Search Results (12,687)

Predicting carbon dioxide (CO₂) solubility in brine is critical for carbon capture and storage. This study employs the Ant Colony Optimization (ACO) algorithm to enhance the predictive accuracy of four machine learning models: Neural Network (NN), Decision Tree (DT), Support Vector Regression (SVR), and Gradient Boosting Machine (GBM). The models were trained and validated on a mineral compound dataset. Performance was evaluated using the coefficient of determination (R²) and error metrics including RMSE and MAE. The GBM model achieved the highest test accuracy (R² = 0.986) with low errors (RMSE = 0.0478, MAE = 0.0362), demonstrating superior ability to model complex, non-linear relationships with minimal overfitting. The optimized NN, featuring three layers and fifteen neurons, delivered strong performance (R² = 0.930) with balanced errors across datasets. The DT model offered excellent interpretability and a strong test score (R² = 0.912), while the SVR model provided robust generalization (R² = 0.889). The results indicate that ACO is an effective tool for hyperparameter tuning across diverse model architectures. For maximum accuracy, GBM is recommended, whereas DT is ideal when interpretability is required. The NN presents a strong middle-ground option with competitive accuracy. This comparative framework assists in selecting the optimal model based on specific project priorities of accuracy, transparency, or computational efficiency for geochemical forecasting. Full article

Background: Artificial intelligence chatbots are increasingly used by patients to obtain health information, including for prostate cancer. While these platforms offer accessible and conversational responses, concerns remain regarding the quality, usability, and clinical relevance of AI-generated content. This study comparatively evaluated patient-directed prostate cancer information generated by commonly used AI chatbots. Methods: Standardised prostate cancer-related prompts were developed using Google Trends and authoritative healthcare resources. Identical queries were submitted to five publicly accessible AI chatbots: ChatGPT 5.2, Google Gemini, Claude AI, Microsoft Copilot, and Perplexity. Responses were independently assessed by two blinded reviewers using the DISCERN instrument for information quality and the Patient Education Materials Assessment Tool for printable materials (PEMAT-P) for understandability and actionability. Inter-rater reliability was assessed using intraclass correlation coefficients (ICCs). Readability was evaluated using the Flesch–Kincaid Reading Ease score. Descriptive statistics were used for comparative and pooled analyses. Results: Overall information quality was moderate, with a pooled median (interquartile range [IQR]) DISCERN score of 56.5 (53.0–61.0). Higher mean DISCERN scores were observed for ChatGPT 5.2 and Microsoft Copilot, whereas lower scores were observed for Claude and Perplexity. PEMAT-P understandability was consistently high across platforms, with a pooled median (IQR) score of 91.7% (83.3–91.7%). In contrast, PEMAT-P actionability was uniformly poor, with a pooled median (IQR) score of 0% (0–0%). Readability analysis demonstrated moderate complexity, with a pooled median (IQR) Flesch–Kincaid Reading Ease score of 50.4 (49.2–52.5) and a median word count of 666 (657–1022). Inter-rater reliability was good for PEMAT understandability (ICC 0.841) and moderate for DISCERN (ICC 0.712). Conclusions: AI chatbots provide highly understandable but only moderately high-quality patient-directed prostate cancer information, with a consistent lack of actionable guidance. Although variation in content quality was observed across platforms, significant limitations remain in evidence transparency and practical patient support. Future development should prioritise integration of evidence-based resources and actionable decision-support tools to enhance the role of AI chatbots in prostate cancer education. Full article

Listeria monocytogenes poses a significant risk in meat-processing plants, especially in micro and small businesses, where structural, organisational and operational limitations make it difficult to control. Although there is evidence of its environmental distribution and recurrence, this information does not always translate into clear operational criteria for risk management. To design an intervention framework for mitigating the risk associated with L. monocytogenes in micro and small meat-processing plants, based on the integration of previously published microbiological and operational evidence, the study integrated results on environmental distribution, recurrence of isolates and risk factors identified in eight plants. Functional prioritisation criteria were defined considering hygienic zoning, the function of sites in the process flow, proximity to the ready-to-eat product, and environmental conditions favourable to “persistence”. Differentiated risk scenarios and a functional hierarchy of priority intervention points were detected, prioritising site types recurrently associated with the presence of Listeria spp. and L. monocytogenes. Based on this hierarchy, the proposed intervention formulation aimed at prevention, control and environmental monitoring, adapted to the operating conditions of micro- and small-scale meat-processing plants. The proposed framework offers a transferable tool to support decisions in the management of L. monocytogenes risk in small-scale plants. Full article

Background/Objectives: Artificial intelligence (AI) tools, particularly large language models (LLMs) such as ChatGPT-4o, are gaining prominence in medicine. While their diagnostic capabilities have been explored across various oncologic domains, their role in clinical decision-making within multidisciplinary tumor boards (MTBs) remains largely unexamined in urologic oncology. This study evaluates the performance of ChatGPT-4o as a decision-support tool in a real-world MTB setting by comparing its recommendations with those of expert clinicians. Materials and Methods: A retrospective study was conducted using 98 anonymized clinical cases discussed by a urologic MTB between June 2024 and February 2025. An independent urologist entered the same cases into ChatGPT-4o using a standardized prompt replicating real-world presentation. Two certified urologists independently assessed the model’s responses. Agreement was analyzed overall and by tumor type, disease stage, clinical context, and treatment strategy. Results: ChatGPT-4o fully agreed with the MTB in 56.1% of cases, was correct but incomplete in 23.5%, and provided partially accurate but flawed recommendations in 18.4%. Overall concordance between ChatGPT-4o and the MTB yielded a Cohen’s kappa of 0.61, indicating moderate-to-good agreement. Discrepancies were most common in metastatic prostate cancer, often due to misclassification of tumor burden or errors in treatment sequencing. Highest agreement rates were observed in bladder and renal tumors, and in standardized therapeutic scenarios such as radiotherapy. Conclusions: ChatGPT-4o demonstrated moderate alignment with expert MTB decisions and performed best in well-defined clinical contexts. While it cannot replace multidisciplinary expertise, it may serve as a supportive tool to enhance access to standardized oncologic care. Full article

Digital capitalism has generated new forms of extractivism that extend beyond natural resources to encompass data, attention, affect, and planetary materials. This article examines how exhibition practices can function as forms of algorithmic resistance by foregrounding material praxis, embodied engagement, and curatorial strategies of care. Drawing on a practice-based research approach, the paper develops a theoretical framework around extractivism, materiality, and relational ethics, and applies it to two case studies: the author’s exhibition Nature Reclaims: Images of Healing, which cultivates regenerative imaginaries through urban rewilding photography, tactile installations, and trauma-informed reflective tools; and Fossil Fables, curated by the Global Extraction Observatory (GEO), which exposes the infrastructural, political, and ideological architectures sustaining extractive industries and digital technologies. Through comparative analysis, the article introduces the concept of symbiotic curation to describe a post-extractive curatorial method that holds critical exposure and regenerative proposition in sustained tension. The findings illustrate how exhibitions can reorganize perception, recalibrate temporality, and render hidden infrastructures visible, while also cultivating embodied relations of care, ecological attunement, and collective reflection. By positioning curatorial practice as an epistemic process in which theoretical propositions are tested through spatial, material, and affective decisions, the article identifies transferable principles for post-extractive cultural work. It argues that exhibitions can operate as laboratories for algorithmic resistance and as sites for rehearsing alternative relations between humans, technologies, and more-than-human worlds. Full article

Water resources management in arid and semi-arid regions has become increasingly challenging due to climate change impacts and upstream water policies, particularly for strategic reservoirs. This study evaluates the applicability of the HEC-HMS model for simulating inflow hydrographs and supporting reservoir operation in data-scarce arid environments, focusing on Haditha Reservoir, the only major dam on the Euphrates River within Iraqi territory. An integrated hydro-meteorological and GIS-based framework was developed using 20 years of data (2004–2024), incorporating basin characteristics and reservoir operation records into the HEC-HMS model. Rainfall–runoff processes were simulated using SCS-based methods and routing techniques, followed by calibration and validation against observed inflows. The results demonstrated satisfactory model performance, with an accurate reproduction of inflow hydrographs during both calibration and validation periods. Subsequently, three reservoir operation scenarios were developed and compared with the actual operating policy (outflow curve operation, outflow structure routing operation and rule-based operation scenarios). The rule-based operation scenario showed superior performance by maintaining higher reservoir storage and water levels during dry periods compared to the existing operation, despite higher supply deficits. Overall, the findings confirm that the HEC-HMS model can be reliably applied as a decision-support tool for evaluating reservoir operation in arid and semi-arid regions under water scarcity conditions. Full article

This study investigates the effects of extraction technique, solvent type, and geographical origin on the recovery of bioactive compounds from Arbutus unedo L. leaves collected from two Croatian islands (Vis and Mali Lošinj) and extracted using conventional, Soxhlet, and ultrasound-assisted extraction (UAE) with green solvents (distilled water, 70% ethanol, and ethyl acetate). Extracts were purified and characterized by thin-layer chromatography, column chromatography, and FTIR spectroscopy. Total phenols, hydroxycinnamic acids, flavonols, condensed tannins, and antioxidant capacity were quantified spectrophotometrically. Solvent type had the greatest influence, with 70% ethanol yielding the highest levels of bioactives and antioxidant capacity. Geographical origin significantly affected total phenolics and condensed tannins, with leaves from Vis outperforming those from Mali Lošinj. UAE was slightly more efficient than conventional and Soxhlet methods, particularly for thermolabile phenolics. Machine learning algorithms were applied as exploratory tools, using total phenols as a proxy variable to estimate selected bioactive compounds and antioxidant capacity based on extraction parameters. Decision Tree and Gradient Boosting models showed high goodness of fit within the experimental dataset (R² > 0.91). These results support the potential of green extraction strategies combined with data-driven screening for the valorization of A. unedo leaf extracts, while highlighting the need for further validation prior to industrial application. Full article

Precision agriculture employs advanced technologies to enhance farm productivity and sustainability; however, selecting the most appropriate tools can be challenging for small and medium-sized farms. This study conducts a comparative analysis of ten key precision agriculture technologies (PATs): remote sensing, GPS, GIS, VRT, soil & crop sensors, DSS, UAVs/Drones, AI & ML-based precision farming, autonomous agricultural machinery, and IoT-based smart farming. The analysis employs a neutrosophic set-based multi-criteria decision-making (MCDM) framework. Domain experts evaluated ten representative technologies using a structured questionnaire based on ten critical criteria, including spatial-temporal accuracy, data acquisition latency, scalability, robustness, interoperability, environmental resilience, economic feasibility, and agro-ecological impact. A hybrid MCDM methodology was employed, integrating neutrosophic entropy and DEMATEL to construct criterion weights. Furthermore, we utilized neutrosophic DEMATEL to identify inter-criterion causal relationships. Neutrosophic TOPSIS, enhanced by a newly proposed hybrid Cosine-Jaccard similarity measure, was introduced to rank the alternatives under conditions of uncertainty. The findings reveal that IoT-based smart farming solutions achieved the highest overall score, followed by remote sensing and decision-support system (DSS) platforms. At the same time, variable-rate technology and sensor networks received lower rankings. The findings underscore the appropriateness of particular PATs for small and medium-scale farming contexts and illustrate the effectiveness of neutrosophic MCDM in addressing ambiguity and indeterminacy. The comparative insights provide direction for researchers, policymakers, and practitioners in prioritizing precision agriculture technologies and strategies to enhance sustainable practices in small and medium-scale farming. Full article

Background: Pneumonia-associated sepsis constitutes a significant portion of all sepsis cases and is a leading cause of sepsis-related morbidity and mortality. The clinical burden is especially pronounced in general ward settings, where delayed recognition can hinder timely intervention. This underscores the necessity for advanced tools that facilitate early detection. Methods: This retrospective, single-center study assessed an AI-driven deep learning model designed to predict in-hospital sepsis up to four hours in advance. We analyzed 7715 pneumonia cases identified through chest radiography or CT. The model’s performance was evaluated using AUROC, sensitivity, specificity, and lead time to sepsis onset and was compared against established scoring systems: NEWS, MEWS, SOFA, and qSOFA. Sepsis was defined according to the CDC Adult Sepsis Event criteria in alignment with Sepsis-3 guidelines. Results: The AI model exhibited strong performance in the early detection of sepsis among pneumonia patients, achieving an AUROC of 0.870, with a sensitivity of 76.7% and specificity of 84.1%. It significantly surpassed conventional scoring systems: NEWS (0.697), MEWS (0.661), SOFA (0.649), and qSOFA (0.678). Importantly, the model identified sepsis a median of 183 min earlier than recognition based on the operational definition. This lead-time advantage was consistent in the pneumonia cohort, where 18.3% of patients developed sepsis. Conclusions: The AI model demonstrated strong predictive capabilities for pneumonia-associated sepsis, facilitating earlier clinical decision-making. Integrating this model into EMR systems could be an effective strategy to enhance sepsis outcomes in general ward settings. Further prospective studies are needed to validate its effectiveness in real-time clinical applications. Full article

The Spanish Public University System (SUPE) assigns student placements through a multi-step application process governed by legal criteria. Analyzing how students move between different degree programs during this process is crucial for universities to optimize and plan their academic offerings. This paper analyzes a decade of student pre-registration data (2014–2023) to track evolving preferences and mobility between degrees. We model this process as a directed graph, mapping student traffic and studying the formation of directional communities within the degree network. A significant challenge is the weakly connected and poorly conditioned nature of these graphs, which impedes standard community detection algorithms. Extending prior work that relied on manually set thresholds for pruning edges, we propose a novel adaptive pruning algorithm that requires no manual intervention. Applying this method to annual data improves community detection performance and reveals gradual shifts in student preferences and demand, particularly in response to new degrees. These insights provide a valuable decision-making tool for higher education institutions, helping them refine their degree offerings in response to evolving trends. Full article

This article examines the governance of Global Value Chains (GVCs) through the lens of economic competence based on a systematic literature review of 32 selected studies. The findings show that economic competence functions as a governance-contingent construct whose effects vary across hierarchical, captive, relational, and modular governance structures. Rather than directly determining upgrading outcomes, competence dimensions operate through governance repositioning and shifts in dependence asymmetries within value chains. The review identifies recurring mechanisms—such as substitutability reduction, coordination cost mitigation, and institutional alignment—that explain how competence and governance interact. The analysis further demonstrates that economic competence is multidimensional, encompassing innovation-oriented, market-oriented, decision-making, relational, and systemic components. These dimensions operate differently depending on coordination complexity and power distribution within the chain. By advancing a contingency-based framework, the study refines GVC governance theory through a micro-foundational explanation of upgrading dynamics. From a managerial perspective, the framework offers a structured tool for aligning competence development strategies with specific governance configurations, supporting informed capability investments and improved strategic positioning. Overall, the study contributes by systematically integrating competence theory with governance typologies and power asymmetries, providing a coherent analytical model for future empirical research. Full article

This study conducts a bibliometric and content analysis of ‘artificial intelligence-enabled auditing’ over three decades. The use of artificial intelligence (AI) tools in auditing has evolved and is now an imperative practice in the auditing space. Using bibliometric methods via Bibliometrix R-package (Biblioshiny) and VOSviewer, this research mainly examines the scholarly discussion on AI-enabled auditing, using the Scopus database. The main themes identified are: Theme 1: AI in auditing: readiness, representation, and implementation; Theme 2: data-driven audit ecosystems and digital technologies; and Theme 3: audit quality, professional skepticism, and ethical governance. On the descriptive end, publication trends, prominent authors, articles, and sources are identified. The findings highlight a significant increase in AI-enabled auditing studies since 2018, coinciding with growing global awareness on the importance of AI across all spheres of business. The outcome of this research contributes to a wide array of stakeholders, including businesses, audit firms, shareholders, and policymakers; it should give insights to business organizations on the capabilities of AI-assisted auditing, while policymakers should have access to verifiable, auditable and regulatory-compliant systems for the implementation of their regulations. Investors may further enhance their knowledge in terms of how AI-assisted auditing increases the quality of their investment decisions and, at the same time, the risks involved. Finally, auditing firms should further invest in improving the application of technology in the auditing environment and ensure quality, evidence-based audit outcomes, and reporting. Full article

Fatigue failure constitutes an issue that cannot be ignored when designing welded steel structures due to the initiation of cracks at weld toes and defects under cyclic loading conditions. Traditional methods, such as the notch stress approach, estimate fatigue life by modeling local stress distributions using idealized weld geometries. While these methods are widely accepted in design codes, they can be limited by complexity and reduced accuracy in real-world applications. This study explores the use of artificial neural networks (ANNs) to enhance fatigue life prediction through data-driven modeling. The proposed method involves training an ANN using synthetic data generated through finite element simulations of S355 steel weldments under various loading histories, rates, and frequencies. The objective is to capture the influence of local geometric and stress features without relying solely on assumptions used in conventional approaches. The FEM-based training data incorporate both classical experimental findings and validated modeling practices. While performance evaluation of the ANN model is reserved for future work, this study lays the groundwork for replacing or supplementing the notch stress approach with a more adaptable and efficient predictive tool. The integration of machine learning into fatigue assessment has the potential to improve reliability, reduce computational burden, and support more informed maintenance and design decisions. Full article

The construction sector is responsible for a significant share of greenhouse gas emissions in Europe, making the decarbonisation of the existing building stock a critical priority. In this context, Digital Building Logbooks (DBLs) are increasingly promoted as digital tools to support renovation planning, data continuity, and circular economy practices across the building lifecycle. Despite growing policy attention, the adoption of DBLs in renovation projects remains limited in practice. This study provides one of the first empirical rankings of perceived barriers, benefits, and drivers influencing DBL adoption in renovation projects across Europe. An exploratory quantitative survey was conducted with a purposively selected sample of stakeholders involved in renovation-related activities. Likert-scale responses were analysed using descriptive ranking statistics and reliability testing, while qualitative data from open-ended responses were analysed using directed content analysis. The results indicate that stakeholders strongly recognise the benefits of DBLs, particularly in terms of improved access to reliable building information, informed decision-making, and support for circular renovation practices. However, adoption is constrained by regulatory uncertainty, limited awareness, and unclear governance and operational frameworks. The most influential drivers identified relate to interoperability with existing digital tools, rising awareness of DBLs among stakeholders, regulatory support, and the availability of standardised and operationally clear frameworks for DBL implementation. Full article

Purpose: Floating knee (FK) injuries are complex high-energy traumas associated with poor functional outcomes. This study aimed to identify independent predictors of functional prognosis and to develop a novel, multidimensional scoring system to predict long-term functional outcomes. Methods: A retrospective analysis was performed on 182 adult patients with ipsilateral femur and tibia fractures treated between January 2010 and December 2023. Functional outcomes were assessed using the Karlström–Olerud criteria and dichotomized as excellent–good versus fair–poor. Variables significant in univariate analysis were entered into a multivariate logistic regression model. Independent predictors were used to construct the FKS-score. Predictive performance was evaluated using ROC analysis. Results: Of the 182 patients, 103 (56.6%) achieved excellent–good outcomes, while 79 (43.4%) had fair–poor results. Multivariate analysis identified Fraser IIA–B (OR 2.12), Fraser IIC (OR 3.85), Gustilo I–II (OR 1.42), Gustilo IIIA–B (OR 2.61), Gustilo IIIC (OR 3.22), segmental fractures (OR 2.18), extensor mechanism injury (OR 2.04), vascular injury (OR 4.89), intra-articular extension (OR 1.92), and patella fracture (OR 1.76) as independent predictors of poor functional outcome. The FKS-score, ranging from 0 to 15, demonstrated high predictive accuracy (AUC = 0.89). An optimal cut-off value of ≥9 points yielded a sensitivity of 78% and specificity of 85%. Conclusions: The FKS-score is the first comprehensive prognostic scoring system specifically developed for floating knee injuries. It provides a reliable, practical tool for early risk stratification and the prediction of long-term functional outcomes, thereby supporting clinical decision-making and patient counseling. Full article