Plamena Zlateva

The paper explores the transformative potential of synergistic integration between Generative AI (GenAI) and Digital Humans in the context of natural disaster management. Traditional crisis notification systems often fail due to structural inelasticity and inability to overcome cognitive tunneling and panic in the threatened population. The paper proposes a new conceptual framework - “Cognitive-Affective Crisis Synergy” - in which GenAI functions as the operational computational core (the brain) and Digital Humans serve as an empathetic, multimodal interface (the face). This is a conceptual and theoretical contribution; the framework is illustrated through practical scenarios rather than empirical validation. Practical scenarios are analyzed across the three phases of crisis management: experiential learning (prevention), scalable tactical dissemination with embedded autonomous sign language translation (response), and virtual psychological triage (recovery). Special attention is paid to ethical and technological challenges, including algorithmic hallucinations, the Uncanny Valley effect under stress, legal liability assignment, and the protection of user biometric sovereignty. The paper concludes with practical recommendations for implementing RAG architectures and Edge AI systems to build a sustainable, inclusive, and humanized technological infrastructure for population protection.

The accelerating development of Artificial Intelligence (AI), data-driven systems and autonomous digital platforms leads to profound transformations in modern e-business. These transformations are increasingly conceptualized through the notion of technological singularity, which is understood not as a singular future event but as an ongoing process of escalating algorithmic autonomy, systemic complexity and diminishing human controllability. The aim of the paper is to discuss the future of the development of e-business in the era of the singularity by analyzing the business model transformation, management and decision-making, cybersecurity and sustainability, and competitiveness in global markets. The main outcome of the work will be that the sustainable development of e-business in the era of the singularity requires a new balance between algorithmic autonomy and human control, between technological efficiency and social responsibility.

Artificial intelligence (AI) is increasingly being established as an important factor in increasing the productivity, efficiency and competitiveness of modern industrial enterprises. Classic efficiency ratios cannot directly assess the different aspects of the impact of AI implementation on the overall activity of an enterprise. This circumstance creates a methodological gap between the processes of digital transformation with AI and the existing tools for assessing the economic efficiency of enterprises. The aim of the paper is to propose modified efficiency ratios for assessing the impact of artificial intelligence on the economic efficiency of industrial enterprises. The developed conceptual methodological framework extends four classic ratios for economic efficiency in term of total costs, production cost, personnel and capital, by including specific variables taking into account the impact of AI on the productivity of an industrial enterprise.

This paper proposes a reference model for a Digital Twin enhanced framework that extends traditional High-Level Architecture (HLA) based simulations toward disaster-aware critical infrastructure management. The framework introduces a disaster-aware conceptual architecture and associated operational views to structure the interaction of simulation components, data flows, and decision-making processes. Real time data streams from sensors, Internet of Things (IoT) devices, and external information sources are integrated with AI driven analytics to create continuously updated, living digital twins of critical infrastructures, urban environments, and relevant natural systems. By embedding these digital twins within standardized HLA federations, the proposed approach enables interoperable, scalable, and reusable simulation components across heterogeneous disaster scenarios. The framework enhances situational awareness, supports continuous risk assessment, and allows decision-makers to simulate, analyze, and compare disaster response and mitigation strategies under dynamically changing conditions. The reference model defines core architectural elements, data exchange mechanisms, synchronization principles, and operational viewpoints required to couple digital twins with HLA - compliant simulations.

Mathematics and computer science constitute the foundational pillars of Generative Artificial Intelligence (GenAI), enabling the design of models capable of learning complex patterns from large-scale data and generating novel, meaningful outputs. Core mathematical disciplines, including probability theory, statistics, linear algebra, and optimization, provide the theoretical basis for generative modeling, while computer science translates these principles into practical algorithms, neural architectures, and scalable computing systems. This tendency adopts the concept of intelligent integration to describe the deep, systemic interaction among mathematics, computer science, and GenAI in education. The paper proposes a Synergistic Innovation Model in Education integrating mathematics, computer science, and GenAI, highlighting how their joint interaction drives innovation in education. The paper aim is to uncover the complex connections between these three disciplines and demonstrate how their unification enables the creation of powerful generative models that can produce new and meaningful knowledge in education.

Data generated by civilian digital ecosystems are increasingly used to train AI algorithms and develop military technologies, creating a new interdependence between the civilian and military spheres. The article introduces the concept of “digital dualism”, which describes the dual role of data. A typology of the main categories of data and the analytical model “Data Militarization Risk Matrix” are proposed to assess the risk of their militarization. The need for new legal frameworks is discussed to reflect the growing role of data and to ensure the protection of civilian infrastructure in the context of modern conflicts. A legal instrument, the “Data Targetability Test”, is proposed, which adapts the principles of international humanitarian law to the digital environment. The aim of the article is to explore and systematize the legal status of civilian data sets in the era of artificial intelligence, by developing a conceptual and analytical framework for assessing their potential transformation into legitimate military targets in the context of international humanitarian law.

Generative Artificial Intelligence (GenAI) rapidly changes modern business. In the recent years, GenAI technologies have not only improved efficiency and innovation in various industries, but also changed the way companies interact with their customers, employees and partners. Sustainable business development becomes increasingly important in the context of global economic, social, and environmental challenges. This means that companies should not only focus on financial success, but also take care of the environmental and social aspects of their activities. The aim of this conceptual paper is to examine the transformation of business models under the influence of GenAI, the economic and market opportunities, the carbon footprint of GenAI infrastructure, ethical dilemmas, the role of regulations and international standards.

The integration of Artificial Intelligence (AI) into Autonomous Weapons Systems (AWS) raises serious ethical, legal, and moral questions about delegating lethal force decisions to nonhuman agents. Basic models of human control have been defined, as well as AI methods that improve the effectiveness of AWS over humans, including speed, accuracy, and data processing. However, the systems present significant risks associated with a lack of moral accountability, algorithmic opacity and the potential for escalation. The article explores the complexity of the issue by examining the basic concepts, the degree of autonomy, and the role of AI in identifying, targeting and neutralizing targets. The aim of the paper is to investigate the conceptual foundations and definitions that shape the understanding of AWS, analyze the legal frameworks applicable in international context, arising from the integration of AI into AWS.

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.