Rate-Dependent Modeling of Piezoelectric Actuators for Nano Manipulation Based on Fractional Hammerstein Model

Piezoelectric actuators (PEAs), as a smart material with excellent characteristics, are increasingly used in high-precision and high-speed nano-positioning systems. Different from the usual positioning control or fixed frequency tracking control, the more accurate rate-dependent PEA nonlinear model is needed in random signal dynamic tracking control systems such as active vibration control. In response to this problem, this paper proposes a Hammerstein model based on fractional order rate correlation. The improved Bouc-Wen model is used to describe the asymmetric hysteresis characteristics of PEA, and the fractional order model is used to describe the dynamic characteristics of PEA. The nonlinear rate-dependent hysteresis model can be used to accurately describe the dynamic characteristics of PEA. Compared with the integer order model or linear autoregressive model to describe the dynamic characteristics of the PEA Hammerstein model, the modeling accuracy is higher. Moreover, an artificial bee colony algorithm (DE-ABC) based on differential evolution was proposed to identify model parameters. By adding the mutation strategy and chaos search of the genetic algorithm into the previous ABC, the convergence speed of the algorithm is faster and the identification accuracy is higher, and the simultaneous identification of order and coefficient of the fractional model is realized. Finally, by comparing the simulation and experimental data of multiple sets of sinusoidal excitation with different frequencies, the effectiveness of the proposed modeling method and the accuracy and rapidity of the identification algorithm are verified. The results show that, in the wide frequency range of 1–100 Hz, the proposed method can obtain more accurate rate-correlation models than the Bouc-Wen model, the Hammerstein model based on integer order or the linear autoregressive model to describe dynamic characteristics. The maximum error (Max error) is 0.0915 μm, and the maximum mean square error (RMSE) is 0.0244.

Statistical estimates bias for data with missing values: Nonresponse study: انحياز التقديرات الإحصائية للبيانات التي تحوي قيماً مفقودة: دراسة حالة عدم الاستجابة

Statistical surveys are usually conducted to obtain data describing a problem in a studied society, and many surveys experience a rise in nonresponse rates, as the rate of nonresponse may affect the bias of the nonresponse in survey estimates. Recent empirical results show instances of nonresponse rate correlation with nonresponse bias, we attempt to translate statistical experiences of nonresponse bias in newly published studies and research into causal models that lead to assumptions about when a lack of response causes bias in estimates. Research studies of the estimates of nonresponse bias show that this bias often exists. The logical question is: what is the advantage of surveys if they suffer from high rates of nonresponse, since post-survey adjustments for nonresponse require additional variables, the answer depends on the nature of the design and the quality of the additional variables.

The Validity of the Abbey Pain Scale for Assessing Pain in Stroke Patient

Background: Apasia and dementia are two of the post-symptoms of stroke disease that cause patients to experience verbal communication disorders, thus requiring nurses to be more sensitive in assessing pain that is felt.Objective: The purpose of this research is to test the validity of the Abbey Pain Scale in assessing pain in stroke patients who are unable to express pain verbally.Methods: This research is a quantitative study using analytic observational research methods. This study used a consecutive sampling technique with the calculation of sample size based on population proportions. Research on one subject was repeated three times at rest and during pain procedures using the Abbey Pain Scale. Data were analyzed using the Pearson and Spearman test.Results: There was a significant correlation (p = 0.001) with a positive and strong correlation coefficient between the Abbey score and the pulse rate (correlation r = 0.699). Then there was a significant correlation (p < 0.001) with a positive and strong correlation coefficient between Abbey scores and mean arterial pressure (correlation r = 0.911). In addition, the Abbey Pain Scale score showed a significant change between the Abbey score at rest and during the pain procedure, both in the morning, afternoon and evening team nurses (p < 0.05).Conclusion: The Abbey Pain Scale is a valid measurement tool in assessing pain in stroke patients.

Locally-correlated kinetics of post-replication DNA methylation reveals processivity and region-specificity in DNA methylation maintenance

DNA methylation occurs predominantly on cytosine-phosphate-guanine (CpG) dinucleotides in the mammalian genome, and the methylation landscape is maintained over mitotic cell division. It has been posited that coupling of methylation activity among neighboring CpGs is critical to collective stability over cellular generations, however the mechanism of this coupling is unclear. We used mathematical models and stochastic simulation to analyze data from experiments that probe genome-wide methylation of nascent DNA post-replication. We find that DNA methylation maintenance shows genomic-region-specific kinetics, indicating influence of local CpG density and chromatin accessibility on methyltransferase activity and inter-CpG coupling. We uncover evidence of processive methylation kinetics in post-replication DNA, which manifests as exponential decay of methylation rate correlation on neighboring CpGs. Our results indicate that processivity is a component of inter-CpG-coupling that occurs globally throughout the genome, but other mechanisms of coupling dominate for inter-CpG distances past $\sim$ 100 basepairs. By decomposing local methylation correlations into processive and non-processive components, we estimate that an individual methyltransferase methylates neighbor CpGs in sequence if they are 36 basepairs apart, on average. Our study demonstrates that detailed information on epigenomic dynamics can be gleaned from replication-associated, cell-based genome-wide measurements, by combining data-driven statistical analyses with hypothesis-driven mathematical modeling.

Comprehensive comparison of Apple Watch and Fitbit monitors in a free-living setting

Objectives The aim of this study was to evaluate the accuracy of three consumer-based activity monitors, Fitbit Charge 2, Fitbit Alta, and the Apple Watch 2, all worn on the wrist, in estimating step counts, moderate-to-vigorous minutes (MVPA), and heart rate in a free-living setting. Methods Forty-eight participants (31 females, 17 males; ages 18–59) were asked to wear the three consumer-based monitors mentioned above on the wrist, concurrently with a Yamax pedometer as the criterion for step count, an ActiGraph GT3X+ (ActiGraph) for MVPA, and a Polar H7 chest strap for heart rate. Participants wore the monitors for a 24-hour free-living condition without changing their usual active routine. MVPA was calculated in bouts of ≥10 minutes. Pearson correlation, mean absolute percent error (MAPE), and equivalence testing were used to evaluate the measurement agreement. Results The average step counts recorded for each device were as follows: 11,734 (Charge2), 11,922 (Alta), 11,550 (Apple2), and 10,906 (Yamax). The correlations in steps for the above monitors ranged from 0.84 to 0.95 and MAPE ranged from 17.1% to 35.5%. For MVPA minutes, the average were 76.3 (Charge2), 63.3 (Alta), 49.5 (Apple2), and 47.8 (ActiGraph) minutes accumulated in bouts of 10 or greater minutes. The correlation from MVPA estimation for above monitors were 0.77, 0.91, and 0.66. MAPE from MVPA estimation ranged from 44.7% to 55.4% compared to ActiGraph. For heart rate, correlation for Charge2 and Apple2 was higher for sedentary behavior and lower for MVPA. The MAPE ranged from 4% to 16%. Conclusion All three consumer monitors estimated step counts fairly accurately, and both the Charge2 and Apple2 reported reasonable heart rate estimation. However, all monitors substantially underestimated MVPA in free-living settings.

Dynamic tensile properties, deformation, and failure testing of impact-loaded coal samples with various water content

Disc coal samples with different water content were tested using the split Hopkinson press bar test system. Their dynamic tensile failure process was monitored via an ultra-high-speed digital image correlation system. The deformation trend and failure characteristics as a function of the water content were analyzed, and the water content effect on dynamic mechanical properties was investigated. The results demonstrated that the dynamic stress–strain curve of the coal samples consisted of four stages. As the water content increased, the coal sample brittleness degraded, while its ductility was enhanced. Quadratic polynomial functions can describe dynamic peak stress, peak strain, and loading pressure. Under different loading pressures, the dynamic peak stress exhibited a concave bending trend as the water content increased. The coal sample's dynamic tensile strength had a strong rate correlation, and the saturated coal sample exhibited the highest rate correlation. Under high-rate loading, the inertia effect and the Stefan effect of water in coal samples hinder the initiation and propagation of coal sample cracks, improving the coal sample's strength. The research results provide a basic theoretical basis for the prevention and control of rock burst in coal mines.