With the burden of dementia growing worldwide and its However, current increasing prevalence, there is an urgent need for treatments that can effectively target the main mechanisms of the neurodegenerative process. A substantial level of hope lies in traditional herbal medicine, particularly Ginkgo biloba, which is believed to not only inhibit the neurodegenerative process but also to alleviate the neuropsychiatric symptoms of dementia the literature rends overlook the demographic-specific effects of Ginkgo biloba, falling to adequately clarify its efficacy across diverse patient populations. De this context, we conducted a thorough search across multiple databases focusing on the Impact of Ginkgo biloba on age, sex, genetic phenoppe, and type of dementia. Our findings revealed varied efficacy across demographic groups, with noticeable benefits in some populations and fewer adverse effects in others. In particular, we identified that groups of older adults with specific genetic markers showed the greatest improvements in cognition, suggesting a strong need for personalised therapeutic strategies that incorporate demographic factors into clinical decision-making. This review highlights the benefits of herbal medicine, particularly Ginkgo biloba, in tailored dementia care, although further research is needed to develop accurate treatment protocols for diverse patient populations.
This study examines und deeply investigates how university students define and negotiate the ethical use of artificial intelligence in higher education within different pedagogical contexts. Focusing on Management Information Systems students at Karadeniz Technical University, the study explores how artificial intelligence is used in technical, theoretical, and project-based courses and how ethical boundaries are shaped based on usage aim. Using a qualitative case study design, data were collected through semi-structured interviews with undergraduate students and analysed using reflexive thematic analysis. The findings show that students do not evaluate the ethical use of artificial intelligence through fixed rules or institutional prohibitions, but rather through context-sensitive judgements based on learning goals, personal effort, responsibility, and course type. Artificial intelligence is mainly positioned as a technical assistant in programming-related courses, as a cognitive support tool in theoretical courses, and as a creative partner in project-based learning. Ethical concerns emerge when artificial intelligence replaces students own thinking, creativity, or responsibility. The chapter concludes that the ethical use of artificial intelligence in higher education should be addressed through flexible, pedagogically informed, and student-centred approaches rather than rigid policies, highlighting the importance of shared responsibility among students, instructors, and institutions.
This study aimed to examine whether behavioural emotion regulation mediated the relationship between burnout and addictive behaviours and whether life satisfaction moderated this association among Romanian healthcare personnel during the COVID-19 pandemic. A total of 137 healthcare professionals (84% women, Mage 41.09, SD = 11.22 Mwork experience 13.76, SD 11.09) filled out a set of online scales. The results indicated positive associations between burnout and maladaptive behavioural emotion regulation strategies (withdrawal and ignoring), as well as a negative association between burnout and the adaptive strategy of active approach. Positive associations between burnout and compulsive eating, between withdrawal and compulsive eating weret also found. In contrast, active approach was negatively associated with alcohol and drug use. Mediation analysis suggested that the association between burnout and compulsive eating was indirectly linked to withdrawal ib = 0.06 CI 1.0055, 1221]). However, moderated mediation analyses showed that life satisfaction did not significantly moderate the relationship between burnout and compulsive eating among healthcare personnel ( R (2) = 0.004 . p = 0.4I ).
The article discusses the implementation of art therapy classes using sandplay. Its relevance stems from the increasing number of children who experience psychological trauma and require psychological support. It aims to clarify such concepts as "art therapy" and "sandplay therapy". It also explores practical approaches to art therapy, while highlighting the effectiveness of sandplay therapy Research methods include a a detailed detailed analysis analysis of relevant scientific sources and a systematic review. The findings reveal that prolonged emotional exhaustion and stress negatively affect the human psyche. In turn, art therapy has proven highly effective as a universal method for psychological intervention across different age groups and mental conditions. Its mission lies in identifying psychological problems and helping the child release outdated or repressed emotions. This can be achieved through symbols, images and metaphors, thereby processing information at the unconscious level of the psyche. Sandplay therapy supports the development of memory, attention, spatial imagination, communication skills, and fine motor abilities.
Artificial intelligence (Al) and neuroscience technologies are transforming the workplace at an accelerating pace, offering significant gains in operational efficiency, talent identification, and workforce analytics, while simultaneously generating profound ethical, legal, and social risks. This paper examines their impact on human resources (HR) management from a rigorous interdisciplinary perspective, integrating recent statistical data from Romania and European Union member states with insights from organisational psychology, applied ethics, and machine-learning theory. We analyse conesele applications in recruitment, performance evaluation, diversity management, and employee well-being, providing empirical data that illuminate prevailing trends and disparities. Key findings include: only 13.5% of EU enterprises had formally adopted Al by 2024, with Romania recording a mere 3.1% adoption rate (Eurostat, 2025), contrasted with a striking ground-level reality in which approximately 35% of Romanian office workers already use Al tools regularly (Romania Journal, 2024). This divergence between formal enterprise adoption and informal employee use is conceptualised as a complex governance problem-rather than a mere technological lag-situated at the intersection of organisational psychology, applied ethics, and machine-learning theory, a unified framing that underlies the entire analytical framework of this paper. We systematically identify ethical risks algorithmic bias in automated hiring (exemplified by Amazon's now-discontinued recruiting engine that penalised women's resumes), covert neuro-surveillance via wearable EEG headsets, and opacity in Al-driven performance appraisal - and ground mitigation strategies in both the EU Al Act (2024) and the emerging Neurotechnology Framework. As a novel contribution, we propose and elaborate a mathematical optimisation model in which organisations maximise a utility function combining productivity gains, bias penalties, and privacy risk costs, subject to formal fairness (equal opportunity) and neurorights constraints. The model, solved via Lagrangian methods, is demonstrated through a detailed case study of a hypothetical Romanian technology firm. This structured, data-driven, ethically grounded approach offers concrete guidance for HR professionals, corporate governance bodies, and policymakers seeking to deploy Al and neurotechnology responsibly and int compliance with EU law.
Objective. The aim of the research is to investigate the relationship between personality traits (conscientiousness and emotional stability) and quality of life in dialysis patients, through psychosocial mediation mechanisms (autonomous motivation, problem-focused coping and social support). The study also examines the longitudinal stability of these relationships and the differences between clinical groups defined according to medical indication and patient attitude towards kidney transplantation. Materials and methods. The preliminary study included 70 dialysis patients, assessed at two time points (TI-beginning of the study, 12 6 months). Standardised instruments were used to assess personality (BF1-50), motivation (TSRQ), coping (Brief COPE), social support (ISEL) and quality of life (KDQOL-SF 1.3). Statistical analyses consisted of PROCESS Model 6 for testing serial mediation and mixed ANOVA for assessing longitudinal stability and between-group differences. Results. Personality influenced quality of life through significant indirect effects of autonomous motivation (TI), problem-focused coping (T2), and social support (T2), confirming the serial mediation model (p < 0.05) A full mediation effect was identified for conscientiousness, whereas emotional stability showed a partial mediation effect, highlighting distinct pathways through which personality traits influence quality of life. Between Tl and T2. significant decreases were observed in physical health, F(1.66) = 2101.67 p < 0.01 mental health, F(1, 66) = 18.41 p < 0.01 as well as in resources (autonomous motivation F(1, 66) = 49.27 p < 0.01 problem-focused coping, F(1.66) = 26.44 p < 0.01 F= 49.37. p < 0D Time group were observed for both physical health and problemfocused -coping F(3, 66) = 3.08 p < 0.05 Conclusions. The preliminary study confirms hypothesis Hl. demonstrating that personality traits influence quality of life through psychosocial mechanisms, and hypothesis H2 was partially confirmed: the relationships of the model remained stable over time, but the differences between groups were limited, except for problem-focused coping. These results emphasise the importance of integrating psychosocial factors into clinical practice and will be complemented by the inclusion of data from 13 to test the robustness and stability of the proposed model.
The article presents a review of existing approaches, theory and practice of music students' development under the conditions of war in Ukraine. The presented material can significantly enrich the content of music pedagogical education on the study of neuro-pedagogical aspect of music students' development under the conditions of war in Ukraine, in the process of implementation of disciplines of professional psychological and pedagogical disciplines.The leading idea of the article is the recognition that music is one of the most powerful means of emotional regulation of the mental activity of music students in the conditions of war in Ukraine. It develops a person's ability to recognise and control their emotions, enhancing their emotional intelligence. In this article, the main types of musical activity - listening to music, its instrumental performance and vocalisation - are used to investigate the neuropedagogical influence on the development of music students under the conditions of war in Ukraine. The effects of listening to various classical works revealed by current science in the development of music students under conditions of war in Ukraine are investigated. Factors influencing the perception of music are discussed, namely, the psycho-emotional state of music students during the war in Ukraine at the moment of listening, as well as the peculiarities of their nervous system, temperament, and personality type. It is noted that significantly enliven the emotional sphere of music students, activate its independence and creative activity, singing and playing musical instruments, the distinctive feature of which is the ability to improvise.
The relationship between diabetes mellitus (DM) and microvascular brain pathology is complex and not very well debated, especially when referred to lacunar strokes, a major subset of cerebral small vessel disease (CSVD). Chronic hyperglycaemia, insulin resistance, and endothelial dysfunction drive microvascular injury, often compounded by coexisting hypertension and dyslipidaemia. Imaging and cohort studies confirm that DM accelerates CSVD progression and increases the risk of lacunar strokes, while antihypertensive and lipid-lowering therapies can modulate this risk. Nonetheless, a differential diagnosis of demyelinating lesions of the brain remains complicated, especially in young patients, as DM increases the likelihood of vascular or infectious mimics, but does not fundamentally influence diagnostic or pharmacological algorithms for acquired demyelinating syndromes. The differential diagnosis for Multiple Sclerosis is also essential in young patients with an autoimmune background. There is also growing evidence that artificial intelligence (Al) and computational modelling are increasingly useful when applied to cerebrovascular disease with DM as a key risk factor, by providing: risk stratification, automated neuroimaging analysis and decision-support systems, that integrate multimodal data to predict risks, classify aetiologies and forecast outcomes (along with retinal imaging and neuroimaging Al). Al-driven cerebrovascular morphology analysis, CSVD markers and perfusion imaging analyses are advancing the ability to predict short-term and longer-term outcomes, including cognitive impairment. For all that, challenges still arise throughout data standardisation, generalisability across diabetic subgroups, integration with electronic health records or regulatory considerations that need further prospective multicenter validation. Although pharmacological management in diabetic patients with lacunar stroke may not benefit from a diabetes-specific protocol beyond standard guidelines, physiotherapy stands as both a rehabilitative and preventive tool, contributing to microvascular risk reduction through exercise, improved metabolic control, and overall lifestyle modification. While calling for larger, diabetes-stratified studies to guide future interventions and refine personalised treatment strategies, the following narrative review, with emphasis on an Al and computational modelling in diabetes-related cerebrovascular disease, offers a brief insight into the current level of knowledge and recent updates in the correlations between DM and lacunar strokes.
This article is the first in international discourse to explore the evolving role of Ukrainian music education in new military and political conditions. It aims to examine global trends in music education for youth and describe the unique context in Ukraine. The article proposes a neuropedagogical model of musical and patriotic education, incorporating elements of music therapy, along with general methodical recommendations. As researchers and ethnic Ukrainians, the authors can observe the growing neurosocial distinctiveness of Ukrainian identity. This identity is reflected in non-verbal, authentic narratives such as music, dance, and cultural symbols. While these expressions are largely suggestive and reflective, they contribute to a broader, holistic perception. The authors have attempted to apply this perception to the sensitive field of music education, especially given its current emotionally charged and patriotically heightened state. Research methods rely on descriptive, reflective, analytical-synthetic, and modelling approaches (both pedagogical and neuroscientific), which have led to several key findings. The first is the confirmation of absolute heteromorphism in the values and goals of patriotic education across different geopolitical regions. The authors also emphasise the special significance of such education in areas experiencing national uncertainty or military-political conflict. Additionally, the authors used a personal-professional anxiety scale for music teachers. The article shows that educational environments in Ukraine are open and highly sensitive. Their participants also display a strong tendency towards reflection. On this basis, the authors of the article have developed two models: a relatively closed, multi-vector model of musical and patriotic education and an open neuropedagogical model intended for use in the current military-political context.
This study presents the results of the first stage of the "COMMON Initiative: Climate-Smart Agriculture Demonstration Plot" project at the University of Central Asia-developing an autonomous smart micro-greenhouse with low-cost IoT equipment (two NodeMCU ESP8266 boards, four 3V relays, and sensors DS18B20/DHT11/LDR/YL-69) and analysing its power consumption in relation to the IoT component. The experiment with eight commonly cultivated plant species (dill, garden strawberry, lettuce, stock, basil, parsley, sorrel, and spinach) in two identical ugreenhouses of size 30x 26x 20cm each (testbed located at the elevation of 800m Bishkek, Kyrgyz Republic) demonstrated that the power consumption is less with lot equipment because the use of plant grow lights and heaters is minimised. Observational findings indicate that six plants (except basil and garden strawberry) grew faster in a smart ugreenhouse. The control algorithm employs one-input hysteresis with a neutral zone to automatically regulate the light and temperature inside a ugreenhouse. The percentage change for two time series (cum-sine IoT equipment) varies from -0.92% to -5.78% during the experiment. The data on temperature/soil moisture inside and the temperature/humidity/light intensity outside a ugreenhouse are provided to the human expert to support the decision-making process on plants' watering. In the second stage of this project, a machine learning algorithm will be employed to further minimise power consumption.
The article examines the concept of 'stress, highlighting both its negative and positive effects on a person's mental and physical state. The aim of the study is to clarify the concept of 'stress", review scientific sources regarding the neurobiological mechanisms of the stress response, and identify the consequences that chronic stress may have on cognitive and emotional brain functions, as well as to explore methods of rehabilitation for such conditions. Research methods: Critical analysis of scientific literature, comparison of scientific theories on the classification of stress and its types, and generalisation of neurobiological data on the effects of prolonged stress on brain structures, including the hippocampus. amygdala, and prefrontal cortex. Research results demonstrate that prolonged stress can transition into chronic stress, which in turn increases the activity of the hypothalamic-pituitary-adrenal axis, leading to the release of stress hormones: adrenaline, cortisol, and noradrenaline. As a result, individuals may experience depression, cognitive impairments, and heightened anxiety. Attention is also drawn to the interconnection between stress, the immune system, and the underlying epigenetic mechanisms. Scientific novelty lies in the holistic approach to analysing stress as a neuropsychobiological phenomenon, encompassing all levels from molecular processes to behavioural manifestations. A conceptual framework is proposed for using psychotechnologies of adaptive self-regulation as a tool for 1 strengthening stress resilience and preventing mental disorders.
Substance use disorders are highly prevalent in adult correctional and forensic populations. However, brief screening instruments are often interpreted without clear differentiation between diagnostic, validation, and predictive purposes. In this study, we synthesize evidence on commonly used substance use screening tools using a stratified meta-analytic framework designed to clarify their legitimate inferential roles in custodial settings. Evidence was organized into three analytic tiers: Tier 1 (CORE: Diagnostic Accuracy) included studies permitting formal estimation of diagnostic accuracy against explicit clinical reference standards. Tier 2 (Extended Forensic Validation) comprised extended forensic validation studies employing context-specific or severity-based frameworks. Tier 3 (Predictive Validity) addressed predictive validity for substance-relevant post-release outcomes. Quantitative synthesis was restricted to Tier 1 studies and indicated high sensitivity with moderate specificity for brief screening instruments when evaluated against structured diagnostic assessments. Given the limited number of eligible studies, these pooled estimates should be interpreted as preliminary indicators rather than stable population parameters. Tier 2 studies demonstrated broadly consistent performance across diverse forensic contexts but substantial heterogeneity in reference standards, precluding pooled diagnostic inference. Limited Tier 3 evidence suggested that screening-derived severity classifications may be associated with substance-relevant post-release outcomes. Overall, the findings indicate that brief screening instruments support distinct, tier-specific functions. Evidence for one inferential purpose should not be generalized to others.
This article explores the integration of intelligent transportation systems (ITS). Smart management of urban resources, including energy, water, and other critical systems, is a tool for enhancing economic efficiency in cities. The article also discusses how digital platforms for territorial governance and the automation of administrative processes create conditions for more transparent and efficient administration, while improving citizen engagement. Special attention is given to the issue of cybersecurity and the protection of digital systems, which ensure the stability and reliability of infrastructure. The article also explores the role of digital technologies and artificial intelligence (AI) in the planning, development, and modernisation of territories and infrastructure. It examines the possibilities of integrating digital governance into urban development, transport, energy, communications, and environmental monitoring. The potential of Al is analysed in terms of processing large volumes of data, forecasting socio-economic processes, optimising resource use, and enhancing the efficiency of decision-making. Particular attention is given to the concepts of "smart cities," digital transformation of regions, and decentralised governance platforms. The article outlines the key advantages, risks, and ethical challenges associated with the implementation of Al in spatial planning and infrastructure renewal.
Forest fires pose a significant threat to Viet a Nam, particularly during the dry season. This study investigates ConvLSTM-based deep learning architecture for early fire detection. Unlike single-frame or threshold-based methods, ConvLSTM jointly models spatial features (via convolutional layers) and temporal dependencies (via LSTM units). We utilize a publicly available dataset of 999 fire and non-fire images. To apply ConvLSTM to static images, we construct temporal sequences using a sliding window over augmented variants. The proposed model achieves 98.3% accuracy, 98.1% precision, 96.8% recall, and a 98.1% Fl-score on the test set. These results are compared with standalone CNN and LSTM models. Limitations include the limited dataset size, class imbalance (75% fire images), and the lack of Viet Nam-specific data. Future work should focus on larger, region-specific datasets and real-time deployment on edge devices.
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder characterised by persistent social communication difficulties together with restricted and repetitive behavioural patterns. Early and accurate diagnosis is critical for timely intervention, however, traditional clinical sessment methods require extended time and extensive resources white assessment results, depend on evaluator judgement. The development of Artificial Intelligence (AI) technologies including Machine Learning (ML) and Deep Learning (DL) has made it possible to automatically identify ASD through the analysis of EEG data and neuroimaging information and eye-tracking results and behavioural signals. The "black-box" characteristics of these models create two main problems which reduce their effectiveness as predictive tools for medical applications. The introduction of Explainable Artificial Intelligence (XAI) techniques, including SHAP, LIME, and Grad-CAM, provides a framework for improving model interpretability and transparency. Quantum Machine Learning (QML) presents two main benefits through its ability to process high-dimensional data while showing improved performance in computational tasks. This review examines the principal datasets, preprocessing techniques, feature extractiont methods, and Al-based detection models used in ASDI diagnosis. However, researchers continue to study three main problems which include standardised biomarker deficiencies, data variability and clinical validation shortages. The article presents future research paths which will lead to systems that achieve interpretability and robustness while maintaining clinical usability.
The success of motor rehabilitation is closely related to the quality of communication between the therapist and the patient. Cognitive impairment is common among acute stroke survivors and is frequently associated with slowed information processing. Under such conditions, verbal instructions often fail to be conveyed in a form that the patient can effectively translate into action. This paper presents two complementary electronic systems developed to provide language-independent feedback for gait re-education. The first is a low-cost device built on an Arduino MKR1000 platform that combines a triaxial accelerometer and three FSR pressure sensors to provide real-time visual feedback during walking, with no AI on board. The second one uses an STM32 microcontroller with six synchronised accelerometers to record multi-segment gait data for offline processing using Gait Flow Analyser, which provides graphical summaries, descriptive statistics, and an optional AI module that detects deviations from a learned normal gait baseline. Preliminary clinical use with 12 patients over six months suggested that visual cues can prompt movement correction without verbal direction. The STM32 system has additionally been evaluated in a parallel investigation on a cohort of 30 participants (15 neurological participants and 15 healthy controls), thereby providing broader empirical context for the descriptive findings reported here. The two systems are not alternatives. Real-time feedback supports the therapeutic session, whereas offline AI-assisted analysis provides the therapist with an objective assessment of gait asymmetry and its evolution over time. Together, they establish a rehabilitation framework in which therapeutic guidance relies less on verbal communication and more on directly interpretable visual feedback.
The increasing presence of pharmaceuticals in aquatic or ecosystems has raised significant concerns due to their detrimental effects on both environmental and human health. The present study discusses the rising concern about pharmaceuticals in aquatic environments, focusing on valproic acid (VPA), an antiepileptic drug identified as a neuroactive contaminant. Its persistence in wastewater and limited removal by conventional treatments, along with its known neuroactive properties, prompted the investigation of its neurobehavioural effects in zebrafish (Danio rerio), a model organism for environmental neurotoxicology. Conventional behavioural scoring techniques frequently suffer from subjectivity and inadequate resolution, especially when evaluating the nuanced effects of low-dose exposure mixture-induced, characteristic in the natural environments. The study highlights the importance of behavioural endpoints as indicators of brain disorders and the role of artificial intelligence (Al) in improving behavioral analysis. By integrating automated video tracking with an Al-assisted exploratory workflow and multivariate analytics, this study illustrates the feasibility of computational approaches for detecting neurobehavioural alterations. After 96 h of exposure, VPA was associated with altered locomotor and spatial behavior in adult zebrafish, evaluated using an optimised low-variance subset 6n = 5 per group) within a proof-of-concept framework. These findings highlight the neuroactive potential of VPA and support the use of Al-enhanced zebrafish behavioural models for exploratory environmental neurotoxicology.
This paper examines the attitudes of university students at Near East University, Northern Cyprus, regarding autonomous learning through the application of the flipped classroom model. A mixed method research design was employed, and quantitative data were collected from 110 undergraduate students using a structured questionnaire while qualitative data were obtained through semi-structured interviews held with 10 students with the purpose of looking into the perceived challenges regarding flipped learning and potential solutions. Quantitative findings revealed that students displayed a positive attitude in general towards using flipped classrooms in the learning process (mean = 3.67). The results of an independent samples 1-test showed that there is no statistical difference between students attitudes based gender. However, significant differences were found according to academic grade, with students in upper grades having developed a more positive attitude towards flipped learning compared to students in lower grades. Multiple regression analysis showed that self-regulation was the strongest predictor of positive attitudes ( beta = 0.425 . p < 0.001 ) followed by technological accessibility and instructional support. Qualitative findings also supported these findings emphasising that increased flexibility, improved student engagement and the promotion of self-determined learning are the basic benefits of the flipped classroom model. In this study, these findings were examined within a neuro-pedagogical framework, and self-regulation skills were linked to prefrontal executive functions. Despite these advantages, students reported challenges including limited access to technological resources, unstable internet connections, and inadequate digital skills. The findings indicate that when technological infrastructure and pedagogical support are adequate, the flipped classroom model can effectively promote autonomous learning in higher education.
This article examines the complex relationship between the rapid development of Smart Cities and the psychosocial and ethical implications of implementing Al and loT technologies. While smart cities promise enhanced efficiency, sustainability, and quality of life through advanced connectivity and data collection, we can argue that this technological focus often neglects crucial psycho-social and ethical challenges. It was identified the main technological problems such as are she rapid pace oft technological change, significant data privacy and seeuelty vulnerabilities. problems with system interoperability, and the vendor lock-in. Even thet pursuit of technological efficiency can have significant negativet psycho-social impacts on residents Efficiency and stress (constanti connectivity and the push for algorithmic efficiency can exponentially increase the rhythm of life, leading to higher levels of stress and anxiety among inhabitants), digital divide and social exclusion (digital systems often favor young, educated, and high-income individuals with high digital literacy. Vulnerable groups, such as the elderly, people with disabilities, or t migrants who do not speak the local language, risk being excluded from essential services, becoming "invisible people" within the urban landscape), loss of cognitive abilities and autonomy (dependence on technology cant lead to cognitive offloading and digital amnesia, progressively limiting critical thinking and complex problem-solving skills) and filter bubbles and echo chambers (search systems and applications often create "filter bubbles" and "echo chambers" that isolate users within information confirming their existing beliefs, limiting access to diverse perspectives and potentially leading to conceptual radicalization) are the most important negative effects. In the last part, it was underlined the ethical principle. based on kantian deonthology: "First, the Human being". The smart cities must prioritize human well-being and ethical behavior such as inclusivity, equity, and transparency. In conclusion, we advocate for a "Social Smart"! approach, arguing that technology should act as an "infrastructure of empathy" rather than an end in itself.
Using artificial intelligence and the latest developments account in neuroscience, the authors investigate the challenges in Improving the efficiency of agent-oriented control in robotic systems. The goal is to develop a theoretical concept taking into the existential-objective, simulation-cognitive, and neurobehavioural levels of control in agent-oriented systems. This will lay the groundwork for improving the hardware and software of such agents. The article employs methods of system-analytical and comparative analysis, neuro-oriented modelling of control concepts, and formal verification approaches. The research results the creation of an original theoretical foundation for Neuro-Agentic Verification Control (NAVC), which combines digital twin simulation, synaptic neural networks, and cognitive-ontological principles. The authors paid significant attention to the integration of formal interfaces and natural language, and other cognitive systems. Finally, the authors provide formal proofs to demonstrate the validity of the key components of the proposed architecture. The article's international significance Is determined by the relevance of addressing challenges related to transparency, safety, and reliability of autonomous robotic systems in critical domains, including transportation, industry, and defence.
