Conflicts between user preferences and automated system behavior already shape the experience of automated mobility. For example, a passenger may prefer assertive driving, yet the vehicle slows down early to follow a conservative policy or yield to other actors. Similar conflicts arise at merges, crossings, or right-of-way situations, where users must accept opaque decisions or attempt to negotiate through interfaces not designed for continuous, multi-actor relationships. This position paper argues that such approaches do not scale as mobility becomes more heterogeneous and automated. Instead, it proposes personal mobility agents that act as proxies for users, encode preferences such as comfort and safety margins, and negotiate traffic behavior with other agents under shared safety rules. The central idea is a shift from moment-to-moment user negotiation interfaces to delegation and oversight interfaces, in which proxy agents manage real-time conflicts while users can shape high-level policies and preferences.
Brayden Hamilton, Tim Cashmore, Peter Driscoll
et al.
Marine biofouling on vessel hulls poses major ecological, economic, and biosecurity risks. Traditional survey methods rely on diver inspections, which are hazardous and limited in scalability. This work investigates automated classification of biofouling severity on the Level of Fouling (LoF) scale using both custom computer vision models and large multimodal language models (LLMs). Convolutional neural networks, transformer-based segmentation, and zero-shot LLMs were evaluated on an expert-labelled dataset from the New Zealand Ministry for Primary Industries. Computer vision models showed high accuracy at extreme LoF categories but struggled with intermediate levels due to dataset imbalance and image framing. LLMs, guided by structured prompts and retrieval, achieved competitive performance without training and provided interpretable outputs. The results demonstrate complementary strengths across approaches and suggest that hybrid methods integrating segmentation coverage with LLM reasoning offer a promising pathway toward scalable and interpretable biofouling assessment.
This investigation discusses the implications of the two most fundamental counter-assumptions of axiology – normative relativism and anti-relativism (absolutism) – for managing values in the process of responsible research and innovation. The study aims to show how these premises determine the goals and means of this practice. The study argues that these premises lead to fundamentally different conceptions of responsibility, with implications for how they are integrated and operationalised in organisational contexts. The study also seeks to open a metaethical perspective to investigate the normative assumptions of managing values further, thereby exploring the interdependence between value concepts and responsible research and innovation. Furthermore, this research shows how philosophical perspectives enhance communication within value management by revealing its underlying normative assumptions. The study is conceptual, and its method is analytical.
ABSTRACT This study aims to predict hard disk drives (HDDs) that pass initial testing but fail during reliability testing, using historical data from 8968 records with 218 features, such as head position and flying height of the read/write head. Since reliability testing is time‐intensive, early failure prediction can significantly accelerate problem detection and resolution. The research focuses on detecting fly height modulation, a key symptom of HDD failure, and introduces an adaptive machine learning (ML) framework integrating AutoML for optimised model selection and hyperparameter tuning with MLOps for deployment, monitoring and continuous updates. Building on a previously proposed dual‐stage classification framework that combines novelty detection and supervised learning, the proposed framework addresses the inefficiencies of manual hyperparameter tuning inherent in the earlier methods. The proposed framework achieves 92% accuracy in novelty detection and 100% in supervised learning, outperforming prior approaches. This integration of AutoML and MLOps offers a scalable, robust solution for early failure prediction, enabling real‐time adaptability with minimal human intervention. Future work will focus on enhancing computational efficiency and responsiveness to data shifts and drifts, advancing data‐driven decision‐making in reliability testing.
Munjung Kim, Marios Constantinides, Sanja Šćepanović
et al.
The rapid rise of AI is poised to disrupt the labor market. However, AI is not a monolith; its impact depends on both the nature of the innovation and the jobs it affects. While computational approaches are emerging, there is no consensus on how to systematically measure an innovation's disruptive potential. Here, we calculate the disruption index of 3,237 U.S. AI patents (2015-2022) and link them to job tasks to distinguish between "consolidating" AI innovations that reinforce existing structures and "disruptive" AI innovations that alter them. Our analysis reveals that consolidating AI primarily targets physical, routine, and solo tasks, common in manufacturing and construction in the Midwest and central states. By contrast, disruptive AI affects unpredictable and mental tasks, particularly in coastal science and technology sectors. Surprisingly, we also find that disruptive AI disproportionately affects areas already facing skilled labor shortages, suggesting disruptive AI technologies may accelerate change where workers are scarce rather than replacing a surplus. Ultimately, consolidating AI appears to extend current automation trends, while disruptive AI is set to transform complex mental work, with a notable exception for collaborative tasks.
Angelos Alexopoulos, Agorakis Bompotas, Nikitas Rigas Kalogeropoulos
et al.
Modern manufacturing demands high flexibility and reconfigurability to adapt to dynamic production needs. Model-based Engineering (MBE) supports rapid production line design, but final reconfiguration requires simulations and validation. Digital Twins (DTs) streamline this process by enabling real-time monitoring, simulation, and reconfiguration. This paper presents a novel platform that automates DT generation and deployment using AutomationML-based factory plans. The platform closes the loop with a GAI-powered simulation scenario generator and automatic physical line reconfiguration, enhancing efficiency and adaptability in manufacturing.
Implementing and deploying advanced technologies are principal in improving manufacturing processes, signifying a transformative stride in the industrial sector. Computer vision plays a crucial innovation role during this technological advancement, demonstrating broad applicability and profound impact across various industrial operations. This pivotal technology is not merely an additive enhancement but a revolutionary approach that redefines quality control, automation, and operational efficiency parameters in manufacturing landscapes. By integrating computer vision, industries are positioned to optimize their current processes significantly and spearhead innovations that could set new standards for future industrial endeavors. However, the integration of computer vision in these contexts necessitates comprehensive training programs for operators, given this advanced system’s complexity and abstract nature. Historically, training modalities have grappled with the complexities of understanding concepts as advanced as computer vision. Despite these challenges, computer vision has recently surged to the forefront across various disciplines, attributed to its versatility and superior performance, often matching or exceeding the capabilities of other established technologies. Nonetheless, there is a noticeable knowledge gap among students, particularly in comprehending the application of Artificial Intelligence (AI) within Computer Vision. This disconnect underscores the need for an educational paradigm transcending traditional theoretical instruction. Cultivating a more practical understanding of the symbiotic relationship between AI and computer vision is essential. To address this, the current work proposes a project-based instructional approach to bridge the educational divide. This methodology will enable students to engage directly with the practical aspects of computer vision applications within AI. By guiding students through a hands-on project, they will learn how to effectively utilize a dataset, train an object detection model, and implement it within a microcomputer infrastructure. This immersive experience is intended to bolster theoretical knowledge and provide a practical understanding of deploying AI techniques within computer vision. The main goal is to equip students with a robust skill set that translates into practical acumen, preparing a competent workforce to navigate and innovate in the complex landscape of Industry 4.0. This approach emphasizes the criticality of adapting educational strategies to meet the evolving demands of advanced technological infrastructures. It ensures that emerging professionals are adept at harnessing the potential of transformative tools like computer vision in industrial settings.
Lights-out factories represent the pinnacle of manufacturing advancement, utilizing automation, digitalization, and intelligent technologies for unmanned production. These factories employ advanced tools such as process control systems, smart sensors, and industrial robots to enhance efficiency. Constructing lights-out factories is complex, involving multiple disciplines, and there is a lack of comprehensive literature on the subject, posing challenges for interested companies. This paper presents a lights-out factory architecture diagram and reviews key literature on the topic, summarizing relevant technologies and their social impact. The lights-out factory model can help enterprises reduce costs, increase efficiency, improve production quality, enhance safety, and promote high-quality development. However, challenges remain, including high costs, reliability issues, personnel requirements, applicability concerns, and technological hurdles. Future advancements in sensor technology, digital twin technology, and other innovations will drive further development of lights-out factories.
Artificial Intelligence (AI) is revolutionising content marketing by automating key processes and enabling personalisation on a massive scale. Using technologies such as Machine Learning and Natural Language Processing, AI can generate content quickly, identify SEO-relevant keywords and improve campaign performance. It analyses user behaviour, anticipates their needs and optimises marketing strategies in real time. However, this automation poses ethical challenges, such as algorithmic bias and over-reliance on technology. To exploit these tools responsibly, it is crucial that companies adopt ethical and transparent practices, respecting data confidentiality rules. In the future, AI will continue to transform marketing, with innovations such as virtual agents and even more personalised strategies, while requiring a balance between technological efficiency and human authenticity.
This comprehensive review explores the advancements and challenges in the field of Artificial Intelligence (AI) applications across various industries. With rapid technological progress, AI has transformed domains such as healthcare, finance, education, and manufacturing, driving innovations in automation, data analysis, and decision-making. This examines the Important and significant AI technologies, including machine learning, natural language processing, and computer vision, highlighting their contributions to enhancing efficiency, accuracy, and problem-solving capabilities, these advancements, several challenges persist, such as ethical concerns, bias in AI systems, data privacy issues, and the need for robust regulatory frameworks. Additionally, the complexity of integrating AI into existing systems and workforce displacement due to automation are critical barriers to widespread adoption and provides a balanced perspective on both the potential and limitations of AI, offering insights into future research directions and strategies for addressing these challenges to ensure the sustainable and ethical development of AI technologies.
Abstract The present article studies the importance of new technologies connected with today’s business environment. From small businesses to corporations, companies are adapting to new high-tech solutions to increase efficiency, productivity, and customer engagement. The increasing use of artificial intelligence, automation, and other digital media is changing the way businesses are conducted. One of the most significant advantages of technology in business is efficiency. Process automation and the use of advanced software can help companies to reduce costs and to optimize their operations. Technology can also improve the quality of products and services through the use of digital testing and quality control tools. Technological innovations such as artificial intelligence, blockchain, and the Internet of Things have made process automation possible and given companies new ways to manage their operations and expand their businesses. The authors highlighted the latest literature related to the analyzed fields, and at the same time, VOSviewer was used as a reinforcement and literature’ tool for the chosen keywords.
Global economic growth is closely tied to energy consumption, with fossil fuels still dominating the global energy mix. The transportation sector, a major energy consumer, significantly impacts global primary energy demand and CO₂ emissions. This paper critically examines the Discourse surrounding technological innovations in transportation and their potential to reduce energy consumption. Using a critical Discourse analysis (CDA) framework, we explore the promotion of technologies such as intelligent automation, self-driving vehicles, and digitalization in transportation. The study highlights a techno-optimistic narrative that often overlooks broader structural challenges, unintended consequences, and the need for systemic changes, including policy innovation and behavior modification. Although technological advancements offer potential efficiency gains, their impact on energy consumption depends on implementation, governance, and socio-economic context. This paper argues for a holistic approach, combining technological innovation with non-technological strategies to achieve sustainability in the transportation sector. Through case studies such as high-speed rail, ride-sharing, and automated vehicles, it examines how policies, user behavior, and structural barriers influence energy use. Findings stress the importance of adaptive governance and inclusive public engagement to foster sustainable, equitable mobility transitions in the evolving transport landscape.
The purpose of the article to analyze the role of technological innovations in improving public financial management, identify barriers to their implementation and offer recommendations for the formation of an effective organizational and legal framework. The author identifies various types of innovations and explains their importance in ensuring the efficiency of public funds management. However, the author pays the main attention to technological innovations. Thus, electronic public procurement platforms, big data analytics, artificial intelligence and robotic process automation were highlighted. The article demonstrates their impact on the transparency, efficiency and accountability of the use of public resources. In addition, the article analyzes specific examples of countries that are already applying relevant innovations and qualitative statistical indicators of their importance for the budget sector. By analyzing successful foreign cases, the most effective models of integrating innovations into public institutions were identified. The author's approach in this paper involves not only describing and analyzing individual successful cases, but also identifying organizational and legal barriers. Author also finds specific ways to overcome negative elements. The proposed recommendations include a roadmap, staff training and regulatory framework improvement. Taking into account the specifics of legislative systems and the capabilities of the national economy, they can be adapted to different countries and regions. In conclusion, the article substantiates that the systematic implementation of innovative technologies can not only increase the efficiency of budget processes and reduce corruption risks, but also contribute to the formation of a new management culture focused on transparency and effectiveness.
Rapid technological advances have produced major disruption in the leather industry in recent decades. The research considers developments in the market, issues related to the environment, enhanced quality, historical viewpoints, and recent breakthroughs. The fundamental foundation is the historical context of leather manufacture. Traditional techniques, which are defined by human labor and artisanal skills, have increasingly been displaced by sophisticated technology. Early initiatives were the first to bring mechanization, which marked a shift in production paradigms. Automation and robotics innovations have further altered the industry all throughout time, having a significant influence on the labor force, improving productivity, and ensuring process accuracy. Furthermore, advancements in lab-grown leather provide promise for a time when animal-derived leather may not be necessary. The environmental consequences of technology breakthroughs are considerable. The implementation of sustainable methods has reduced the industry's total environmental effect. Initiatives to decrease trash and recycle have grown in popularity, strengthening the business's dedication to environmentally friendly practices. Furthermore, the leather industry is currently driven by technological innovation with the goal to meet stringent environmental regulations. Finally, technological advances have irreversibly altered the landscape that includes the leather industry.
Background It is currently observed that some landfills are experiencing severe overloading, with some having ceased operations. However, they continue to threaten the environment and public health. There is an urgent need for governance, although the process is complex and requires more intelligent and efficient governance approaches. Methods This study explored the application of digital twin technology based on multi-agent systems in the environmental governance of abandoned landfills. This paper addresses the demands of landfill governance by integrating modules, including twin models, mechanisms, and big data, and integrating each module with corresponding intelligent agents, forming a thoughtful, collaborative, and adaptive digital twin agent system. Results This method can collect and analyze on-site data more systematically and provide feedback to management personnel to guide the adjustment of on-site plans and improve the on-site management efficiency by 30%. Conclusions Through application cases, the operation process of this system in specific landfill environmental governance scenarios was demonstrated, confirming its superiority in environmental governance. This system can facilitate environmental monitoring, intelligent analysis, and decision control during the governance of abandoned landfills.
We study of millions of scientific, technological, and artistic innovations and find that the innovation gap faced by women is far from universal. No gap exists for conventional innovations. Rather, the gap is pervasively rooted in innovations that combine ideas in unexpected ways - innovations most critical to scientific breakthroughs. Further, at the USPTO we find that female examiners reject up to 33 percent more unconventional innovations by women inventors than do male examiners, suggesting that gender discrimination weakly explains this innovation gap. Instead, new data indicate that a configuration of institutional practices explains the innovation gap. These practices compromise the expertise women examiners need to accurately assess unconventional innovations and then "over-assign" women examiners to women innovators, undermining women's innovations. These institutional impediments negatively impact innovation rates in science but have the virtue of being more amenable to actionable policy changes than does culturally ingrained gender discrimination.
This study introduces a novel software tool leveraging large language model (LLM) prompts, designed to automate the generation of academic articles from Python code a significant advancement in the fields of biomedical informatics and computer science. Selected for its widespread adoption and analytical versatility, Python served as a foundational proof of concept; however, the underlying methodology and framework exhibit adaptability across various GitHub repo's underlining the tool's broad applicability (Harper 2024). By mitigating the traditionally time-intensive academic writing process, particularly in synthesizing complex datasets and coding outputs, this approach signifies a monumental leap towards streamlining research dissemination. The development was achieved without reliance on advanced language model agents, ensuring high fidelity in the automated generation of coherent and comprehensive academic content. This exploration not only validates the successful application and efficiency of the software but also projects how future integration of LLM agents which could amplify its capabilities, propelling towards a future where scientific findings are disseminated more swiftly and accessibly.
This study investigates the controversial role of Intellectual Property Rights (IPRs) in climate technology transfer and innovation in developing countries. Using a systematic literature review and expert interviews, we assess the role of IPRs on three sources of climate technology: (1) international technology transfer, (2) adaptive innovation, and (3) indigenous innovation. Our contributions are threefold. First, patents have limited impact in any of these channels, suggesting that current debates over IPRs may be directed towards the wrong targets. Second, trademarks and utility models provide incentives for climate innovation in the countries studied. Third, drawing from the results, we develop a framework to guide policy on how IPRs can work better in the broader context of climate and trade policies, outlining distinct mechanisms to support mitigation and adaptation. Our results indicate that market mechanisms, especially trade and demand-pull policies, should be prioritised for mitigation solutions. Adaptation differs, relying more on indigenous innovation due to local needs and low demand. Institutional mechanisms, such as finance and co-development, should be prioritised to build innovation capacities for adaptation.