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

Abstract

Przedmiotem zgłoszenia jest urządzenie do automatycznego sadzenia sztobr, zwłaszcza zdrewiałych sadzonek wierzby energetycznej i topoli, wyposażone w ramę (1) podpartą kołem napędowo-podporowym (6) charakteryzujące się tym, że koło napędowo-podporowe (6) połączone jest przekładnią cięgnową (8) z bębnem (9) będącym elementem mechanizmu selekcji (3) i mającym równomiernie rozmieszczone na obwodzie wzdłużne wybrania równoległe do osi wzdłużnej bębna (9) o długości równej jego wysokości i szerokości większej niż średnica sztobr, a rama (1) jest na przeciwległym końcu wyposażona w rolki dociskowe (7), przy czym pomiędzy kołem napędowo-podporowym (6), a rolkami dociskowymi (7) znajduje się redlica (5), nad którą zawieszony jest lej (4) prowadzący sztobry, nad którym z kolei znajduje się podpora (10) do siłowej zmiany pozycji poziomej sztobr wypadających z bębna (9) mechanizmu selekcyjnego (3), ponadto urządzenie wyposażone jest w otwarty od góry kontener (2) załadowczy o jednej parze ścian równoległych, przy czym odległość ścian równoległych od siebie jest większa od długości sztobr oraz o wzdłużnej szczelinie w dnie, równoległej do osi wzdłużnej bębna (9), której szerokość jest większa od średnicy sztobr.

Kowalchyk Z., Kubon, M., Kwasniewski, D., Mudryk, K., Glowacki, S., Hutsol T., Yermakov S., Kukharets S.: Urządzenie do automatycznego sadzenia sztobr, zwłaszcza zdrewiałych sadzonek wierzby energetycznej i topoli: Patent 248384 (2025).

This paper presents a methodological framework for integrating UAV-based photogrammetry and GIS technologies to generate a high-accuracy digital elevation model (DEM) for urban land-use planning. The study was conducted in an urbanized area characterized by heterogeneous topography, mixed vegetation cover, and fragmented land use, which complicate high-resolution terrain modeling. UAV surveys were performed using multiple photogrammetric blocks with centimeter-level ground sample distance and a dense ground control network supported by geoid-based height corrections. The resulting DEM was independently validated using control points derived from large-scale topographic data. The achieved vertical accuracy (RMSE ≈ 0.25 m) confirms the applicability of UAV-derived DEMs for large-scale mapping (1:1000–1:2000) and urban spatial analysis. Unlike studies focused on runoff simulation, this work emphasizes the accuracy-controlled generation and validation of DEMs as a primary spatial dataset for urban planning applications. The results demonstrate that DEM accuracy depends strongly on flight planning, ground control distribution, and hybrid automatic–manual point cloud refinement.

Keywords
unmanned aerial vehicles; unmanned aerial vehicle; digital elevation model; DEM; photogrammetry; GIS; urban terrain modeling; ground control points; elevation accuracy

Kulikovska, O.; Kolb, I.; Kovalyshyn, O.; Kolodiy, P.; Stupen, R.; Trzyniec, K.; Vasyuk, V.; Hutsol, T. Integration of UAV Photogrammetry and GIS for Digital Elevation Modeling in Urban Land Use Planning. Sustainability 2026, 18, 3047. https://doi.org/10.3390/su18063047

Open article

Abstract
Soil fertility is determined mainly by its level of compaction. For a single wheel, this level depends on the pressure, which is calculated by dividing the wheel’s vertical load by its contact patch area. The latter is directly related to the length of the tire’s contact patch with the soil (). In this regard, this article aims to develop a strategy for selecting the parameters of a tractor chassis system based on the analysis of such an analytical dependence for determining the length of the wheel-soil contact line, which would contain the radius () and the tire width (); the vertical load on the tire () and the air pressure in it (); the soil characteristic in the form of the coefficient of volumetric compression (). The results of theoretical and experimental studies of such a dependence have established that an increase in the contact line length of the wheel with the soil causes an increase in the values ​​of the parameters and on the one hand, and a decrease in the values ​​of , and parameters on the other. However, to practically implement the functional link of increasing the parameter and subsequently reduce the compaction effect of the wheel on the soil, it is necessary to increase the wheel radius and tire width, while reducing the tire air pressure and the vertical load on the wheel. From an agronomic perspective, it is necessary to create a soil structure with a lower coefficient of volumetric compression.

Keywords:
Vertical load, Wheel radius; Tire air pressure; Tire width; Soil volumetric; Compression

Nadykto, V., Horetska, I., Glowacki, S. et al. Determination of the contact patch length in tractor tire–soil interaction. Sci Rep (2026). https://doi.org/10.1038/s41598-026-37868-2

Abstract
Production of dairy products is a crucial component of food security. The situation in the dairy sector affects not only the supply of the population with dairy products but also the overall sustainable development of the country. The main purpose of this publication is to determine the forecasted need for investment in innovations for Ukrainian enterprises engaged in the production of milk and cream in order to achieve sustainable development goals. The study employed the following economic research methods: the inductive method—for collecting, systematizing, and processing information; the deductive method—for theoretical interpretation of the problem; analysis and synthesis—for assessing the investment attractiveness of dairy enterprises and examining the components of sustainable development and their interrelationships. The primary data on enterprises engaged in the production of milk and cream were collected and systematized for large, medium, and small enterprises based on the information from the State Statistics Service of Ukraine. The study substantiates the impact of investments in the modernization of the dairy industry on achieving sustainable development goals. Integral indicators of the investment attractiveness of Ukrainian milk and cream producers were calculated, revealing that large enterprises are the most suitable for absorbing investments aimed at production modernization. An analysis of milk and cream production volumes by large enterprises in Ukraine for 2014–2024 was conducted, demonstrating that in 2023–2024, production began to grow after the crisis of 2021–2022. Based on historical production dynamics, a forecast for 2026–2030 was developed. It was determined that under the pessimistic scenario, production will reach 291.79 thousand tons in 2030, under the realistic scenario, 349.84 thousand tons, and under the optimistic scenario, 407.88 thousand tons. The key factors influencing the pessimistic, realistic, and optimistic projections were identified. Since the realistic scenario enables the most comprehensive consideration of influencing factors, the calculation of investment needs for the modernization of large milk and cream producers was based on this scenario. It was established that to meet EU product quality standards, comply with sustainable development goals, and accommodate the projected increase in production, the total investment required for the modernization of large enterprises engaged in the production of milk and cream in Ukraine should amount to 126 million euros by 2030.

Keywords
innovations; investments; dairy industry; product quality and safety; European integration; sustainable development

Stoliarchuk, N.; Kielbasa, P.; Dibrova, A.; Dibrova, L.; Nahorna, O.; Kukharets, V.; Hutsol, T. Attracting Investment in the Modernization of Ukrainian Dairy Enterprises as a Tool for Sustainable Development. Sustainability 2026, 18, 996. https://doi.org/10.3390/su18020996

Abstract
In the studied process of moisture removal there is an increase in the driving force, due to centrifugation during rotor rotation, the emergence of electroosmotic pressure when creating conditions for one-sided diffusion, the filtering of the technological mass of the load through the rotor perforations, as well as the introduction of low-frequency oscillations of the dryer’s actuators. Therefore, the purpose of this scientific study is to substantiate the operating modes of the vibration convective dryer by evaluating the amplitude–frequency parameters of the beet pulp dehumidification process. According to the results of the studies, the use of the angular velocity of the drive shaft of the vibrator in the range of 80…110 rad/s and the amplitude of oscillations within 2.5…3.0 mm allow the process to be carried out at maximum energy consumption of about 700…750 W. The developed technology involves the sequential implementation of vibration, filtration, and electroosmotic technological action, which allows for a reduction in the duration of beet pulp processing during dehumidification by almost two times compared to the duration when performing filtration moisture removal in a stationary layer of products. Low-frequency oscillations with force field acceleration (of the order of 2…3 g) are used to create a pseudo rapid layer of products before convective processing, and when this parameter is reduced to (0.9…1.0 g), they ensure maximum compaction of the pulp mass, which significantly increases the efficiency of electroosmotic moisture removal. Such a combination of the noted physical and mechanical factors makes it possible to reduce the specific energy consumption for the removal of 1 kg of moisture by 2.7 times compared to traditional convective drying.

Keywords:
vibrocentrifugal moisture removal; electroosmotic effect; amplitude–frequency characteristics; beet pulp; pectin-containing raw materials; low-frequency oscillations

Share and Cite
Palamarchuk, I.; Priss, O.; Zozulyak, O.; Kiurcheva, L.; Vasylenko, O.; Dyadyura, K.; Romanowska-Duda, Z.; Obraniak, A.; Szufa, S.; Hutsol, T. Hybrid Technology of Beet Pulp Dewatering with Process Intensification in a Convection Dryer as an Element of Sustainable Processing of Agro-Industrial Waste into Bioenergy. Sustainability 2025, 17, 10327. https://doi.org/10.3390/su172210327

Abstract:

The growing consumer interest in plant-based alternatives has encouraged the development of non-dairy versions of traditionally dairy-based products such as ice cream and frozen desserts. Soy milk, with its high protein content and favorable nutritional profile, is a promising alternative to cow’s milk in frozen desserts. The aim of this study was to develop frozen dessert recipes containing soy milk and assess their physical, chemical, and sensory properties. Physicochemical analyses of frozen dessert included measurements of pH, specific gravity, total solids, moisture content, ash content, overrun, and melting resistance. Air sell size distribution was examined microscopically. Sensory evaluation was conducted by a trained panel. Nutritional composition was also analyzed. The pH of the frozen dessert increased from 7.2 to 7.5 with higher soy milk content. Specific gravity of the frozen dessert rose from 589.0 to 634.4 kg/m3, while total solids content increased from 20.61 to 28.99%, and ash content rose from 0.33 to 0.98%. Overrun of the frozen dessert dropped from 73.2% to 50.1%, and melting resistance declined from 72.8% to 54.1% with increased soy milk levels. Frozen dessert samples containing soy milk demonstrated high sensory scores due to their smooth texture, pleasant flavor, and uniform consistency. Replacing cow’s milk with soy milk in frozen dessert recipes is a feasible strategy for producing plant-based frozen desserts with acceptable quality and improved nutritional attributes. Future research should explore flavor enhancement and functional enrichment, particularly through the incorporation of freeze-dried fruits and berries, to diversify the product range and meet consumer demands for health-oriented desserts. This research supports sustainable food innovation by reducing dependence on animal-derived ingredients and promoting the use of plant-based proteins. The development of soy milk-based frozen dessert aligns with global efforts to lower the environmental impact of food production and respond to growing consumer demand for eco-conscious dietary choices.

Keywords:

ice cream; frozen dessert; cow’s milk; plant-based milk; soy milk; overrun; melting resistance;

Share and Cite:
Dudarev, I.; Shemet, V.; Sydoruk, T.; Andrushchenko, M.; Semenov, A.; Borusiewicz, A.; Hutsol, T. Physicochemical and Sensory Properties of Frozen Dessert Containing Soy Milk. Appl. Sci. 2025, 15, 11455. https://doi.org/10.3390/app152111455

JCR category rank
Q2: Engineering, Multidisciplinary

CiteScore category rank
Q1: General Engineering
Q1: Instrumentation

Source

Abstract
This study presents a system model developed for collecting and analyzing spatial data on the project environment of transport infrastructure development in the post-war context, with a focus on supporting sustainable management and recovery planning. The model utilizes the OpenStreetMap Overpass Application Programming Interface (Overpass API) to extract structured geospatial information from OpenStreetMap (OSM), enabling efficient and accurate assessments of settlements affected by armed conflict. Python 3.11-based software modules were created to process OSM data, evaluate 17 relevant attributes of transport infrastructure objects, and visualize key characteristics for decision-makers. A case study was conducted on 23 Ukrainian settlements with partially damaged infrastructure, demonstrating how the proposed model facilitates timely and informed decisions for infrastructure redevelopment. By improving the accessibility and quality of spatial data, the model enhances the capacity for sustainable management of post-war transport infrastructure projects. To ensure the quality of spatial data obtained from OSM, a verification procedure was carried out by cross-checking with satellite images and official national geospatial data. The results showed an average deviation of ±4.4% in the length of road sections, confirming the reliability and accuracy of spatial objects obtained from OSM for use in transport infrastructure planning. The findings offer valuable insights for regional planners, public administrators, and policymakers involved in sustainable reconstruction and digital governance. Future research will focus on developing a comprehensive information system for identifying and prioritizing infrastructure development projects within defined administrative units such as municipalities and local communities.

Tryhuba, A.; Glowacki, S.; Zachko, O.; Tryhuba, I.; Slobodian, S.; Demchyna, V.; Horetska, I.; Hutsol, T. System Model for Spatial Data Collection in Post-War Transport Infrastructure Planning. Sustainability 2025, 17, 7676. https://doi.org/10.3390/su17177676

Keywords:
sustainable management; transport infrastructure; post-war recovery; spatial data; OpenStreetMap; project environment; system modeling

JCR category rank
Q2: Environmental Studies
Q2: Environmental Sciences
Q3: Green and Sustainable Science and Technology

CiteScore category rank
Q1: Geography, Planning and Development
Q1: Environmental Science (miscellaneous)

This study addresses the problem of minimizing the environmental impact of livestock production while improving the efficiency of livestock waste-to-energy conversion. A sustainable management approach based on neural network models is proposed and substantiated, focusing on predicting livestock waste generation and optimizing farm configurations. The novelty of this work lies in the development and validation of a multilayer perceptron (MLP) neural network model tailored to regional and production characteristics of cattle farms in western Ukraine. The approach includes 11 stages, from data preprocessing and feature selection to multiobjective optimization and emission assessment. The optimized MLP model demonstrated high predictive performance, achieving a mean squared error (MSE) of 0.0005 and a mean absolute percentage error (MAPE) of 6.51%, compared to 8.01% for the baseline model. The study confirms that feeding rations significantly affect both waste generation and greenhouse gas emissions. Farms using protein-rich or concentrated diets show increased biogas production potential but also higher CO₂-equivalent emissions (kg/year), while roughage-based diets reduce methane (CH₄) and nitrous oxide (N₂O) emissions, lowering environmental impact. Optimization results reveal a non-linear relationship between herd size and sustainability indicators. The most effective emission reduction was achieved on farms with 900 cattle, where CO₂-equivalent emissions decreased from 4,560,590 to 3,830,100 kg/year. Simultaneously, biogas production increased by 7–16%, exceeding 1,100,002 m3/year on large farms. These findings support the use of anaerobic digestion technologies and intelligent modelling for planning farm modernization. The proposed approach can inform decision-making at the farm and policy levels, contributing to climate resilience and the sustainable development of livestock farming.

Keywords

Sustainable management
Waste
Animal husbandry
Neural network model
Biogas
Energy supply
Emissions
Environmental sustainability

Subjects

Energy science and technology
Engineering
Environmental sciences
Mathematics and computing

Cite this article

Tryhuba, A., Mudryk, K., Tryhuba, I. et al. Models for sustainable management of livestock waste based on neural network architectures. Sci Rep 15, 28082 (2025). https://doi.org/10.1038/s41598-025-13150-9

https://www.nature.com/srep/about