Although cognitive robotics is still a work in progress, the trend is to “free” robots from the assembly lines of the third industrial revolution and allow them to “enter human society” in large numbers and many forms, as forecasted by Industry 4.0 and beyond. Cognitive robots are expected to be intelligent, designed to learn from experience and adapt to real-world situations rather than being preprogrammed with specific actions for all possible stimuli and environmental conditions. Moreover, such robots are supposed to interact closely with human partners, cooperating with them, and this implies that robot cognition must incorporate, in a deep sense, ethical principles and evolve, in conflict situations, decision-making capabilities that can be perceived as wise. Intelligence (true vs. false), ethics (right vs. wrong), and wisdom (good vs. bad) are interrelated but independent features of human behavior, and a similar framework should also characterize the behavior of cognitive agents integrated in human society. The working hypothesis formulated in this paper is that the propensity to consolidate ethically guided behavior, possibly evolving to some kind of wisdom, is a cognitive architecture based on bio-inspired embodied cognition, educated through development and social interaction. In contrast, the problem with current AI foundation models applied to robotics (EAI) is that, although they can be super-intelligent, they are intrinsically disembodied and ethically agnostic, independent of how much information was absorbed during training. We suggest that the proposed alternative may facilitate social acceptance and thus make such robots civilized.
Mechanical engineering and machinery, Electronic computers. Computer science
Ali Nasiri Khiavi, Mehdi Vafakhah, Dongkun Kim
et al.
ABSTRACT This study develops a comprehensive framework for mapping flood susceptibility and vulnerability in the Cheshmeh‐Kileh forest watershed in northern Iran by integrating remote sensing (RS), local knowledge, and machine learning (ML) algorithms. This was accomplished through the application of various MLs, such as K‐nearest neighbor (KNN), random forest (RF), support vector regression (SVR), and Naive Bayes. In this study, flood susceptibility refers to the physical propensity of an area to experience flooding, influenced by geo‐environmental factors, while flood vulnerability captures the socio‐economic and institutional dimensions that determine a community's ability to cope with and recover from flood events. This research first identified critical geo‐environmental factors influencing flood susceptibility and utilized remote sensing to locate areas prone to runoff generation. Flood risk zoning was then implemented using machine learning techniques in Python. To assess flood vulnerability, data were collected from local residents via questionnaires, focusing on economic, infrastructural‐physical, institutional‐policy, and social‐cultural aspects. The flood vulnerability map was created by integrating these survey results with population density data to identify areas where high social exposure coincides with high physical susceptibility. Findings indicated that the combined remote sensing‐SVR model was the most effective for sensitivity classification, identifying sub‐watersheds 2 and 8 in the Sehezar River (a major basin within the study area) as the areas with the highest and lowest flooding susceptibility, respectively, with sub‐watershed 10 in the Dohezar River (another major basin) being the most vulnerable. The estimated values for Mean Absolute Error (0.041), Mean Square Error (0.042), Root Mean Square Error (0.205), and Area Under the Curve (0.980) demonstrated high model accuracy. The Friedman statistical test showed that the average scores for the different dimensions of vulnerability decreased in the order of: economic (0.48), social‐cultural (0.44), infrastructural‐physical (0.34), and institutional‐policy (0.28). Consequently, the economic dimension was prioritized for its highest score. Flood vulnerability mapping revealed that sub‐watersheds 5, 11, 14, and 15, which had higher population densities, were naturally more vulnerable to floods. This finding reflects a direct relationship between population density and flood vulnerability. Overall, this study underscores the urgent need for effective planning and preventive strategies to mitigate flood risks and enhance resilience in the region.
River protective works. Regulation. Flood control, Disasters and engineering
Carlos Henrique Vieira-Vieira, Sarang Sanjay Kulkarni, Adam Zalewski
et al.
The pharmaceutical industry faces pressure to improve the drug development process while reducing costs in an evolving regulatory landscape. This paper presents the Preclinical Information Center (PRINCE), a cloud-hosted data integration platform developed by Bayer AG in collaboration with Thoughtworks. PRINCE integrates decades of structured and unstructured safety study reports, leveraging a multi-agent architecture based on Large Language Models (LLMs) and advanced data retrieval methodologies, such as Retrieval-Augmented Generation and Text-to-SQL. In this paper, we describe the three-step evolution of PRINCE from a data search tool based on keyword matching to a resourceful research assistant capable of answering complex questions and drafting regulatory-critical documents. We highlight the iterative development process, guided by user feedback, that ensures alignment with evolving research needs and maximizes utility. Finally, we discuss the importance of building trust-based solutions and how transparency and explainability have been integrated into PRINCE. In particular, the integration of a human-in-the-loop approach enhances the accuracy and retains human accountability. We believe that the development and deployment of the PRINCE chatbot demonstrate the transformative potential of AI in the pharmaceutical industry, significantly improving data accessibility and research efficiency, while prioritizing data governance and compliance.
Bu çalışma “yapay zekâ” (YZ) ile elde edilen bilginin niteliği üzerine bir değerlendirme içermektedir. Bu bağlamda YZ ile elde edilen bilginin üretim süreçleri, bilim içindeki rolü, avantajları ve dezavantajları, toplumsal karşılığı, ideoloji, hegemonya ve etik boyutunu ele almaktadır. Yapılan değerlendirmelerde, YZ ile elde edilen bilginin objektifliği, şeffaflığı, bilimselliği, gerçekliği, eşitliği ve etikliği sorgulanmıştır. Tersinden söylersek, YZ ile elde edilen bilginin yanlı, ideolojik, tahakkümcü, antidemokratik ve emperyal biçimli olup olmadığıyla ilgili anlamlı sonuçlar ortaya koyulmaya çalışılmıştır. Çalışmada içerik çözümleme yöntemi bağlamında tematik (semantik//latent) teknik kullanılmıştır. Bu süreç, popüler bir YZ uygulaması olan ChatGPT ve son günlerde adını duyuran DeepSeek uygulamalarına, kullanıcılarına sundukları bilginin kaynağı ve niteliğine yönelik sorular sorarak gerçekleştirilmiştir. Her iki YZ uygulamasının verdiği cevaplar, aynı işleyişe sahip tüm YZ uygulamalarının yanlı, ideolojik, sübjektif, eşitsiz, etik dışı ve zaman zaman hukuka uymayan bilgi aktarımı yaptığı yönündedir. Bu durum, modernite paradigması ile şekillenen Batı merkezli bilimsel anlayışın ürettiği YZ teknolojisinin epistemolojik hegemonyaya dayalı işleyiş biçiminin zorunlu bir sonucudur.
Bunga Laelatul Muna, Sudianto Sudianto, Muhammad Lulu Latif Usman
Academic services are an important element in education, as they provide students with access to information and support. At Telkom University Purwokerto, there are obstacles to the efficiency of academic services, especially due to information delays and the high burden of onsite services. To overcome this challenge, a Telegram-based chatbot, "SiAkif," was developed using the Large Language Model (LLM) model from Gemini AI. Gemini AI's selection is based on its ability to understand complex conversational contexts and generate accurate and relevant responses. This research aims to implement the Telegram chatbot that utilizes Gemini AI for Indonesian-language academic services. The implementation showed satisfactory results, with the chatbot "SiAkif" recording an average BLEU score of 0.88, which reflects good performance and response. This chatbot effectively reduces information delays, expands service accessibility, and improves student experience in interacting with institutions. Through "SiAkif," the institution is expected to strengthen the interaction between students and academic services, making it a potential solution for digital transformation in education.
Catherine L. Jan, Sanil Joseph, Algis J. Vingrys
et al.
Abstract There are no prospective clinical studies evaluating artificial intelligence implementation for glaucoma detection in real-world settings. We developed an automated retinal photography and AI-based screening system and prospectively assessed its accuracy, feasibility, and acceptability in Australian general practice (GP) clinics. Adults aged 50 years or older were recruited during routine GP visits, with retinal images captured using an automated fundus camera and analysed by the AI system for glaucoma risk classification. Of 414 participants, 277 (66.9%) had analysable images, with a total of 483 eyes included. The AI system achieved an AUROC of 0.80, sensitivity of 65.0%, and specificity of 94.6%. Among 161 previously undiagnosed patients, 18 (11.2%) were identified as referable glaucoma. Patient feedback was positive, and clinic staff supported AI-assisted screening to enhance glaucoma care. Despite challenges such as lower sensitivity and image acquisition limitations, the system shows promise for opportunistic screening in primary care settings.
Computer applications to medicine. Medical informatics
Cherelle Atallah, Ayline El Abiad, Marita El Abiad
et al.
Human skin microbiome plays a fundamental role in maintaining skin health, immunity, and appearance. While current microbiome-friendly cosmetics emphasize the use of probiotics and prebiotics, recent advances in bioengineering are paving the way for a new generation of personalized and sustainable skincare solutions. This evolution is increasingly necessary given the limitations of conventional dermatological treatments in addressing individual variability. Emerging technologies such as artificial intelligence (AI), synthetic biology, and high-throughput microbiome sequencing now enable precise skin analysis and the development of tailored, more effective cosmetic formulations. This review critically examines these technological breakthroughs, including genetic modification of microbial strains, engineered delivery systems, and quorum sensing modulation, with a focus on their cosmetic and therapeutic applications. These innovations not only facilitate product customization but also reduce environmental impact by minimizing resource use, synthetic chemicals, and testing burdens aligning with sustainability goals. Several structured tables synthesize the latest findings on microbial targets, bioengineered ingredients, delivery platforms, and mechanistic pathways, providing a practical reference for researchers and product developers. Additionally, this review addresses key regulatory and safety considerations, particularly those associated with genetically modified organisms (GMOs) in cosmetic products. It highlights the need for region-specific clinical trials, toxicity assessments, and microbial safety screening to ensure consumer protection. While current frameworks offer a foundation, further ethical and environmental guidelines may be necessary as synthetic biology advances. Thus, AI-integrated synthetic biology and microbiome transplantation emerge as transformative pathways for advancing sustainable, personalized skincare innovation.
Marta Miškufová, Martina Košíková, Petra Vašaničová
et al.
The digital economy, driven by innovative technologies and artificial intelligence (AI), is transforming economic systems and increasing the demand for accurate assessments of digital competitiveness. This study addresses the inconsistencies in country rankings derived from global digital indices and aims to determine whether these rankings differ due to methodological variations. It also examines whether the rankings correlate significantly across different evaluation frameworks. The research focuses on 29 European countries and analyzes rankings from four widely recognized indices: the World Digital Competitiveness Ranking (WDCR), Network Readiness Index (NRI), AI Readiness Index (AIRI), and Digital Quality of Life Index (DQLI). To assess the consistency and variability in rankings from 2019 to 2024, the study applies Friedman’s ANOVA and Kendall’s coefficient of concordance. The results demonstrate strong correlations at the level of country rankings, indicating a high degree of consistency, but also confirm statistically significant differences in rankings among the indices, which reflect the diversity of their conceptual foundations. Countries such as Finland, the Netherlands, and Denmark consistently achieve top rankings, indicating convergence, while more variability is observed in indices like the DQLI. These findings highlight the importance of rank-based, multidimensional assessments in evaluating digital competitiveness. They support the use of such assessments as policy tools for monitoring progress, identifying gaps, and promoting inclusive digital development.