One of the food preservation technologies is the drying process, which requires heat and is significantly energy-intensive, resulting in high costs. This caused the search for new design solutions for dryers, which requires continuous experimental research and the creation of new decision-supporting models for the optimization of drying processes. In this work, four models of the kinetics of convective onion drying were developed using Artificial Neural Networks (ANNs), taking into account pre-treatment before drying and the different temperatures of the drying agent. The moisture content in the dried material at a specific moment in time was taken as the dependent variable (ANN output). The following were accepted as independent variables (ANN inputs): drying temperature, initial sample thickness, initial moisture content, initial mass of the sample, time of drying, and material pre-treatment (no pre-treatment—blanching–osmotic dehydration). Four semantic models were formulated, the general Ann1 model taking into account all input variables and three detailed Ann2 models for individual types of pre-treatment. For the best Ann1, the MAPE values were 5.88–7.02% (for different data: Training, Test, Validation). For the detailed Ann2 models, the error values were more than twice lower. The MAPE values ranged from 1.14% to 3.12%.

One of the methods for obtaining zinc oxide nanoparticles (ZnO NPs) is electrochemical synthesis. In this study, the anodic dissolution process of metallic zinc in alcohol solutions of LiCl was used to synthesize ZnO NPs. The products were obtained as colloidal suspensions in an electrolyte solution. Due to the small size and ionic nature of the zinc oxide molecule, colloidal nanoparticles tend to cluster into larger groupings, so the size of nanoparticles in solutions will differ from the size of nanoparticles observed in ZnO powders after solvent evaporation. The main goal of this research is to investigate the influence of the temperature of synthesis and the kind of alcohol on the size of ZnO NP micelles. Nanocrystals of zinc oxide were obtained in all tested alcohols: methanol, ethanol, and 1-propanol. The particle size was determined using the Dynamic Light Scattering (DLS) method. It was observed that the particles synthesized in methanol were the largest, followed by smaller particles in ethanol, while the smallest particles were obtained in 1-propanol. Additionally, the particles obtained in ethanol were the most uniform in size, showing the highest level of size homogeneity.

Brake pad wear forecasting, due to its complex nature, is very difficult to describe using engineering formulas. Therefore, the aim of this publication is to create high-quality brake pad wear forecasts based on three stochastic quantitative models based on multiple regression models (linear model, inverted linear model, and power model). The matrix of explanatory variables was extracted from the controllers of 29 vehicles: A—the driver’s style of using the brake pedal specified on a 4-point scale and B—the number of vehicle load ranges specified on a 5-point scale. Methodology: A matrix of explanatory variables was obtained over a 2-year period from trucks carrying oversize loads via OBD2 socket. The trucks operated under similar operating conditions. The created models were verified in terms of their fit to the source data and by analyzing the residuals of the models. It should be emphasized that only the linear model met all the required criteria. The inverted linear and power-law models were rejected. Results: The verified linear model is characterized by very small MAPE errors. The model was validated on 4 trucks and the brake pad wear prediction errors ranged from −0.39% to 7.03%.

A neural model was developed to predict the distribution of ZnO nanoparticles obtained by electrochemical synthesis. It is a three-layer multilayer perceptron (MLP) artificial neural network (ANN) with five neurons in the input layer, eight neurons in the hidden layer, and one neuron in the output layer. This network has a hyperbolic tangent activation function for the neurons in the hidden layer and an exponential activation function for the neuron in the output layer. The input (independent) variables are particle size (nm), solvent type, and temperature (°C), and the output (dependent) variable is fraction share (%). The best neural model (ann08) has a root mean square error (RMSE) 0.84% for the training subset, 0.98% for the testing subset, and 1.27% for the validation subset. The RMSE values are therefore small, which enables practical use of the ANN model.

Modern research laboratories are constantly evolving to meet the growing demands for precision, quality, and flexibility in scientific work. The modernization of existing experimental test benches plays a crucial role in improving efficiency, optimizing processes, and ensuring operational safety. This requires updates to their design, experimental methods, data collection, and results recording—all of which provide the foundation for developing new research concepts. An increasing number of innovations are now guided by the principle of minimizing environmental impact. In line with this approach, an innovative modernization of a tube furnace research station was carried out, based on the concepts of sustainable development and the zero-waste philosophy. To enable thermogravimetric analyses of coffee waste, a previously incomplete tube furnace was refurbished using recycled components. The primary objective was to expand the research capabilities of the existing workstation. As part of the modernization, three indicators of reuse efficiency were calculated: the quantitative indicator W_re-use, the mass indicator $W_{re\text{-}use}^{mass}$, and the cost indicator $W_{re\text{-}use}^{value}$. A quantitative index of 78% and a mass index of approximately 76% were achieved, while the economic value of the recovered components accounted for 11% of the total value of the revitalized research station. This strategy significantly reduced waste generation, carbon dioxide emissions, and the consumption of primary raw materials.