Volume 16, Issue 2

Overview Podcast about this issue

DOI: http://dx.doi.org/10.70594/brain/16.2

Table of Contents

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Neurology and Neurophysiology

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Authors:
Ioannis Mavroudis , Foivos Petridis , Dimitrios Kazis , Cătălina Ionescu , Bogdan Novac , Antoneta Dacia Petroaie , Otilia Novac , Irina Luciana Gurzu , Bogdan Gurzu
Abstract:

Traumatic brain injury (TBI), particularly repetitive mild TBI, has emerged as a significant risk factor for the development of Alzheimer’s disease (AD)-like pathology. Neuropathological studies have identified shared features between TBI and AD, such as amyloid-beta (Aβ) deposition and hyperphosphorylated tau (p-tau) aggregates. However, distinct regional patterns differentiate chronic traumatic encephalopathy (CTE) from AD, making the relationship between TBI and neurodegenerative diseases a complex and debated issue. Diagnostic challenges are compounded by overlapping clinical symptoms and the limitations of current imaging and biomarker techniques, which hinder precise differentiation between TBI-associated neurodegeneration and classical AD. Despite these challenges, recent advances in tau-specific imaging technologies and blood-based biomarkers hold promise for enhancing diagnostic accuracy and distinguishing TBI-related changes from AD pathology. This study aims to enhance the understanding of the mechanisms linking TBI and AD by examining shared and distinct pathological features. It explores how TBI may trigger or accelerate neurodegenerative processes leading to AD-like pathology. By focusing on amyloid-beta and tau patterns in TBI, we aim to clarify the role of TBI in AD development and identify potential biomarkers for early detection and intervention. The study also emphasizes the need for longitudinal research and personalized therapeutic strategies to mitigate TBI's long-term effects on brain health.


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Authors:
Florin Zamfirache , Gabriela Prundaru , Cristina Dumitru , Beatrice Mihaela Radu
Abstract:
Major depressive disorder (MDD), one of the most prevalent and debilitating mental health conditions, is found to have the highest rates of treatment resistance and recurrence. The current therapeutic approach is primarily based on pharmacological and psychotherapeutic interventions, which fail to achieve complete remission for a significant part of the patients. New interest is emerging in combining pharmacological treatments with neuromodulation and environmental improvements to enhance therapeutic outcomes in this context. This one-year longitudinal study investigated the individual and combined effects of social isolation, chronic restraint stress, sertraline administration, transcranial direct current stimulation (tDCS), and social rehabilitation on depression-like behaviours in male Sprague Dawley rats. Behavioural outcomes were assessed using the Open Field Test, Sucrose Preference Test, Elevated Plus Maze, and Novel Object Recognition Test across ten experimental phases. Social isolation and chronic restraint effectively induced depression-like behaviours, as indicated by reduced exploration and sucrose preference. Sertraline treatment yielded partial improvements in affective and motivational behaviour, though inter-individual variability was notable. The addition of tDCS produced more consistent behavioural benefits and appeared to stabilise recovery trajectories. Social rehabilitation also contributed to behavioural improvement but was influenced by prior treatment history. Depression treatment usually has a high risk of relapse, so continuous interventions and individualised strategies are needed to increase the chance of effective treatment. Our findings support a multimodal approach to depression treatment, where combining pharmacological, neuromodulatory, and environmental interventions offers more significant potential for recovery than single treatments alone. These results underscore the complexity of depressive disorders and the need for long-term, personalised therapeutic strategies.

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Authors:
Raluca – Maria Rusu Andron , Romulus-Dan Nicoară , Horia-George Coman
Abstract:
Alzheimer's dementia is a progressive degenerative brain disease marked by cognitive decline and often accompanied by psychiatric symptoms, with depression being one of the most prevalent non-cognitive symptoms. Various antidepressants have been investigated for their efficacy in treating depression in dementia patients. This study analyses the outcomes of randomised clinical trials (RCTs) regarding the use of antidepressants to treat depression associated with Alzheimer’s dementia (AD). As method, a comprehensive search was conducted for randomised, placebo-controlled, double-blind studies with a minimum duration of 4 weeks. The search included studies evaluating one or more antidepressants. Keywords used were "RCT, depression, dementia," supplemented by terms for each specific antidepressant or class of antidepressant drugs.

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Authors:
Cătălina Mihaela Dascălu , Paul Lucaci , Marius Neculaeș , Beatrice-Aurelia Abalașei , Alexandru-Rareș Puni
Abstract:
Falls among older adults are often associated with age-related decline in sensorimotor integration and postural control. Perturbation-based balance training (PBT) is increasingly recognised for its role in enhancing stability through targeted motor adaptations. This study examines the functional effects of a two-week PBT intervention on balance and fall risk in elderly individuals (aged 75–92), using Tinetti, TUG, and Romberg assessments. Significant post-intervention improvements in the experimental group suggest that PBT may facilitate sensorimotor recalibration and promote neuroadaptive balance strategies. While neurophysiological mechanisms were not directly assessed, the observed functional gains point to enhanced central integration of proprioceptive feedback and motor planning. These findings underscore the potential of task-specific, reactive balance training as a neurorehabilitation strategy for fall prevention in geriatric cohorts. Despite the absence of neurophysiological instrumentation, the improvements suggest underlying cortical and subcortical adaptations that warrant further investigation. Future studies should incorporate wearable sensors, electromyography, or neuroimaging modalities to characterise neuroplastic modifications and assess the retention of motor learning. These additions could help optimise intervention design and better personalise fall prevention strategies for older adults.

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Authors:
Ana-Maria Dumitrescu , Roxana-Gabriela Cobzaru , Lucia Corina Dima-Cozma , Claudia-Florida Costea , Dragoș-Andrei Chiran , Carmen Valerica Rîpă , Andrei-Ionuț Cucu , Ana Maria Slănină , Ana-Marina Rădulescu , Cristina Elena Dobre , Crînguța-Mariana Paraschiv , Anca Sava
Abstract:
This study investigates the correlation between anatomical variants of the Circle of Willis (CoW) and the presence of intracranial aneurysms (IcAs), a relationship insufficiently addressed in current literature. We conducted a 12-year retrospective observational study on 221 adult autopsied patients admitted to the “Prof. Dr. N. Oblu” Emergency Clinical Hospital in Iași, Romania. Demographic data, aneurysm morphology (location, diameter), and CoW anatomical variants were analyzed. IcAs were found in 29 cases (13.12%), most frequently located at the Anterior Communicating Artery (55.2%), with a majority measuring under 10 mm in diameter. A strong association was observed between IcAs and atypical CoW configurations (96.55%), particularly when three or four anatomical variants coexisted. No gender predisposition was identified. The mean age of patients increased with aneurysm size, yet larger aneurysms were less often associated with progression to death. Our findings suggest that the presence of multiple CoW variants significantly increases the likelihood of developing IcAs. These results may enhance prognosis and support clinical decision-making frameworks for managing cerebral aneurysms.

Neuroscience, Neurolinguistics, and Neuroetichs

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Authors:
Olena Baklanova , Lyubov Lytvyn , Gennadii Riabtsev , Olha Maltseva , Valentyn Susidenko , Zorislav Makarov
Abstract:
This article examines the neuroeconomic aspect of innovative activity in the scope of global factors affecting the world economy. The megatrend of recent decades is digitalisation and globalisation, which have contributed to the formation and spread of the information society. The purpose of the article is to study neuroeconomic approaches to managing innovative activities in the conditions of information society. A wide range of globalisation's impact on states leads to their closer interaction. Supporters of globalisation emphasise the positive effects of its action, but critics see negative consequences for national economies and local producers. Thus, although the issue of globalisation is not new, there are still antagonistic opinions about its impact on society and the economy. The transition to digitalisation, imbalances and inequalities, as well as other previously existing global trends and problems have been exposed by the Covid-19 pandemic sway. Synchronically, as a great threat, the pandemic revealed the need for global cooperation. It has been established that the positive impact of the globalisation of the world economy is manifested in the openness of markets, which sharpens competition and encourages entrepreneurs to develop innovative activities. The neuroeconomic approach to decision-making makes it possible to respond adequately to the prevailing conditions. In order to rationally manage innovative activities, it is necessary to single out the stages of genesis and formation of the concept, the process of its execution  and the realisation of the innovation, at each of which one of the most important factors of effectiveness is the system of decisions made by managers.

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Authors:
Myroslava Hnatyuk , Volodymyr Diakiv , Oksana Kalashnyk , Halyna Stupnytska , Olha Sopina , Liliia Sobol
Abstract:

In the context of the study, the essence of the psycholinguistic features of the use of Anglo-American words in youth slang was clarified. The definition of the concept of slang and psycholinguistics is revealed. A theoretical and methodological analysis of the work of researchers as a basis for the formation of trends in postmodern society has been carried out. In the course of the study, a linguistic analysis of concepts and terms characterising Ukrainian youth slang was carried out. The research problem is determined by close integration and globalisation processes. The theoretical significance of the research lies in the development of a neurolinguistic approach to the analysis of Anglo-American loanwords in slang; in expanding the idea of the psychologically real meaning of slangisms; when systematising data on the functioning of borrowings in slang, a technique is used that involves a comprehensive consideration of the research object. The work contributes to the study of the ethno-cultural specificity of the linguistic consciousness of speakers of the American variant of the English language and slang as its meaningful form. The article defines the trends of the neuropsychological aspect in the context of rethinking values and determining priority bases for the formation of neurolinguistic analysis of lexical units. In order to realise the research goal, the method of synthesis and analysis, integration, as well as research, descriptive and scientific methods were applied. The method of generalisation formed an idea of the neurolinguistic essence of English slang in speech and communication interpretations.


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Authors:
Oksana Kozachyshyna , Natalia Myronova , Uliana Zhornokui , Liubov Slyvka , Lesia Shahala , Tetiana Marchenko
Abstract:

This research delves into the intricate interplay of intertextuality and translation within the digital learning landscape, with a specific focus on the cognitive and neurolinguistic dimensions. By scrutinising the utilisation of intertextuality in e-learning environments, the study aims to unravel the mechanisms through which intertextual approaches can be employed to enrich language learning and elevate translation proficiency. Using a qualitative research approach, the study systematically analyses relevant literature to explore how intertextual strategies enhance language acquisition and translation proficiency. Drawing critical insights from e-learning contexts, this article not only unveils the potential advantages but also addresses the challenges inherent in integrating intertextuality into translation practices in the digital age. The findings indicate that incorporating intertextual elements – such as hyperlinks to related texts, multimedia resources, and cultural references – significantly improves learners’ comprehension, critical thinking, and contextual awareness. Specifically, leveraging intertextual cues such as hyperlinks to pertinent texts, multimedia resources, and cultural references proves instrumental in enhancing learners’ comprehension, stimulating critical thinking, and facilitating the establishment of connections between diverse texts and contexts. The research underscores the cognitive processes involved in intertextual comprehension, including memory activation, attention regulation, and semantic integration, which are crucial for effective translation in e-learning settings. Furthermore, the study underscores the significance of adopting a collaborative approach to both translation and intertextuality within the digital realm. The imperative for learners to actively participate in dialogue and idea exchange emerges as a key element in fostering cross-cultural understanding, aligning with the broader cognitive and neurolinguistic aspects of intertextuality comprehension. The conducted research not only sheds light on the transformative potential of intertextuality and translation in e-learning environments but also accentuates their profound implications for cognitive and neurolinguistic processes. 


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Authors:
Olga Pravotorova , Oleksii Shumilo , Iryna Talanchuk , Tetiana Lien , Nataliia Zlenko , Vadym Podorozhnyi
Abstract:
The article presents an analysis of the origins and development of legal misconduct from a historical perspective within the framework of legal studies. The relevance of the study is determined by modern conflicts in society, the cause of which is legal misconduct in law. It is important for a person in the new society to understand how to resolve conflicts on the basis of law. The main purpose of the article is to consider the features of neuroethics as a new scientific discipline, which claims to be not only a form of applied ethics, but also an apology of morality within the framework of a naturalistic paradigm based on new data from neurobiology and cognitive science, which determines the main content of the genesis of the legal aspect. The article explored the evolution of scientific research on the problem of legal formation in the postmodern worldview. It is established that the importance of legal culture in society is due to the practical value for the establishment of justice, equality, and human dignity. Legal science is the object of study of historical figures in different historical periods. In the postmodern era in the life of society legal misconduct is the cause of many conflicts. The article examines legal knowledge as an aspect of neuroethical formation in historical retrospect. The concept of neuroethics, formed from neurophysiology, which influences self-awareness and the formation of morality of will in the context of the formation of political and legal relations, is defined. Scientific and methodological basis of the study are the conclusions of Ukrainian and foreign researchers in the field of law and legal responsibility. On the basis of scientific works the tasks of the study and the goals that form the main theoretical and methodological approaches of this study are defined. Theoretical and methodological approaches formed the results of the study, in particular the genesis of the formation of legal misconduct in the law. Methods of synthesis, analysis, descriptive and historical method were used for effective research. The method of generalisation was used to determine the main results of the study.

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Authors:
Ana-Maria Danila , Irina-Luciana Gurzu , Bogdan Novac , Otilia Novac , Antoneta Dacia Petroaie , Alin Ciobica , Ioana Vata , Gabriel Dăscălescu , Bogdan Gurzu
Abstract:
This article investigates how artificial intelligence can support the inclusive education of students and patients with albinism, a genetic condition that manifests itself in visual deficits and cognitive challenges. Starting from a systematic synthesis of specialist studies and a case study of some AI platforms already in use, we aim to identify technologies capable of personalising teaching materials and adjust digital interfaces according to the visual and mental needs of the beneficiaries. The analysis reveals that while these tools can provide adaptive content and real-time feedback, their effectiveness depends on fine-tuning the parameters for the particularities associated with albinism. Furthermore, we argue that integrating neuroscientific perspectives, particularly those on neural plasticity and attentional processes, in the design of AI solutions can lead to truly personalised educational strategies. We conclude that the responsible adoption of artificial intelligence in inclusive environments requires careful reflection on the ethical and neuro-educational implications to guarantee an equitable and effective learning experience.

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Authors:
Yana Topolnyk , Roman Gurevych , Ihor Debenko , Oleksandr Klochok , Zhanna Cherniakova , Alla Yarova , Borys Maksymchuk
Abstract:
This article explores the urgent issue of developing digital skills and stimulating neuroplasticity in adults during rapid digital transformation. It examines how information and communication technologies (ICT) and artificial intelligence (AI) contribute to effective adult education. The article analyses the theoretical foundations of digital literacy and neuroplasticity. It also discusses practical strategies for enhancing these areas through ICT and AI. To support this analysis, the study applies several research methods. These include a theoretical review of scientific literature, comparative analysis, and case study approaches. The methods are used to investigate real-life examples of adult learning with tools such as online courses, AI-powered tutors, and neurofeedback systems. The findings show that ICT and AI significantly expand access to education and support personalised learning. Besides, they help improve cognitive functioning, increase learning motivation, and promote neuroplasticity in adult learners. The article presents some examples of digital tools that personalise learning and support cognitive development. These include AI tutors, online learning platforms, and neurofeedback technologies. In addition, the article discusses the challenges and future ways of integrating ICT and AI into adult education. It highlights key issues such as ethics and accessibility. Finally, the article emphasises that digital literacy and neuroplasticity are essential for successful ageing and lifelong learning. It concludes by underlining the critical role of ICT and AI in creating inclusive, engaging, and effective learning environments for adults.

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Authors:
Nadiia Smolikevych , Оlena Kyriazova , Oleh Radchenko , Iryna Hryshchenko , Oksana Kravchuk , Oksana Snigovska
Abstract:
The importance of the article is due to the fact that in recent years psycholinguists and linguistic theorists in many countries around the world have shown great interest in the research of the linguo-cultural aspect of emotional intelligence development in the process of learning a foreign language neurodidactics. Of course, teaching foreign languages using a special method based on neurolinguistic programming in the study of the linguo-cultural aspect of the development of emotional intelligence in the process of learning a foreign language will allow to master intercultural communicative competence in the shortest possible time due to the production needs. Researchers who conduct such research want to help teachers to teach the linguo-cultural aspect of the development of emotional intelligence in the process of learning a foreign language, taking into account the individual and age features of the brain activity of students. They also emphasise the importance of the emotional component of the learning process, although they analyse the full range of challenges for teachers. Thus, many neurodidactic issues related to the improvement of the educational process are still waiting to be solved. The article explores the role of neuroscientific knowledge in the didactics of learning a foreign language; ways of developing emotional intelligence in the process of learning a foreign language are defined; the role of the linguocultural aspect in the process of learning a foreign language is outlined.

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Authors:
Andrii Fesenko , Roman Gurevych , Alla Yarova , Volodymyr Gotsuliak , Oksana Dovhalets , Nataliia Perekhodko
Abstract:

This article explores the intersection of historical memory, national identity, and neuroethics within the educational paradigm, focusing on the complex legacy of Polish-Ukrainian relations. Against the backdrop of geopolitical tensions and historical traumas—such as the Volhynia massacre and the Soviet-era distortions—the study analyzes how educational institutions mediate collective memory and identity formation. By integrating neuroethical perspectives, the article highlights the cognitive and moral dimensions of historical education, emphasizing the importance of memory as a neurological and ethical construct. In a postmodern context marked by digital memory, value crises, and neurocapitalist influences, education emerges as a strategic tool for shaping socio-political consciousness. The authors argue that rethinking the politics of memory through neuroethics and critical pedagogy enables societies to transcend inherited narratives of victimhood and hostility, promoting reconciliation and cooperative futures. Methodologically, the study draws on discourse analysis, critical historiography, and educational theory to formulate new approaches for historical teaching that foster reflective citizenship and ethical awareness.


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Authors:
Vasyl Ovcharuk , Yurii Yurchyshyn , Yevgen Pavlyuk , Rostyslav Polishchuk , Andrey Kernas , Oleksandr Lohvynenko
Abstract:
The significance of the article lies in its exploration of the historical aspects related to the genesis of physical education and the shaping of its content, which the authors believe have philosophical roots in the development of one’s physical culture. This historical journey can be divided into two distinct periods. The first period, from the emergence of primitive communal systems to the 19th century, can be tentatively described as a pre-scientific phase. During this time, two phases are discernible: empirical and experimental, which reflect the initial stages of physical culture development. The second period, spanning roughly a century and a half from the late 19th century to the early 21st century, represents the era when the problem evolved into a more scientifically focused field of study. Within this timeframe, four distinct stages can be identified, illustrating the evolution of the scientific, theoretical, methodological and technological foundations of physical education, sports, as well as the management of physical education. Currently, sports are the dominant culture in many areas of human activity. It directly influences the material, social, and spiritual values of society and individuals. Therefore, the impact of physical culture on brain function is of paramount importance as physical activity is intrinsically linked to the body’s functioning through the brain. The article studies the history of one’s physical culture development, discussing the role of the state system, lifestyle and specific state requirements in organising physical education. It also explores the current development of one’s physical culture and its impact on brain development.

Psychiatry

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Authors:
Diana Patraș , Anamaria Ciubară , Alexandru Bogdan Ciubară , Lucreția Anghel
Abstract:

Psychiatric disorders, chronic obstructive pulmonary disease (COPD), and type 2 diabetes (T2D) share common mechanisms, including neuroinflammation, oxidative stress, systemic inflammation, and gut microbiota alterations. This literature review evaluates key molecular and clinical intersections across these diseases. Analyzing peer-reviewed studies (2020–2025) from databases like PubMed and Scopus, our findings highlight oxidative stress and systemic inflammation as major drivers of disease progression, with hypoxia-induced neuroinflammation and gut dysbiosis emerging as critical factors. Early psychiatric screening in COPD and T2D patients is essential, alongside precision medicine approaches such as biomarker diagnostics and microbiome-based therapies. Addressing inflammation and autonomic dysfunction could help break the cycle of disease progression. This study advocates for a multidisciplinary healthcare approach, emphasising integrated, patient-centered management strategies.


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Authors:
Seila Musledin , Eduard Circo , Elena Ciciu , Olesea Scrinic
Abstract:

Objectives: Depressive syndrome is commonly observed in patients with autoimmune thyroid diseases, often coexisting with specific endocrine symptoms. This study highlights a higher prevalence of depression in patients from urban environments, potentially influenced by occupational, educational, and psychosocial factors, as well as environmental endocrine disruptors. Depression may develop at any age, with increased frequency among older individuals due to chronic health conditions. Early identification using tools such as the Beck Depression Inventory can support timely intervention and appropriate treatment strategies.

Methods:This paper presents a clinical observational study involving 80 patients with autoimmune thyroid disease from the Dobrogea region, Romania. Participants were divided into two groups: Group 1 with chronic autoimmune thyroiditis (CAT, n=62) and Group 2 with Graves’ disease (GD, n=18), reflecting the lower prevalence of GD. Inclusion criteria required confirmed autoimmune thyroid disease and residence in Constanța County. Patients underwent clinical and paraclinical evaluations, including hormonal and antibody testing, as well as vitamin D level assessment. Depression was screened using the Beck Depression Inventory II (BDI-II), a 21-item tool used to assess the presence and severity of depressive symptoms, particularly in relation to vitamin D deficiency.

Results: The study found that depression was more prevalent among GD patients (66.7%) compared to those with CAT (53.2%), with higher incidence in urban areas and among older individuals. Increased depression severity was associated with lower vitamin D levels, higher ATPO and TRAb levels, and suboptimal thyroid function. Mild depression was most common and linked to vitamin D deficiency in both groups. The Beck Depression Inventory-II effectively captured varying depression degrees across clinical and biochemical parameters.

Conclusions: Depression is a common comorbidity in autoimmune thyroid disorders and should be systematically assessed, with a multidisciplinary approach and attention to vitamin D status using cost-effective screening methods.


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Authors:
Monica Calderaro , Vincenzo Mastronardi , Ionut Virgil Serban
Abstract:

Starting from the updated data on the topic and inherent to the research of the author (Calderaro M., and coordinated by Mastronardi V.) and carried out with the CSS (Superior Health Council) which approved the new concept of addiction as developed by the Working Group in charge and published on 12 October 2022 (see sitography) and ultimately for the new 'concept of addiction', that is, a "psychic condition, (maladaptive) which does not exclude physical discomfort resulting from the interaction between a dysfunctional behaviour and/or a substance and an organism, characterised by an absolute need to continue to experience the inebriating effects of the same psychophysical and biological satisfactions, therefore with an increase in endorphinic gratification, albeit fictitiously, involving the limbic area of our brain with its centres of gratification and punishment". The deepest motivations that drive and/or support dysfunctional addictive behaviour can be considered common to almost all addictions and can be summarised as follows: a) to soothe pain, b) out of curiosity or exploratory instinct, c) for pleasure, d) for escape, e) out of desperation, f) out of nonconformity, g) for imitation, h) to create and ritualise social bonds, i) for compensation [for a psychoneurotic syndrome]. The new official research by the Study Group of the Higher Health Council on addictions not due to the use of substances, has drawn inspiration from the need for assistance by the National Health Service to guarantee assistance not only for cases of "addiction linked to the use of substances", but also for that "not due to the use of substances". In fact, for some time now we no longer speak of SERT, but of SERD where the last letter is no longer (Drug Addiction), but the letter D which stands for Dependence Tout Court. Obviously the difficulty lies in finding the evaluation tools capable of discerning what constitutes a pathological dependence from a non-pathological one deserving of assistance by the National Health Service itself. For this topic, please refer to the specific chapter. In the English language, ‘addiction’ means the behaviour that leads to psychological dependence, vice versa, fore ‘dependence’ means that dependence that involves both a physical and chemical dependence in order to function. Therefore, this work has the purpose of examining, among others, diagnostic-evaluative aspects in function of the operational treatment tools relating to the individual behavioural dependencies that are listed below.


Psychology

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Authors:
Hanna Ivanochko , Yevheniia Kaliuzhna , Larysa Matviienko , Viktoriia Levchenko , Yuliia Danchuk , Oksana Lohvina
Abstract:
The article analyses the key issues surrounding emotional burnout syndrome in the teaching profession at higher education institutions. It highlights that burnout is driven by the specific demands of the teaching profession and one’s typological characteristics. Depending on the burnout stage, recovery can be achieved through personal resources or with the help of specialists. Traditionally, emotional health issues among university teachers have been examined through burnout indicators correlated with personality traits or stress-related occupational factors, which are now recognised as affecting university teachers. Recent research has shifted focus away from studying burnout concerning psychophysiological characteristics, temperament, and character, instead emphasising moral disruptions as a contributing factor to emotional burnout. This article aligns with recent studies conceptualising emotional burnout as a complex, dynamic phenomenon rooted in axiological and moral determinants. The primary emphasis is on neuropsychological tools and diagnostic methods for preventing professional burnout. The article defines emotional and professional burnout among university teachers and presents diagnostic findings on burnout syndrome in this group. Furthermore, it identifies neuropsychological tools to prevent it. The article also proves that incorporating neuropsychological tools into teaching and assessment can greatly improve the effectiveness of adaptive learning platforms and cognitive evaluations. Finally, it examines AI-driven mental health monitoring tools for tracking stress levels in teachers.

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Authors:
Dan Octavian Rusu , Cristian Delcea
Abstract:
Sexual desire disorder (SDD) and major depressive disorder (MDD) are closely related syndromes of substantial impact on mental health, relationship quality, and quality of life. We aim to analyse the psychological impacts of SDD and therapeutic strategies to include cognitive-behavioural therapy (CBT) and couples therapy. The bio-psychosocial model not only addresses these issues but emphasises relapse prevention and improving treatment effectiveness, bolstered by a review of the current literature. This consists of a systematic review of recent scientific literature, including longitudinal and cross-sectional studies, as well as clinical case series. Psychological and physiological mechanisms underpinning the association between SDD and MDD in these studies have been emphasised. It extends beyond relational and social factors to encompass individual influences including family history and factors such as emotional trauma and vulnerability to stressors. Indeed, for children of parents with SDD, there is a high association of emotional distress such as depression, and anxiety, which increases relationship problems. Psychotherapeutic interventions such as CBT and couples therapy have shown efficacy in mitigating symptoms, improving communication patterns, and increasing relationship satisfaction. In addition, techniques like social support and cognitional reorganisation are crucial in preventing subsequent relapse. This research highlights the need for integrative therapeutic strategies for SDD in the context of MDD to be used. Psychotherapeutic interventions can improve symptoms, avoid relapses, and assist in the interpersonal relationship when taken with personalised strategies. The findings highlight the importance of interprofessional teamwork and individualised medicine that result in appropriate response to patients needs.

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Authors:
Catalin Plesea-Condratovici , Alina Plesea-Condratovici , Ciprian Adrian Dinu , Pantelie Nicolcescu , Karina Robles-Rivera , Mark Weiser , Mihai Mutica , Lucian Stefan Burlea , Anamaria Ciubara
Abstract:
This study examines the relationship between digital media consumption, gaming time, and academic performance among medical and nursing students at the University of Galați during the academic year 2024-2025. Data from 209 students were collected using PsyToolkit and analysed using SPSS v27, Excel, and Python for statistical analysis. The research explores correlations between the Bergen Social Media Addiction Scale (BSMAS), time spent on social media and gaming, and students' academic grades. The results indicate significant differences in digital media usage between medical and nursing students, with the latter group spending more time on social applications. Gaming behaviour was similar across groups, suggesting it serves as a recreational activity rather than an academic detriment. However, no significant correlation was found between gaming time and academic performance in the same group. Regression analysis found no significant predictors for academic performance, highlighting the need for further research and supplementary data. Regression analysis found no significant predictors for academic performance, highlighting the need for further research and supplementary data. Additionally, the most commonly used social media applications and their differentiated usage based on age categories were revealed. Findings emphasise the importance of balanced digital consumption and structured academic support to optimize student success.

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Authors:
Soufien Jaffali , Nesrine Khelifi
Abstract:
Social media platforms have evolved into powerful arenas for public discourse, significantly influencing opinions, behaviours, and policy decisions. This study explores the relationship between sentiment and user engagement in online conversations related to public health. Using advanced sentiment analysis techniques powered by the BERT model, we analyse how emotional tone affects interactions such as likes, replies, and shares. Our findings reveal that positive sentiments drive higher engagement in terms of likes and replies, while negative sentiments generate more shares, amplifying their reach.. Neutral sentiments, in contrast, exhibit lower engagement across all metrics. Statistical analyses confirm significant differences in engagement based on sentiment, underscoring the critical role emotions play in shaping online conversations. These insights offer valuable guidance for policymakers, health communicators, and digital strategists aiming to craft more effective messaging strategies. By understanding how sentiment influences engagement, organisations can better connect with audiences, foster meaningful discussions, and enhance the impact of public communication campaigns.

Artificial Intelligence in Medicine

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Authors:
Virgilijus Sakalauskas , Dalia Kriksciuniene
Abstract:
Stroke is found to be a leading cause of mortality and long-term disability worldwide, forcing effective predictive models to identify at-risk individuals and optimise treatment plans. In this study, we evaluate the performance of various machine learning (ML) algorithms in predicting stroke-related mortality. Five binary classification models—Logistic Regression (LR), Random Forest (RF), Gradient Boosting Machines (XGBoost), Support Vector Machine (SVM), and Neural Networks (MLPClassifier)-were applied to a dataset containing clinical and demographic features of stroke patients registered by the neurology department of the Clinical Centre of Montenegro. Each model was trained and evaluated using standard classification metrics: accuracy, precision, recall, and F1-score. Also, the importance of the feature was analysed to find the key predictors of stroke mortality across different models. The research shows the Random Forest and XGBoost performance over simpler models, proposing superior accuracy and interpretability. By analysing how precision, recall, and accuracy changes across a range of classification thresholds, we gained deeper insight into the model’s reliability under different clinical conditions. This analysis revealed clear trade-offs: lower thresholds improve recall (reducing the risk of missed death predictions), while higher thresholds enhance precision (minimising false positives). The findings support the selection of threshold values tailored to specific clinical priorities, such as early warning, balanced risk assessment, or high-confidence decision-making.

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Authors:
Gülay Demir , Prasenjit Chatterjee
Abstract:
Personalised treatment approaches have become increasingly important in the field of neuroscience, aiming to improve healthcare quality and patient satisfaction. This study contributes to the development of personalised treatment strategies by integrating fuzzy multi-criteria decision-making (FMCDM) techniques. Given the uncertainties and multidimensional criteria involved in treatment evaluations, FMCDM provides a robust framework to enhance decision-making in healthcare. The primary objectives of this study are to manage uncertainties in treatment evaluations, to develop an integrated decision-making approach for patient monitoring, and to evaluate treatment criteria on an individual patient basis. The prioritization of treatment criteria for each patient was performed using Fuzzy Logarithm Methodology of Additive Weights (FLMAW), while personalised treatment approaches were evaluated with Fuzzy Ranking of Alternatives with Weights of Criteria (FRAWEC) method. The results demonstrated that the criteria for Patient A and Patient B, as recommended by the expert team, were distinct, leading to different personalised treatment approaches for each. The proposed model integrates FLMAW and FRAWEC methods to optimise personalised treatment strategies by addressing uncertainties and evaluating key factors such as biometric data, treatment response, and psychosocial aspects. By prioritising treatment criteria and ranking interventions based on individual patient profiles, the model facilitates tailored treatment plans that address both physical and psychological health needs. This study discusses practical implications for healthcare professionals and management strategies for implementing these innovative approaches. Future research highlights the need for broader expert collaboration and the continuous integration of advanced technologies to update monitoring criteria.

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Authors:
Preena Prasad , Anitha J. , Alexander Zakharov , Natalia Romanchuk , D. Jude Hemanth
Abstract:
Magnetic resonance imaging (MRI) is an essential imaging modality for brain, but it is affected by noise which degrades the quality of the images. Traditional methods often fail to maintain the fine details of the image while providing denoising efficiency. Even though deep learning methods perform well, integrating them with traditional approaches for medical image denoising is under explored. In this work, we developed CNLNet, Deep learning(DL) model that combines convolutional neural network (CNN) with Non-local means (NLM) layer for MR brain image denoising. Local feature extraction is done by utilising CNN. NLM layer refines long-range dependencias, delivering better denoising performance. Model was trained on Kaggle MRI brain dataset with varying Gaussian noise of standard deviation 0.1, 0.3, 0.5. Quantitative results of experimentation reveal that CNL Net performs well when compared to the CNN model, traditional median, Wiener, NLM filters in terms of PSNR, SSIM, MSE, MAE. Visual comparison also highlights that CNLNet preserves significant fine details along with noise removal. This approach improves the quality of MRI brain images demonstrating potential for clinical diagnostics applications and offers a more efficient denoising solution compared to conventional methods.

Artificial Intelligence

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Authors:
Marius Susa , Kristijan Cincar , Andrea Amalia Minda
Abstract:
This paper explores thesecurity challengesassociated withdata transmissioninprecision agriculture, emphasising thevulnerabilities introduced by wireless sensor networks (WSNs). To mitigate these risks, we provide acomprehensive analysisof different types ofcyberattackstargeting WSNs, along withdetection, prevention, and defense strategiesto safeguard agricultural data and ensure the reliability of smart farming systems.

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Authors:
Sorin Ionuț Conea , Valer Nimineț
Abstract:
This paper investigates the development and implementation of a hybrid transportation system that integrates the advantages of trucks and drones to optimise delivery processes. Considering the continuous growth in demand for rapid and efficient delivery services, our approach presents an innovative model that combines the reliability of road transport with the agility and speed of aerial delivery. Starting from a detailed analysis of logistics networks in Bacău County, Romania, our study develops and applies a routing algorithm using Google's OR-Tools. For truck transportation, real distances between towns were utilised, allowing the calculation of routes for both transportation modes, taking into account their specific characteristics. Computational experiments demonstrate that the proposed method is both fast and reliable.

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Authors:
Nina Asenovska , Asya Stoyanova-Doycheva
Abstract:
 The design of software architecture remains a critical and complex task, often requiring expert knowledge, significant time investment, and the ability to interpret evolving requirements. This paper presents Archescape, a prototype system that supports the automated generation of software architecture from structured requirements using a hybrid approach that integrates machine learning algorithms with generative language models, specifically ChatGPT. The proposed system guides users through an interactive interface to collect detailed project requirements and produces architectural suggestions based on both algorithmic analysis and natural language generation. The system aims to assist both experienced architects seeking alternative perspectives and less experienced developers in need of guidance. Experimental results show that combining machine learning with large language models yields more adaptive, context-aware, and user-friendly architectural solutions than using either technique in isolation. Additionally, the platform supports educational purposes by enhancing understanding of the link between requirements and architecture. Limitations and future improvements, including architecture validation and domain specific tuning, are discussed. The results demonstrate the potential of AI-assisted tools to streamline the architectural design process and improve communication between technical and non-technical stakeholders.

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Authors:
Ivan Stoyanov , Veneta Tabakova-Komsalova , Asya Stoyanova-Doycheva , Stanimir Stoyanov , Emil Doychev
Abstract:
 This paper presents the design and implementation of an integrated software platform that connects air quality monitoring systems with smart agriculture tools, with a focus on secondary air quality standards. The study investigates the correlation between air pollution and the development of tomato crops in the Plovdiv region of Bulgaria. The system integrates two intelligent agents: Air Agent, responsible for analysing real-time pollutant data, and Farmer Agent, which monitors the vegetation dynamics of tomato plants. Experimental results from the 2023 growing season indicate that elevated concentrations of PM2.5 and PM10 during critical growth stages significantly inhibited both plant height and stem development. The proposed platform utilises ontologies, asynchronous communication protocols, and data analytics to provide actionable insights for farmers and to support the practical application of environmental standards in agriculture. These findings underscore the importance of incorporating secondary air quality considerations into agricultural decision-making and environmental policy.

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Authors:
Mihai-Lucian Voncilă , Nicolae Tarbă , Cosmin-Dumitru Oprea , Costin Anton Boiangiu , Nicolae Goga
Abstract:
Digital images often contain noise introduced during acquisition, storage, or transmission, which can hinder the performance of Optical Character Recognition systems. Effective noise reduction is essential for improving the accuracy of these systems, as noise can obscure text and reduce recognition rates. The problem of removing noise from images is widely studied in computer vision but remains challenging due to the variety of noise types and the risk of introducing artifacts or blurring. In this work, we propose a new preprocessing algorithm that is used in conjunction with the Tesseract engine, in order to improve its overall accuracy. We test this method against the SmartDoc dataset, which contains images taken from mobile devices, and obtain an improvement over the original accuracy of 6.5%. The method is also compared to several other classical algorithms such as Mean Filter, Median Filter, Bilateral Filter, Adaptive Smoothing, and others showing improved results over each individual one.

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Authors:
Andrei Gabriel Nascu
Abstract:
Nowadays, with the increase of technological advancement, AI can be found anywhere, from smartphones with facial recognition, voice assistant, and text autocorrect, to security systems to detect unauthorised persons, cyberbullying, cyberattacks, or even in healthcare to assist doctors in giving diagnostics and treatments. The intersection point of all these use cases is that they all need a lot of labelled data to obtain good results in classification processes. Labelling is tedious work that can consume many resources, like time, workforce, computer power, and money. The labelling process differs according to the type of data that you are labelling (audio, video, images, text). This paper focuses on the image type of data. To solve this issue, the paper proposes the use of an auto-labelling algorithm by employing a combination of a Convolutional Neural Network (CNN)architecture and morphological operations with a set of hyperparameters. To quantify how well the algorithm performs, an efficiency metric given by two components, a local evaluation of the results and a global evaluation of the results, is established by comparing the labels given by the algorithm with the labels manually done. Furthermore, the paper analyses the impact of the hyperparameters on the algorithm using the established efficiency metric. Finally, the results for the best configuration of the hyperparameter are explored using both global and local data analysis. Using the efficiency metric given by the overlap area, the algorithm achieved a mean overlap percentage of 89.33%.

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Authors:
Anita E. Stoyanova , Emil G. Delinov , Daniela A. Orozova
Abstract:
The Analytic Hierarchy Process (AHP), is a multi-criteria group decision making (MCGDM) methodology. It generally required aggregating the Individual Judgments (IJ) or Individual Priorities (IP) of a group of experts. Until now, in the scientific community has accepted the Geometric Mean (GM) and the Arithmetic Mean (AM) as aggregation variables. In this paper we offer the statistical Mode as a possible aggregation variable. This could simplify the process of finding the aggregated priorities/weights in the AHP process, particularly in the context of large, homogeneous groups of experts. We argue that the mode offers a viable, efficient, and representative solution under specific distributional constraints. Our analysis includes both empirical validations based on our long-term observations in various applications of AHP and theoretical, mathematical justification. It is complemented by using simulated datasets based on a controlled range around the Mode. Since we have not yet encountered AI that applies Mode as an aggregation variable, our subsequent efforts will be aimed at training an AI model and/or agent that works with Mode as an aggregation variable. The ultimate goal is for the agent to acquire not only the ability to apply AHP with the Mode as an aggregation variable, but also to analyze which is the most appropriate aggregation approach depending on the collected, experts output data.

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Authors:
Angel Nikolov , Toncho Kolev , Zlatin Zlatev , Neli Grozeva , Daniela Orozova
Abstract:
This development proposes an IoT-based application for automatic bread making, providing new opportunities for convenience and customisation of the process. The use of IoT technologies allows connectivity with other smart devices in the home while improving efficiency and control over baking. The developed system includes local control, which provides speed and security through physical connectivity to the home network and eliminates dependence on an Internet connection. The IoT functionality of the machine provides monitoring of basic parameters such as temperature and time for fermentation and baking. The application of a PID regulator significantly improves temperature control, reducing deviations from 15°C with relay control to 4°C after refinement. This leads to more precise baking and improved quality of the final product. The traditional buttons and small displays of commercially available bread machines have been replaced by an intuitive HMI interface and web monitoring via ESP8266, which facilitates machine management. Research has shown that controlling the temperature below 220°C reduces the formation of acrylamide in the bread crust, with the machine set to operate at 162°C. Research in this area has been expanded, and programs have been implemented to prepare bread with alternative flours rich in nutrients. Future development opportunities include the application of AI and predictive models to optimise fermentation and baking processes, as well as integration with smart AI enabled systems such as Alexa+ and Google Home.

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Authors:
Tiberiu Socaciu , Paul Pașcu
Abstract:

PINNs (Physics-Informed Neural Networks) are neural networks designed to solve Partial Differential Equations (PDEs) by integrating physical knowledge into the learning framework. Constructing a PINN involves defining a neural network to approximate the PDE solution, with the total loss calculated as a combination of the losses associated with the PDE, boundary conditions, initial conditions, and measured data. This concept is employed in practical applications to solve various PDEs, such as the Black-Scholes and Heston equations, which are fundamental in financial option pricing. This approach enables the modelling and pricing of financial options, with the added advantage of parallelising the training process across multiple economic scenarios.


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Authors:
Rehana Farhat , Temoor Anjum , Muhammad Khalid Sohail
Abstract:
Amidst the ongoing competitive challenges in the business environment, AI plays a fundamental role. Despite the pace of AI adoption has shown slow advancement in emerging markets, particularly in Pakistan. Forthcoming years will become essential for organisations to integrate AI into different facets of task management like recruitment and selection (R&S). The core purpose of the research is to address how numerous factors influence the adoption of AI under technology-organisation-environment (TOE) model. The study introduces a basis to examine the importance and interrelationship of key success factors in the adoption of AI. Six key factors influencing AI adoption were derived from a comprehensive review of the literature. The structural model was empirically validated using data collected via a Google-based mail survey conducted in Pakistan. The data is analysed through Structural Equation Modelling, revealing that factors such as relative advantage under Technology factor, technological competency, and support from top-management for Organisational factor, while competitive pressure and vendor support for Environment factor have a significant association with AI adoption regarding R&S in selected firms in Pakistan. The study’s findings offer valuable insights for organisations in Pakistan to refine their AI adoption strategies, particularly in R&S, helping them gain a competitive edge. It also highlights key factors specific to the Pakistani context, enhancing understanding of how local dynamics shape AI adoption in HR. These insights can guide organisations in overcoming challenges and optimising AI’s potential for organisation success.