Nowadays, natural disasters, due to available climate changes, increase their negative impact on tangible fixed assets of companies by damaging or destroying them. Therefore, in order to continue their production activities, companies need to repair existing assets or purchase new ones. The purpose of the paper is to propose a depreciation approach to be used to reduce these consequences for business from natural disasters. The main idea is to use the funds accumulated through depreciation expediently for the acquisition of new tangible fixed assets to replace the destroyed ones. It is assumed that the term ‘depreciation’ refers to an accounting method used to allocate the cost of a tangible fixed asset over its useful life. Various depreciation methods are considered: linear and nonlinear (declining and ascending). The essence of the proposed approach is to suggest suitable optimal method for depreciation of a given tangible fixed asset, taking into account its specific features, in order to accumulate as mush depreciation funds as possible during the first years. Thus, the asset destroyed from natural disasters can be replaced with new one as quickly as possible. To verify the approach, several examples are presented with specific tangible fixed assets showing the results of using various depreciation methods.

The main idea of this study is to address the need for improved scenario simulation techniques in disaster risk management and to explore the potential of the High-Level Architecture (HLA) standard in this area. The aim of this research is to propose an innovative framework for performing object-oriented (OO) simulations based on the HLA standard of various scenarios for natural disaster risk management. The proposed approach enables collaboration, interoperability, and scalability by integrating various OO simulation components, including hazard models, infrastructure models, human behavior models, and decision support systems, within a unified platform. The results demonstrate the feasibility and effectiveness of the proposed innovative framework based on the HLA standard for simulations of the disaster risk management scenarios.

This paper examines the impact of simulator training duration on the performance of mobile transport platform (MTP) operators in the early stages of skill development. The primary hypothesis is that the learning effect is uneven, structurally localized, and manifests itself at specific stages of task performance. An experimental study was conducted involving operators with no practical experience who underwent simulator training for 15 and 30 min under identical conditions. An unmanned aerial vehicle (UAV) was used as an aerial case study of mobile transport platform (MTP) control, ensuring accurate recording of trajectory parameters. The experimental task involved driving a standardized route with checkpoints, divided into time segments. To analyze structural effects, a relative segment effect metric was introduced, allowing for quantitative assessment of differences between training conditions at the level of individual trajectory segments. In addition to the overall task completion time, segmental characteristics, the number of incidents, and missed checkpoints were analyzed using nonparametric statistical methods. The results showed that longer simulator training was associated with a statistically significant reduction in overall task completion time. However, the effect is heterogeneous: significant differences are localized in specific trajectory segments characterized by increased control complexity. High variability in performance was also revealed in the early stages of training, along with a lack of a strong relationship between execution speed and control accuracy. These results demonstrate that assessing the effectiveness of operator training should consider not only integral indicators but also the task structure. Although the experiment was conducted using a UAV-based aerial case study, the proposed approach is applicable to a wide range of mobile transport platforms.

The increasing frequency and intensity of natural disasters pose serious challenges to business resilience and continuity of organizational processes. Traditional approaches to management training often prove insufficient to prepare managers in conditions of high uncertainty and dynamically developing crises. The aim of this article is to propose a generative AI-based framework for managerial training aimed at increasing business resilience to natural disasters. The framework will integrate scenario generation, simulation-based decision-making and adaptive learning mechanisms. By using generative models, realistic and dynamic scenarios will be created that allow managers to develop strategic and operational competencies in a controlled learning environment. The proposed approach provides personalized training, real-time feedback and multicriteria performance assessment. The conceptual demonstration shows the potential of the framework to improve decision quality and organizational readiness. The results highlight the transformative role of Generative AI in the modernization of management education and strengthening business resilience.

This article examines the fundamental challenges facing the education system in the context of the approaching technological singularity, where artificial intelligence (AI) surpasses human cognitive abilities and begins to improve itself exponentially. The traditional educational model, inherited from the industrial era and based on linearity, standardization, and reproduction of an established volume of knowledge, turns out to be incompatible with the dynamics of exponential technological progress. Education can no longer be conceived as a system designed merely to transmit established volumes of knowledge. It becomes a dynamic, adaptive, and meta-cognitive infrastructure for cultivating meaning, judgment, and human agency within environments characterized by exponential change. Education in the age of technological singularity must transition from an industrial-era model of standardization to a post-industrial, cognitively augmented model focused on human-AI symbiosis, ethical reasoning, creative synthesis, and lifelong adaptability. The aim of the article is to identify and analyze the main dimensions and critical issues of the transition of education to technological singularity.

Natural disasters such as floods, hurricanes, landslides, extreme rainfall, and earthquakes have a serious impact on the sustainability of businesses related to transportation systems. There are interruptions or changes in transportation routes, delays in deliveries, increases in planned costs, disruptions in supply chains due to unforeseen changes in demand and supply, and other adverse events. The basic transportation task, which aims to minimize transportation costs under certain constant constraints (static costs and fixed capacities), does not take into account the uncertainty in the transportation logistics system due to natural disasters. The purpose of this article is to propose a modified model of the basic transportation task, which includes an assessment of the risk of natural disasters as a key variable in the optimization of transportation logistics systems. The proposed modified model extends the basic transportation task by taking into account factors related to natural disasters: the level of potential risk along different routes, changes in supply chain capacity and demand levels. This modified model includes several additional variables such as the risk level and probability of occurrence of a natural disaster, disaster intensity, infrastructure sensitivity, and route vulnerability. These additional variables are used to assess the accessibility of the routes and dynamically recalculate the values in the model. The proposed modified model of the transportation task allows to minimize the total transport costs, taking into account both standard logistical constraints and various risk variables associated with potential natural disasters along the transport routes. Numerical results are presented, which show the behaviour of the modified model of the transportation task at different levels of risk of natural disasters.

Modern solutions of artificial intelligence (AI) give significant opportunities for improving efficiency of multiple sociotechnical systems, including transport systems. At the same time, the issues of distribution of functions between an intelligent assistant and a human operator remain open. The issues of organizing such interaction between a human and artificial intelligence, which would exclude their confrontation or rivalry as well as the phenomenon of cognitive dissonance in a human when making decisions, have not been fully resolved. This paper proposes a scheme of interaction between a human and AI as part of complex systems and a developed model of human-machine interaction with an intelligent assistant. It shows that for organization of advanced AI-based automation an intelligent assistant needs to receive information not only on the state of a technical system, a work object and an ambient environment but also on the state of a human operator themselves. This could ensure "awareness" of a human operator by artificial intelligence and provide a base for predicting operator behavior in different conditions.

Generative Artificial Intelligence (GenAI) is emerging as a transformative tool with the potential to personalize learning, increase student engagement and expand access to high quality educational resources. GenAI can enhance the learning process through simulations, code generation, visualizations and adaptive resources. GenAI offers the development of critical and ethical thinking and supports educators through the automation of routine tasks, as well as opportunities for the inclusion of learners with special needs. On the other hand, significant ethical issues are identified, such as violation of academic integrity, bias, digital inequality, cognitive passivity and lack of institutional regulations. The purpose of this paper is to examine the opportunities and ethical issues and arising from the application of GenAI into STEM+C education (science, technology, engineering, mathematics, computer science). Specific recommendations are proposed for the responsible use of GenAI in STEM-C, covering educator and learner training, policy development, equitable access and ethical pedagogical practices.

Generative AI technologies are revolutionizing various business sectors, providing new ways to automate, personalize and innovate. The aim of the paper is to explore the future of business innovation through generative AI, highlighting its impact on design, manufacturing, logistics, marketing, healthcare and education. Generative AI offers significant benefits such as accelerating new product development, optimizing processes and improving customer experience. However, the use of these technologies also brings challenges, including ethical issues related to intellectual property and employment, as well as the need for regulations. The article predicts that in the coming years, generative AI will play a key role in transforming business models and will continue to drive innovation across industries.

Generative Artificial Intelligence (GenAI) is becoming a powerful tool in modern entrepreneurship, providing new opportunities for automation, personalization, and the creation of innovative products and services. Through emerging technologies such as language models, image and audio generation systems, entrepreneurs can significantly accelerate their workflows and reduce costs. However, the implementation of GenAI is associated with a number of significant challenges. The technological barriers include high requirements for computing resources, complexity in integration, and the need for specialized knowledge. The lack of trained personnel and resistance to technological changes are another obstacle in its sustainable implementation. From a business perspective, the unpredictability of the GenAI results and the lack of trust among users can slow down the growth. The emerging global legal regulations impose additional responsibilities on the entrepreneurs. The aim of this conceptual paper is to analyze these multi-layered challenges and offer guidelines for the responsible and effective use of GenAI in entrepreneurship. A framework for using GenAI in entrepreneurship is proposed too.

The aim of the paper is to examine the role of the break-even point (BEP) as a business management tool in the conditions of natural disasters and to propose various managerial scenarios for effective decision-making after severe damage to production equipment. The break-even point of a company represents the sales volume or revenue level at which total revenues equal total costs (both fixed and variable), meaning that the enterprise neither makes a profit nor incurs a loss. Below this threshold, the company operates at a loss, while above it, profitability begins to appear. This study explores four managerial scenarios: Scenario for increasing fixed costs and decreasing unit variable costs; Scenario for increasing fixed costs and increasing unit variable costs; Scenario for increasing fixed costs, increasing unit variable costs, and increasing unit price; Scenario for reducing fixed costs without changing unit variable costs. By using the break-even point as a business management tool the companies can successfully design strategies for sustainable recovery and profitability under the conditions of natural disasters.

Quantum Computing (QC) is a new generation of computing technologies based on the principles of quantum mechanics. It can offer significant advantages over traditional computer systems in solving complex problems that would take years for classical computers. The ability of QC to process vast amounts of information at incredible speeds and find solutions to previously unsolvable problems could accelerate innovation, revolutionize industries and create new business opportunities. QC can be used to solve specific, real-world business problems that can include big data processing, complex calculations, process optimization, and cybersecurity. To realize the potential of QC, companies must invest in partnerships with technology companies, train specialists and experiment with pilot projects. Early adopters will gain a competitive advantage by optimizing operations, creating innovative products and transforming business models. The aim of the paper is to examine the way QC can accelerate innovation processes in business, as well as to outline practical aspects and challenges to help implement this disruptive technology in the corporate environment.

The combination of Generative Artificial Intelligence (GenAI) and augmented reality (XR) technologies opens up new opportunities for innovation in the real estate business. GenAI provides automated predictions and personalized recommendations, while XR provides interactive virtual experiences. These technologies are revolutionizing old business models and offering new ways to connect with customers, manage properties, and optimize investments. These innovations will create new business opportunities, but will also require new approaches to governance, data protection, and social impacts. The purpose of this conceptual paper is to explore the issues of the convergence of GenAI and XR and to propose a conceptual framework for real estate business innovation.

The aim of the paper is to analyze the key ethical aspects of the application of Generative Artificial Intelligence (GenAI) in disaster risk reduction (DRR), identifying the challenges and proposing a framework for their responsible solving. GenAI is a transformative technology in DRR, which offers new possibilities for predicting, preventing and responding to natural disasters. GenAI can create detailed simulations for training, analyze large data sets in real time, generate personalized warnings and optimize logistics. Despite the significant potential, its implementation is accompanied by a set of ethical challenges that require careful consideration. The GenAI inherent bias in algorithms can lead to discriminatory forecasting models that underestimate risks for marginalized communities or redirect resources to more privileged areas. The GenAI model complexity makes them difficult to interpret, creating problems with responsibility. GenAI can create convincing fake images, videos and text messages that can be used to manipulate public opinion during natural disasters, creating additional threats to public safety. GenAI operations require the processing of huge volumes of sensitive data, which raises privacy issues and possible misuse. Thus, a comprehensive ethical framework and governance approaches are needed to address these challenges.

The integration of Generative Artificial Intelligence (GenAI) with the Metaverse for a next-generation education is a complex but challenging task. The GenAI-enhanced Metaverse classrooms require innovative instructional designs that use virtual reality and augmented reality to enhance engagement and personalized learning. Educators must adapt to new roles over traditional teaching methods, while learners need to develop digital literacy skills that are essential for navigating and inhabiting in these environments. Such learning environments require significant advancements in real-time processing, scalability and interoperability of different platforms, while ensuring data privacy and security. The equity of access to high-speed internet and advanced devices still remains a serious barrier, which can increase the potential existing inequalities between different educational environments. Ethical considerations, including the responsible use of GenAI, the creation of unbiased educational content, and the psychological impacts of extended usage of virtual reality, are also of important consideration. The aim of the paper is to explore in detail the different challenges through a comprehensive analysis of the obstacles and potential solutions and to propose a collaborative framework involving educators, technologists, policymakers and industry stakeholders to address the effective implementation of the integration of GenAI and the Metaverse for a next generation education.

In recent years, a sharp rise in the frequency and intensity of climate-related hazards is observed worldwide. This increases the vulnerability of businesses to climate-related hazards, which in turn adversely affects the economic and sustainable development of the companies. The aim of this paper is to propose an approach for vulnerability analysis of businesses to climate-related hazards. It is assumed that the level of vulnerability for each business depends on the levels of the potential impact of the specific climate-related hazards in the given geographical region. The potential impact depends on the levels of a business’s sensitivity and exposure to climate-related hazards. The approach consists of several steps. For each of the variables related to the vulnerability, five levels are defined (Very low, Low, Middle, High, Very high). The usefulness and peculiarities of the proposed approach for vulnerability analysis of businesses to climate-related hazards are validated with particular examples. The proposed approach for vulnerability analysis can successfully help the managers to make informed decisions about the choice of targeted measures to adapt businesses to climate change.

Generative AI (GenAI) is a powerful tool capable of creating text, images, music and other forms of content, but its use raises serious questions about privacy and data protection. Models trained on large amounts of data often include private or sensitive information, leading to risks of data leakage and inadvertent reproduction of confidential data. The main challenges include the collection of data from different sources, the storage of personal information in the models and the possibility of generating content that reveals sensitive information. Regulatory and other data protection laws play an important role in limiting these risks, imposing requirements for transparency, consent and the right to erasure of personal data. The aim of the paper is to additionally explore the key privacy risks in GenAI, analyze the applicable regulatory requirements, and propose strategies for managing personal data that comply with modern laws and ethical standards.

Generative artificial intelligence (GenAI) is a cutting-edge technology capable of creating new content–text, images, video and sound based on predefined data. Despite the enormous potential for innovation, this technology carries with it serious risks and threats. The article aims to discuss the technical risks, ethical and social challenges, labor market and economic consequences, and legislative and regulatory challenges. Recommendations are proposed to reduce these risks through more responsible use, transparency and the development of adequate regulations.