Standard Error Measures Research Articles

Intra-rater and inter-rater reliability of a handheld myotonometer measuring myofascial stiffness of lower lumbar myofascial tissue in healthy adults.

Biomechanical muscle properties, such as stiffness, can be valuable indicators of tissue health and show promise as a diagnostic and treatment measure for chronic low back pain (CLBP). The development of accessible assessment technologies, such as the MyotonPRO portable device, allows for the convenient quantification of muscle tone and stiffness changes. The aim of this study is to assess the reliability of lumbar erector spinae muscle stiffness with the MyotonPRO in healthy adults and to compare stiffness changes between prone and sitting position. Thirty asymptomatic participants (N = 15 women and N = 15 men) aged between 18 and 65 years were recruited to participate in this study. Two examiners tested muscle stiffness at the palpable muscle belly, one finger away from the spinous process at the level of the L4 vertebra, first from the left and then from the right side, both in prone position and after in sitting position. For inter-rater reliability, all participants were tested by two examiners on the same day, and intra-rater reliability was calculated using the same examiner's assessment results with an exact 24-h interval. Intraclass correlation coefficients (ICC), standard error measures (SEM) and minimum detectable change (MDC) with a 95% confidence interval were calculated to assess intra-rater and inter-rater reliability. Statistical analysis revealed good intra-rater reliability with an ICC of 0.88 (95% CI [0.76-0.94]) for the stiffness of the left erector spinae and excellent intra-rater reliability with an ICC of 0.91 (95% CI [0.82-0.95]) for the right erector spinae, both in the prone position. Intra-rater reliability in the sitting position was excellent to very good with an ICC of 0.91 (95% CI [0.82-0.96]) for the left side and an ICC of 0.89 (95% CI [0.78-0.95]) for the right side. The results for the left-sided prone position showed good inter-rater reliability with an ICC of 0.87 (95% CI [0.73-0.94]). The prone position on the right side also showed good inter-rater reliability with an ICC of 0.84 (95% CI [0.68-0.92]). The inter-rater reliability for the left and right side in the sitting position was excellent with an ICC of 0.96 (95% CI [0.92-0.98]) for the left side and an ICC of 0.95 (95% CI [0.90-0.97]) for the right side. This study demonstrated high reliability in measuring lumbar erector spinae muscle stiffness with the MyotonPRO in healthy adults and the ability of the device to detect even small changes in erector spinae muscle stiffness, testing both the right and left sides and measuring in both prone and sitting positions. The use of the sitting position to assess lumbar tissue tension in individuals may serve as a valuable substitute for the prone position, particularly for patients who experience discomfort in the prone position, and could have additional practical significance in clinical settings.

Evaluating the effectiveness of safety countermeasures at highway–railway grade crossing based on a machine learning framework

Objective This research aims to cluster similar highway–railway grade crossings (HRGCs) to examine the safety countermeasures at HRGCs. Methods The methodology integrates inventory and collision data from Federal Railroad Association (FRA) data set during years 2010 to 2022 . The XGBoost and random forest (RF) algorithms are employed to identify influential collision severity factors. Then, the deep latent class analysis (DLCA) method is utilized on selected inventory factors as important features to cluster similar HRGCs. Afterward, collision modification factor (CMF) and standard error (SE) measures are computed for each countermeasure through collisions within each HRGC cluster. Results XGBoost successfully identified 20 important collision and inventory factors with importance levels exceeding 94%, such as the number of daily trains and the surface material. Then, the DCLA method achieved 4 distinct clusters optimized by high similarity within each cluster and significant independence among clusters. The effectiveness of countermeasures was computed in terms of CMF and SE. The CMF results demonstrated that bells achieved superior safety compared to other countermeasures in clusters with sharper track angles and high maximum train speeds. Implementing bells decreased collisions across Clusters 1 and 4, with reductions of 53% (CMF = 0.47) and 46% (CMF = 0.54), respectively. Conclusions The results highlight XGBoost’s capability to identify important collision and inventory factors, successfully uncovering 20 of the most important factors. The DCLA clustering method forms 4 distinct groups marked by substantial internal similarity within each cluster. This approach contributes to a clearer understanding of how each countermeasure impacts collision frequency. The findings highlight the varying effectiveness of different countermeasures across clusters, improving decision making for safety at HRGCs. The study highlights the efficacy of crossbucks in addressing safety concerns during moderate traffic conditions, particularly evident in environments with a highway speed limit between 100 and 125 mph. Additionally, bells demonstrate notable effectiveness in areas with sharper track angles.

Agreement of synchronous remote and in-person application of the Alberta Infant Motor Scale: Cohort study.

Using standardized scales to assess motor development via telemedicine can increase access for low-income populations. Our aim was to verify the agreement and feasibility between remotely and synchronously applying the Alberta Infant Motor Scale (AIMS) and the in-person format. This was a concordance study, with 77 typical infants aged 4-18 months (mean = 13 months). The AIMS was applied remote via video calls and face-to-face. We applied a questionnaire to caregivers to verify feasibility. There was a high level of agreement between the remote and in-person assessments, with intraclass correlation coefficients above 0.98 and low standard error measure values (<1 item for each posture, <2 items for the total raw score, and =5% for the normative score). The smallest detectable change was between 1.67 and 2.45 for each posture, 3 for the total raw score, and 6% for the normative score. The Bland-Altman analysis showed low bias with the mean difference close to zero (<0.80) and low error with little dispersion of the difference points around the mean. Caregivers' perspectives on the synchronous remote assessment were positive, with good quality, clear information during the assessment, and comfort with the method. The synchronous remote application of the AIMS may be an alternative for families without access to in-person services that assess motor development.

Data-based modelling of arrays of wave energy systems: Experimental tests, models, and validation

One of the key steps towards economic feasibility of wave energy conversion technology concerns scaling up to farms of multiple devices, in the attempt to reduce installation costs by sharing infrastructure, and a consequent drop in levelised cost of energy. Moreover, whenever wave energy systems are deployed in proximity (in so-called arrays), the exploitation of the hydrodynamic interactions between single devices is fully enabled, potentially increasing the final energy outcome. To achieve this in real (operational) time, the employed energy-maximising control strategies require control-oriented array models, able to efficiently describe the dynamics of these interconnected systems in a representative fashion. This can be, nonetheless, a difficult task when considering first principles alone, under small motion assumptions, for modelling purposes. Recognising the uncertainty associated to array numerical models obtained from the linearisation of simplified system equations around their equilibria, this paper presents models of several array configurations identified following a frequency domain approach on the basis of experimental data. Tailored tests on laboratory-scale devices have been designed and conducted in the Aalborg University (Denmark) wave tank facility, with the purpose of performing representative system identification of the wave energy systems arrays. The obtained models are validated on different representative sea states configurations, in controlled and uncontrolled motion operational conditions. The validation results are fully discussed and analysed in terms of standard error measures and time lag, while the obtained models are made freely accessible via a linked repository (named OCEAN), in the attempt to openly provide validated models for different array configurations.

Software reliability prediction using ensemble learning with random hyperparameter optimization

The paper investigates software reliability prediction by using ensemble learning with random hyperparameter optimization. Software reliability is a significant problem with software quality that developers face. It involves accurately predicting the next failure. In recent years, machine learning techniques and ensemble learning approaches have been applied to improve software reliability prediction. These approaches aim to analyze historical data and develop models that can accurately forecast when failures are likely to occur. The article proposes an ensemble learning regression model using Ridge, Bayesian Ridge, Support Vector Regressor (SVR), K-Nearest Neighbors Algorithm (KNN), Regression tree, Random Forest, Neural network, and Decision Tree as base learners. Ridge is used as a combiner model. Each base learner hyperparameter is tuned using a random search algorithm automatically. A random hyperparameter search optimization algorithm selects the hyperparameter and adjusts it for overfitting and underfitting. The base models are tuned to minimize bias and variance. The performances of the models are evaluated using standard error measures such as Mean Squared Error (MSE), Sum of Squared Error (SSE), and Normalized Root Mean Square Error (NRMSE). The proposed ensemble model is compared with existing models using a benchmark dataset. The Iyer,and Lee, and Musa datasets are used for the experiment. The dataset is scaled using standard methods like logarithmic scaling, lagging, and linear interpolation. The results of the statistical comparison show better performance by our proposed model as compared to existing models.

Linking error measures to model questions

Models for forecasting various ecosystem properties have great potential that comes with a need for model validation. Before we can perform such validation, we need to define what it means for the model to perform well, which depends on the question being asked. Often, it seems easy to ignore the model question and take a standard well-known error measure for comparing the model to the available data. The question is whether this practice is adequate. Here, we defined different types of model-data mismatches that may be more or less relevant to different types of questions. We show that error measures differ in their sensitivity to the type of mismatch and robustness to sparse and noisy data. The results imply that a careful selection of error measures, using a clearly defined ecological question as a starting point, is vital to proper model evaluation. While we present our results as generally applicable to the validation of any type of forecasting model, we also illustrate them using cyanobacterial bloom modelling as a detailed example of a case where different questions could be asked of the same model.

LABOUR PRODUCTIVITY ANALYSIS OF MANUFACTURING SECTOR IN TURKEY AGAINST EU

This study offers an in-depth analysis of labour productivity of manufacturing sector in Turkey and provides a comparison with EU27 and EA19 countries utilizing Eurostat time series data of 63 quarters covering 2005/first quarter-2020/third quarter time interval. Productivity trends are identified and interpreted by relating them with the key macroeconomic events and factors. Multiple linear and non-linear regression equations, and ARIMA model with different parameters are applied to the time series data considering the periods with and without covid effect. Future projections are made for the periods 2020–2023 for Turkey manufacturing sector based on the best fitting regression and ARIMA solutions and they are compared. Findings revealed that extreme covid conditions of even two quarters of data have significant impact on the forecasted values for Turkey, EU27 and EA19 countries. ARIMA analysis with 12 different parameter settings provided accurate results, supported by Thiel’s inequality coefficients and standard error measures. Analysis has shown consistent patterns between EA19 and EU27 countries. ARIMA results represent better compatibility with the regression results for Turkey. Study is valuable by providing comprehensive and comparative analysis, revealing future forecasts and covid effect and degree of recovery from the pandemic.

Missing data imputation using utility-based regression and sampling approaches

Data are often missing not at random (MNAR) in scientific experiments. We treat the MNAR problem as an imbalanced learning task. Standard predictive error measures of regression (e.g., mean squared error) are not suitable for imbalanced learning problems, such as in clinical trials where extreme values tend to be MNAR. We investigate hybrid imbalanced learning approaches that combine utility-based regression (UBR) with synthetic minority oversampling technique for regression (SMOTER) in cross-sectional trial settings. UBR optimizes the product of the conditional probability density (estimated by quantile regression forests) and a utility function which takes the relevance of the target variable value and the prediction error into account. SMOTER oversamples the relevant rare cases. Simulations show that the proposed method provides plausible predictions and reduces the bias for realistic missing data scenarios when compared with standard approaches like random forests and multiple imputation (systematic bias is observed in those methods, i.e., a tendency to underestimate the mean and standard deviation given the presence of MNAR in the area of high values of the target variable). The proposed method is implemented in a real dataset from an antidepressant clinical trial, and similar pattern of the systematic bias from commonly used methods is observed in the real data compare to the proposed method. Therefore, we encourage the integration of utility-based learning strategies for handling of missing data in the analysis of clinical trials.

Variations in adolescent cybercrime awareness concerning geographical location and parental relationships

This paper investigates the adolescents’ importance of parental relationships in preventing them from cybercrime victimization. In addition, the role of locality in the level of internet addiction is studied. A sample comprised of 360 students of age group 15-17 years, pursuing their studies in 13 CBSE schools of New Delhi was selected randomly. These selected students were in classes XI and XII. The internet addiction and the satisfaction level of parental relationships were analyzed to understand the adolescents’ online behaviors. The mean, SD, and standard error measures of several factors were analyzed. Findings reveal that there exists a significant difference in internet addiction between urban and rural adolescents. The findings also confirmed that 84.75 % of cyber-victimized adolescents have a dissatisfied parental relationship.

Requirements Engineering: Conflict Detection Automation Using Machine Learning

The research community has well recognized the importance of requirement elicitation. Recent research has shown the continuous decreasing success rate of IS projects in the last five years due to the complexity of the requirement conflict refinement process. Requirement conflict is at the heart of requirement elicitation. It is also considered the prime reason for deciding the success or failure of the intended Information System (IS) project. This paper introduces the requirements conflict detection automation model based on the Mean shift clustering unsupervised machine learning model. It utilizes the advantages of Artificial Intelligence in detecting and classifying the requirement conflicts occurring in the requirement elicitation phase. An experiment of the proposed model was conducted, composed of 207 observations and 11 parameters. The results show that the correct detection accuracy for the (Conflicted Requirements, Partial Conflicted Requirements & Conflict Free Requirements). The proposed model findings provide a promising and effective detection process regarding requirements classification. The model validation process provides a performance comparison between the model output vs. the output produced by the requirement conflict verification phase, detailing the Standard Error (SE) measure of accuracy values and the detected clusters. The implications of this study could be used to promote the automatization of the requirement elicitation process. Thus, increasing the potentiality of enhancing the produced systems designs.

Reliability and Repeatability of the Footwork Plantar Pressure Plate System.

Plantar pressure plate instruments are commonly used in clinical practice and biomechanical analysis and are useful to establish a relationship between gait disorders and foot pressure. The aim of this study was to verify the reliability and repeatability of the Footwork pressure plate system for static and dynamic conditions. Forty healthy adults, without apparent gait pathology, were recruited. For the static condition, participants were asked to stand static on the Footwork pressure plate for 5 sec in natural position (arms on either side of the body, feet shoulder-width apart in a comfortable angle, and looking ahead). For the dynamic condition, subjects were told to step five times with each foot on the plate following the three-step protocol. Both conditions were performed in two testing sessions spaced by 1 week. Intrasession and intersession reliability for both conditions showed substantial to almost perfect intraclass correlation coefficient (ICC) values, and low coefficient of variation, low standard error measure, and low percentage error. Intrasession ICCs were 0.724 to 0.993 for static condition evaluation and 0.639 to 0.986 for dynamic condition evaluation. Intersession reliability ICCs ranged from 0.850 to 0.987 for the static condition and from 0.781 to 0.996 for the dynamic condition. Coefficient of variation values were below 8% in both cases and percentage error calculated from standard error measure were less than 10%. The present work demonstrates that the Footwork plantar pressure plate system is a reliable instrument for collecting plantar pressures in static and dynamic conditions. Reliability data were higher for the static trials, probably because of the individual physiologic fluctuations, which are larger during dynamic gait. Reliability for intersession and average intrasession trials were higher than single-test reliability. The results from the present work can be used as a starting point for future research and to establish a basis for sample sizes for investigations that would use the Footwork platform.

Investigation of design and performance improvements on solid resilient tires through numerical simulation

Solid tires are often utilized to bear excessive loads. Therefore, sidewall cracks and internal heat build-up affect their durability more significantly. It is a challenge to minimize these factors and improve tire performance while maintaining the functionality of the solid tire. Moreover, there are no specific standard guidelines for modifying the solid tire design to achieve this objective. This study proposes several design and performance improvements to the solid resilient tire and investigates the performance of these modified designs using the Finite Element (FE) method under static and dynamic conditions. For these FE simulations, suitable hyperelastic models are obtained using curve fitting combined with three standard error measures. The results show that the Mooney-Rivlin, Ogden, and Yeoh material models show good agreement with experimental data for modelling the base, cushion, and tread layers of the tire, respectively. The developed FE models are validated using experimental data obtained from a leading tire manufacturing company in Sri Lanka. The validated model is used to develop and analyse two distinct tire models which have two different cavity geometries (circular and elliptical) in their cushion layers to minimize heat build-up and sidewall cracks. The tire reinforcements are also rearranged to further improve the performance of the models. The introduction of cavities helps to reduce strain energy dissipation and to increase the dissipation of internal heat by convection. Furthermore, the stress intensity on the side walls is also reduced, thereby minimizing sidewall cracks. Numerical experiments show that the modified designs have a lower strain energy density, lower stress concentration, and less material utilization when compared to the basic tire design.

SMOOTHING IN NEURAL NETWORK FOR UNIVARIAT TIME SERIES DATA FORECASTING

Time series data is interesting research material for many people. Not a few models have been produced, but very optimal accuracy has not been obtained. Neural network is one that is widely used because of its ability to understand non-linear relationships between data. This study will combine a neural network with exponential smoothing to produce higher accuracy. Exponential smoothing is one of the best linear methods is used for data set transformation and thereafter the new data set will be used in training and testing the Neural Network model. The resulting model will be evaluated using the standard error measure Root Mean Square Error (RMSE). Each model was compared with its RMSE value and then performed a T-Test. The proposed ES-NN model proved to have better predictive results than using only one method.

Finding "Bright Spots": Using Multiple Measures to Examine Local-Area Racial Equity in Cancer Mortality Outcomes.

In this article, we present a variety of measures that quantify equity in cancer mortality outcomes, demonstrate how the measures perform with various cancer types, and identify counties, or "bright spots," that meet the criteria of those measures. Using county-level age-adjusted mortality rates for 2007-2016 from the National Center for Health Statistics, we identified counties that had both equitable and optimal outcomes for Black and White death rates across 5 types of cancer: cancers of the lung/bronchus, prostate, female breast, colorectum, and liver. The number of counties that met the criteria ranged from 0 to 442, depending on cancer type and measure used. Prostate cancer and male liver cancer consistently had the lowest number of "bright spots," with a maximum of 3 counties meeting the most lenient criteria. This paper presents several ways to examine equity, using rate ratios and standard error measures, in cancer mortality outcomes. It highlights areas with positive progress toward equity and areas with a potential need for equity-focused cancer-control planning. Examining local areas of positive deviance can inform cancer-control programming and planning around health equity.

Modeling and Forecasting Medium-Term Electricity Consumption Using Component Estimation Technique

The increasing shortage of electricity in Pakistan disturbs almost all sectors of its economy. As, for accurate policy formulation, precise and efficient forecasts of electricity consumption are vital, this paper implements a forecasting procedure based on components estimation technique to forecast medium-term electricity consumption. To this end, the electricity consumption series is divided into two major components: deterministic and stochastic. For the estimation of deterministic component, we use parametric and nonparametric models. The stochastic component is modeled by using four different univariate time series models including parametric AutoRegressive (AR), nonparametric AutoRegressive (NPAR), Smooth Transition AutoRegressive (STAR), and Autoregressive Moving Average (ARMA) models. The proposed methodology was applied to Pakistan electricity consumption data ranging from January 1990 to December 2015. To assess one month ahead post-sample forecasting accuracy, three standard error measures, namely Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE), were calculated. The results show that the proposed component-based estimation procedure is very effective at predicting electricity consumption. Moreover, ARMA models outperform the other models, while NPAR model is competitive. Finally, our forecasting results are comparatively batter then those cited in other works.

Three-axis measurements with a novel system for 3D plantar foot scanning: iPhone X

Three-dimensional (3D) surface scanning technologies are being used to produce digitized representation of human anatomy parts. The purpose of this study was to determine the agreement between two 3D scanners (Structure iSense and TrueDepth iPhone X) to obtaining anthropometric data from the foot. Fourteen feet from seven voluntary healthy adults (four males and three females) were scanned. 3D landmarks were placed on 1st metatarsal head (1MTH), 5th metatarsal head (5MTH), heel (H) and on the plantar side of the scaphoid tubercle (ST) to acquire measurements in the three space-axes (X, Y and Z). Intraclass correlation coefficient (ICC) > 0.9 was obtained for Structure iSense for the measurements taken in the three axes. ICC > 0.9 was obtained TrueDepth iPhone X for X- and Y-axes and ICC < 0.75 for Z-axis. Low standard error measure (SEM) was obtained in both scanners. The two methods provide moderate to excellent reliability measuring the foot in the three space-axes.

Does corporate governance matter for stock returns volatility in the Brazilian context?

PurposeThis paper aims to examine the Brazilian stock market behavior and volatility term structure of two portfolios that, theoretically, the companies that comprise them have different degrees of idiosyncratic risk: one portfolio consists of firms with good corporate governance and the other comprises firms with poor corporate governance.Design/methodology/approachThe sample comprises corporate firms listed in the Brazilian stock market during the period from January 2008 to December 2017. Generalized autoregressive conditional heteroskedasticity models were applied.FindingsThe results show that the portfolio of firms with good corporate governance practices presents fluctuations that are more often temporary and reactive, with trends’ persistence of shorter durations, when considering the punctual volatility of the parameters estimated. This opposed expectation that the portfolio comprised of companies with good governance practices are better protected from short-term movements. However, over time and with standard error measures in consideration, both portfolios’ volatilities behave in similar ways. These findings may be related to Brazilian market characteristics, such as ownership concentration, ineffective corporate boards and the ever-developing nature of the stock market in Brazil. Any one of these characteristics present challenges to effective enforcement of the corporate governance practices in the Brazilian context.Originality/valueThe findings are potentially to the interest of researchers and practitioners for several reasons. First, this paper contributes to the growing literature on the relationship between corporate governance and market volatility. Second, it informs that volatility in the Brazilian context is likely only partially, if at all, influenced by corporate governance practices. Third, longitudinally, both indices follow the same pattern and converge to the same place.

A Short Note on Application of Bachelier Option Model to FRAs; with Comment on Black 1976

This Short Note arises from questions about applying the Bachelier model to FRAs which arose as interest rates went negative leading to a failure of the Black 1976 model used by convention as the market price model. The paper reviews the Black ’76 model form highlighting reasons for this failure. Alternate Bachelier model based on log normal and yield standard error measures are shown to have similar limitations. Other extant models derived from the Black-Scholes model form have consistent failings. Given this, the author defines a Bachelier spread model in terms of yields and the related note price. It is shown these create the same price outcomes for all positive, negative and nil values of the note yield. It can then be shown the Black ’76 model for instance systematically misprices as the Note yields go to zero and fails below zero. Recommendation is the price model be moved from the log normal, yield standard error model form to the Bachelier style spread model formulated consistent with that detailed in this paper.

Recession forecasting using Bayesian classification

We demonstrate the use of a Naïve Bayes model as a recession forecasting tool. The approach is closely connected with Markov-switching models and logistic regression, but also has important differences. In contrast to Markov-switching models, our Naïve Bayes model treats National Bureau of Economic Research business cycle turning points as data, rather than as hidden states to be inferred by the model. Although Naïve Bayes and logistic regression are asymptotically equivalent under certain distributional assumptions, the assumptions do not hold for business cycle data. As a result, Naïve Bayes has a larger asymptotic error rate, but converges to the error rate more quickly than logistic regression, resulting in more accurate recession forecasts with limited data. We show that Naïve Bayes outperforms competing models and the Survey of Professional Forecasters consistently for real-time recession forecasting up to 12 months in advance. These results hold under standard error measures, and also under a novel measure that varies the penalty on false signals, depending on when they occur within a cycle; for example, a false signal in the middle of an expansion is penalized more heavily than one that occurs close to a turning point.

Cruise missile head shape optimisation using an adaptive sampling surrogate model

ABSTRACTHigh-precision response of the surrogate model is desired in the process of optimisation. An excessive number of sampling points will increase the cost of the calculation. The appropriate number of sampling points cannot only guarantee the accuracy of the surrogate model but also save the calculation cost. The purpose of this research is to demonstrate the eventuality of using an adaptive surrogate model for optimisation problems. The adaptive surrogate model is built on an adaptive sampling approach and an extended radial basis function (ERBF). The adaptive sampling is an approach that new sampling points are placed in the blank area and all the sampling points are uniformly distributed in the design region using Multi-Island GA. The precision of the ERBF surrogate model is checked using standard error measure to determine whether the surrogate model should be updated or not. This adaptive surrogate model is used to optimise a cruise missile head shape. Aerodynamic and stealthy performance of the cruise missile head shape are considered in this research. Different global objective function and different weight factor are used to research the aerodynamic and stealthy performance in this optimisation process. The results show that the drag is reduced with a slender head shape and the radar-cross section (RCS) value is reduced with a short head shape.