The extended Kalman filter (EKF) is a cornerstone of nonlinear state estimation, yet its performance is fundamentally limited by noise-model mismatch and linearization errors. We develop a residual-aware distributionally robust EKF that addresses both challenges within a unified Wasserstein distributionally robust state estimation framework. The key idea is to treat linearization residuals as uncertainty and absorb them into an effective uncertainty model captured by a stage-wise ambiguity set, enabling noise-model mismatch and approximation errors to be handled within a single formulation. This approach yields a computable effective radius along with deterministic upper bounds on the prior and posterior mean-squared errors of the true nonlinear estimation error. The resulting filter admits a tractable semidefinite programming reformulation while preserving the recursive structure of the classical EKF. Simulations on coordinated-turn target tracking and uncertainty-aware robot navigation demonstrate improved estimation accuracy and safety compared to standard EKF baselines under model mismatch and nonlinear effects.
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.
The influence of a black silicon (b-Si) interlayer on the photovoltaic characteristics of tandem perovskite/silicon cells was investigated by numerical modeling in the SCAPS-1D software environment. It is shown that a 640 nm thick nanotextured b-Si interlayer increases the efficiency of the modeled device from 27.17 to 28.97
This paper presents a method for automatic detection of typical analog circuit building blocks from transistor-level SPICE descriptions using artificial intelligence and machine learning techniques. The relevance of the problem is associated with the fact that modern integrated circuits contain numerous repeating analog structures whose identification is important for design automation, reverse engineering, technical analysis, and integration into CAD environments. SPICE netlists are widely used as textual circuit representations; however, they do not explicitly describe the functional hierarchy of a circuit, which makes their automatic interpretation a challenging task. The proposed framework includes preprocessing of netlists, removal of com-ments, normalization of node names, extraction of structural features, and supervised classification. The extracted features include the total number of transistors, NMOS/PMOS ratio, presence of shared gate nodes, symmetry indicators, diode-connected devices, and connectivity density. Based on the generated feature vectors, a Random Forest classifier is applied to recognize typical analog structures such as cur-rent mirrors and differential amplifiers. Experimental studies demonstrate that the proposed method provides reliable accuracy for small and medium-size circuits while maintaining computational effi-ciency. A decrease in recognition accuracy is observed for larger netlists due to in-creased structural complexity and limited training data. Nevertheless, the approach remains scalable and suitable for practical implementation. The developed method can be used in automated circuit analysis, reverse en-gineering systems, and electronic design automation tools. Future work may include expansion of the dataset, hierarchical block recognition, and application of graph-based neural network models for improved structural understanding. Keywords: analog circuits, SPICE netlist, circuit recognition, structural analy-sis, machine learning, Random Forest.
Ուսումնասիրվել է ՋԷԿ-երի և ՋԷՑ-երի մակերևութային կոնդենսատորների խողովակների ներքին մակերևույթի մակերեսների աղտոտվածության ազդեցությունը նրանց աշխատանքի արդյունավետության վրա։ Քանակական գնահատումները կատարվել են T-110/120-130 էներգա¬բլոկի համար՝ օգտագործելով փորձնական և հաշվարկային տվյալներ, որոնց հիման վրա կառուցվել են կախվածություններ կոնդենսատորի խողովակներում նստվածքագոյացման շերտի հաստության և էլեկտրական հզորության անկման միջև՝ տարբեր սկզբնական ջերմաստիճանային պայմաններում։
