Tillage is one of the most energy-intensive operations in crop production, leading to high fuel consumption and the emission of harmful gases such as CO₂ and NO_x. This study was conducted under real field conditions to explore how soil parameters influence variations in fuel use and exhaust emissions. A machine learning approach based on the XGBoost algorithm was applied to develop predictive models for CO₂ concentrations in exhaust gases and specific fuel consumption. The CO₂ prediction model achieved an accuracy exceeding 80%, while the model for fuel consumption reached over 65%. Although not optimized for high precision, these models offer a valuable basis for preliminary assessments and highlight the potential of data-driven approaches for improving energy efficiency and environmental sustainability in agricultural mechanization.

In the realm of agricultural advancement, the relentless quest for agricultural efficiency amidst the vagaries of climate change has positioned greenhouse technology as a linchpin for secure and sustainable food production. The precise management of greenhouse microclimatic conditions i.e., the ability to accurately predict and maintain ideal temperature and relative humidity, is crucial for enhancing plant growth and health, optimizing resource use, and ensuring sustainable agricultural practices. However, maintaining optimal microclimatic conditions is a significant challenge due to the dynamic nature of external environmental influences. This study aims to address the critical need for advanced predictive tools that can enhance the control and management of greenhouse microclimates, thereby supporting sustainable agricultural practices and food security. Our research introduces a novel integration of building transient simulation (TRNSYS) and artificial neural networks (ANNs) to predict temperature and relative humidity inside a greenhouse across the calendar year, based on external atmospheric conditions. The TRNSYS model meticulously simulates the greenhouse’s thermal load, incorporating real-world data to ensure a high level of accuracy in describing the facility’s dynamic behavior. Our ANN model, composed of three layers, underwent optimization to identify the ideal number of neurons, learning rates, and epochs, settling on a model configuration that minimized prediction errors. The evaluation metrics, including root mean square error (RMSE) and mean absolute error (MAE), demonstrated the model’s effectiveness, with an RMSE of 0.3166 °C for temperature and 5.9% for relative humidity, and MAE values of 0.1002° and 3.4%, respectively. These findings underscore the model’s potential as a powerful tool for greenhouse climate control, offering substantial benefits in terms of energy efficiency, resource optimization, and overall sustainability in agriculture. By leveraging detailed dynamic simulations and advanced neural network algorithms, this study contributes significantly to the field of precision agriculture, presenting a novel approach to managing greenhouse environments in the face of changing climatic conditions.

Prostate cancer is the second most commonly occurring cancer in men. Regardless of statistics, screening for prostate cancer is an individual decision and most male patients come for their first examination with an already developed disease, as they are not adequately informed. The study aimed to emphasize the importance of preventive tests for urological diseases in the Republic of Serbia, raise awareness about urinary problems, and present social marketing strategies for prevention. The results confirm the generally lower awareness of respondents under the age of 30, followed by those who finished university, go to the doctor two or three times a year, and receive information other than by watching TV. Implemented research indicates the influence of the marketing principles and social marketing strategies on possible target groups of the male population over 50, which is aimed at raising awareness of the importance of prevention of urological diseases and the expected changes in the health behavior of the target population.

The research explores the drying kinetics of strawberry slices (5 mm thick with initial moisture content of 88.04% wb) through the application of both traditional mathematical models and advanced machine learning method. The study aims to optimize th...

Tillage in agricultural production is one of the most demanding in terms of the tractor's energy requirements, consuming a large amount of fuel and therefore releasing the harmful products of fuel combustion into the atmosphere, where carbon dioxide and nitrogen oxides are released. The experimental test was carried out under real operating conditions on the property, where an analysis was made of all the important parameters that can be correlated with ground resistance and thus the changes in the observed parameters, i.e. the changes in specific fuel consumption and the changes in exhaust emissions. Machine learning techniques were used as highly effective modelling techniques to predict the appropriate tractor operating conditions and soil condition. XGBoost model was selected due to its flexibility and high level of learning. The models were built to predict the CO2 content in the exhaust gases and the specific fuel consumption. Although the statistical parameters were not the highest, the prediction of CO2 content based on the data analysis has an accuracy of over 80%, and the prediction of specific fuel consumption is over 65%, which can be used for a quick or initial analysis, but also represents a challenge for further research on this topic.