Search Results (60)

In the era of precision medicine, effective data visualisation plays a pivotal role in supporting clinical decision-making by translating complex, multidimensional datasets into intuitive and actionable insights. This paper explores the foundational principles of visual perception, with a specific focus on pre-attentive attributes such as colour, shape, size, orientation, and spatial position, which are processed automatically by the human visual system. Drawing from cognitive psychology and perceptual science, we demonstrate how these attributes can enhance the clarity and usability of medical visualisations, reducing cognitive load and improving interpretive speed in high-stakes clinical environments. Through detailed case studies and visual examples, particularly within the field of oncology, we highlight best practices and common pitfalls in the design of dashboards, nomograms, and interactive platforms. We further examine the integration of advanced tools—such as genomic heatmaps and temporal timelines—into multidisciplinary workflows to support personalised care. Our findings underscore that visually intelligent design is not merely an aesthetic concern but a critical factor in clinical safety, efficiency, and communication, advocating for user-centred and evidence-based approaches in the development of health data interfaces. Full article

This research explores the impact of ownership structure on the financial performance of Leveraged Buyout (LBO) transactions in the MENA region, a key emerging market region. Drawing on agency theory by Jensen & Meckling and the capital structure theory of Modigliani and Miller, the study investigates how different shareholder configurations, particularly managerial equity participation, influence LBO outcomes. Based on a sample of 233 transactions conducted between 2000 and 2023, the research adopts a quantitative methodology grounded in a hypothetico-deductive approach. The analysis focuses on the interactions between managerial ownership, leverage, target firm size, and operational performance. The findings support the agency theory premise that managerial ownership aligns interests and enhances performance, showing a positive relationship between managerial equity stakes and financial outcomes. Conversely, the effect of leverage, central to Modigliani and Miller’s propositions, proves more nuanced, reflecting the region’s unique financial constraints and market imperfections. Firm size, meanwhile, shows no direct correlation with performance improvement. These insights underscore the complex mechanisms behind LBO success in the MENA context and offer practical and theoretical implications, particularly regarding governance practices and institutional frameworks. The study also outlines avenues for future research, including a deeper examination of regional governance dynamics. Full article

This paper proposes a technological framework designed to mitigate the inherent risks associated with the deployment of artificial intelligence (AI) in decision-making and task execution within the management processes. The Agreement Validation Interface (AVI) functions as a modular Application Programming Interface (API) Gateway positioned between user applications and LLMs. This gateway architecture is designed to be LLM-agnostic, meaning it can operate with various underlying LLMs without requiring specific modifications for each model. This universality is achieved by standardizing the interface for requests and responses and applying a consistent set of validation and enhancement processes irrespective of the chosen LLM provider, thus offering a consistent governance layer across a diverse LLM ecosystem. AVI facilitates the orchestration of multiple AI subcomponents for input–output validation, response evaluation, and contextual reasoning, thereby enabling real-time, bidirectional filtering of user interactions. A proof-of-concept (PoC) implementation of AVI was developed and rigorously evaluated using industry-standard benchmarks. The system was tested for its effectiveness in mitigating adversarial prompts, reducing toxic outputs, detecting personally identifiable information (PII), and enhancing factual consistency. The results demonstrated that AVI reduced successful fast injection attacks by 82%, decreased toxic content generation by 75%, and achieved high PII detection performance (F1-score ≈ 0.95). Furthermore, the contextual reasoning module significantly improved the neutrality and factual validity of model outputs. Although the integration of AVI introduced a moderate increase in latency, the overall framework effectively enhanced the reliability, safety, and interpretability of LLM-driven applications. AVI provides a scalable and adaptable architectural template for the responsible deployment of generative AI in high-stakes domains such as finance, healthcare, and education, promoting safer and more ethical use of AI technologies. Full article

Preschool as a place of learning consists of various communities and belongings created in the intersection between children, teachers, and policy. The very essence of a good life in preschool is characterised by the sort of communities and the opportunities for belonging available to children. Being able to participate in various communities and to experience belonging is central to the wellbeing of every child in preschool. The aim of this study is to gain knowledge about processes of belonging in preschool peer communities. The following research questions guide the study: What categorisations, positionings, and identifications appear in the interactions between the children? What ethical and political value preferences are at stake? The study applies Nira Yuval-Davis’s theory about the politics of belonging and her analytical framework is used to interpret and understand data, including analytical concepts such as categorisation and social positioning, identifications, and ethical and political value systems. A case study has been used to explore processes of belonging in rich detail. Data consist of video observations of interactions between children in a Swedish Early Childhood Education (ECE) institution. Findings reveal how the children’s categorisations lead to various positions, identifications, and opportunities for belonging. We show how a grounded position is established, and how categorisations are used to justify exclusion. The study also illustrates various approaches used by the children to gain a sense of belonging in peer communities, here conceptualised as confrontation, adaptation, and defence. A conclusion is that children’s categorisations are deeply embedded in the social structures of preschool and that the work with belonging needs to be on every teacher’s agenda. Full article

Our study investigates how the sequencing of text and image inputs within multi-modal prompts affects the reasoning performance of Large Language Models (LLMs). Through empirical evaluations of three major commercial LLM vendors—OpenAI, Google, and Anthropic—alongside a user study on interaction strategies, we develop and validate practical heuristics for optimising multi-modal prompt design. Our findings reveal that modality sequencing is a critical factor influencing reasoning performance, particularly in tasks with varying cognitive load and structural complexity. For simpler tasks involving a single image, positioning the modalities directly impacts model accuracy, whereas in complex, multi-step reasoning scenarios, the sequence must align with the logical structure of inference, often outweighing the specific placement of individual modalities. Furthermore, we identify systematic challenges in multi-hop reasoning within transformer-based architectures, where models demonstrate strong early-stage inference but struggle with integrating prior contextual information in later reasoning steps. Building on these insights, we propose a set of validated, user-centred heuristics for designing effective multi-modal prompts, enhancing both reasoning accuracy and user interaction with AI systems. Our contributions inform the design and usability of interactive intelligent systems, with implications for applications in education, medical imaging, legal document analysis, and customer support. By bridging the gap between intelligent system behaviour and user interaction strategies, this study provides actionable guidance on how users can effectively structure prompts to optimise multi-modal LLM reasoning within real-world, high-stakes decision-making contexts. Full article

The deployment of emotion-recognition technologies expands across healthcare education and gaming sectors to improve human–computer interaction. These systems examine facial expressions together with vocal tone and physiological signals, which include pupil size and electroencephalogram (EEG), to detect emotional states and deliver customized responses. The technology provides benefits through accessibility, responsiveness, and adaptability but generates multiple complex ethical issues. The combination of emotional profiling with biased algorithmic interpretations of culturally diverse expressions and affective data collection without meaningful consent presents major ethical concerns. The increased presence of these systems in classrooms, therapy sessions, and personal devices makes the potential for misuse or misinterpretation more critical. The paper integrates findings from literature review and initial emotion-recognition studies to create a conceptual framework that prioritizes data dignity, algorithmic accountability, and user agency and presents a conceptual framework that addresses these risks and includes safeguards for participants’ emotional well-being. The framework introduces structural safeguards which include data minimization, adaptive consent mechanisms, and transparent model logic as a more complete solution than privacy or fairness approaches. The authors present functional recommendations that guide developers to create ethically robust systems that match user principles and regulatory requirements. The development of real-time feedback loops for user awareness should be combined with clear disclosures about data use and participatory design practices. The successful oversight of these systems requires interdisciplinary work between researchers, policymakers, designers, and ethicists. The paper provides practical ethical recommendations for developing affective computing systems that advance the field while maintaining responsible deployment and governance in academic research and industry settings. The findings hold particular importance for high-stakes applications including healthcare, education, and workplace monitoring systems that use emotion-recognition technology. Full article

This research presents an innovative blockchain-based solution for the charging and energy trading of electric vehicles (EVs). By combining the strengths of two prominent consensus mechanisms, Proof of Work (PoW) and Proof of Stake (PoS), the proposed system balances security, decentralization, and energy efficiency. PoW secures the blockchain, while PoS enhances energy efficiency and scalability, key factors in meeting the growing demand for EV infrastructure. The system’s decentralized nature allows for EV owners, charging stations, and stakeholders to interact and transact transparently, without relying on centralized entities. The research conducts a comprehensive simulation to assess the performance of the proposed hybrid blockchain model, demonstrating significant improvements in cost-effectiveness, scalability, and energy management. Additionally, dynamic pricing mechanisms within the blockchain enable real-time energy trading, optimizing charging times and balancing grid demand efficiently. Through the use of smart contracts, automated pricing adjustments, and incentive-driven user behaviors, the proposed system paves the way for more sustainable, cost-effective, and efficient energy solutions in the future. Full article

The concept of a digital twin (DT), rooted in mid-20th-century ideas, has recently gained significant traction even outside software simulation and engineering modeling. The recent advancements in computational power and the development of model integration methodologies have enabled the creation of virtual replicas of complex physical objects. The success of DTs in engineering has also pushed for the exploration of their use in other domains, especially where complex systems are at stake. One of these cases, which is the focus of this paper, is the modeling of cities and the way they are transformed via technologies into so-called smart cities. In these systems, the huge amount of data that are made accessible and constantly updated via sensor networks suggests that one can use DTs dedicated to the urban scenario as data-driven decision-making devices. However, the concept of a DT was not developed for socio-technical systems and requires careful analysis when applied to urban scenarios. While technologies and information systems have become integrated into city management, this has not reduced the complexity of the city. Relying only on sensory data for city modeling and management seems pretentious since detectable data (what is made accessible via sensor networks) do not seem suitable to inform on all important aspects of the city. Urban DTs hold promise, yet their development necessitates careful consideration of both opportunities and limitations. For this goal, it can be helpful to exploit an ontological analysis due to its neutral and systematic approach and to look at a city as a system of intertwined relationships across its components, such as places, agents, and knowledge. The variety of interactions that the components manifest highlights aspects of the city that the type of data we can collect today leaves unexplored. The paper presents a preliminary example of this issue by studying cases of city squares. The final part of this paper is a call to analyze DTs’ potential role in urban contexts and become aware of the intrinsic limitations of the data they rely upon. Full article

Large language models (LLMs) are increasingly being utilized to develop tools and services in various domains, including education. However, due to the nature of the training data, these models are susceptible to inherent social or cognitive biases, which can influence their outputs. Furthermore, their handling of critical topics, such as privacy and sensitive questions, is essential for responsible deployment. This study proposes a framework for the automatic detection of biases and violations of responsible use using a synthetic question-based dataset mimicking student–chatbot interactions. We employ the LLM-as-a-judge method to evaluate multiple LLMs for biased responses. Our findings show that some models exhibit more bias than others, highlighting the need for careful consideration when selecting models for deployment in educational and other high-stakes applications. These results emphasize the importance of addressing bias in LLMs and implementing robust mechanisms to uphold responsible AI use in real-world services. Full article

Gaps remain in our understanding of which factors contribute to concussion disclosure and how they contribute to this process, thereby limiting our ability to improve disclosure. This study aimed to characterize the most relevant extra-personal determinants of SC disclosure and to describe their influence on the disclosure process. To that aim, the first author conducted substantive qualitative interviews with nine university student–athletes and analyzed their content via constant comparative analysis (guided by Straussian grounded theory). Eleven (11) extra-personal concepts influencing concussion reporting were identified and described across two categories: Contextual Incentives and Socio-Cultural Pressures. These findings suggest that each identified concept can individually shape the context around the injury, creating either higher-stakes conditions that deter disclosure or lower-stakes conditions that encourage it. Further, the results posit that these concepts interact and collectively influence athletes’ decision-making process by modulating the perceived stakes of disclosing a concussion. If these findings hold true in more diverse populations and contexts, they suggest that adapting concussion prevention efforts to consider these contextual variables could improve SC disclosure. This study also highlights the benefits of using qualitative methods in the investigation of concussion reporting. Full article

Generating accurate SQL queries from natural language is critical for enabling non-experts to interact with complex databases, particularly in high-stakes domains like healthcare. This paper presents an extensive evaluation of state-of-the-art large language models (LLM), including LLaMA 3.3, Mixtral, Gemini, Claude 3.5, GPT-4o, and Qwen for transforming medical questions into executable SQL queries using the MIMIC-3 and TREQS datasets. Our approach employs LLMs with various prompts across 1000 natural language questions. The experiments are repeated multiple times to assess performance consistency, token efficiency, and cost-effectiveness. We explore the impact of prompt design on model accuracy through an ablation study, focusing on the role of table data samples and one-shot learning examples. The results highlight substantial trade-offs between accuracy, consistency, and computational cost between the models. This study also underscores the limitations of current models in handling medical terminology and provides insights to improve SQL query generation in the healthcare domain. Future directions include implementing RAG pipelines based on embeddings and reranking models, integrating ICD taxonomies, and refining evaluation metrics for medical query performance. By bridging these gaps, language models can become reliable tools for medical database interaction, enhancing accessibility and decision-making in clinical settings. Full article

This preliminary empirical study delves into the “agrafa”, the “unwritten” or “uncharted” parts of a Greek adoption phenomenon and Greek–American relations that may, however, still be accessed via archival investigations, mixed research methods, and efforts to hone life writing skills. At stake is the case of the post-WWII adoptions of some 4000 Greek children who were sent to the United States between the years 1950 and 1975. This study asks how the related negotiations were transacted, especially in the early years of the intercountry adoption phenomenon. It challenges the researcher today to create a life writing narrative out of scant snippets and dense allusions and to disclose the dynamics of overlooked interactions, such as the consumerist and occasionally racist attitudes of some, though certainly not all, prospective adoptive parents. Thus, this article highlights formerly dismissed interactions, not necessarily numerically representative interactions, given that the window of opportunity has passed to interview adoptive parents of Greek children who pursued these foreign adoptions in the 1950s–1960s and to quantify their actions and reactions more systematically. Many of the adoptive parents of the 1950s–1960s, however, left their impressions, demands, and frustrations in writing. Those writings have yet to be studied, and their more deliberate, explicit language must be acknowledged, even amid generally more positive depictions of postwar intercountry adoption. I show that the victorious post-WWII era saw a sense of American entitlement emerge among the prospective adoptive parents that has since been whitewashed. Waiving the banner of altruism or humanitarianism (as a couple or as a superpower, respectively), some adoptive parents embarked on adoptions from Greece from a position of cultural as well as political and economic superiority. Their expectation was that the “destitute” partner should comply, that the Greeks themselves should not “talk back” when “poor orphans” were about to be “saved” from “illegitimacy” and lack of prospects. Full article

Starting from a symmetrical multiple-choice individual, I build a sociophysics model of decision-making. Reducing the choices to two and interactions to pairs recovers the Ising model from physics at zero temperature. The associated equilibrium state results from a spontaneous symmetry breaking, with the whole group sharing a unique choice, which is selected at random. However, my focus departs from physics, which aims at identifying the true equilibrium state, discarding any possible impact of the initial conditions, the size of the sample, and the update algorithm used. Memory of past history is erased. In contrast, I claim that dealing with a social system, the history of the system must be taken into account in identifying the relevant social equilibrium state, which is always biased by its history. Accordingly, using Monte Carlo simulations, I explore the spectrum of non-universal equilibrium states of the Ising model at zero temperature. In particular, I show that different initial conditions with the same value of the order parameter lead to different equilibrium states. The same applies for different sizes and different update algorithms. The results indicate that in the presence of a social network composed of agents sharing different initial opinions, it is their interactions that lead them to share a unique choice and not their mere membership in the network. This finding sheds a new light on the emergence of echo chambers, which appear to be the end of a dynamical process of opinion update and not its beginning with a preferential attachment. Furthermore, polarization is obtained as a side effect of the random selection of the respective unanimous choices of the various echo chambers within a social community. The study points to social media exchange algorithms, which are purely technical levers independent of the issue and opinions at stake, to tackle polarization by either hindering or accelerating the completion of symmetry breaking between agents. Full article

Specifications and mastery grading schemes have been growing in popularity in higher education over the past several years, and reports of specifications grading and other alternative grading systems are emerging in the chemistry education literature. The general goal of these alternative grading approaches is to reduce the reliance on high-stakes exams and give students a more transparent pathway to achieving the course learning outcomes. More importantly, relying less on infrequent high-stakes exams may help reduce historical equity gaps in introductory gateway STEM courses. Herein, we describe the implementation of two versions of mastery grading systems in large enrollment general chemistry courses at a public R1 institution. Class-wide course outcomes, equity gaps in performance on a common final exam, and student feedback on their experience navigating these grading schemes are presented. We show that combining mastery grading with interactive courseware tools improved the average performance on a common final assessment for under-represented minority (URM) students by 7.1 percentage points relative to an active control course that used infrequent high-stakes exams. Full article

The Deccan in the first half of the second millennium is marked by political and religious ferment. The Cōḻas, Gaṅgas, Rāṣṭrakūṭas, and Cāḷukyas are contesting its mundane territory, while the Śaivas, Jainas, and Vaiṣṇavas are contesting its spiritual geography. Unlike the interactions of the earthly rulers which spill real blood, the bloodshed of the spiritual gurus is merely metaphorical. But, the animosity driving their interactions is no less intense, for survival is at stake for them just as it is for their secular counterparts. In this essay, I explore the Jaina point of view in sectarian contestations between the twelfth and the fourteenth centuries through the texts of three Kannada authors: Dharmāṁṛtam of Nayasēna (1112CE), Samayaparīkṣe of Brahmaśiva (c.1200CE), and Dharmaparīkṣe of Vṛttavilāsa (c.1360CE). My objective is to identify the sectarian ‘other’ that these authors address, dispute with and vilify, and to explore the changing nature of this sectarian ‘other’ and the shifting attitudes of these authors towards their opponents. Full article