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Abstract Chronic and systematic inflammation have been related to increased risks of osteopenia and related fracture. However, studies concerning the association between low-grade inflammation and the bone mineral density (BMD) and strength of the femoral neck are still few and inconsistent. This study aimed to examine the relationships between blood inflammatory biomarkers and BMD and femoral neck strength in an adult-based cohort. We retrospectively analyzed a total of 767 participants included in the Midlife in the United States (MIDUS) study. The blood levels of inflammatory markers, including interleukin-6 (IL6), soluble IL-6 receptor, IL-8, IL-10, TNF-α and C-reactive protein (CRP), in these participants were measured, and their associations with the BMD and strength of the femoral neck were determined. We analyzed these 767 subjects with data concerning the BMD, bending strength index (BSI), compressive strength index (CSI), and impact strength index (ISI) in the femoral neck and inflammatory biomarkers. Importantly, our results suggest that strongly negative associations exist between the blood soluble IL6 receptor levels and the BMD (per SD change, Sβ = −0.15; P

Surface soil moisture (SSM) reflects the dry and wet states of soil. Microwave remote sensing technology can accurately obtain regional SSM in real time and effectively improve the level of agricultural drought monitoring, and it is of great significance for agricultural precision irrigation and smart agriculture construction. Based on Sentinel-1, Sentinel-2, and Landsat-8 images, the effect of vegetation was removed by the water cloud model (WCM), and SSM was retrieved and validated by a radial basis function (RBF) neural network model in bare soil and vegetated areas, respectively. The normalized difference vegetation index (NDVI) calculated by Landsat-8 (NDVI_Landsat-8) had a better effect on removing the influence the of vegetation layer than that of NDVI_Sentinel-2. The RBF network model, established in a bare area (R = 0.796; RMSE = 0.029 cm3/cm3), and the RBF neural network model, established in vegetated areas (R = 0.855; RMSE = 0.024 cm3/cm3), have better simulation effects on SSM than a linear SSM inversion model with single polarization. The introduction of surface parameters to the RBF neural network model can improve the accuracy of the model and realize the high-accuracy inversion of SSM in the study area.

Wavefront sensors and processors are vital components of adaptive optical (AO) systems, directly impacting the operating bandwidth. As application scenarios become increasingly complex, AO systems are confronted with more extreme atmospheric turbulence. Additionally, as optical systems scale up, the data processing demands of AO systems increase exponentially. These challenges necessitate advancements in wavefront sensing and processing capabilities. To address this, this paper proposes an integrated wavefront sensing and processing method based on the optical neural network architecture, capable of directly providing control coefficients for the wavefront corrector. Through simulation and experimentation, this method demonstrates high sensing precision and processing speed, promising to realize large-scale, high-bandwidth AO systems.

Optical isolation with high-quality, large-aperture polarizers is commonly used in high-power laser facilities to suppress retro-reflected pulses. However, it is hard to manufacture these polarizers. We propose an approach of optical isolation with two plasma-electrode Pockels cells instead of large-aperture polarizers. In this approach, Nd:glass slabs placed at the Brewster′s angle are used as polarizers. The analysis results and the application performances in the Xingguang (Star Light) XG-III PW beamline indicate that this approach can supply good protection to optical components in laser facilities.

IntroductionCompensatory movements usually occur in stroke survivors with hemiplegia, which is detrimental to recovery. This paper proposes a compensatory movement detection method based on near-infrared spectroscopy (NIRS) technology and verifies its feasibility using a machine learning algorithm. We present a differential-based signal improvement (DBSI) method to enhance NIRS signal quality and discuss its effect on improving detection performance.MethodTen healthy subjects and six stroke survivors performed three common rehabilitation training tasks while the activation of six trunk muscles was recorded using NIRS sensors. After data preprocessing, DBSI was applied to the NIRS signals, and two time-domain features (mean and variance) were extracted. An SVM algorithm was used to test the effect of the NIRS signal on compensatory behavior detection.ResultsClassification results show that NIRS signals have good performance in compensatory detection, with accuracy rates of 97.76% in healthy subjects and 97.95% in stroke survivors. After using the DBSI method, the accuracy improved to 98.52% and 99.47%, respectively.DiscussionCompared with other compensatory motion detection methods, our proposed method based on NIRS technology has better classification performance. The study highlights the potential of NIRS technology for improving stroke rehabilitation and warrants further investigation.

Performing data mining on large waveform datasets of a high-power laser facility is an important way to achieve precise regulation of a device. However, there are currently issues with missing values, noise, and inconsistency in this database of measuring pulse waveform in a current high-power laser facility. In this paper, a UNet of a series residual module is presented to predict the pulse waveform of a front-end chained system in a current high-power laser facility. The designed network is trained on grouped sequences formed by experimentally measuring pulse waveforms of a high-power laser facility. The strategies of relay output and relay loss are employed in training in order to enable the network to predict two kinds of pulse waveforms simultaneously. The trained network achieved an RMSE of 3.38% on the testing set of measuring pulse waveform at a frequency of 1 Hz, and an RMSE of 0.84% on the testing set of setting the voltage of the Arbitrary Waveform Generator (AWG). These results indicate that this method can accurately fill in paired missing values in the waveform database of a high-power laser facility. The main advantage of this method is that it can quickly couple operational parameters for prediction, and this method can be applied to predicting laser performance, cleaning one-dimensional sequences, and maintaining a waveform database.

With the increasing number of stroke patients, there is an urgent need for an accessible, scientific, and reliable evaluation method for stroke rehabilitation. Although many rehabilitation stage evaluation methods based on the wearable sensors and machine learning algorithm have been developed, the interpretable evaluation of the Brunnstrom recovery stage of the lower limb (BRS-L) is still lacking. The paper propose an interpretable BRS-L evaluation method based on wearable sensors. We collected lower limb motion data and plantar pressure data of 20 hemiplegic patients and 10 healthy individuals using seven Inertial Measurement Units (IMUs) and two plantar pressure insoles. Then we extracted gait features from the motion data and pressure data. By using feature selection based on feature importance, we improved the interpretability of the machine learning-based evaluation method. Several machine learning models are evaluated on the dataset, the results show that k-Nearest Neighbor has the best prediction performance and achieves 94.2% accuracy with an input of 18 features. Our method provides a feasible solution for precise rehabilitation and home-based rehabilitation of hemiplegic patients.

BackgroundCardiac autonomic neuropathy in population with diabetes mellitus (DM) is frequent and linked with high risk of cardiovascular mortality. However, studies on whether blood hemoglobin A1c (HbA1c) levels are related to adverse differences in heart rate variability (HRV) in individuals with DM are scarce.AimWe aimed to investigate the association of blood HbA1c levels with adverse differences in HRV, which is an indicator of cardiac autonomic control, in adult individuals with and without DM.MethodsData were collected from the Midlife in the United States (MIDUS) study, and 928 individuals were analyzed for the relationship between blood HbA1c levels and HRV through a cross-sectional analysis.ResultsParticipants with DM had significantly higher HRV than those without DM. The smooth curve suggested inverse relationships between blood HbA1c levels and HF- and LF-HRV seen in participants with DM but not in those without DM after controlling for all covariates (age, sex, BMI, smoking, number of drinking years and exercise). Furthermore, linear regression analysis demonstrated that elevated blood HbA1c levels did contribute to adverse differences in HF-HRV (Sβ= -0.118; 95% CI -0.208, -0.027; P=0.012) and LF-HRV (Sβ= -0.097; 95% CI -0.177, -0.017; P=0.019) after controlling for these covariates in participants with DM, while in participants without DM, blood HbA1c was not significantly related to adverse differences in HF-HRV (Sβ=0.095; 95% CI -0.059, 0.248; P=0.228) or LF-HRV (Sβ=0.043; 95% CI -0.103, 0.189; P=0.565). DM has a significant modifying effect on associations between blood HbA1c and adverse differences in HF-HRV (P for interaction=0.019) and LF-HRV (P for interaction=0.029).ConclusionsWe reported strong evidence that elevated blood levels of HbA1c were associated with adverse differences in HRV in the diabetic population but not in the nondiabetic population. This finding supported that long-term hyperglycemia is related to autonomic nerve injury in the diabetic population. Blood HbA1c might be a good indicator of cardiac autonomic neuropathy.

In this letter, we report a diode-pumped nano-second laser with the output energy 10 J, the repetition rate 50 Hz, and the average power 500 W. The main amplifier was made up of eight amplify modules. Each amplify module was pumped by an 808 nm laser diode and cooled by water from the back end of the Nd:YAG slab. To our knowledge, this represents the highest pulse repetition rate for the 10 J class nano-second single-aperture Nd:YAG laser. The beam quality was controlled by means of mechanical design and adjustment and compensation by a home-made deforming mirror. The beam quality was controlled well with the beam quality of the laser 2.61DL.

The utilization of waste rubber tires is of great value for environment protection and resource recovery, which can also improve the properties of matrix asphalt. The adhesion characteristics were evaluated for crumb rubber modified asphalt and limestone aggregate using the surface free energy (SFE) approach. Four types of matrix asphalt and four rubber contents were used to prepare the crumb rubber modified asphalt. The contact angle of matrix and crumb rubber modified asphalt was obtained, and the SFE indicators (dispersion, polar component, and compatibility rate—CR) were calculated. Moreover, the water stability tests were conducted using one matrix and rubber modified asphalt in order to investigate the relationship between SFE and water stability indicators. Results showed that the total SFE, dispersion component, adhesion work, and CR increased with the addition of crumb rubber, while the polar component and spalling work decreased. The types of asphalt had different influences on SFE indicators. The results from analysis of variation (ANOVA) indicated asphalt type and rubber content had significant influence on the adhesion work, spalling work and CR, and the influence of asphalt type was greater than that of rubber content. Additionally, the retained Marshall Stability and tensile strength ratio had better correlation with adhesion work and CR, but less with spalling work. The presented results demonstrated that the type of matrix asphalt played an important role in the adhesion characteristics for the crumb rubber modified asphalt.

This article introduces the study of peripheral clamping for large-aperture laser mirrors in high power laser facilities. Some multi-point clamping schemes were experimentally tested, the results of the experiments show that these schemes cannot meet the technical requirements, and in the simulation analysis, we explain the reason for this phenomenon. It is concluded that the additional bending moment caused by the non-ideal process factors in the multi-point clamping is the main cause for the surface distortion. Based on the above conclusions, we carried out research on minor-point clamping. Experimental verification of the minor-point clamping were done, the results show that the minor-point clamping can meet the requirements of technical indicators in whole process. This work can provide a reference for the design of the large-aperture transport mirror clamping structure, which may be used in huge laser devices and telescopes. [ABSTRACT FROM AUTHOR]