Industrial revolutions have always marked breakthrough periods that fundamentally changed the way of production, work organisation and the overall functioning of industry. From the first mechanised machines in the 18th century to today’s intelligent technologies, industry has undergone enormous changes affecting production, logistics, transport and trade. Each industrial revolution has brought new technologies that have helped businesses become more efficient and better respond to market demands. The research and the submitted manuscript aimed to examine different ways of managing the supply chain through innovative information technologies and to analyse their effectiveness and risks at individual stages of the supply chain. The study also focused on identifying optimal ways of implementing these technologies and their possible impacts on the performance and competitiveness of the company.

This paper presents a comparative analysis of various segmentation methods for extracting tall vegetation objects from LiDAR data, including clustering-based methods, voxelization strategies, and Canopy Height Model (CHM)-based segmentation. First, an overview of Light Detection and Ranging (LiDAR) technology is provided, focusing on its underlying principles and the unique advantages it offers in forestry management, such as detailed canopy structure mapping. Subsequently, an extensive experimental evaluation is conducted on real forest datasets, where each segmentation technique is assessed in terms of accuracy using Intersection over Union (IoU) metric. Through these experiments, the paper highlights key trade-offs between accuracy and runtime performance, revealing that methods leveraging voxelization can achieve high IoU values while remaining computationally feasible. The findings underscore the importance of selecting an appropriate segmentation algorithm for forestry applications, as it can significantly impact the fidelity of canopy measurements and downstream forest management decisions. The experimental results demonstrate that combining voxelization with DBSCAN algorithm yields superior segmentation performance compared to other evaluated CHM-based methods.

Abstract The building information modeling represents the management of construction projects based on relevant information and work with them. In the era of digitization, it represents a space for modeling information about constructions to implement and increase the digital level in the construction industry as well. One of the goals of implementing new technologies is to improve performance and efficiency. The current situation within the pressure for sustainability and so-called green solutions brings new challenges, but also perspectives on technologies with the aim of increasing sustainability in every phase of the construction project. One of the tools should be the very implementation of these solutions to support the use of information and information systems to support the achievement of goals. Improving performance and achieving goals in circular economy and a higher level of sustainability itself is intended to quantify performance and change. Key performance indicators need to be monitored and evaluated. It is believed that building information modeling can achieve selected performance indicators. Due to the principles of the circular economy, there is pressure to monitor indicators that will lead to an increase in performance and indicators associated with this concept. In the research, based on relevant studies, the key performance indicators of the circular economy in the management of construction projects were determined. Due to the perception of sustainability through economic sustainability, material costs, material consumption, recycling costs, rate of use of recycled materials, waste management costs were monitored. The goal of the research was to quantify the impacts of building information modeling on selected performance indicators within the concept of circular economy. The aim of the research was to analyze the impact of using BIM on selected performance indicators. The research took place in three countries, namely Slovakia, Croatia, and Slovenia. The respondents were the main participants in the construction project. Data processing was based on the search for relationships and dependencies between the rate of use of information building modeling and selected key performance indicators.

Abstract Construction is an important sector of human activity that significantly impacts the environment. The impact of this sector can be analysed from different perspectives, such as consumption of natural resources, waste generation, energy intensity, and environmental change. The sector is increasingly promoting using renewable materials, energy-efficient practices, and planning those respects ecological processes and biodiversity. Against this background, it is important to take coordinated action across the sector and move to net-zero carbon standards through immediate action to raise awareness, implement innovation, and improve carbon management and reporting processes. Tools supporting the reduction of the adverse impacts of construction activities include artificial intelligence tools. The construction industry has long been considered a conservative and traditional industry but is now experiencing a technological revolution. Gradually, artificial intelligence (AI) principles and tools are beginning to be integrated into the various lifecycle processes of construction projects. This paper analyses the AI tools used to analyse carbon footprinting in the construction sector in terms of selected functionalities. The results of the research will form the basis for the development of a strategic plan for the development of AI within the research activities at the Faculty of Civil Engineering in Košice.

Abstract The paper deals with the simulation of selected activities in the warehouse. Routine logistics activities are the receipt of the goods, the control of the goods, and the goods’ storage in the warehousing system on the side of the entry into the warehouse. On the output side, these are activities such as picking, packing and shipping. The economy of warehouse operations depends on the efficiency of the logistics activities performed and on deployed the technical means. The paper’s main goal is to present a created simulation model for a selected warehouse activity—receipt of goods into the warehouse. This operation involves unloading the goods from the trucks, moving them to the physical receipt, where the quantitative and qualitative control of the goods is performed (acceptance of goods). An analysis of this activity preceded the creation of the simulation model under operating conditions. The research was performed by observing and measuring the forklift’s work cycle, which unloaded pallets from the trucks. The obtained data from the analysis were the input to the simulation model. The EXTENDSIM8 simulation system was used to create a simulation model. The several experiments were performed on the model after its creating and verifying the functionality. The paper evaluates 3 experiments and analyzes their results. The main benefit, novelty, of the article is the simulation of a selected activity for a specific company’s conditions. The experiments results are a reasonable basis for the creation of an operational tool for the company.