Search Results (436)

Mispronunciation detection (MD) is a key component in computer-assisted pronunciation training (CAPT) and speaking tests. Most MD systems adopt a production view, measuring phone-level deviation from a canonical pronunciation (Native Norm) or the expected pronunciation of a target population (Target Norm). Yet, pronunciation assessment is fundamentally perceptual: listeners map speech to linguistic categories under uncertainty and with individual psychological priors, so judgments are inherently subjective and lack a single gold standard. Labels are therefore often aggregated (e.g., voting), but aggregation rules are themselves subjective, require many annotators, and entangle individual perception with social consensus, complicating model training. In this paper, we propose a “Perception Norm”, which models MD as the decision process of individual annotators and trains models to simulate single listeners rather than an annotator pool. To support this study, we introduce UY/CH-CHILD-MA, a corpus of Uyghur-accented child Mandarin words and phrases with four independent phone-level annotations. Our experiments reveal substantial inter-annotator variation and show that a Transformer with pre-training and fine-tuning can learn annotator-specific patterns with high accuracy. Finally, we present a committee ensemble that combines annotator models using application-matched aggregation rules to produce task-specific assessments. The data and source code will be made publicly available upon publication. Full article

Background: An appropriate and effective management of haemophilia is currently based on a multidimensional evaluation of treatment adequacy. Current clinical practice however is still lacking standardised tools able to combine clinical, functional, and patient-reported outcomes. In this study a structured Monitoring Tool for haemophilia A and B was developed and validated through a Delphi-based expert consensus process. This study represents an expert consensus-based validation of a monitoring framework, rather than a clinical validation in patient cohorts. The tool is intended for use by haemophilia treaters during routine follow-up visits to support structured treatment reassessment. Score categories reflect the need for clinical re-evaluation or potential treatment optimisation, rather than disease severity. Methods: Italian haemophilia specialists were asked to participate to a panel over a two-round Delphi process. Experts rated the relevance of several predefined clinical domains—pharmacokinetics, bleeding episodes, joint health, adherence and quality of life (QoL)—and the individual items within each domain for patients on prophylactic or on-demand treatment. Consensus was defined by responses within an interquartile range (IQR) < 8. Section and item weights and Likert-based scoring values were used to reach a composite score between 0 and 100. Results: Consensus was achieved for all domains and items across haemophilia types and treatments, prophylaxis and on demand (Haemophilia A: 16 and 12 participants; Haemophilia B: 12 and 9, respectively). With reference to prophylaxis domains, bleeding episodes received the highest domain weight (31–32%), followed by joint health (27–29%) and adherence/QoL (21–23%) and pharmacokinetics (18–19%). For on-demand treatment, pharmacokinetics was excluded; bleeding episodes (38–40%) and joint health (35–37%) remained dominant. At the item level, dynamic joint health indicators (HJHS and HEAD-US changes) and longitudinal QoL changes consistently received the highest weights. The final scoring system categorised results as Excellent (0–25), Suboptimal (26–50), Poor (51–75), or Critical (76–100). Conclusions: The Delphi-validated Monitoring Tools provide a structured, weighted, and clinically relevant framework for assessing treatment adequacy in haemophilia A and B across prophylactic and on-demand settings. These tools allow multidimensional outcome assessment and may support a more consistent, personalised therapeutic decision-making. A prospective validation of the tool in clinical cohorts is warranted. Full article

Background/Objectives: Acute cor pulmonale is a critical clinical condition often encountered in acute care settings. Optimal management demands coordinated, interdisciplinary care. The aim of this study was to assess the current knowledge and management strategies for acute cor pulmonale among different groups of physicians involved in acute care in Switzerland. Methods: A structured questionnaire, extrapolated from the Acute Cardiovascular Care Association of the European Society of Cardiology clinical consensus statement on the diagnosis and treatment of cor pulmonale, was distributed among physicians of four specialties. Results: A total of 110 physicians participated in this multicenter survey, including 15 “experts,” 71 “generalists” (internal and emergency medicine), and 24 “specialists” (cardiology and intensive care). Experts validated 29 out of 40 questionnaire items (Fleiss Kappa 0.63), which were then used for analysis. Overall, there was substantial agreement with the experts’ answers among non-experts, with most correct response rates exceeding 60%. Significant differences were observed for only two items: experts more frequently recognized the prognostic value of clinical models (87% vs. 59%, p = 0.046) and the correct indications for systemic thrombolysis (100% vs. 76%, p = 0.037). Between generalists and specialists, differences in knowledge were minimal. Specialists more accurately identified the role of repeated arterial blood gas analysis, while generalists showed better awareness of clinical prognostic models. Conclusions: The study highlights a sound knowledge of acute cor pulmonale among acute care physicians, regardless of specialty. Despite comparable levels of knowledge, some variations reflect their clinical roles and information sources. The results emphasize the value of existing educational efforts and support the need for comprehensive, accessible guidelines to standardize care in complex conditions like acute cor pulmonale. Full article

To address voltage over-limit and transformer overload issues in distribution grids caused by large-scale distributed PV integration, this paper proposes a distributed cooperative-based intelligent load optimization technique for distribution grids. First, by analyzing the limitations of traditional centralized control in communication burden, response speed, and fault tolerance, the necessity of distributed cooperative control is demonstrated. Subsequently, leveraging the bidirectional power regulation capability of energy storage systems, a distributed PV-storage system cooperative control model based on a consensus algorithm is constructed. This model comprehensively considers PV output fluctuations, energy storage state of charge, and grid regulation demands. Through multi-node information exchange and iterative updates of consensus variables, the model achieves coordinated power allocation among systems and voltage overlimit mitigation. Simulation results demonstrate that the proposed method effectively smooths PV fluctuations and alleviates local overloads in distribution grids. It simultaneously accommodates capacity differences and operational constraints across energy storage systems, enhancing system response speed and robustness. This provides effective technical support for the safe operation of distribution grids under high penetration of distributed renewable energy. Full article

In social network-based group decision-making, achieving consensus often entails costs, leading to an inherent trade-off between cost and consensus. To address this issue, we propose a dual-semantic, multi-objective consensus optimization model that simultaneously minimizes cost and maximizes consensus. The resulting Pareto set offers decision makers (DMs) multiple trade-off solutions between cost and consensus. Specifically, we first develop a 2-tuple trust propagation model that incorporates path knowledge and path length to improve the completeness and accuracy of indirect trust inference. Building on this foundation, we adaptively adjust DM weights by combining trust relationships with dynamic incentive weights. This design balances individual influence and adjustment willingness throughout the consensus-reaching process. Finally, we formulate a multi-objective decision optimization model. This model integrates minimum cost and maximum consensus to generate a modified decision matrix for efficiently aggregating group opinions. A multi-physician collaboration case in a medical diagnostic decision-support system validates the effectiveness of the proposed method. Full article

Introduction: Artificial intelligence (AI) and robot-assisted platforms are increasingly influencing cardiothoracic surgery. AI enhances risk prediction, imaging interpretation, and early complication detection, while robotics improves visualization, dexterity, and minimally invasive access. This systematic review evaluates the current evidence supporting these technologies and their implications for clinical practice. Methods: A systematic literature search was conducted across PubMed, Embase, Scopus, Web of Science, and Google Scholar (January 2000–May 2025) following PRISMA 2020 guidelines. After screening and eligibility assessment, 67 studies met predefined inclusion criteria and were incorporated into the qualitative synthesis. Additional high-impact reviews and consensus documents were consulted for contextual interpretation. Results: Machine learning models demonstrated modest but consistent improvements in predictive performance compared with EuroSCORE II and STS scores, particularly in high-risk cohorts. Robot-assisted mitral and coronary procedures showed reduced postoperative pain, blood loss, ICU stay, and recovery time in experienced centers, though early learning phases were associated with longer operative, cross-clamp, and bypass times. AI-enabled intraoperative tools, such as video analysis, workflow recognition, and real-time anatomical segmentation, emerged as promising adjuncts for surgical precision. Structured robotic training programs, especially simulation-based and dual-console pathways, accelerated proficiency acquisition. Conclusions: AI and robotic systems act as augmentative technologies that enhance rather than replace the surgeon’s role. Their safe and effective adoption requires standardized training, transparent AI decision pathways, and clear ethical and medico-legal governance. Full article

Maritime Autonomous Surface Ships (MASS) are progressing from proof-of-concept to engineering test and initial application phases due to advancements in intelligent sensing, automatic control, and communication technologies. However, numerous studies have shown that the improvement of automation level does not linearly reduce human factor risks. Instead, it exhibits more complex evolutionary characteristics at the medium automation level. In particular, MASS Level 2 (MASS L2) features a “system-dominated, human-supervised” operational mode, and its human factor risks have become one of the key factors restricting the safe operation, large-scale application and sustainable long-term deployment of autonomous ships. This study employs a systematic literature review to analyze 89 core articles (2020–2025) and summarizes the theoretical basis, risk characteristics, and evolutionary trends of human factor risk research in MASS L2. The review results indicate that the current research consensus has gradually shifted from the traditional “human error”-centered explanatory paradigm to a systematic understanding of “information mismatches, opacity, and coupling failures in the human-machine-shore collaborative system”. Typical human factor risks in MASS L2 are mainly manifested as the degradation of supervisory cognition and situation awareness, imbalance in trust in automation, vulnerability in mode switching and takeover, skill degradation, and structural risks in ship-shore collaboration. Based on these findings, this study constructs a classification system and a comprehensive analysis framework for human factor risks in MASS L2, reveals the interaction relationships and dynamic evolution mechanisms among different risk types from a system-level perspective, and further discusses the limitations of existing research in terms of methods, data, and engineering applicability. Finally, considering the development trends of autonomous ship technology, this study proposes future research directions in human factor theoretical modeling, dynamic risk assessment, system design, and operation management. This study aims to provide a systematic knowledge framework for human factor risk research in MASS L2 and offer references for the safety design, safety management, and development of higher-level automation of autonomous ships, while supporting the sustainable and safe advancement of the global intelligent shipping industry. Full article

This paper introduces D³O-GT, a distributed optimization framework designed to operate under partial, heterogeneous, and delayed information—conditions commonly encountered in large-scale logistics and networked decision support systems. The proposed approach integrates gradient tracking with delay-aware updates to address the steady-state bias and instability that often affect classical distributed gradient methods. We formulate a consensus optimization model that captures decentralized decision variables while preserving global optimality, and we develop an algorithmic structure that balances convergence accuracy, communication efficiency, and robustness to asynchronous updates. Extensive numerical experiments demonstrate that D³O-GT achieves machine precision convergence in synchronous settings and remains stable under bounded communication delays, converging to a small neighborhood of the optimum. In contrast, conventional distributed gradient descent exhibits significant residual error under the same conditions. Scalability analyses further indicate that the proposed method maintains favorable iteration complexity as the number of agents increases. These results position D³O-GT as a practical and scalable solution for distributed decision-making environments, with direct relevance to logistics-oriented applications such as resource allocation, coordination of networked services, and real-time operational planning. Full article

Background/Objectives: Accurate and reproducible lymph node (LN) measurement is essential for oncologic staging and therapy monitoring but is subject to inter-reader variability. This study evaluated the accuracy and reproducibility of a fully automated artificial intelligence (AI)-based LN measurement workflow in contrast-enhanced chest CT, using multi-reader manual measurements as reference. Methods: Sixty thoracic LNs from seven patients were independently measured by 13 radiologists in two reading rounds. The median of all measurements served as the ground truth (GT). Automated short- and long-axis diameters were derived from fully automated 3D CNN-based segmentations. Agreement between AI and manual measurements was assessed using Friedman testing, intraclass correlation coefficients (ICCs), and concordance correlation coefficients (CCCs). Measurement stability was evaluated across repeated runs on different hardware systems. Results: A total of 2280 manual measurements were analyzed. Manual assessment showed significant inter-reader variability (p < 0.01), while intra-reader agreement was high. No significant differences were observed between AI-based measurements and the GT (all p > 0.01). Agreement was good, with CCC values of 0.86 (SAD) and 0.79 (LAD). AI-based measurements were numerically stable across hardware configurations. Conclusions: Fully automated AI-based LN measurements in chest CT scans provide strong agreement with multi-reader consensus and high numerical stability. Automated measurement may support more standardized and reproducible oncologic imaging assessment. Full article

Current peer-to-peer (P2P) energy trading systems face important challenges in decentralised trading environments, particularly in managing participant trustworthiness, preventing dishonest behaviour, and mitigating transaction defaults. These limitations reduce transaction reliability and weaken trust among participants in community-scale energy trading markets. Although P2P energy trading enables communities to exchange locally generated renewable energy in smart environments, existing platforms often lack effective mechanisms to regulate participant behaviour and support reliable transactions. This paper proposes RepuTrade, a blockchain-based P2P energy trading platform tailored for community-scale microgrids. The proposed framework integrates a reputation-based consensus mechanism and a dynamic collateral management scheme that is directly linked to participant reputations such that trading reliability can be strengthened through behavioural incentives. In addition, a reputation-driven matching algorithm preferentially pairs highly reputable participants to improve market stability and trust. Simulation-based evaluation, involving 200 users across 8 trading rounds, shows that the RepuTrade framework consistently achieves higher trade success rates (92–99% compared to 83–95% in the baseline) and reduces defaults by more than 40% (27–44 vs. 55–72 per run). The results further reveal a strong negative correlation between user reputation and default probability, indicating that higher reputation is associated with a lower likelihood of dishonest behaviour. Overall, under the simulated settings considered in this study, the proposed framework improves transaction reliability and execution efficiency by reducing failed trades and lowering consensus validation latency. These findings contribute to the design of trust-aware decentralised energy trading mechanisms and provide simulation-based insights for developing more reliable and transparent community-scale renewable energy markets. Full article

Echocardiographic assessment is essential for evaluating patients with cardiogenic shock (CS) and determining their potential need for mechanical circulatory support (MCS) implantation. The use of Impella devices has increased significantly in recent years, paralleling the growing recognition of their hemodynamic benefits in selected patient populations. As the clinical experience with these devices has expanded, the need for a more standardized imaging approach has emerged. Both transthoracic echocardiography (TTE) and transesophageal echocardiography (TEE) play complementary roles in guiding the pre-implantation evaluation, placement procedure, and post-implantation management of Impella devices. Currently, no comprehensive guidelines exist concerning the echocardiographic evaluation of Impella devices throughout their entire clinical course, from initial patient selection and device implantation to ongoing monitoring and eventual weaning. This gap in standardized guidance has led to significant variability in clinical practice across different institutions and healthcare systems. This comprehensive review examines the role of transthoracic echocardiography (TTE) and transesophageal echocardiography (TEE) in managing patients on Impella support across five distinct phases: candidate identification and pre-implantation assessment, intraoperative procedural guidance and device positioning, postoperative monitoring and haemodynamic optimisation, complication detection and troubleshooting, and weaning strategies with post-explantation surveillance. Both left-sided devices (Impella CP, CP Smart Assist, and Impella 5.5) and right-sided support (Impella RP) are covered, including combined configurations with VA-ECMO (ECPella). For each phase, we detail the recommended echocardiographic views, essential measurements and their evidence-based thresholds, signs of device malposition, and practical corrective strategies. A level-of-evidence approach is adopted throughout, specifying whether proposed thresholds derive from randomised trials, observational studies, expert consensus, or manufacturer recommendations. Summary tables and a bedside workflow are provided to facilitate immediate clinical application. Full article

The decentralisation of autonomous Unmanned Aircraft Systems (UASs) introduces significant challenges in terms of establishing secure communication and consensus in contested, resource-constrained environments. This research addresses these challenges by conducting a comprehensive performance evaluation of two cryptographic technologies: Messaging Layer Security (MLS) for group key exchange, and threshold signatures (FROST and BLS) for decentralised consensus. Seven leading open-source libraries were methodically assessed through a series of static, network-simulated, and novel bulk-signing benchmarks to measure their computational efficiency and practical resilience. This paper confirms that MLS is a viable solution, capable of supporting the group sizes and throughput requirements of a UAS swarm. It corroborates prior work by identifying the Cisco MLSpp library as unsuitable for dynamic environments due to poorly scaling group management functions, while demonstrating that OpenMLS is a highly performant and scalable alternative. Furthermore, the findings show that operating MLS in a ‘key management’ mode offers a dramatic increase in performance and resilience, a critical trade-off for UAS operations. For consensus, the benchmarks reveal a range of compromises for developers to consider, while identifying the Zcash FROST implementation as the most effective all-around performer for sustained, high-volume use cases due to its balance of security features and efficient verification. Full article

Background: Left ventriculo-arterial coupling (VAC) integrates the interaction between left ventricular contractility and the arterial system, representing a key determinant of cardiovascular efficiency. In rapidly changing hemodynamic states such as septic or cardiogenic shock, conventional indices of pressure or flow alone may be misleading. VAC provides a unified physiological framework to assess global cardiovascular performance and guide therapy. Objective: To review the physiological foundations, bedside assessment, and therapeutic applications of VAC in critically ill patients with rapidly fluctuating circulatory conditions. Methods and Content: The article revisits the underlying principles of VAC, expressed as the ratio between arterial elastance (Ea) and end-systolic elastance (Ees), and discusses their derivation from the pressure–volume relationship. Practical echocardiographic methods for bedside estimation, including the non-invasive single-beat approach, are outlined with illustrative figures. The review further examines how VAC patterns evolve in sepsis, cardiogenic shock, and heart failure and how this integrative index clarifies paradoxical responses to vasoactive and inotropic therapies. Specific therapeutic phenotypes are proposed according to Ea/Ees profiles, providing a structured approach to optimise coupling and restore circulatory efficiency. Summary: VAC offers a physiology-based perspective on cardiovascular performance, enabling clinicians to interpret complex hemodynamic changes beyond traditional measures of ejection fraction or mean arterial pressure. Its dynamic tracking may refine the assessment of therapeutic trajectories and improve bedside decision-making. Conclusions: By integrating ventricular and arterial function into a single measure, VAC bridges cardiovascular physiology and clinical practice. Its incorporation into routine critical care monitoring could enhance individualised hemodynamic management and serve as a foundation for future outcome-driven studies. Methodology: This narrative review was conducted using a structured literature search to ensure comprehensive coverage of contemporary evidence regarding ventriculo-arterial coupling (VAC) in critical care and shock states. A systematic search of PubMed/MEDLINE, Embase, and Scopus databases was performed from database inception through October 2025. The following key search terms were used: “ventriculo-arterial coupling”; “arterial elastance”; “end-systolic elastance”; “Ea/Ees”; “pressure–volume loops”; “septic shock”; “cardiogenic shock”; “critical care echocardiography”; “point-of-care ultrasound”; “mechanical circulatory support”. Reference lists of relevant articles, review papers, and consensus documents were also manually screened to identify additional pertinent studies. Only English-language publications were included. Both seminal foundational studies and recent contemporary investigations were reviewed to provide historical context and up-to-date clinical applicability. Full article

Context: Sport-related concussion (SRC) management has evolved substantially over the past 25 years. Early paradigms emphasized prolonged physical and cognitive rest; however, growing evidence has demonstrated that recovery following SRC is multidimensional and influenced by interacting neurological, vestibular, autonomic, cervical, cognitive, and psychological systems. Consequently, contemporary clinical practice has shifted toward active, multifaceted rehabilitation approaches. Objective: We aimed to synthesize and contextualize the evidence supporting a multifaceted approach to sport-related concussion management from 2000 through 2025, with emphasis on implications for athletic training practice. Data Sources: A structured literature search was conducted using PubMed, SPORTDiscus, CINAHL, and Web of Science to identify peer-reviewed publications related to SRC evaluation, management, and rehabilitation. Study Selection: Studies published between 1 January 2000, and 31 December 2025 involving human participants with sport-related concussion or sport-like mechanisms of mild traumatic brain injury were included. Evidence from randomized controlled trials, cohort studies, systematic and narrative reviews, and major consensus or position statements was considered. Data Extraction: Relevant studies were reviewed and synthesized across key domains of SRC management, including aerobic exercise, vestibular and oculomotor rehabilitation, cervical spine management, multimodal and profile-based rehabilitation, return-to-learn strategies, psychological and behavioral health considerations, and implementation patterns within athletic training settings. Results: A total of 182 publications contributed evidence to one or more components of multifaceted SRC management. Across domains, evidence supports early, symptom-limited aerobic exercise; targeted vestibular and cervical rehabilitation; structured return-to-learn planning; and the integration of psychological support. Multimodal rehabilitation and profile-based clinical categorization approaches were associated with shorter recovery timelines and improved functional outcomes compared with rest-only strategies. Despite strong evidence, implementation variability persists across athletic training settings. Conclusions: Evidence accumulated over the past 25 years supports a shift toward active, individualized, and multidisciplinary approaches to SRC management. Athletic trainers are uniquely positioned to coordinate multifaceted care addressing the diverse contributors to concussion recovery. Full article

There is a growing consensus that we must look beyond IQ to understand the mechanisms of talent development. Grounded in the Actiotope Model of Giftedness, this study adopts a resource-based approach and examines the incremental and interactive contributions of educational and learning capital to STEM achievement beyond IQ. Data were collected from 318 German secondary school students (grades 6–10; M_age = 12.08; 50.3% male) using domain-specific measures of educational and learning capital, a nonverbal matrix intelligence test, and STEM grades. Robust regression and mediation analyses showed that learning capital significantly predicted STEM achievement beyond general intelligence, whereas educational capital exerted no direct effect. Instead, the relationship between educational capital and achievement was fully mediated by learning capital. Moreover, the interaction term of educational and learning capital predicted achievement. A further interaction indicated that the positive effect of learning capital on STEM achievement was stronger for students with higher intelligence, consistent with an intelligence utilization (Matthew) effect. These findings support a systemic interpretation of achievement in which intelligence reflects prior resource utilization and functions as a catalyst, while current learning resources constitute the proximal determinants of STEM performance. Full article