Organisations collect large amounts of safety data concerning safety events. However, many organisations are still in their infancy in this regard, especially when it comes to utilising large datasets containing textual safety data to support their safety management. Fast-developing Artificial Intelligence (AI) and machine learning methods offer new possibilities for efficient analysis of textual data. In this paper, we discuss aspects related to using AI to support safety management. The main contribution is to examine the possibilities and challenges of using machine learning methods to analyse unstructured textual safety data from organisations’ incident reports and accident investigation texts in order to identify and classify the contributing factors. To this end, we fine-tuned a pretrained Large Language Model (LLM), using Human Factors (HF) and the HF Tool as a framework, to classify the contributing factors mentioned in report texts. The HF Tool supports the systemic analysis of safety incidents and guides the identification of contributing factors at individual, work, group/team, and organisational levels. Furthermore, the LLM developed in this study, AN-HF-classifier-V1, was evaluated on two distinct datasets comprised of excerpts, which were classified according to HF Tool by human factors experts. Our findings suggested that this classifier may support the identification of HF from large textual datasets. However, we found that the quality of textual safety data needs to be improved to include more comprehensive and accurate factors contributing to incidents. The need to improve the quality of data applies regardless of whether AI is used in the analysis or not.
Maria Tiikkaja, Henriikka Kannisto, A. Nurmi et al.· Safety Science· 0 citations
Bimetallic nanoparticles (BimNPs) have emerged as multifunctional nanomaterials with superior properties compared to their monometallic counterparts, owing to synergistic interactions between metal pairs. This review comprehensively explores the green synthesis of BimNPs using phytochemical-rich fruit peels as sustainable, low-cost reducing and stabilizing agents. We detail mechanisms of nanoparticle formation driven by fruit-peel polyphenols and flavonoids. Commonly used fruit peels including banana, citrus, pomegranate, and papaya are analysed with respect to phytochemical composition, extraction techniques, and resultant BimNP characteristics. We discuss biomedical, environmental remediation, and sensing applications. Challenges related to reproducibility, scalability, toxicity, and regulatory gaps are highlighted, alongside future strategies involving artificial intelligence, advanced characterization, and the valorisation of underexplored peel sources. The review underscores the promising role of fruit-peel-based green nanotechnology in sustainable nanomaterial innovation.
Johan S. Umesh, Ashok Shettar· Next Materials· 0 citations
This article presents a narrative review of Multimodal Learning Across Text, Image, and Sound 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 multimodal and vision-language 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
Prioritizing T-cell receptor (TCR) candidates for defined peptide-HLA targets is an important step in TCR-based immunotherapy development, but it still relies heavily on laborious and expensive experimental screening. Recent advancements in generative artificial intelligence have demonstrated promising power in protein design and engineering. In this regard, we propose a pre-trained transformer model, termed Epitope-Receptor-Transformer (ERTransformer), for the epitope-conditioned generation of candidate TCR β-chain CDR3 sequences. ERTransformer is built on EpitopeBERT and ReceptorBERT, which are trained using 1.9 million epitope sequences and 33.1 million TCR sequences, respectively. To demonstrate the model capability, we generate 1,000 candidate TCR β-chain CDR3 sequences for each of the five epitopes with known natural TCRs. The generated candidates show low sequence similarity to natural TCR β-chains while retaining plausible CDR3 length, amino-acid composition, and conservative substitution patterns. We further conduct wet-lab experiments using flow cytometry in defined TCR/pMHC contexts and find that the level of T cell activation induced by selected artificial TCRs is either comparable to or even surpasses that of natural ones. Our work suggests that ERTransformer can expand and prioritize candidate TCR β-chain CDR3 sequences for downstream experimental screening in defined peptide-HLA and TCR-chain contexts.
Jiannan Yang, Bing He, Lei Guan et al.· Genome biology· 0 citations
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Gastric precancerous lesions (GPLs) are critical stages in gastric carcinogenesis, where early and precise identification and intervention are pivotal for improving patient prognosis. Recent advancements in artificial intelligence (AI) and organoid technology have revolutionized GPL research, with their synergistic applications progressively overcoming limitations of traditional methodologies. This review systematically summarizes the latest progress in AI and organoid technology for GPLs, focusing on mechanistic exploration, diagnostic optimization, and therapeutic innovation. We highlight how AI enhances diagnostic accuracy in endoscopy and pathology, while organoids provide unparalleled models for studying disease progression and drug screening. Critically, we discuss the pioneering integration of both technologies, wherein AI analyzes dynamic organoid phenotypes and fuses multimodal data for risk prediction. Current challenges hindering clinical translation, such as data standardization and model interpretability, are critically examined. We conclude that the future of GPL management lies in harnessing this synergy through interdisciplinary collaboration, which is poised to bridge the bench-to-bedside gap and usher in a new paradigm of mechanism-based precision prevention for gastric cancer.
This article presents a narrative review of Multimodal Learning Across Text, Image, and Sound 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 multimodal and vision-language 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
Purpose: This study aims to examine how dynamic capabilities—detecting, utilizing, and transforming—mediate the adoption of Generative Artificial Intelligence (Generative AI) to optimize content marketing strategies among culinary Micro, Small, and Medium Enterprises (MSMEs) in Jatinangor. Research Method: A qualitative descriptive approach with a multiple case study design was employed to explore how culinary MSMEs integrate Generative AI into content marketing while addressing digital and resource constraints. Results and Discussion: The findings show that detecting capability develops organically through a bottom-up process, with creative staff acting as information gatekeepers. This capability is reflected in tactical budgeting for premium AI accounts and independent experimentation. Transforming capability emerges by restructuring conventional workflows into a human–AI hybrid model, in which AI generates ideas and drafts, while creative staff perform cultural and local curation. This integration reduces content production time by 50%–60% without compromising brand authenticity or local identity. Implications: Generative AI serves as a capability enhancer, increasing creative productivity and supporting digital creativity among resource-constrained MSMEs. Originality: This study contributes by explaining Generative AI adoption through a dynamic capabilities perspective and demonstrating how human–AI collaboration enables productive yet culturally authentic content marketing at the micro-business level.
Anthonius S. Hutabarat, Dewi Tamara, Irawan R D Budianto et al.· Advances in Human Resource M...· 0 citations
This article presents a narrative review of Multi-Agent Systems and Emergent Cooperation 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 multi-agent and cooperation 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 article presents a narrative review of Multi-Agent Systems and Emergent Cooperation 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 multi-agent and cooperation 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
Abstract Objective Researchers are increasingly using generative artificial intelligence (GenAI) to support tasks such as literature review, academic writing, programming, data interpretation, and knowledge synthesis. We developed a Rapid Assessment Method for Responsible Use of Generative AI in Scientific Research (RAM-GenAI) to provide a practical approach for evaluating responsible practices in low- and middle-income settings like Uganda. Results description Rather than presenting long checklists of many individual requirements, RAM-Gen AI have five major areas of assessment. These are groups related to the principles of responsible GenAI use. They includes, transparency of AI use, verification of AI-generated outputs, data responsibility, human oversight, and reproducibility of AI-assisted research workflows. Each domain contains assessment criteria designed to identify strengths, risks, and areas requiring improvement. RAM-GenAI developed provides researchers and institutions with a practical approach for evaluating responsible GenAI practices before, during, or after AI-assisted research activities.
Global land degradation affects approximately 2 billion hectares, threatening food security, biodiversity, and climate stability while undermining the United Nations Sustainable Development Goals (SDGs). The concurrent urgency to decarbonize the energy system and mobilize green finance for sustainable transitions has created a rare policy window in which AI-optimized biofuel production on degraded lands can simultaneously serve multiple imperatives. This study presents a comprehensive secondary data analysis of AI-based optimization frameworks for deploying biofuel production systems on degraded lands, integrating an explicit green finance dimension that has been largely absent from prior synthesis literature. Drawing on 152 peer-reviewed studies and authoritative datasets from FAO, IEA, IRENA, UNCCD, the Green Climate Fund (GCF), and the World Bank, we analyze machine learning, deep learning, reinforcement learning, and hybrid AI architectures applied to feedstock selection, soil remediation, yield prediction, supply-chain logistics, and green finance risk-return optimization. Our findings reveal that AI-optimized biofuel systems on degraded lands recover 75-94% of prime-land bioenergy yields, sequester 8.3-10.5 t CO2e ha-1 over 30 years, reduce lifecycle GHG emissions by 55-88%, and generate internal rates of return of 9-22% when green finance instruments are systematically integrated. Green bonds, Article 6 carbon credits, GCF concessional finance, and blended finance structures are identified as the most impactful instruments, collectively capable of reducing project risk scores by 30-45% and expanding the investable universe of degraded-land biofuel projects by an estimated 340%. We develop the AI-Biofuel-Land Restoration (ABLR) conceptual framework with explicit green finance routing pathways and identify critical policy enablers for global deployment. This study advances the evidence base for policy-makers, investors, researchers, and development practitioners working at the intersection of artificial intelligence, bioenergy, green finance, and sustainable land management.
ANJALI CHAUDHARY, Hebah Shalhoob, Kholoud Y. Bajunaied et al.· Processes· 0 citations
As organizations that are explicitly values-driven, public libraries play a critical role in building and maintaining a democratic, equitable, and sustainable information environment. With the growing potential of artificial intelligence (AI) to reshape library collections, services, and workflows, public libraries must determine how to engage with these technologies while maintaining longstanding library values. Despite widespread discussion of AI’s impact on public libraries, to our knowledge there exists no published analysis of American and Canadian public library AI policies to date. In this paper, we address this gap first through an environmental scan of public library websites to identify publicly available AI policy statements. We then analyze these policy statements according to how they include library values. In our scan of over 200 library websites, we identified just 16 publicly available AI policies. ese policies all govern internal or staff usage rather than patron usage. All policies reference at least two library values, with privacy and security the most frequently cited.
Kathryn FitzGerald, Benjamin Charles Germain Lee· Public Library Quarterly· 0 citations
