Purpose This study examines factors associated with older adults' intention to use artificial intelligence (AI) for personal financial management in China and Vietnam by integrating the technology acceptance model (TAM) and the knowledge–behavior gap (KBG) model. Design/methodology/approach Using cross-sectional survey data from 713 respondents, including 407 respondents from Vietnam and 306 from China, the proposed model was examined using partial least squares structural equation modeling (PLS-SEM). Findings Information diagnosticity was positively associated with assessment perceived utility in Vietnam, whereas the corresponding association was not statistically significant in China. Social influence was positively associated with intention to use AI for personal financial management in China but showed no statistically significant association with intention in Vietnam. AI self-efficacy and AI literacy showed significant positive associations with several technology-evaluation and acceptance constructs in both countries. Neither personal innovativeness nor brand reputation significantly moderated the association between acceptance and intention in either national sample. Originality/value Research on AI adoption has predominantly focused on technologically experienced or younger populations. This study extends the literature by examining AI-supported personal financial management among middle-aged and older adults and by comparing the structural associations observed in two Asian countries with different technological and institutional environments.
Hà Vy Nguyễn, Ngoc Kim Ngan Le, Thi Hoang Trang Nguyen et al.· China Finance Review Interna...· 0 citations
AB156 CPF-Camouflage Police Force (Enhanced) A Preventive, Rehabilitative and Adaptive Policing Framework for Safer Cities and Communities Original Idea: 01 December 2019Author: Muhammad Asim – Global Progress VolunteerIndependent Researcher ID / ORCID: 0000-0002-8575-4447Relevant United Nations Sustainable Development Goal: SDG 11 – Sustainable Cities and Communities Abstract Street crime and urban insecurity remain important challenges for cities and communities worldwide. Although crime patterns differ considerably between countries and cities, effective public safety requires approaches that combine prevention, lawful enforcement, rehabilitation, community participation and evidence-based policing. This paper introduces the Camouflage Police Force (CPF), an original policing concept first developed by the author on 01 December 2019. CPF proposes an integrated three-stage framework: (1) root-cause prevention, (2) lawful surrender, rehabilitation and reintegration, and (3) adaptive camouflage policing. The distinctive element of CPF is its proposal to complement conventional visible policing with strategically less-obvious police deployment. As an initial research hypothesis, CPF proposes testing a 70% adaptive/non-obvious and 30% visible deployment model. This ratio is not presented as an established universal standard; rather, it should be experimentally evaluated and adjusted according to local crime patterns, legal requirements, operational capacity, community expectations and empirical results. The framework also proposes responsible use of artificial intelligence and data-driven tools for crime-pattern analysis and resource allocation, while emphasizing human oversight, privacy, due process, proportionality and independent accountability. CPF is aligned with the broader objective of United Nations Sustainable Development Goal 11, which calls for cities and human settlements to become inclusive, safe, resilient and sustainable [1]. It also reflects established evidence supporting multisectoral violence prevention, attention to risk factors, social reintegration and participatory urban safety strategies [2–6]. The paper concludes that CPF represents a researchable alternative to predominantly reactive policing by integrating prevention, rehabilitation and adaptive protection into a single framework. Keywords: Camouflage Police Force, CPF, street crime, urban safety, crime prevention, adaptive policing, hidden policing, rehabilitation, reintegration, artificial intelligence, SDG 11, sustainable cities, public safety.
Muhammad Asim - Global Progress Volunteer Muhammad Asim - Global Progress Volunteer· Zenodo (CERN European Organi...· 0 citations
This article presents a narrative review of Explainability and Interpretability of Black-Box Models 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 explainable AI and interpretability 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
This study proposes the Inclusive Didactic Mediation Model Based on Artificial Intelligence for University Mathematics Education in Latin America (MMDIAI-LAT), structured around four interdependent dimensions: pedagogical-didactic, ethical-inclusive, technological-functional, and sociocultural-Latin American contextual. The research adopts a critical integrative review (Torraco, 2005) of eighteen high-impact sources. The model positions AI as a didactic mediator supporting visualization, accessibility, and conceptual construction in university mathematics, with equity as a constitutive design condition rather than a secondary aspiration. This preprint is submitted for peer review.
Mariana Gabriela Torres· Zenodo (CERN European Organi...· 0 citations
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This study proposes the Inclusive Didactic Mediation Model Based on Artificial Intelligence for University Mathematics Education in Latin America (MMDIAI-LAT), structured around four interdependent dimensions: pedagogical-didactic, ethical-inclusive, technological-functional, and sociocultural-Latin American contextual. The research adopts a critical integrative review (Torraco, 2005) of eighteen high-impact sources. The model positions AI as a didactic mediator supporting visualization, accessibility, and conceptual construction in university mathematics, with equity as a constitutive design condition rather than a secondary aspiration. This preprint is submitted for peer review.
Mariana Gabriela Torres· Zenodo (CERN European Organi...· 0 citations
The development of Artificial Intelligence (AI) has brought significant changes to the field of education, particularly in supporting students in completing their final projects. This study aims to analyze the effect of Artificial Intelligence usage on the effectiveness of undergraduate students' final project completion at Bina Adinata Institute of Technology and Business. This research employed a descriptive quantitative method by collecting data through a Likert-scale questionnaire distributed to 47 students selected using purposive sampling. The collected data were analyzed using descriptive statistical analysis based on five indicators: AI usage intensity, ease of AI use, AI utilization for information retrieval, AI utilization for academic writing, and AI output quality. The results indicate that the majority of respondents agreed and strongly agreed with all research indicators. AI was perceived as helpful in searching for information, finding references, understanding research materials, improving grammar, organizing final project content, and providing information relevant to research topics, thereby enhancing the effectiveness and efficiency of final project completion. The findings imply that Artificial Intelligence can serve as an effective supporting technology to improve students' productivity and the quality of final project writing. However, AI should be used responsibly by balancing it with critical thinking skills, verification of information through credible academic sources, and adherence to academic integrity to maintain the quality of scientific work.
Ade Budi Setiawan, M. Yasin, Yunella Yunella et al.· Jurnal Publikasi Teknik Info...· 0 citations
Recent years have witnessed considerable progress in computer-aided drug discovery, driven by the incorporation of computational technologies within both academic and pharmaceutical environments. This evolution is marked by a significant accumulation of data pertaining to detailed three-dimensional structural information, ligand properties, and their interactions with therapeutic targets. The augmentation of computational capabilities and the accessibility of extensive chemical libraries containing billions of drug-like small molecules have further facilitated this transition. To effectively utilize these resources, it is imperative to employ rapid computing methods for virtual screening, which encompass structure-driven in silico screening across vast molecular spaces, supported by efficient recurrent profiling techniques. Furthermore, advancements in deep learning methodologies are required to improve the accuracy of prediction concerning target functionalities and ligand characteristics, even when complete receptor structures are not available. Here, this review discusses the expansion of chemical space, advanced virtual screening, deep learning, molecular dynamics (MD) simulations, Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) modelling, and challenges for drug discovery. It examines how experimental validation integrates computational predictions with laboratory testing for effective candidate selection. Finally, it outlines future research directions for artificial intelligence-driven, ultra-high performance computing (UHPC) in drug discovery, offering new prospects for the economical creation of safer and more efficacious molecule-level therapies.
Lunna Li, Lianna D. Soriano, W. M. Kedir et al.· Molecular Biomedicine· 0 citations
Introduction: The integration of artificial intelligence (AI) into healthcare is an inevitable global trend. Healthcare students’ perceptions and attitudes are critical in shaping future AI adoption and informing educational reform. This study aimed to examine students’ perceptions and attitudes towards AI in healthcare and to assess the relationship between these constructs. Methods: A cross-sectional study was conducted among 967 healthcare students from December 2024 to January 2025. Data were collected using a structured questionnaire assessing perceptions and attitudes towards AI. Statistical analyses were performed using SPSS version 26.0. Descriptive statistics summarised the data, while Pearson’s correlation and multivariate linear regression were used to examine associations (p < .05). Results: Participants demonstrated moderate perceptions (3.43 ± .64) and attitudes (3.45 ± .64) towards AI. Most students recognised AI’s potential in preventive health (65.0%), patient documentation (65.4%), and population health surveillance (54.5%). Additionally, 64.9% of participants reported that AI makes medicine more engaging, and over 61% believed it enhances healthcare delivery and supports career development. A strong positive correlation was observed between perceptions and attitudes (r=.724; p<.001). In the regression analysis, perception remained a strong independent predictor of attitude (β = .718, p < .001) after adjustment. Conclusion: Healthcare students exhibited moderate readiness for AI adoption, with perceptions strongly influencing attitudes. These findings underscore the importance of integrating AI into healthcare curricula, with emphasis on practical exposure and discipline-specific training. Future research should incorporate perspectives from educators, and healthcare organisations to provide a more comprehensive understanding.
Thuy Thi Luu, Giang Huong Nguyen, Hoai Thi Yen Nguyen et al.· The Asia Pacific Scholar· 0 citations
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
