AI Reloaded: ACAT2017 Conference Summary

This volume of Journal of Physics: Conference Series is dedicated to scientific contributions presented at the 14th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2011) which took place on 5–7 September 2011 at Brunel University, UK. The workshop series, which began in 1990 in Lyon, France, brings together computer science researchers and practitioners, and researchers from particle physics and related fields in order to explore and confront the boundaries of computing and of automatic data analysis and theoretical calculation techniques. It is a forum for the exchange of ideas among the fields, exploring and promoting cutting-edge computing, data analysis and theoretical calculation techniques in fundamental physics research.This year's edition of the workshop brought together over 100 participants from all over the world. 14 invited speakers presented key topics on computing ecosystems, cloud computing, multivariate data analysis, symbolic and automatic theoretical calculations as well as computing and data analysis challenges in astrophysics, bioinformatics and musicology. Over 80 other talks and posters presented state-of-the art developments in the areas of the workshop's three tracks: Computing Technologies, Data Analysis Algorithms and Tools, and Computational Techniques in Theoretical Physics. Panel and round table discussions on data management and multivariate data analysis uncovered new ideas and collaboration opportunities in the respective areas.This edition of ACAT was generously sponsored by the Science and Technology Facility Council (STFC), the Institute for Particle Physics Phenomenology (IPPP) at Durham University, Brookhaven National Laboratory in the USA and Dell. We would like to thank all the participants of the workshop for the high level of their scientific contributions and for the enthusiastic participation in all its activities which were, ultimately, the key factors in the success of the workshop.Further information on ACAT 2011 can be found at http://acat2011.cern.ch Dr Liliana Teodorescu Brunel UniversityThe PDF also contains details of the workshop's committees and sponsors.

This study investigates the scientific development of Artificial Intelligence (AI) and Machine Learning (ML) applications in Human Resource Management (HRM) through bibliometric analysis. To this end, 522 academic publications indexed in the Scopus database between 2020 and 2024 were analyzed using the R-based bibliometrix package and VOSviewer software. Descriptive analysis, scientific productivity metrics, and content analysis techniques were employed. The findings revealed three main patterns. First, research on AI and ML applications in HRM has grown significantly—particularly between 2022 and 2024—driven by post-pandemic digital transformation. Second, India, China, and the USA lead in research output, while the UK and France demonstrate strong citation impact, indicating a globally expanding research ecosystem. Third, the thematic focus of research is shifting from technical infrastructure toward more human-centered and ethical dimensions. Additionally, keyword co-occurrence network analysis identified three major thematic clusters: HRM functions, AI applications, and machine learning analytics, highlighting the field’s interdisciplinary nature. Compared to the previous studies, this research provides a more comprehensive bibliometric analysis of AI and ML applications in HRM. It is the first extensive study to map the intellectual evolution of the field from a multidisciplinary perspective. Furthermore, it charts research trends and collaboration networks, revealing a shift from technical implementations to strategic integration. In conclusion, this analysis offers new insights to the literature by illustrating the technological evolution in HRM and highlighting the growing significance of cutting-edge approaches such as AI and ML, reaffirming the field as a timely and impactful area of research.

This paper utilizes artificial intelligence (AI) and machine learning (ML) application to the field of forecasting public finances, where a specific paradigm shift is observed as the complex AI-based models replace traditional econometric models. This paper systematically analyzes AI and ML application to the field of forecasting public finances, where a specific paradigm shift is observed as the complex AI-based models replace traditional econometric models. It critically looks at the latest development in the field of deep learning, natural language processing (NLP), ensemble learning and hybrid AI methods in explaining how they have been utilized in predicting key fiscal variables, including government revenues, expenditures, budget deficits, debt sustainability, tax compliance, and fiscal risks.

Artificial intelligence for patient scheduling in the real-world health care setting: A metanarrative review

Artificial intelligence and Machine Learning approaches in sports: Concepts, applications, challenges, and future perspectives

10th International Conference on Physics and Its Applications (ICOPIA) 26 August 2020, Surakarta, Indonesia International Conference on Physics and Its Applications (ICOPIA) is a biannual conference, started in 2001. This year of 2020, ICOPIA is the tenth one. This conference is mainly supported by Department of Physics, Universitas Sebelas Maret, Indonesia.ICOPIA is a forum for presenting and discussing current research in physics and its applications. Varied studies about material physics, optics, geophysics, instrumentation, magnetics and theoretical physics have been presented in the 10th ICOPIA. All the papers were presented orally.The keynote presentations by qualified speakers have been delivered in this conference. The keynote presentation included Rutile-Phased TiO2 Nanostructured Towards Electronic Devices Application, Deep Learning for Biomedical Application, Control of nanoparticle-size in cobalt ferrite system and Improvement of Functional Oxide Properties for Applications. The keynotes speakers were from Université du Littoral Côte d’Opale, France, University of Technology Sydney, Australia, Universiti Tun Hussein Onn Malaysia and Universitas Sebelas Maret, Indonesia.This proceeding contains the papers presented in ICOPIA 2020. The papers are divided into sections: Acoustic, Computational Physics, Geophysics, Instrumentation, Material Physics, Optics, Theoretical Physics, and other topics related to Physics. This structure is made so that readers are easier to find an article in this proceeding.We would like to thank to all the participants attending this conference and also to the committees for their contribution to this high-level conference and its overall success. We also would like to thank to the reviewers for their positive contribution to maintain the quality of the articles presented in this conference.Nuryani, Ph.DGeneral Chair of ICOPIA2020List of titles General Chair, Organizer, Advisory Board, Organizing Committee are available in this Pdf.

Communication is a crucial element of every health care profession, rendering communication skills training in all health care professions as being of great importance. Technological advances such as artificial intelligence (AI) and particularly machine learning (ML) may support this cause: it may provide students with an opportunity for easily accessible and readily available communication training. This scoping review aimed to summarize the status quo regarding the use of AI or ML in the acquisition of communication skills in academic health care professions. We conducted a comprehensive literature search across the PubMed, Scopus, Cochrane Library, Web of Science Core Collection, and CINAHL databases to identify articles that covered the use of AI or ML in communication skills training of undergraduate students pursuing health care profession education. Using an inductive approach, the included studies were organized into distinct categories. The specific characteristics of the studies, methods and techniques used by AI or ML applications, and main outcomes of the studies were evaluated. Furthermore, supporting and hindering factors in the use of AI and ML for communication skills training of health care professionals were outlined. The titles and abstracts of 385 studies were identified, of which 29 (7.5%) underwent full-text review. Of the 29 studies, based on the inclusion and exclusion criteria, 12 (3.1%) were included. The studies were organized into 3 distinct categories: studies using AI and ML for text analysis and information extraction, studies using AI and ML and virtual reality, and studies using AI and ML and the simulation of virtual patients, each within the academic training of the communication skills of health care professionals. Within these thematic domains, AI was also used for the provision of feedback. The motivation of the involved agents played a major role in the implementation process. Reported barriers to the use of AI and ML in communication skills training revolved around the lack of authenticity and limited natural flow of language exhibited by the AI- and ML-based virtual patient systems. Furthermore, the use of educational AI- and ML-based systems in communication skills training for health care professionals is currently limited to only a few cases, topics, and clinical domains. The use of AI and ML in communication skills training for health care professionals is clearly a growing and promising field with a potential to render training more cost-effective and less time-consuming. Furthermore, it may serve learners as an individualized and readily available exercise method. However, in most cases, the outlined applications and technical solutions are limited in terms of access, possible scenarios, the natural flow of a conversation, and authenticity. These issues still stand in the way of any widespread implementation ambitions.

The paper presents pilot studies exploring students’ understanding of machine learning with an AI-Robotics tool. This is a follow-up to the “Promoting AI Literacy through AI-Robotics” [1] presented at the SITE 2021 conference. Artificial Intelligence (AI) is influencing practically every aspect of our lives as it has rapidly been developed to increase its complexities. Children are not an exception. What is specifically urgent is to prepare primary and secondary students for their future professions, which might not currently exist, and become citizens capable of understanding and utilizing AI-enhanced technologies in ways that do not benefit some populations over others. There have been several AI-tools focusing on teaching students about AI developed and used in education settings. However, how deep their understanding of AI becomes one of the remaining questions. This paper explores and reports how using Teachable Machine, one of the AI-tools, with a robotic tool deepens the understanding of AI among students. Two pilot studies using Teachable Machine and AI-Robotics tool are explained and how the process that a group of undergraduate students went through in developing AI-powered educational robotics lessons helped them deepen their own understanding of machine learning and its application in physical computing is reported.KeywordsMachine learningAI-powered roboticsArtificial Intelligence literacy

Chemical heuristics have been fundamental to the advancement of chemistry and materials science. These heuristics are typically established by scientists using knowledge and creativity to extract patterns from limited datasets. Machine learning offers opportunities to perfect this approach using computers and larger datasets. Here, we discuss the relationships between traditional heuristics and machine learning approaches. We show how traditional rules can be challenged by large-scale statistical assessment and how traditional concepts commonly used as features are feeding the machine learning techniques. We stress the waste involved in relearning chemical rules and the challenges in terms of data size requirements for purely data-driven approaches. Our view is that heuristic and machine learning approaches are at their best when they work together.

Public health epidemiology is continuously evolving, driven by the integration of innovative methodologies that address the complex nature of health challenges. With the increased availability of high-dimensional, population-level, and cross-national data, advanced methodologies offer new opportunities for modeling the complex interactions between risk factors, health outcomes, and disparities across populations. This workshop will focus on the application of cutting-edge methods, such as causal inference models, machine learning, and artificial intelligence (AI)-driven analysis, in the context of epidemiology. These methodologies enable novel insights into disease mechanisms, improve risk prediction, and identify vulnerable populations, ultimately guiding more effective public health interventions. Topics will include causal decomposition techniques to assess interventions targeting cardiovascular health disparities, AI-driven analyses to improve hypertension control strategies across diverse populations, and federated machine learning methods for privacy-preserving cardiovascular risk prediction. Moreover, a key focus of the workshop will be the importance of reproducibility in public health research. Ensuring that findings are transparent, reproducible, and open to further development is critical for advancing scientific knowledge. Special attention will be given to the role of causal graphs, which help make causal assumptions explicit and promote the reuse and modification of epidemiological models. The session aims to foster critical discussion around these methods, encouraging a proactive exchange of ideas among epidemiologists, public health professionals, scientists, and policymakers. Following an interactive format, the session will facilitate open discussion and exchange of ideas among participants. Attendees will have the opportunity to actively engage with speakers to explore advanced and emerging methods in epidemiology, fostering a collective debate on methodological innovation and its implications for public health policy. In conclusion, this workshop aims to foster a deeper understanding of the latest methodologies in public health epidemiology, with a focus on their application to cardiovascular disease. By facilitating discussions on innovative methods and reproducibility, the workshop will provide a platform for sharing knowledge, fostering collaboration, and advancing the field of public health epidemiology. Key messages • Advanced analytical techniques can uncover subtle epidemiological patterns, deepening scientific understanding and guiding more equitable and effective public-health interventions. • Transparency and reproducibility are critical for generating credible evidence that informs policy and practice.

Background/Objectives: The integration of Artificial Intelligence (AI) and Machine Learning (ML) in pharmaceutical research and development is transforming the industry by improving efficiency and effectiveness across drug discovery, development, and healthcare delivery. This review explores the diverse applications of AI and ML, emphasizing their role in predictive modeling, drug repurposing, lead optimization, and clinical trials. Additionally, the review highlights AI’s contributions to regulatory compliance, pharmacovigilance, and personalized medicine while addressing ethical and regulatory considerations. Methods: A comprehensive literature review was conducted to assess the impact of AI and ML in various pharmaceutical domains. Research articles, case studies, and industry reports were analyzed to examine AI-driven advancements in predictive modeling, computational chemistry, clinical trials, drug safety, and supply chain management. Results: AI and ML have demonstrated significant advancements in pharmaceutical research, including improved target identification, accelerated drug discovery through generative models, and enhanced structure-based drug design via molecular docking and QSAR modeling. In clinical trials, AI streamlines patient recruitment, predicts trial outcomes, and enables real-time monitoring. AI-driven predictive maintenance, process optimization, and inventory management have enhanced efficiency in pharmaceutical manufacturing and supply chains. Furthermore, AI has revolutionized personalized medicine by enabling precise treatment strategies through genomic data analysis, biomarker discovery, and AI-driven diagnostics. Conclusions: AI and ML are reshaping pharmaceutical research, offering innovative solutions across drug discovery, regulatory compliance, and patient care. The integration of AI enhances treatment outcomes and operational efficiencies while raising ethical and regulatory challenges that require transparent, accountable applications. Future advancements in AI will rely on collaborative efforts to ensure its responsible implementation, ultimately driving the continued transformation of the pharmaceutical sector.

Aim: The majority of machine learning (ML) models in healthcare are built on retrospective data, much of which is collected without explicit patient consent for use in artificial intelligence (AI) and ML applications. The primary aim of this study was to evaluate whether clinicians and scientific researchers themselves consent to provide their own data for the training of ML models. Materials and Methods: This survey was conducted through an anonymous online survey, utilizing platforms such as Telegram, LinkedIn, and Viber. The target audience comprised specific international groups, primarily Russian, German, and English-speaking, of clinicians and scientific researchers. These participants ranged in their levels of expertise and experience, from beginners to veterans. The survey centered on a singular, pivotal question: “Do You Consent to the Use of Your Biological and Private Data for Training Machine Learning and AI Models?” Respondents had the option to choose from three responses: “Yes” and “No”. Results: The survey was conducted in January 2024. A total of 119 unique and verified individuals participated in the survey. The results revealed that only 50% of respondents (63 persons) expressed consent to provide their own data for the training of ML and AI models. Conclusion: In the development of ML and AI models, particularly open-source ones, it is crucial to ascertain whether participants are willing to provide their private data. While ML algorithms can transform the nature of data, it is important to remember that the primary owner of this data is the individual. Our findings show that in 50% of the cases, even participants from scientific research and clinical backgrounds – individuals typically accountable for ensuring data quality in AI and ML model development – do not consent to the use of their data in AI and ML settings. This highlights the need for more stringent consent processes and ethical considerations in the utilization of personal data in AI and ML research.

This annual report of the Physics, Computer Science and Mathematics Division describes the scientific research and other work carried out within the Division during 1977. The Division is concerned with work in experimental and theoretical physics, with computer science and applied mathematics, and with the operation of a computer center. The major physics research activity is in high-energy physics, although there is a relatively small program of medium-energy research. The High Energy Physics research program in the Physics Division is concerned with fundamental research which will enable man to comprehend the nature of the physical world. The major effort is now directed toward experiments with positron-electron colliding beam at PEP. The Medium Energy Physics program is concerned with research using mesons and nucleons to probe the properties of matter. This research is concerned with the study of nuclear structure, nuclear reactions, and the interactions between nuclei and electromagnetic radiation and mesons. The Computer Science and Applied Mathematics Department engages in research in a variety of computer science and mathematics disciplines. Work in computer science and applied mathematics includes construction of data bases, computer graphics, computational physics and data analysis, mathematical modeling, and mathematical analysis of differential and integral equations resulting from physical problems. The Computer Center provides large-scale computational support to LBL's scientific programs. Descriptions of the various activities are quite short; references to published results are given. 24 figures. (RWR)

Brief Introduction of Physics Researches in Chinese Academy of Sciences

Artificial intelligence (AI) is an influential technology, which has helped many branches of science and technology accomplish an unimaginable progress. The field of nanoparticles has had more than a decade-long tryst with AI, but it is yet to fully realize the benefits of AI to the extent other branches of technologies have. In recent times, there has been a renewed interest in applying machine learning and similar AI concepts to produce new nanoparticles, which has resulted in exponential growth of research in that direction and resulting data. This research paper critically analyzes that data to explore the prevalent trends/methods in applying machine learning (ML) and data mining techniques in the field of nanoparticles. Beginning with delineating the challenges faced in implementing ML in the nanoparticles segment, this paper goes on to explore the various learning algorithms relevant to the field of nanomaterials, their applications, suitability, merits, and demerits. In the latter section of the paper, a detailed analysis (in the form of a flow chart) spells out the considerations to be taken while collecting and constructing data, choosing and applying appropriate ML algorithms. This paper reviews various ML algorithms used in recent articles. In essence, this paper compendiously provides information and prediction of nanoparticles necessary to successfully customize ML for nanomaterials vertical, especially for nanoparticles of medical significance and applications.KeywordsMachine learningArtificial intelligenceNanoparticlesData mining