Search Results (843)

Intelligent oilfield development is experiencing an increasingly deep integration between localized automation and integrated, data-centric ecosystems. To systematically delineate the knowledge structure and technological trajectories within this field, this study analyzes 225 high-quality publications. This study innovatively employs a custom toolchain based on the Dart language for heterogeneous data cleaning and standardization, ensuring high accuracy and scientific rigor in the analysis samples. The investigation reveals a distinct dual-core driving mechanism underpinning recent advancements: a cognitive cluster centered on Artificial Intelligence and Deep Learning for complex data interpretation and prediction, and a decision-making cluster focused on Operational Optimization and Predictive Modeling for production enhancement. These two clusters respectively encompass eight sub-clusters: “artificial intelligence,” “machine learning,” “deep learning,” “performance,” “enhanced oil recovery,” “model,” “optimization,” and “predication.” This dual-core framework signifies a paradigm shift from experience-based practices to a synergistic “AI-enabled + mathematical optimization” approach. The analysis further explores emerging trends, including the potential of deep reinforcement learning for dynamic decision-making and the critical role of cybersecurity and model robustness in safety risk management. By mapping the current landscape and core mechanisms, this study provides a foundational reference for researchers and practitioners to navigate the future development of intelligent oilfields towards more resilient and efficient ecosystems. Full article

Artificial Intelligence (AI) has transitioned from a specialized research area to a ubiquitous socio-technical infrastructure influencing sectors from healthcare and law to manufacturing and defense. In tandem with its transformative promise, AI has created an exponentially expanding ethics literature questioning, fairness, transparency, accountability, and justice. This review synthesizes publications and key policy developments between 2019 and 2025, bringing sectoral discourses together with cross-cutting frameworks. Grounded in a systematic scoping review methodology, we frame the field along four meta-dimensions: trust and transparency, bias and fairness, governance & regulation, and justice, while we investigate their expression across diverse sectors. Special attention is dedicated to healthcare (patient trust and algorithmic bias), education (integrity and authorship), media (misinformation), law (accountability), and the industrial sector (data integrity, intellectual property protection, and environmental safety). We ground abstract principles in concrete case studies to illustrate real-world harms and mitigation strategies. Furthermore, we incorporate pluralistic ethics (e.g., Ubuntu, Islamic perspectives), environmental ethics, and emerging challenges posed by Generative AI and neuro-AI interfaces. To bridge theory and practice, we propose an operational governance framework for organizations. We contend that success involves transitioning from principles toward ethics-by-design, pluralistic governance, sustainability, and adaptive oversight. This review is intended for scholars, practitioners, and policymakers who need a comprehensive and actionable framework for navigating the complex landscape of AI ethics. Full article

Although artificial intelligence (AI) capabilities have emerged as a critical driver of corporate innovation in the contemporary business landscape, how they facilitate ambidextrous green innovation (AGI) during the manufacturing sector’s green transition—and under what conditions these benefits are most pronounced—remains unclear. Drawing on the Resource-Based View (RBV) and Knowledge-Based View (KBV), this study investigates the mechanism by which AICs foster AGI through the mediating role of green knowledge management (GKM), while further examining how Human–Organization–Technology (HOT) fit moderates these pathways. An analysis of survey data from 238 Chinese manufacturing firms using PLS-SEM reveals that AICs significantly drive AGI, with GKM playing a pivotal mediating role. Furthermore, the study confirms that Human–Organization–Technology (HOT) fit acts as a boundary condition, moderating the impact of AICs on GKM. These findings clarify the underlying mechanisms and boundary conditions of AICs, offering actionable insights for manufacturers seeking to boost green innovation capabilities by optimizing HOT alignment and leveraging green knowledge management systems. Full article

Radiation-responsive promoters represent a functionally distinct class of transcriptional regulatory elements that translate genotoxic stress signals into quantifiable gene expression outputs. These promoters occupy a unique mechanistic position within the broader radiation biomarker landscape: rather than directly measuring molecular damage products, they report the cellular interpretation of radiation-induced stress through coordinated gene regulatory networks. This review provides a systematic analysis of five major classes of radiation-responsive promoters—microRNA (miRNA) promoters, tRNA-derived small RNA (tsRNA) promoters, acute-phase protein gene promoters, DNA repair gene promoters, and long non-coding RNA (lncRNA) promoters—with emphasis on their regulatory logic, dose-response characteristics, and current evidence for clinical deployment. We further describe four complementary screening strategies: homology-based conservation analysis, functional genomics and transcriptomics, epigenetic modification profiling, and synthetic biology promoter engineering. Applications spanning biosensor development, biological dosimetry, treatment response prediction, and radiation-guided gene therapy are evaluated within a two-track framework that distinguishes biomarker-oriented applications (Track A) from tool-oriented reporter gene systems (Track B). Critical appraisal of current limitations—including insufficient clinical-grade validation, absence of standardized dose-response curves, and reproducibility deficits—is integrated throughout. Future priorities include multi-center prospective validation studies, FAIR-compliant data infrastructure, AI-driven multi-omics integration, and point-of-care detection platforms. Radiation-responsive promoter biology holds significant potential for advancing precision radiotherapy and nuclear emergency medical response, contingent upon systematic closure of the current evidence gap relative to established gold-standard cytogenetic methods. Full article

Mapping land cover, monitoring its changes, and simulating future alterations are essential tasks for sustainable land management. These processes enable accurate assessment of environmental impacts, support informed policymaking, and assist in the planning needed to mitigate risks related to urban expansion, deforestation, and climate change. This study proposes a GeoAI-based framework leveraging Multilayer Perceptron (MLP), a class of Artificial Neural Networks (ANNs), to predict land cover changes in the Aosta Valley region (NW Italy). The model uses Copernicus Earth Observation data, specifically Sentinel-1 and Sentinel-2 imagery, and is trained and validated on land cover maps derived from different time periods previously validated with ground truth data. The objective is to provide a predictive tool capable of simulating potential future landscape configurations, supporting proactive regional land use planning including regulatory constraints under the current land use plan. Model performance is evaluated using accuracy metrics. The land cover classification methodology follows established approaches in the scientific literature, adapted to the specific geomorphological characteristics of the Aosta Valley. To explore and visualize potential future land cover transitions, Sankey and chord diagrams are used in combination with zonal statistics and thematic plots. These provide detailed insights into the intensity, direction, and magnitude of landscape dynamics. Training data were stratified-sampled across the study area, covering a diverse set of land cover classes to ensure robustness and generalization of the MLP model. This GeoAI approach offers a scalable and replicable methodology for anticipating land cover dynamics, identifying vulnerable areas, and informing adaptive environmental management strategies at the regional scale, while simultaneously considering the latest urban planning regulations. Full article

Clarifying the relationship between landscape patterns and runoff coefficient, along with identifying key influencing pathways, is crucial for formulating sustainable water resource management strategies. Since the launch of the Grain-for-Green (GfG) project in 1999, the landscape pattern of the Loess Plateau has been profoundly reshaped, altering regional rainfall-runoff processes. Assessment across 27 catchments selected in the central Loess Plateau demonstrated forest and grassland areas expanded by 738.8 km² and 480.4 km², respectively, paralleled by a 20.1% enhancement in vegetation coverage. Correspondingly, surface runoff decreased by 28.1–90.6% in the 2000s and 12.8–95.5% in the 2010s compared to the 1960s, with a similar decline in runoff coefficient. This study further developed a novel landscape unit mapping method, integrating vegetation coverage, land use, slope, and soil type to compute landscape metrics. Partial least squares regression (PLSR) and piecewise structural equation modeling (piecewiseSEM) were constructed to systematically analyze the linkage between landscape patterns and surface runoff. The constructed landscape metrics explained 64.6% of the variance in the runoff coefficient, with perimeter area fractal dimension (PAFRAC), mean perimeter-area ratio (PARA_MN), and aggregation index (AI) exerting significant influence. The findings provide a scientific basis for water resource management in regions with similar environmental characteristics. Full article

Context. The grocery sector is undergoing a massive shift in consumer behavior, with global chatbot usage projected to reach 8.4 billion units by 2024—surpassing the total human population—and online grocery revenue per shopper expected to hit USD 449.00 by 2023. In this competitive landscape, small grocery stores must adopt AI-driven tools to modernize their operations. However, these businesses often face significant inefficiencies in manual inventory management, resulting in errors and reduced competitiveness. Objective. This research aims to develop and validate a chatbot application using Large Language Models and Retrieval-Augmented Generation (RAG) for operational management of grocery stores. Method. The method employed a quantitative experimental approach with a five-component system architecture: a web interface, a FastAPI API, a Mistral-7B-Instruct-v0.2 model, a dynamic SQL generator, and a custom RAG application with an FAISS vector database, all integrated through SQLAlchemy 2.0.40. Results. The results demonstrate that a chatbot achieves an average response time of 0.08 s with 80% overall accuracy, showing a 96.2% improvement in information query time and a 92.9% reduction in operational errors. Conclusions. Major conclusions suggest that the chatbot system is effective for retail environments and has the potential to enhance the operational efficiency of grocery stores, serving as a foundation for future research in applied conversational assistance. Full article

Artificial Intelligence (AI) is increasingly embedded in project management within the financial sector, yet existing research remains fragmented and largely focused on isolated technical applications. A systemic understanding of how AI reshapes financial project management as an integrated socio-technical capability is still lacking. This study addresses this gap through a systematic literature review of 62 peer-reviewed articles (2022–2025), combined with BERTopic-based thematic analysis supported by large language model-assisted topic representation. The findings reveal the emergence of Agentic AI as a dominant theme, marking a shift from analytical support tools toward autonomous and collaborative agents embedded in project processes. While predictive analytics and automation are relatively mature, governance-oriented and human-centric dimensions remain underdeveloped and weakly integrated. This study contributes by: (1) presenting a computationally enhanced systematic mapping study that integrates a systematic literature review with BERTopic-based topic modelling to map the evolving research landscape; (2) identifying Agentic AI as a pivotal interface between technical execution and strategic governance; and (3) proposing a socio-technical target architecture that offers a structured roadmap for AI-enabled transformation in financial project management systems. Full article

In the contemporary digital landscape, AI (Artificial Intelligence) emerged as a pivotal tool in enhancing the defense technologies developed across the entire network infrastructure. As reliance on AI-based decision-making grew, so did the imperative need for interpretability, transparency, and trustworthiness, leading to the development and integration of XAI (eXplainable Artificial Intelligence). This research paper provides a comprehensive overview of the current state of the art in XAI approaches that can be effectively implemented for network traffic monitoring, especially in critical digital infrastructures. The main contribution of this research article consists of the comparative analysis of the XAI SHAP (Shapley Additive Explanation) method applied to different datasets obtained from real-time network traffic monitoring, utilizing several representative parameters, which demonstrates the performance, vulnerabilities, and limitations of the proposed method, and also the security implications of the system resources from a cybersecurity perspective. Experimental results show that Ethernet networks offer higher predictability and clearer decision boundaries. Consequently, they are a safer solution for deployment in sensitive network architectures. In contrast, BYOD (Bring Your Own Device) Wi-Fi environments exhibit greater randomness. Full article

The accelerated expansion of advanced technologies—particularly artificial intelligence, intelligent systems, and interactive digital environments—is influencing contemporary media ecosystems and contributing to changes in educational practices. This study provides a systematic and descriptive bibliometric mapping of recent scientific production on artificial intelligence in education, algorithmic ethics, and digital inequality. A total of 229 Scopus-indexed documents published between 2021 and 2026 were analyzed using Biblioshiny and VOSviewer to examine publication patterns, influential authors and sources, and the conceptual structure of the field. Results indicate a marked increase in research output since 2024, with an annual growth rate of 47.58%, an average of 8.68 citations per document, and an international co-authorship rate of 24.45%. These indicators reflect an expanding and increasingly collaborative research landscape, accompanied by a diversification of thematic priorities within the field. The analysis identifies five thematic clusters: (1) the technical foundations of AI and digital transformation; (2) intelligent and immersive learning environments; (3) personalized and adaptive learning systems; (4) AI literacy and pedagogical integration; and (5) ethical considerations, including algorithmic bias and educational robotics. The findings highlight the need for explicit justification of database selection, strengthened critical AI literacy, and context-sensitive strategies that address disparities in access, skills, and institutional capacity. Overall, this study offers a coherent overview of a research area that is currently expanding and undergoing conceptual reorganization, providing evidence-informed insights for future research, policy development, and the design of equitable AI-driven educational technologies. Full article

The application of artificial intelligence (AI) and machine learning (ML) to the optimisation of agrivoltaic systems represents a promising frontier for enhancing dual land-use efficiency. Insights from the literature identify substantial opportunities for the transfer of mature AI methodologies from renewable energy and agriculture applications to the emerging field of agrivoltaics. Despite agrivoltaic systems achieving reported Land Equivalent Ratios (LERs) of between 1.2 and 1.6—corresponding to a 20 to 60% increase in combined energy and crop productivity per unit of land—the adoption of dynamic, real-time optimisation remains limited. Key research gaps include the absence of cross-domain learning architectures, the limited integration of economic considerations within optimisation frameworks, and the lack of adaptive, multi-temporal modelling approaches. This perspective paper proposes a research roadmap for the development of next-generation AI systems capable of simultaneously optimising energy generation and agricultural productivity, thereby supporting sustainable land-use transitions in integrated agri-energy landscapes. Full article

This systematic review maps the landscape of microlearning research within software engineering education, critically examining how this pedagogical approach is being applied to develop the multifaceted competencies required of modern software professionals. Following PRISMA-ScR guidelines, the review synthesized 21 empirical studies from 2015 to 2026, analyzing their pedagogical approaches, technological integrations, curriculum coverage, and evidence of effectiveness. The findings reveal a field marked by creative experimentation yet significant fragmentation: while microlearning effectively engages students and conveys discrete programming and project management knowledge through gamified, mobile, and project-based formats, its application remains narrowly concentrated on introductory coding, leaving advanced competencies such as software architecture, requirements engineering, and testing strategies virtually unexplored. The review further exposes critical gaps in the evidence base, including the absence of longitudinal and transfer studies, the conflation of platform engagement with learning, and methodologically fragile claims of effectiveness. Enthusiasm for microcredentials and AI-personalized learning considerably outstrips empirical support, with implemented systems relying on rule-based logic rather than adaptive intelligence and credentialing frameworks lacking validation of employer recognition or employment outcomes. This review concludes that while microlearning holds genuine potential for just-in-time skill development in a rapidly evolving discipline, its role in software engineering education must be strategic and supplemental rather than comprehensive. The field must urgently move from promotional advocacy toward rigorous, comparative, and longitudinal research that assesses higher-order competencies and authentic professional capability, lest its promise remain unfulfilled. Full article

The increasing complexity and uncertain system of modern discrete event system (DES) challenge traditional model-based control approaches, while artificial intelligence (AI) techniques offer powerful data-driven decision-making capabilities but lack formal guarantees. This review surveys recent research on the integration of AI with DES and supervisory control theory. Following a systematic literature mapping methodology, the literature is organized using a taxonomy based on three orthogonal perspectives: control and decision paradigm, system capability and property, and application and operational objectives. The review highlights how learning-based methods enhance adaptability and performance in DES, while also exposing persistent challenges related to safety, nonblocking behavior, data efficiency, and interpretability. By structuring existing approaches and identifying open issues, this review provides a coherent overview of the current research landscape and outlines key directions for future work on AI-enabled DES. Full article

Background: Chronic kidney disease (CKD) and end-stage renal disease (ESRD) represent a growing global health burden, affecting nearly one in ten adults worldwide. CKD is associated with high morbidity, premature mortality, reduced quality of life and enormous healthcare costs, and is primarily driven by dialysis and kidney transplantation. The silent and progressive nature of CKD means that most patients are diagnosed late, when irreversible damage has already occurred and costly kidney replacement therapies (KRT) become necessary. Dialysis services are resource-intensive, requiring significant infrastructure, specialized staff, and consumables, which makes them especially challenging to sustain in low- and middle-income countries. Traditional models of nephrology, care center-based dialysis and fragmented follow-up are increasingly inadequate in meeting the demands of a rising CKD population. These challenges highlight the urgent need for innovative approaches that enhance efficiency, improve patient outcomes, and expand access. Objective: This review aims to analyze the current landscape of information and communication technology (ICT) applications in nephrology and to evaluate how digital innovations are reconfiguring kidney therapy. Specifically, it seeks to identify the major ICT tools that are currently in use, assess their clinical and operational impact, and discuss their role in creating more sustainable, patient-centered kidney care models. This study reviews and analyzes ICT tools that are reconfiguring nephrology, including remote monitoring, AI, wearables, patient engagement apps and data dashboards. Methods: Narrative and scoping review of recent innovations in nephrology, including remote patient monitoring (RPM), telehealth, artificial intelligence (AI) analytics, wearable sensors, and clinical decision support platforms. Results: ICT tools such as Sharesource, Versia, telenephrology platforms, medical assistant for Chronic Care Service (MACCS), AI-based predictive analytics, wearable devices and patient engagement apps have improved patient outcomes, adherence, and early detection of complications. Key metrics include technique survival, hospitalization rate, patient-reported outcomes, workflow efficiency, and prediction accuracy. The relevant literature describing the potential of digital health technologies, including ICT platforms, artificial intelligence tools, and remote monitoring systems, to transform nephrology care was retrieved and screened for inclusion in this narrative review. Conclusions: ICT has shifted nephrology from reactive to proactive care, enhancing accessibility, patient empowerment and clinical efficiency. Future directions include precision nephrology, fully wearable kidneys, AI integration and large language models for education and triage. Challenges include digital divide, regulatory heterogeneity, cost and the need for long-term evidence. Full article

Background and Objectives: Rapid diagnosis is fundamental to acute ischemic stroke management; however, access to neuroradiological expertise remains limited. This scoping review maps the diagnostic accuracy, workflow impact, and cost-effectiveness of leading AI platforms (Brainomix, Aidoc, RapidAI, and Viz.ai), characterizing industry and peer-reviewed metrics. Materials and Methods: Following PRISMA-ScR guidelines, we searched PubMed, Cochrane Library, and HTA repositories for studies (2019–2025). Using a PICO-based framework, 29 studies were included for thematic mapping of the technological landscape. Results: Twenty-nine studies were included. Platforms show high proximal LVO sensitivity (78–97%), while performance for distal/MVO and posterior circulation occlusions was more variable. RapidAI is frequently mapped using historical perfusion trial parameters; however, volumetric discrepancies with platforms like Viz.ai indicate outputs are not interchangeable. Brainomix shows extensive validation for automated NCCT ASPECTS in triage. Aidoc demonstrates operational advantages via worklist prioritization, while. Viz.ai is associated with door-to-puncture time reductions (11–25 min). Economically, cost-effectiveness is driven by improved functional outcomes and expanded access to thrombectomy, rather than labor substitution. Conclusions: AI platforms function as diagnostic safety nets and workflow optimizers. Reported roles, such as perfusion-centric analysis (RapidAI) or workflow coordination (Viz.ai), reflect current research trends rather than definitive technological superiority. Institutional selection should consider these evidence clusters alongside local validation and specific clinical priorities. Full article