Average Peak Research Articles

The EEG signals steganography based on wavelet packet transform-singular value decomposition-logistic

Brain-computer interface (BCI) technology is widely used in online medicine for the diagnosis and treatment of brain diseases. However, during brain-computer interaction, Electroencephalogram (EEG) signals and private information may be leaked when transmitted through unsecured Internet channels. To protect private information and EEG signal security, this paper proposes a steganography algorithm based on Wavelet Packet Transform-Singular Value Decomposition-Logistic (WPT-SVD-Logistic). The algorithm utilized wavelet packet transform (WPT) to conceal more private information while maintaining better perceptual fidelity. After two-level WPT processing, the EEG signal is decomposed into 4 sub-band signals, and then the singular value decomposition (SVD) method is used to embed private information into these sub-band signals. Additionally, the algorithm employed the Logistic map to confuse private information further and enhance its security. Experimental results show that WPT is more suitable for information hiding in EEG signals. The average peak signal-to-noise ratio of the algorithm on four different datasets is 96.5 dB, indicating that adding private information has a weak impact on the EEG signal. Compared with similar methods, this algorithm has smaller errors and stronger robustness, so it has more potential to become the main means of EEG signal steganography.

Feasibility of identifying proliferative active bone marrow with fat fraction MRI and multi-energy CT

Objective. Active bone marrow (ABM) can serve as both an organ at risk and a target in external beam radiotherapy. 18F-fluorothymidine (FLT) PET is the current gold standard for identifying proliferative ABM but it is not approved for human use, and PET scanners are not always available to radiotherapy clinics. Identifying ABM through other, more accessible imaging modalities will allow more patients to receive treatment specific to their ABM distribution. Multi-energy CT (MECT) and fat-fraction MRI (FFMRI) show promise in their ability to characterize bone marrow adiposity, but these methods require validation for identifying proliferative ABM. Approach. Six swine subjects were imaged using FFMRI, fast-kVp switching (FKS) MECT and sequential-scanning (SS) MECT to identify ABM volumes relative to FLT PET-derived ABM volumes. ABM was contoured on FLT PET images as the region within the bone marrow with a SUV above the mean. Bone marrow was then contoured on the FFMRI and MECT images, and thresholds were applied within these contours to determine which threshold produced the best agreement with the FLT PET determined ABM contour. Agreement between contours was measured using the Dice similarity coefficient (DSC). Main results. FFMRI produced the best estimate of the PET ABM contour. Compared to FLT PET ABM volumes, the FFMRI, SS MECT and FKS MECT ABM contours produced average peak DSC of 0.722 ± 0.080, 0.619 ± 0.070, and 0.464 ± 0.080, respectively. The ABM volume was overestimated by 40.51%, 97.63%, and 140.13% by FFMRI, SS MECT and FKS MECT, respectively. Significance. This study explored the ability of FFMRI and MECT to identify the proliferative relative to ABM defined by FLT PET. Of the methods investigated, FFMRI emerged as the most accurate approximation to FLT PET-derived active marrow contour, demonstrating superior performance by both DSC and volume comparison metrics. Both FFMRI and SS MECT show promise for providing patient-specific ABM treatments.

Enhanced Power Law Transformation for Histopathology Images of Breast Cancer

Different image enhancement techniques are applied to improve the visual quality of an image on a display device. Contrast stretching, intensity level slicing with and without background, histogram equalization, logarithmic transformation and power law transformation are some image enhancement techniques. Most of the research work focuses on adaptive gamma correcting factors for better visualization of extremely low contrast images, giving less importance to the constant for enhanced visualization. This research proposes an efficient and less complex enhanced power law transformation (EPLT) approach to improve the contrast of dimmed and extremely bright images. The approach is a quick way to compute the value of C, i.e. constant for enhanced visualization. For better picture quality, it is very important to determine C automatically and the gamma correcting factor. This technique offers a novel and unique perspective on image contrast manipulation. The proposed enhancement technique is experimented on histopathology images of breast cancer, bright images and extremely dark images. The average peak signal-to-noise ratio (PSNR) for clinical data and BreakHis dataset is high for the proposed method are 16.52487 and 17.69335 respectively. The average RMSE for clinical data and BreakHis dataset is low for the proposed method are 40.88251 and 44.2546 respectively. It is observed that the proposed method yields the most satisfactory contrast enhancements based on performance comparison with other state-of-art enhancement algorithms and works efficiently on all types of images.

Greening of Svalbard

Svalbard, located between 76°30′N and 80°50′N, is among the regions in the world with the most rapid temperature increase. We processed a cloud-free time-series of MODIS-NDVI for Svalbard. The dataset is interpolated to daily data during the 2000–2022 period with 232 m pixel resolution. The onset of growth, with a clear phenological definition, has been mapped each year. Then the integrated NDVI from the onset (O) of growth each year to the time of average (2000–2022) peak (P) of growth (OP NDVI) have been calculated. OP NDVI has previously shown high correlation with field-based tundra productivity. Daily mean temperature data from 11 meteorological stations are compared with the NDVI data. The OP NDVI values show very high and significant correlation with growing degree days computed from onset to time of peak of growth for all the meteorological stations used. On average for the entire Svalbard, the year 2016 first had the highest greening (OP NDVI values) recorded since the year 2000, then the greening in 2018 surpassed 2016, then 2020 surpassed 2018, and finally 2022 was the year with the overall highest greening by far for the whole 2000–2022 period. This shows a rapid recent greening of Svalbard very strongly linked to temperature increase, although there are regional differences: the eastern parts of Svalbard show the largest variability between years, most likely due to variability in the timing of sea-ice break-up in adjacent areas. Finally, we find that areas dominated by manured moss-tundra in the polar desert zone require new methodologies, as moss does not share the seasonal NDVI dynamics of tundra communities.

Robust image hiding network with Frequency and Spatial Attentions

Convert Image Communication (CIC) is a promising technology to protect the privacy of images. Recently, the emergence of robust CIC resistant to JPEG compression has gained due to the widespread use of JPEG compression in image communication. This paper introduces a R̲obust image hiding network with F̲requency and S̲patial A̲ttentions (RFSA) to implement robust CIC. RFSA can hide an image within another image with high robust. It incorporates multiple image attentions corresponding to imperceptibility, recovered image quality, and resistance to JPEG compression, which ensure that secret images are hidden within regions that cause little distortion and can well withstand JPEG compression. Additionally, two encoders, that is, a frequency encoder and a spatial encoder, are mixed to adaptively embed secret images across both frequency and spatial domains. Experimental results demonstrate that the proposed scheme not only maintains high image quality and capacity but also exhibits exceptional resistance to JPEG compression compared to other state-of-the-art image hiding methods. The average Peak Signal-to-Noise Ratio (PSNR) of the recovered image remains at 24.96 dB even under JPEG compression with a quality factor of 55.

Auditory Sensory Gating: Effects of Noise.

Cortical auditory evoked potentials (CAEPs) indicate that noise degrades auditory neural encoding, causing decreased peak amplitude and increased peak latency. Different types of noise affect CAEP responses, with greater informational masking causing additional degradation. In noisy conditions, attention can improve target signals' neural encoding, reflected by an increased CAEP amplitude, which may be facilitated through various inhibitory mechanisms at both pre-attentive and attentive levels. While previous research has mainly focused on inhibition effects during attentive auditory processing in noise, the impact of noise on the neural response during the pre-attentive phase remains unclear. Therefore, this preliminary study aimed to assess the auditory gating response, reflective of the sensory inhibitory stage, to repeated vowel pairs presented in background noise. CAEPs were recorded via high-density EEG in fifteen normal-hearing adults in quiet and noise conditions with low and high informational masking. The difference between the average CAEP peak amplitude evoked by each vowel in the pair was compared across conditions. Scalp maps were generated to observe general cortical inhibitory networks in each condition. Significant gating occurred in quiet, while noise conditions resulted in a significantly decreased gating response. The gating function was significantly degraded in noise with less informational masking content, coinciding with a reduced activation of inhibitory gating networks. These findings illustrate the adverse effect of noise on pre-attentive inhibition related to speech perception.

Sulfate Formation Driven by Wintertime Fog Processing and a Hydroxymethanesulfonate Complex With Iron: Observations From Single‐Particle Measurements in Hong Kong

AbstractFog processing has a significant impact on sulfur chemistry in the atmosphere. This study analyzed three winter fog events in Hong Kong using single‐particle aerosol mass spectrometry (SPAMS) and a Monitor for AeRosols and GAses in ambient air (MARGA). Black carbon (BC)‐related carbonaceous particles with substantial sulfate amounts comprised the largest particle number fraction (56.7%). Sulfate mass concentration decreased during fog due to the cloud's effective scavenging, but fog processing notably promoted sulfate formation at the single‐particle level (average peak area increases of 31.2%). Hydroxymethanesulfonate (HMS), an important S(IV) compound and fog tracer, was identified accounting for up to 12% by particle number fraction. Although pH showed a positive correlation (r = 0.53–0.69) with HMS particles in each fog scenario, a negative overall correlation (r = −0.51) was observed. Further analysis revealed that the higher aerosol acidity (pH 0.65–3.11), promoted Fe dissolution, leading to 49% of HMS particles being mixed with Fe, which potentially facilitated sulfate formation via the Fenton reaction. Additionally, around 40% of HMS‐Fe particles are mixed with oxalate, thereby warranting further attention for their potential to cause more intricate sulfur oxidation processes. This study reveals the initial identification of a high mixed‐state of HMS‐Fe, which could potentially serve as a crucial avenue for the formation of sulfate on individual particulate matter. Considering the persistent augmentation of aerosol acidity in the Asian region, this phenomenon necessitates further investigation and attention.

Enhancing thermal performance: Utilizing carbon nanoparticles from tobacco waste butts in evacuated tube collectors

Sustainable Energy device's efficiency enhancement by utilizing the waste of tobacco is focused on this research. The carbon nanoparticles derived from the waste of tobacco through a novel approach. The produced Carbon nanoparticles were used to improve the efficiency of sustainable solar collectors in the form of nanofluid. When it comes to improving thermal performance, using nanofluids in evacuated tube collectors (ETSC) is the best option because of the improved thermo-physical characteristics of the working fluid. In this investigation, Syltherm oil was preferred for base fluid to prepare nanofluids with the concentrations of Carbon nanoparticles 0.05 vol%, 0.1 vol%, and 0.2 vol%. Additionally, a spiral coil heat exchanger was introduced to facilitate heat transfer between the nanofluid and water for heating applications. Results indicate that Syltherm/0.2 % carbon nanofluid exhibits higher thermal performance compared to neat Syltherm oil and other nanofluids. Notably, Syltherm/0.2 % carbon nanofluid demonstrated an average peak heat acquisition of 769.1 W and the lowest heat loss of 156.7 W. Furthermore, the thermal and exergy efficiencies are measured at 68.2 % and 42.5 %, respectively. These findings suggested that higher concentrations of carbon nanoparticles lead to increased heat transfer rate and improved efficiency compared to low-concentrated Nanofluids in the ETSC system.

Pulsed power supply of Tr-RC and Tr-RLC topologies for improvement of micro electrical discharge machining

Pulsed power supply is one of the key technologies in micro-electrical discharge machining (micro-EDM), particularly in machining micro-holes with high aspect ratios. Through the disadvantageous waveform feature analysis of the controllable RC pulsed power supply with random interpulse voltage, insufficient capacitor charging, and uncontrolled reversed fluctuations, an improved Tr-RC pulsed power supply with a promoted function is proposed. Discharge pulses with high power density have the potential to synchronously improve machining efficiency and accuracy. Increasing the discharge peak current by optimizing the pulsed-power topology is a feasible means of improving the power density. A pulsed power supply of the Tr-RLC topology introducing an inductor-controlled branch according to Faraday’s law of electromagnetic induction was further proposed based on the improved Tr-RC pulsed power structure. The discharge waveforms of Tr-RLC pulsed power supply achieve an average peak current of 1.3 A with inductance of 20 μH, compared to 0.5 A with Tr-RC pulsed power supply. For machining micro-holes with aspect ratios of 10:1, 20:1, and 30:1, the Tr-RLC pulsed power supply exhibited an efficiency improvement of 25%, 22%, and 17%, respectively, with reduced taper error and a slight decrease in tool wear rate compared to the Tr-RC pulsed power supply.

Microstructural and micromechanical characterization of Cr diffusion barrier in ATR irradiated U-10Zr metallic fuel

Chromium (Cr) has been recognized as a promising diffusion barrier candidate to mitigate the Fuel Cladding Chemical Interaction (FCCI) failure in metallic fuels for sodium-cooled fast reactor. This paper, for the first time, conducted an in-depth post-irradiation examination of the microstructure and composition evolution, and micromechanics of the Cr diffusion barrier in U-10Zr fuel/HT9 cladding irradiated in the Advanced Test Reactor (ATR) to 8.7% burnup at an averaged Peak Inner Cladding Temperature of 540–550 °C. Transmission Electron Microscope (TEM) characterization confirmed the preferential intergranular diffusion of Zr and U in the Cr diffusion barrier, suggesting that grain boundaries served as fast path for the diffusion of Zr and U into the Cr diffusion barrier. The interaction zone is dominated by nano crystalline α-ZrCr2 Laves phase. Despite the interaction, there is no microcracks being observed in the preserved Cr diffusion barrier and HT9 cladding, serving as a good barrier to mitigate FCCI under the studied in-reactor irradiation conditions. High density cavities in uniform distribution are observed inside Cr grains, nano particles contains Cr, Mn, and O are confirmed by Electron Energy Loss Spectroscopy (EELS) analysis. However, it is unclear whether the cavities will become an issue to the barrier integrity at higher burnup. In-situ Scanning Electron Microscopy (SEM) micro-tensile testing uncovered mechanical softening in the HT9 cladding nearing the Cr diffusion barrier, possibly due to the coarsening of lath structure and carbides precipitates.

Flash Flood Simulation for Hilly Reservoirs Considering Upstream Reservoirs—A Case Study of Moushan Reservoir

With the advancement of society and the impact of various factors such as climate change, surface conditions, and human activities, there has been a significant increase in the frequency of extreme rainfall events, leading to substantial losses from flood disasters. The presence of numerous small and medium-sized water conservancy projects in the basin plays a crucial role in influencing runoff production and rainwater confluence. However, due to the lack of extensive historical hydrological data for simulation purposes, it is challenging to accurately predict floods in the basin. Therefore, there is a growing emphasis on flood simulation and forecasting that takes into account the influence of upstream water projects. Moushan Reservoir basin is located in a hilly area of an arid and semi-arid region in the north of China. Flooding has the characteristics of sudden strong, short confluence time, steep rise, and steep fall, especially floods caused by extreme weather events, which have a high frequency and a wide range of hazards, and has become one of the most threatening natural disasters to human life and property safety. There are many small and medium-sized reservoirs in this basin, which have a significant influence on the accuracy of flood prediction. Therefore, taking Moushan Reservoir as an example, this paper puts forward a flash flood simulation method for reservoirs in hilly areas, considering upstream reservoirs, which can better solve the problem of flood simulation accuracy. Using the virtual aggregation method, the 3 medium-sized reservoirs and 93 small upstream reservoirs are summarized into 7 aggregated reservoirs. Then, we construct the hydrological model combining two method sets with different runoff generation and confluence mechanisms. Finally, after model calibration and verification, the results of different methods are analyzed in terms of peak discharge error, runoff depth error, difference in peak time, and certainty coefficient. The results indicate that the flooding processes simulated by the proposed model are in line with the observed ones. The errors of flood peak and runoff depth are in the ranges of 2.3% to 15% and 0.1% to 19.6%, respectively, meeting the requirements of Class B accuracy of the “Water Forecast Code”. Method set 1 demonstrates a better simulation of floods with an average flood peak error of 5.63%. All these findings illustrate that the developed model, utilizing aggregate reservoirs and dynamic parameters to reflect regulation and storage functions, can effectively capture the impact of small water conservancy projects on confluence. This approach addresses challenges in simulating floods caused by small and medium-sized reservoirs, facilitating basin-wide flood prediction.

The relationship between hamstring strength tests and sprint performance in female Gaelic footballers: A correlation and linear regression analysis.

To investigate the relationships between handheld dynamometer (HHD), isokinetic and Nordic hamstrings exercise (NHE) measurements of knee flexor strength and their association with sprinting performance. Cross-sectional. The relationships between HHD (prone isometric, prone break and supine break knee flexor strength tests), isokinetic and NHE peak knee flexor strength measures were examined using Pearson product correlations on 38 female footballers. A linear regression analysis was also performed for each pair of dependent variables (10 and 30 metre sprint times) and independent predictor variables (average relative peak torque for HHD, isokinetic and NHE testing). There were good correlations between HHD tests (r = 0.81-0.90, p < 0.001, R2 = 0.65-0.82), moderate correlations between HHD and isokinetic peak torque, (r = 0.61-0.67, p < 0.001, R2 = 0.37-0.44) and poor association between the HHD peak torques and isokinetic work (r = 0.44-0.46, p = 0.005-0.007, R2 = 0.20-0.21) and average power (r = 0.39-0.45, n = 36, p = 0.006-0.019, R2 = 0.15-0.22). There was a poor association between NHE peak torque and isokinetic total work (r = 0.34, p = 0.04, R2 = 0.12). No associations between knee flexor strength and sprint times were observed (p = 0.12-0.79, r2 = 0.002-0.086). Moderate to good correlations within HHD testing and poor to moderate correlations between HHD and isokinetic testing were observed. HHD knee flexor torque assessment may be useful to regularly chart the progress of hamstring rehabilitation for female footballers. Knee flexor strength assessments were not associated with sprint times in female footballers. Other aspects of knee flexor strength and sprint performance should be investigated to assist clinicians in making return to running and sprinting decisions in this population.

Efficient Haze Removal from a Single Image Using a DCP-Based Lightweight U-Net Neural Network Model.

In this paper, we propose a lightweight U-net architecture neural network model based on Dark Channel Prior (DCP) for efficient haze (fog) removal with a single input. The existing DCP requires high computational complexity in its operation. These computations are challenging to accelerate, and the problem is exacerbated when dealing with high-resolution images (videos), making it very difficult to apply to general-purpose applications. Our proposed model addresses this issue by employing a two-stage neural network structure, replacing the computationally complex operations of the conventional DCP with easily accelerated convolution operations to achieve high-quality fog removal. Furthermore, our proposed model is designed with an intuitive structure using a relatively small number of parameters (2M), utilizing resources efficiently. These features demonstrate the effectiveness and efficiency of the proposed model for fog removal. The experimental results show that the proposed neural network model achieves an average Peak Signal-to-Noise Ratio (PSNR) of 26.65 dB and a Structural Similarity Index Measure (SSIM) of 0.88, indicating an improvement in the average PSNR of 11.5 dB and in SSIM of 0.22 compared to the conventional DCP. This shows that the proposed neural network achieves comparable results to CNN-based neural networks that have achieved SOTA-class performance, despite its intuitive structure with a relatively small number of parameters.

Visual Object Tracking Based on the Motion Prediction and Block Search in UAV Videos

With the development of computer vision and Unmanned Aerial Vehicles (UAVs) technology, visual object tracking has become an indispensable core technology for UAVs, and it has been widely used in both civil and military fields. Visual object tracking from the UAV perspective experiences interference from various complex conditions such as background clutter, occlusion, and being out of view, which can easily lead to tracking drift. Once tracking drift occurs, it will lead to almost complete failure of the subsequent tracking. Currently, few trackers have been designed to solve the tracking drift problem. Thus, this paper proposes a tracking algorithm based on motion prediction and block search to address the tracking drift problem caused by various complex conditions. Specifically, when the tracker experiences tracking drift, we first use a Kalman filter to predict the motion state of the target, and then use a block search module to relocate the target. In addition, to improve the tracker’s ability to adapt to changes in the target’s appearance and the environment, we propose a dynamic template updating network (DTUN) that allows the tracker to make appropriate template decisions based on various tracking conditions. We also introduce three tracking evaluation metrics: namely, average peak correlation energy, size change ratio, and tracking score. They serve as prior information for tracking status identification in the DTUN and the block prediction module. Extensive experiments and comparisons with many competitive algorithms on five aerial benchmarks, UAV20L, UAV123, UAVDT, DTB70, and VisDrone2018-SOT, demonstrate that our method achieves significant performance improvements. Especially in UAV20L long-term tracking, our method outperforms the baseline in terms of success rate and accuracy by 19.1% and 20.8%, respectively. This demonstrates the superior performance of our method in the task of long-term tracking from the UAV perspective, and we achieve a real-time speed of 43 FPS.

Effect of tube bend angle on liquid propulsion system priming event pressures

Experimental work was performed to measure the priming event pressure transient levels in unrestricted liquid propulsion system tubing. An existing water hammer experimental setup was utilized, where system tubing diameter, length, and bend angle were examined. Experiments were performed under both atmospheric and low-pressure pre-test element conditions to understand any impact of tube bend angle on the mitigation of priming event pressure transients inside the liquid system. Experimental results showed pressure levels were influenced by a combination of tube diameter, length, and pre-test pressures; however, tube bend angle had minimal to no discernible change on the average peak pressure levels or rise times to peak pressure for all conditions examined.

Deep learning decryption approach for asymmetric computer-generated holography (CGH) cryptosystem

Deep-learning-based optical image decryption has attracted attention due to its remarkable advantages of keyless managements. Here, a high-fidelity deep learning (DL) decryption strategy is proposed, aiming for the asymmetric DRPE-based CGH cryptosystem, which is combined with phase truncation technique and chaotic iris phase masks. First, a mass of ciphertext and plaintext image pairs are generated to create a dataset. Then, a deep neural network, namely ACGHC-Net (network for the asymmetric DRPE-based CGH cryptosystem), is designed and trained in a supervised learning manner. After the model training and tuning, the ACGHC-Net can quickly and accurately decrypt the ciphertext images. The average cross-correlation coefficient (CC) of the decrypted images achieves 0.998, the average structural similarity (SSIM) 0.895, and the average peak signal-to-noise ratio (PSNR) 31.090 dB. Furthermore, we conducted anti-noise and anti-clipping analysis on the ACGHC-Net. The results prove that the proposed ACGHC-Net can successfully decrypt the encrypted complex grayscale images, and has good anti-noise and anti-cropping robustness for the asymmetric DRPE-based CGH cryptosystem. The proposed method will be expected to further boost keyless decryption in image encryption systems.

Experimental study on the behavior of damaged CFRP and steel rebars RC columns retrofitted with externally bonded composite material

The structural behavior of concrete columns reinforced with CFRP bars has been the focus of many researches over the past two decades. There is a dearth of comprehensive research concerning the axial compressive performance of retrofitted CFRP bars reinforced concrete (RC) columns. To fill this gap a thorough investigation was carried out to understand failure mode, load–displacement response, axial deflection, axial load-bearing strength, ductility index (DI), stiffness index (KI), and strength index (SI) of retrofitted CFRP bars RC columns. The experimental matrix comprised 14 short circular columns, seven reinforced with CFRP bars and hoops and the other seven with steel bars and hoops. Initial damage was induced through axial loading until a 35% reduction in peak axial strength was observed, after which damaged columns were repaired and retrofitted using CFRP sheets. The parameters of the study include concentric and eccentric compressions, transverse reinforcement spacing, CFRP wrapping, steel, and CFRP reinforcements. According to the findings, lowering transverse hoop spacing enhanced the utmost capacity and ductility of both types of retrofitted columns. The average peak strength and corresponding axial displacement of CFRP RC columns were 22.63% and 14.87%, and ductility (at a 20% load decrease) was 118% higher after CFRP retrofitting. The axial stiffness of damaged CFRP bars RC columns after strengthening with CFRP sheet were not fully rehabilitated and on average SI under concentric loading was 3.22% lower while DI under eccentric loading was 2.71% higher than steel RC columns.

Microstructure, mechanical properties and multiphase synergistic strengthening mechanisms of LPBF fabricated AlZnMgZr alloy with high Zn content

In this work, a novel aluminum alloy with high Zn content was designed for laser powder bed fusion (LPBF) forming process. Aluminum alloy powder containing 14 wt% Zn and 2 wt% Zr was produced to conduct LPBF experiments with different process parameters. The analysis of the specimens revealed that the up facing surface exhibited an average microhardness peak value of 170.6 HV, achieving the ultimate tensile strength (UTS) of 573 MPa and the elongation of 11.6 %. There was no significant change in grain size and a large amount of plate-like η' (a metastable precipitate MgZn2 phase) phase precipitated within the grains after aging treatment (120°C, 21 h). The specimen reached the average microhardness peak of 181.7 HV and the UTS of 605 MPa. The principal strengthening mechanisms in the LPBF-formed samples were solid solution strengthening and grain refinement strengthening. After aging treatment, the precipitation of the second phase from solute atoms led to grain refinement strengthening and second-phase strengthening becoming the predominant factors to enhance the sample's strength. Al14ZnMgZr alloy have great application prospect in high strength and tough service environment.

Reliability of Hip Strength Assessment in Young Active People with Hip Dysplasia: An Examination in Student Circus Artists.

Developmental dysplasia of the hip (DDH) is common in performing artists and other young active individuals and involves abnormalities in bony morphology of the acetabulum and proximal femur that can negatively impact walking biomechanics, muscular strength, quality of life, and sleep. Rehabilitation for hip-related conditions should target known modifiable impairments such as hip muscle strength, though a reliable method of assessment in this population remains unclear. To determine the inter- and intra-rater reliability of hip muscle strength assessments using handheld dynamometry (HHD) in young active circus artists with DDH. Reliability of hip strength in all planes was assessed using HHD in 21 adult performing circus arts students (mean age 21.3 yrs [3.2]; 13 M, 5 F, 3 NB) with symptomatic radiologically and clinically diagnosed hip dysplasia. The reliability of average peak force and absolute peak force were expressed for each position tested. Reliability was assessed using intraclass correlation coefficients (ICC) with standard error of measurement (SEM) and minimal detectable change (MDC) values calculated to improve clinical interpretability. Good to excellent inter-rater reliability resulted for all hip muscle strength testing positions, ICC=0.88 (95%CI 0.70 to 0.95) to ICC=0.97 (0.92 to 0.99), except average peak hip flexion strength, ICC=0.71 (0.28 to 0.88). Absolute peak hip abduction, ICC=0.77 (0.16 to 0.94), and adduction strength, ICC=0.72 (-0.55 to 0.92), demonstrated the lowest intra-rater reliability. Transverse plane strength measures (rotation) produced the lowest SEM and MDC values followed by the frontal plane (abduction/adduction) and sagittal plane (flexion/extension). HHD is an appropriate and reliable method to assess hip muscle strength in circus artists with DDH.

DF-GAM: Cross-Domain Ultrasound Image High-Quality Reconstruction Using a Dual Frequency-Domain Guided Adaptation Model

ObjectiveTo enhance the quality of low-resolution (LR) ultrasound images and mitigate artifacts and speckle noise, which can impede accurate medical diagnosis, a novel method called the dual frequency-domain guided adaptation model (DF-GAM) is proposed. The method aims to achieve high-quality image reconstruction across diverse domains, including different ultrasound machines, diseases and phantom images. MethodsDF-GAM utilizes a dual-branch network architecture combined with frequency-domain self-adaptation and self-supervised edge regression. This approach enables cross-domain enhancement by focusing on the reconstruction of clear tissue structures and speckle patterns. The model is designed to adapt to various ultrasound imaging (USI) scenarios, ensuring its applicability in real-world clinical settings. ResultsExperimental evaluations of DF-GAM were conducted using five different datasets. The results demonstrated the method's effectiveness, with DF-GAM outperforming existing enhancement techniques. The average peak signal-to-noise ratio (PSNR) achieved was 34.62, and the structural similarity index (SSIM) was 0.91, indicating a significant improvement in image quality compared to other methods. ConclusionDF-GAM shows great potential in improving medical image diagnosis and interpretation. Its ability to enhance LR ultrasound images across various domains without the need for extensive training data makes it a valuable tool for clinical use. The high PSNR and SSIM scores validate the method's effectiveness, suggesting that DF-GAM could significantly contribute to the field of USI diagnostics.