Search Results (3,515)

Background/Objectives: Cognitive decline and compromised attention control serve as early indicators of neurodysfunction that manifest as broader psychopathological symptoms, yet conventional mental health assessment relies predominantly on subjective self-report measures lacking objectivity and temporal granularity. We propose Neurosense, an AI-driven brain health assessment framework using electroencephalography (EEG) to non-invasively capture neural dynamics. Methods: Our Dual-path Spatio-Temporal Adaptive Gated Encoder (D-STAGE) architecture processes temporal and spatial EEG features in parallel through Transformer-based and graph convolutional pathways, integrating them via adaptive gating mechanisms. We introduce a two-stage paradigm: first training on cognitive task EEG for reaction time prediction to acquire cognitive performance-related representations, then featuring parameter-efficient adapter-based transfer learning to estimate p-factor—a transdiagnostic psychopathology dimension. The adapter-based transfer achieves competitive performance using only 1.7% of parameters required for full fine-tuning. Results: The model achieves effective reaction time prediction from EEG signals. Transfer learning from cognitive tasks to mental health assessment demonstrates that cognitive efficiency representations can be adapted for p-factor prediction, outperforming direct training approaches while maintaining parameter efficiency. Conclusions: The Neurosense framework reveals hierarchical relationships between neural dynamics, cognitive efficiency, and mental health dimensions, establishing foundations for a promising computational framework for mental health assessment applications. Full article

Liver fibrosis, the progressive accumulation of scar tissue resulting from chronic liver disease, is increasingly recognized as a multi-system condition, the effects of which extend beyond the liver, affecting brain health. Dementia, characterized by progressively impaired cognition sufficient to impede daily functioning, is a major global health issue with incompletely defined risk factors and pathogenic precursors. To examine the relationship between liver fibrosis and cognitive outcomes, we conducted a comprehensive PubMed literature search, and human studies published in English were included. Evidence is synthesized on the pathophysiology and clinical significance of liver fibrosis, types of dementia, and studies supporting the association between liver fibrosis and cognitive impairment. Meta-analytic data indicate that liver fibrosis is associated with an approximately 30% increased risk of incident dementia (pooled hazard ratio ~1.3), with progressively higher risks across more advanced fibrosis stages. Putative pathomechanisms, potentially modulated by age and sex, include chronic systemic and neuro-inflammation, insulin resistance, vascular dysfunction, and a perturbed intestinal microbiota–liver–brain axis. Non-invasive liver fibrosis diagnostics, advanced neuroimaging, and biomarkers represent key tools for assessing risk. In conclusion, liver fibrosis is a systemic condition that can affect brain health. Early detection, thorough risk assessment and interventions, such as lifestyle changes, metabolic therapies, and antifibrotic treatments, may help protect neural function. Key research gaps are identified, with suggestions for improving understanding of liver fibrosis’s connection to dementia or cognitive impairment. Full article

Reliable and low-latency seizure detection from electroencephalography (EEG) is critical for continuous clinical monitoring and emerging wearable health technologies. Spiking neural networks (SNNs) provide an event-driven computational paradigm that is well suited to real-time signal processing, yet achieving competitive seizure detection performance with constrained model complexity remains challenging. This work introduces a hybrid spike encoding scheme that combines Delta–Sigma (change-based) and stochastic rate representations, together with two spiking architectures designed for real-time EEG analysis: a compact feed-forward HybridSNN and a convolution-enhanced ConvSNN incorporating depthwise-separable convolutions and temporal self-attention. The architectures are intentionally designed to operate on short EEG segments and to balance detection performance with computational practicality for continuous inference. Experiments on the CHB–MIT dataset show that the HybridSNN attains 91.8% accuracy with an F1-score of 0.834 for seizure detection, while the ConvSNN further improves detection performance to 94.7% accuracy and an F1-score of 0.893. Event-level evaluation on continuous EEG recordings yields false-alarm rates of 0.82 and 0.62 per day for the HybridSNN and ConvSNN, respectively. Both models exhibit inference latencies of approximately 1.2 ms per 0.5 s window on standard CPU hardware, supporting continuous real-time operation. These results demonstrate that hybrid spike encoding enables spiking architectures with controlled complexity to achieve seizure detection performance comparable to larger deep learning models reported in the literature, while maintaining low latency and suitability for real-time clinical and wearable EEG monitoring. Full article

Sulfamethoxazole (SMX), a widely used antibiotic, poses potential threats to ecosystems and human health due to its persistence and residues in aquatic environments. This study developed a novel intelligent water treatment system, namely Intelligent Pulsed Electrochemical Activation of NaClO₂ (IPEA_NaClO2), which integrates a FeCuC-Ti₄O₇ composite electrode with machine learning (ML) to achieve efficient SMX removal and energy consumption optimization. Six key operational parameters—initial SMX concentration, NaClO₂ dosage, reaction temperature, reaction time, pulsed potential, and pulsed frequency—were systematically investigated to evaluate their effects on removal efficiency and electrical specific energy consumption (E-SEC). Under optimized conditions (SMX 10 mg L⁻¹, NaClO₂ 60~90 mM, pulsed frequency 10 Hz, temperature 313 K) for 60 min, the IPEA_NaClO2 system achieved an SMX removal efficiency of 89.9% with a low E-SEC of 0.66 kWh m⁻³. Among the ML models compared (back-propagation neural network, BPNN; gradient boosting decision tree, GBDT; random forest, RF), BPNN exhibited the best predictive performance for both SMX removal efficiency and E-SEC, with a coefficient of determination (R²) approaching 1 on the test set. Practical application tests demonstrated that the system maintained excellent stability across different water matrices, achieved a bacterial inactivation rate of 98.99%, and significantly reduced SMX residues in a simulated agricultural irrigation system. This study provides a novel strategy for the intelligent control and efficient removal of refractory organic pollutants in complex water bodies. Full article

Weed management has become a critical agricultural practice, as weeds compete with crops for nutrients, host pests and diseases, and cause major economic losses. The invasive weed Ambrosia artemisiifolia (common ragweed) is particularly problematic in Hungary, endangering crop productivity and public health through its fast proliferation and allergenic pollen. This review examines the current challenges and impacts of A. artemisiifolia while exploring sustainable approaches to its management through precision agriculture. Recent advancements in unmanned aerial vehicles (UAVs) equipped with advanced imaging systems, remote sensing, and artificial intelligence, particularly deep learning models such as convolutional neural networks (CNNs) and Support Vector Machines (SVMs), enable accurate detection, mapping, and classification of weed infestations. These technologies facilitate site-specific weed management (SSWM) by optimizing herbicide application, reducing chemical inputs, and minimizing environmental impacts. The results of recent studies demonstrate the high potential of UAV-based monitoring for real-time, data-driven weed management. The review concludes that integrating UAV and AI technologies into weed management offers a sustainable, cost-effective, mitigate the socioeconomic impacts and environmentally responsible solution, emphasizing the need for collaboration between agricultural researchers and technology developers to enhance precision agriculture practices in Hungary. Full article

Background: Lung adenocarcinoma in non-smoking women represents a distinct clinical entity that cannot be fully explained by traditional exposure-centered carcinogenic models. Although ambient air pollution is a recognized risk factor, sex-specific vulnerability suggests the involvement of additional biological modulators shaping inflammatory, immune, and proliferative responses. Main body: In this conceptual review, we integrate epidemiological, experimental, and mechanistic evidence to propose a multisystem framework of lung carcinogenesis in non-smoking women. We delineate a central carcinogenic spine encompassing lung epithelial injury, chronic inflammation, growth factor signaling activation—particularly epidermal growth factor receptor (EGFR) pathways—and tumor microenvironment remodeling. Within this framework, three interacting domains function as biological modulators that amplify carcinogenic processes: chemosensory–neural–immune modulation, hormonal–endocrine signaling including estrogen–EGFR crosstalk, and psychosocial stress–hypothalamic–pituitary–adrenal (HPA) axis dysregulation. These domains converge through feedback mechanisms that reinforce systemic dysregulation and tumor-promoting microenvironments. Implications: This integrative model provides a biologically grounded perspective on female-specific vulnerability to lung adenocarcinoma and informs precision prevention, risk stratification, and ESG-informed public health strategies beyond conventional exposure reduction. Full article

Accurate detection of structural damage is essential for ensuring the safety and reliability of bridges. However, traditional vibration-based approaches often struggle to capture rich feature representations and adequately model spatial dependencies among sensors. This study proposes a novel bridge damage detection framework, LGSTA-GNN, which integrates local–global spatiotemporal learning with graph neural networks. The framework first extracts multi-scale temporal–frequency features using a multi-scale feature extraction module. A local graph feature extraction module then models intrinsic spatial relationships through graph convolutions, while a global graph attention module adaptively captures inter-sensor dependencies by emphasizing structurally informative nodes. A benchmark dataset generated from a scaled bridge model under progressive damage states is used to evaluate the proposed method. Extensive experiments demonstrate that LGSTA-GNN outperforms multiple graph neural network variants and conventional deep learning techniques, achieving superior accuracy, precision, recall, and F1-score. The confusion matrix and t-SNE visualization further verify its enhanced discriminative capability and robustness. Ablation studies confirm the contribution of each module, highlighting the effectiveness of global attention in identifying subtle structural deterioration. Overall, LGSTA-GNN provides an effective and interpretable solution for intelligent bridge damage detection, with strong potential for practical structural health monitoring and real-time safety assessment. Full article

Background/Objectives: Rapid and accurate diagnosis of acute appendicitis is crucial for patient health and management, and the diagnostic process can be prolonged due to varying clinical symptoms and limitations of diagnostic tools. This study aims to shorten the timeframe for these vital processes and increase accuracy by developing a quantum-inspired hybrid model to identify appendicitis types. Methods: The developed model initially selects the two most performing architectures using four convolutional neural networks (CNNs) and two Transformers (ViTs). Feature extraction is then performed from these architectures. Phase-based trigonometric embedding, low-order interactions, and norm-preserving principles are used to generate a Quantum Feature Map (QFM) from these extracted features. The generated feature map is then passed to the Multiple Head Attention (MHA) layer after undergoing Hadamard fusion. At the end of this stage, classification is performed using a multilayer perceptron (MLP) with a ReLU activation function, which allows for the identification of acute appendicitis types. The developed quantum-inspired hybrid model is also compared with six different CNN and ViT architectures recognized in the literature. Results: The proposed quantum-inspired hybrid model outperformed the other models used in the study for acute appendicitis detection. The accuracy achieved in the proposed model was 97.96%. Conclusions: While the performance metrics obtained from the quantum-inspired model will form the basis of deep learning architectures for quantum technologies in the future, it is thought that if 6G technology is used in medical remote interventions, it will form the basis for real-time medical interventions by taking advantage of quantum speed. Full article

Remote photoplethysmography (rPPG) enables non-contact acquisition of human physiological parameters using ordinary cameras, and has been widely applied in medical monitoring, human–computer interaction, and health management. However, most existing studies focus on estimating specific physiological metrics, such as heart rate and heart rate variability, while paying insufficient attention to reconstructing the underlying rPPG waveform. In addition, publicly available datasets typically record facial videos accompanied by fingertip PPG signals as reference labels. Since fingertip PPG waveforms differ substantially from the true photoplethysmography (PPG) signals obtained from the face, deep learning models trained on such datasets often struggle to recover high-quality rPPG waveforms. To address this issue, we collected a new dataset consisting of palm-region videos paired with wrist-based PPG signals as reference labels, and experimentally validated its effectiveness for training neural network models aimed at rPPG waveform reconstruction. Furthermore, we propose a generative adversarial network (GAN)-based pulse-wave synthesis framework that produces high-quality rPPG waveforms by denoising the mean green-channel signal. By incorporating time-domain peak-aware loss, frequency-domain loss, and adversarial loss, our method achieves promising performance, with an RMSE (Root Mean Square Error) of 0.102, an MAPE (Mean Absolute Percentage Error) of 0.028, a Pearson correlation of 0.987, and a cosine similarity of 0.989. These results demonstrate the capability of the proposed approach to reconstruct high-fidelity rPPG waveforms with improved morphological accuracy compared to noisy raw rPPG signals, rather than directly validating health monitoring performance. This study presents a high-quality rPPG waveform reconstruction approach from both data and model perspectives, providing a reliable foundation for subsequent physiological signal analysis, waveform-based studies, and potential health-related applications. Full article

Diabetes develops through a mix of clinical, metabolic, lifestyle, demographic, and environmental factors. Most current classification models focus on traditional biomedical indicators and do not include environmental exposure biomarkers. In this study, we develop and evaluate a supervised machine learning classification framework that integrates heterogeneous demographic, anthropometric, clinical, behavioral, and environmental exposure features to classify physician-diagnosed diabetes using data from the National Health and Nutrition Examination Survey (NHANES). We analyzed NHANES 2017–2018 data for adults aged ≥18 years, addressed missingness using Multiple Imputation by Chained Equations, and corrected class imbalance via the Synthetic Minority Oversampling Technique. Model performance was evaluated using stratified ten-fold cross-validation across eight supervised classifiers: logistic regression, random forest, XGBoost, support vector machine, multilayer perceptron neural network (artificial neural network), k-nearest neighbors, naïve Bayes, and classification tree. Random Forest and XGBoost performed best on the balanced dataset, with ROC AUC values of 0.891 and 0.885, respectively, after imputation and oversampling. Feature importance analysis indicated that age, household income, and waist circumference contributed most strongly to diabetes classification. To assess out-of-sample generalization, we conducted an independent 80/20 hold-out evaluation. XGBoost achieved the highest overall accuracy and F1-score, whereas random forest attained the greatest sensitivity, demonstrating stable performance beyond cross-validation. These results indicate that incorporating environmental exposure biomarkers alongside clinical and metabolic features yields improved classification performance for physician-diagnosed diabetes. The findings support the inclusion of chemical exposure variables in population-level diabetes classification and underscore the value of integrating heterogeneous feature sets in machine learning-based risk stratification. Full article

Alzheimer’s disease (AD) is a chronic neurodegenerative disorder characterized by progressive loss of cognitive function. Its main pathological features include accumulation of Amyloid-beta (Aβ) plaques, excessive phosphorylation of microtubule-associated protein tau (tau protein), and neuroinflammation. In recent years, studies have confirmed intestinal flora is closely connected to AD. Gut–brain axis has an important part in AD. Intestinal flora can achieve signal communication between gut and brain through metabolic, immune, neural, and endocrine pathways, thereby slowing down AD. It has been discovered that exercise is not only beneficial to physical health but also has a positive impact on the brain function. In recent years, more and more studies have found exercise can alleviate AD through the following four major pathways: regulating the diversity of intestinal flora, strengthening the blood–brain barrier (BBB), regulating immune homeostasis, and upregulating the brain-derived neurotrophic factor (BDNF). In this review, we have summarized intestinal flora in AD and systematically expounded potential regulatory pathways of exercise in modulating intestinal flora for AD. This provides a more theoretical basis for subsequent research targeting “gut–brain axis” to regulate AD. At the same time, this review also summarizes differences in different exercise types on improving intestinal flora for alleviating AD, providing new ideas and strategies for AD. Full article

The limitations of conventional diabetes management are increasingly evident. As a result, both type 1 and 2 diabetes in pediatric populations have become major global health concerns. As new technologies emerge, particularly artificial intelligence (AI), they offer new opportunities to improve diagnostic accuracy, treatment outcomes, and patient self-management. A PRISMA-based systematic review was conducted using PubMed, Web of Science, and BIREME. The research covered studies published up to February 2025, where twenty-two studies met the inclusion criteria. These studies examined machine learning algorithms, continuous glucose monitoring (CGM), closed-loop insulin delivery systems, telemedicine platforms, and digital educational interventions. AI-driven interventions were consistently associated with reductions in HbA1c and extended time in range. Furthermore, they reported earlier detection of complications, personalized insulin dosing, and greater patient autonomy. Predictive models, including digital twins and self-learning neural networks, significantly improved diagnostic accuracy and early risk stratification. Digital health platforms enhanced treatment adherence. Nonetheless, the barriers included unequal access to technology and limited long-term clinical validation. Artificial intelligence is progressively reshaping pediatric diabetes care toward a predictive, preventive, personalized, and participatory paradigm. Broader implementation will require rigorous multiethnic validation and robust ethical frameworks to ensure equitable deployment. Full article

The integration of flexible electronics and machine learning (ML) algorithms has become a revolutionary force driving the field of intelligent sensing, giving rise to a new generation of intelligent devices and systems. This article provides a systematic review of core technologies and practical applications of ML in flexible electronics. It focuses on analyzing the theoretical frameworks of algorithms such as the Long Short-Term Memory Network (LSTM), Convolutional Neural Network (CNN), and Reinforcement Learning (RL) in the intelligent processing of sensor signals (IPSS), multimodal feature extraction (MFE), process defect and anomaly detection (PDAD), and data compression and edge computing (DCEC). This study explores the performance advantages of these technologies in optimizing signal analysis accuracy, compensating for interference in high-noise environments, optimizing manufacturing process parameters, etc., and empirically analyzes their potential applications in wearable health monitoring systems, intelligent control of soft robots, performance optimization of self-powered devices, and intelligent perception of epidermal electronic systems. Full article

Diplodia shoot blight is an opportunistic fungal pathogen infesting many conifer species and it has a global distribution. Depending on the duration and severity of the disease, affected needles appear yellow (chlorotic) for a brief period before becoming red or brown in colour. These symptoms can occur on individual branches or over the entire crown. Aerial sketch-mapping or the manual interpretation of aerial photography for tree health surveys are labour-intensive and subjective. Recently, however, the application of deep learning (DL) techniques to detect and classify tree crowns in high-spatial-resolution imagery has gained significant attention. This study evaluated two complementary DL approaches for the detection and classification of Pinus radiata trees infected with diplodia shoot blight across five geographically dispersed sites with varying topographies over two acquisition years: (1) object detection using YOLOv12 combined with Segment Anything Model (SAM) and (2) pixel-level semantic segmentation using U-Net, SegFormer, and EVitNet. The three damage classes for the object detection approach were ‘yellow’, ‘red-brown’ (both whole-crown discolouration) and ‘dead tops’ (partially discoloured crowns), while for the semantic segmentation the three classes were yellow, red-brown, and background. The YOLOv12m model achieved an overall mAP50 score of 0.766 and mAP50–95 of 0.447 across all three classes, with red-brown crowns demonstrating the highest detection accuracy (mAP50: 0.918, F1 score: 0.851). For semantic segmentation models, SegFormer showed the strongest performance (IoU of 0.662 for red-brown and 0.542 for yellow) but at the cost of longest training time, while EVitNet offered the most cost-effective solution achieving comparable accuracy to SegFormer but with a superior training efficiency with its lighter architecture. The accurate identification and symptom classification of crown damage symptoms support the calibration and validation of satellite-based monitoring systems and assist in the prioritisation of ground-based diagnosis or management interventions. Full article

Air pollution, particularly fine particulate matter (PM_2.5), poses significant public health and environmental risks. This study explores the effectiveness of spatiotemporal graph neural networks (ST-GNNs) in forecasting PM_2.5 concentrations by integrating remote-sensing hyperspectral indices with traditional meteorological and pollutant data. The model was evaluated using data from Switzerland and the Gauteng province in South Africa, with datasets spanning from January 2016 to December 2021. Key performance metrics, including root mean squared error (RMSE), mean absolute error (MAE), probability of detection (POD), critical success index (CSI), and false alarm rate (FAR), were employed to assess model accuracy. For Switzerland, the integration of spectral indices improved RMSE from 1.4660 to 1.4591, MAE from 1.1147 to 1.1053, CSI from 0.8345 to 0.8387, POD from 0.8961 to 0.8972, and reduced FAR from 0.0760 to 0.0719. In Gauteng, RMSE decreased from 6.3486 to 6.2319, MAE from 4.4891 to 4.4066, CSI from 0.9555 to 0.9560, and POD from 0.9699 to 0.9732, while FAR slightly increased from 0.0154 to 0.0181. Error analysis revealed that while the initial one-day ahead forecast without spectral indices had a marginally lower error, the dataset with spectral indices outperformed from the two-day ahead mark onwards. The error for Swiss monitoring stations stabilized over longer prediction lengths, indicating the robustness of the spectral indices for extended forecasts. The study faced limitations, including the exclusion of the Planetary Boundary Layer (PBL) height and K-index, lack of terrain data for South Africa, and significant missing data in remote sensing indices. Despite these challenges, the results demonstrate that ST-GNNs, enhanced with hyperspectral data, provide a more accurate and reliable tool for PM_2.5 forecasting. Future work will focus on expanding the dataset to include additional regions and further refining the model by incorporating additional environmental variables. This approach holds promise for improving air quality management and mitigating health risks associated with air pollution. Full article