Traditionally, health records are kept in siloed data storages of different health organizations. Today, all patients’ EHRs (Electronic Health Records) are used and shared with different institutes and research facilities without their consent. To protect and overcome pitfalls of generic systems, we introduce a new hybrid system called Hybrid Patient Data Vault (HPDV). This hybrid system can help patients securely share their health information in a manner that could allow them to share only what is necessary or in need-to-know basis. We detail the system’s components, workflows, and emergency protocols, emphasizing patient-centric design. Through a STRIDE-based threat model and simulations of key metrics like transaction latency and ZKP generation time, we demonstrate HPDV’s security and feasibility. Our evaluation shows it outperforms monolithic approaches in auditability and privacy, with ZKP proofs generated in under 7 seconds on standard devices. This work demonstrates a practical modern approach for secure, patient-controlled health data exchange.

Within the scope of the study an analysis of a quantum-resistant key management infrastructure (KMI) is conducted. Key Management Infrastructure, in this context, encompasses cloud services, on-premises HSM clusters, and hybrid or edge solutions. Asymmetric algorithms underpinning modern protection protocols demonstrate vulnerability to quantum methods of Shor and Grover. The objective of this study is to perform a holistic reference architecture for KMI that enables a seamless and secure transition from classical to post-quantum solutions. The methodology includes a systematic analysis of existing KMI architectures, a detailed evaluation of algorithms standardized by NIST for post-quantum cryptography, as well as the modeling of a hybrid cryptographic scheme. As a result, a multi-layer architectural model is proposed, featuring a “Crypto-Agility Engine” for dynamic algorithm replacement, hybrid key encapsulation protocols and a phased migration strategy to post-quantum primitives. The model maintains backward compatibility with legacy systems, minimizes the load on mission-critical components such as fintech platforms and ensures an unchanged level of performance. The study conclusions confirm the practical feasibility of this approach for long-term protection of data confidentiality and integrity in the post-quantum era. This work is of interest to information security architects, software engineers and specialists engaged in the protection of critically important information across diverse infrastructures.

The article provides principles for creating efficient project schedules in extreme climatic conditions. The rationale for the relevance of the work is essentially defined, considering recent trends regarding the increasing frequency and magnitude of weather-related catastrophes. The purpose here is to develop a methodology for adaptive planning based on quantitative diagnostics regarding climate, probabilistic windows related to favorable conditions, and the dynamic redistribution of tasks. It will be new in that non-stationary extremes will be integrated within an analysis approach to evaluate changes in parameters regarding climate distributions; climate windows shall be calculated based on multi-year meteorological data and NOW-cast forecasts; adaptive buffers shall be applied within the critical chain by APD rule and modular decomposition of tasks; the digital twin will function to perform operational revision of priorities; finally parametric insurance as well as climate clauses shall be included in contracts. The main conclusions affirm that the application of the above-stated rules allows up to 28% acceleration of the calendar with budget maintenance, reduces downtime uncer­tainty by 15%, and keeps schedule shifts within 5% of the planned date for up to three years. This paper is likely to find its best audience among project managers, planners, and risk management specialists working in the building construction, infrastructure, and maritime sectors.

This article explores the transformative impact of Artificial Intelligence (AI) integration within SAP S/4HANA Cloud, focusing on its applications in enterprise management, particularly in finance and controlling operations. It examines how AI enhances various aspects of financial management, including cash management, robotic process automation, and advanced analytics. The article delves into the synergistic relationship between AI capabilities and human expertise, highlighting how this collaboration is reshaping traditional financial processes and decision-making paradigms. The article also discusses the future trajectory of AI in SAP S/4HANA Cloud, anticipating further advancements in areas such as predictive analytics and natural language processing. Additionally, it addresses the challenges and considerations that arise from this technological integration, including the need for balancing automation with human oversight, the ethical implications of AI-driven decision-making, and the imperative for workforce skill development. By providing a comprehensive overview of the current state and future potential of AI in SAP S/4HANA Cloud, this article offers valuable insights for organizations seeking to leverage AI technologies to enhance their financial operations and strategic decision-making processes

Corporate training seldom provides structured, realistic practice, resulting in low retention and limited behavior change. We built a curriculum-centric, multi-agent framework for role-play simulations (could be adapted for text and voice) that deliver sequenced, educationally grounded training conversations for sales employees. The framework automates delivering curriculum of scenarios by coordinating several agents: the Orchestrator manages flow, the Curriculum Manager sequences role-play scenarios with embedded learning objectives and rubrics, the Conversation Agent enacts realistic dialogues, the Data Agent tracks progress, Telemetry logs outcomes, and the Guard enforces safety. We designed a workflow prototype in n8n that simulates the behavior of a phone-based conversational agent. For this paper, the system is demonstrated through a chat-based interface that reproduces the curriculum sequencing and orchestration logic, rather than a production telephony deployment. Pilot learners told us that sequenced curricula improved practice consistency and in turn confidence. Learners appreciated how realistic practice was and specific feedback via Telemetry, although challenges still remain around sustaining engagement and avoiding repetitive feedback. Through these findings we canconclude that a curriculum-centric, multi-agent role play simulation can improve learning outcomes in employee training, bridging the gap between adaptive e-learning and real-world application. Our objective is to examine whether curriculum-guided AI role play can improve soft skills when compared to unstructured. This paper contributes a curriculum-centric framework, functional prototype and findings from a formative pilot with three sales hires.

The paper discusses the great change at the high end of game development brought about by generative artificial intelligence — not simply a minor tooling upgrade but rather a machine that can be used in automating the building of adaptive systems and increasing the gravitational pull of engagement for players. At a studio level, adoption has already moved beyond pilot projects to institutional practice-a shift that turns mere efficiency gains into matters of existence for quite many teams. The paper’s novel input is a neat merge of three paths: first, the lift of a game’s “skeleton” to a formal meta-model that lets big language models set up event graphs; second, a change in how hardship is made real — moving from simple time factors (D1/D7) to group-level keeping and leaving-risk hints so that tuning helps long-term involvement rather than short-term win rates; and third, the use of a feedback loop — watcher-helper sending possible changes to a skilled checker, with checker fixes sent back as top-notch training samples to lessen false guesses methodically. AI-fueled automation substantially speeds up prototyping and can enhance extended player engagement; however, these advances are precarious — dependent on legally verifiable data origin, strict dual-path (algorithmic + human) examination, and transparent intervention mechanisms — if not present, then adaptive systems may stray from customization to hidden control. This paper aims at academics and business professionals who seek disciplined, practical methods for implementing AI in design while maintaining creative freedom and responsibility.