ABSTRACT Lignite has poor hydrophobicity and is difficult to adhere to bubbles. Particle size and collectors play critical roles in lignite flotation. This study conducted flotation kinetics test with lignite particle size of 0.50–0.25, 0.25–0.125, 0.125–0.074, 0.074–0.045, −0.045 mm (D50 = 21.79 μm), and −0.045 mm (D50 = 11.32 μm), and used tetradecane and mixed collector SYS as collector. Adhesion/desorption behavior between lignite and bubbles was investigated via induction time tests, oscillatory desorption tests, and adhesion/desorption force tests. Results showed particle size and collectors significantly affect flotation recovery. Optimal particle size facilitates flotation: the −0.045 mm (D50 = 21.79 μm) lignite achieved the highest combustible recovery (72.36% combined with SYS), accompanied by an induction time of only 10 ms and the highest critical desorption frequency. The particle size is much smaller than the optimal particle size for high-rank coal flotation. Coarse particles experienced 1–2 orders of magnitude higher vibration forces than fine particles, making it more prone to detachment from bubbles with same flotation conditions. SYS significantly increased interaction forces between coal particles and bubbles, enabling stable particle attachment to bubbles and float. This study systematically investigated adhesion/desorption behavior between lignite and bubbles from particle size and collectors two key aspects, enriching the theoretical basis of lignite flotation.

Belt conveyor idlers frequently fail under high-load harsh conditions, causing system shutdowns. Existing deep learning-based fault diagnosis methods suffer from insufficient frequency resolution and poor dynamic adaptability. To address this, this paper proposes a fault diagnosis framework based on adaptive frequency-band KAN: First, an adaptive frequency-band Mel filter bank designed based on fault mechanisms enhances resolution in critical fault frequency bands through non-uniform frequency-axis remapping and third-order peak detection. Second, a temporal convolutional network is integrated to expand the receptive field and capture cross-period features. A Kolmogorov-Arnold Networks (KAN) layer is introduced to dynamically analyze nonlinear coupling relationships in the frequency domain using learnable B-spline basis functions. This model achieves synergistic optimization of feature resolution enhancement and dynamic modeling, significantly improving diagnostic accuracy and cross-condition generalization capability for roller faults. Under actual conveyor roller operating conditions, fault prediction accuracy reaches 81.25%, fully validating the model's adaptability to real-world industrial scenarios.

Rapid advances in materials chemistry and data-driven approaches have accelerated the development of aquatic chemical sensors, yet accurate, real long-term nutrient monitoring remains a significant challenge. Reliable real-time detection of nitrate (NO3-), nitrite (NO2-), and ammonium (NH4+) ions is essential for understanding aquatic biogeochemistry, mitigating eutrophication, ensuring precision fertigation, and ensuring sustainable water resource and crop management. Conventional electrochemical sensors can achieve low detection limits, but issues of accuracy, reproducibility, and stability under variable conditions hinder their broader application. In this preliminary study, electrochemical impedance spectroscopy (EIS) was employed in a three-electrode system to capture impedance responses over a wide frequency range, where the electrode-electrolyte interface was modelled using an equivalent circuit comprising resistive, capacitive and impedance elements. Impedance features including the real part, imaginary part, amplitude, and phase were analyzed as functions of concentration and frequency for the three target ions. To address the inherent nonlinearities of EIS data, advanced machine learning models were applied, with extreme gradient boosting (XGBoost) used for feature extraction, principal component analysis (PCA) for dimensionality reduction and a stacked ensemble (SVR-MLP-ridge regression) yielding the highest overall predictive accuracy (R2 = 0.99, RMSE < 0.921 ppm, MAE < 0.808 ppm, EV = 0.99) across all analytes. The developed hybrid tree-PCA-ML framework enables interpretable frequency-based analysis consistent with the physicochemical interpretations from the equivalent electrical circuit models. This combined EIS-ML approach not only enhances predictive accuracy for nutrient concentrations but also identifies critical frequency regions governing the sensing mechanisms, offering a pathway toward high-precision, real-time water quality monitoring.

Engineering experience shows that in noise and vibration control for ships and aircraft, measures like stiffening can reduce vibration but may fail to lower radiated noise, as they disrupt structural continuity and create new radiation sources. This paper investigates the sub-critical frequency sound radiation from the boundaries of semi-infinite plates using analytical and discrete Fourier transform methods. A force-excited finite plate is also considered, generating its supersonic sound intensity map. Results demonstrate that the near- and far-field sound pressure and supersonic sound intensity manifest the radiation patterns of different boundary conditions as monopole, dipole, and quadruple pole patterns. At resonance, boundary acoustic radiation dominates in finite structures. Furthermore, as the plate size and frequency increase, the radiation differences among various boundary conditions converge to the results observed in semi-infinite plates. This study provides a mechanistic understanding of the relationship between vibration and sound radiation, offering valuable insights for controlling sound radiation in engineering applications.

Complex network time-series analysis by the Visibility Graph (VG) method is applied to an experimental set of drain current signals from nano-transistor devices (nano-MOSFETs). Electric current in nano-MOSFETs has noisy fluctuations produced by different physical mechanisms, including thermal, electron-hole recombination, and the effect of ion traps present at the gate region. The combination of these mechanisms results in a complex power spectrum, which may contain "corners" at one or more critical frequencies, switching from the well-known 1/f "flicker" noise to 1/f 2 "Brownian" tails, or contain flat regions at low frequencies. More recent studies have shown that current fluctuations in a fully depleted nano-MOSFET may contain low-dimensional chaotic dynamics with critical intermittent behavior. Consequently, noisy current signals in nano-MOSFETs constitute an excellent testbed to assess the ability of the VG method in capturing and discerning subtle features of dynamics under complex scenarios. Using graph metrics, such as clustering coefficient, assortativity, and the rich-club coefficient, we show that the VG structure consistently discerns differences in the nature of noise between fresh and stressed (faulty) transistors and between stochastic and low-dimensional chaotic dynamics. Using three types of surrogate time series, we show that these differences are statistically significant. Moreover, we show that graph metrics other than the degree distribution are crucial in capturing features of complex system dynamics that are mixtures of various types of noise and possibly deterministic chaos. In the case of noisy signals from nano-devices, this has a direct application to device classification and fault detection.

Covert hepatic encephalopathy (CHE) is a frequent and clinically relevant complication of liver cirrhosis, affecting approximately 30-70% of patients. Despite the absence of overt neurological symptoms, CHE is associated with impaired quality of life and increased risks of falls, traffic accidents, hospitalization, progression to overt HE (OHE), and mortality. The pathophysiology of HE, including CHE and OHE, is multifactorial and involves complex interactions among hyperammonemia, systemic inflammation, oxidative stress, gut dysbiosis, bile acid dysregulation, and sarcopenia along the gut-liver-brain axis. Several diagnostic tools are available, including psychometric batteries, computerized neuropsychological assessments, the Stroop test, critical flicker frequency, and the inhibitory control test. However, time and resource constraints hinder their routine implementation in real-world clinical settings, leading to substantial underdiagnosis of CHE. Although treatment strategies for CHE have not yet been fully established, non-absorbable disaccharides and rifaximin have emerged as promising ammonia-lowering therapies and microbiota-targeted interventions for improving cognitive function and reducing the risk of progression to overt HE. Early recognition and multidisciplinary intervention for CHE are essential to prevent disease progression and improve clinical outcomes. This review summarizes the current evidence on the epidemiology, pathophysiology, diagnosis, clinical significance, and therapeutic approaches for CHE in cirrhosis, with the aim of enhancing its recognition and optimizing patient management.

Effect of aroma shiatsu massage therapy on cognitive function in healthy adolescents: A non-randomized controlled trial.

EDSF-Net : An enhanced dynamic spatiotemporal-frequency attention network for robust EEG decoding in motor imagery.

Many of life’s biggest dilemmas can be summed up as a tension between holding on and letting go. The very language evokes a notion of intentionality which, for the most part, has evaded scientific understanding. How might we even get a window into it? Important insights have come from a seemingly simple task: wiggling one’s fingers to and fro to the beat of a metronome. As the metronome pace increases to some critical frequency, one coordinative pattern becomes unstable and switches spontaneously to another. Such transitions are typically preceded by critical fluctuations, a predicted feature of self-organization in complex, dynamical systems. Here we address the nature and source of these fluctuations, usually assumed to be: (1) random; (2) of external origin; and (3) of fixed magnitude. We performed an experiment in which participants were instructed to oscillate their fingers in either an in-phase or anti-phase pattern in time with a metronome and instructed them to either “hold-on” or “let-go” should they feel the pattern begin to change, yielding a 2 by 2 within-subjects design. We observed that as the metronome frequency was increased from 1.00 to 3.00 Hz, fluctuations in the relative phase between the fingers were significantly altered both by the starting coordinative pattern as well as the participant’s intention to “hold it on” or “let it go”. Specifically, the intention to hold on to the anti-phase pattern delayed the spontaneous transition to in-phase, an effect that was paired with increased fluctuations beyond the critical frequency. These observations were analyzed under the extended Haken–Kelso–Bunz (HKB) model which describes the non-linear stochastic dynamics of the order parameter (relative phase) as a gradient descent on a certain potential. Our analysis, in line with experimental results, suggests that intention transforms the HKB potential not only by stabilizing unstable coordination states but also (paradoxically) by increasing fluctuations around them. Such findings may offer new interpretative light on the relation between intention and fluctuations in the coordination dynamics of living things.

The critical frequency foF2 of the ionosphere’s F2 layer plays a key role in shortwave communications. Its variability is influenced by factors such as solar activity and geomagnetic conditions, making accurate prediction of foF2 essential for reliable communication in navigation, aviation, and emergency scenarios. This paper presents a global foF2 prediction model based on occultation data from 2007 to 2023 and the eXtreme Gradient Boosting (XGBoost) algorithm. The model incorporates the SHAP method for interpretability analysis, which identifies the core factors driving its predictions. The results show a coefficient of determination (R2) of 0.886 and a root mean square error (RMSE) of 0.964 MHz on the test set. The model captures key ionospheric features, including the single peak at the magnetic equator, the double peak of the equatorial anomaly, the Weddell Sea anomaly, and the winter anomaly. Verification with independent datasets from the GRACE satellite and GIRO radiosonde demonstrates that the model outperforms the IRI‑2020, NPDM, and random forest (RF) models, achieving the highest R2 and reducing errors by over 50% at mid‑ and high‑latitudes. When COSMIC‑2 data were added, the model’s performance improved, with a 2.39% reduction in mean absolute error (MAE) and a 2.63% reduction in RMSE for latitudes between –42.55° and 42.91°. SHAP analysis highlighted the core driving factors of the model, including latitude, time of day, annual cumulative day, F10.7 index, and longitude. Feature ablation experiments revealed that only six features were needed to achieve core accuracy, with an R2 greater than 0.85 and a ΔR2 of –0.038.

This paper investigates the effects of geomagnetic disturbances that occurred in August 2017, during the declining phase of Solar Cycle 24, when two geomagnetic storms driven by high-speed stream (HSS) and stream interaction region (SIR) structures and associated with HILDCAA intervals took place on 4–6 August and 17–21 August, together with a coronal mass ejection (CME) embedded in the solar wind that impacted the Earth on 22–23 August. The focus of the study is the ionospheric response over the Brazilian sector. For this purpose, we use CADI ionosonde measurements from an equatorial station (Araguatins) and two low-latitude stations (Jataí and São José dos Campos). The empirical model of Fejer and Scherliess (1997) is applied to estimate the equatorial vertical plasma drifts and to assess the impact of prompt penetration electric fields (PPEFs) and disturbance dynamo electric fields (DDEFs) on the F-region ionosphere over Brazil. The results show pronounced variability in the F-layer height parameters ( h ′ F and hpF 2 ) and in the critical frequency ( foF 2 ) throughout August. At the equatorial station, deviations reached approximately ± 80 –160 km ( ± 20 –40%) in h ′ F , ± 160 –200 km ( ± 40 –50%) in hpF 2 , and decreases of about 1–5 MHz in foF 2 . At the low-latitude stations, the corresponding height variations were smaller, typically ± 40 –80 km ( ± 10 –20%), while foF 2 variations remained within the range of -1–5 MHz, with amplitudes decreasing progressively with increasing distance from the magnetic equator. Because storm-time signatures are not always unambiguously identifiable in the raw ionospheric time series, a wavelet-based coherence analysis is employed to quantify how PPEF- and DDEF-related drifts modulate the ionospheric parameters ( h ′ F and foF 2 ) in the time–frequency domain. The time–frequency results support the interpretation that recurrent HSS/SIR structures modulate both the intensity of PPEFs and the equatorial F-layer height. A latitudinal comparison of the wavelet-coherence patterns demonstrates the increasing importance of DDEFs away from the magnetic equator, particularly at Jataí and São José dos Campos. In addition, an analysis of the occurrence of intermediate layers (CIs) is carried out. The CIs, which formed predominantly by detachment from the lower part of the F region followed by downward motion into the ionospheric valley region, exhibited typical virtual heights between 140 and 170 km and peak frequencies between 3 and 4.5 MHz. At Araguatins, the largest number of CI occurrences coincides with geomagnetically disturbed intervals, suggesting that storm-time PPEFs and DDEFs may contribute to the formation or maintenance of CIs near the magnetic equator. At Jataí and São José dos Campos, however, such correspondence is not evident, indicating that additional drivers, such as neutral wind shear, atmospheric tides, and gravity waves, likely play a relatively more important role at those latitudes.

Burden of Minimal Hepatic Encephalopathy in Literate Cirrhotics Aged 15-50 Years: Time to Switch to Digital Diagnostic Tools?

Modulation of a ramp-induced shock wave/boundary layer interaction through extended frequency range pulsed discharge

This paper presents a literature review on the state of the art in modelling and experimenting with radial passenger car tires operating on contaminated road surfaces. To successfully model and experiment with radial passenger car tires, validation is required for the tire itself, material interactions, contaminant modelling, contact theories, and combined interactions. This work presents a review of previous studies. The review includes modelling and validation of passenger car tires in the finite element environment. Both static and dynamic validation techniques are reviewed, focusing on tire footprint, vertical stiffness, lateral stiffness, critical vertical frequency, and combined slip conditions. The modelling and physical interactions of contaminants such as sand, clay, ice, gravel, and snow are also presented for tire interactions on both on-road and off-road surfaces. Techniques such as Smoothed Particle Hydrodynamics (SPH), Finite Element Analysis (FEA), and Discrete Element Methods (DEM) are employed. The contact algorithms used or derived are also reviewed, as they are vital to the accurate prediction of contact forces between two objects. The contact algorithms presented are both physical and modelled, using contact theories such as the Hertzian contact theory, point contact models, and Lumped LeGre Friction models. Additionally, studies including tire-road and tire-terrain interaction characteristics are presented and discussed. These studies vary loading conditions through speed, loading, road surface, and temperature. Furthermore, research on hydroplaning prediction is presented, as water is a commonly found surface contaminant and presents a threat to all drivers. Finally, this review discusses shortcomings and best techniques as they pertain to winter-contaminated road surface modelling with tire interactions.

BackgroundThe escalating prevalence of screen-related eye fatigue has become a health burden in the digital era worldwide, yet routine monitoring relies largely on subjective reports. This underscores the urgent need for clinically applicable, objective diagnostic solutions. Ocular metrics provide an objective method to assess computer vision syndrome, or asthenopia.ObjectiveThis study aimed to develop and evaluate an integrated at-home system for predicting short-term deteriorated asthenopia using objective ocular metrics. This system classifies the short-term risk level for practical monitoring and automatically generates a session report that summarizes metrics to complement symptom-based evaluation.MethodsWe developed EyeFatigue Tracker, an integrated at-home system delivered via a desktop app, comprising a head-mounted device to record binocular infrared eye videos, a deep learning (DL) model to extract ocular metrics, and a machine learning (ML) classifier to estimate asthenopia risk. The DL model, trained on an in-house dataset, segments the palpebral fissure, pupil, and iris from recorded videos to derive ocular metrics. To build the prediction model, participants were recruited to complete a 1-hour computer gameplay session. Changes in the Computer Vision Syndrome Questionnaire (CVS-Q) scores served as the primary outcome measure to classify participants into deteriorated and nondeteriorated asthenopia groups. Metrics showing significant between-group differences were used as inputs for four ML models, including support vector machine (SVM), decision tree, extreme gradient boosting (XGBoost), and random forest, to identify deteriorated asthenopia. Model performance was evaluated with fivefold cross-validation.ResultsThis study enrolled 38 participants aged 19-31 (mean 24.8, SD 3.11) years. Following visual tasks, participants’ CVS-Q scores were higher compared to baseline values (mean 9.21, SD 4.57, vs mean 6.76, SD 3.76; P<.001). Alongside the critical flicker fusion frequency (CFF), nine key features were selected as predictive indicators, with the top five reflecting fissure length variability (variance, coefficient of variation, and SD), average blink duration, and pupil size variability (coefficient of variation). Most ML models exhibited high discriminative ability, with the random forest achieving the best overall performance (mean accuracy 0.720, SD 0.035; mean area under the receiver operating characteristic curve 0.850, 95% CI 0.830-0.860).ConclusionsThe findings highlight the potential of objective indicators in identifying individuals at risk for asthenopia following computer gameplay. The ML models using ocular biomarkers identified in this study achieved plausible discriminative ability in detecting deteriorated asthenopia. EyeFatigue Tracker functions as an integrated, at-home system that produces a risk level prediction and a concise session report, supporting early detection and informing preventive care in real-world settings.

Partially liquid-filled rotor systems subjected to lateral excitation exhibit pronounced fluid–structure interaction, leading to complex and highly sensitive vibration responses. To enable efficient probabilistic prediction under parametric uncertainty, this study develops a deterministic–data-driven framework for a rigid hollow rotor partially filled with liquid. Based on small-perturbation flow theory, the liquid-induced feedback forces are analytically derived and incorporated into the coupled rotor–liquid dynamic equations, yielding a closed-form steady-state solution. The results reveal that lateral excitation in one direction induces coupled vibration in the orthogonal direction, resulting in an elliptical whirl trajectory of the rotor center. The vibration characteristics depend jointly on excitation frequency and rotor angular velocity, and for a given angular velocity, two critical excitation frequencies are identified at which the response amplitude increases sharply. Surrogate models based on a backpropagation neural network (BPNN) and a support vector machine (SVM) are constructed and validated, with the BPNN demonstrating superior predictive accuracy. Uncertainty analysis further shows that the maximum vibration amplitude exhibits asymmetric, non-Gaussian distributions even under normally distributed inputs, and excessive amplification may occur beyond certain uncertainty levels. The proposed framework provides a robust tool for probabilistic vibration assessment and uncertainty-informed design of partially liquid-filled rotor systems.

The research focuses on the durability of the rotor blades of a marine gas turbine engine. The goal of the research is to determine the maximum equivalent stresses and the durability of the rotor blades of a ship gas turbine engine under the action of the working fluid flow. To achieve the aim of the research, the following research tasks were addressed: developing a high-precision mathematical model for calculating the fatigue strength of marine gas turbine engine rotors, which reliably reflects the actual operating conditions while ensuring the accuracy of the results; determining the influence of the gas flow temperature and pressure on blade durability; and analyzing the influence of blade’s cooling cavity design on the level of maximum equivalent stresses. The methods employed include numerical methods, in particular the finite element method. The following results were obtained: the problem of determining the equivalent dynamic stresses and durability of gas turbine blades was solved in the work. The numerical results were used to determine the operational lifespan of the rotor blades. Equivalent stresses and service life were determined for the most critical frequencies of forced oscillations across all turbine stages, that constitute the rotor. A marine gas turbine engine must be as compact as possible. Thus, its rotor consists of only three stages, which results in considerable vibration and thermal loads on the rotor blades. It was established that the blades of the first stage, which are exposed to the highest thermal and vibration loads, have the lowest durability. The scientific novelty lies in the development of an improved mathematical model for determining the durability of blades of marine and stationary gas turbine engines. Conclusions. The calculation results show that at a surface temperature of approximately 1000 °C, the maximum equivalent stresses are approximately equal to the endurance limit of the blade material. The influence of the geometric parameters of the blade cooling cavity on the value of the maximum equivalent stresses was also investigated. The results obtained can be used for subsequent research related to rotor creep and the initiation of fatigue cracks on the blades surfaces.

The idea of using the available large arrays of ionogram processing results from vertical radiosonde of the ionosphere as training datasets for building predictive models using machine learning methods is put forward. The most common formats for saving the results of ionogram processing are considered, as well as some Internet resources with archives of freely available files of these formats. These datasets are used by us to build predictive models, including time series of critical frequencies of ionospheric layers. It is also possible to use some datasets of ionogram processing results to train models designed for automatic ionogram processing.

Introduction: The emergence of SARS-CoV-2 Variants of Concern (VOCs) has provided a powerful empirical demonstration of the importance of a phenotypic perspective in evolutionary dynamics. Materials and methods: This study utilizes high-resolution genomic surveillance data from the USA, UK, Germany, and Denmark (extracted from the CoVariants database) to model the competition between SARS-CoV-2 variants. The analytical approach bridges classical population genetics—specifically the Price equation and Fisher’s Fundamental Theorem—with epidemiological growth frameworks. A recursive fitting procedure was applied to the initial empirical frequency data of emerging Variants of Concern (VOCs) to assess the reliability of early-stage fitness estimates compared to full-series benchmarks. Results: The analysis demonstrates that fitness estimates derived from as few as the first 3 to 5 weeks of genomic data show a rapid convergence toward final values. These early estimates remain highly stable across diverse geographic regions, suggesting that the intrinsic transmission advantage of a VOC is a dominant driver. The results indicate that the selective coefficient of a variant remains largely decoupled from local environmental noise and stochastic fluctuations once a critical frequency threshold is surpassed. Conclusions: This study shows that the spread advantage of new COVID-19 variants is stable and can be accurately predicted using only early infection data, bypassing the need for long-term tracking. By analyzing genomic data from four countries, this method provides a crucial early warning window to help public health officials prepare for new variants before they become dominant.

Abstract The Atlantic marsh fiddler crab, Minuca pugnax (Smith, 1870), spans a broad latitudinal range from Maine to Florida, USA, experiencing chronic thermal variation that may drive physiological adaptation. We investigated how acclimatization with latitude alters acute thermal effects on oxygen consumption rate and visual speed in populations from Rhode Island, Delaware, and South Carolina. Whole-animal oxygen consumption rate and retinal critical flicker fusion frequency (CFF; visual speed) were measured at acute temperatures of 15°C, 20°C, and 25°C within each population during summer. Oxygen consumption rate varied with acute temperature and latitude, with crabs from the Rhode Island population showing the highest rates and lowest thermal sensitivity (Q10= 0.86), consistent with metabolic cold adaptation. Crabs from the South Carolina and Delaware populations exhibited lower oxygen consumption rates but higher thermal sensitivity (Q10= 1.63 and 1.85 respectively). CFF did not differ significantly (P = 0.1207) among locations or temperatures, suggesting visual speed is more thermally stable than oxygen consumption. While metabolism in fiddler crabs varies with latitude-dependent physiological compensation, visual performance appears more rigid, highlighting differential thermal sensitivity across physiological systems in response to environmental variability.