The paper presents the issue of container handling processes at a railroad intermodal terminal. The main purpose of this paper is the assessment of the handling equipment utilization and the associated energy consumption. The authors analyze how the road vehicle availability at the moment specified in the containers loading schedule influences the total handling equipment operation time as well as the necessary number of handling equipment. It is assumed that vehicles planned for loading of import containers may be late for loading, which causes some interruptions in the loading schedule. Such interruptions are identified with the necessity to handle the next container for which the road vehicle is already waiting, which influences the handling equipment utilization and, finally, energy consumption. The general mathematical model of the problem developed in the FlexSim simulation software was presented. Based on the simulation research, it pointed out that proper road vehicles loading sequencing can significantly reduce handling equipment operation time, and thus energy consumption, costs, and CO₂ emissions. The literature analysis presented in the paper indicates that most of the research in the field of intermodal transport is focused on operations optimization in container ports. There are differences between two types of intermodal terminals in operation procedures and rules. That is why the authors decided to undertake the problem of road vehicle sequencing including their random availability and its influence on handling device operation time, which has not been considered in the literature so far.

Modeling and simulation are key performance analysis and control techniques to optimize decision-making as well as design and operate complex production systems. They are also indicated as one of the technological pillars of modern industry and IT solutions supporting the implementation of the roadmap toward Industry 4.0 in the areas of digital transformation and automation. In the context of the required rapid transformation of today’s enterprises, it becomes extremely important to look for solutions that allow the use of the existing infrastructure, information, and energy, so as to minimize the negative impact of new technologies and the transformation process itself on the environment. The article presents an approach to modeling large and complex production systems with the use of distributed Petri net models allowing the use of the possessed IT infrastructure as consistent with the idea of sustainable development in the activities of enterprises. This eliminates two major problems that render traditional models unusable. The first is related to the difficulties in analyzing and verifying models of enormous size and infinite space of states. The second is related to the required computing power, if such analyzes are to be performed on one computing unit, which would force the producers to replace the IT infrastructure. For this purpose, modular Petri nets are introduced. Other benefits of modularization, such as smaller components that can be independently analyzed, are also presented in the paper. The proposed modular Petri net has been implemented in the proprietary GPenSIM software. The paper is complemented by a practical example of industrial modeling of production systems with automated guided vehicles (AGVs) using the Modular Model with Intelligent Petri Modules.

The structural, morphological and photoluminescent properties of thermally evaporated neodymium oxide (Nd2O3) thin films deposited onto nanostructured silicon (Si-ns) are reported. Si-ns embedded in silicon nitride (SiN) thin films are prepared by plasma-enhanced chemical vapour deposition (PECVD). SiN and Nd2O3 thin films uniformity and Si-ns formation are confirmed by atomic force microscopy (AFM) and scanning electron microscopy (SEM). The presence of neodymium (Nd), silicon (Si), oxygen (O), and phosphorus (P) is investigated by energy-dispersive spectroscopy (EDS) and secondary ion mass spectrometry (SIMS). Post-annealing SIMS profile indicates an improvement of the homogeneity of activated P distribution in Si bulk. The X-ray diffraction (XRD) combined with Raman spectroscopy and Fourier-transform infrared spectroscopy (FTIR) have been employed to determine amorphous silicon (a-Si), crystalline silicon (c-Si), Nd2O3 and SiN phases present in the Nd2O3-SiN bilayers with their corresponding chemical bonds. After annealing, a Raman shift toward lower wavenumbers is recorded for the Si peak. XPS data reveal the formation of Nd2O3 thin films with Nd-O bonding incorporating trivalent Nd ions (Nd3+). Strong room-temperature photoluminescence is recorded in the visible light range from the Si-ns. Nd-related photoluminescent emission in the near infrared (NIR) range is observed at wavelengths of 1025–1031 nm and 1083 nm, and hence is expected to improve light harvesting of Si-based photovoltaic devices.

Neuroscience deals with the issue of moral judgment. That term already has a long history in philosophical reflection. Both fields, the neurosciences and the philosophy, use different methodologies when applying it. The approach of neuroscientists tends to be reductionist. This article seeks to overcome this reductionism. The main question is: How the term “moral judgment” is understood in neurosciences? Is its understanding very different from that which is present in moral philosophy? To answer, in the first part of the article, the author investigates the meaning of the term “moral judgment” in four scientifical models: in the moral intuitionism of experimental psychology, in Social Intuitionist Model by Jonathana Haidt, in Dual-Process Theory by Joshua Green, and finaly, in Somatic Marker Hypothesis by Antonio Damasio. These reflections introduce the second part of which the subject is an examination of Christian moral philosophy and its confrontation with the findings of neuroscientists.