Search Results (57)

In the initial phase of large-scale disasters, delayed external relief supplies make scientific local emergency supply allocation crucial—not only for reducing casualties, but also for advancing sustainable disaster response, a key link in enhancing post-disaster resilience. Existing research mostly focuses on cross-regional material allocation while overlooking local challenges like low resource efficiency and unbalanced supply–demand dynamics. To tackle these limitations in the existing research, this study develops a multi-objective collaborative local emergency supply allocation model centered on sustainability. It uses an improved TOPSIS method to quantify the urgency of needs in disaster-stricken areas, prioritizing material distribution to vulnerable regions in line with the principle of “no vulnerable area left neglected in relief efforts”. The study also integrates the entropy weight method and analytic hierarchy process (AHP) to ensure rational indicator weighting, and designs a double-layer encoded genetic algorithm to obtain optimal allocation schemes that balance efficiency, fairness, and sustainability. Validated using the 2013 Ya’an Earthquake case study, the model outperforms traditional local allocation approaches: it boosts resource utilization efficiency by reducing material shortage rates, accelerates post-disaster recovery by shortening response times, and improves allocation fairness. Findings provide empirical support for the establishment of “local–external” collaborative rescue systems and sustainable disaster risk reduction frameworks. Empirical calculations using case-specific data and real-world estimates verify the model’s practical applicability: it meets the requirements for fair and rapid allocation needs, aligns with the goals of sustainable disaster management, and lowers the carbon footprint of relief operations by lessening reliance on long-distance external materials. Full article

Machine Learning and Knowledge Extraction have evolved from algorithmic tools for pattern recognition into a unifying foundational scientific framework underpinning virtually all of today’s groundbreaking advances, enabling systematic discovery, interpretation and understanding across domains. This paper introduces a comprehensive research agenda that defines currently the future of innovation in Artificial Intelligence. We identify ten interrelated research frontiers that collectively map the transition from data-driven learning to knowledge-centric, trustworthy, and sustainable intelligence. These frontiers span the full spectrum of future AI research: from physics-informed and hybrid architectures that embed causality and domain knowledge, to multimodal and embedded intelligence that ground AI in real-world contexts; from interpretable and responsible design principles that ensure transparency and fairness, to safe and sustainable deployment in open-world environments. Together, these directions delineate a coherent roadmap toward AI systems that not only predict but also explain, reason, and collaborate. Future AI can be seen as a new member of your research lab, an active participant in knowledge creation, driven by interdisciplinary integration, global cooperation, ethical responsibility, and human oversight. By embedding principles of transparency, sustainability, and societal alignment from the outset, we envision AI as both a catalyst for scientific discovery and a cornerstone of responsible technological progress. Full article

The increasing availability of free-text components in electronic medical records (EMRs) offers unprecedented opportunities for machine learning research, enabling improved disease phenotyping, risk prediction, and patient stratification. However, the use of narrative clinical data raises distinct ethical challenges that are not fully addressed by conventional frameworks for structured data. We conducted a narrative review synthesizing conceptual and empirical literature on ethical issues in free-text EMR research, focusing on privacy, fairness, autonomy, interpretability, and governance. We examined technical methods, including de-identification, differential privacy, bias mitigation, and explainable AI, alongside normative approaches, such as participatory design, dynamic consent models, and multi-stakeholder governance. Our analysis highlights persistent risks, including re-identification, algorithmic bias, and inequitable access, as well as limitations in current regulatory guidance across jurisdictions. We propose ethics-by-design principles that integrate ethical reflection into all stages of machine learning research, emphasize relational accountability to patients and stakeholders, and support global harmonization in governance and stewardship. Implementing these principles can enhance transparency, trust, and social value while maintaining scientific rigor. Ethical integration is therefore not optional but essential to ensure that machine learning research using free-text EMRs aligns with both clinical relevance and societal expectations. Full article

Data quality is fundamental to scientific integrity, reproducibility, and evidence-based decision-making. Nevertheless, many datasets lack transparency in their collection and curation, undermining trust and reusability across research domains. This narrative review synthesizes scientific and technical literature published between 1996 and 2025, complemented by international standards (ISO/IEC 25012, ISO 8000), to provide an integrated overview of data quality frameworks, governance, and ethical considerations in the era of Artificial Intelligence (AI). Sources were retrieved from PubMed, Scopus, Web of Science, and grey literature. Across sectors, accuracy, completeness, consistency, timeliness, and accessibility consistently emerged as universal quality dimensions. Evidence from healthcare, business, and public administration suggests that poor data quality leads to substantial financial losses, operational inefficiencies, and erosion of trust. Emerging frameworks are increasingly integrating FAIR principles (Findability, Accessibility, Interoperability, Reusability) and incorporating ethical safeguards, including bias mitigation in AI systems. Data quality is not solely a technical issue but a socio-organizational challenge that requires robust governance and continuous assurance throughout the data lifecycle. Embedding quality and ethical governance into data management practices is crucial for producing trustworthy, reusable, and reproducible data that supports sound science and informed decision-making. Full article

For more than a decade (2014–2025), the Consortium “3D for Digital Humanities” has been advancing the use of 3D technologies in the Humanities and Social Sciences (HSS) while structuring and supporting the research community. It now brings together more than 30 teams, primarily from academic research, but also increasingly from the cultural sector. Under its coordination, significant achievements have been realized, including best-practice guides, an infrastructure for the publication of 3D data, and dedicated software for documentation, dissemination, and archiving, as well as a metadata schema, all fully aligned with FAIR principles. The Consortium has developed national training programs, particularly on metadata and ethical practices, and contributed to important initiatives such as the reconstruction of Notre-Dame de Paris, while actively engaging in European projects. It has also fostered international collaborations to broaden perspectives, share methodologies, and amplify impacts. Looking ahead (2025–2033), the Consortium aims to address the environmental impact of 3D data production and storage by proposing best practices for digital sustainability and efficiency. It is also expanding the National 3D Data Repository, enhancing interoperability, and adopting emerging standards to meet evolving scientific needs. Building on its past achievements, the Consortium intends to further advance 3D research and its applications across disciplines, positioning 3D data as a key component of future scientific data clouds. Full article

Artificial intelligence (AI) is profoundly transforming heritage tourism through the incorporation of technological solutions that reconfigure the ways in which cultural heritage is conserved, interpreted, and experienced. This article presents a critical and systematic review of current AI applications in this field, with a special focus on their impact on destination management, the personalization of tourist experiences, universal accessibility, and the preservation of both tangible and intangible assets. Based on an analysis of the scientific literature and international use cases, key technologies such as machine learning, computer vision, generative models, and recommendation systems are identified. These tools enable everything from the virtual reconstruction of historical sites to the development of intelligent cultural assistants and adaptive tours, improving the visitor experience and promoting inclusion. This study also examines the main ethical, technical, and epistemological challenges associated with this transformation, including algorithmic surveillance, data protection, interoperability between platforms, the digital divide, and the reconfiguration of heritage knowledge production processes. In conclusion, this study argues that AI, when implemented in accordance with principles of responsibility, sustainability, and cultural sensitivity, can serve as a strategic instrument for ensuring the accessibility, representativeness, and social relevance of cultural heritage in the digital age. However, its effective integration necessitates the development of sector-specific ethical frameworks, inclusive governance models, and sustainable technological implementation strategies that promote equity, community participation, and long-term viability. Furthermore, this article highlights the need for empirical research to assess the actual impact of these technologies and for the creation of indicators to evaluate their effectiveness, fairness, and contribution to the Sustainable Development Goals. Full article

Over the past twenty years, significant efforts have been made to digitize natural collections. This process represents a crucial step in preserving and enhancing biodiversity data. In this context, the phycology team from the Institute for Water Research (CNR-IRSA) in Taranto (southern Italy), as a partner of the NRRP Project ITINERIS, and within the nascent European Research Infrastructure “Distributed System of Scientific Collections” (DiSSCo), answered to the challenge of digitizing and sharing the extensive biodiversity data preserved in the marine macrophyte collection Herbarium TAR. This herbarium includes over 500 species collected between 1982 and 2025. Digitization was carried out in accordance with international standards for imaging and in compliance with FAIR principles for metadata curation. A total of 353 digital specimens were produced, including 152 species of seaweeds (76 Rhodophyta, 47 Heterokontophyta, and 29 Chlorophyta) and 3 species of Spermatophyta. Notably, 15 non-indigenous species were documented. Part of the metadata, structured using the Darwin Core standard, has been published on GBIF. This initiative, carried out within the ITINERIS framework, highlights the value of both long-term biodiversity monitoring and digital data in supporting research on climate change, biological invasions, and the conservation of marine ecosystems. Full article

The United Arab Emirates (UAE) has a long history and tradition in fishing, yet its role in regional tuna management remains yet to be fully defined. This is the case specifically of tuna species, such as yellowfin, which are highly migratory and require coordinated efforts in the context of a corresponding international governance framework, particularly in ecologically important areas like the Northern Indian Ocean and the Sea of Oman. Data collection and species identification present significant complexities for these species, yet accuracy is crucial for effective conservation and fair allocation of management shares. Although UAE fisheries are partly within the area of competence of the Indian Ocean Tuna Commission (IOTC), the country has only recently begun to give consideration to the process toward participating in this Regional Fisheries Management Organisation (RFMO) which, in turn, would provide for the relevant governance framework for the species examined in this paper. This paper explores the factors behind these developments and assesses their implications for regional tuna management. Based on scientific sampling, we developed estimates of past landing volumes and propose mechanisms for ensuring data collection instrumental to an informed participation by the UAE in the regional tuna management framework under the IOTC. Finally, we explored the implications that this development would have under public international law, departing from the traditional principle “ex facto oritur ius” (Latin: the law arises from facts), which embodies the notion that certain legal consequences attach to particular developments. With regard to the specific developments being addressed by this paper, there could be certain legal consequences for UAE; following the reconstruction of landings and the enhancement of international datasets, we postulate that there would be legal ground for UAE to exercise historical fishing rights and seek a potential allocation of quotas within the framework of IOTC. Full article

The creation of the LitUCA lithotheque represents a significant methodological advance in geoarchaeological research in the southwest of Spain. This article presents a systematic framework for the conservation, documentation, and digital integration of lithic collections, with particular emphasis on data traceability, reproducibility, and interoperability. The methodology adopted is inspired by international standards, adapted to the regional context, and incorporates rigorous protocols for sampling, analytical documentation, and a relational database system. The collection comprises over 5000 items, all of which are catalogued, photographed, and characterised both petrographically and morphometrically, with metadata being progressively aligned with FAIR principles, aiming for full compliance in the future. Preliminary analysis demonstrates the collection’s capacity to facilitate comparative studies of procurement, mobility, and lithic technological organisation. Furthermore, the digital infrastructure developed promotes remote access and fosters both academic and societal collaboration. Despite ongoing challenges regarding sample representativeness and interoperability, LitUCA stands as a scalable and versatile model for the management of lithotheques. This study highlights the importance of integrated lithotheques for scientific progress, heritage management, and interdisciplinary education. Full article

The construction industry has been troubled by a shortage of skilled labor and safety accidents in recent years. Therefore, more and more robots are introduced to undertake dangerous and repetitive jobs, so that human workers can concentrate on higher-value and creative problem-solving tasks. Nevertheless, although human–robot collaboration (HRC) shows great potential, most existing evaluation methods still focus on the single performance of either the human or robot, and systematic indicators for a whole HRC team remain insufficient. To fill this research gap, the present study constructs a comprehensive evaluation framework for HRC team performance in construction projects. Firstly, a detailed literature review is carried out, and three theories are integrated to build 33 indicators preliminarily. Afterwards, an expert questionnaire survey (N = 15) is adopted to revise and verify the model empirically. The survey yielded a Cronbach’s alpha of 0.916, indicating excellent internal consistency. The indicators rated highest in importance were task completion time (µ = 4.53) and dynamic separation distance (µ = 4.47) on a 5-point scale. Eight indicators were excluded due to mean importance ratings falling below the 3.0 threshold. The framework is formed with five main dimensions and 25 concrete indicators. Finally, an AHP-TOPSIS method is used to evaluate the HRC team performance. The AHP analysis reveals that Safety (weight = 0.2708) is prioritized over Productivity (weight = 0.2327) by experts, establishing a safety-first principle for successful HRC deployment. The framework is demonstrated through a case study of a human–robot plastering team, whose team performance scored as fair. This shows that the framework can help practitioners find out the advantages and disadvantages of HRC team performance and provide targeted improvement strategies. Furthermore, the framework offers construction managers a scientific basis for deciding robot deployment and team assignment, thus promoting safer, more efficient, and more creative HRC in construction projects. Full article

A vast proportion of scientific data remains locked behind dynamic web interfaces, often called the deep web—inaccessible to conventional search engines and standard crawlers. This gap between data availability and machine usability hampers the goals of open science and automation. While registries like FAIRsharing offer structured metadata describing data standards, repositories, and policies aligned with the FAIR (Findable, Accessible, Interoperable, and Reusable) principles, they do not enable seamless, programmatic access to the underlying datasets. We present FAIRFind, a system designed to bridge this accessibility gap. FAIRFind autonomously discovers, interprets, and operationalizes access paths to biological databases on the deep web, regardless of their FAIR compliance. Central to our approach is the Deep Web Communication Protocol (DWCP), a resource description language that represents web forms, HyperText Markup Language (HTML) tables, and file-based data interfaces in a machine-actionable format. Leveraging large language models (LLMs), FAIRFind combines a specialized deep web crawler and web-form comprehension engine to transform passive web metadata into executable workflows. By indexing and embedding these workflows, FAIRFind enables natural language querying over diverse biological data sources and returns structured, source-resolved results. Evaluation across multiple open-source LLMs and database types demonstrates over 90% success in structured data extraction and high semantic retrieval accuracy. FAIRFind advances existing registries by turning linked resources from static references into actionable endpoints, laying a foundation for intelligent, autonomous data discovery across scientific domains. Full article

The fast growth of artificial intelligence during recent years has resulted in its implementation across various sectors. The broad implementation of AI systems has generated substantial ethical issues because AI algorithm decisions can affect basic rights such as privacy, fairness, security and individual autonomy. With these concerns, governments, international organizations, and academic institutions have established guidelines and regulations to ensure that artificial intelligence systems are designed and implemented in a manner that upholds fundamental ethical principles. This work presents the results of a Systematic Literature Review using the PRISMA approach and aims to identify which approaches/methods are the most suitable ones for being used to integrate ethics and trustworthiness into AI tools for industrial engineering applications. Therefore, the review considered 38 pertinent scientific works published between 2019 and the end of August 2024. Full article

A holistic scientific measurement of the internet traffic across all devices in Germany has quantified the extreme extent of digital monopolization. Due to the high level of concentration, provider pluralism and fair competition in the field of digital media have been systematically and intentionally abolished. As a result of the digital transformation, it can be assumed that the GAFA (the known acronym for Google, i.e., Alphabet, Amazon, Facebook, i.e., Meta, Apple) players will take control of the German media system in the coming years (due to comparable market structures, the situation will be similar in other Western democracies). From a German and a European perspective, it is the more alarming that this development can hardly be stopped on the basis of existing legislation. However, already the status quo is in striking contradiction to the anti-monopolistic principles of classic German media law. It is time for a fundamental debate and quick legislative actions to open the media markets again for competition and plurality and safeguard media freedom for the future. Full article

This article introduces a novel hybrid database architecture that combines graph and time-series databases to enhance the storage and management of transportation data, particularly for electric vehicles (EVs). This model addresses a critical challenge in modern mobility: handling large-scale, high-velocity, and highly interconnected datasets while maintaining query efficiency and scalability. By comparing a naive graph-only approach with our hybrid solution, we demonstrate a significant reduction in query response times for large data contexts-up to 64% faster in the XL scenario. The scientific contribution of this research lies in its practical implementation of a dual-layer storage framework that aligns with FAIR data principles and real-time mobility needs. Moreover, the hybrid model supports complex analytics, such as EV battery health monitoring, dynamic route optimization, and charging behavior analysis. These capabilities offer a multiplier effect, enabling broader applications across urban mobility systems, fleet management platforms, and energy-aware transport planning. By explicitly considering the interconnected nature of transport and energy data, this work contributes to both carbon emission reduction and smart city efficiency on a global scale. Full article

The digitization of cultural and intellectual heritage is expanding the research scope and methodologies of the scientific discipline of Humanities. Culturally diverse epigraphic systems reveal a range of methodological impediments on the way to their integration into digital epigraphic data preservation systems—EAGLE and FAIR ontologies predominantly based on Greco-Roman cultural categorization. We suggest an interdisciplinary approach—drawing from Heritage Studies, Cultural Epistemology, and Social Semiotics—to ensure the comprehensive encoding, preservation, and accessibility of at-risk cultural artifacts. Heritage Studies emphasize inscriptions as material reflections of historical memory. Cultural Epistemology helps us to understand how different knowledge systems influence data categorization, while semiotic analysis reveals how inscriptions function within their social and symbolic contexts. Together, these methods guide the integration of culturally specific information into broader digital infrastructures. The case of Ukrainian epigraphy illustrates how this approach can be applied to ensure that local traditions are accurately represented and not flattened by standardized international systems. We argue that the same methodology can also support the digitization of other non-Greco-Roman heritage. FAIR Ontology and EAGLE vocabularies prioritize standardization and interoperability, introducing text mining, GIS mapping, and digital visualization to trace patterns across the vast body of texts from different historical periods. Standardizing valuable elements of cultural categorization and reconstructing and integrating lost or underrepresented cultural narratives will expand the capacity of the above systems and will foster greater inclusivity in Humanities research. Ukrainian epigraphic classification systems offer a unique, granular approach to inscription studies as a worthwhile contribution to the broader cognitive and epistemological horizons of the Humanities. Through a balanced use of specificity and interoperability principles, this study attempts to contribute to epigraphic metalanguage by challenging the monocentric ontologies, questioning cultural biases in digital categorization, and promoting open access to diverse sources of knowledge production. Full article