Search Results (229)

Background/Objectives: Effective teamwork is crucial for minimizing human error in healthcare settings. Medical teams, typically composed of physicians and nurses, supported by auxiliary professionals, achieve better outcomes when they possess strong collaborative competencies. High-quality teamwork is associated with fewer adverse events and complications and lower mortality rates. Based on this background, the objective of this study is to analyze the perception of non-technical skills and immediate learning outcomes in interprofessional simulation settings based on E-CRM items. Methods: A cross-sectional observational study was conducted involving participants from the official postgraduate Medicine and Nursing programs at the Catholic University of Murcia (UCAM) during the 2024–2025 academic year. Four interprofessional E-CRM simulation sessions were planned, involving randomly assigned groups with proportional representation of medical and nursing students. Teams worked consistently throughout the training and participated in clinical scenarios observed via video transmission by their peers. Post-scenario debriefings followed INACSL guidelines and employed the PEARLS method. Results: Findings indicate that 48.3% of participants had no difficulty identifying the team leader, while 51.7% reported minor difficulty. Role assignment posed moderate-to-high difficulty for 24.1% of respondents. Communication, situation awareness, and early help-seeking were generally managed with ease, though mobilizing resources remained a challenge for 27.5% of participants. Conclusions: This study supports the value of interprofessional education in developing essential competencies for handling urgent, emergency, and high-complexity clinical situations. Strengthening interdisciplinary collaboration contributes to safer, more effective patient care. Full article

In industrial systems, sensors often classify collected time series data for incipient fault diagnosis. However, time series data from sensors during the initial stages of a fault often exhibits minor fluctuation characteristics. Existing time series classification (TSC) methods struggle to achieve high classification accuracy when these minor fluctuations serve as the primary distinguishing feature. This limitation arises because the low-amplitude variations of these fluctuations, compared with trends, lead the classifier to prioritize and learn trend features while ignoring the minor fluctuations crucial for accurate classification. To address this challenge, this paper proposes a novel graph-based time series classification framework, termed MFSI-TSC. MFSI-TSC first extracts the trend component of the raw time series. Subsequently, both the trend series and the raw series are represented as graphs by extracting the “visible relationship” of the series. By performing a subtraction operation between these graphs, the framework isolates the differential information arising from the minor fluctuations. The subtracted graph effectively captures minor fluctuations by highlighting topological variations, thereby making them more distinguishable. Furthermore, the framework incorporates optimizations to reduce computational complexity, facilitating its deployment in resource-constrained sensor systems. Finally, empirical evaluation of MFSI-TSC on both real-world and publicly available datasets demonstrates its effectiveness. Compared with ten benchmark methods, MFSI-TSC exhibits both high accuracy and computational efficiency, making it more suitable for deployment in sensor systems to complete incipient fault detection tasks. Full article

Post-viral conditions, Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and Long COVID (LC), share > 95% of their symptoms, but the connection between disturbances in their underlying molecular biology is unclear. This study investigates DNA methylation patterns in peripheral blood mononuclear cells (PBMC) from patients with ME/CFS, LC, and healthy controls (HC). Reduced Representation Bisulphite Sequencing (RRBS) was applied to the DNA of age- and sex-matched cohorts: ME/CFS (n = 5), LC (n = 5), and HC (n = 5). The global DNA methylomes of the three cohorts were similar and spread equally across all chromosomes, except the sex chromosomes, but there were distinct minor changes in the exons of the disease cohorts towards more hypermethylation. A principal component analysis (PCA) analysing significant methylation changes (p < 0.05) separated the ME/CFS, LC, and HC cohorts into three distinct clusters. Analysis with a limit of >10% methylation difference and at p < 0.05 identified 214 Differentially Methylated Fragments (DMF) in ME/CFS, and 429 in LC compared to HC. Of these, 118 DMFs were common to both cohorts. Those in promoters and exons were mainly hypermethylated, with a minority hypomethylated. There were rarer examples with either no change in methylation in ME/CFS but a change in LC, or a methylation change in ME/CFS but in the opposite direction in LC. The differential methylation in a number of fragments was significantly greater in the LC cohort than in the ME/CFS cohort. Our data reveal a generally shared epigenetic makeup between ME/CFS and LC but with specific, distinct changes. Differences between the two cohorts likely reflect the stage of the disease from onset (LC 1 year vs. ME/CFS 12 years), but specific changes imposed by the SARS-CoV-2 virus in the case of the LC patients cannot be discounted. These findings provide a foundation for further studies with larger cohorts at the same disease stage and for functional analyses to establish clinical relevance. Full article

Global data reveal ongoing inequalities faced by minorities, often reinforced by media portrayals that depict them as threats, victims, or passive individuals without agency. While media framing has been extensively studied, especially in terms of media content and representation, few studies have examined how journalists perceive and navigate the coverage of minorities. This study addresses that gap by examining how Greek journalists perceive mainstream media coverage of refugees and migrants, LGBTQ+ individuals, and people with mental health challenges, with particular attention to their sourcing practices and sense of ethical responsibility. Fourteen journalists participated in semi-structured interviews, and thematic analysis was applied to identify key patterns. Journalists described dominant media narratives as fragmented, stereotypical, and dehumanizing, noting the frequent use of linguistic inaccuracies, misinformation, and the absence of personal stories. At the same time, they reported opportunities within their own sourcing practices to promote more inclusive and accurate coverage. Ethical concerns were expressed on three levels—union; corporate; and personal—with calls for clearer editorial guidelines and dedicated training. Many participants emphasized the role of personal ethics as a guiding compass in navigating complex newsroom pressures. Full article

The aircraft fuel pump is a critical component of the aviation fuel supply system, and its fault diagnosis is essential in ensuring flight safety. However, in practical operating conditions, fault samples are scarce and data distributions are highly imbalanced, which severely limits the ability of traditional models to identify minority-class faults. To address this challenge, this paper proposes a fault diagnosis method for aircraft fuel pumps based on adaptive synthetic data augmentation using a Conditional Variational Autoencoder (CVAE). The CVAE generates semantically consistent and feature-diverse minority-class samples under class-conditional constraints, thereby enhancing the overall representational capacity of the dataset. Simultaneously, the Adaptive Synthetic (ADASYN) approach adaptively augments hard-to-classify samples near decision boundaries, enabling fine-grained control over sample distribution. The integration of these two techniques establishes a “broad coverage + focused refinement” augmentation strategy, effectively mitigating the class imbalance problem. Experimental results demonstrate that the proposed method significantly improves the recognition performance of minority-class faults on real-world aircraft fuel pump fault datasets. Full article

Accurate classification of electrocardiogram (ECG) signals is vital for reliable arrhythmia diagnosis and informed clinical decision-making, yet real-world datasets often suffer severe class imbalance that degrades recall and F1-score. To address these limitations, we introduce MAK-Net, a hybrid deep learning framework that combines: (1) a four-branch multiscale convolutional module for comprehensive feature extraction across diverse waveform morphologies; (2) an efficient channel attention mechanism for adaptive weighting of clinically salient segments; (3) bidirectional gated recurrent units (BiGRU) to capture long-range temporal dependencies; and (4) Kolmogorov–Arnold Network (KAN) layers with learnable spline activations for enhanced nonlinear representation and interpretability. We further mitigate imbalance by synergistically applying focal loss and the Synthetic Minority Oversampling Technique (SMOTE). On the MIT-BIH arrhythmia database, MAK-Net attains state-of-the-art performance—0.9980 accuracy, 0.9888 F1-score, 0.9871 recall, 0.9905 precision, and 0.9991 specificity—demonstrating superior robustness to imbalanced classes compared with existing methods. These findings validate the efficacy of multiscale feature fusion, attention-guided learning, and KAN-based nonlinear mapping for automated, clinically reliable arrhythmia detection. Full article

Epileptic seizure detection and classification from EEG recordings faces significant challenges due to extreme class imbalance. Analysis of the Temple University Hospital Seizure (TUSZ) dataset reveals imbalance ratios of 150:1 between common and rare seizure types, with high temporal heterogeneity (seizure durations of 1–1638 s). We propose a cascaded deep learning architecture with two specialized CNNs: a binary detector followed by a multi-class classifier. This approach decomposes the classification problem, reducing the maximum imbalance from 150:1 to manageable levels (9:1 binary, 5:1 type). The architecture implements a high-confidence filtering mechanism (threshold = 0.9), creating a 99.5% pure dataset for type classification, dynamic class-weighted optimization proportional to inverse class frequencies, and information flow refinement through progressive stages. Loss dynamics analysis reveals that our weighting scheme strategically redistributes optimization attention, reducing variance by 90.7% for majority classes while increasing variance for minority classes, ensuring all seizure types receive proportional learning signals regardless of representation. The binary classifier achieves 99.64% specificity and 98.23% sensitivity (ROC-AUC = 0.995). The type classifier demonstrates >99% accuracy across seven seizure categories with perfect (100%) classification for three seizure types despite minimal representation. Cross-dataset validation on the University of Bonn dataset confirms robust generalization (96.0% accuracy) for binary seizure detection. This framework effectively addresses multi-level imbalance in neurophysiological signal classification with hierarchical class structures. Full article

Satellite image-based farmland classification plays an essential role in agricultural monitoring. However, typical tiling-based classification approaches, which extract patches at fixed offsets within each image during training, often suffer from structural issues such as patch duplication, limiting training diversity. Additionally, farmland classification frequently exhibits class imbalance due to uneven cultivation areas, resulting in biased training toward majority classes and poorer performance on minority classes. To overcome these issues, we propose Class-Balanced Random Patch Training, which combines Random Patch Extraction (RPE) and Class-Balanced Sampling (CBS). This method improves patch-level diversity and ensures balanced class representation during training. We evaluated our method on the FarmMap dataset, using a validation set from the same region and year as the training data, and a test set from a different year and region to simulate domain shifts. Our approach improved the F1 scores of minority classes and overall performance. Furthermore, our analysis across varying levels of class difficulty showed that the method consistently outperformed other configurations, regardless of minority-class difficulty. These results demonstrate that the proposed method offers a practical and generalizable solution for addressing class imbalance in tiling-based remote sensing classification, particularly under real-world conditions with spatial and temporal variability. Full article

The widespread adoption of web applications has led to a significant increase in the number of automated cyberattacks. Parameter tampering attacks pose a substantial security threat, enabling privilege escalation and unauthorized data exfiltration. Traditional pattern-based detection tools exhibit limited efficacy against such threats, as identical parameters may produce varying response patterns contingent on their processing context, including security filtering mechanisms. This study proposes a machine learning-based detection model to address these limitations by identifying parameter tampering vulnerabilities through a contextual analysis. The training dataset aggregates real-world vulnerability cases collected from web crawls, public vulnerability databases, and penetration testing reports. The Synthetic Minority Over-sampling Technique (SMOTE) was employed to address the data imbalance during training. Recall was adopted as the primary evaluation metric to prioritize the detection of true vulnerabilities. Comparative analysis showed that the XGBoost model demonstrated superior performance and was selected as the detection model. Validation was performed using web URLs with known parameter tampering vulnerabilities, achieving a detection rate of 73.3%, outperforming existing open-source automated tools. The proposed model enhances vulnerability detection by incorporating semantic representations of parameters and their values using BERT embeddings, enabling the system to learn contextual characteristics beyond the capabilities of pattern-based methods. These findings suggest the potential of the proposed method for scalable, efficient, and automated security diagnostics in large-scale web environments. Full article

Synthetic aperture radar (SAR) automatic target recognition (ATR) plays a pivotal role in SAR image interpretation. While existing approaches predominantly rely on batch learning paradigms, their practical deployment is constrained by the sequential arrival of training data and high retraining costs. To overcome this challenge, this paper introduces a divergence-constrained incremental dictionary learning framework that enables progressive model updates without full data reprocessing. Specifically, firstly, this method learns class-specific dictionaries for each target category via sub-dictionary learning, where the learning process for a specific class does not involve data from other classes. Secondly, the intra-class divergence constraint is incorporated during sub-dictionary learning to address the challenges of significant intra-class variations and minor inter-class differences in SAR targets. Thirdly, the sparse representation coefficients of the target to be classified are solved across all sub-dictionaries, followed by the computation of corresponding reconstruction errors and intra-class divergence metrics to achieve classification. Finally, when the targets of new categories are obtained, the corresponding class-specific dictionaries are calculated and added to the learned dictionary set. In this way, the incremental update of the SAR ATR system is completed. Experimental results on the MSTAR dataset indicate that our method attains >96.62% accuracy across various incremental scenarios. Compared with other state-of-the-art methods, it demonstrates better recognition performance and robustness. Full article

Lung cancer remains the leading cause of cancer-related death in the US and worldwide. Recent advances in molecular profiling and targeted therapies have revolutionized the management of non-small cell lung cancer (NSCLC), particularly in oncogene-driven subtypes. These therapies selectively target key molecular alterations in EGFR, ALK, KRAS, ROS1, MET, RET, ERBB2 (HER2), BRAF V600E, and NTRK, resulting in substantial improvements in survival rates and quality of life for lung cancer patients. However, disparities in molecular diagnostics and precision treatments persist, disproportionately affecting minority patients. These inequities include underrepresentation in clinical trials, disparities in molecular testing, and barriers to treatment access. The limited participation of racial and ethnic minorities in landmark clinical trials raises concerns about the generalizability of findings and their applicability to diverse populations. In this review, we examine the current landscape of molecular diagnosis and precision medicine in minority patients with oncogene-driven lung cancer, highlighting challenges, opportunities, and future directions for achieving equity in precision oncology. Additionally, we discuss differences in the prevalence of oncologic driver mutations across populations and emphasize the urgent need for greater diversity in clinical research. Addressing these gaps is critical to improving survival outcomes and ensuring equitable access to personalized lung cancer care for all patients. Full article

Rockfall poses a significant hazard in steep terrain, where complex ground interactions cause falling boulders to deviate from straight-line paths. While lateral dispersion is commonly used to describe the distribution of deposited boulders from rockfall events, it does not provide any insight into the complexity of boulder trajectories while in motion. This study introduces tortuosity, a metric typically applied in porous media hydraulic analysis, as a novel approach for quantifying the deviation of rockfall paths from linearity. Using high-resolution UAV-based LiDAR data and RocFall3 (Version 1.017) simulation software, this research investigates the effects of terrain model resolution, boulder shape, and boulder mass on tortuosity values for 20,000 simulated rockfalls on a columnar jointed basalt slope in Boise, ID, USA. Results show that increasing terrain resolution leads to higher tortuosity values due to the increased presence of terrain asperities. Spherical boulders exhibited higher tortuosity than hexagonal ones, and tortuosity decreased with increasing mass for spheres, likely due to their momentum overcoming minor terrain features. Hexagonal boulders, constrained by their angular shape, showed less variability in tortuosity across resolutions and sizes. These findings emphasize the limitations of low-resolution publicly available LiDAR data and highlight the critical influence of accurate boulder representation in simulation models. Full article

Heart attack is a leading cause of mortality, necessitating timely and precise diagnosis to improve patient outcomes. However, timely diagnosis remains a challenge due to the complex and nonlinear relationships between clinical indicators. Machine learning (ML) and deep learning (DL) models have the potential to predict cardiac conditions by identifying complex patterns within data, but their “black-box” nature restricts interpretability, making it challenging for healthcare professionals to comprehend the reasoning behind predictions. This lack of interpretability limits their clinical trust and adoption. The proposed approach addresses this limitation by integrating predictive modeling with Explainable AI (XAI) to ensure both accuracy and transparency in clinical decision-making. The proposed study enhances heart attack prediction using the University of California, Irvine (UCI) dataset, which includes various heart analysis parameters collected through electrocardiogram (ECG) sensors, blood pressure monitors, and biochemical analyzers. Due to class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied to enhance the representation of the minority class. After preprocessing, various ML algorithms were employed, among which Artificial Neural Networks (ANN) achieved the highest performance with 96.1% accuracy, 95.7% recall, and 95.7% F1-score. To enhance the interpretability of ANN, two XAI techniques, specifically SHapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), were utilized. This study incrementally benchmarks SMOTE, ANN, and XAI techniques such as SHAP and LIME on standardized cardiac datasets, emphasizing clinical interpretability and providing a reproducible framework for practical healthcare implementation. These techniques enable healthcare practitioners to understand the model’s decisions, identify key predictive features, and enhance clinical judgment. By bridging the gap between AI-driven performance and practical medical implementation, this work contributes to making heart attack prediction both highly accurate and interpretable, facilitating its adoption in real-world clinical settings. Full article

We present a systematic evaluation of population health reviews from the Cochrane Database (January 2013–February 2023) to evaluate how indicators of inequity or disadvantage are considered and reported in population health evidence syntheses. Descriptive analyses explored a representation of reviews across health-determinant categories (primary and secondary categories), summarised equity-focused reviews, and examined proportions and types of reviews that planned/completed a subgroup analysis using ≥1 indicators from the PROGRESS-Plus framework. Of 363 reviews included, a minority focused on interventions targeting wider determinants of health (n = 83, 22.9% as primary category), with a predominance related to individual lifestyle factors (n = 155, 42.7%) or health care services intervention (n = 97, 26.7%). An explicit equity focus was evident in 21 (5.8%) reviews that used PROGRESS/PROGRESS-Plus, and 28 (7.7%) targeting vulnerable groups. Almost half (n = 165, 45.6%) planned a subgroup analysis by ≥1 PROGRESS-Plus indicator, which was completed in 63 reviews (38.2% of 165). The non-completion of planned subgroup analyses was attributed to insufficient data (too few primary studies, data not reported by subgroups). Among the 165 reviews planning a subgroup analysis, age was the most cited indicator (n = 91, 55.2%), followed by gender/sex (n = 67, 40.6%), place (n = 47, 28.5%) and socio-economic status (n = 37, 22.4%). This study highlighted missed opportunities for learning about the impacts of health equity in population health evidence syntheses due to insufficient data. We recommend routine use of PROGRESS-Plus and greater consistency in socio-economic proxies (occupation, education, income, disadvantage measures) to facilitate meta-analyses and subgroup analyses. Full article

Background/Objectives: We address the ongoing under-representation of ethnic minority groups in cardiovascular research in this opinion paper—a challenge that limits both scientific validity and equitable healthcare outcomes. We aim to outline the underlying causes of this issue and propose concrete strategies to address it. Methods: To engage ethnic minorities in cardiovascular research, we thoroughly studied the existing literature and gathered expert opinions to identify barriers and formulate practical solutions. Results: Our findings highlight the need for a multifaceted approach, including culturally appropriate educational outreach, interactive multimedia information, community ambassador programs, and improved, but ethically sound, ethnicity registration practices. Conclusions: To promote ethnic minority participation in cardiovascular research, a thorough improvement plan is required. Our proposed solutions, which align with insights from the current literature, suggest that addressing cultural, structural, and informational barriers can help achieve a more representative and inclusive participant population. This is an essential step towards improving cardiovascular outcomes for all. Full article