Type of the article: Research ArticleAbstractSustainable leadership has become one of the widely addressed topics over the last decade, both in business and academia. This paper examines how personal, organizational, and industry characteristics influence sustainable leadership competencies across Turkic nations and how the country variable moderates these relationships. The study utilized the results of a comprehensive leadership assessment based on managers’ involvement in a business simulation, operated by artificial intelligence and rooted in scientific management. Assessment results of 1,756 managers from Hungary (N = 695), Kazakhstan (N = 647), and Türkiye (N = 414), employed in manufacturing and construction, education and research, finance, production and trade, information technologies, public relations and services, were analyzed. The two-step approach to structural equation modeling was implemented using Python software. The results showed that strategic and normative competences were partially dependent on managers’ gender (β = 1.450, p = 0.022), age (β = 5.678, p < 0.001; β = –3.587, p < 0.001), tenure (β = 3.767, p < 0.001; β = –1.898; p = 0.020) and industry (β = 1.194, p = 0.031; β = 3.092, p = 0.001), whereas interpersonal and anticipatory competencies remained unaffected. The country demonstrated a significant positive moderation in the relationships associated with normative competence (β = 3.260, p = 0.039) and interpersonal competence (β = 5.667, p = 0.041). In contrast, it exhibits a noteworthy negative moderation in strategic competence (β = –3.665, p = 0.043) and systems thinking competence (β = –3.853, p = 0.006). The moderation effect between the managers’ age, tenure, industry, and sustainable competencies was most pronounced in Kazakhstan, followed by Hungary, and least in Türkiye. This nuanced understanding highlights the diverse impact the country has on different aspects of competence, underscoring the importance of context in these relationships.AcknowledgmentThis research has been funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. AP22687001) within the project titled “Designing a Conceptual Model of Managers’ Leadership Competencies in Kazakhstan through the Gender Potential Prism”.

In this paper, we investigate universal objects in the class of positive preorders with respect to computable reducibility, we constructed a computable numbering of this class and proved theorems on the existence of a universal positive lattice and a universal weakly precomplete.

Secure Shell (SSH) intrusions against Linux servers remain a dominant vector of opportunistic and targeted cyber incidents, yet operational honeypots treat attacker commands either as isolated lookup keys for static templates or as input to a single uniform language model. Two research directions partially address this gap: LLM-driven response generation raises interaction realism but incurs a fidelity–latency trade-off, while semantic command analytics classifies attack stages from embeddings yet applies a single fine-tuned model uniformly across sessions. This work introduces the phase-adaptive model routing framework (PAMR), which explicitly couples both views. A lightweight online phase estimator infers the current attack stage—reconnaissance, exploitation, or persistence—from a sliding window of recent commands via compact semantic embeddings; a router then dispatches each command to one of several heterogeneous LLM backends according to estimated phase, command complexity, and a confidence-weighted latency budget. The routing decision is formulated as a constrained optimization problem and realized at runtime as an $O\left(1\right)$ lookup in a precomputed dispatch table. PAMR is evaluated on a controlled, reproducible benchmark of 412 stage-annotated command sessions that combines representative Linux command–response pairs with synthesized attacker traces; we explicitly state that this is a laboratory benchmark rather than live attacker traffic, and we scope our claims accordingly. Relative to uniform-model baselines, PAMR reduces mean response latency by approximately 38% against an API-hosted high-capacity backend and by approximately 45% against a locally hosted mid-capacity backend, while keeping token-level response-fidelity metrics close to the high-capacity baseline (cosine similarity ≈ 0.39 vs. 0.40) and maintaining an online stage-classification macro-F1 above 0.87. We further provide a first-order analytical treatment of the timing side-channel that any backend-routing architecture introduces, and we frame the contribution as a latency/token-fidelity trade-off, leaving validation of operational realism against live adversaries to future work.

Type of the article: Research ArticleAbstractStrategic planning is a driver of leaders’ performance in today’s turbulent and demanding environment, yet the relative importance of soft versus hard skills within it remains insufficiently researched. This study aims to test how gender, age, and cultural context influence the distribution of soft and hard skills in strategic planning and managers’ goal attainment. Utilizing a behavioral outcome measure (high performance), we analyzed a sample of 1,468 managers from Kazakhstan (n = 694) and Hungary (n = 774), who participated in the scientifically grounded AI business simulation. A two-step analytical approach, involving PLS-SEM and logistic regression, was implemented in Python. The results demonstrate that soft skills are the dominant driver of high performance, with an Odds Ratio (OR) of 3.44, indicating they are 2.5 times more effective than hard skills. Leaders’ age exerted a negligible effect on their performance (OR = 1.02, p = 0.728). The overall sample showed no gender differences, but the country-specific analysis revealed a moderated mediation pattern where the link between gender and soft skills varies by country (p = 0.018). Managers in Kazakhstan exhibited slightly higher average softskill scores than their counterparts in Hungary (p = 0.014). Despite cultural differences, the model demonstrated consistent predictive utility in both settings, with an overall accuracy of 72.9% in Kazakhstan and 72.4% in Hungary. The designed competency model is highly stable and cross-culturally valid for predicting managerial performance. Thus, managerial excellence is driven by competencies that are largely independent of demographic characteristics across different cultural settings.AcknowledgmentThis research has been funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. AP22687001) within the project titled “Designing a Conceptual Model of Managers’ Leadership Competencies in Kazakhstan through the Gender Potential Prism”.