This study addresses the critical issue of Food Waste (FW) across the 27 European Union (EU) member states by investigating its correlation with key socio-economic and environmental factors. Utilizing panel data regression with a fixed-effects model, this research controls for inherent country-specific characteristics to isolate the influence of variables, such as GDP per capita, educational attainment, environmental taxes, and economic burden on FW levels. The analysis reveals that FW is shaped by a complex interplay of factors, where economic affluence (GDP per capita) and financial stress (housing cost overburden) both exhibit a positive and statistically significant relationship with increased FW. Conversely, higher educational attainment, particularly at the bachelor’s and master’s degree levels, is strongly associated with reduced FW, emphasizing education’s role in promoting sustainable behavior. Environmental policy variables, including environmental taxes and circular material use, are negatively correlated with waste, suggesting effective indirect reduction. Notably, government support for agriculture demonstrates a positive association with FW, potentially indicating incentives for overproduction. These findings highlight the multidimensional nature of FW in the EU, necessitating comprehensive policy responses that integrate educational initiatives, economic levers, and sustainability-oriented reforms to promote resource-efficient consumption across the continent. By clarifying these relationships, this study contributes to the literature by providing one of the few examples of cross-country, EU-wide panel analyses that jointly consider economic, educational, and policy dimensions of FW. The findings offer practical implications for policymakers, emphasizing that FW reduction requires integrated strategies: strengthening environmental taxation and circularity initiatives, aligning agricultural subsidies with sustainability goals, and expanding educational programs that cultivate food-responsible behavior. Together, these insights support the design of more targeted and evidence-based interventions to reduce FW and promote resource-efficient consumption across the EU.

The North-South Road Corridor Investment Program is a huge infrastructural program that aims to connect the south of the country with the north through the 556km long Meghri-Yerevan-Bavra road 1in full compliance with the European standards. The construction of this strategically important road will ensure a safe travel from the southern border of Armenia up to the Georgian border and further to the ports of the Black Sea, will allow cargo and passenger transportation in compliance with the European standards, will provide serious development opportunities to all human settlements in Armenia from the south to the north.

The integration of artificial intelligence (AI) into the financial system, especially in the banking sector, has become one of the most important directions of technological progress. The aim of the article is to reveal the specifics of the application of AI in banking services, emphasizing the role of chatbots and virtual assistants in the customer-centric services and risk management system. The article presents the theoretical foundations and main directions of application of AI in the banking system. Four main directions are analyzed: customer-centric solutions, process optimization, banking services market management, and improvement of regulatory mechanisms. The experience of international banks (Bank of America, HSBC, DBS, Armenians banks, and others) indicates that the use of AI contributes to reducing operating costs and accelerating and personalizing customer service. However, the integration of AI also raises challenges related to data privacy, cybersecurity, legislative regulations, and the transformation of professional skills. Special attention is paid to the field of credit scoring, where machine learning methods allow for a more accurate assessment of borrower behavior and reduce financial risks. The relevance of the research is due to the fact that AI is no longer an additional technology for banks but a necessary tool for maintaining competitiveness and sustainable development. Received: 21 September 2025 | Revised: 5 January 2026 | Accepted: 10 March 2026 Conflicts of InterestThe authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in PES at https://doi.org/10.24874/PES06.02.023, reference number [31], in ASPUR at https://doi.org/10.61552/JAI.2024.01.004, reference number [32], in International Accountancy Training Centre at https://doi.org/10.59503/29538009-2024.2.14-121, reference number [33]. Author Contribution Statement Suren H. Parsyan: Conceptualization, Methodology, Investigation, Resources, Writing – original draft, Writing – review & editing, Supervision, Project administration. Frida F. Baharyan: Conceptualization, Investigation, Resources, Data curation, Writing – original draft. Gayane A. Avagyan: Conceptualization, Methodology, Validation, Formal analysis, Resources, Writing – original draft, Writing – review & editing. Sergo A. Episkoposian: Methodology, Validation, Visualization. Vardan S. Aleksanyan: Software, Investigation, Supervision. Ararat Kostanian: Writing – review & editing, Visualization. Lilik M. Beglaryan: Methodology, Visualization, Writing – review & editing.

Науковий журнал для працівників науки та викладачів закладів освіти. Публікація статей, матеріалів конференцій, досліджень та методичних розробок у відкритому доступі. Scientific journal for researchers and university educators. Publication of articles, conference papers, research and methodological developments in open access.

Artificial intelligence (AI)in banking increasingly requires models and representations that are not only predictive but also interpretable, auditable, and stable under model updates. Fraud screening,anti-money-laundering triage, and customer-risk monitoring are high-stakes structured-data tasks in which explanations must be reproducible and connected to documented feature processing. This paper proposes WHSDA-AI, a Walsh-Hadamard Spectral Deviation Analysis framework for structured and profile-based AI in the banking sector and related tabular risk-analysis settings. The proposed framework maps normalized feature profiles into a fixed orthogonal signed-contrast space. Each retained coefficient represents a deterministic comparison between two documented groups of input features. The novelty of the approach is not the classical Walsh-Hadamard transform itself but its integration into a governance oriented AI layer: semantic feature-map versioning, a fixed coefficient dictionary,reference-normalized deviation analysis, leakage-free coefficient selection, bootstrap stability assessment, and compact explanation-card generation. The full Walsh-Hadamard transform is an isometry and cannot improve Euclidean rankings by itself. Practical value arises through coefficient selection, reference deviations, template comparison, risk weighting, and integration with supervised models. Experiments on banking-style diagnostic data, credit-style fraud data, and public structured-data benchmarks compare WHSDA-AI with raw-feature models, sparse logistic regression, principal component analysis (PCA), Haar contrasts, random signed projections, random forests, gradient boosting, XGBoost, and LightGBM. Results are reported with repeated-split uncertainty and conservative robustness interpretation. The framework is positioned as an ante-hoc representation, governance, and feature-engineering layer rather than as a universal accuracy booster. Received: 25 May 2026 | Revised: 6 July 2026 | Accepted: 23 August 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data available on request from the corresponding author upon reasonable request. Author Contribution Statement Sergo A. Episkoposian: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Suren H. Parsyan: Conceptualization, Methodology, Investigation, Resources, Writing – review & editing, Supervision, Project administration.