BACKGROUND: Knee osteoarthritis (OA) commonly causes increase in the patients’ disability and reduce their function because it causes pain, limitation in knee range of motion and decrease in muscle power.AIM: This study investigated the effect of three different physical therapy programs on knee range of motion , pain level, muscle strength and functional condition in patients with knee OA. DESIGN: Randomized comparative study.SETTING: Outpatient Rehabilitation clinic of Tongji Hospital (Wuhan, China).POPULATION: Seventy-two patients of Knee OA (aged 40-70) enrolled and randomly allocated into three groups. Group A (n=24) received physical agents and isometric quadriceps exercises, group B (n=24) received same physical agents as group A in addition to open kinetic chain exercises and group C (n=24) received physical agents as group A in addition to closed kinetic chain exercises.METHODS: knee range of motion (by electro goniometer), knee pain level (by VAS), quadriceps muscle strength (by isokinetic dynamometer) and functional condition (by WOMAC) were assessed before and after 5 weeks of treatment.RESULTS: In between group analysis, there were significant differences (P<0.05) between the three groups for all outcome measure. In within-group analysis, group C was the only group that showed significant differences (P<0.05) in all the outcomes. Group A and B showed significant differences (P<0.05) in quadriceps muscle strength and knee pain and no significant differences (P>0.05) in knee ROM and functional condition.CONCLUSIONS: After the study, our solid conclusion is that in physical therapy programs or protocols, closed kinematic chain exercise is very effective, it improves knee ROM, and help in reducing knee pain, muscle strength, function and disability for patients with knee osteoarthritis.CLINICAL REHABILITATION IMPACT: Comparison between different physical therapy programs provides the rehabilitation team with the best treatment intervention that is more effective in treating the problems associated with knee osteoarthritis.

Most existing data synthesis methods are designed to tackle problems such as dataset imbalance, data anonymization and insufficient sample size. There is a lack of effective synthesis methods for the limited number of datasets which contain a large of features and unknown noise to expand the size of the dataset. We propose a data synthesis method, named Adaptive Subspace Interpolation for Sample Optimization (ASISO). The idea is to divide the original feature space into several subspaces with an equal number of samples, and then perform interpolation for the samples in the adjacent subspaces. This method can adaptively adjust the size of the dataset containing unknown noise, and the expanded data typically contain minimal error with actual. Moreover, it adjusts the structure of the samples, which can significantly reduce the proportion of samples with large errors. In addition, the hyperparameters of this method have an intuitive explanation and usually require little calibration. Experimental results on artificial data and benchmark data sets demonstrate that ASISO is a robust and stable method to optimize samples.

Soil organic matter is an important component that reflects soil fertility and promotes plant growth. The soil of typical Chinese tea plantations was used as the research object in this work, and by combining soil hyperspectral data and image texture characteristics, a quantitative prediction model of soil organic matter based on machine vision and hyperspectral imaging technology was built. Three methods, standard normalized variate (SNV), multisource scattering correction (MSC) and smoothing, were first used to preprocess the spectra. After that, random frog (RF), variable combination population analysis (VCPA) and variable combination population analysis and iterative retained information variable algorithm (VCPA-IRIV) algorithms were used to extract the characteristic bands. Finally, the quantitative prediction model of nonlinear support vector regression (SVR) and linear partial least squares regression (PLSR) for soil organic matter was established by combining nine color features and five texture features of hyperspectral images. The outcomes demonstrate that, in comparison to single spectral data, fusion data may greatly increase the performance of the prediction model, with MSC+VCPA-IRIV+SVR (R2C=0.995, R2P=0.986, RPD=8.155) being the optimal approach combination. This work offers excellent justification for more investigation into nondestructive methods for determining the amount of organic matter in soil.

During the 2008 financial crisis, central banks (such as the Fed) adopted quantitative easing (QE) policy to stimulate their economies and overcome severe economic and financial recessions. However, apart from stimulating the economy by issuing a substantial amount of currency to purchase long-term bonds and suppress interest rates, QE policy also contributed to a boom in the real estate and construction sectors. Therefore, this study employs Data Envelopment Analysis to measure the business performance (BP) of construction companies and explores the impact of QE policy on the BP of construction companies between 2004 and 2015 using ordinary least squares regression. Also, we examine the moderating role of debt ratio on the relationship. Focused on publicly listed construction companies in Taiwan, this research reveals three encouraging find-ings. Firstly, QE policy indeed enhanced the BP of Taiwanese construction companies. Secondly, the performance improvement of construction companies due to QE policy shows a time-diminishing trend, suggesting the importance of seizing the initial policy benefits of QE implementation. Lastly, construction companies with appropriate financial leverage may exhibit better BP. These findings can provide valuable insights for relevant government entities and de-cision-makers in the industry for policy and investment decisions.