Search Results (10,177)

The rapid diffusion of generative artificial intelligence (GenAI) is reshaping organisational systems and digital transformation strategies, yet it remains unclear which organisational conditions are associated with long-term competitive performance in business-to-business (B2B) contexts. This study adopts a systems-informed perspective and examines how ethical governance, environmental dynamism, exploratory and exploitative innovation, and GenAI adoption are associated with long-term competitive performance in B2B firms. Using survey data from 104 Portuguese B2B managers and Partial Least Squares Structural Equation Modelling (PLS-SEM), the findings show that ethical governance is the strongest organisational correlate of long-term competitive performance, underscoring the central role of governance structures in responsible GenAI use. GenAI adoption is positively associated with performance, but its role is complementary rather than dominant. Exploratory innovation does not show a significant direct association with performance; instead, its association with performance operates through GenAI adoption in the estimated model, suggesting that experimentation becomes more performance-relevant when translated into digitally enabled routines. In contrast, exploitative innovation is directly associated with performance through incremental efficiency mechanisms. These findings challenge technology-deterministic assumptions and suggest that long-term competitive performance in B2B firms is more closely associated with the organisational alignment of governance structures, innovation capabilities, and GenAI adoption than with technology adoption alone. Full article

High-availability (HA) systems—essential in many contemporary contexts—are designed to guarantee the availability of processes and data for more than 99% of their operational time. These systems are typically implemented as Cloud/Edge infrastructures that are properly maintained by human operators and intelligent agents in order to guarantee the required level of availability. Moreover, we are witnessing the widespread adoption of AI-based automation across many industries. AI-based software agents are increasingly being adopted to introduce more automation in highly available systems, particularly for monitoring and fault detection, fault prediction, recovery, and optimization processes. In this review paper, we discuss the state of the art of AI-based solutions for HA systems. In particular, we focus on the use of AI for the core operational mechanisms of monitoring, failure detection, and recovery. Our discussion begins by reviewing a few key background concepts of HA architectures, then we review recent work on AI-based solutions for monitoring, fault detection and recovery in HA systems. Full article

Coral reef ecosystems are declining rapidly due to climate change, disease, and anthropogenic stressors, driving the expansion of land-based coral propagation for reef restoration. A major bottleneck in these efforts is the manual measurement of coral recruit tissue area from microscopy images, which requires 2–7 min per image and limits scalability. We present a hierarchical deep learning pipeline that automates this measurement by integrating YOLO-based detection with Segment Anything Model (SAM) segmentation. YOLO localizes recruits and classifies them by developmental stage; stage-specific fine-tuned SAM models then segment live tissue using bounding box and background point prompts to suppress segmentation leakage and improve boundary precision. Surface area is computed directly from the segmented masks using pixel size extracted from image metadata. The pipeline reduces processing time to approximately 3–5 s per image—a 24–140× speedup over manual tracing. Evaluated on 3668 microscopy images from two national coral research facilities, the system achieves a mean IoU exceeding 95% and an auto-acceptance rate (AAR) of 71.51%, where predicted-to-ground-truth area ratios fall within a ±5% tolerance of expert annotation, substantially reducing manual workload while maintaining measurement reliability across species, developmental stages, and imaging conditions. This workflow addresses a critical bottleneck in restoration research and demonstrates the broader applicability of AI-based image analysis in marine ecology. Full article

Effective port and coastal surveillance require persistent monitoring, flexible deployment, and reliable target detection in dynamic maritime environments. This paper presents a system- and deployment-oriented autonomous tethered drone architecture, integrated with AI-based perception, for persistent maritime surveillance in ports and coastal areas. Mounted on a moving maritime platform and powered through a tether, the drone provides a persistent elevated viewpoint without the endurance limitations of conventional battery-powered Unmanned Aerial Vehicles (UAVs). The system combines maritime platform integration, tethered flight operation, fail-safe and safety mechanisms, and a distributed Artificial Intelligence (AI) pipeline for real-time object detection and tracking. The perception module is based on YOLOv8m for vessel detection and BoT-SORT for multi-object tracking, enabling continuous monitoring of maritime targets in realistic operational scenarios. Field trials conducted from moving vessels in maritime environments demonstrate autonomous take-off and landing, stable surveillance operation under realistic wind and wave conditions, and effective vessel detection and tracking on real image sequences. The results show the potential of AI-enabled tethered drone surveillance as a persistent and operationally relevant tool for maritime monitoring and security. Full article

The increasing demand for innovative strategies in sustainable food production—driven by rapid global population growth, particularly in sub-Saharan Africa (SSA)—necessitates urgent attention to agricultural resilience. Recent technological advancements have enhanced crop productivity, post-harvest preservation, and environmentally sustainable farming practices. However, three critical bottlenecks remain: (i) the lack of accurate, maize-specific yield prediction methods tailored to SSA; (ii) limited multimodal modeling approaches capable of capturing complex, nonlinear interactions among heterogeneous data sources; and (iii) a lack of explainability mechanisms, which render high-performing models “black boxes” and hinder stakeholder trust. To address these gaps, this study presents an explainable machine learning framework for smart maize yield prediction. We integrate multimodal SSA-specific soil, crop, and weather data to capture the multi-dimensional drivers of maize productivity. Six diverse algorithms—including extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), categorical boosting (CatBoost), support vector machine (SVM), random forest (RF), and an artificial neural network (ANN) combined with a k-nearest neighbors (kNN)—were benchmarked to evaluate predictive performance. To ensure robustness against spatial heterogeneity, we employed a Leave-One-Plot-Out (LOPO) cross-validation strategy. Empirical results on unseen test data identify CatBoost as the best-performing model, achieving a coefficient of determination of ($R^2 =~76\%$), demonstrating its ability to capture complex, nonlinear relationships in agricultural data. To enhance transparency and stakeholder trust, we integrated Local Interpretable Model-agnostic Explanations (LIME), providing plot-level insights into the physiological and environmental drivers of maize yield. Together, these contributions establish a scalable and interpretable modeling framework capable of supporting data-driven agricultural decision-making in SSA. Full article

This paper analyzes “prompt engineering” through the economic lens of self-insurance against the risk of errors from noisy AI systems. To formalize this approach, we model an agent under cognitive load, allocating effort between working unassisted and prompting an AI assistant. The theoretical model demonstrates that an agent’s optimal prompting effort is driven by the agent’s attitude toward risk. Specifically, the model proves that risk-averse agents rationally “over-invest” in prompting effort, while risk-seeking agents “under-invest” relative to the risk-neutral benchmark. This outcome stems from the covariance between the marginal utility of performance and the marginal product of prompting. This alignment is positive for risk-averse agents, effectively boosting the AI’s perceived productivity. The novel implication is that prompting effort is an economically meaningful behavior that can be informative about an individual’s underlying attitude toward downside AI risk. These results offer a new perspective for understanding heterogeneity in AI adoption and oversight. They also suggest that, under comparable task conditions and controlling for prompting ability, observed prompting effort may be informative about attitudes toward downside AI risk. The framework therefore provides a risk-management perspective for understanding heterogeneity in AI governance in high-stakes settings such as healthcare and finance. Full article

Background/Objectives: Trace metal homeostasis is regulated by nutritional status and is crucial for maintaining redox balance, vascular function, and neuroinflammation. Dysregulation of systemic copper (Cu) metabolism, especially an elevated level of non-ceruloplasmin-bound copper (NCC), has been linked to oxidative stress and early cognitive decline. However, the nutritional and molecular pathways that connect Cu imbalance to mild cognitive impairment (MCI) are not well understood. Methods: We compared the serum Cu and zinc levels of individuals with normal cognition (NC; n = 116) and MCI (n = 184). An exploratory serum proteomic analysis using pooled samples was conducted to investigate patterns related to Cu dysregulation. We identified proteins using pattern correlation analysis and then performed a protein–protein interaction analysis using STRING and functional annotation and biological and Kyoto Encyclopedia of Genes and Genomes pathways. Results: The individuals with MCI had higher NCC levels than those with NC, indicating disrupted Cu metabolism influenced by nutrition and metabolism. The proteomic analysis revealed changes in proteins related to lipid transport, metal balance, and inflammation, including transthyretin, transferrin, apolipoprotein A-I, alpha-1 antitrypsin, antithrombin III, and alpha-2-macroglobulin, which respond to oxidative stress and vascular injury. Conclusions: In this cross-sectional analysis of baseline data, NCC levels were associated with cognitive status and specific circulating proteomic profiles. These findings suggest a potential relationship between copper-related biomarkers and mild cognitive impairment; however, longitudinal studies are required to clarify temporal relationships and potential mechanistic pathways. Full article

Agricultural intelligent agent technology features autonomy in multimodal perception, scalability for cross-scenario collaboration and adaptability via closed-loop optimization, serving as a core technological pillar for industrial intelligent upgrading and refined production management. This paper systematically elucidates its technical essence and methodological framework, focusing on five key aspects: multimodal heterogeneous data perception and fusion, scenario-oriented knowledge modeling and dynamic memory, intelligent decision-making and planning, embodied artificial intelligence, and closed-loop feedback optimization. On this basis, the paper outlines its core agricultural applications in four domains: crop cultivation, efficient utilization of agricultural resources, intelligent upgrading of agricultural technologies and equipment, and collaborative governance of the entire agricultural industry chain. From an interdisciplinary “AI + Agriculture” perspective, the paper further analyzes its future development directions, aiming to provide insights for improving agricultural intelligent agent technologies and promoting their industrial application to accelerate agricultural intelligent transformation. This study constructs a three-dimensional integrated methodological framework encompassing technological analysis, application mapping and trend forecasting, systematically summarizes its agricultural application scenarios and technological evolution characteristics, enriches the theoretical system and methodological construction of agricultural intelligent agent research, and provides a reusable analytical paradigm for agricultural intelligent agent research and practice. Full article

Background/Objectives: Tablet development requires simultaneous optimization of multiple quality attributes under limited experimental budgets, yet formulation–property relationships are highly nonlinear in mixture systems. To support pre-formulation decision-making prior to extensive tablet prototyping, this study proposes an AI framework that organizes formulation and process data together with raw-material property records into a reusable database, and enriches conventional composition/process features with physically motivated mixture descriptors derived from raw-material properties and formulation/process settings. Methods: Mixture-level scalar descriptors are constructed by composition-weighted aggregation of material properties, and particle size distribution (PSD) is incorporated via a compact set of summary statistics computed from composition-weighted mixture PSDs. Three feature sets are compared: (i) Materials + Processes (MP), (ii) MP with scalar Descriptors (MPD), and (iii) MPD with PSD summaries (MPDD). Five target properties are modeled: hardness, disintegration time, flow function, cohesion, and thickness. We train and evaluate Random Forest, Extra Trees Regressor, Lasso, Partial Least Squares, Support Vector Regression, and a multi-branch neural network that processes the three feature blocks separately and concatenates them for prediction. For interpolation assessment, repeated Train/Dev/Test splitting (5:3:2) across multiple random seeds is used, and the effect of feature augmentation is quantified by paired RMSE improvements with bootstrap confidence intervals and paired Wilcoxon signed-rank tests. To assess robustness under practical formulation updates, rolling-origin time-series splits are employed and Applicability Domain indicators are computed to characterize out-of-distribution coverage. Results: Across interpolation evaluations, mixture-descriptor augmentation (MPD/MPDD) improves hardness and disintegration time in most settings, whereas gains for flow function are smaller and cohesion/thickness show mixed effects under limited sample sizes. Conclusions: Under extrapolation-oriented evaluation, the descriptors can improve hardness but may degrade disintegration-time prediction under covariate shift, emphasizing the need for careful descriptor selection and dimensionality control when deploying pre-formulation predictors. Full article

The evolving digital landscape, including artificial intelligence (AI) and its generative forms, is changing how younger generations learn. As students utilize generative AI systems, they cultivate trust in such technology to support their current and long-term learning. The objective of this study was to investigate the relationship between generative AI use among students in health professions and their trust in this technology to support their lifelong learning as future health professionals. This study employed a survey methodology using a cross-sectional study design. The survey included sociodemographic variables and questions regarding students’ generative AI use and their trust in this technology to support their lifelong learning. Descriptive and inferential statistical procedures were used to analyze the data. A total of 558 students representing various health professions responded to the survey. In the regression analysis, after controlling for student’s sex and location variables, greater generative AI use was associated with students’ increased trust in this technology to support their lifelong learning (beta = 0.58, p < 0.001), explaining close to 40% of the total variance. Given the rapidly evolving digital landscape, this finding warrants further study, with implications for training of the future health workforce. Full article

This study investigates the acceptance of an AI-powered decision-support chatbot among professionals in a marketing and sales context, addressing a gap in technology acceptance research by examining data-intensive decision environments that remain underexplored. Building on the Unified Theory of Acceptance and Use of Technology (UTAUT), the study proposes an extended model incorporating Behavioral Intention, Performance Expectancy, Effort Expectancy, Social Influence, Output Quality, Time Saving, Source Trustworthiness, Cognitive Load, and Chatbot Self-Efficacy. An experimental study was conducted with 106 professionals using a chatbot-enhanced business analytics platform to complete marketing KPI analysis tasks. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results demonstrate that Behavioral Intention to use decision-support chatbots is significantly influenced by Performance Expectancy, Effort Expectancy, and Social Influence. Performance Expectancy is strongly driven by Output Quality, Time Saving, and Source Trustworthiness, while Effort Expectancy is significantly shaped by reduced Cognitive Load and higher Chatbot Self-Efficacy. The findings suggest that chatbot acceptance in professional decision-making depends not only on usability and performance beliefs but also on cognitive relief, trust in information sources, and efficiency gains, highlighting important implications for both theory and the design of AI-based decision-support systems. Full article

With the increasing penetration of converter-based devices, harmonic distortion has become a major challenge for power quality monitoring in large-scale power systems. This study presents a systematic review of methods for modeling harmonic sources and their applicability to real-time monitoring of power distribution systems. The review was conducted following PRISMA guidelines, considering literature published between 2000 and 2026. Searches were performed across Scopus, IEEE Xplore, Web of Science, ScienceDirect, and MDPI using predefined keywords. A total of 128 peer-reviewed journal articles were included. Potential sources of bias were qualitatively assessed, including selection, retrieval, and classification bias; however, residual bias may still arise from database selection, keyword design, and study classification. A structured comparative framework is introduced, based on a six-dimension coverage scoring scheme and maturity analysis, enabling consistent evaluation across both methodological and deployment aspects. The robustness of this framework was evaluated using leave-one-out and perturbation analyses, indicating low variability in coverage scores and stable rankings across both corpora. A taxonomy of harmonic source modeling approaches is proposed. Comparative synthesis indicates that measurement-based approaches, particularly those leveraging distribution-level PMUs, show strong potential for real-time monitoring. Key challenges include D-PMU placement, data integration, and computational scalability. Future work should focus on physics-informed AI and digital twin-based monitoring. Full article

Histopathological evaluation of tissue sections remains the gold standard for the diagnosis, classification, and grading of breast cancer (BC). The widespread adoption of whole-slide imaging (WSI) has enabled the digitization of histological slides and facilitated the development of artificial intelligence (AI) approaches for computational pathology. In recent years, machine learning and deep learning (DL) algorithms have been increasingly investigated for the analysis of hematoxylin and eosin (H&E)-stained images, with potential applications in tumor detection, histological classification, prognostic stratification, and prediction of treatment response. This narrative review summarizes recent developments in AI-driven models applied to BC histopathology and discusses their potential role in supporting diagnostic and prognostic assessment. Several studies have demonstrated the promising performance of DL algorithms in tasks such as the detection of lymph node metastases, assessment of residual tumor after neoadjuvant therapy, and prediction of clinical outcomes from histopathological images. Emerging research has also explored the possibility of inferring molecular and biomarker information from histology images, although these approaches currently identify statistical associations rather than direct molecular measurements. Despite the rapid expansion of this research field, significant barriers remain before routine clinical implementation can be achieved. Key challenges include dataset bias, variability in staining and image acquisition, limited external validation across institutions, and the need for transparent and reproducible model development. In addition, the translation of AI-based systems into clinical practice requires compliance with regulatory frameworks governing software used for medical purposes, such as those established by the U.S. Food and Drug Administration. Overall, AI represents a promising research direction in computational pathology and may contribute to decision-support tools capable of assisting pathologists in the analysis of digital slides. Continued efforts toward methodological rigor, large multicenter datasets, and prospective validation studies will be essential to determine the future role of AI in BC histopathology. Full article

This study investigates how participants conceptualise sustainability and sustainability citizenship, as well as how these conceptualisations relate to perceived agency. Drawing on two open-ended prompts, it analyses participants’ visions of a sustainable future and the roles they would like to play within it. The dataset was based on 1714 coded response segments from 164 participants. Methodologically, the study combines qualitative content analysis, independent human-AI double coding, manual validation, inter-rater reliability assessment, and residual-based co-occurrence analysis within a qualitatively grounded mixed-methods design. The results show that sustainability is predominantly framed in civic, symbolic, and ecological terms, whereas strategic competence and professionally articulated agency remain less visible. Sustainability meanings and role conceptions also vary systematically across disciplinary contexts. In addition, the analyses reveal patterned gaps between participants’ future visions and their self-attributed roles in sustainability transformations. The study contributes empirical insights into sustainability meaning-making and perceived agency and shows how LLM-assisted coding can be embedded in a transparent mixed-methods workflow. For sustainability education, the findings underline the importance of strengthening strategic and systemic dimensions of competence and linking civic engagement more closely to professional pathways of action. Full article

Artificial intelligence (AI) is increasingly being adopted for decision support in sustainable building risk management, yet the trustworthiness of AI-supported sustainability risk decisions depends as much on data quality as on analytical capability. Poor data conditions can amplify sustainability risks by producing unreliable decision support, yet existing studies provide limited insights into which data quality dimensions should be prioritized to enable trustworthy AI outcomes. This study identifies and prioritizes the critical data quality dimensions for trustworthy AI-supported decisions in sustainable building risk management. A questionnaire survey was conducted of accredited sustainable building professionals and their expert judgements were analyzed through an Analytic Hierarchy Process (AHP). The findings reveal that system-dependent dimensions, particularly traceability and interoperability, are prioritized over intrinsic dimensions like accuracy and consistency. The findings suggest that trustworthy AI-supported sustainability decisions depend strongly on a verifiable data provenance, cross-system integration and interpretable outputs rather than data correctness alone. This study reframes data quality from a general prerequisite to a prioritized, context-sensitive construct underpinning trustworthy AI applications, extending data-driven decision theory in the sustainable building domain. Ultimately, a phased data governance approach is recommended to prioritize traceability and interoperability as the foundational conditions for construction organizations implementing trustworthy AI in sustainable building risk management. Full article