National Institutes of Health Stroke Scale as an Outcome in Stroke Research: Value of ANCOVA Over Analyzing Change From Baseline

National Institutes of Health Stroke Scale (NIHSS), measured a few hours to days after stroke onset, is an attractive outcome measure for stroke research. NIHSS at the time of presentation (baseline NIHSS) strongly predicts the follow-up NIHSS. Because of the need to account for the baseline NIHSS in the analysis of follow-up NIHSS as an outcome measure, a common and intuitive approach is to define study outcome as the change in NIHSS from baseline to follow-up (ΔNIHSS). However, this approach has important limitations. Analyzing ΔNIHSS implies a very strong assumption about the relationship between baseline and follow-up NIHSS that is unlikely to be satisfied, drawing into question the validity of the resulting statistical analysis. This reduces the precision of the estimates of treatment effects and the power of clinical trials that use this approach to analysis. ANCOVA allows for the analysis of follow-up NIHSS as the dependent variable while adjusting for baseline NIHSS as a covariate in the model and addresses several challenges of using ΔNIHSS outcome using simple bivariate comparisons (eg, a t test, Wilcoxon rank-sum, linear regression without adjustment for baseline) for stroke research. In this article, we use clinical trial simulations to illustrate that variability in NIHSS outcome is less when follow-up NIHSS is adjusted for baseline compared to ΔNIHSS and how a reduction in this variability improves the power. We outline additional, important clinical and statistical arguments to support the superiority of ANCOVA using the final measurement of the NIHSS adjusted for baseline over, and caution against using, the simple bivariate comparison of absolute NIHSS change (ie, delta).

Dynamics of Perception Distortions of Human Body Physical Dimensions in Virtual Reality

Controlling characters in a virtual reality (VR) environment can lead to the interiorization of their body dimensions by the recipients. The possible preservation of these distortions in their psyche will indicate a high degree of psychological impact of a VR on a person and the potential danger of developing depersonalization of the recipients and their dependence on such stimulation. The study of the stability of these distortions is necessary in the context of ensuring the safety of the impact of VR environments on the human psyche. The main focus of the study is on the perception distortions of human body dimensions, as they are sensed by people immersed in a VR environment, and their dynamics depending on the number of immersions. The impact of the virtual reality environment was simulated using the Freedom Locomotion VR application. One virtual reality immersion session took 15 minutes. To obtain psychometric indicators of the subjects perception of their own body dimensions, the technique Measurements according to M. Feldenkrais was used. All the participants (N = 45, three experimental groups) underwent a mandatory preliminary measurement using this technique (several hours before exposure) and a final measurement (one day after the last exposure). At the same time, the results of preliminary measurements were taken as indicators of the subjects habitual perception of their own body dimensions and were considered in each data processing as a comparison group. Free movement in a VR environment leads to distortions in the subjects perception of their own body dimensions. In all the experimental groups, there was a tendency to exaggerate body dimensions immediately after immersions, which indicates the qualitative similarity of these distortions. The effect of repetitive immersion in a VR environment on the perception of body dimensions is that it increases awareness in perceiving body parts that are least active at the time of immersion. Controlling an anthropomorphic character in a VR leads to an increase in the subjective significance of the recipients own body perception and an increased concentration of attention on the parameters least involved in immersion. The results of the study show that an increased level of awareness in perceiving their own bodies is characteristic of the subjects who have experience of repetitive immersions in a VR environment in the guise of an anthropomorphic character. Controlling a bodily projection in a VR headset does not cause its long-term interiorization, but has a positive effect on the formation of personal corporeality.

Resistance training induces similar adaptations of upper and lower-body muscles between sexes

The purpose of the study was to compare sex adaptations in hypertrophy, strength and contractile properties of upper and lower-body muscles induced by resistance training (RT). Eighteen RT untrained male (MG) and female (FG) students (aged 24.1 ± 1.7 years, height: 1.75 ± 0.08 m, weight: 70.4 ± 12.3 kg) undervent 7 weeks of biceps curl and squat training (2 days/week, 60–70% repetition maximum, 3–4 sets, 120 s rest intervals, reps until muscular failure). At baseline and final measurement, thickness and cross-section area, one-repetition maximum and tensiomyography parameters (contraction time − Tc and radial displacement − Dm) of elbow flexors (biceps brachii) and knee extensors (4 quadriceps muscles) were evaluated. Although MG tends to display greater absolute strength gains for upper- (p = 0.055) and lower-body (p = 0.098), for relative changes ANCOVA revealed no sex-specific differences for either of the tested variables. Significant hypertrophy was observed for all tested muscles, except for vastus intermedius in FG (p = 0.076). The Dm significantly decreased for biceps brachii (MG by 12%, p < 0.01 and FG by 13.1%, p < 0.01) and rectus femoris (MG by19.2%, p < 0.01 and FG by 12.3%, p < 0.05), while Tc values remain unchanged. These results indicate that initial morphological, functional and contractile alterations following RT are similar for males and females, and that there are no specific sex adaptations either for the upper- or lower-body muscles. The study was registered with ClinicalTrials.gov (NCT04845295).

Excluding false negative error in certification of quantum channels

Certification of quantum channels is based on quantum hypothesis testing and involves also preparation of an input state and choosing the final measurement. This work primarily focuses on the scenario when the false negative error cannot occur, even if it leads to the growth of the probability of false positive error. We establish a condition when it is possible to exclude false negative error after a finite number of queries to the quantum channel in parallel, and we provide an upper bound on the number of queries. On top of that, we found a class of channels which allow for excluding false negative error after a finite number of queries in parallel, but cannot be distinguished unambiguously. Moreover, it will be proved that parallel certification scheme is always sufficient, however the number of steps may be decreased by the use of adaptive scheme. Finally, we consider examples of certification of various classes of quantum channels and measurements.

Development of an optical pH measurement system based on colorimetric effect

<p>This paper proposed an optical pH measurement system developed to measure the changes of pH levels based on colorimetric reactions with phenol red reagent. The optical sensing was achieved through the implementation of a pair of light emitting diode (LED) and photodiode. The detection mechanism was based on different absorbance of light intensity at pH values of 2.36, 8.32, 9.08 and 12.83. The data processing method was carried out using LabVIEW software and interfaced with NI USB DAQ 6008. The measured voltage showed a good correlation in relation to the pH level with R2 equals to 0.9624. This relationship was used as a calibration curve in the final system testing. The final measurement of pH showed a good agreement between the actual and measured values with an error of less than 5%, thus indicating the reliability of the proposed system.</p>

Exploring the measurement accuracy of flush air data sensing based on normal cloud model and multi-objective programming

As far as airborne sensors are concerned, the measurement accuracy is an important indicator that cannot be ignored and may directly affect final measurement results. In order to improve the measurement accuracy of a flush air data sensing (FADS), which is an advanced sensor, this paper proposed a new method based on the normal cloud model and the multi-objective programming (MOP). First, the high-precision FADS model is established by using the database obtained with the CFD software and aerodynamics knowledge. Meanwhile, the uncertainty and randomness of signals caused by measurement noise are quantitatively analyzed by using the normal cloud model. Then, in the process of data fusion, a new method for calculating the weights is proposed based on the slack variable method and the Lagrange multiplier method. The simulation results show that the proposed method can improve the measurement accuracy by 3.2% and reduce the dispersion of measurement data by 68.88%.

ELABORATION OF ACTIVE KNOWLEDGE SHARING LEARNING MODEL TO IMPROVE HIGH ORDER THINKING SKILLS INTEGRATED 4C

This research is an experimental study through elaborates the Active Knowledge Sharing (AKS) learning model to improve students' High Order Thinking Skills (HOTS). The low ability of students to analyze, evaluate, and create is due to the lack of application of questions that deliver students to solve problems that contain HOTS. The four stages in the AKS will serve as a reference for students’ HOTS integrated 4Cs. The sample was a student in Chemical Engineering at the State Polytechnic of Ujung Pandang. The increase in the average aspects of HOTS (analysis, evaluation, and create) in the experimental class (N-gain = 94%) higher than the control class (N-gain = 57%). In the experimental class, the average score for HOTS of students was 37.7 and there was an increase in the average score in the posttest final measurement of 96.5 or an increase of 59 points with the average high-level thinking ability of students in the very good category. This indicates that the AKS learning model is effective in increasing the ability of HOTS as well as having an impact on students' 4Cs abilities.

Effect of PNF and NDT Bobath Concepts on Ischemic Strokes Patients for Trunk Rehabilitation – A Randomized Pilot Study

Introduction: Recently, various systematic reviews and evidence-based clinical practice guidelines have evaluated the efficacy of stroke rehabilitation interventions. Across them proprioceptive neuromuscular facilitation (PNF) and neuro-developmental treatment (NDT) Bobath resulted to be an effective and widely used therapeutic approaches. Objective of the work: The aim of the study was to evaluate the therapeutic effectiveness of PNF and NDT Bobath concept for improvement of trunk motor control in patients after ischemic stroke. Materials and methods: The study consisted of 30 patients up to 3 months after ischemic stroke. Participants were randomly assigned to two study groups. Both group consisted of 15 patients underwent rehabilitation according to the PNF or NDT Bobath concept. Comparison of the changes in the Trunk Impairment Scale was the primary outcome. Results: The analysis of the values of total TIS scale showed a statistically significant improvement in both group. Within group analysis showed statistical significance among baseline and intermediate measurement, and among baseline and final measurement within both groups. The analysis of total TIS scale indicators between the PNF and Bobath groups showed no statistically significant differences. Conclusions: Both the PNF and Bobath concepts have proven to be effective in the treatment of posture control and there have been significant improvements in patients from both groups.

A Possibilistic Kalman Filter for the Reduction of the Final Measurement Uncertainty, in Presence of Unknown Systematic Errors

A Kalman filter is a concept that has been in existence for decades now and it is widely used in numerous areas. It provides a prediction of the system states as well as the uncertainty associated to it. The original Kalman filter can not propagate uncertainty in a correct way when the variables are not distributed normally or when there is a correlation in the measurements or when there is a systematic error in the measurements. For these reasons, there have been numerous variations of the original Kalman filter, most of them mathematically based (like the original one) on the theory of probability. Some of the variations indeed introduce some improvements, but without being completely successful. To deal with these problems, more recently, Kalman filters have also been defined using random-fuzzy variables (RFVs). These filters are capable of also propagating distributions that are not normal and propagating systematic contributions to uncertainty, thus providing the overall measurement uncertainty associated to the state predictions. In this paper, the authors make another step forward, by defining a possibilistic Kalman filter using random-fuzzy variables which not only considers and propagates both random and systematic contributions to uncertainty, but also reduces the overall uncertainty associated to the state predictions by compensating for the unknown residual systematic contributions.

IoT Based Sewage Gas Monitoring System with Overflow Detection

The sewage system is an important component of urban infrastructure. Most of the cities adopted efficient sewage systems to maintain the cleanliness of the cities. If the sewage maintenance is not proper, infectious disease may spread. Current sewage monitoring system is not efficient, as it is difficult to find out the poisonous gas level of sewage water. In the existing system, water level and temperature sensors are used to detect the overflow and the inside temperature of the sewage plant. An “IoT BASED SEWAGE GAS MONITORING SYSTEM WITH OVERFLOW DETECTION” is proposed to overcome the above mentioned limitations of existing systems. Internet of Things (IoT) is a network of physical objects that use sensors to capture data and exchange information over the internet which can be adopted to detect and monitor the exaction location of sewage system and the amount of poisonous gases present in the sewage plant.One of the advantage of this project is to prevent of sewage water get mixed with drinking water ,so that the environmental health issues can be solved. The advanced MQ5 sensor is used instead of MQ4 sensor as it detects only methane gas.MQ5 sensor helps to detect the poisonous and flammable gases such as carbon monoxide, methane & LPG. Electrochemical oxygen sensor is provided to detect oxygen level. Then the final measurement will be sent through the WiFi module to the database . The GSM and GPS module is used to send the emergency message and current location to a specific smartphone . In future the project can be upgraded with the aid of modern technology like artificial intelligence.