Search Results (113)

Background/Objectives: Medical training prepares students to diagnose known diseases and barely at all for the uncertainty that fills emergency practice, where symptom-based labels are common and nearly half of emergency medicine residents report difficulty discussing what remains unknown. This article sets out a six-category framework that separates medical uncertainty into distinct epistemological forms, so that communication can be matched to the uncertainty at hand. Methods: I used critical interpretive synthesis with iterative taxonomy development under prespecified ending conditions, arriving at six categories: (1) known and treatable, (2) unexplained, (3) untreatable, (4) heterogeneous, and (5) contested diseases, plus (6) depathologized conditions. Derivation ran iteratively against a purposive set of exemplar conditions, and existing uncertainty taxonomies were examined as alternatives. For each category I identified its epistemological features, the patient experience, and the communication it requires then built recommendations for teaching, assessment, and faculty development. Results: The framework supports structured reasoning and category-specific communication at three points in the emergency encounter: initial assessment, diagnostic testing, and disposition. Keeping a “known and treatable” category is what shows learners that apparently settled knowledge is provisional and context-dependent, which guards against false confidence. Categories are usefully assignable without being mutually exclusive; a condition may occupy several at once and may migrate as knowledge changes. Conclusions: The framework replaces generic advice to “tolerate ambiguity” with concrete tools for category recognition, emergency department safety-netting, disposition communication, and assessment. It is conceptually derived; its categories, scripts, and rubric require empirical validation through curriculum implementation and educational outcomes research. Full article

Adaptive decision-making has emerged as an important direction in audio classification. Reinforcement learning (RL) and multi-armed bandit (MAB) methods offer principled frameworks for sequential and localized decision-making. However, despite growing interest in these approaches and the availability of review studies on audio classification and audio-based learning, their role within the audio classification pipeline remains fragmented and has not been systematically synthesized. This study presents a systematic review of RL- and MAB-based approaches in audio classification, to characterize how adaptive decision-making is formulated, where it is applied within the pipeline, and what impact it has on system performance, robustness, and efficiency. The review followed PRISMA guidelines. A literature search across IEEE Xplore, Scopus, Nature, and Google Scholar identified 1896 records. Thirty-one studies met the predefined inclusion criteria and data were extracted for analysis. Reporting quality was assessed using the TRIPOD framework, and methodological reliability was evaluated using a domain-specific risk-of-bias tool. The findings show that adaptive decision-making in audio classification is evolving toward a control-centric paradigm. RL-based optimization and control approaches dominated the literature, whereas bandit-based approaches remained comparatively underused. A central finding of the review is a structural mismatch between problem type and method choice. Many adaptive tasks are inherently local and repeated decision problems, yet they are predominantly addressed using full RL frameworks rather than lighter bandit formulations. This review introduces a taxonomy of adaptive decision-making mechanisms and proposes a unified framework that reframes audio classification as a layered decision-making process under uncertainty. The evidence suggests that the future of audio classification lies not only in improved prediction, but in adaptive decision-making architectures capable of managing uncertainty, variability, and real-world deployment constraints. Full article

Background: Berberine, an isoquinoline alkaloid present in Berberis spp., Coptis chinensis and Hydrastis canadensis, is among the most widely consumed metabolic-health supplements, popularized as “nature’s Ozempic”. Concurrent, often undisclosed use with prescription drugs is common in older adults, yet berberine is far from inert. Objective: To synthesize the evidence on berberine as a perpetrator of supplement–drug interactions, propose a four-axis mechanistic taxonomy, with product quality treated separately as a modifier of exposure rather than as a mechanism, and derive a clinically actionable risk-stratification framework. Methods: Structured narrative review, prepared per the SANRA quality criteria; PubMed/MEDLINE, Scopus, Web of Science and Embase were searched up to May 2026. Results: Despite very low systemic exposure (oral bioavailability 0.68% in rats; low ng/mL plasma concentrations in humans), high luminal, enterocytic and hepatic concentrations generate interaction liability, documented in humans for a few pairs and mechanistic for most, along four mechanistic axes: inhibition, and transcriptional induction, of CYP3A4, with CYP2D6/CYP2C9 inhibition that is quasi-irreversible through a metabolite-intermediate complex; transporter modulation (P-glycoprotein, OCT1/OCT2, and MATE1); pharmacodynamic additivity (hypoglycemia, hypotension, and QT prolongation); and microbiome- and gut-barrier-mediated effects, the last of these being a candidate axis rather than a demonstrated one. Product-quality variability is treated separately, as a modifier of exposure. The clinical anchor is increased cyclosporine exposure in renal-transplant recipients (AUC +34.5%; trough 29.3% above control). These elements are integrated into a three-tier risk-stratification framework that combines perpetrator potency, victim-drug vulnerability, and patient vulnerability, with each tier being linked to a defined pharmacy action. Conclusions: In patients on multiple medications, and particularly when berberine is co-administered with drugs of narrow therapeutic index, it should be managed as an active pharmacological perpetrator rather than as an inert supplement. Unstandardized product quality and an unsettled European regulatory framework, under which national limits differ by more than an order of magnitude, further widen the uncertainty around the dose actually delivered. Berberine use should therefore be elicited routinely at medication reconciliation and stratified by mechanism, by victim-drug vulnerability, and by patient risk, with particular attention to metabolic self-medication in the GLP-1 era. Full article

The Internet of Medical Things (IoMT) is a promising technology for enabling smart and efficient healthcare systems through continuous physiological monitoring, early anomaly detection, and proactive patient management. However, IoMT sensors are vulnerable to False Data Injection Attacks (FDIAs), in which adversaries can manipulate sensor measurements to compromise diagnostic accuracy, mislead clinical decision-making, and threaten patient safety. Existing detection approaches often rely on population-level statistical models that may not fully capture individual physiological variations or residual-based thresholds designed for relatively simple attack scenarios, limiting their ability to exploit the spatio-temporal dependencies of multi-sensor physiological streams and detect stealthy or adversarial FDIAs. This paper proposes a patient-specific FDIA detection framework based on a Graph Convolutional Network–Gated Recurrent Unit (GCN–GRU) digital twin that learns an individual patient’s normal physiological behaviour from clean baseline telemetry. The trained digital twin is integrated into a Kalman filter as the state prediction model, and the resulting standardised innovation residuals are used as detection features. To characterise stealthy attack behaviours, four complementary window-based feature groups are extracted from the innovation sequence: innovation statistics, sensor correlation drift, temporal smoothness, and uncertainty mismatch. A CNN-1D classifier is then trained to learn discriminative temporal attack patterns from these features for accurate detection. A structured attack taxonomy comprising five stealthy and adversarial FDIA scenarios is developed, where attacks are injected as smooth gradual or abrupt coordinated modifications to sensor measurements while remaining within plausible physiological ranges. Experiments conducted on the WUSTL-EHMS-2020 benchmark dataset demonstrate that the proposed framework achieves an F1-score of 94.3%, outperforming Isolation Forest and PCA Reconstruction by 34 percentage points. Furthermore, the proposed framework reduces the false alarm rate to 3.6%, compared with 35.1% and 9.2% achieved by Isolation Forest and PCA Reconstruction, respectively. These results demonstrate the effectiveness of the proposed framework for reliable detection of stealthy FDIAs in IoMT-based healthcare systems. Full article

The shift from passive predictive models to autonomous agents capable of tool use and multi-step planning moves the AI safety landscape from prediction error to control failure: small misjudgements become irreversible actions, and risks compound across long horizons and populations of interacting systems. We present a structured review and taxonomy of open scientific problems in agentic AI safety, mapped explicitly onto the EU AI Act and the NIST AI Risk Management Framework. The corpus follows a PRISMA-ScR scoping review, assembled through anchor-based citation chaining and curated reading lists across arXiv, the major machine-learning conferences, and selected security and fairness venues, with a primary March 2026 search cut-off (extended to May 2026 during revision for a small number of high-relevance governance and agentic-safety sources), explicit eligibility criteria, and an analytical distinction between open scientific problems and deployment risks. The taxonomy identifies eight problem families spanning reinforcement-learning policies and language-model planners: goal specification, inner alignment, safe learning and robustness, scalable oversight, interpretability, tool-use security, multi-agent safety, and evaluation and assurance. Mapping these onto the two frameworks shows close alignment for some families and notable absences for others, with multi-agent safety surfacing as a regulatory gap. We add a per-family research roadmap with concrete milestones and a practitioner-facing deployment-posture triage, arguing that progress on inner alignment, interpretability for deceptive-alignment detection, and multi-agent safety would most directly reduce compliance uncertainty. Full article

The family Limoniidae is the most species-rich within the order Diptera. The classification of this family is still unresolved and is based largely on morphological studies and the opinions of a few specialists. The rapid advancement of molecular techniques has created new opportunities to address long-standing systematic challenges. Nevertheless, relatively few studies have used molecular data to refine our understanding of Limoniidae and its relationships with allied lineages. In the present study, we reviewed publications that applied molecular approaches to improve the classification of Limoniidae and clarify its relationships with related groups. Relevant studies were identified through the literature section of the Catalogue of the Craneflies of the World using the keywords “classification”, “systematics”, and “molecular”. We retained only studies that employed molecular data to investigate the classification of Limoniidae, the phylogenetic position of the family within Tipuloidea, and the placement of Tipulomorpha within the dipteran tree. The studies reviewed consistently support the conclusion that Limoniidae is not monophyletic. In contrast, Tipulomorpha is consistently recovered as a monophyletic lineage, although its position within the dipteran tree remains controversial. Consequently, a satisfactory taxonomic framework has yet to be achieved. Recent advances in high-throughput sequencing and the generation of large-scale molecular datasets provide unprecedented opportunities to resolve these systematic uncertainties. We therefore advocate expanding both taxon sampling and the number of genetic markers included in phylogenetic analyses. Ultimately, a coordinated effort among research groups and institutions is likely to represent the most effective strategy for resolving the taxonomy and evolutionary relationships of Limoniidae. Full article

The concept of Meta-Action Units (MAUs) has emerged as a promising paradigm for decomposing machine tool motion into fundamental action units, providing new insights into error propagation and accuracy stability in CNC machine tools. This paper presents a comprehensive review of accuracy stability from the MAU perspective. Fluctuation mechanisms induced by geometric errors, thermal effects, load-dependent deformations and wear-related degradation are systematically reviewed. Existing modeling, identification, and compensation methods are critically analyzed. A key contribution is the synthesis of a novel MAU-centric taxonomy integrating research on key MAU identification, precision remaining useful life prediction under incomplete maintenance, cascading fault propagation and reliability coupling mechanisms. The integration of screw theory, multi-body systems, and active learning Kriging is examined, along with hybrid approaches combining physics-based models with machine learning. The alignment of MAU-based digital twins with Industry 4.0 and Industry 5.0 is discussed. MAU decomposition provides a physically interpretable framework for accuracy formation. Hybrid Wiener–GPIM models achieve PRUL prediction errors below ten percent. Five research gaps are identified: uncertainty propagation, robust parameter identification, benchmark datasets, cost–benefit frameworks, and transfer learning. Addressing these gaps will guide the development of next-generation high-accuracy and intelligent CNC machine tools. Full article

The medical device manufacturing industry in Mexico faces a critical risk of losing competitiveness and sustainability due to its concentration on low-value-added manufacturing activities and limited integration into advanced stages of the value chain, such as R&D. This research addresses the lack of validated quantitative methodologies to identify the critical factors that promote sectoral sustainability in the national context. Through a literature review and the analysis of MCDM, a taxonomy of the problem was developed that integrates dimensions of governance, technological innovation, and human capital. The findings emphasize the need to transition toward circular economy and additive manufacturing models, supported by hybrid algorithms such as AHP, TOPSIS, and DEMATEL to mitigate uncertainty in strategic decision making. As a main result, an innovation management flow aligned with international standards and several maturity levels (TRLs, MRLs, CRLs, and PRLs) is proposed, providing a structured roadmap to scale the Mexican industry toward more-sophisticated global segments. Full article

Financial statement fraud inflicts large and recurring losses on capital markets, yet the dominant detection paradigm still relies on single, black-box classifiers (e.g., RUSBoost) trained on structured accounting ratios alone. Two limitations follow: (i) the rich, unstructured Management Discussion and Analysis (MD&A) narrative of the 10-K filing is discarded, and (ii) the resulting scores are difficult for auditors to trust because they carry no transparent, standards-aligned rationale. Recent large language model (LLM) systems have shown that multi-agent collaboration is more robust than a single LLM for anomaly detection, but no study has systematically transferred this paradigm to listed-company statement fraud. We propose FraudDebate-Agent, a four-role multi-agent system in which a Quantitative Analyst agent scores 28 raw accounting items and 14 ratios with gradient-boosted and tabular attention models, a Narrative Auditor agent quantifies tone, linguistic uncertainty, and year-over-year textual novelty of the MD&A with FinBERT, and an Industry Peer agent uses retrieval-augmented generation to measure industry-relative anomaly. A Critic–Debate agent then orchestrates a pair-wise Evidence-based Multi-Agent Debate (EMAD) that reconciles disagreement across modalities and arbitrates a reconciled fraud-risk assessment, which is aggregated over a tri-modal evidence graph. Our contributions are as follows: (1) the first use of an evidence-grounded debate mechanism for accounting fraud, which materially reduces LLM hallucination; (2) a numerical–textual–peer evidence graph that fuses heterogeneous signals; and (3) an explainable report aligned with the PCAOB AS 2401 fraud-risk taxonomy. On AAER-labelled firm-years linked across a SEC financial dataset and EDGAR-CORPUS, FraudDebate-Agent improves the area under the ROC curve and the rare-event ranking metric NDCG@k over the strongest single-modality and single-LLM baselines while producing substantially more faithful explanations. We frame the system as a fraud-risk screening and risk-ranking tool for AAER-labelled misstatement risk rather than a determination of fraudulent intent. We report results over multiple seeds to reflect real-world stochasticity and discuss limitations and cross-domain applications. Full article

The Markets in Crypto-Assets Regulation (MiCA) harmonizes market rules across the European Union, but it does not itself determine how entities should classify, measure, document, and tax crypto-asset transactions. This study examines whether Slovakia’s recent implementation measures have translated formal harmonization into operational accounting clarity. An anonymous online survey of 34 accounting, tax, finance, and business professionals recruited through purposive and convenience sampling was analyzed using counts, percentages, a descriptive cross-tabulation with Cramér’s V, and a documented but limited coding of two open-ended items. Because the sample is small and non-probability, all results are interpreted as indicative of the observed respondents rather than as population estimates. In the sample, 52.9% assessed the direction of legislative development positively, 72.7% of valid respondents considered current valuation rules inadequate, 60.6% reported that the reforms had not increased accounting clarity, and 60.6% perceived greater uncertainty. Tax obligations (50.0%) and record-keeping and documentation (35.3%) were selected more often than bookkeeping mechanics (14.7%). Practical experience co-varied with legislative monitoring in the realized sample (Cramér’s V = 0.62), although no population-inferential p-values or confidence intervals are reported. The pattern suggests a regulatory–operational clarity gap: legal taxonomy and market supervision have advanced faster than implementable valuation and documentation guidance. The proposed implementation framework combines survey indications with regulatory analysis, prior literature, and the authors’ professional judgment; it is a non-ranked proposal for consultation and further testing rather than a set of empirically validated policy priorities. Although derived from Slovakia, the framework is relevant to other EU jurisdictions translating MiCA’s common market rules into national accounting and tax practice. Full article

Over the past two decades, air-quality prediction has moved from a mainly single-model paradigm toward ensemble systems that make explicit use of diversity across models, observations, and data streams. This review connects developments that are often treated separately: chemical transport model (CTM) ensembles, tree-based and hybrid machine learning ensembles, deep learning architectures, physics-informed neural networks, and distributed approaches such as federated learning. Evidence summarized from recent systematic reviews and coordinated modeling initiatives indicates that, within comparable validation settings, ensembles often outperform individual models for PM_2.5, PM₁₀, O₃, NO₂, CO, and SO₂ across a broad range of spatial scales and standard error metrics, including RMSE, MAE, and correlation. Operational CTM ensembles, such as the Copernicus Atmosphere Monitoring Service (CAMS) European system with eleven regional models, improve both forecast skill and uncertainty characterization for ozone and particulate matter. In data-driven applications, tree-based ensembles (Random Forest, gradient boosting, XGBoost, LightGBM) and hybrid deep architectures (CNN–LSTM models, attention-based multi-branch networks, graph neural networks) now form a core part of the state of the art for AQI (Air Quality Index) and particulate-matter estimation from structured and multi-source data. Reported performance can be very high on well-structured tabular datasets, with $R^2$ values above 0.99 in selected benchmarks and RMSE reductions of 23–45% relative to classical statistical baselines in multi-modal studies; however, these values are not directly interchangeable because pollutant type, prediction horizon, monitoring density, and validation design differ among studies. This review proposes a practical taxonomy of ensemble strategies and uses it to explain why diversity, rather than model count alone, is central to reliable air-quality prediction. Drawing on coordinated European and North American model-evaluation initiatives (AQMEII, HTAP) and on case studies in topographically and meteorologically complex Italian regions (the Po Valley, the Naples metropolitan area, and Campania), we show that effective ensemble design requires a balance among diversity, redundancy, computational feasibility, and interpretability. On the basis of a structured narrative synthesis, the main research gaps concern physics-informed and explainable ensemble frameworks, transferable and adaptive models, standardized benchmarks, severe-pollution-episode forecasting, and scalable distributed architectures. Open questions include how to design compact non-redundant CTM sub-ensembles and how to couple deep learning with chemical-transport physics in next-generation operational systems. Full article

Short-term electricity demand forecasting is a critical enabler of the secure and efficient operation of modern power systems, particularly amid increasing renewable energy integration, smart grid expansion, and the broader energy transition. This paper presents a rigorous comparative analysis of electricity demand forecasting models, encompassing statistical methods, Machine Learning (ML), Deep Learning (DL), and hybrid architectures. A structured taxonomy is proposed to classify models according to their methodological family, application horizon, and data requirements, thereby providing a unified reference framework for researchers and energy-sector practitioners. Models are evaluated using a multi-criteria framework comprising accuracy, robustness, scalability, interpretability, computational cost, and the capacity to handle exogenous variables. The analysis identifies critical research gaps, including the limited integration of probabilistic forecasting into operational contexts and the absence of standardized evaluation protocols under real-world conditions. Future research directions are outlined, with particular emphasis on uncertainty quantification, adaptive learning strategies, and hierarchical forecast coherence in systems with high penetration of distributed energy resources. Full article

This paper presents a metrology-oriented review of vision-based displacement and deformation measurement for civil structural health monitoring (SHM), with an emphasis on field robustness and uncertainty quantification (UQ). The review focuses on image- and video-based methods that convert visual information into quantitative physical measurements, such as displacement, strain, or derived dynamic indicators. The literature is organized according to the main stages of the measurement chain: image formation, image-plane motion estimation, and geometric conversion to metric motion. Within this framework, measurement pipelines are interpreted through three levels of geometric mapping, namely, a scalar scale-factor model, a planar homography-based model, and a full Jacobian-based model. The review synthesizes major method families, including marker-based and markerless tracking, feature-based tracking, optical flow, digital image correlation (DIC), phase-based motion magnification, edge-based estimators, fixed- and moving-camera configurations, UAV-based acquisition with ego-motion compensation, hybrid vision–sensor fusion, and deep-learning-enhanced pipelines. A structured taxonomy of uncertainty sources is then presented along the processing chain, covering camera geometry and calibration, imaging noise and blur, quantization, timing and synchronization, environmental disturbances, optical turbulence and heat haze, platform motion, algorithmic failure modes, and reference-sensor uncertainty. The paper also compares UQ practices, including GUM-aligned analytical propagation, Monte Carlo methods, DIC-specific error budgets, bootstrap and resampling strategies, and probabilistic deep learning. The main contribution of this review is to connect computer-vision-based displacement pipelines with metrological requirements by explicitly linking measurement models, uncertainty sources, UQ methods, and field-validation evidence within a unified framework. A practical uncertainty-budget template is compiled to support traceable reporting across different pipelines and deployment scenarios. The paper concludes with prioritized research gaps and future directions, including standardized benchmarks and datasets, traceable UQ for moving-camera systems, multi-sensor fusion with end-to-end uncertainty propagation, long-term drift characterization, optical-turbulence and adverse-weather modeling, validated subpixel limits at extreme range, probabilistic deep learning–metrology integration, and standardized reporting practices. Full article

The spacecraft reachable domain has become increasingly important for orbital game analysis due to growing on-orbit activities such as servicing, debris removal, and space situational awareness. This paper provides a comprehensive review of reachable domain theory and its applications in orbital games. A unified mathematical framework is established through three complementary classification dimensions: spatial attributes that distinguish absolute from relative reachable domains, temporal attributes that differentiate free-time from fixed-time reachable domains, and informational attributes that contrast deterministic and predictive reachable domains. Solution methods are systematically reviewed according to this taxonomy, covering analytical and semi-analytical methods, numerical optimization approaches, and geometric and sampling methods for spatial-scale reachable domains, as well as linearized ellipsoidal approximation, exact envelope determination, and fast analytical approximation for time-scale reachable domains. Applications are examined through three representative scenarios: one-on-one pursuit-evasion games, multi-agent cooperative games, and threat-avoidance and defense games. Key limitations of existing approaches are identified, including modeling fidelity, computational efficiency, and scalability under uncertainty. Future research directions are outlined to address these challenges. Full article

Business Process Management (BPM) process models represent routing behaviour through control-flow constructs, yet BPMN 2.0 does not provide a native mechanism for quantifying uncertainty associated with routing decisions. This study presents the Information Entropy Performance Indicator (IEPI) as a deterministic BPMN analytics artifact for evaluating routing uncertainty under externally specified routing probabilities. The IEPI framework integrates construct-level routing diagnostics, viability assessment, diagnostic flagging, compositional uncertainty propagation, and process-level reporting within a unified analytical workflow. The IEPI engine accepts as input a BPMN 2.0 process representation, a routing-probability map, and analyst-specified viability thresholds. It computes (i) construct-level diagnostics based on normalized entropy and responsiveness, (ii) block-level uncertainty and responsiveness quantities using fixed composition rules for XOR, OR, and LOOP routing constructs, and (iii) a bounded process-level viability-band reporting index. The framework is evaluated using four analytically constructed BPMN authorisation workflows designed to exercise the complete routing-construct taxonomy supported by the artifact. Results demonstrate that construct-level classifications, propagated uncertainty quantities, and process-level IEPI values are well defined and reproducible under fixed inputs. Threshold sensitivity analysis shows that local viability classifications and aggregate reporting outputs vary deterministically with threshold settings and remain consistent with the underlying routing diagnostics. The findings highlight the distinction between uncertainty propagation and viability-band compliance. While propagated uncertainty quantities characterize the accumulation of routing uncertainty within a process structure, the IEPI score provides a reporting-oriented assessment of aggregate compliance with analyst-defined viability criteria. The proposed artifact offers a reproducible and extensible analytical framework for routing-uncertainty evaluation in BPMN-based process models. Full article