High Frequency Components Research Articles

Accommodation microfluctuation in individuals with mTBI and the potential effect of chromatic filter on this parameter.

Individuals with mild traumatic brain injury (mTBI) exhibit significant accommodative abnormalities. Accommodation microfluctuation (MFs) plays an important role in the accommodation steady-state control. This study was set to investigate the characteristics of the MFs in mTBI subjects and whether chromatic filter (CF) or neutral density filter (ND) would impact these parameters. 54 healthy and 30 mTBI young adults participated. Tonic accommodation (TA) was recorded in complete darkness after dark adaptation. The steady-state accommodation response (AR) for various stimulus distances was recorded at baseline (no filter; NF), with CF and ND. The low and high frequency component (LFC and HFC respectively) of MFs was analyzed. Subjects with mTBI exhibited significantly lower MFs, especially at higher stimulus levels. There was a strong negative correlation between the accommodation error (AE) and the LFC. Increased lag of accommodation (AE > 0 D) was associated with lower LFC and vice versa. Both filter conditions exerted a minimum impact on the MFs overall. A strong positive correlation between MFs and TA was revealed for both groups. Our study showed that mTBI significantly impaired the MFs of accommodation. The observed lower MFs power in mTBI appears to be subsequent to the higher lag of accommodation, suggesting an underlying deficit in the accommodative motor controller to generate sufficient response. The strong correlation between TA and MFs, as well as the lack of effect from different filters, further supports the theory that sensory blur detection does not play a significant role in MFs of accommodation.

Deep Feature Blend Attention: A New Frontier in Super Resolution Image Generation

Super Resolution (SR) images contain more useful information than Low Resolution (LR) images. Generally, SR images are highly preferred over LR images in the medical field due to their high quality. Generally, SR images are affected by many factors, like blur, noise, and decimation. Therefore, the present study proposes Deep Feature Blend Attention Mechanism for generating SR images with optimal outcomes. In this study, a Deep Learning (DL) based encoder-decoder is used to extract the detailed information from the LR image, assist in noise removal, and improve image quality. Using Gaussian blur, an LR image is produced, which is given as input to the encoder-decoder. The Attention mechanism with feature blending is performed to produce the reconstructed SR image. The low and high frequency component issues from feature map are improved using the attention mechanism. Also, feature blend attention mechanism is implied to reconstruct SR images from LR images. The present study utilizes the feature blend technique, which helps select the optimal features and blending to attain the optimal feature set. The study is performed with and without the feature blend technique in attention mechanism to expose the impact of the feature blend technique, and it is used to avoid overfitting issues and produce optimal results. The performance of the proposed system is assessed by using PSNR and SSIM to show the system's efficiency. The proposed system is compared with other state-of-the-art studies to expose the efficacy of the present system.

Hilbert transform analysis of the mouse scotopic electroretinogram reveals two distinct bursts of oscillatory potentials with progressively dimmer flashes.

Study the scotopic oscillatory potentials (OPs) in mice over a wide range of flash luminance levels using the Hilbert transform (HT) to extract new features of the high frequency components of the electroretinogram (ERG). Scotopic ERGs [Intensity: - 6.3 to 0.9 log cd∙s∙m-2; 12h of dark-adaptation] were obtained from adult mice (C57BL/6; n = 7). The Hilbert transform (HT) was obtained within 3 consecutive frequency bands (65-90Hz, 90-115Hz and 115-140Hz), with OPs being denoised, automatically identified and analyzed. Measurements included: number of OPs, duration of the OP response, surface-under-the-curve (SUC) of the HT envelopes, implicit times, and instantaneous frequency at the HT envelope peak, mean peak time differences (PTD) between the envelopes of each frequency band (measuring their synchrony), correlation coefficient and lag between consecutive HT envelopes, as well as the number of peaks on the HT envelopes. The OP response duration, number of OPs and PTD all peaked for flashesbetween the level corresponding to the RodVmax (maximal b-wave amplitude of the rod ERG; i.e., the first asymptote of the scotopic luminance-response curve) and K (the flash luminance at which the amplitude of the b-wave is half of that of the RodVmax;), i.e., between -3.9 and -2.4 log cd∙s∙m-2. The correlation between consecutive envelopes is close to 1 at flashes > -1.2 log cd∙s∙m-2, with small lags (min. = 1.93 ± 0.45ms at - 1.2 log cd∙s∙m-2), then gradually drops to 0.81 ± 0.02 at the dimmest flash intensity (with a max. lag = 14.76 ± 8.92ms at - 5.1 log cd∙s∙m-2). Finally, we found that the single OP burst (i.e., a single HT envelope peak) seen at flash intensities > - 1.2 log cd∙s∙m-2 progressively divided in two (or more) OP bursts (i.e., multiple HT envelope peaks) with gradually dimmer flashes. Our HT method enabled the analysis of the OP response without the subjective interpretation of the experimenter. Analysis of the scotopic OPs at dim flashes with the HT revealed a novel feature of the OP response not yet reported elsewhere, namely: a split of the OP response into two (or more) distinct bursts. Furthermore, the synchrony peak (measured with the PTD) matched the peak in OP response duration between K and RodVmax, suggesting a disorganization (or dephasing) of the retinal signal in ERGs evoked for weaker flashes. The increased synchronization and correlation of the single burst observed for the strongest flashes could suggest an optimization or saturation of the retinal response. We believe that these novel features of the OP components of the ERG went unnoticed given that previous studies did not use weak enough flashes and failed to recognize the added value that time and frequency domain analysis of the ERG (such as what is achieved with the HT) brings to the interpretation (and our understanding) of the retinal response.

Spontaneous oxycodone withdrawal disrupts sleep, diurnal, and electrophysiological dynamics in rats.

Opioid dependence is defined by an aversive withdrawal syndrome upon drug cessation that can motivate continued drug-taking, development of opioid use disorder, and precipitate relapse. An understudied but common opioid withdrawal symptom is disrupted sleep, reported as both insomnia and daytime sleepiness. Despite the prevalence and severity of sleep disturbances during opioid withdrawal, there is a gap in our understanding of their interactions. The goal of this study was to establish an in-depth, temporal signature of spontaneous oxycodone withdrawal effects on the diurnal composition of discrete sleep stages and the dynamic spectral properties of the electroencephalogram (EEG) signal in male rats. We continuously recorded EEG and electromyography (EMG) signals for 8 d of spontaneous withdrawal after a 14-d escalating-dose oxycodone regimen (0.5-8.0 mg/kg, 2×d; SC). During withdrawal, there was a profound loss (peaking on days 2-3) and gradual return of diurnal structure in sleep, body temperature, and locomotor activity, as well as decreased sleep and wake bout durations dependent on lights on/off. Withdrawal was associated with significant alterations in the slope of the aperiodic 1/f component of the EEG power spectrum, an established biomarker of arousal level. Early in withdrawal, NREM exhibited an acute flattening and return to baseline of both low (1-4 Hz) and high (15-50 Hz) frequency components of the 1/f spectrum. These findings suggest temporally dependent withdrawal effects on sleep, reflecting the complex way in which the allostatic forces of opioid withdrawal impinge upon sleep and diurnal processes. These foundational data based on continuous tracking of vigilance state, sleep stage composition, and spectral EEG properties provide a detailed construct with which to form and test hypotheses on the mechanisms of opioid-sleep interactions.

Denoising low-field MR images with a deep learning algorithm based on simulated data from easily accessible open-source software

In this study, we introduce a denoising method aimed at improving the contrast ratio in low-field MRI (LFMRI) using an advanced 3D deep convolutional residual network model. Our approach employs synthetic brain imaging datasets that closely mimic the contrast and noise characteristics of LFMRI scans, addressing the limitation of available in-vivo LFMRI datasets for training deep learning models. In the simulation data, the Relative Contrast Ratio (RCR) increased, and similar improvements were observed in the in-vivo data across different imaging conditions. Comparative evaluations demonstrate that our model performs better than the widely used non-deep learning method, BM4D, in enhancing RCR and maintaining high spatial frequency components in in-vivo data.

Efficacy of Arogya Rakshak Panchatantra (Five Lifestyle Principles) on Heart Rate Variability and Menstrual Symptoms in Primary Dysmenorrhea: A Randomized Controlled Trial.

Introduction: Primary dysmenorrhea (PD) is associated with decreased performance and reduced quality of life in young adults. This study aims to evaluate the effectiveness of implementing Arogya Rakshak Panchatantra (ARP), a naturopathic lifestyle practice, among young female adults with PD. Method: This open-label, parallel-arm, randomized controlled trial included 52 young female adults with PD aged between 16 and 25 years. They were randomly assigned to the ARP group practicing the ARP module for 2 months or the control group maintaining their routine lifestyle. The primary outcome was changes in heart rate variability (HRV), while secondary outcomes included menstrual distress, quality of life, pain, and daytime sleepiness, all evaluated at baseline and during the first three menstrual cycles in both groups. To evaluate the feasibility, the study also monitored self-reported symptoms, emotions, adverse events, calorie intake, and adherence to the intervention. The effectiveness of ARP was evaluated using repeated measures analysis of variance and a generalized linear model. Results: In the intervention group, we observed significant improvements in the high frequency component of HRV (p = 0.007), as well as reductions in weight (p = 0.017), visual analog scale pain (p = 0.000), and retrospective symptom scale score (p < 0.011). There were no significant changes in other HRV metrics, quality of life, or daytime sleepiness. Participants reported mild symptoms such as headaches, colds, body pain, diarrhea, constipation, fever, and weakness, with no serious adverse events. Discussion: This trial is the first to examine the long-term impact of ARP on young females with PD, suggesting it could be an effective and feasible management option. Further research with larger sample sizes is needed to confirm these findings.

Immediate Effect of Uddiyana Bandha on Heart Rate Variability in Patients with Hypertension: A Randomised Controlled Study.

Hypertension, a prevalent cardiovascular condition, is linked to autonomic nervous system dysregulation, often evidenced by reduced heart rate variability (HRV). Yoga, particularly Uddiyana Bandha, may influence autonomic control and improve HRV, thus offering potential benefits for hypertension management. This study intended to assess the impact of the regular use of Uddiyana Bandha practice on HRV among hypertensive patients. This randomised controlled study included 60 hypertensive patients aged between 40 and 60. Participants were randomised into an intervention group practicing Uddiyana Bandha (n = 30) and a control group with no intervention (n = 30). The outcome measures included time and frequency domain HRV variables, specifically the standard deviation of NN intervals (SDNN), the square root of the mean squared differences of successive NN intervals (RMSSD), high-frequency (HF) and low-frequency (LF) components in normalised units (n.u), and the LF/HF ratio. These were assessed both before and immediately after the intervention. The intervention group performed three rounds of Uddiyana Bandha under supervision, while the control group rested quietly for an equivalent duration. The Uddiyana Bandha group demonstrated significant (p < .05) improvements in HRV parameters post-intervention compared to the control group. Mean RR interval, SDNN and RMSSD increased immediately after Uddiyana Bandha practice. The HF component (n.u) increased, while the LF component (n.u) and LF/HF ratio decreased (p < .05), indicating enhanced parasympathetic activity. No significant changes were observed in the control group. Uddiyana Bandha's practice immediately has notable effects on HRV in hypertensive patients, showing better autonomic balance and cardiovascular performance. These results suggest Uddiyana Bandha as a possible non-pharmacological strategy for hypertension management. Future studies with larger sample sizes and longer durations are needed to support these results and explore long-term effects.

Changes in heart rate variability after rhinosurgical interventions

Purpose: to study changes in heart rate variability (HRV), as an indicator of the stress response, after rhinosurgical interventions.Material and methods. A study of heart rate variability was conducted in 98 patients in the perioperative period who underwent open rhinoplasty (1st group, 33 patients), open rhinoplasty with simultaneous septoplasty (2nd group, 33 patients), and septoplasty (3rd group, 32 patients). Heart rate variability was assessed before surgery, 3, 6, 24 and 48 hours after surgery. The low frequency component (LF), high frequency component (HF), and very low frequency component (VLF) of HRV were studied.Results. In the 3rd group of patients, compared with the data before surgery, the high-frequency component of HRV significantly decreased on the 3rd and 24th hours after surgery (p &lt; 0.001), but increased after two days (p &lt; 0.001). 3 hours after surgery, HF HRV in patients of 1st group was significantly higher than in patients of groups 2nd and 3rd (p &lt; 0.001). In the second group, a significant decrease in HF was noted in the first 3 (p &lt; 0.001), 6 hours (p &lt; 0.01), 24 hours (p &lt; 0.05) and 48 hours (p &lt; 0.001) of the postoperative period, compared with data before surgery. In 3rd group, at intervals after surgery of 3 and 24 hours (p &lt; 0.001), as well as 6 hours (p &lt; 0.001), LF was significantly lower than before septoplasty. 3 hours after surgery, the VLF in the rhinoplasty group was significantly higher than in the other groups (p &lt; 0.001). At the 6th hour there were no differences between the groups. After 24 hours, in the septoplasty group, VLF was significantly lower than in 11st and 2nd groups (p &lt;0.01).Conclusion. Septoand rhinosptoplasty provoke an inadequate low response from the sympathetic nervous system in the first hours after surgery, which is manifested by low LF power and indicates a violation of adaptive responses maladaptation. A decrease in VLF power in the first 3 hours after septoand rhinoseptoplasty indicates a pronounced stressor effect, which leads to low adaptive capabilities compared to rhinoplasty, that is, maladaptation within the specified period.

DESRGAN: Detail-enhanced generative adversarial networks for small sample single image super-resolution

Image super-resolution involves reconstructing a blurry, low-resolution image with limited information into a clear, high-resolution image containing more detailed information. The images generated by super-resolution reconstruction can enhance the performance of downstream computer vision tasks, and hold wide application prospects in fields such as industrial fault detection, plant phenotype parameter extraction, medical imaging, and more. High-frequency components in images, such as edges and texture details, typically require more attention. However, when the training samples are limited, effectively recovering clear high-frequency details of images becomes highly challenging. Therefore, this paper proposes a single-image super-resolution method based on generative adversarial networks, named DESRGAN. Compared to existing methods, DESRGAN achieves better reconstruction of image details even with a limited number of training samples. DESRGAN introduces several key innovations: a shallow generator structure to address overfitting issues in small sample scenarios, a dual-stream feature extraction network with dilated convolutions to capture multi-scale contextual information and expand the receptive field, and an artifact loss designed to eliminate artifacts and preserve the true high-frequency details of the super-resolved images. Extensive ablation experiments and comparative studies with multiple state-of-the-art models are conducted on two small sample datasets, "Root" and "Leaves," as well as five publicly available datasets. The results demonstrate that the proposed DESRGAN achieves superior performance in small sample single image super-resolution tasks, with improvements of 1.39 dB in PSNR and 0.013 in SSIM. The generated high-resolution images exhibit clear texture and edge structures, presenting favorable subjective visual effects. Moreover, the model displays strong generalization capabilities.

Hybrid Preprocessing for Neural Network-Based Stock Price Prediction

In the domain of stock price prediction, the intricate interdependencies within multivariate time series data present significant challenges for accurate forecasting. This paper introduces a groundbreaking hybrid preprocessing technique to tackle this issue. By leveraging the Empirical Wavelet Transform (EWT), we adeptly extract both low-frequency and high-frequency components from the time series. We then apply Dynamic Time Warping (DTW) and Differential Dynamic Time Warping (DDTW) to measure component similarities, identifying correlated patterns within the stock price series. High-frequency components are managed using sliding windows and Principal Component Analysis (PCA), while PCA is directly applied to low-frequency components. Integrating these techniques into neural network models, our approach yields a substantial 30% improvement in prediction accuracy compared to traditional methods. This significant advancement underscores the potential of our hybrid preprocessing method in enhancing stock price prediction accuracy, offering valuable insights for financial market analysis.

Electrochemical Properties of Co and Yb Doped BaZrO3 and Its Application to PCFC Cathodes

Protonic ceramic fuel cells (PCFC) are expected to have high power density and high energy efficiency because they can operate at 500-700°C and do not dilute the fuel. BaZrO3 doped with Yb is a candidate electrolyte for PCFC because it has high proton conductivity and chemical stability, does not react with Ni in the anode to form complex oxides, and can be manufactured industrially [1]. Many researchers have been exploring the cathode, but a material with higher activity is needed to improve the power density and efficiency. In this study, we investigated the electrochemical and PCFC cathodic properties of BaZr0.875-x Yb0.125Co x O3-δ (x=0.0-0.75), which is a Yb-doped BaZrO3 electrolyte with Co added to provide electronic conductivity.To clarify the electrochemical properties of BaZr0.875-x Yb0.125Co x O3-δ, the electrical conductivity was measured by the two-terminal AC method and the proton concentration was evaluated by TGA. The Co valence was confirmed by XPS and the coefficient of thermal expansion was evaluated by XRD. The electrical conductivity was found to increase with increasing Co content. The TGA results showed that the proton concentration decreased with Co addition, and increased with Co addition above 0.375. XPS results indicate that Co was mainly tetravalent below x=0.375, but at x=0.5, Co was both tetravalent and trivalent. The increase in proton concentration can be attributed to the increase in oxide ion vacancies due to compensation of trivalent Co. The coefficient of thermal expansion was found to be 9.8×10-6/K at x=0.5, which is close to that of BaZr0.8Yb0.2O3-δ, a candidate electrolyte for PCFC.The anode-supported PCFCs using BaZr0.875-x Yb0.125Co x O3-δ as cathode were constructed and power density was investigated, with the highest power output (545 mWcm-2 at 600°C) obtained at x=0.5. Further DRT analysis of the cell impedance showed that the electrode impedance of the anode-supported PCFC could be separated into five component. The isotope effect of the impedance was investigated by replacing the anode gas from H2O-H2 to D2O-D2, and the isotope effect was observed in the two components on the high frequency under OCV, indicating that it is attributable to the anodic reaction. On the other hand, no isotope effects were observed in the three high-frequency components under OCV, but isotope effects were observed in the highest-frequency component under bias (0.6 V). This component is considered to be the component attributed to the water vapor formation reaction and was found to have the highest resistance among the impedance components of the cathode. Acknowledgment This paper is based on results obtained from a project, (JPNP20003), commissioned by the New Energy and Industrial Technology Development Organization (NEDO).[1] T. Kuroha, Y. Niina, M. Shudo, G. Sakai, N.Matsunaga, T. Goto, K. Yamauchi, Y. Mikami, and Y. Okuyama, Journal of power sources, 506 (2021) 230134.

Gravitational bremsstrahlung from Yukawa and nucleon collisions

We obtain the gravitational emission from particles scattering via the Yukawa interaction, presenting both classical and approximate quantum results. We also estimate the contribution from the tensor part of the internucleon interaction. This emission is the main source of a very high frequency component to the stochastic background in the Solar System and in neutron stars. The emission from the Sun (allowing for Debye screening) and from a typical neutron star are obtained. The gravitational wave luminosity of the Sun is 41±10 MW. Published by the American Physical Society 2024

Acupuncture improves the symptoms, serum ghrelin, and autonomic nervous system of patients with postprandial distress syndrome: a randomized controlled trial

BackgroundWhether gastrointestinal hormones in patients with postprandial distress syndrome (PDS) are altered by acupuncture, and whether such alterations are related to the autonomic nervous system (ANS), remains an open question.ObjectiveThis study aims to investigate the effects of acupuncture on symptoms, serum hormones, and ANS in PDS patients.MethodsThis randomized controlled clinical trial was conducted at Beijing Hospital of Traditional Chinese Medicine affiliated with Capital Medical University. Sixty-two PDS patients were randomly assigned equally to acupuncture or sham acupuncture arm (3 sessions per week for 4-week). The main outcome measures which were evaluated at baseline and 4-week included cardinal symptoms, serum hormones including ghrelin, vasoactive intestinal peptide (VIP), substance P (SP), and ANS.ResultsAmong the 62 randomly assigned participants, 51 (82%) were included in the baseline characteristics and outcome analysis. Gastrointestinal symptoms including response rate (p = 0.001) and dyspepsia symptom severity (p = 0.002) were significantly improved after acupuncture treatment. Serum ghrelin concentration was significantly higher in acupuncture group than in sham acupuncture group (8.34 ± 3.00 ng/ml versus 6.52 ± 2.00 ng/ml, p = 0.022) after 4-week treatment, instead of VIP and SP (p > 0.05). The acupuncture group had significantly higher vagal activity, showing with increasing of high-frequency component (HF, p ≤ 0.001) and decreasing of the ratio of low-frequency and HF (p ≤ 0.001). In relationship analysis, the HF component exhibited a significant inverse correlation with symptom severity (R = − 0.501, p ≤ 0.001), but not with ghrelin level (R = 0.026, p = 0.865).ConclusionAcupuncture may improve the symptoms and increase the ghrelin level of PDS patients, the therapeutic effect of acupuncture was associated with the alteration of vagal activity.Trial registration: The trial is registered with the ISRCTN registry, ISRCTN12511434. Registered 31 March 2017, https://www.isrctn.com/.

Sex-differences in neuromuscular control of hip abductors during isometric submaximal tasks

BackgroundOne key focus in the literature is the need to better understand how males and females perform neuromuscular control, which requires consideration of muscle morphology, as it may change neural drive during force production. Although previous studies focused on muscles around the knee and ankle, it is crucial to explore the behavior of other muscles, such as the hip abductors, since many lower limb conditions more common in females have been associated with alterations in hip muscles. Research questionsAre electromyography (EMG) variables (amplitude and low- and high frequency bands) of hip abductors during submaximal isometric tasks different between males and females? How is muscle size associated with EMG responses during these tasks? MethodsThirty-six participants (males, n = 18; females, n = 18) had muscle and subcutaneous thickness of gluteus medius (GMed) and tensor fascia latae (TFL) measured. They then performed an estimated one-repetition maximum (1RM) test in a side-lying hip abduction exercise, followed by two 10-s submaximal tasks: (i) side-lying hip abduction with 50 % 1RM and (ii) with 20 % 1RM. GMed and TFL EMG amplitude and frequency bands (low and high frequency components) were analyzed. ResultsFemales had higher GMed EMG amplitude, lower low-frequency, and higher high-frequency components than males in both tasks (p = 0.001–0.040). No differences were observed in TFL EMG variables. Greater GMed thickness was associated with lower amplitudes in the 50 % 1RM task (r = − 0.35; p = 0.03), while greater TFL thickness was associated with lower low-frequency [20 % 1RM: r = − 0.70; p = 0.002; 50 %-1RM: r = − 0.65; p = 0.005] and higher high-frequency components [20 % 1RM: r = 0.74; p = 0.001; 50 %-1RM: r = 0.76; p < 0.001] only in females. SignificanceMales and females employ different neuromuscular control strategies, which vary among the synergists for hip abduction.

Multiresolution Analysis of HRRR Meteorological Parameters and GOES-R AOD for Hourly PM2.5 Prediction.

High-resolution, comprehensive exposure data are crucial for accurately estimating the human health impact of PM2.5. In recent years, satellite remote sensing data have been increasingly utilized in PM2.5 models to overcome the limited spatial coverage of ground monitoring stations. However, data gaps in satellite-retrieved parameters such as aerosol optical depth (AOD), the sparsity of regulatory air quality monitors for model training, and nonlinear relationships between PM2.5 and meteorological conditions can affect model performance and cause data gaps in most existing PM2.5 models. In this study, spatial gaps in AOD obtained from Geostationary Operational Environmental Satellite-16 are filled using Goddard Earth Observing System Composition Forecasting AOD estimations. Furthermore, to improve model performance, meteorological predictors such as temperature from the High-Resolution Rapid Refresh model are preprocessed using Daubechies wavelet to extract low and high frequency components. The spatially gap-filled AOD, along with meteorological data, are ingested into a machine learning model to predict hourly PM2.5 at a 1 km spatial resolution in California. The model evaluation metrics (OOB (out-of-bag) R2 = 0.86 and RMSE (root-mean-square error) = 9.27 μg/m3 and 10-fold spatial cross-validation R2 = 0.82 and RMSE = 9.82 μg/m3) demonstrate the model's reliability in predicting ambient PM2.5, especially for states like California that experience frequent episodes of wildfires.

Integration of polyoxometalate into defective UiO-66-NH2(Zr/Hf) for visible-light-driven hydrogen photogeneration

UiO-66 plays a crucial role in photocatalytic systems due to its superior stability and tunability. However, the charge transfer from organic ligands to metals is rarely involved in the photo-excitation of the pristine material. Herein, several Keggin-type Cs2.5H0.5PW12O40 (CsPW) were successfully incorporated into a bimetallic metal-organic framework (MOF) using a one-pot, one-step method, resulting in a series of defective CsPW@UNH(Hf-Zr) as optimized photocatalysts for visible-light-driven hydrogen evolution. Further investigations demonstrate that a Z heterojunction mechanism proceeds in the CsPW@UNH(0.65Hf) heterojunction to boost the charge separation via the chemical interaction between the CsPW and UNH(0.65Hf) components. The hydrogen evolution rate for 5 %CsPW@UNH(0.65Hf) is nearly two orders of magnitude greater than that of the bare UNH(Zr). The coexistence of Hf3+ and Zr3+ was observed in defective polyoxometalate-encapsulated MOF (POM@MOF) for the first time during photocatalysis, confirming the successful conjugated π electrons transition from the amine-containing ligand to bimetallic cluster. This study offers a novel guideline for exploring efficient MOF-based photocatalysts through defect engineering.

Transient flow and noise characteristics of vortex-turbulence-noise interaction in centrifugal pump

Centrifugal pump operation noise is an avoidable common phenomenon in engineering practices, and its characteristics are closely influenced by the flow characteristics of the fluid. In this paper, a numerical simulation of centrifugal pump operation with different flow rates is carried out using the detached eddy simulation turbulence model and compared with the experimental results. Then the noise source and far-field radiated noise characteristics of the centrifugal pump are investigated using Lighthill’s analogy. The results show that the noise of centrifugal pump is closely related to the flow characteristics of impeller, especially the generation and development of vortex. The relationship between turbulent kinetic energy (TKE) and vortex is complex, involving the generation, development and shedding of vortex. The influence of flow rate variation on vortex structure in impeller is studied by vortex transport equation. It is found that the relative vortex stretching (RVS) term is the main driving force of vortex distribution. The distribution of the Coriolis force (CORF) term is mainly concentrated in the inlet and outlet areas of the flow channel, which is related to the rotational motion of the impeller and the dynamic characteristic of the fluid. The power spectral density inside the impeller is mainly concentrated in the low frequency region, indicating that the low frequency noise component is dominant in energy. The directivity curve of far-field noise pressure level is influenced by hydrodynamic effect, impeller rotation direction and pump design structure, and will have a certain deviation. The spectral curve analysis of velocity, vorticity, pressure and sound pressure level shows that these physical quantities have strong correlation on the spectrum, in which the low frequency component is large, and the high frequency component is complex and volatile.

Relationship Between High Frequency Component of Heart Rate Variability and Delta EEG Power During Sleep in Women With Irritable Bowel Syndrome Compared to Healthy Women.

To explore the relationship between the high frequency (HF) heart rate variability (HRV) and electroencephalogram (EEG) delta band power in women with irritable bowel syndrome (IBS) versus healthy control women. Twenty women with IBS and twenty healthy controls were studied over three consecutive nights using polysomnography in a sleep laboratory. To avoid the first night effect, only second-night data were analyzed. Power spectral analysis was applied to HRV and EEG recordings. The linear system coherence/phase analysis assessed the relationship between normalized HF power of HRV and normalized delta band power of EEG during the first four NREM-REM sleep cycles. Women with IBS exhibited a significantly higher percentage of NREM sleep, higher normalized HF, lower normalized low frequency (LF) and decreased LF/HF ratio of HRV in the first four NREM-REM sleep cycles compared to controls. Additionally, their normalized delta band power was significantly lower in these sleep cycles and over the whole night. The phase shift between HF and delta band power was significantly longer in the IBS group. While the coherence between normalized HF and normalized delta band power was lower in the IBS group, the difference was not statistically significant. The coherence/phase analysis showed a dysregulated interaction between autonomic and central nervous systems in women with IBS, manifested by increased lag time between cardiac and EEG delta band power compared to healthy controls. Whether this dysregulation contributes to the pathophysiology of IBS remains to be determined.

Forecasting Soil Moisture in Caragana Shrubland Using Wavelet Analysis and NARX Neural Network

It is important for sustainable use of soil water resource and high-quality development to forecast the soil moisture in forestland of water-limited regions. There are some soil water models. However, there is not the best model to forecast the change of soil moisture in the caragana shrubland. In this paper, the plant water relationship has been investigated at the same time for a long term in the caragana shrubland of semiarid region of the Loess Plateau of China. The data of soil moisture was divided and then NARX neural network was used to build model I and model II. For model I, low frequency component was the input variable, and for model II, low frequency component and high frequency component were predicted. The results showed the average relative error for model I is 3.5% and for model II is 0.3%. The average relative error of predicted soil moisture in 100 cm layer using model II is 0.8%, then soil water content in the 40 cm and 200 cm soil depth is selected and the forecast errors are 4.9% and 0.4%. The results showed that using model II to predict soil water is well Predicting soil water using model II will be important for sustainable use of soil water resource and high-quality development.

The contribution of dynamic cues to externalization of virtual source in virtual auditory display

It is well known that static virtual auditory display with headphone is inclined to create the auditory perception of in-head-localization, especially for target virtual sources in the median plane. By introducing the dynamic variation of binaural pressures caused by head turning, dynamic virtual auditory display eliminates the in-head-localization effectively. The present work investigates the contributions of dynamic interaural time difference (ITD) and dynamic interaural level difference (ILD) in binaural pressures to the externalization of auditory perception. By modifying the head-related transfer functions used in a dynamic virtual auditory display, binaural signals with dynamic ITD or dynamic ILD alone are created and presented by headphone. A psychoacoustic experiment is conducted to evaluate the extent of perceived externalization. The experiment results prove that, for the target virtual source in the median plane, dynamic ITD and dynamic ILD contribute to the externalization of low and high frequency components of the stimuli respectively. For wideband stimulus with low and high frequency components, both dynamic ITD and dynamic ILD contribute externalization but dynamic ITD contributes more than that of dynamic ILD.