Search Results (178)

Background: Cardiovascular diseases (CVDs) remain the primary cause of global mortality, with arterial hypertension, ischemic heart disease (IHD), and cerebrovascular accident (CVA) forming a progressive continuum from early risk factors to severe outcomes. While numerous studies focus on isolated biomarkers, few integrate multidimensional visualization with artificial intelligence to reveal hidden, clinically relevant patterns. Methods: We conducted a comprehensive analysis of 106 patients using an integrated framework that combined clinical, biochemical, and lifestyle data. Parameters included renal function (glomerular filtration rate, cystatin C), inflammatory markers, lipid profile, enzymatic activity, and behavioral factors. After normalization and imputation, we applied correlation analysis, parallel coordinates visualization, t-distributed stochastic neighbor embedding (t-SNE) with k-means clustering, principal component analysis (PCA), and Random Forest modeling with SHAP (SHapley Additive exPlanations) interpretation. Bootstrap resampling was used to estimate 95% confidence intervals for mean absolute SHAP values, assessing feature stability. Results: Consistent patterns across outcomes revealed impaired renal function, reduced physical activity, and high hypertension prevalence in IHD and CVA. t-SNE clustering achieved complete separation of a high-risk group (100% CVD-positive) from a predominantly low-risk group (7.8% CVD rate), demonstrating unsupervised validation of biomarker discriminative power. PCA confirmed multidimensional structure, while Random Forest identified renal function, hypertension status, and physical activity as dominant predictors, achieving robust performance (Accuracy 0.818; AUC-ROC 0.854). SHAP analysis identified arterial hypertension, BMI, and physical inactivity as dominant predictors, complemented by renal biomarkers (GFR, cystatin) and NT-proBNP. Conclusions: This study pioneers the integration of multidimensional visualization and AI-driven analysis for CVD risk profiling, enabling interpretable, data-driven identification of high- and low-risk clusters. Despite the limited single-center cohort (n = 106) and cross-sectional design, the findings highlight the potential of interpretable models for precision prevention and transparent decision support in cardiovascular aging research. Full article

Modern energy systems are undergoing a profound transformation characterized by the active replacement of conventional fossil-fuel-based power plants with renewable energy sources. This transition aims to reduce the carbon emissions associated with electricity generation while enhancing the economic performance of electric power market players. However, alongside these benefits come several challenges, including reduced overall inertia within energy systems, heightened stochastic variability in grid operation regimes, and stricter demands on the rapid response capabilities and adaptability of emergency controls. This paper presents a novel methodology for selecting effective control laws for low-inertia energy systems, ensuring their dynamic stability during post-emergency operational conditions. The proposed approach integrates advanced techniques, including feature selection via decision tree algorithms, classification using Random Forest models, and result visualization through the Mean Shift clustering method applied to a two-dimensional representation derived from the t-distributed Stochastic Neighbor Embedding technique. A modified version of the IEEE39 benchmark model served as the testbed for numerical experiments, achieving a classification accuracy of 98.3%, accompanied by a control law synthesis delay of just 0.047 milliseconds. In conclusion, this work summarizes the key findings and outlines potential enhancements to refine the presented methodology further. Full article

This paper presents an automatic system for the classification of musical instruments from audio recordings. The project leverages deep learning (DL) techniques to achieve its objective, exploring three different classification approaches based on distinct input representations. The first method involves the extraction of Mel-Frequency Cepstral Coefficients (MFCCs) from the audio files, which are then fed into a two-dimensional convolutional neural network (Conv2D). The second approach makes use of mel-spectrogram images as input to a similar Conv2D architecture. The third approach employs conventional machine learning (ML) classifiers, including Logistic Regression, K-Nearest Neighbors, and Random Forest, trained on MFCC-derived feature vectors. To gain insight into the behavior of the DL model, explainability techniques were applied to the Conv2D model using mel-spectrograms, allowing for a better understanding of how the network interprets relevant features for classification. Additionally, t-distributed stochastic neighbor embedding (t-SNE) was employed on the MFCC vectors to visualize how instrument classes are organized in the feature space. One of the main challenges encountered was the class imbalance within the dataset, which was addressed by assigning class-specific weights during training. The results, in terms of classification accuracy, were very satisfactory across all approaches, with the convolutional models and Random Forest achieving around 97–98%, and Logistic Regression yielding slightly lower performance. In conclusion, the proposed methods proved effective for the selected dataset, and future work may focus on further improving class balance techniques. Full article

Cervical lymph node metastasis is the strongest prognostic factor in oral tongue carcinoma, yet current clinical guidelines rely primarily on depth of invasion to guide elective neck dissection. This approach results in unnecessary surgery in up to 70% of patients. Cancer-testis antigens (CTAs) are a family of genes associated with tumor aggressiveness and may serve as predictive biomarkers for nodal spread. A multi-step analysis integrating large-scale public datasets, including microarray (GSE78060), bulk RNA-seq emerging from the cancer genome atlas (TCGA), and single-cell RNA-seq (GSE103322), was employed to identify CTA genes active in oral tongue cancer. Selected genes were validated using NanoString nCounter RNA profiling of 16 patients undergoing curative glossectomy with elective neck dissection. Machine learning algorithms, including decision trees, t-distributed stochastic neighbor embedding (t-SNE), and convolutional neural networks (CNN), were applied to assess predictive power for nodal metastasis. Computational analysis initially identified 40 cancer-active CTA genes, of which four genes (LY6K, MAGEA3, CEP55, and ATAD2) were most indicative of nodal spread. In our patient cohort, NanoString nCounter profiling combined with machine learning confirmed these four genes as highly predictive. We present a proof-of-concept CTA-based genetic diagnostic tool capable of discriminating nodal involvement in oral tongue cancer. This approach may reduce unnecessary neck dissections, minimizing surgical morbidity. Full article

(1) Background: Quantitative EEG (QEEG) offers potential for identifying objective neurophysiological biomarkers in psychiatric disorders and guiding neurofeedback interventions. This study examined whether three nonlinear QEEG metrics—Lempel–Ziv Complexity, Tsallis Entropy, and Renyi Entropy—can distinguish children with autism spectrum disorder (ASD) from typically developing (TD) peers, and assessed their relevance for neurofeedback targeting. (2) Methods: EEG recordings from 19 scalp channels were analyzed in children with ASD and TD. The three nonlinear metrics were computed for each channel. Group differences were evaluated statistically, while machine learning classifiers assessed discriminative performance. Dimensionality reduction with t-distributed Stochastic Neighbor Embedding (t-SNE) was applied to visualize clustering. (3) Results: All metrics showed significant group differences across multiple channels. Machine learning classifiers achieved >90% accuracy, demonstrating robust discriminative power. t-SNE revealed distinct ASD and TD clustering, with nonlinear separability in specific channels. Visual processing–related channels were prominent contributors to both classifier predictions and t-SNE cluster boundaries. (4) Conclusions: Nonlinear QEEG metrics, particularly from visual processing regions, differentiate ASD from TD with high accuracy and may serve as objective biomarkers for neurofeedback. Combining complexity and entropy measures with machine learning and visualization techniques offers a relevant framework for ASD diagnosis and personalized intervention planning. Full article

Transcriptomic profiling is a powerful tool for dissecting the cellular and molecular complexity of ocular tissues, providing insights into retinal development, corneal disease, macular degeneration, and glaucoma. With the expansion of microarray, bulk RNA sequencing (RNA-seq), and single-cell RNA-seq technologies, artificial intelligence (AI) has emerged as a key strategy for analyzing high-dimensional gene expression data. This review synthesizes AI-enabled transcriptomic studies in ophthalmology from 2019 to 2025, highlighting how supervised and unsupervised machine learning (ML) methods have advanced biomarker discovery, cell type classification, and eye development and ocular disease modeling. Here, we discuss unsupervised techniques, such as principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection (UMAP), and weighted gene co-expression network analysis (WGCNA), now the standard in single-cell workflows. Supervised approaches are also discussed, including the least absolute shrinkage and selection operator (LASSO), support vector machines (SVMs), and random forests (RFs), and their utility in identifying diagnostic and prognostic markers in age-related macular degeneration (AMD), diabetic retinopathy (DR), glaucoma, keratoconus, thyroid eye disease, and posterior capsule opacification (PCO), as well as deep learning frameworks, such as variational autoencoders and neural networks that support multi-omics integration. Despite challenges in interpretability and standardization, explainable AI and multimodal approaches offer promising avenues for advancing precision ophthalmology. Full article

Bearings are critical components whose failures in industrial machinery can lead to catastrophic breakdowns and costly downtime; yet, accurate early-stage diagnosis remains challenging due to the non-stationary, nonlinear nature of vibration signals and noise interference. This study proposes a multidataset-integrated bearing fault diagnosis methodology incorporating variational mode decomposition (VMD), refined composite multiscale weighted permutation entropy (RCMWPE) feature extraction, and whale optimization algorithm (WOA)-optimized support vector machine (SVM). Addressing the non-stationary and nonlinear characteristics of bearing vibration signals, raw signals are first decomposed via VMD to effectively separate intrinsic mode functions (IMFs) carrying distinct frequency components. Subsequently, RCMWPE features are extracted from each IMF component to construct high-dimensional feature vectors. To address visualization challenges and mitigate feature redundancy, the t-distributed stochastic neighbor embedding (t-SNE) algorithm is employed for dimensionality reduction. Finally, WOA optimizes critical SVM parameters to establish an efficient fault classification model. The methodology is validated on two public bearing datasets: PRONOSTIA and CWRU. For four-class fault diagnosis on the PRONOSTIA dataset, the model achieves 96.5% accuracy. Extended to ten-class diagnosis on the CWRU dataset, accuracy reaches 99.67%. Experimental results demonstrate that the proposed method exhibits exceptional fault identification capability, robustness, and generalization performance across diverse datasets and complex fault modes. This approach offers an effective technical pathway for early bearing fault warning and maintenance decision making. Full article

Traditional landslide susceptibility assessment (LSA) methods typically adopt a global modeling strategy, which struggles to account for the pronounced spatial heterogeneity arising from variations in topography, geology, and vegetation conditions within a region. Furthermore, model predictive performance is often undermined by feature redundancy. To address these limitations, this study focuses on the landslide disaster early-warning demonstration area in Honghe Prefecture, Yunnan Province. It proposes an ensemble learning model termed heterogeneity feature optimized stacking (HF-stacking), which integrates spatial heterogeneity partitioning (SHP) with feature selection to improve the scientific rigor of LSA. This method initially establishes an LSA system comprising 15 static landslide conditioning factors (LCFs) and two dynamic factors representing the average annual deformation rates derived from interferometric synthetic aperture radar (InSAR) technology. Based on landslide inventory data, an SHP method combining t-distributed stochastic neighbor embedding (t-SNE) and iterative self-organizing (ISO) clustering was developed to divide the study area into subregions. Within each subregion, a tailored feature selection strategy was applied to determine the optimal feature subset. The final LSA was performed using the stacking ensemble learning approach. The results show that the HF-stacking model achieved the best overall performance, with an average AUC of 95.90% across subregions, 4.23% higher than the traditional stacking model. Other evaluation metrics also demonstrated comprehensive improvements. This study confirms that constructing an SHP framework and implementing feature selection strategies can effectively reduce the impact of spatial heterogeneity and feature redundancy, thereby significantly enhancing the predictive performance of LSA models. The proposed method contributes to improving the reliability of regional landslide risk assessments. Full article

Background/Objectives: Fatigue is a highly prevalent and burdensome symptom among individuals with Marfan syndrome (MFS), yet its heterogeneity and underlying psychosocial and clinical correlates remain underexplored. This study aimed to identify and characterize distinct fatigue-related profiles in MFS patients using a data-driven cluster analysis approach. Methods: A cross-sectional study was conducted involving 127 patients with MFS from a specialized connective tissue disorder center in Italy. Participants completed self-reported measures of fatigue severity (Fatigue Severity Scale, FSS), depressive symptoms (Patient Health Questionnaire-9, PHQ-9), and insomnia (Insomnia Severity Index, ISI). The body mass index (BMI) and clinical data were also collected. A t-distributed stochastic neighbor embedding (t-SNE) analysis was performed to reduce dimensionality, followed by hierarchical clustering (Ward’s method), exploring solutions from k = 2 to k = 10. The optimal cluster solution was identified based on silhouette scores and clinical interpretability. Results: Three distinct clusters emerged: (1) a cluster characterized by low fatigue with minimal psychological and sleep-related symptoms (younger patients, lower PHQ-9 and ISI scores), (2) a cluster characterized by moderate fatigue with moderate psychological and sleep-related symptoms (intermediate age, moderate PHQ-9 and ISI scores), and (3) a cluster characterized by high fatigue with elevated psychological and sleep-related symptoms (older patients, higher PHQ-9, ISI, and FSS scores). Significant differences were observed across clusters in age, BMI, depressive symptoms, insomnia severity, and fatigue levels (all p < 0.05). Conclusions: Our findings highlight the heterogeneity of fatigue experiences in MFS and suggest the importance of profiling patients to guide personalized interventions. This approach may inform precision medicine strategies and enhance the quality of life for individuals with this rare disease. Full article

Background/Objectives: Obstructive sleep apnea (OSA) is a highly prevalent disorder that significantly impacts quality of life and daily functioning. While continuous positive airway pressure (CPAP) therapy is effective, long-term adherence remains a challenge. This single-arm observational study aimed to evaluate clinical outcomes and adherence patterns during telemedicine-supported CPAP therapy and identify distinct phenotypic response clusters in Romanian patients with OSA. Methods: This prospective observational study included 86 adults diagnosed with OSA, treated with ResMed Auto CPAP devices at “Victor Babeș” University Hospital in Timișoara, Romania. All patients were remotely monitored via the AirView™ platform and received monthly telephone interventions to promote adherence when necessary. Clinical outcomes were assessed through objective telemonitoring data. K-means clustering and t-distributed stochastic neighbor embedding (t-SNE) were employed to explore phenotypic response patterns. Results: During telemedicine-supported CPAP therapy, significant clinical improvements were observed. The apnea–hypopnea index (AHI) decreased from 42.0 ± 21.1 to 1.9 ± 1.3 events/hour. CPAP adherence improved from 75.5% to 90.5% over six months. Average daily usage increased from 348.4 ± 85.8 to 384.2 ± 65.2 min. However, post hoc analysis revealed significant concerns about the validity of self-reported psychological improvements. Self-esteem changes showed negligible correlation with objective clinical measures (r < 0.2, all p > 0.1), with only 3.3% of variance being explained by measurable therapeutic factors (R² = 0.033). Clustering analysis identified four distinct adherence and outcome profiles, yet paradoxically, patients with lower adherence showed greater self-esteem improvements, contradicting therapeutic causation. Conclusions: Telemedicine-supported CPAP therapy with structured monthly interventions was associated with substantial clinical improvements, including excellent AHI reduction (22-fold) and high adherence rates (+15% after 6 months). Data-driven phenotyping successfully identified distinct patient response profiles, supporting personalized management approaches. However, the single-arm design prevents definitive attribution of improvements to telemonitoring versus natural adaptation or placebo effects. Self-reported psychological outcomes showed concerning patterns suggesting predominant placebo responses rather than therapeutic benefits. While the overall findings demonstrate the potential value of structured telemonitoring for objective CPAP outcomes, controlled trials are essential to establishing true therapeutic efficacy and distinguishing intervention effects from measurement bias. Full article

Personality is a fundamental psychological trait that exerts a long-term influence on human behavior patterns and social interactions. Automatic personality recognition (APR) has exhibited increasing importance across various domains, including Human–Robot Interaction (HRI), personalized services, and psychological assessments. In this study, we propose a multimodal personality recognition model that classifies the Big Five personality traits by extracting features from three heterogeneous sources: audio processed using Wav2Vec2, video represented as Skeleton Landmark time series, and text encoded through Bidirectional Encoder Representations from Transformers (BERT) and Doc2Vec embeddings. Each modality is handled through an independent Self-Attention block that highlights salient temporal information, and these representations are then summarized and integrated using a late fusion approach to effectively reflect both the inter-modal complementarity and cross-modal interactions. Compared to traditional recurrent neural network (RNN)-based multimodal models and unimodal classifiers, the proposed model achieves an improvement of up to 12 percent in the F1-score. It also maintains a high prediction accuracy and robustness under limited input conditions. Furthermore, a visualization based on t-distributed Stochastic Neighbor Embedding (t-SNE) demonstrates clear distributional separation across the personality classes, enhancing the interpretability of the model and providing insights into the structural characteristics of its latent representations. To support real-time deployment, a lightweight thread-based processing architecture is implemented, ensuring computational efficiency. By leveraging deep learning-based feature extraction and the Self-Attention mechanism, we present a novel personality recognition framework that balances performance with interpretability. The proposed approach establishes a strong foundation for practical applications in HRI, counseling, education, and other interactive systems that require personalized adaptation. Full article

This study aims to rapidly in situ identify starch sausage samples with the improper addition of chicken bone paste. Chicken bones play important roles in building materials, biomass fuels, and as food additives after enzymatic hydrolysis, but no current research indicates that chicken bones can be directly added to food for consumption. Especially in starch sausages, the addition of chicken bone paste is highly controversial due to potential risks of esophageal laceration and religious concerns. This paper first uses laser-induced breakdown spectroscopy (LIBS) to investigate the elemental differences between starch sausages and chicken bone paste. By preparing mixtures of starch sausages and chicken bone paste at different ratios, the relationships between the spectral peak intensities of elements, such as Ca, Ba, and Sr, and the proportion of chicken bone paste were determined. Through processing methods such as normalization with reference spectral lines, selection of the signal of the second laser pulse at the same position, and electron temperature correction, the determination coefficients (R²) of each element’s spectral lines have significantly improved. Specifically, the R² values for Ca I, Ca II, Ba II, and Sr II have increased from 0.302, 0.694, 0.857, and 0.691 to 0.972, 0.952, 0.970, and 0.982, respectively. Finally, principal component analysis (PCA) was used to distinguish starch sausages, chicken bone paste, and their mixtures at different ratios, with further effective differentiation achieved through t-distributed stochastic neighbor embedding (t-SNE). The results show that LIBS technology can serve as an effective and rapid method for detecting elemental composition in food and distinguishing different food products, providing safety guarantees for food production and supervision. Full article

Seismic waveform feature extraction is a critical task in seismic exploration, as it directly impacts reservoir prediction and geological interpretation. However, large-scale seismic data and nonlinear relationships between seismic signals and reservoir properties are challenging for traditional machine learning methods. To address these limitations, this paper proposes a novel framework combining Convolutional Neural Network (CNN) and Uniform Manifold Approximation and Projection (UMAP) for seismic waveform feature extraction and analysis. The UMAP-CNN framework leverages the strengths of manifold learning and deep learning, enabling multi-scale feature extraction and dimensionality reduction while preserving both local and global data structures. The evaluation experiments, which considered runtime, receiver operating characteristic (ROC) curves, embedding distribution maps, and other quantitative assessments, illustrated that the UMAP-CNN outperformed t-distributed stochastic neighbor embedding (t-SNE), locally linear embedding (LLE) and isometric feature mapping (Isomap). A case study in the Ordos Basin further demonstrated that UMAP-CNN offers a high degree of accuracy in predicting coal seam thickness. Furthermore, our framework exhibited superior computational efficiency and robustness in handling large-scale datasets. Full article

Background/Objectives: Patients with headache experience depression, anxiety, and reduced quality of life, which are individually associated with pain intensity and painkiller use, but their comprehensive combined effect remains unclear. Methods: Comprehensive patient groups were formed based on unsupervised clustering using machine learning algorithms, and their associations were analyzed via ordinary least square regression. K-means and t-distributed stochastic neighbor embedding (t-SNE) combined with hierarchical density-based spatial clustering of applications with noise (HDBSCAN) were applied for clustering. Results: A total of 813 patients were subdivided via K-means clustering (2 clusters) and t-SNE + HDBSCAN clustering (4 clusters). In the K-means clustering, Cluster 1 showed significantly lower peak pain intensity (coefficient [95% CI]: −0.7 [−1 to −0.4]) and frequency of painkiller use (−2.3 [−3.4 to −1.3]) compared to Cluster 0. In the t-SNE + HDBSCAN clustering, Clusters 2 and 3 showed higher peak pain intensity (1.1 [0.5–1.7] and 1.6 [1.0–2.2], respectively) and more frequent painkiller use (2.5 [0.4–4.5] and 4.4 [2.2–6.7], respectively) than Cluster 1. Conclusions: The clustering approach successfully generated groups that reflected a comprehensive profile of depression-, anxiety-, and headache-related quality of life. The clusters demonstrated significant differences which can help better characterize patients based on their psychological and functional impact. Full article

Rapid urban growth and the spread of the concept of smart cities force an increasing need to understand how cities become “smart” and apply their experience where it will best take root. Understanding which experience will be most suitable is not a trivial task and requires labor-intensive analysis. This study aims to identify smart cities that are most similar to Almaty and Astana in terms of key indicators by applying quantitative methods. Using a sample of smart cities, this paper successively employs three methods—principal component analysis, hierarchical cluster analysis, and t-distributed stochastic neighbor embedding. The results showed that Denver and Ottawa are the closest to Almaty and Astana, followed by Ankara and Phoenix. The proposed methodology allowed us to assess the similarity of urban development conditions, with an assumption that similar development conditions determine approaches to the development of smart cities, and thus the relevance of experiences from other smart cities worldwide could be applied to Almaty and Astana. This approach is intended to contribute to the effectiveness of transferring advanced solutions of smart city development to the context of Kazakhstan. The obtained conclusions can be used to form recommendations for the development strategy of Almaty and Astana, as well as other cities facing similar challenges. Full article