Biodegradable materials are used in the manufacture of packaging and compostable films and various types of medical products. These have demonstrated high potential in medical applications: cardiac, vascular and orthopaedic conditions in adults as well in children. In our research, the extrusion-cooking technique was used to obtain environmentally friendly loose-fill foams as packaging. Potato starch was the basic raw material. Polyvinyl alcohol was used as an additive in the amount of 1%, 2% and 3% to replace starch. The components were mixed and moistened with water to various initial moisture contents of the blend (17%, 18% and 19%). The processing of starch foams employed the TS-45 single screw extruder-cooker (Gliwice, Poland) with the L/D ratio of 12. The foams were processed with various screw speeds (100 and 130 rpm) and with two types of forming dies (circular and ring die). The extrusion-cooking process efficiency (kg h⁻¹) and the energy consumption (kWh kg⁻¹) during the processing were also measured. The results showed that the processing efficiency of potato starch foams varied depending on the level of polyvinyl alcohol, the shape of the forming die and the screw speed applied. The analysis of energy consumption, mechanical properties and FTIR analyses demonstrated that the type of the forming die and the initial moisture level had the most significant impact on specific energy demands during the processing of potato starch-based foams.

The possibility of charging and possibly discharging electric cars can influence not only the balancing of power demand profiles in the grid and the stabilization of voltage profiles but also the appropriate management of electricity within the grid of an industrial plant equipped with its own RES resources. For this purpose, the concept of “power supply modes” can be introduced, which involves intelligent demand-side management. Each technological process in an industrial plant should be assigned a specific level of importance and priority. These priorities can be numbered according to their importance (weights) and marked with appropriate colors. One thus obtains a qualitative assessment of energy consumption within the plant (demand side) through the lens of power modes. With respect to the ability to charge electric vehicles within the plant grid, such priorities can also be assigned to individual charging options. If a given RES has sufficient generation capacity during a particular time period, the cost of charging is low. However, if the RESs are not operational during a given period (e.g., nighttime in the case of photovoltaics or during calm weather in the case of wind turbines), vehicles can still be charged but according to a different priority, which, of course, involves higher costs. By having access to data on the generation capacity of distributed RESs and knowing the preferences of employees, including the number of electric cars and the expected periods of vehicle charging, it is possible to predict the degree of use of available green energy and manage it efficiently. The analyses presented in the article represent an original approach to the flexibility of operation not only of the electricity grid but also of the internal energy system of industrial plants. It offers a novel perspective aimed at maximizing the share of RESs in the overall energy balance and minimizing the costs associated with the operation of RESs. The theoretical opportunity of sustainable sharing with employees a dedicated charging mode named “free charging”, powered by RESs, could represent an appropriate solution for CO₂ emission reduction within Scope 3, Category 3, “employee commuting”, according to the GHG Protocol requirements. The original methodology proposed in the article aligns with activities related to the energy transition.

The main goal of the article is to share some experimental view onto VR training applications, that allows for adaptation of the directed training scenario to a user being trained. The resulted application allows to the three scenarios being selected in any combination of ones, as well as the tasks modification through the input files, before a test starts. The program was developed in the Unity engine, using the modern UnityXR framework providing extensive support for leading virtual reality hardware. The resulting solution presents the possibility of an efficient training in Virtual Reality while modifying the course of training without any needs to recompile the program, and also it shows some positive values of using VR technology as a didactic solution.

This manuscript introduces a multi-criteria decision-making (MCDM) methodology to address the complexities of the multi-depot heterogeneous vehicle routing problem with time windows (MDHVRPTW), incorporating pickups and deliveries, and validates it through a Fast-Moving Consumer Goods (FMCG) industry case study. The primary scientific achievement is the development and validation of a four-stage VRP algorithm that comprehensively models and solves real-world operational challenges. These include managing split customer orders (FTL/LTL), intricate vehicle capacity and axle load balancing (considering unloading sequences), route duration constraints (via tachograph data), precise delivery time windows, and an integrated multi-depot network structure. The methodology effectively minimizes routes/vehicles and maximizes route efficiency (e.g., km/ton), while strategically managing vehicle relocations. The findings demonstrate that the proposed approach is an effective decision support tool for VRPs with multiple, conflicting criteria. Furthermore, the research establishes a coherent set of evaluation criteria that are applicable across diverse problem instances, enabling robust solution comparison and fostering an objective basis for complex route planning.