Search Results (12,931)

The mining industry is in need of automation due to increasing requirements like higher global demands for resources and deposits, which are deeper and more complex. Progressing underground mines lead to longer travel times to the mining face and thus a loss in productive working time, which has to be compensated by automation. Ultimately, stricter health and safety regulations and a decreasing number of skilled operators accelerate the need for automation further. Within the the drill-and-blast cycle in underground mining, the drilling of blast holes is a central step. While semi-automated and supporting systems exist that allow the automated execution of single process steps under supervision, to date, no system is available for the unsupervised blast hole drilling of a mine face. A precondition for unsupervised operation is a perception system, which allows independent decision-making of the machine. To address this gap, this work presents a novel vision system capable of segmenting a mine face into drillable and non-drillable areas, which can serve as the basis for the autonomous adaption of a drilling pattern. An area of the mine face is considered drillable if no leftover blast holes from the previous blast cycle are present and the surface angle is below a certain threshold. The system presented is based on a stereo camera setup mounted on a drill jumbo. The resulting 2D and 3D data are processed by software that employs AI-based computer vision techniques, as well as traditional algorithms. The system was validated, and the performance was verified in the K+S Zielitz mine. Experts assisted in the determination of operational parameters and empirically validated the system’s performance. Additionally, the blast hole detection algorithm underwent a data-based analytical verification. Full article

Brain tumor detection from magnetic resonance imaging (MRI) is fundamental to computer-aided diagnosis, yet automated models must remain robust to heterogeneous imaging conditions. Despite strong recent progress, many deep learning and transformer-based approaches primarily optimize performance accuracy without explicitly improving feature selectivity and spatial localization, and they typically produce deterministic output without uncertainty estimates, which limits reliability. To overcome these limitations, we introduce UA-EffNet-DA, an uncertainty-aware EfficientNet framework that addresses these limitations through three complementary components. First, EfficientNet-B4 serves as an efficient backbone for discriminative feature extraction. Second, lightweight dual attention modules, comprising channel and spatial attention in parallel, are applied to the model to emphasize what and where discriminative features to focus within MRI slices. Third, Monte Carlo dropout is employed during inference to quantify predictive uncertainty and enable confidence-aware decision. Experiments on two public benchmarks demonstrate strong performance, yielding accuracies of 98.73% on the Figshare dataset and 99.23% on the Kaggle dataset. In addition, explainable AI analysis using Gradient-weighted Class Activation Mapping (Grad-CAM) further indicates that the proposed model concentrates on diagnostically relevant tumor regions rather than background structures, supporting transparent decision-making. Ablation studies confirm the complementary contribution of dual attention refinement and uncertainty-aware inference. Overall, the proposed UA-EffNet-DA framework offers an efficient and interpretable approach for brain tumor detection that supports more reliable clinical decision support through uncertainty-aware predictions. Full article

Background and Objective: Active surveillance (AS) describes the active monitoring of men with low- to intermediate-risk prostate cancer (PCa), before active management (AM) is needed due to disease progression. A substantial proportion of patients require a transition to AM within a few years of diagnosis. Proclarix is a blood-based diagnostic test that predicts clinically significant PCa (csPCa) and the Proclarix risk score has been shown to correlate with tumor aggressiveness. This study aimed to assess whether Proclarix can predict the likelihood of transition from AS to AM and to compare it to PSA density (PSAD). Methods: We retrospectively evaluated the Proclarix risk scores in serum samples from a Danish cohort of 132 men recruited from the PerPros prostate biobank. Most participants had low- to intermediate-risk PCa and were considered eligible for AS at diagnosis. Blood samples were collected before the initial biopsies, and clinical follow-up data were available for every patient for a minimum of 3 and up to 9.5 years. The primary endpoint was the ability of the Proclarix risk score to predict the transition from AS to AM. The secondary endpoint was to assess whether Proclarix could identify patients at risk of progression to csPCa. For both endpoints, PSA density was also included in the analysis for comparison. Results: Overall, 48 of 132 men (36%) transitioned from AS to AM during follow-up. A baseline Proclarix risk score of ≥50% was associated with a 79% estimated cumulative probability of switching to AM (HR = 4.4, 95% CI: 2.3–8.3, p < 0.001), compared to the 58% (HR = 3.1, 95% CI: 1.7–5.7, p < 0.001) for PSAD At the 5-year follow-up, 82% of men with a Proclarix score ≥ 50% and 57% with PSAD ≥ 0.15 ng/mL/cm³ had progressed to AM. Additionally, 67% and 54% of men showed progression to csPCa with, respectively, Proclarix and PSAD at the confirmatory biopsy. In contrast, among men with a Proclarix score < 50%, only 28% progressed to AM and 32% to csPCa, whereas for PSAD < 0.15 ng/mL/cm³, 17% transitioned to AM and 23% progressed to csPCa. Conclusions: The Proclarix risk score may support clinical decision-making in AS by identifying patients at higher risk of progression and informing follow-up intensity. However, the results should be confirmed in a larger prospective study. Full article

Enterprise spatial data supply chains (SDSCs) increasingly support high-stakes decision-making; yet, the provenance in operational geospatial systems is often fragmented across metadata records, workflow logs, and application-specific formats. This limits traceability, reproducibility, auditability, and fitness-for-purpose assessment, particularly when organisations need to explain how spatial products were created, with which parameters, spatial references, and dependencies. This study proposes the Enterprise Spatial Data Provenance Knowledge Infrastructure (ESDPKI), a standards-aligned framework that treats provenance as enterprise knowledge infrastructure rather than passive metadata. Using a design science research approach, the study synthesised the literature-derived requirements, standards-based interoperability constraints, and representative spatial data supply chain workflows to develop four artefacts: a six-layer reference architecture, the GeoPROV minimal semantic profile, a validation-gated ingestion and governance mechanism, and a reproducible evaluation blueprint with service-level objectives. Together, these artefacts support provenance capture, semantic normalisation, validation, queryable lineage, catalogue linkage, and policy-aware disclosure across enterprise environments. The resulting design makes geospatial operations, parameters, geometry, and coordinate reference system context machine-actionable, enabling lineage tracing, impact analysis, discovery-time fitness-for-purpose assessment, and stronger governance at scale. ESDPKI therefore provides a coherent architectural pathway for operationalising trustworthy, explainable, and scalable spatial provenance in enterprise settings. Full article

Background/Objectives: Healthcare facilities are among the most energy-intensive public buildings, yet hospital decision-support models rarely integrate energy-related performance indicators alongside operational metrics. This study aims to address this gap by developing a discrete-event simulation framework capable of jointly evaluating clinical efficiency and energy consumption in elective orthopedic surgical pathways. Methods: A comprehensive discrete-event simulation model was developed to represent the diagnostic imaging and orthopedic surgical process. The model was parameterized using a hybrid data-collection approach that combined clinical activity data, scientific literature, and expert judgment. Energy consumption was modeled by differentiating fixed loads, such as heating, ventilation, and air-conditioning systems and lighting, from activity-dependent loads associated with diagnostic and surgical equipment. Baseline performance was assessed and compared with alternative scenarios for organizational and technological improvements. Results: The analysis showed that fixed infrastructural loads, particularly HVAC systems, were the main drivers of per-patient energy consumption, with inefficient space utilization and prolonged idle times. Scenario analysis demonstrated that organizational interventions, such as increasing operating room throughput and optimizing MRI scheduling, can substantially reduce energy intensity by diluting fixed loads and decreasing idle consumption. Technological interventions, such as replacing conventional surgical lamps with LED systems, produced smaller but still beneficial reductions. The combined implementation of organizational and technological strategies yielded the greatest overall improvement. Conclusions: Integrating energy metrics into discrete-event simulation provides effective support for hospital decision-making by revealing the interaction between workflow design, resource utilization, and environmental performance. The findings indicate that organizational redesign, particularly when combined with technological upgrades, can significantly improve both operational efficiency and sustainability in hospital settings. This study highlights discrete-event simulation as a promising tool for energy-aware healthcare planning. Full article

Prefabricated steel structures have been increasingly adopted in modern construction due to their high efficiency, sustainability, and industrialized production. However, their construction quality and efficiency are often compromised by accumulated geometric deviations during fabrication, transportation, assembly, and welding, while traditional construction control and welding processes remain highly dependent on manual measurements and empirical operations. To address these challenges, this study proposes a digital construction framework for prefabricated steel structures, integrating high-precision three-dimensional (3D) laser scanning, Building Information Modeling (BIM), and intelligent welding technologies. First, high-precision 3D laser scanning is employed to capture the as-built geometric information of prefabricated steel components, generating dense point cloud data for construction-stage deviation detection and quantitative comparison with BIM-based design models. Based on deviation analysis, a digital construction control strategy is established to support real-time feedback, error compensation, and assembly adjustment. An engineering case study involving a complex prefabricated steel structure is conducted to validate the proposed framework. The results demonstrate that the integrated digital construction and intelligent welding approach significantly improves assembly accuracy, weld positioning precision, and construction efficiency, while reducing manual intervention and error accumulation. Overall, this study contributes to the body of knowledge by proposing a unified closed-loop digital construction paradigm that integrates geometric perception, deviation-driven decision-making, and intelligent welding execution, thereby bridging the gap between construction control and robotic fabrication in prefabricated steel structures. Full article

Background: Kounis syndrome is an allergic acute coronary syndrome precipitated by coronary vasospasm, plaque destabilization, stent thrombosis, or bypass occlusion. Cardiac surgery represents a uniquely high-risk setting due to cardiopulmonary bypass–associated inflammation and exposure to multiple pharmaceutical agents. Importantly, Kounis syndrome remains underrecognized in this context, as classical signs of anaphylaxis may be masked under general anesthesia and cardiopulmonary bypass, while ischemic manifestations may be misattributed to other perioperative conditions. Methods: A narrative review of PubMed-indexed literature was conducted to synthesize current evidence on the pathophysiology, perioperative triggers, clinical presentation, diagnostic strategies, and management of Kounis syndrome in cardiac surgery, with emphasis on intraoperative recognition and surgical decision-making. Published cases were retrieved involving perioperative cardiac surgery patients with a definite diagnosis of Kounis syndrome. Additionally, cases presenting with severe perioperative anaphylaxis and life-threatening cardiovascular involvement (grade III with cardiovascular collapse and grade IV with cardiac arrest) were included as possible Kounis syndrome, reflecting real-world diagnostic uncertainty in the intraoperative setting. Results: The literature review identified five cases of definite Kounis syndrome and ten cases of possible Kounis syndrome, including three cases with cardiovascular collapse and seven cases with cardiac arrest. Recurrent episodes were reported in several patients, particularly due to re-exposure to the triggering agent. In the context of cardiac surgery, Kounis syndrome is most frequently triggered by chlorhexidine, protamine, antibiotic prophylaxis, and anesthetic agents. The clinical presentation is often subtle during cardiopulmonary bypass. Vasoplegia, pulmonary hypertension, ventricular dysfunction, new regional wall-motion abnormalities, and hyperdynamic ventricles on transesophageal echocardiography commonly precede overt electrocardiographic changes. Diagnosis is primarily clinical and relies on intraoperative ultrasound, hemodynamic monitoring, serum tryptase, serum troponin, and, when indicated, coronary angiography. A dual-pathway approach addressing both anaphylaxis and myocardial ischemia is essential; however, one component may predominate, particularly in perioperative patients with limited clinical information, potentially leading to misdiagnosis. A multidisciplinary approach is therefore required for rapid diagnosis and individualized management. In refractory cases, cardiopulmonary bypass or ventricular assist devices may provide lifesaving support. Conclusions: Kounis syndrome remains underrecognized in cardiac surgery but carries significant morbidity. Increased clinical awareness, multidisciplinary collaboration, structured diagnostic approaches, and preventive strategies are essential to improve outcomes and reduce the risk of recurrence during future procedures. Full article

This systematic review synthesized the existing evidence on neurofeedback interventions applied to football players, aiming to clarify their effects on cognitive, technical–tactical, physical and psychological performance. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, four databases (PubMed, Web of Science, SCOPUS and SportsDiscus) were searched up to November 2025. Seven studies met the inclusion criteria, involving 133 players across youth, amateur, national and elite levels. Neurofeedback protocols primarily targeted alpha or sensorimotor rhythm (SMR) activity, and some were combined with heart rate variability (HRV) biofeedback. Across studies, neurofeedback may be associated with improvements in several cognitive outcomes, including improvements in working memory, visuospatial memory, task switching, mental rotation and decision-making. Limited evidence suggests potential improvements in technical skills (particularly shooting accuracy) and tactical decision-making. Some studies reported changes in physiological markers and stress-recovery capacity, although their interpretation remains uncertain. However, the evidence base remains constrained by small samples, heterogeneous protocols and limited use of randomized controlled designs. Overall, neurofeedback appears to be a potentially promising but still experimental tool to support cognitive and psychophysiological readiness in football, warranting more rigorous and standardized research to establish efficacy and optimal training parameters. Full article

Traditional wood identification models are vulnerable to out-of-distribution (OOD) substitution in the global timber trade. In response to this issue, this study presents a dual-stage cascade authentication architecture using in situ Raman spectroscopy and machine learning. First, a physically informed preprocessing strategy, integrating adaptive truncation (>1749 cm⁻¹) and first-derivative filtering, is developed to extract a 1309-dimensional composite feature matrix. This step effectively decouples non-linear fluorescence and converts physical detector saturation into highly discriminative features. To mitigate data leakage, the system utilizes a cross-validated Random Forest engine for Stage-1 closed-set discriminative screening. Subsequently, it cascades a high-dimensional One-Class Support Vector Machine (OCSVM) for Stage-2 open-set non-linear boundary verification in the Reproducing Kernel Hilbert Space. This design avoids the “variance trap” of traditional linear dimensionality reduction (e.g., PCA), preserving weak but critical secondary metabolite signals. Under a controlled OOD benchmarking scenario involving three taxonomically and chemically similar substitute species, the optimized Stage-1 engine maintains a 91.67% closed-set accuracy on known species. Crucially, Stage-2 verification achieves an open-set detection AUROC of 0.9722 and limits the FPR95 to 3.33%. Feature importance mapping indicates that the model effectively incorporates macroscopicoptical surrogate features (e.g., fluorescence decay boundaries) for decision-making. Overall, this study offers a robust, controlled non-destructive approach for real-world wood authenticity verification. Full article

Background/Objectives: Efficient management of intensive care unit (ICU) resources is a critical challenge for modern healthcare systems, which must balance high-quality patient care with operational and financial performance. ICU length of stay (LOS) is a key metric of clinical complexity and hospital efficiency. However, traditional methods for predicting LOS often fail to capture the complex, nonlinear interactions among physiological, demographic, and treatment-related variables. Machine learning (ML) and deep learning (DL) models have emerged as promising tools for enhancing predictive accuracy and supporting data-driven decision-making. Methods: This study presents a systematic review and meta-analysis of ML and DL approaches for predicting ICU LOS in adult patients. Following PRISMA guidelines, eight scientific databases were searched, yielding 33 eligible studies published between 2015 and 2025. Results: Mixed medical–surgical ICUs were the most common setting (51.5%), and 45.5% of datasets were sourced from public repositories. Most studies (19/33) focused on binary classification of prolonged stays, although thresholds ranged from >48 h to ≥14 days. The pooled results from ten studies yielded an AUROC of 0.9005 (95% CI: 0.8890–0.9121), indicating strong predictive capability across diverse clinical contexts. Subgroup analyses showed comparable performance between specialized surgical and general ICUs. Conclusions: These findings suggest that AI-driven LOS prediction models exhibit strong discriminatory power for ICU LOS prediction, supporting hospital capacity planning. However, to translate this into reliable clinical support, the methodological heterogeneity, scarcity of external validation, and near absence of calibration reporting identified in this review need to be addressed. Full article

Natural resource management and area-based conservation are increasingly recognised as outcomes of complex interactions between ecological conditions and social systems, shaped by local knowledge, governance arrangements, and environmental pressures. The Social-Ecological Systems Framework (SESF), developed by Elinor Ostrom, provides a comprehensive framework to analyse these dynamics; however, most applications remain context-specific, limiting cross-site comparability. This study introduces the Conservation and Human Use Index (CHUI), a standardised diagnostic tool that operationalizes SESF principles for comparative analysis across conservation-important areas. CHUI comprises 134 qualitative questions structured across four equally weighted dimensions: (i) Natural Values and Ecosystem Services, (ii) Threats and Pressures, (iii) Governance, and (iv) Social Perceptions. Using an ordinal 0–3 scale with a “Not Applicable” option, the Index enables consistent, flexible application through both desk-based assessments and participatory processes. It generates aggregate and disaggregated outputs that help identify pressure hotspots, governance gaps, and conservation-use synergies. CHUI’s primary innovation lies in translating SESF into a pragmatic and participatory instrument that supports real-world decision-making. Rather than replacing detailed ecological or socio-economic assessments, it functions as a collaborative diagnostic compass to guide targeted investigation and intervention. Its participatory design fosters shared learning, transparency, and co-production of context-specific management pathways, supporting adaptive stewardship and community empowerment. Developed within the Horizon Europe PRO-COAST project and tested across ten European coastal case studies, CHUI advances both the operationalization of SESF and the practice of inclusive, adaptive conservation management. Full article

The rise of generative artificial intelligence (GenAI) is creating new opportunities for assessment design in universities, particularly in subjects that emphasize analytical and creative skills. This paper introduces the Educator–GenAI Partnership Model, an iterative five-stage model that helps educators create assessments that foster higher-order thinking (HOT). The model is grounded in constructive alignment and Bloom’s taxonomy, with a central emphasis on preserving human oversight to ensure educators retain control over assessment validity, academic integrity, and the ethical use of AI. The model maps out the unique strengths and responsibilities of both educators and GenAI, showing how each plays a distinct role in the assessment design process. It illustrates how GenAI can support the rapid generation of assessment tasks and marking rubrics, while positioning educators as critical decision-makers who only review, adapt, and iteratively refine AI-generated outputs to ensure alignment with higher-order learning outcomes. Overall, this paper presents a structured and practical model for utilizing GenAI responsibly in assessment design, thereby strengthening academic rigor while enhancing efficiency for educators. Full article

Bees are essential pollinators for agricultural systems, making accurate, automated monitoring of their behavior critical for assessing colony health and ecosystem stability. Recent advances in computer vision and artificial intelligence have enabled large-scale bee traffic monitoring at hive entrances; however, most existing event classification methods focus exclusively on simple entrance and exit events. This simplification overlooks compound movements—such as U-turns and guarding behaviors—that represent a substantial portion of bee activity and can lead to inaccurate trajectory reconstruction and misleading behavioral interpretations. In this work, we systematically analyze existing event classification strategies used in automatic bee traffic monitoring, evaluating their performance on both simple and compound movements. We then propose extended classification methods that explicitly model compound events by incorporating bidirectional movement patterns derived from positional and angular cues. Using a manually annotated dataset of computer-vision-based hive entrance recordings, we compare threshold-based, displacement-based, and angle-based approaches under simple and mixed-event conditions. Our results demonstrate that compound events account for over one-third of all detected movements and that classification methods explicitly designed to handle bidirectional behavior substantially outperform traditional approaches in both accuracy and robustness. In particular, threshold-based bidirectional classification achieves near-perfect performance when full trajectories are available, while displacement-based methods provide a reliable alternative under partial observations. These findings highlight the importance of modeling compound behaviors in automated bee monitoring systems and contribute to more accurate flight reconstruction, behavioral analysis, and AI-driven decision support for precision agriculture and pollinator management. Full article

The Camino de Santiago, a UNESCO-listed pilgrimage route, has experienced sustained growth in visitor numbers, challenging municipalities to preserve cultural integrity while ensuring service quality. This study reviews people-counting technologies and proposes a smart pilgrim management framework grounded in flux measurement systems to support data-driven and sustainable decision-making. Drawing on the smart tourism literature, the conceptual framework integrates infrared counters, mobile tracking solutions, and GPS/Wi-Fi data to generate real-time insights into pilgrim flows. A pilot simulation illustrates how these data can inform operational and strategic planning. The framework enables local authorities to monitor pedestrian movements, anticipate service demands (sanitation, accommodation, and safety), and detect overcrowding in sensitive heritage areas. By incorporating technological solutions into traditionally low-tech pilgrimage settings, municipalities can transition from reactive to proactive management approaches. The paper contributes a scalable and ethically grounded framework tailored to heritage pilgrimage routes, advancing smart tourism applications in culturally significant contexts. Full article

We present a parametrised charging infrastructure model developed to support the design of a hybrid electric zero-emission vessel with corresponding charging infrastructure for operation along the Norwegian Coastal Express route. The charging model includes functionalities to analyse the required battery storage capacity and power ratings and locations of charging facilities for achieving battery-electric operation. We demonstrate the use of the charging model to analyse different zero-emission scenarios for the Norwegian Coastal Express route. In the presented example scenarios, the model takes as input the estimated energy demand for a new zero-emission vessel design for the Coastal Express in different weather conditions, and includes functionality to consider realistic port stays based on existing timetables and historical data of delays. The analyses show minimal required battery capacities and illustrate a trade-off between charging power and battery capacity, as well as exemplifying the impact of different timetables and historic deviations on charging and energy delivered from the battery. The charging model presented is general and can be used for other routes than the Norwegian Coastal Express, as a tool for decision-makers to optimize for battery-electric operation whilst keeping the need for onboard storage capacity and charging infrastructure installations at a minimum. Full article