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The accuracy of power measurement affects the economic benefits and operating costs of power supply companies, and even affects corporate decision-making. Analyzing the causes of errors in measurement data and making improvements to reduce errors is of great significance to both power companies and users. The article analyzes the causes of power measurement errors, and proposes improvement measures to reduce the errors.

We investigate the reliability of individual differences of four quantities measured by magnetic resonance imaging‐based multiparameter mapping (MPM): magnetization transfer saturation (MT), proton density (PD), longitudinal relaxation rate (R 1 ), and effective transverse relaxation rate (R 2 *). Four MPM datasets, two on each of two consecutive days, were acquired in healthy young adults. On Day 1, no repositioning occurred and on Day 2, participants were repositioned between MPM datasets. Using intraclass correlation effect decomposition (ICED), we assessed the contributions of session‐specific, day‐specific, and residual sources of measurement error. For whole‐brain gray and white matter, all four MPM parameters showed high reproducibility and high reliability, as indexed by the coefficient of variation (CoV) and the intraclass correlation (ICC). However, MT, PD, R 1 , and R 2 * differed markedly in the extent to which reliability varied across brain regions. MT and PD showed high reliability in almost all regions. In contrast, R 1 and R 2 * showed low reliability in some regions outside the basal ganglia, such that the sum of the measurement error estimates in our structural equation model was higher than estimates of between‐person differences. In addition, in this sample of healthy young adults, the four MPM parameters showed very little variability over four measurements but differed in how well they could assess between‐person differences. We conclude that R 1 and R 2 * might carry only limited person‐specific information in some regions of the brain in healthy young adults, and, by implication, might be of restricted utility for studying associations to between‐person differences in behavior in those regions.

Magnetic resonance is an exceptionally powerful and versatile measurement technique. The basic structure of a magnetic resonance experiment has remained largely unchanged for almost 50 years, being mainly restricted to the qualitative probing of only a limited set of the properties that can in principle be accessed by this technique. Here we introduce an approach to data acquisition, post-processing and visualization—which we term ‘magnetic resonance fingerprinting’ (MRF)—that permits the simultaneous non-invasive quantification of multiple important properties of a material or tissue. MRF thus provides an alternative way to quantitatively detect and analyse complex changes that can represent physical alterations of a substance or early indicators of disease. MRF can also be used to identify the presence of a specific target material or tissue, which will increase the sensitivity, specificity and speed of a magnetic resonance study, and potentially lead to new diagnostic testing methodologies. When paired with an appropriate pattern-recognition algorithm, MRF inherently suppresses measurement errors and can thus improve measurement accuracy. A new approach to magnetic resonance, ‘magnetic resonance fingerprinting', is reported, which combines a data acquisition scheme with a pattern-recognition algorithm that looks for the ‘fingerprints’ of interest within the data. Raising the profile of NMR Although nuclear magnetic resonance is a powerful analytical tool for many scientific and medical disciplines, usually only a fraction of its potential power is harnessed. Most implementations are qualitative, and restricted in the range of properties that are probed. Dan Ma and colleagues introduce a new approach — termed magnetic resonance fingerprinting — aimed at greatly enhancing the amount of quantitative information that can be obtained in one measurement. Their approach combines a data-acquisition scheme that is indiscriminate in the material properties that it probes with pattern-recognition algorithms that look for the 'fingerprints' of interest within the data. Magnetic resonance fingerprinting has the potential to detect and analyse early indicators of disease or complex changes in materials, as well as increasing the sensitivity, specificity and speed of magnetic resonance studies.

There is a great deal of evidence that method bias is really sure influences item validities, measurement error, correlation and covariance between latent constructs and thus leading the researchers to erroneous conclusion due to inflation or deflation during hypothesis testing. To remedy this, the study provides a guideline to minimize the method bias in the context of structural equation modeling employing the covariance method (CB-SEM) using medical tourism model. A practical approach is illustrated for the identification of method bias based on the new construct namely common latent factor. Using this latent construct, we managed to identify which item has potential to permeate more variance from common latent factor. Nevertheless, we figure out that the method bias is do not exist in our developed model. Therefore, this measurement model is appropriate for structural model in order to achieve the research hypotheses. We hope that this discussion will help the researchers anticipate which items are likely exposed on method bias before proceed to advance modeling.

Measuring head impacts in sports can further our understanding of brain injury biomechanics and, hopefully, advance concussion diagnostics and prevention. Although there are many head impact sensors available, skepticism on their utility exists over concerns related to measurement error. Previous studies report mixed reliability in head impact sensor measurements, but there is no uniform approach to assessing accuracy, making comparisons between sensors and studies difficult. The objective of this paper is to introduce a two-phased approach to evaluating head impact sensor accuracy. The first phase consists of in-lab impact testing on a dummy headform at varying impact severities under loading conditions representative of each sensor’s intended use. We quantify in-lab accuracy by calculating the concordance correlation coefficient (CCC) between a sensor’s kinematic measurements and headform reference measurements. For sensors that performed reasonably well in the lab (CCC ≥ 0.80), we completed a second phase of evaluation on-field. Through video validation of impacts measured by sensors on athletes, we classified each sensor measurement as either true-positive and false-positive impact events and computed positive predictive value (PPV) to summarize real-world accuracy. Eight sensors were tested in phase one, but only four sensors were assessed in phase two. Sensor accuracy varied greatly. CCC from phase one ranged from 0.13 to 0.97, with an average value of 0.72. Overall, the four devices that were implemented on-field had PPV that ranged from 16.3 to 91.2%, with an average value of 60.8%. Performance in-lab was not always indicative of the device’s performance on-field. The methods proposed in this paper aim to establish a comprehensive approach to the evaluation of sensors so that users can better interpret data collected from athletes.

Batra et al. (Journal of Marketing 76, 1–16, 2012) created a new conceptualization of brand love but did not develop a pragmatically useful measure for studies where questionnaire length is a constraint. The current research develops a more parsimonious brand love scale, with three nested versions of 26, 13, and 6 items, respectively. This research also validates the scales, and in so doing conducts several important validity tests not considered by Batra et al. The 26-item scale is able to predict consumer loyalty, word of mouth, and resistance to negative information, with an R² of .90, after correcting for measurement error.

Wearable inertial measurement systems (IMS) allow for three-dimensional analysis of human movements in a sport-specific setting. This study examined the concurrent validity of a IMS (Xsens MVN system) for measuring lower extremity and pelvis kinematics in comparison to a Vicon motion analysis system (MAS) during kicking. Thirty footballers from Australian football (n = 10), soccer (n = 10), rugby league and rugby union (n = 10) clubs completed 20 kicks across four conditions. Concurrent validity was assessed using a linear mixed-modelling approach, which allowed the partition of between and within-subject variance from the device measurement error. Results were expressed in raw and standardised units for assessments of differences in means and measurement error, and interpreted via non-clinical magnitude-based inferences. Trivial to small differences were found in linear velocities (foot and pelvis), angular velocities (knee, shank and thigh), sagittal joint (knee and hip) and segment angle (shank and pelvis) means (mean difference: 0.2–5.8%) between the IMS and MAS in Australian football, soccer and the rugby codes. Trivial to small measurement errors (from 0.1 to 5.8%) were found between the IMS and MAS in all kinematic parameters. The IMS demonstrated acceptable levels of concurrent validity compared to a MAS when measuring kicking biomechanics across the four football codes. Wearable IMS offers various benefits over MAS, such as, out-of-laboratory testing, larger measurement range and quick data output, to help improve the ecological validity of biomechanical testing and the timing of feedback. The results advocate the use of IMS to quantify biomechanics of high-velocity movements in sport-specific settings.

Validity and reliability as scientific quality criteria have to be considered when using optical motion capture (OMC) for research purposes. Literature and standards recommend individual laboratory setup evaluation. However, system characteristics such as trueness, precision and uncertainty are often not addressed in scientific reports on 3D human movement analysis. One reason may be the lack of simple and practical methods for evaluating accuracy parameters of OMC. A protocol was developed for investigating the accuracy of an OMC system (Vicon, volume 5.5×1.2×2.0m3) with standard laboratory equipment and by means of trueness and uncertainty of marker distances. The study investigated the effects of number of cameras (6, 8 and 10), measurement height (foot, knee and hip) and movement condition (static and dynamic) on accuracy. Number of cameras, height and movement condition affected system accuracy significantly. For lower body assessment during level walking, the most favorable setting (10 cameras, foot region) revealed mean trueness and uncertainty to be −0.08 and 0.33mm, respectively. Dynamic accuracy cannot be predicted based on static error assessments. Dynamic procedures have to be used instead. The significant influence of the number of cameras and the measurement location suggests that instrumental errors should be evaluated in a laboratory- and task-specific manner. The use of standard laboratory equipment makes the proposed procedure widely applicable and it supports the setup process of OCM by simple functional error assessment. Careful system configuration and thorough measurement process control are needed to produce high-quality data.

We propose watch-type sweat rate sensors capable of automatic natural ventilation by integrating miniaturized thermo-pneumatic actuators, and experimentally verify their performances and applicability. Previous sensors using natural ventilation require manual ventilation process or high-power bulky thermo-pneumatic actuators to lift sweat rate detection chambers above skin for continuous measurement. The proposed watch-type sweat rate sensors reduce operation power by minimizing expansion fluid volume to 0.4 ml through heat circuit modeling. The proposed sensors reduce operation power to 12.8% and weight to 47.6% compared to previous portable sensors, operating for 4 hours at 6 V batteries. Human experiment for thermal comfort monitoring is performed by using the proposed sensors having sensitivity of 0.039 (pF/s)/(g/m 2 h) and linearity of 97.9% in human sweat rate range. Average sweat rate difference for each thermal status measured in three subjects shows (32.06 ± 27.19) g/m 2 h in thermal statuses including ‘comfortable’, ‘slightly warm’, ‘warm’, and ‘hot’. The proposed sensors thereby can discriminate and compare four stages of thermal status. Sweat rate measurement error of the proposed sensors is less than 10% under air velocity of 1.5 m/s corresponding to human walking speed. The proposed sensors are applicable for wearable and portable use, having potentials for daily thermal comfort monitoring applications.

Intra-individual processes are thought to continuously unfold across time. For equally spaced time intervals, the discrete-time lag-1 vector autoregressive (VAR(1)) model and the continuous-time Ornstein–Uhlenbeck (OU) model are equivalent. It is expected that by taking into account the unequal spacings of the time intervals in real data between observations will lead to an advantage for the OU in terms of predictive accuracy. In this paper, this is claim is being investigated by comparing the predictive accuracy of the OU model to that of the VAR(1) model on typical ESM data obtained in the context of affect research. It is shown that the VAR(1) model outperforms the OU model for the majority of the time series, even though time intervals in the data are unequally spaced. Accounting for measurement error does not change the result. Deleting large abrupt changes on short time intervals (that may be caused by externally driven events) does however lead to a significant improvement for the OU model. This suggests that processes in psychology may be continuously evolving, but that there are factors, like external events, which can disrupt the continuous flow.