentation of innovative solutions, and the development of partnerships in the fields of sustainable development and renewable energy sources.

The conference program included online and onsite presentations, official welcome addresses, scientific and business discussions, poster presentations, cultural activities, and study visits. During the event, participants presented contemporary scientific research, innovative technologies, and practical solutions related to energy efficiency, decarbonization, and sustainable development.

As part of the conference program, the Foundation delivered several thematic presentations. The principal topic focused on “International Cooperation of Public and Private Institutions in Sustainable Development Projects”. Participants discussed prospects for international cooperation among universities, local communities, scientific institutions, and businesses in the implementation of joint innovative projects.

UUE-F’s participation as a co-organizer confirms the organization’s active role in supporting international scientific initiatives, promoting innovation, and contributing to the development of a modern educational and research environment within the renewable energy sector.

We sincerely thank our partners, participants, and organizers for their cooperation, trust, and joint efforts in making this international event successful. We highly value the partnerships established during the conference and look forward to further collaborative initiatives in the fields of sustainable development and renewable energy.