Search Results (1,645)

This comprehensive review explores the critical roles of microRNAs (miRNAS) in cardiovascular diseases, emphasizing their regulatory functions in gene expression and their involvement in disease progression. miRNAS are small, evolutionarily conserved non-coding RNAs that regulate gene expression post-transcriptionally and play essential roles in various cardiac conditions, including fibrosis, cardiac remodeling, apoptosis, ischemia/reperfusion injury, hypertrophy, heart failure, arrhythmias, coronary artery disease (CAD), congenital heart diseases (CHDs), cardiomyopathies, and valvular heart disease (VHD). miRNAS are increasingly recognized as sensitive and specific biomarkers for early diagnosis, disease monitoring, and evaluation of therapeutic responses across the cardiovascular disease spectrum. Ischemia/reperfusion injury leads to significant cardiac damage through elevated oxidative stress, mitochondrial dysfunction, and apoptosis. CAD, a major contributor to global morbidity and mortality, is primarily driven by atherosclerosis and chronic inflammation. Cardiac hypertrophy is initially an adaptive response to stress but may progress to heart failure if sustained. Arrhythmias arise from electrical disturbances such as reentry, abnormal automaticity, and triggered activity. Heart failure is a complex and progressive syndrome marked by poor prognosis and increasing global prevalence. VHD involves intricate molecular alterations, including myocardial fibrosis and calcification, which contribute to disease progression and adverse outcomes. Cardiomyopathies—including hypertrophic, dilated, restrictive, and arrhythmogenic forms—are influenced by genetic mutations, systemic diseases, and disrupted molecular signaling. CHDs, the most common congenital malformations, stem from structural abnormalities in cardiac development and remain a major cause of infant morbidity and mortality. Novel therapeutic methods, such as antisense oligonucleotides, miR mimics, and exosome-based delivery mechanisms, demonstrate the translational promise of miRNAs in the realm of personalized cardiovascular medicine. However, issues such as small sample sizes, inconsistent results, interspecies differences, and delivery challenges restrict the clinical application of miRNA-based therapies. This review integrates mechanistic insights, critiques the quality of available evidence, and identifies translational shortcomings. It highlights the diagnostic, prognostic, and therapeutic potential of miRNAs in reshaping cardiovascular disease treatment. Full article

This paper presents an adaptive fuzzy filter applied to processing a signal from a voltage sensor fed to the input of an object in an automatic temperature control system with a PI controller. (1) The research goal was to develop an algorithm for processing the signal from an RMS voltage sensor, measured at the terminals of a heating element in a temperature control system with a PI controller, in a way that ensures good dynamic properties while maintaining an appropriate level of accuracy. (2) The paper presents a method for designing an adaptive fuzzy filter by synthesizing a first-order low-pass infinite impulse response (IIR) filter and a fuzzy model of the dependence of this filter parameter value on the modulus of the derivative of the measured quantity. The application of a model with a symmetric input and output structure and a modified fuzzy model with asymmetry resulting from the uneven distribution of modal values of singleton fuzzy sets at the output was shown. The innovation in the proposed solution is the use of a signal from a PI controller to determine the derivative module of the measured quantity and, using a fuzzy model, linking its instantaneous value with a digital filter parameter in the measurement chain with a sensor monitoring the signal at the input of the controlled object. It is demonstrated that the signal generated by the PI controller can be used in a control system to continuously determine the modulus of the time derivative of the signal measured at the input of the controlled object, also indicating the limitations of this method. The signal from the PI controller can also be used to select filter parameters. In such a situation, it can be treated as a reference signal representing the useful signal. The mean square error (MSE) was adopted as the criterion for matching the signal at the filter output to the reference signal. (3) Based on a comparative analysis of the results of using an adaptive fuzzy filter with a classic first-order IIR filter with an optimal parameter in the MSE sense, it was found that using a fuzzy filter yields better results, regardless of the structure of the fuzzy model used (symmetric or asymmetric). (4) The paper demonstrates that in the tested temperature control system, introducing a simple fuzzy model with one input characterized by three fuzzy sets, relating the modulus of the derivative of the signal developed by the PI controller to the value of the first-order IIR filter parameter, into the voltage sensor signal-processing algorithm gave significantly better results than using a first-order IIR filter with a constant optimal parameter in terms of MSE. The best results were obtained using a fuzzy model in which an intentional asymmetry in the modal values of the output fuzzy sets was introduced. Full article

Breast cancer is a leading cause of mortality among women, disproportionately affecting Hispanic populations in the U.S., particularly those with limited health literacy and language access. To address these disparities, we present a bilingual, web-based educational platform tailored to low-literacy Hispanic users. The platform supports full navigation in English and Spanish, with seamless language switching and both written and spoken input options. It incorporates automatic speech recognition (ASR) capable of handling code-switching, enhancing accessibility for bilingual users. Educational content is delivered through culturally sensitive videos organized into four categories: prevention, detection, diagnosis, and treatment. Each video includes embedded and post-video assessment questions aligned with Bloom’s Taxonomy to foster active learning. Users can monitor their progress and quiz performance via a personalized dashboard. An integrated chatbot, powered by large language models (LLMs), allows users to ask foundational breast cancer questions in natural language. The platform also recommends relevant resources, including nearby treatment centers, and support groups. LLMs are further used for ASR, question generation, and semantic response evaluation. Combining language technologies and LLMs reduces disparities in cancer education and supports informed decision-making among underserved populations, playing a pivotal role in reducing information gaps and promoting informed healthcare decisions. Full article

Collecting crop growth data in field environments is crucial for breeding research. The team’s current autonomous soybean phenotyping system requires manual control to start and stop data collection. To address the aforementioned issues, this study innovatively proposes an elliptical calibration rotating geofencing technique. Preprocess coordinates using Z-scores and mean fitting perform global error calibration via weighted least squares, calculate the inclination angle between the row direction and the relative standard direction by fitting a straight line to the same row of data, and establish a rotation model based on geometric feature alignment. Results show that the system achieves an average response time of 0.115 s for geofence entry, with perfect accuracy and Recall rates of 1, meeting the requirements for starting and stopping geographic fencing in soybean ridge trial plots. This technology provides the critical theoretical foundation for enabling a dynamic, on-demand automatic start–stop functionality in smart data collection devices for soybean field trial zones within precision agriculture. Full article

In response to the limitations of traditional single-signal approaches, which fail to comprehensively reflect fault conditions, and the difficulties of existing feature extraction methods in capturing subtle fault patterns in transformer fault diagnosis, this paper proposes an innovative fault diagnosis methodology. Initially, to address common severe faults in excitation transformers, Principal Component Analysis (PCA) is applied to reduce the dimensionality of multi-source feature data, effectively eliminating redundant information. Subsequently, to mitigate the impact of non-stationary noise interference in voiceprint signals, a Deep Belief Network (DBN) optimized using the Hunter–Prey Optimization (HPO) algorithm is employed to automatically extract deep features highly correlated with faults, thus enabling the detection of complex, subtle fault patterns. For temperature and electrical parameter signals, which contain abundant time-domain information, the Random Forest algorithm is utilized to evaluate and select the most relevant time-domain statistics. Nonlinear dimensionality reduction is then performed using an autoencoder to further reduce redundant features. Finally, a multi-classifier model based on Adaptive Boosting with Support Vector Machine (Adaboost-SVM) is constructed to fuse multi-source heterogeneous information. By incorporating a pseudo-label self-training strategy and integrating a working condition awareness mechanism, the model effectively analyzes feature distribution differences across varying operational conditions, selecting potential unseen condition samples for training. This approach enhances the model’s adaptability and stability, enabling real-time fault diagnosis. Experimental results demonstrate that the proposed method achieves an overall accuracy of 96.89% in excitation transformer fault diagnosis, outperforming traditional models such as SVM, Extreme Gradient Boosting with Support Vector Machine (XGBoost-SVM), and Convolutional Neural Network (CNN). The method proves to be highly practical and generalizable, significantly improving fault diagnosis accuracy. Full article

Water-quality monitoring plays a vital role in protecting and managing water resources, maintaining ecological balance and safeguarding human health. At present, the traditional monitoring technology is associated with risks of low sampling efficiency, long response time, high economic cost and secondary pollution of water samples, and cannot guarantee the accuracy and real-time determination of monitoring data. Remote sensing (RS) technology and sensors are used to automatically realize the real-time monitoring of water quality. In this paper, the principles and composition of remote monitoring systems are systematically summarized. For the RS technology, indicators including chlorophyll-a, turbidity and total suspended matter/solids, colored dissolved organic matter, electrical conductivity (EC), dissolved oxygen (DO), temperature and pH value were considered, and for sensors monitoring, the parameters of pH value, temperature, oxidation reduction potential, DO, turbidity, EC and salinity, and total dissolved solids were analyzed. The practical applications of remote monitoring in surface water, marine water and wastewater are introduced in this context. In addition, the advantages and disadvantages of remote monitoring systems are evaluated, which provides some basis for the selection of remote monitoring systems in the future. Full article

In the context of achieving sustainable development goals and promoting a sustainable, low-carbon global energy transition, accurately quantifying and proactively managing the carbon intensity of power systems is a core challenge in monitoring the sustainability of the power sector. However, existing demand response methods often overlook the dynamic characteristics of power system carbon emissions and fail to accurately characterize the complex relationship between power consumption and carbon emissions, which results in suboptimal emission reduction results. To address this challenge, this paper proposes and validates an innovative low-carbon demand response optimization scheduling method as a sustainable tool. The core of this method is the development of a dynamic carbon emission factor (DCEF) assessment model. By innovatively integrating marginal and average carbon emission factors, it becomes a dynamic sustainability indicator that can measure the environmental performance of the power grid in real time. To characterize the relationship between power consumption behavior and carbon emissions, we employ an adaptive Dirichlet process mixture model (DPMM). This model does not require a preset number of clusters and can automatically discover patterns in the data, such as grouping holidays and working days with similar power consumption characteristics. Based on the clustering results and historical data, a dual long short-term memory (LSTM) deep learning network architecture is designed to achieve a coordinated prediction of power consumption and DCEFs for the next 24 h. On this basis, a method is established with the goal of maximizing carbon emission reduction while considering constraints such as fixed daily power consumption, user comfort, and equipment safety. Simulation results demonstrate that this approach can effectively reduce regional carbon emissions through accurate prediction and optimized scheduling. This provides not only a quantifiable technical path for improving the environmental sustainability of the power system but also decision-making support for the formulation of energy policies and incentive mechanisms that align with sustainable development goals. Full article

The cyber–physical integrated power distribution system is poised to become the predominant trend in the development of future power systems. Although the highly intelligent panoramic link information system in substations facilitates the efficient, cost-effective, and secure operation of the power system, it is also exposed to dual threats from both internal and external factors. Under intentional cyber information attacks, the operational data and equipment response capabilities of the panoramic link information system within smart substations can be illicitly manipulated, thereby disrupting dispatcher response decision-making and resulting in substantial losses. To tackle this challenge, this paper delves into the research on automatic verification and active defense mechanisms for the cyber–physical power distribution system under panoramic link attacks in smart substations. Initially, to mitigate internal risks stemming from the uncertainty of new energy output information, this paper utilizes a CGAN-IK-means model to generate representative scenarios. For scenarios involving external intentional cyber information attacks, this paper devises a fixed–flexible adjustment resource response strategy, making up for the shortfall in equipment response capabilities under information attacks through flexibility resource regulation. The proposed strategy is assessed based on two metrics, voltage level and load shedding volume, and computational efficiency is optimized through an enhanced firefly algorithm. Ultimately, the efficacy and viability of the proposed method are verified and demonstrated using a modified IEEE standard test system. Full article

With the increasing integration of variable renewable energy into power systems, the frequency regulation capability of hydroelectric units has become crucial for ensuring grid stability. In response to grid disturbances, where Primary Frequency Regulation (PFR) and Automatic Generation Control (AGC) are activated sequentially in actual operation, this paper employs parameter characteristic analysis to systematically investigate the influence of several factors—including turbine operating head, PWM parameters, and governor parameters—on the active power regulation process of hydroelectric units. The study first compares the response characteristics under different heads and PWM/pulse parameters within the AGC framework. It then examines the effects of pulse duration limits and integral adjustments on guide vane movement and correction efficiency. Finally, under the PFR framework, the impacts of head, steady-state slip coefficient, and integral gain on the amplitude and speed of frequency response are analyzed. Simulation results demonstrate that as the set value of Tkmax increases, the operating range of the guide vane opening within the pulse cycle expands, and the time required for power correction is significantly reduced. Specifically, when Tkmax is increased from 0.2 to 0.55, the regulation time is shortened by 44%. These findings offer theoretical guidance and practical insights for parameter optimization and operational scheduling of hydropower units. Full article

Background: The study of psychophysiological responses to disgust-evoking stimuli has long been neglected in favour of other emotional stimuli, especially those evoking fear. While the basic cascade of responses to a frightening stimulus is relatively well-understood, psychophysiological responses to disgust-related threats, such as parasites or rotten food, are scarcely studied. Methods: Here, we aimed to assess skin resistance (SR) change as a measure of electrodermal response to visual cues that signal the presence of disgust-relevant threats. To this aim, we recruited 123 participants and presented them with one of the following varieties of disgust-relevant threats: disgust-evoking animals (e.g., parasites, worms), spoiled food, threat of pandemic, or pollution and toxicity. The latter two represented modern threats to test whether also these modern stimuli can initiate immediate automatic reaction. Results: We found significant differences between the categories: Participants responded with the highest probability to disgust-evoking animals (38%) and sneezing (52%), suggesting that only ancestral cues of pathogen disgust trigger automatic physiological response. Moreover, we found significant inter-sexual differences: women exhibited more SR change responses than men, and the amplitude of these responses was overall larger. Finally, we report a weak effect of subjectively perceived disgust intensity on reactivity to threat stimuli. Conclusions: We discuss heterogeneity of disgust-relevant threats, their adequate behavioural responses, and subsequent heterogeneity of respective SR responses. We conclude that large interindividual variability might eclipse systematic differences between participants with high or low sensitivity to disgust, and that subjectively perceived intensity of disgust is only a weak predictor of electrodermal response to its elicitor. Full article

Levees are critical for flood defence, but their integrity is threatened by hazards such as piping and seepage, especially during high-water-level periods. Traditional manual inspections for these hazards and associated emergency response elements, such as personnel and assets, are inefficient and often impractical. While UAV-based remote sensing offers a promising alternative, the effective fusion of multi-modal data and the scarcity of labelled data for supervised model training remain significant challenges. To overcome these limitations, this paper reframes levee monitoring as an unsupervised anomaly detection task. We propose a novel, fully automated framework that unifies geophysical hazards and emergency response elements into a single analytical category of “abnormal targets” for comprehensive situational awareness. The framework consists of three key modules: (1) a state-of-the-art registration algorithm to precisely align infrared and visible images; (2) a generative adversarial network to fuse the thermal information from IR images with the textural details from visible images; and (3) an adaptive, unsupervised segmentation module where a mean-shift clustering algorithm, with its hyperparameters automatically tuned by Bayesian optimization, delineates the targets. We validated our framework on a real-world dataset collected from a levee on the Pajiang River, China. The proposed method demonstrates superior performance over all baselines, achieving an Intersection over Union of 0.348 and a macro F1-Score of 0.479. This work provides a practical, training-free solution for comprehensive levee monitoring and demonstrates the synergistic potential of multi-modal fusion and automated machine learning for disaster management. Full article

The accurate calibration of radiochromic films is critical for high dose rate (HDR) brachytherapy dosimetry. Conventional workflows frequently rely on manually determined regions of interest (ROIs), which might increase operator variability. In this investigation, Gafchromic EBT3 films were irradiated under clinical settings at nominal doses of 0–10 Gy and evaluated using a MATLAB (R2024b)-based tool that allows for both manual and automated ROI selection. The calibration curves were modeled with a second-order polynomial and rational model, and performance was assessed using statistical measures. The study found that the rational model fits better than the polynomial model. Additionally, the automatic ROI approach outperformed the manual method in both models, resulting in higher calibration accuracy and reproducibility (R² = 0.999, RMSE = 0.118 Gy, MAE = 0.103 Gy vs. R² = 0.986, RMSE = 0.448 Gy, MAE = 0.388 Gy). Although manual ROI occasionally produced greater dose–response slopes at higher doses, it was more susceptible to operator bias and film non-uniformity. In contrast, automatic ROI reduced variability by consistently picking homogeneous sections, resulting in steady curve fitting across the entire dose range. Furthermore, a companion module transformed calibrated films into 2D false-color maps and 3D dosage surfaces, allowing for visual assessment of dose uniformity, detection of scanner-related aberrations, and quantitative verification for quality assurance. These findings demonstrate that automated ROI selection provides a more stable and reproducible foundation for film calibration in HDR brachytherapy, minimizing operator dependency while facilitating routine clinical quality assurance. Full article

The high spatiotemporal resolution of optically pumped magnetometers (OPMs) makes them an essential tool for functional brain imaging, enabling accurate recordings of neuronal activity. However, physiological signals such as eye blinks and cardiac activity overlap with neural magnetic signals in the frequency domain, resulting in contamination and creating challenges for the observation of brain activity and the study of neurological disorders. To address this problem, an automatic physiological artifact removal method based on OPM magnetic reference signals and a channel attention mechanism is proposed. The randomized dependence coefficient (RDC) is employed to evaluate the correlation between independent components and reference signals, enabling reliable recognition of artifact components and the construction of training and testing datasets. A channel attention mechanism is subsequently introduced, which fuses features from global average pooling (GAP) and global max pooling (GMP) layers through convolution to establish a data-driven automatic recognition model. The backbone network is further optimized to enhance performance. Experimental results demonstrate a strong correlation between the magnetic reference signals and artifact components, confirming the reliability of magnetic signals as artifact references for OPM-MEG. The proposed model achieves an artifact recognition accuracy of 98.52% and a macro-average score of 98.15%. After artifact removal, both the event-related field (ERF) responses and the signal-to-noise ratio (SNR) are significantly improved. Leveraging the flexible and modular characteristics of OPM-MEG, this study introduces an artifact recognition framework that integrates magnetic reference signals with an attention mechanism. This approach enables highly accurate automatic recognition and removal of OPM-MEG artifacts, paving the way for real-time, automated data analysis in both scientific research and clinical applications. Full article

Dynamic equivalence of active distribution networks (ADNs) is emerging as one of the most important issues for the backbone network security analysis due to high penetration of distributed generations (DGs) and electricity vehicles (EVs). The multiscale and multimodal fusion deep learning (MMFDL) method proposed in this paper contains two modalities, one of which is a CNN + attention module to simulate Newton Raphson power flow calculation (NRPFC) for the important feature extraction of a power system caused by disturbance, which is motivated by the similarities between NRPFC and convolution network computation. The other is a long short-term memory (LSTM) + fully connected (FC) module for load modeling based on the fact that LSTM + FC can represent a load′s differential algebraic equations (DAEs). Moreover, to better capture the relationship between voltage and power, the multiscale fusion method is used to aggregate load modeling models with different voltage input sizes and combined with CNN + attention, merging as MMFDL to represent the dynamic behaviors of ADNs. Then, the Kepler optimization algorithm (KOA) is applied to automatically tune the adjustable parameters of MMFLD (called KOA-MMFDL), especially the LSTM and FC hidden layer number, as they are important for load modeling and there is no human knowledge to set these parameters. The performance of the proposed method was evaluated by employing different electric power systems and various disturbance scenarios. The error analysis shows that the proposed method can accurately represent the dynamic response of ADNs. In addition, comparative experiments verified that the proposed method is more robust and generalizable than other advanced non-mechanism methods. Full article

The P300 evoked potential, recorded via electroencephalography, serves as a relevant marker of attentional allocation and cognitive workload. This work extracts and analyzes event-related potentials that reflect variations in the cognitive state of military pilots during a complex simulated flight scenario coupled with simultaneous mental arithmetic tasks. The experiment was conducted at the Academia General del Aire (Spain) with 14 military pilots using a high-fidelity flight simulator. The experimental protocol involved dynamic flight instructions combined with arithmetic tasks designed to elicit varying cognitive loads. The results revealed a significant decrease in P300 amplitude across successive sessions, indicating a progressive reduction in attentional engagement due to task habituation and increased cognitive automaticity. Concurrently, P300 latency for correct responses decreased significantly, demonstrating enhanced efficiency in cognitive stimulus evaluation over repeated exposure. However, incorrect responses failed to yield robust results due to an insufficient number of trials. These findings validate the use of P300 as an objective indicator of cognitive workload variations in realistic aviation contexts. Full article