Faculty Scholarship Celebration 2024

The illegal file sharing of digital content is a widespread activity with far-reaching ramifications. In Greece, studies indicate that “digital piracy” has become an accepted form of criminal practice, disrupting relevant market developments. To explore this, we conducted an online questionnaire that documented practices of Greek audiences, regarding the piracy of movies and television series. This paper goes beyond the labelling of the practice to an analysis of its socio-cultural character. We argue that these findings point to a changing market, and the disruption piracy causes is explained as an effort by consumers to participate in a global culture that transcends national borders.

Function approximation is a fundamental process in a variety of problems in computational mechanics, structural engineering, as well as other domains that require the precise approximation of a phenomenon with an analytic function. This work demonstrates a unified approach to these techniques, utilizing partial sums of the Taylor series in a high arithmetic precision. In particular, the proposed approach is capable of interpolation, extrapolation, numerical differentiation, numerical integration, solution of ordinary and partial differential equations, and system identification. The method employs Taylor polynomials and hundreds of digits in the computations to obtain precise results. Interestingly, some well-known problems are found to arise in the calculation accuracy and not methodological inefficiencies, as would be expected. In particular, the approximation errors are precisely predictable, the Runge phenomenon is eliminated, and the extrapolation extent may a priory be anticipated. The attained polynomials offer a precise representation of the unknown system as well as its radius of convergence, which provides a rigorous estimation of the prediction ability. The approximation errors are comprehensively analyzed for a variety of calculation digits and test problems and can be reproduced by the provided computer code.

Despite the concerns that recent developments in Large Language Models (LLMs) have raised, they undoubtedly revealed a novel potential of Artificial Intelligence (AI) algorithms in educational environments. Whether they are used for tutoring, in a manner similar to that of Intelligent Tutoring Systems (ITS), or to support assessment design and delivery, their impact in a learning setting is remarkable. In this paper, we propose an interactive tutoring approach, utilizing ChatGPT’s API. By exploiting ChatGPT’s programming interface, we can develop customized interactive problem-solving and tutoring sessions on specific topics of interest. The API’s versatility allows for dynamic interactions, fostering a deeper understanding of subjects taught and effective problem-solving skills. We demonstrate the application of the developed code in an applied educational setting with specific use cases.

Data-driven models utilizing powerful artificial intelligence (AI) algorithms have been implemented over the past two decades in different fields of simulation-based engineering science. Most numerical procedures involve processing data sets developed from physical or numerical experiments to create closed-form formulae to predict the corresponding systems’ mechanical response. Efficient AI methodologies that will allow the development and use of accurate predictive models for solving computational intensive engineering problems remain an open issue. In this research work, high-performance machine learning (ML) algorithms are proposed for modeling structural mechanics-related problems, which are implemented in parallel and distributed computing environments to address extremely computationally demanding problems. Four machine learning algorithms are proposed in this work and their performance is investigated in three different structural engineering problems. According to the parametric investigation of the prediction accuracy, the extreme gradient boosting with extended hyper-parameter optimization (XGBoost-HYT-CV) was found to be more efficient regarding the generalization errors deriving a 4.54% residual error for all test cases considered. Furthermore, a comprehensive statistical analysis of the residual errors and a sensitivity analysis of the predictors concerning the target variable are reported. Overall, the proposed models were found to outperform the existing ML methods, where in one case the residual error was decreased by 3-fold. Furthermore, the proposed algorithms demonstrated the generic characteristic of the proposed ML framework for structural mechanics problems.

The present study aims to explore the differences in the manifestation of cognitive decline and psychiatric symptoms across the different ages of MCI onset: early onset (EOMCI: <65 years old), middle onset (MOMCI: 65–75 years old), and late onset (LOMCI: >75 years old). It was hypothesized that individuals with EOMCI will preserve their cognitive functions to a greater extent as compared to individuals with LOMCI, even after adjusting the cognitive performance for age and education through the use of published Greek norms. The level of cognitive decline concerning MOMCI was evaluated for extracting more precise conclusions regarding the impact of the age of onset on the patterns of MCI symptomatology. The analyses of data were conducted in a Greek population of individuals with MCI, who were consecutive visitors of the Outpatient Memory Clinic of Nestor Alzheimer’s Centre in Athens, Greece. The sample consisted of 297 participants who fulfilled the following inclusion criteria: MCI diagnosis based on Petersen’s criteria, Greek mother language, and absence of a psychiatric history or chronic and incurable organic disease. The overall results support the presence of a cognitive advantage of the EOMCI group compared to the LOMCI group. In the MOMCI group, cognitive performance displayed a tendency to remain intermediate compared to the other two groups. Nonetheless, significant differences were observed when this group was compared with the LOMCI group. The current findings indicate that the age of onset should be taken under consideration in the neuropsychological assessment of individuals with MCI. The specific parameters could have implications in terms of prognosis as well as the design and implementation of tailored interventions.

Despite the concerns that recent developments in Large Language Models (LLMs) have raised, they undoubtedly revealed a novel potential of Artificial Intelligence (AI) algorithms in educational environments. Whether they are used for tutoring, in a manner similar to that of Intelligent Tutoring Systems (ITS), or to support assessment design and delivery, their impact in a learning setting is remarkable. In this paper, we propose an interactive tutoring approach, utilizing ChatGPT’s API. By exploiting ChatGPT’s programming interface, we can develop customized interactive problem-solving and tutoring sessions on specific topics of interest. The API’s versatility allows for dynamic interactions, fostering a deeper understanding of subjects taught and effective problem-solving skills. We demonstrate the application of the developed code in an applied educational setting with specific use cases.

This chapter presents a novel approach for enhancing security and trust in supply chains through the use of blockchain technology. Supply chains lie at the base of world’s economy and typically comprise numerous stake-holders, which share no trust relationships while at the same time they need to interact and cooperate through complex processes. Cooperation between stakeholders presumes agreement between them at the different stages of their interaction, something that may be challenging to achieve. This chapter shows how blockchains can be used for logging stakeholder interactions, guaranteeing consensus among them, and implementing complex service agreements that may involve financial transactions through escrow accounts.

The PLURAL project developed key technologies, called Plug-and-Use kits (‘PnU kits’), for the deep renovation of EU buildings, aiming at reaching the goal of near-zero energy buildings (NZEB). A data mining algorithm is utilised to find all minimal configuration settings of a PnU kit that practically ensures the NZEB status of the building after the configuration is installed.

High stakes decision making requires that any decision support systems must be able to come up with plausible explanations about the decisions they propose to the user. Several popular approaches to explaining black-box AI systems, such as neural networks, focus either on highlighting the features that matter the most in one particular decision as in the SHAP models, or on developing a local to the particular instance data model that is explainable by nature, such as a decision tree. ML systems that are by default explainable and/or interpretable, such as decision trees, or rule-based systems do not require such third-party approaches, as they are themselves explainable. Nevertheless, presenting a consistent (small) set of features to the users as explanations for any given proposed decision can increase the confidence of the users towards the reliability of the system. For this reason, we have developed a system that given a set of rules that hold on a training dataset, finds a minimal cardinality set of features that are used in a set of rules that together cover the entire training dataset. We develop a parallel heuristic algorithm for finding such a minimal variables set, and we show it outperforms all state-of-the-art optimization solvers for finding the solution to a MIP formulation of the problem. Experiments with data from use cases applying AI in public policy decision making as well as in medical use cases show that the proposed small set of features is sufficient to explain all the cases in the test dataset via rules containing only variables from the proposed set of features.

We are concerned with the personalized student course plan (PSCP) problem of optimizing the plan of courses students at the American College of Greece will need to take to complete their studies. We model the constraints set forth by the institution so that we guarantee the validity of all produced plans. We formulate several different objectives to optimize the resulting plan, including the fastest completion time, course difficulty balance, and maximization of the expected student grade point average given the student’s performance in passed courses. All resulting problems are mixed-integer linear programming problems with a number of binary variables, that is, the max number of terms times the number of courses available for the student to take. The resulting mathematical programming problem is solvable in less than 10 seconds on a modern commercial off-the-shelf PC, whereas the manual process used to take more than one hour of advising time for every student and, as measured by the objectives set forth, resulted in suboptimal schedules.

Nowadays public policymakers are offered with opportunities to take data-driven evidence-based decisions by analyzing the very large volumes of policy-related data that are generated through different channels (e.g., e-services, mobile apps, social media). Machine learning (ML) and artificial intelligence (AI) tehcnologies ease and automate the analysis of large policy-related datasets, which helps policymakers to realize a shift toward data-driven decisions. Nevertheless, the deployment and use of AI tools for public policy development is also associated with significant technical, political, and operation challenges. For instance, AI-based policy development solutions must be transparent and explainable to policymakers, while at the same time adhering to the mandates of emerging regulations such as the AI Act of the European Union. This paper introduces some of the main technical, operational, regulatory compliance challenges of AI-based policymaking. Accordingly, it introduces technological solutions for overcoming them, including: (i) a reference architecture for AI-based policy development, (ii) a virtualized cloud-based tool for the specification and implementation of ML-based data-driven policies, (iii) a ML framework that enables the development of transparent and explainable ML models for policymaking, and (iv) a set of guidelines for using the introduced technical solutions to achieve regulatory compliance. The paper ends up illustrating the validation and use of the introduced solutions in real-life public policymaking cases for various local governments.

The changes triggered by the global financial crisis in 2008, the immigration flows and the covid-19 pandemic in contemporary societies have transformed the way individuals communicate, create content, and ‘consume’ publicly available information. Consequently, political, societal, and financial pressures have led to alternative forms of media practice and representations and disrupted the core relationships and dynamics between politics, journalism, and society.

In this context, several challenges emerge which are related to deeper social and cultural changes. Such challenges influence political communication and its relationship with the media and further impact the boundaries between private and public domains. Some of these challenges also constitute a direct challenge to democratic values and in some cases work against the preservation and strengthening of democracy. Moreover, all these developments are taking place at a time when democracy itself and its ‘chronic diseases’ are under criticism by new forms of authoritarianism and totalitarianism.

This edited book examines the key challenges in political discourse and journalistic practice in times of crisis. It focuses on European paradigms and links political rhetoric and media challenges with the societal, political, and financial crises from 2008 until the present.

JSB Library Discovery record

The thematic topic of resilience has been studied for many consecutive years and by numerous scientists across the globe. Despite the current plethora of literature aiming to grapple with an understanding of the construct itself, the “psychology” of resilience has also generated scientific support, with the APA suggesting that resilience refers to the ability to adapt to stress and adversity. Moreover, the scientific topic of resilience has rendered itself of pivotal importance amongst organizational psychologists and on a global scale. An example of a trait that has been associated with resilience is coping, which refers to cognitive and behavioural efforts aiming to reduce the intensity of stressful events and recover one's resources. Moreover, other positive psychosocial resources, such as hope, optimism, and meaning in life have a negative correlation with psychological distress and can play a protective role for the individual enhancing resilience.

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This international and thought-provoking volume addresses both theoretical and conceptual issues of resilience in modern organizations, looking at areas of concern and providing suggestions for future preventative measures.

In recent years, organizations across the world have been subjected to major upheavals as several crises, including the COVID-19 pandemic, the World Economic Crisis, and the Migratory Crisis, have contributed to the changing landscape of work. Individuals, organizations, and societies have been forced to re-think, re-adjust, and re-align in the face of adversity. The “survivors” of such upheavals are those who come to grips with the new realities of our times and encompass resilience in its entirety. This timely collection assesses resilience on critically important variables, such as socio-economic status, occupational type, and gender differences, and highlights preventative measures that organizations and individuals should take to maximise wellbeing and adjustment in these everchanging and challenging times.

Essential reading for students, scholars, practitioners, and policy makers, this volume sheds light on the multi-faceted ways to enhance the resilience paradigm and offers insights into implications for future research in the area.

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Cancer is a condition characterized by genomic instability and gross chromosomal aberrations. The inability of the cell to timely and efficiently complete its replication cycle before entering mitosis is one of the most common causes of DNA damage and carcinogenesis. Phosphorylation of histone 2AX (H2AX) on S139 (γH2AX) is an indispensable step in the response to DNA damage, as it is required for the assembly of repair factors at the sites of damage. γH2AX is also a marker of DNA replication stress, mainly due to fork collapse that often follows prolonged replication stalling or repair of arrested forks, which involves the generation of DNA breaks. Although the role of γH2AX in the repair of DNA breaks has been well defined, the function of γH2AX in replicative stress remains unclear. In this review, we present the recent advances in the field of replication stress, and highlight a novel function for γH2AX that is independent of its role in the response to DNA damage. We discuss studies that support a role for γΗ2ΑΧ early in the response to replicative stress, which does not involve the repair of DNA breaks. We also highlight recent data proposing that γH2AX acts as a chromatin remodeling component, implicated in the efficient resolution of stalled replication forks. Understanding the mechanism by which γH2AX enables cellular recovery after replication stress will allow identification of novel cancer biomarkers, as well as new targets for cancer therapies.

In response to research that suggests that in tertiary institutions, student learning and emotional wellbeing are impacted significantly by academic demands, this paper investigates the pedagogical and psychological value of including nursery rhymes in a university course syllabus. It is based on the personal experience of teaching a children’s literature course and informed by the scholarship of teaching and learning (SoTL) that invites instructors to reflect on their teaching practices and classroom experiences as well as to share insights to promote scholarly discussion. To answer the research question of whether and to what extent the inclusion of different versions of one select nursery rhyme affected the students’ learning experience, an exploratory research design was employed. Validity was established through the application of triangulation by using multiple datasets, both quantitative and qualitative data collection methods, and assessment through two learning theories. Elements of the Kirkpatrick evaluation model were adapted to understand the impact on student performance and satisfaction as well as classroom atmosphere and morale. While contradictory insights resulted from the quantitative and qualitative analysis, they indicate that using select versions of one nursery rhyme as a scaffolding tool can be useful, particularly for students unfamiliar with analytical reading as well as critical synthesizing and contextualizing. Such scaffolding allows for a more gradual introduction to literary analysis that can activate prior knowledge, encourage curiosity, and increase active learning, thereby reducing academic anxiety that often results in declining interest and negative psychological wellbeing.

Observational fear-learning studies in genetically modified animals enable the investigation of the mechanisms underlying the social transmission of fear-related information. Here, we used a three-day protocol to examine fear conditioning by proxy (FCbP) in wild-type mice (C57BL/6J) and mice lacking the β2-subunit of the nicotinic acetylcholine receptor (nAChR). Male animals of both genotypes were exposed to a previously fear-conditioned (FC) cage mate during the presentation of the conditioned stimulus (CS, tone). On the following day, observer (FCbP) mice were tested for fear reactions to the tone: none of the β2-KO mice froze to the stimulus, while 30% of the wild-type mice expressed significant freezing. An investigation of the possible factors that predicted the fear response revealed that only wild-type mice that exhibited enhanced and more flexible social interaction with the FC cage mate during tone presentations (Day 2) expressed fear toward the CS (Day-3). Our results indicate that (i) FCbP is possible in mice; (ii) the social transmission of fear depends on the interaction pattern between animals during the FCbP session and (iii) β2-KO mice display a more rigid interaction pattern compared to wild-type mice and are unable to acquire such information. These data suggest that β2-nAChRs influence observational fear learning indirectly through their effect on social behaviour.

The 11th edition of Biopsychology is a clear, engaging introduction to current biopsychological theory and research. Intended for use as a core textbook in a one- or two-semester course in Biopsychology with various titles, such as Biopsychology, Physiological Psychology, Brain and Behavior, Psychobiology, Behavioral Neuroscience, or Behavioral Neurobiology.

The defining characteristic of Biopsychology is that it uniquely combines the biopsychological science and the personal, reader-oriented discourse. Rather than presenting the concepts of biopsychology in the usual way, the chapters speak directly to the students and interweave the fundamentals of the field with clinical case studies, social issues, personal implications, helpful metaphors, and memorable anecdotes. Like previous editions, the 11th edition of Biopsychology has thoroughly incorporated recent developments in the field and contains more than 950 article or book references that did not appear in the previous edition.

The goal of this paper is the performance evaluation of a deep learning approach when deployed in fifth-generation (5G) millimeter wave (mmWave) multicellular networks. To this end, the optimum beamforming configuration is defined by two neural networks (NNs) that are properly trained, according to mean square error (MSE) minimization. The first network has as input the requested spectral efficiency (SE) per active sector, while the second network has the corresponding energy efficiency (EE). Hence, channel and power variations can now be taken into consideration during adaptive beamforming. The performance of the proposed approach is evaluated with the help of a developed system-level simulator via extensive Monte Carlo simulations. According to the presented results, machine learning (ML)-adaptive beamforming can significantly improve EE compared to the standard non-ML framework. Although this improvement comes at the cost of increased blocking probability (BP) and radiating elements (REs) for high data rate services, the corresponding increase ratios are significantly reduced compared to the EE improvement ratio. In particular, considering 21.6 Mbps per active user and ML adaptive beamforming, the EE can reach up to 5.3 Mbps/W, which is significantly improved compared to the non-ML case (0.9 Mbps/W). In this context, BP does not exceed 2.6%, which is slightly worse compared to 1.7% in the standard non-ML case. Moreover, approximately 20% additional REs are required with respect to the non-ML framework.

Research in Multiple Sclerosis (MS) has attracted extensive attention from the scientific field of machine learning due to its dependency on a variety of medical parameters. Therefore, a vast amount of produced clinical data can shed light on the nature of MS disease in terms of evaluation and classification through a machine learning exploration framework. Accordingly, the current work elaborates on the behaviour of neural conduction between two points of the nervous system through machine learning information analysis provided from a published multi-year dataset. Such data has been derived by motor evoked potential (MEP) measurements carried out during medical visits of MS patients. Based on the aforementioned MEP measurements, the present work suggests a prediction model quantifying the damage to the central nervous system through the Expanded Disability Status Scale (EDSS) parameter. To achieve this, different machine learning regression models were utilised, having as essential input parameters the properties of the propagating signal in the central nervous system. The obtained accuracy of the prediction model is 96%.

Multiple sclerosis (MS) is a disease that deteriorates the central human nervous system, which can potentially cause significant brain, spinal cord and visual problems. Based on recent studies, MS has affected 3 million people with a prevalence rate of 3.9%. To this end, a wealth of information about MS has been produced, which makes MS the ideal candidate for applying artificial intelligence (AI) techniques for early diagnosis through a machine learning (ML) exploration framework. Accordingly, the current work studies to what extent the nervous system has been degenerated by analyzing data from a recently published dataset. Such data has been derived by motor evoked potential (MEP) measurements conducted in each patient hospital visit. Therefore, five machine learning models have been trained with cross-validation, in order to obtain the best one with good generalization properties. We compare the accuracy of all models utilizing various metrics (maximum obtained accuracy is ~96% with XGBoost model). Furthermore, we use sensitivity analysis in order to explain the dependence of the target variable on the input parameters statistically.

This collection of critical essays explores how contemporary British authors engage with the theme of crisis in their fiction. Of interest to scholars and students of literary and cultural studies, this volume investigates crisis as a complex phenomenon: not only as a cultural concept involving sociopolitical systems but also as a mode of challenge to established power structures and modes of representation across narrative traditions. Through the examination of a variety of leading authors such as Kazuo Ishiguro, and award-winning texts like Julian Barnes’ The Sense of an Ending (2011), this collection foregrounds the theme of crisis as a critical commonality emerging among vastly different stylistic expressions of local and global concerns. Bringing together a variety of scholars from Germany, Italy, Greece, the UK and the US, this collection provides diverse disciplinary perspectives and highlights the significance of social and ethical concerns in contemporary British fiction through the investigation of the theme of crisis.

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This volume fills a gap in studies of literature, media, culture, and art by exploring depictions of contagious diseases in different genres since the dawn of humanity and compiling a history of such representations of pestilence from a post-human and environmental perspective. Indulging in humankind’s struggle with calamities throughout history, the collection discusses several media that portray real or imagined futures based on past and present facts. In today’s world, which is stricken with global warming and the COVID-19 pandemic, these narratives, termed ‘plague literature’, hold a crucial position in guiding humanity towards a greater ecological awareness. The book will appeal to scholars, students, organisations, and individuals who are interested in studies of literature, history, media, art, and environmental humanities.

JSB Library Discovery record

Ian McEwan's fictional world has always focused on crisis, violence and turmoil, evident in works engaged with major concerns of the English novel since the Victorian age: identity, personal and national; the essence and representation of consciousness; the meaning of being and of being human. This chapter explores four McEwan novels, namely, Sweet Tooth, The Children Act, Nutshell, and Machines Like Me, all published in the 2010s, which explore various crises in the contemporary world. As this discussion suggests, McEwan creates in his work an uncomfortable but recognizable "troubled home" for his characters, a term which appears on the last page of McEwan's Machines Like Me (2019) and which constitutes an apt metaphor for a thematic focus on complex crises related to familial relationships and societal contexts, implying the decay of the modern family and the corruption of human bonds.

JSB Library Discovery record

The illegal file sharing of digital content is a widespread activity with far-reaching ramifications. In Greece, studies indicate that “digital piracy” has become an accepted form of criminal practice, disrupting relevant market developments. To explore this, we conducted an online questionnaire that documented practices of Greek audiences, regarding the piracy of movies and television series. This paper goes beyond the labelling of the practice to an analysis of its socio-cultural character. We argue that these findings point to a changing market, and the disruption piracy causes is explained as an effort by consumers to participate in a global culture that transcends national borders.

Considering perspectives on creative advertising through a unique media and communications lens, this book encompasses both the theory and practical tools needed to approach and understand creativity in advertising with an original eye.

Drawing from diverse subject areas including Social Anthropology, Narrative Theory, Consumer Psychology, Semiotics and Cultural Studies, Creative Advertising Concept and Copy provides a solid grounding in advertising education away from the traditional business and marketing literature. Notwithstanding the need for independent inspiration and originality, the author guides readers through the entire process of campaign planning, moving from strategy to creative idea to finished piece whilst employing concepts and principles relevant to ‘design thinking’. Taking into account ethics and regulations, the use of text and images, and storytelling across radio, TV and video platforms, readers will come to a holistic understanding of what advertising can (and cannot) do, and how to achieve the best results.

Written for students involved in creative advertising as an area of academic research and professional practice, this book will also be of interest to early-career advertising professionals seeking a fresh perspective on their work.

Perceptual systems that integrate different principles and/or a different architecture at large may produce substantially varied outputs from the same scene. I call this thesis perceptual pluralism and I bring related empirical evidence to bear on the epistemological debate on perception. I argue that perceptual pluralism, coupled with the position that at least some different kinds of perception are equally successful in guiding interaction with the world, serve to undermine the thesis that the typical human perception is uniquely veridical. Last, I consider perspectival realism as the epistemological stance that is prospectively best suited to this state of affairs, and provide a method for evaluating whether this prospect holds up.

The neurodiversity movement has introduced a new era for autism research. Yet, the neurodiversity paradigm and the autism clinic remain largely unconnected. With the present work, we aim to contribute to filling this lacuna by putting forward phenomenology as a foundation for developing neurodiversity-affirming clinical interventions for autism. In the first part of this paper, we highlight that autistic people face a severe mental health crisis. We argue that approaches focused on reducing autistic ‘symptoms’ are unlikely to solve the problem, as autistic mental health is positively correlated with autism acceptance and perceived quality of support provided, not necessarily with lack of ‘symptomatologic severity’. Therefore, the development and dissemination of neurodiversity-affirming clinical interventions is key for addressing the autism mental health crisis. However, therapists and researchers exploring such neurodiversity-affirming practices are faced with two significant challenges. First, they lack concrete methodological principles regarding the incorporation of neurodiversity into clinical work. Second, they need to find ways to acknowledge rightful calls to respect the ‘autistic self’ within the clinic, while also challenging certain beliefs and behaviors of autistic clients in a manner that is sine qua non for therapy, irrespective of neurotype. In the second part of the paper, we introduce phenomenological psychology as a potential resource for engaging with these challenges in neurodiversity-affirming approaches to psychotherapy. In this vein, we put forward specific directions for adapting cognitive behavioral and interpersonal psychotherapy for autism.

Cancer is a condition characterized by genomic instability and gross chromosomal aberrations. The inability of the cell to timely and efficiently complete its replication cycle before entering mitosis is one of the most common causes of DNA damage and carcinogenesis. Phosphorylation of histone 2AX (H2AX) on S139 (γH2AX) is an indispensable step in the response to DNA damage, as it is required for the assembly of repair factors at the sites of damage. γH2AX is also a marker of DNA replication stress, mainly due to fork collapse that often follows prolonged replication stalling or repair of arrested forks, which involves the generation of DNA breaks. Although the role of γH2AX in the repair of DNA breaks has been well defined, the function of γH2AX in replicative stress remains unclear. In this review, we present the recent advances in the field of replication stress, and highlight a novel function for γH2AX that is independent of its role in the response to DNA damage. We discuss studies that support a role for γΗ2ΑΧ early in the response to replicative stress, which does not involve the repair of DNA breaks. We also highlight recent data proposing that γH2AX acts as a chromatin remodeling component, implicated in the efficient resolution of stalled replication forks. Understanding the mechanism by which γH2AX enables cellular recovery after replication stress will allow identification of novel cancer biomarkers, as well as new targets for cancer therapies.

Background

The causes for immune-mediated inflammatory diseases (IMIDs) are diverse and the incidence trends of IMIDs from specific causes are rarely studied. The study aims to investigate the pattern and trend of IMIDs from 1990 to 2019.

Methods

We collected detailed information on six major causes of IMIDs, including asthma, inflammatory bowel disease, multiple sclerosis, rheumatoid arthritis, psoriasis, and atopic dermatitis, between 1990 and 2019, derived from the Global Burden of Disease study in 2019. The average annual percent change (AAPC) in number of incidents and age standardized incidence rate (ASR) on IMIDs, by sex, age, region, and causes, were calculated to quantify the temporal trends.

Findings

In 2019, rheumatoid arthritis, atopic dermatitis, asthma, multiple sclerosis, psoriasis, inflammatory bowel disease accounted 1.59%, 36.17%, 54.71%, 0.09%, 6.84%, 0.60% of overall new IMIDs cases, respectively. The ASR of IMIDs showed substantial regional and global variation with the highest in High SDI region, High-income North America, and United States of America. Throughout human lifespan, the age distribution of incident cases from six IMIDs was quite different. Globally, incident cases of IMIDs increased with an AAPC of 0.68 and the ASR decreased with an AAPC of −0.34 from 1990 to 2019. The incident cases increased across six IMIDs, the ASR of rheumatoid arthritis increased (0.21, 95% CI 0.18, 0.25), while the ASR of asthma (AAPC = −0.41), inflammatory bowel disease (AAPC = −0.72), multiple sclerosis (AAPC = −0.26), psoriasis (AAPC = −0.77), and atopic dermatitis (AAPC = −0.15) decreased. The ASR of overall and six individual IMID increased with SDI at regional and global level. Countries with higher ASR in 1990 experienced a more rapid decrease in ASR.

Interpretation

The incidence patterns of IMIDs varied considerably across the world. Innovative prevention and integrative management strategy are urgently needed to mitigate the increasing ASR of rheumatoid arthritis and upsurging new cases of other five IMIDs, respectively.

Funding

The Global Burden of Disease Study is funded by the Bill and Melinda Gates Foundation. The project funded by Scientific Research Fund of Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital (2022QN38).

Background

Anaemia is a major health problem worldwide. Global estimates of anaemia burden are crucial for developing appropriate interventions to meet current international targets for disease mitigation. We describe the prevalence, years lived with disability, and trends of anaemia and its underlying causes in 204 countries and territories.

Methods

We estimated population-level distributions of haemoglobin concentration by age and sex for each location from 1990 to 2021. We then calculated anaemia burden by severity and associated years lived with disability (YLDs). With data on prevalence of the causes of anaemia and associated cause-specific shifts in haemoglobin concentrations, we modelled the proportion of anaemia attributed to 37 underlying causes for all locations, years, and demographics in the Global Burden of Disease Study 2021.

Findings

In 2021, the global prevalence of anaemia across all ages was 24·3% (95% uncertainty interval [UI] 23·9–24·7), corresponding to 1·92 billion (1·89–1·95) prevalent cases, compared with a prevalence of 28·2% (27·8–28·5) and 1·50 billion (1·48–1·52) prevalent cases in 1990. Large variations were observed in anaemia burden by age, sex, and geography, with children younger than 5 years, women, and countries in sub-Saharan Africa and south Asia being particularly affected. Anaemia caused 52·0 million (35·1–75·1) YLDs in 2021, and the YLD rate due to anaemia declined with increasing Socio-demographic Index. The most common causes of anaemia YLDs in 2021 were dietary iron deficiency (cause-specific anaemia YLD rate per 100 000 population: 422·4 [95% UI 286·1–612·9]), haemoglobinopathies and haemolytic anaemias (89·0 [58·2–123·7]), and other neglected tropical diseases (36·3 [24·4–52·8]), collectively accounting for 84·7% (84·1–85·2) of anaemia YLDs.

Interpretation

Anaemia remains a substantial global health challenge, with persistent disparities according to age, sex, and geography. Estimates of cause-specific anaemia burden can be used to design locally relevant health interventions aimed at improving anaemia management and prevention.

Funding

Bill & Melinda Gates Foundation.

Background

Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period.

Methods

22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution.

Findings

Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations.

Interpretation

Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic.

Funding

Bill & Melinda Gates Foundation.

The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) is a multinational collaborative research study with >10,000 collaborators around the world. GBD generates a time series of summary measures of health, including prevalence, cause-specific mortality (CSMR), years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life years (DALYs) to provide a comprehensive view of health burden for a wide range of stakeholders including clinicians, public and private health systems, ministries of health, and other policymakers. These estimates are produced for 371 causes of death and 88 risk factors according to mutually exclusive, collectively exhaustive hierarchies of health conditions and risks. The study is led by a principal investigator and governed by a study protocol, with oversight from a Scientific Council, and an Independent Advisory Committee.1 GBD is performed in compliance with Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER).2 GBD uses de-identified data, and the waiver of informed consent was reviewed and approved by the University of Washington Institutional Review Board (study number 9060).

This almanac presents results for 18 cardiovascular diseases (CVD) and the CVD burden attributed to 15 risk factors (including an aggregate grouping of dietary risks) by GBD region.

This paper explores the potential of Artificial Intelligence (AI) in safeguarding public health from the risks posed by climate change. In addition, it examines AI as a contributor to climate change. Climate change is increasingly impacting human health through various channels, including extreme weather events, such as high temperatures, extreme cold, droughts, fires, floods, huriccanes and monsoons and the impact on natural resources, such as reduced access to drinking water, adverse impacts on food production as well as the increased risks related to infectious diseases such as zoonoses and vector-borne diseases. By leveraging AI technologies, such as machine learning, deep learning, data analysis, and predictive modeling, it becomes possible to enhance public health resilience through engaging into adapted responses. This paper through literature review focuses on AI uses as a tool and various present applications in addressing climate change-related health challenges. It also discusses the potential benefits, limitations, and future directions of AI-enabled solutions. The findings suggest that AI has the potential to revolutionize public health and healthcare preparedness and response to climate change, contributing to the well-being of individuals and communities. At the same time it poses threats which demand development of widely accepted AI strategies across the globe and careful regulatory mechanisms operating at various levels. As a result, it is imperative that scientists, legislators, philosophers and social scientists interact directly with informaticians in order to benefit humanity as a whole.

A WorldWide Teach-In workshop was organized and hosted on March 22, 2023, at Deree-The American College of Greece (ACG), where students from different courses of the Environmental Studies, Biomedical Sciences, and the International Honors Programs presented the work they have undertaken related to biodiversity, climate change/crisis and climate justice. Within the framework of this project, students conducted field and lab work, and used the Deree-ACG forest as a living laboratory. The three authors, who are Deree-ACG Professors, and their students supported the worldwide effort initiated by Bard College in USA-Graduate programs in sustainability. A climate teach-in is a bottom-up hands-on educational event, focusing on just solutions to the climate crisis in local communities and globally, aiming to bring the WorldWide Teach-In on climate and justice to hundreds of Universities, Colleges, Schools, and various Communities in more than fifty countries. The specific Deree-ACG workshop was based on three Learning Objects (to be published in a LO repository), developed by the three authors. In this paper, the overall outcomes of the workshop (both individual and student group work of students) are presented, as well as the various ways in which the work of the students was communicated locally and globally. Future recommendations are made.

Many species of marine mammals are threatened and their populations decline worldwide due to the increase of anthropogenic activities such as fishing, shipping, pollution and climate change. Our understanding of marine mammal behavior/ecology remains poor and under-documented since studies of the species are extremely challenging in practice and theory. Applied anthropology can help provide effective strategies based on cultural compatibility. Marine biology has already validated that biodiversity is beneficial to the ecosystem and so should anthropology promote the significance of mutualism in society.

Anthology of papers from the 2022 ISNS Conference in Athens, Platonism and Its Heritage.

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Paper presented at the 2022 ISNS Conference in Athens, Platonism and Its Heritage, and included in the published conference proceedings.

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Introduction to the published conference proceedings of the 2022 ISNS Conference in Athens, Platonism and Its Heritage.

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Despite many twenty-first century efforts to minimize corporate corruption, initiatives taken by local governments, global organizations, academic institutions, or the corporate world itself, it is clear that corporate corruption is perpetuating itself. In this paper, I apply the Aristotelian concept of “akrasia” (moral weakness) in order to provide an interpretation of corporate corruption as an act of moral failure and misapprehension of the right thing to do, if not an act of wickedness, which originates with lack of integrity. By utilizing Aristotle’s philosophical framework, I attempt a redefinition of integrity not only with reference to the Aristotelian moral and intellectual virtues but also with a combination of theoretical and practical wisdom, that is, a connection between theory and action, definitive of moral consistency. Through this interpretation, I also wish to further emphasize the need for a philosophical background of contemporary managerial practices for a more effective countering of corporate corruption.

In this paper, we try to establish a connection between Heidegger’s conception of authenticity and the Aristotelian σπουδαῖος. Through an exploration of Heidegger’s reference to authenticity, Aristotle’s concept of σπουδαῖος, and a reinterpretation of Heidegger’s reappropriation of certain Aristotelian concepts, such as αἴσθησις, πίστις and προαίρεσις, we wish to show that the authentic Dasein has assumed full responsibility of its role in life, and it has developed ontologically, in the same way a σπουδαῖος man acknowledges one’s full potentiality in purposeful action. The σπουδαῖος as the authentic Dasein grasps the moment, gives rise to the occasion by directing one’s judgement towards the praxis, and comes to provide its own stance in the facticity of life. In fact, the σπουδαῖος, like an authentic Dasein, in a way, comes to reflect the synthesis between θεωρία and πράξις as well as an implicit effort for Dasein to uncover itself to the world and to allow the world to uncover to it.

This chapter provides an overview of the characteristics, prevalence, and potential psychological impact of perinatal bereavement and discusses how the model of posttraumatic growth (PTG) has provided new understandings and clinical implications. Initially, definitions and literature on the characteristics of perinatal bereavement and its prevalence are presented. Then, empirical findings on PTG and parental bereavement are reviewed. Finally, the chapter discusses addressing PTG in psychotherapy in general and provides clinical suggestions for those supporting perinatally bereaved parents.

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Despite the concerns that recent developments in Large Language Models (LLMs) have raised, they undoubtedly revealed a novel potential of Artificial Intelligence (AI) algorithms in educational environments. Whether they are used for tutoring, in a manner similar to that of Intelligent Tutoring Systems (ITS), or to support assessment design and delivery, their impact in a learning setting is remarkable. In this paper, we propose an interactive tutoring approach, utilizing ChatGPT’s API. By exploiting ChatGPT’s programming interface, we can develop customized interactive problem-solving and tutoring sessions on specific topics of interest. The API’s versatility allows for dynamic interactions, fostering a deeper understanding of subjects taught and effective problem-solving skills. We demonstrate the application of the developed code in an applied educational setting with specific use cases.

We are concerned with the personalized student course plan (PSCP) problem of optimizing the plan of courses students at the American College of Greece will need to take to complete their studies. We model the constraints set forth by the institution so that we guarantee the validity of all produced plans. We formulate several different objectives to optimize the resulting plan, including the fastest completion time, course difficulty balance, and maximization of the expected student grade point average given the student’s performance in passed courses. All resulting problems are mixed-integer linear programming problems with a number of binary variables, that is, the max number of terms times the number of courses available for the student to take. The resulting mathematical programming problem is solvable in less than 10 seconds on a modern commercial off-the-shelf PC, whereas the manual process used to take more than one hour of advising time for every student and, as measured by the objectives set forth, resulted in suboptimal schedules.

We are concerned with the personalized student course plan (PSCP) problem of optimizing the plan of courses students at the American College of Greece will need to take to complete their studies. We model the constraints set forth by the institution so that we guarantee the validity of all produced plans. We formulate several different objectives to optimize the resulting plan, including the fastest completion time, course difficulty balance, and maximization of the expected student grade point average given the student’s performance in passed courses. All resulting problems are mixed-integer linear programming problems with a number of binary variables, that is, the max number of terms times the number of courses available for the student to take. The resulting mathematical programming problem is solvable in less than 10 seconds on a modern commercial off-the-shelf PC, whereas the manual process used to take more than one hour of advising time for every student and, as measured by the objectives set forth, resulted in suboptimal schedules.

The poems in this collection feature well-known, but also unknown, figures of the Renaissance, who with their work shaped an entire era, whose achievements determined the evolution of European culture.

The 39 portraits of the book give Haris Vlavianos the opportunity to analyse in depth issues - religious, political, ethical, aesthetic - related to the effort of artists, poets and scholars to maintain their freedom of thought, within the adverse conditions of the Reformation and the Counter-Reformation movements, that is, of religious fanaticism, and to defend, even with their lives, their creations.

At a time when intellectual and artistic life is threatened by the defenders of a controversial "rightness", the collection of Haris Vlavianos opens an important field of dialogue and reflection.

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