To investigate the effects of wearable near-infrared ray-emitting knee pads on knee pain among elderly adults in Korea. Randomized controlled trial evaluating the effects of near-infrared rays (NIR) on knee pain in Korean elderly adults. Five community-based research facilities (two welfare centers, a senior citizen center, and two churches). Forty-seven participants aged 65 years and older who had experienced knee pain. The experimental group (n = 25) wore NIR-emitting knee pads for one month at nighttime while sleeping. The control group (n = 22) wore knee pads without NIR. Demographic characteristics, intensity and duration of knee pain, amount of analgesic medication used, range of motion, gait speed, and health-related quality of life were collected using questionnaires. The experimental group showed decreased intensity (t = -6.17, p < 0.001) and duration (t = -3.34, p = 0.002) of knee pain and reduced analgesic use (t = -2.30, p = 0.026) compared to the control group. NIR may be an effective non-pharmacological option for relieving knee pain in elderly adults.

We studied the structure of symmetric group of rubik's cube with oriented face. In conclusion, we have shown that the symmetric group of rubik's cube with oriented face is isomorphic to C46 / 2 × (C37 ≀ S8) × (C210 ≀ S12).

With the recent introduction of artificial intelligence (AI) technology, the use of data is rapidly increasing, and newly generated data is also rapidly increasing. In order to obtain the results to be analyzed based on these data, the first thing to do is to classify the data well. However, when classifying data, if only one classification technique belonging to the machine learning technique is applied to classify and analyze it, an error of overfitting can be accompanied. In order to reduce or minimize the problems caused by misclassification of the classification system such as overfitting, it is necessary to derive an optimal classification by comparing the results of each classification by applying several classification techniques. If you try to interpret the data with only one classification technique, you will have poor reasoning and poor predictions of results. This study seeks to find a method for optimally classifying data by looking at data from various perspectives and applying various classification techniques such as LDA and QDA, such as linear or nonlinear classification, as a process before data analysis in data analysis. In order to obtain the reliability and sophistication of statistics as a result of big data analysis, it is necessary to analyze the meaning of each variable and the correlation between the variables. If the data is classified differently from the hypothesis test from the beginning, even if the analysis is performed well, unreliable results will be obtained. In other words, prior to big data analysis, it is necessary to ensure that data is well classified to suit the purpose of analysis. This is a process that must be performed before reaching the result by analyzing the data, and it may be a method of optimal data classification.

The purpose was to examine the factors related to subjective poor oral health in middle school and high school adolescents using data from '2019 Youth Health Behavior Online Survey'. Independent variables related to sociodemographic status and oral health related behaviors were the following:gender, grade, household economy, academic achievement, residence, frequency of daily and after lunch toothbrushing, smocking, alcohol,annual dental visit and preventive treatment. Almost all variables revealed a significant difference in poor oral health among boys and girls in school except resident area of girls and annual dental visit of boys. The odds ratios of subjective poor oral health were as follows:the highest ORs was subjective household economy and the second was frequency of daily toothbrushing in boys. The highest ORs was subjective household economy and the second was subjective academic achievement in girls.

In this paper, we propose a method to detect concept drift by applying Convolutional Neural Network (CNN) in a data stream environment. Since the conventional method compares only the final output value of the CNN and detects it as a concept drift if there is a difference, there is a problem in that the actual input value of the data stream reacts sensitively even if there is no significant difference and is incorrectly detected as a concept drift. Therefore, in this paper, in order to reduce such errors, not only the output value of CNN but also the probability vector are used. First, the data entered into the data stream is patterned to learn from the neural network model, and the difference between the output value and probability vector of the current data and the historical data of these learned neural network models is compared to detect the concept drift. The proposed method confirmed that only CNN output values could be used to reduce detection errors compared to how concept drift were detected.

To monitor all elements simultaneously of variance-covariance matrix Σ of several correlated quality characteristics under multivariate normal process Np($\underline{\mu}$, Σ), multivariate exponentially weighted moving average (EWMA) chart and cumulative sum (CUSUM) chart are considered and compared. Numerical performances of the considered variable sampling interval (VSI) charts are evaluated using average run length (ARL), average time to signal (ATS), average number of switches (ANSW) to signal, and the probability of switch Pr(switch) between two sampling interval d1 and d2 where d1 < d2. For small or moderate changes of Σ, the performances of multivariate EWMA chart is approximately equivalent to that of multivariate CUSUM chart.

Modern people live in the age of plastics. It has been widely used due to its easy molding processing, mass production, and excellent durability. However, over-produced plastics for convenience cause plastic disasters and adversely affect the ecosystem. Since the COVID-19 outbreak, the use of single-use plastic waste due to the use of delivery services has increased. The COVID-19 pandemic has caused a plastic pandemic. Currently, domestic recycling policies depend only on recycling collection companies and market prices of recyclable resources. This paper confirms whether the outbreak of COVID-19 has affected the price of plastic waste. It also shows that the price of plastic waste is more unstable than metals with a high recycling rate. This urges businesses to share the cost of recycling on plastic waste, no longer being dependent on market prices for recyclable resources.

Abnormal regulation, hyperphosphorylation, and aggregation of the tau protein are the hallmark of several types of dementia, including Alzheimer's Disease. Increased activity of Glycogen Synthase Kinase-3β (GSK-3β) in the Central Nervous System (CNS), increased the tau hyperphosphorylation and caused the neurofibrillary tangles (NFTs) formation in the brain cells. Over the last two decades, numerous adenosine triphosphate (ATP) competitive inhibitors have been discovered that show inhibitory activity against GSK-3β. But these compounds exhibited off-target effects which motivated researchers to find new GSK-3β inhibitors. In the present study, we have collected the dataset of 31 C-Glycosylflavones derivatives that showed inhibitory activity against GSK-3β. Among the dataset, the most active compound was docked with the GSK-3β and molecular dynamics (MD) simulation was performed for 50 ns. Based on the 50 ns MD pose of the most active compound, the other dataset compounds were sketched, minimized, and aligned. The 3D-QSAR based Comparative Molecular Field Analysis (CoMFA) model was developed, which showed a reasonable value of q2=0.664 and r2=0.920. The contour maps generated based on the CoMFA model elaborated on the favorable substitutions at the R2 position. This study could assist in the future development of new GSK-3β inhibitors.