Credibility of simulation data has always been fundamental in aerodynamic vehicle development, as a significant amount of early design phase work is conducted virtually before a physical test property is made. As the automotive industry pivots toward artificial intelligence and machine learning techniques to assist in aerodynamic development, training these models with simulation data requires a comprehensive understanding of the accuracy and validity of the underlying simulation. It is critical these systems are trained from reliable data with a full understanding of both the limitations and predictive performance of the computational fluid dynamics (CFD) process and the wind tunnel facility it is benchmarked against. Validation and verification studies have been a long-established set of guidelines to determine if the simulation model appropriately reflects reality (validation) or if it has been set with robust numerical schemes, mesh settings, or boundary conditions (verification). The work presented here shows a comprehensive validation study with more than 400 test configurations and 18 vehicle properties. It evaluates Reynolds-averaged Navier–Stokes (RANS) and detached eddy simulation (DES) approaches using moving reference frame (MRF) and rigid body motion (RBM) to account for wheel rotation and comparing STAR-CCM+ CFD process and the FKFS Aeroacoustic Wind Tunnel (AAWT). The results demonstrate that DES—particularly when wheel rotation is modeled using RBM—provides the highest overall predictive performance, with a drag accuracy from −2% to +4% for 80% of cases with corrections applied, which gets to ±2% for over 95% cases with an additional calibration step. A metric-based assessment criterion that combines key performance metrics into a single detection event (DE) score derived from failure mode effects analysis (FMEA) principles is proposed with an example shown for the 2021 Range Rover Velar. The benefit being that it removes a more judgement-based, qualitative approach, aiding toolset selection and methods development gaps.
Christopher Beves, Nicholas Simmonds, Eric Dalmau Graells· SAE technical papers on CD-R...· 0 citations
This article presents a narrative review of The Transformer Revolution in Sequence Modeling in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpretive synthesis of representative contributions, the review reconstructs the historical development of the area, examines the conceptual foundations and definitional disputes that organize its debates, and maps the contemporary landscape of research, including the methodological shift toward data-intensive approaches and the institutional pressures that shape publication practice. Particular attention is given to the role of transformers and attention as organizing themes, and to the conditions under which findings from different research traditions can be brought into productive comparison. The review identifies three synthetic conclusions: the literature is cumulatively strong but organizationally weak; methodological pluralism is better understood as a resource than as a defect; and the growing practical salience of the topic raises the stakes of its unresolved conceptual questions. An agenda for future work is proposed, emphasizing integrative research designs, transparent synthesis practices, and the protection of definitional and infrastructural work on which cumulative progress depends. The article is intended as both a reference map for newcomers and a provocation for specialists in Artificial Intelligence.
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations
The growing use of digital technology in healthcare has increased the importance of computational skills in pharmacy. Python is a simple, open-source programming language widely used for data analysis, visualization, modelling, and artificial intelligence. In pharmacy, Python can be applied to pharmacokinetics, pharmaceutical research, drug discovery, formulation development, pharmacovigilance, and clinical data analysis. Introducing basic Python programming into pharmacy education may improve students' analytical abilities and prepare them for modern, technology-driven pharmaceutical research.
Subhajit Samanta*, Dr. Dhrubo, Jyoti Sen· Zenodo (CERN European Organi...· 0 citations
This article presents a narrative review of The Transformer Revolution in Sequence Modeling in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpretive synthesis of representative contributions, the review reconstructs the historical development of the area, examines the conceptual foundations and definitional disputes that organize its debates, and maps the contemporary landscape of research, including the methodological shift toward data-intensive approaches and the institutional pressures that shape publication practice. Particular attention is given to the role of transformers and attention as organizing themes, and to the conditions under which findings from different research traditions can be brought into productive comparison. The review identifies three synthetic conclusions: the literature is cumulatively strong but organizationally weak; methodological pluralism is better understood as a resource than as a defect; and the growing practical salience of the topic raises the stakes of its unresolved conceptual questions. An agenda for future work is proposed, emphasizing integrative research designs, transparent synthesis practices, and the protection of definitional and infrastructural work on which cumulative progress depends. The article is intended as both a reference map for newcomers and a provocation for specialists in Artificial Intelligence.
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations
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The growing use of digital technology in healthcare has increased the importance of computational skills in pharmacy. Python is a simple, open-source programming language widely used for data analysis, visualization, modelling, and artificial intelligence. In pharmacy, Python can be applied to pharmacokinetics, pharmaceutical research, drug discovery, formulation development, pharmacovigilance, and clinical data analysis. Introducing basic Python programming into pharmacy education may improve students' analytical abilities and prepare them for modern, technology-driven pharmaceutical research.
Subhajit Samanta*, Dr. Dhrubo, Jyoti Sen· Zenodo (CERN European Organi...· 0 citations
Abstract Quantum technologies (QT) are advancing rapidly, promising advancements across a wide spectrum of applications but also raising significant ethical, societal, and geopolitical impacts, including dual-use capabilities, varying levels of access, and impending quantum divide(s). To address these, the Responsible Quantum Technologies (ResQT) community was established to share knowledge, perspectives, and best practices across various disciplines. Its mission is to ensure QT developments align with ethical principles, promote equity, and mitigate unintended consequences. Initial progress has been made, as scholars and policymakers increasingly recognize principles of responsible QT. However, more widespread dissemination is needed, and as QT matures, so must responsible QT. This paper provides a structured, situated synthesis of the ResQT community's current work and identifies future directions from within this community perspective. Drawing on historical lessons from artificial intelligence and nanotechnology, actions targeting the quantum divide(s) are addressed, including the implementation of responsible research and innovation, fostering wider stakeholder engagement, and sustainable development. These actions aim to build trust and engagement, facilitating the participatory and responsible development of QT. The ResQT community advocates that responsible QT should be an integral part of quantum development rather than an afterthought so that quantum technologies evolve toward a future that is technologically advanced and beneficial for all.
Adrian Schmidt, Alexandre Artaud, Arsev Umur Aydınoğlu et al.· Ethics and Society· 1 citation
Beyond their socio-economic implications, disruptive technologies influence all instruments of state power, especially the military. Artificial intelligence (AI), Machine Learning (ML), and autonomous systems are transforming traditional ways of thinking, leadership concepts, and processes, necessitating new approaches to prepare military decision-makers at all levels of command for a complex, technology-driven environment. This article explores how these technologies are reshaping military decision-making and outlines why adapting education and processes is critical to maintaining operational effectiveness in future conflicts. In most Western armed forces, the operational level of command serves as an interface between the tactical and military-strategic levels. It enables the translation of strategic objectives into tactical actions. In addition to its vertically hierarchical role, the operational level also performs a horizontal interface function with other state and non-state actors. These environmental systems (System of Systems), involving a wide range of actors, increase the complexity of the operational level of command.
Oliver Hochfellner· Zenodo (CERN European Organi...· 0 citations
Beyond their socio-economic implications, disruptive technologies influence all instruments of state power, especially the military. Artificial intelligence (AI), Machine Learning (ML), and autonomous systems are transforming traditional ways of thinking, leadership concepts, and processes, necessitating new approaches to prepare military decision-makers at all levels of command for a complex, technology-driven environment. This article explores how these technologies are reshaping military decision-making and outlines why adapting education and processes is critical to maintaining operational effectiveness in future conflicts. In most Western armed forces, the operational level of command serves as an interface between the tactical and military-strategic levels. It enables the translation of strategic objectives into tactical actions. In addition to its vertically hierarchical role, the operational level also performs a horizontal interface function with other state and non-state actors. These environmental systems (System of Systems), involving a wide range of actors, increase the complexity of the operational level of command.
Oliver Hochfellner· Zenodo (CERN European Organi...· 0 citations
Modern businesses increasingly rely on big data and artificial intelligence. However, behind every data point lies a human decision. To be successful, managers must also understand the psychological processes that drive decision-making. Traditional research methods, such as surveys and focus groups, provide valuable insights, but may fail to capture the subconscious processes that influence decisions. This dissertation demonstrates how neuroscientific methods can generate novel business insights by measuring these processes in the brain. Using functional magnetic resonance imaging (fMRI), this dissertation studies brain activity in three distinct domains of business. In marketing, it shows that storytelling advertisements persuade consumers not because they share a character’s feelings, but because they engage brain networks involved in understanding a character’s intentions. In finance, it finds that reward-related brain activity of professional investors is associated with future stock performance, even when their explicit market forecasts are not. In organizational management, brain activity reveals how biases shape social judgments: while expectations of cooperation are based on perceived similarity and attractiveness, actual cooperation depends primarily on the explicit commitments that people make. Together, the findings of this dissertation leverage neuroscientific methods to reveal psychological processes underlying economic decision-making in business contexts. By illuminating the hidden mechanisms of narrative persuasion, financial intuition, and interpersonal cooperation, brain activity informs business practices in ways that self-report studies cannot. Even as artificial intelligence continues to transform businesses, these findings prove that the biological mind remains an indispensable source of insights.
Leo van Brussel· EUR Research Repository (Era...· 0 citations
Artificial Intelligence may be the latest technology transforming education, but the challenges institutions face are not new. From learning management systems and online learning to mobile devices and collaboration tools, every major technology shift has followed a familiar pattern: experimentation, uncertainty, governance, and ultimately scale. The technology changes, but the leadership and learning challenges remain. This session explores what previous waves of educational technology can teach us about navigating the AI era. Participants will examine how AI is changing long-held assumptions about teaching, learning, and institutional decision-making, while exploring practical strategies for evaluating impact, increasing institutional visibility, and creating responsible governance frameworks. The discussion will focus on maintaining human judgment, discernment, and accountability as AI becomes increasingly integrated into learning environments. Attendees will leave with a framework for moving beyond the excitement and anxiety surrounding AI to focus on what matters most: fostering meaningful learning, supporting learner growth, and using technology in ways that align with educational purpose and values.
Jennifer Grevis, Amanda Perez, Lee Lewis· Scholar Works at Harding (Ha...· 0 citations
The digital landscape is undergoing a seismic shift, moving beyond the era of simple data storage into an age where the true value of information lies in its ability to forecast the future. AI-Powered Predictive Analytics for Intelligent Decision Support Systems is designed as a comprehensive guide to navigating this transition, blending the technical rigor of machine learning with the pragmatic needs of modern industrial and academic decision-making. In an increasingly complex world, the scope of global systems—spanning healthcare, finance, and manufacturing—has begun to exceed the limits of unassisted human intuition. This book explores the vital synergy between Artificial Intelligence (AI) and Decision Support Systems (DSS), illustrating how predictive modeling transforms raw, historical data into a strategic asset that anticipates trends, mitigates risks, and optimizes outcomes in real-time. While many existing texts focus solely on the mathematical algorithms of machine learning or the administrative management of information systems, this work bridges the gap by covering the entire system lifecycle. It takes the reader on a structured journey from the foundational theories of predictive analytics to the cutting edge of autonomous decision systems. Throughout these chapters, we delve into the intricate nuances of feature engineering, data governance, and the deployment of models within modern MLOps frameworks. Furthermore, the book provides deep technical explorations of supervised learning, ensemble methods, and deep learning architectures like CNNs and LSTMs, while placing a heavy emphasis on Explainable AI (XAI) to ensure that automated decisions remain transparent and trustworthy. The theory presented in these pages is anchored by extensive case studies that reflect both global and regional perspectives, providing a balanced view of how these technologies are implemented across diverse economic and regulatory environments. As we move toward a future of fully autonomous systems, the book addresses the critical challenges of algorithmic bias, data privacy, and the indispensable role of human-AI collaboration. This text is intended for researchers, data scientists, and business leaders alike—anyone who seeks to understand the strategic implications and technical requirements of building systems that are not only intelligent and efficient but also ethical and transparent. It is our hope that this roadmap serves as a vital resource for those looking to harness the analytical power of machines to solve the most pressing challenges of our time.
Mr PRAVEEN NAINAR BALASUBRAMANIAN, Dr A ANANTHI CHRISTY, Dr A K DASARATHY et al.· Zenodo (CERN European Organi...· 0 citations
The digital landscape is undergoing a seismic shift, moving beyond the era of simple data storage into an age where the true value of information lies in its ability to forecast the future. AI-Powered Predictive Analytics for Intelligent Decision Support Systems is designed as a comprehensive guide to navigating this transition, blending the technical rigor of machine learning with the pragmatic needs of modern industrial and academic decision-making. In an increasingly complex world, the scope of global systems—spanning healthcare, finance, and manufacturing—has begun to exceed the limits of unassisted human intuition. This book explores the vital synergy between Artificial Intelligence (AI) and Decision Support Systems (DSS), illustrating how predictive modeling transforms raw, historical data into a strategic asset that anticipates trends, mitigates risks, and optimizes outcomes in real-time. While many existing texts focus solely on the mathematical algorithms of machine learning or the administrative management of information systems, this work bridges the gap by covering the entire system lifecycle. It takes the reader on a structured journey from the foundational theories of predictive analytics to the cutting edge of autonomous decision systems. Throughout these chapters, we delve into the intricate nuances of feature engineering, data governance, and the deployment of models within modern MLOps frameworks. Furthermore, the book provides deep technical explorations of supervised learning, ensemble methods, and deep learning architectures like CNNs and LSTMs, while placing a heavy emphasis on Explainable AI (XAI) to ensure that automated decisions remain transparent and trustworthy. The theory presented in these pages is anchored by extensive case studies that reflect both global and regional perspectives, providing a balanced view of how these technologies are implemented across diverse economic and regulatory environments. As we move toward a future of fully autonomous systems, the book addresses the critical challenges of algorithmic bias, data privacy, and the indispensable role of human-AI collaboration. This text is intended for researchers, data scientists, and business leaders alike—anyone who seeks to understand the strategic implications and technical requirements of building systems that are not only intelligent and efficient but also ethical and transparent. It is our hope that this roadmap serves as a vital resource for those looking to harness the analytical power of machines to solve the most pressing challenges of our time.
Mr PRAVEEN NAINAR BALASUBRAMANIAN, Dr A ANANTHI CHRISTY, Dr A K DASARATHY et al.· Zenodo (CERN European Organi...· 0 citations
