Hyperion University

In the context of accelerated digital transformation, organizations increasingly operate as complex systems in which strategic decision-making is challenged by uncertainty, data heterogeneity, and bounded rationality. The integration of artificial intelligence (AI) into organizational processes is therefore redefining how decisions are supported and enacted. This study develops and validates an integrated conceptual model that explains how trust in AI-based decision support systems (AI-DSSs), data transparency and quality, perceived usefulness, and ease of use influence decision-making efficiency and the intention to adopt AI-DSS in complex organizational contexts. The empirical analysis is based on a questionnaire survey administered to 324 respondents from Romanian organizations operating in IT, services, industry, and public administration. Data were analyzed using partial least squares structural equation modeling (PLS-SEM) implemented in SmartPLS 4. The results show that data transparency and quality strongly enhance trust in AI-DSS (β = 0.784, p < 0.001). Trust positively influences both perceived usefulness (β = 0.229, p < 0.01) and perceived ease of use (β = 0.482, p < 0.001), confirming its role as a key psychological enabler of favorable technology perceptions. Furthermore, perceived ease of use significantly affects perceived usefulness (β = 0.597, p < 0.001). Regarding adoption-related attitudes, perceived usefulness (β = 0.352, p < 0.001), trust (β = 0.311, p < 0.001), and perceived ease of use (β = 0.135, p < 0.05) exert significant positive effects on the intention to adopt AI-DSS, which in turn demonstrates a strong association with decision-making efficiency (β = 0.544, p < 0.001). By extending traditional technology acceptance models (TAM) with AI-specific dimensions—namely transparency, data quality, and trust—this study contributes to the literature on decision-making in complex systems and offers practical insights for organizations seeking to improve decision effectiveness through AI-based support.

The transition to a low-carbon economy is profoundly reshaping European labour markets, creating both opportunities for sustainable employment and challenges for regions reliant on carbon-intensive sectors. Assessing how prepared EU Member States are for this shift remains difficult due to the lack of unified evaluation tools. This study introduces the Green Labour Market Readiness Index (GLMRI)—a composite measure assessing the adaptability of national labour markets to the energy-driven green transformation in nine EU countries: Germany, France, Sweden, Spain, Italy, Greece, Poland, Romania, and the Czech Republic. The index integrates five dimensions—education and skills, investment and infrastructure, policy and institutional quality, labour market structure, and innovation—based on harmonized data from 2010 to 2024. Panel econometric models (Fixed and Random Effects), combined with Hausman tests, are used to examine how structurally independent external energy-system characteristics, institutional capacity, and macro-structural labour-market conditions are associated with observed variation in labour-market readiness, as captured by the GLMRI composite outcome. Machine learning algorithms (Random Forest, XGBoost, LSTM) are employed to forecast readiness trajectories until 2040 under alternative policy scenarios. Results reveal persistent asymmetries between Northwestern and Southeastern Europe, showing that successful energy transition is closely associated not only with investment and innovation but also with human capital and governance quality. These associations are interpreted as diagnostic rather than causal, highlighting how external structural conditions shape the translation of energy-transition pressures into differentiated labour-market outcomes. The GLMRI provides a methodological and policy-relevant framework, helping decision-makers prioritize resources and design measures that make Europe’s energy transition sustainable, inclusive, and equitable.

Background and Objectives: Breast cancer remains one of the most prevalent malignancies affecting women and one of the most emotionally distressing diagnoses. Emerging evidence suggests that chronic psychological stress may influence cancer progression via neuroendocrine and immune mechanisms. This rapid narrative review explores three key arguments for integrating psycho-oncologists into the multidisciplinary care of breast cancer patients: (1) early detection and reduction of psychological distress, (2) improvement of treatment adherence, and (3) enhancement of quality of life through personalized psychological interventions. Materials and Methods: The review was conducted through comprehensive searches in PubMed, Scopus, and Web of Science for peer-reviewed articles published between 2010 and 2025. Inclusion criteria comprised randomized controlled trials, systematic reviews, meta-analyses, and theoretical papers. Of 246 identified articles, 50 met the inclusion criteria. Results: Selected studies show that psycho-oncological interventions—including cognitive–behavioral therapy, mindfulness-based techniques, narrative therapy, and guided imagery—significantly reduce anxiety, depression, and fear of recurrence. These approaches improve adherence to endocrine therapy and chemotherapy, enhance emotional resilience, and promote overall well-being. Also, recent research concepts emphasize the role of psycho-oncologists in encouraging post-traumatic growth and helping patients redefine cancer as an opportunity for transformation. Conclusions: Integrating psycho-oncologists into standard breast cancer care improves psychological and clinical outcomes. By addressing emotional distress, strengthening coping mechanisms, and supporting existential resilience, psycho-oncologists contribute to a holistic, patient-centered model of oncology care. Wider implementation of psycho-oncological services is warranted as a core component of comprehensive cancer management.

We work in the physical model of hydrogen-like atoms and use the wave functions provided by solutions of the Schrödinger equation with Coulomb potential. For the hydrogen spectra, we develop a theoretical-spectroscopy method to identify the significant quantum states with the highest probability of transition. We exemplify this method for the α, β, γ Lyman transitions, namely for a fixed principal quantum number n less than 5, and find (2 1 0), (3 2 0), (4 3 0) as significant quantum states. Besides this, we obtain other various results: the numerical tables for the radial distributions and the electron density functions, and some observables like θ nodal angles, nodal surfaces, along with the ordered sequences of the average-radii of the sub-shells for a fixed n, such as: 18a =〈r〉 < 〈r〉 < 〈r〉 <〈r〉 = 24a (for the shell n = 4 and a - the first Bohr radius). Afterwards, we build the scientific 3D-visualization of the orbitals shapes and describe them via the nodal values for θ angles.

PurposeIn recent years, the frequency and severity of cybersecurity incidents have prompted customers to seek out specialized insurance products. However, this has also presented insurers with operational challenges and increased costs. The assessment of risks for health systems and cyber–physical systems (CPS) necessitates a heightened degree of attention. The significant values of potential damages and claims request a solid insurance system, part of cyber-resilience. This research paper focuses on the emerging cyber insurance market that is currently in the process of standardizing and improving its risk analysis concerning the potential insured entity.Design/methodology/approachThe authors' approach involves a quantitative analysis utilizing a Likert-style questionnaire designed to survey cyber insurance professionals. The authors' aim is to identify the current methods used in gathering information from potential clients, as well as the manner in which this information is analyzed by the insurers. Additionally, the authors gather insights on potential improvements that could be made to this process.FindingsThe study the authors elaborated it has a particularly important cyber and risk components for insurance area, because it addresses a “niche” area not yet proper addressed in specialized literature – cyber insurance. Cyber risk management approaches are not uniform at the international level, nor at the insurer level. Also, not all insurers can perform solid assessments, especially since their companies should first prove that they are fully compliant with international cyber security standards.Research limitations/implicationsThis research has concentrated on analyzing the current practices in terms of gathering information about the insured entity before issuing the cyber insurance policy, level of details concerning the cyber security posture of the insured entity and way such information should be analyzed in a standardized and useful manner. The novelty of this research resides in the analysis performed as detailed above and the proposals in terms of information gathered, depth of analysis and standardization of approach made. Future work on the topic can focus on the standardization process for analyzing cyber risk for insurance clients, to improve the proposal based also on historical elements and trends in the market. Thus, future research can further refine the standardization process to analyze in more depth the way this can be implemented and included in relevant legislation at the EU level.Practical implicationsProposed improvements include proposals in terms of the level of detail and the usefulness of an independent centralized approach for information gathering and analysis, especially given the re-insurance and brokerage activities. The authors also propose a common practical procedural approach in risk management, with the involvement of insurance companies and certification institutions of cyber security auditors.Originality/valueThe study investigates the information gathered by insurers from potential clients of cyber insurance and the way this is analyzed and updated for issuance of the insurance policy.

Analogia 17 (2023)