Error Variance Research Articles

Electrical power projects: the role of risk management and reliability

The management of power outages caused by severe weather disasters such as earthquakes, tornadoes, and weather disturbances is critical in power transmission and distribution systems. The transmission system is the key to any power system and can trigger severe or significant effects if it fails. Different causes of fires, extreme weather conditions, ageing components, poor maintenance and malfunctions, human error, mal-operative procedures, and high operating network variables may cause transmission components to fail. System reliability is the probability that, despite component failures, a system remains available or functional. Risk management of the failures describes it. There are several methods to predict the failure rate, including exponential and Bayesian distributions. In this study, Poisson distribution forecasted the outage patterns for transmission and distribution networks for one and five years. A probability distribution function generated this pattern. Moreover, previous data were obtained from the National Electric Power Regulatory Authority (NEPRA). On comparing the acquired results with previous outage data, it was reported that the average outages in the transmission system were 567. The average outages in the distribution system stood at 37 for five years with a 100% confidence level. However, the probability of getting 620 transmission outages and 50 distribution outages was the highest in the probability distribution function for five years. Poisson distribution proved to be a useful tool to assess the transmission and distribution system reliability by estimating the failure rate over the years. It would allow the risk professionals to schedule, reduce, and track the systems’ risks. It would also help to improve the system’s reliability.

Analisis Kebijakan Moneter melalui Jalur Harga Aset di Negara Muslim Berkembang

This research aims to analyze the contribution of variables from the interaction of monetary policy transmission variables (SBI, STOCK, INVESTMENT, GDP, INFLATION). This research uses secondary data or time series, namely from the first quarter of 2011 to the first quarter of 2016. The data analysis model in this research is Vector Autoregression (VAR) model and sharpened with Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD) analysis. The results of the VAR analysis show that the past variable (t-1) contributes to the current variable both the variable itself and other variables and from the estimation results it turns out that there is a reciprocal relationship between the variables where all the variables are monetary policy transmission variables (SBI, STOCK, INVESTMENT, GDP, INFLATION) contribute to each other. The results of the IRF analysis show that the stability of the response of all variables is formed in period 5 or the medium and long term, where the response of other variables to changes in one variable shows different variations from positive to negative responses or vice versa, and there are variables whose responses remain positive or remains negative from the short term to the long term. The results of the FEVD analysis show that there are variables that have the largest contribution to the variable itself in the short, medium and long term, such as GDP and SB. Meanwhile, other variables that have the greatest influence on the variable itself in the short, medium and long term are GDP which is most influenced by INVESTMENT, Inflation is most influenced by GDP, INVESTMENT is most influenced by Inflation, SB is most influenced by INVESTMENT while it is most influenced by SB. The results of the analysis of the interaction of each monetary policy transmission variable in the path of asset prices in developing Muslim countries in the short, medium and long term show that monetary policy transmission can increase economic growth and achieve the inflation targets of developing Muslim countries.

Position Computation of Underwater Target Using Geodesic Curvature for Sub-Aquatic Internet of Underwater Things

To estimate the accurate position of the target, the geodesic curvature of the transmitter sensor is proposed in the present work. The geodesic curvature of the transmitter sensor traces the motion of a sensor as the shortest distance along a curved path, which measures the optimal distance on a curved path, accurate measurement of moving targets in an environment with complex geodesics, enhances the realism of the simulation, and underwater surveillance. Theorem 2.1 describes the geodesic curvature between the transmitter sensor and the target. A proposed non-cooperative moving target state measurement (NMTSM) algorithm quantifies the degradation of localization performance and mitigates the stratification effect and geodesic spreading losses. The localization error, localization error variance, and localization accuracy with respect to the number of anchor nodes are compared with standard methods. The proposed non-cooperative moving target state measurement (NMTSM) algorithm improves the localization performance by 33.46% and reduces position MSE by 35.46%, and velocity MSE by 38.86% up to 650 meters. It reduces the localization error by 32.23%, the localization error variance by 34.86% and improves the localization accuracy by 42.21%. The proposed algorithm is useful for the sub-aquatic Internet of Underwater Things (IoUT).

The relationship between the inflation rate and the unemployment rate in Poland and their long-term associations with selected macroeconomic variables

The purpose of this paper is to examine the relationship between the inflation and unemployment rates in Poland and to analyze their long-term relationships with selected macroeconomic variables: the WIG20 index, consisting of the 20 largest Polish companies noted on Warsaw Stock Exchange, the USD/PLN exchange rate, the Brent crude oil index, and the interest rate on 10-year government bond yields. The main objective of the study is to determine the relationship between inflation and unemployment. In this study, a vector error correction model (VECM) was used to study long-run dependence. The impulse response function and forecast error variance decomposition were also used to examine the interactions between variables. There is one long-run relationship between the factors studied. Both the values of the VECM model parameters and the results of the impulse response function indicate that there is a negative relationship between inflation and unemployment in the short term. In the long term, there is a positive relationship, resulting in the stagflation phenomenon.

An Empirical Research on the Antecedents of Psychological Ownership: Wise Leadership and the Crab Syndrome

The purpose of this research is to examine the effects of wise leadership and the crab syndrome, which are new concepts in the literature, on psychological ownership within the scope of the conservation of resources theory. For this purpose, a quantitative method was followed in the study. The study is significant since it was conducted to reveal the antecedents of psychological ownership at the level of individuals and leaders. The sample of the research is the participants working in the banking sector. The questionnaire method was used as the data collection technique, and the items of the dependent variables took place before the independent variables in the questionnaire form to minimize the common method variance error. The analysis of the data was carried out in two stages such as preliminary data analysis and hypothesis testing. While descriptive statistics were included in the preliminary data analysis, research hypotheses were tested using the two-step process of the Structural Equation Model (SEM) in hypothesis testing. In the obtained findings, it was observed that wise leadership predicted psychological ownership positively, and crab syndrome negatively. Within the scope of the conservation of resources theory, the results were discussed, and the recommendations and limitations of the research were stated.

Capital Market and Economic Growth in Nigeria

In spite of its expected role of bridging the saving and investment gap for economic growth, the impact of the Nigerian capital market on the growth of the economy remains unclear. This study looked at the influence of the capital market on economic growth in Nigeria between 1981 and 2022, using vector autoregressive (VAR) analysis. The variables used included: real gross domestic product (RGDP), market capitalization (MCAP), total value of securities traded (TVST), and gross fixed capital formation (GFCF). The study concluded that while RGDP and MCAP influenced their own outcome as well as the outcome of other variables positively in both the short and long run, TVST and GFCF influenced their own outcome as well as the outcome of other variables negatively in both the short and long run, using the VAR system tools of impulse response function and forecast error variance decomposition. Based on the findings of this study, we recommended among others the urgent need to boost the perception of local firms listed in the Nigerian stock exchange both locally and internationally in terms of the total value of their corporate assets and performance measured by price-to-earnings, price-to-sales and return-on-equity metrics. This can be improved upon when these firms are able to increase the quality of their products and services thereby making them better competitors in the global space.

Quantifying Careless Responses in Student Evaluation of Teaching and Justifying Removal for Data Validity

Surveys are typical for student evaluation of teaching (SET). Survey research consistently confirms the negative impacts of careless responses on research validity, including low data quality and invalid research inferences. SET literature seldom addresses if careless responses are present and how to improve. To improve evaluation practices and validity, the current study proposed a three-step procedure to screen SET data for quantifying careless responses, delete and justify the removal of careless responses, and assess if removing careless responses improved the internal structure of the SET data. For these purposes, a convenience sample was taken from a Chinese university. A web-based survey was administered using a revised version of the Students’ Evaluation of Education Quality. One hundred ninety-nine students evaluated 11 courses with 295 responses. Longstring and Rasch outlier analyses identified 49% of nonrandom and random careless responses. The careless responses impacted evaluation results substantively and were deleted. The subsequent study demonstrated that removal improved data validity, using reliability, separation, and inter-rater agreement from the multi-facet Rasch model and G- and D-coefficients, signal-noise ratios, and error variance from generalizability theory. Removing careless responses improved the data validity in terms of true score variance and discrimination power of the data. Data screening should be a prerequisite to validating SET data based on the research results. Data removal is necessary to improve the research validity only if there is a noticeable change in the estimated teaching abilities. Suggestions and implications were discussed, including developing sound evaluation practices and formative use of SET.

Grouped Heterogeneity in Linear Panel Data Models with Heterogeneous Error Variances

We develop a procedure to identify latent group structures in linear panel data models that exploits a grouping in the error variances of cross-sectional units. To accommodate such grouping, we introduce an objective function that avoids a singularity that arises in a pseudolikelihood approach. We provide theoretical and numerical evidence showing when allowing for variance groups improves classification. The developed procedure provides new evidence on the relation between firm-level research and development (R&D) investments and the business cycle. We find a well-defined group structure in the variances that ex-post can be related to firm size. Our estimates indicate stronger procyclical investment patterns at medium-size firms compared to large firms.

Arc Length Spacing-Driven Cooperative Elliptical Encircling Control of Robots: Design and Experiments

Most studies of cooperative enclosing control are centered around moving along the shared circle with available position information. Note that Global Positioning System (GPS) is typically inaccessible under hostile scenar- ios, and elliptical circumnavigation may provide a more flexible and high-efficiency entrapping solution, whilst the non-orthogonality of axial and tangential velocities and the lack of GPS enable elliptical entrapping to be more challeng- ing, and the related outcomes are still rare. Following this line, a novel coordinated error in terms of arc length spacing is skillfully formulated and included in elliptical encircling control to characterize cooperative behaviors without trigg- ering frequent speed variation and unstable observation. Particularly, to remedy the dependence on accessible global position, a new bearing-based estimator under a local frame is proposed to recover the relative coordinate, then a modified cooperative elliptical encircling control strategy is formulated without entailing global position data. Lyapunov stability analysis demonstrates that all error variables are ultimately bounded. Both simulations and experiments are implemented to illustrate the efficacy of proposed control algorithm.

Effects of errors-in-variables on the internal and external reliability measures

The reliability theory has been an important element of the classical geodetic adjustment theory and methods in the linear Gauss-Markov model. Although errors-in-variables (EIV) models have been intensively investigated, little has been done about reliability theory for EIV models. This paper first investigates the effect of a random coefficient matrix A on the conventional geodetic reliability measures as if the coefficient matrix were deterministic. The effects of such geodetic internal and external reliability measures due to the randomness of the coefficient matrix are worked out, which are shown to depend not only on the noise level of the random elements of A but also on the values of parameters. An alternative, linear approximate reliability theory is accordingly developed for use in EIV models. Both the EIV-affected reliability measures and the corresponding linear approximate measures fully account for the random errors of both the coefficient matrix and the observations, though formulated in a slightly different way. Numerical experiments have been carried to demonstrate the effects of errors-in-variables on reliability measures and compared with the conventional Baarda's reliability measures. The simulations have confirmed our theoretical results that the EIV-reliability measures depend on both the noise level of A and the parameter values. The larger the noise level of A, the larger the EIV-affected internal and external reliability measures; the larger the parameters, the larger the EIV-affected internal and external reliability measures.

Consistent Individual Tendencies in Motor Speed-Accuracy Trade-Off.

The literature on speed-accuracy trade-off (SAT) in motor control has evidenced individuality in how individuals trade moments (e.g.,mean and variance) of spatial and temporal errors. These individual tendencies could grasp tendencies of the system given previous experiences and constraints of the organism, a signature of the system control. Nonetheless, such tendency must be robust to small perturbations. Thirty participants performed nine conditions with different time and spatial criteria over 2days (scanning). In between these scanning conditions, individuals performed a practice condition that required modifications of the individuals' preferred spatial and temporal tendency in the SAT. Our results demonstrated that there were no systematic effects of practice in SAT preferences. However, individual analyses demonstrated significant changes for 25 out of 30 individuals. The latter either attests against a consistent preference or to a more complex characterization of individual SAT tendencies.

Predicting the onset of myopia in children by age, sex, and ethnicity: Results from the CLEERE Study.

Clinicians and researchers would benefit from being able to predict the onset of myopia for an individual child. This report provides a model for calculating the probability of myopia onset, year-by-year and cumulatively, based on results from the largest, most ethnically diverse study of myopia onset in the United States. This study aimed to model the probability of the onset of myopia in previously nonmyopic school-aged children. Children aged 6 years to less than 14 years of age at baseline participating in the Collaborative Longitudinal Evaluation of Ethnicity and Refractive Error (CLEERE) Study who were nonmyopic and less hyperopic than +3.00 D (spherical equivalent) were followed up for 1 to 7 years through eighth grade. Annual measurements included cycloplegic autorefraction, keratometry, ultrasound axial dimensions, and parental report of children's near work and time spent in outdoor and/or sports activities. The onset of myopia was defined as the first visit with at least -0.75 D of myopia in each principal meridian. The predictive model was built using discrete time survival analysis and evaluated with C statistics. The model of the probability of the onset of myopia included cycloplegic spherical equivalent refractive error, the horizontal/vertical component of astigmatism (J0), age, sex, and race/ethnicity. Onset of myopia was more likely with lower amounts of hyperopia and less positive/more negative values of J0. Younger Asian American females had the highest eventual probability of onset, whereas older White males had the lowest. Model performance increased with older baseline age, with C statistics ranging from 0.83 at 6 years of age to 0.92 at 13 years. The probability of the onset of myopia can be estimated for children in the major racial/ethnic groups within the United States on a year-by-year and cumulative basis up to age 14 years based on a simple set of refractive error and demographic variables.

"I Can’t Exercise Because My Foot Hurts Me Too Much." Does BMI Change after Common Foot and Ankle Surgeries? A Retrospective Review

Introduction/Purpose: The World Health Organization reports that 1.9 billion people worldwide have a body mass index (BMI) that classifies them as being either overweight (BMI > 25) or obese (BMI > 30). Patients often claim, prior to orthopaedic surgery, that their physical activity and exercise is negatively impacted by their orthopaedic condition. Hip and Knee arthroplasty literature has established that patients with high preoperative BMI continue to have high BMI post-operatively, despite also reporting improvements in their joint pain. No such studies exist in foot and ankle surgery. The purpose of this study was to analyze changes in BMI after many common foot and ankle surgeries. Methods: A retrospective review of all patients undergoing bunion correction, flatfoot correction, 1st MTP arthrodesis, and midfoot arthrodesis procedures between November 2018 and December 2022 was conducted in this IRB approved, single center study. BMI data was collected from the electronic medical record for preoperative and 12 months postoperative time points. Descriptive statistics were reported for age, gender, diabetes, A1c for diabetic patients, smoking status, and overall follow up time. Repeated measures of analysis of variation were conducted with one between-groups factor being type of surgery. Results: 196 patients were included in the study. The average age was 62.1 years, 151(76.6%) patients were female. Average BMI amongst all patients preoperatively was 29.58 +/- 5.21, and postoperatively 29.79 +/- 5.33, with an overall average increase in BMI of 0.2182 +/- 2.21. Upon subgroup analysis, all surgical cohorts yielded an increase in postoperative BMI (bunion +0.2489 +/- 2.06, double arthrodesis +0.4767 +/- 1.37, 1st MTP fusion +0.2293 +/- 2.07, midfoot arthrodesis +0.0251 +/- 3.10) although these changes did not reach significance (p value 0.425). There was no significant difference observed in changes in BMI between surgical groups (p value 0.958). BMI outcomes were normally distributed with acceptable equality of error variances (Levene’s test p- values > 0.270). Conclusion: The current statistics surrounding the prevalence of overweight and obesity are staggering. The results of this study demonstrate that elective orthopaedic surgery of common foot and ankle procedures do not facilitate a change in BMI one year postoperatively, despite the goal of reducing chronic pain and increasing physical function, which is often cited as the primary restriction to exercise amongst patient populations. Additional studies are warranted to further elucidate changes in BMI to adequately council patients regarding postoperative expectations. Study Data Summary

Accounting for residual errors in atmosphere–ocean background models applied in satellite gravimetry

The Atmosphere and Ocean non-tidal De-aliasing Level-1B (AOD1B) product is widely used in precise orbit determination and satellite gravimetry to correct for transient effects of atmosphere–ocean mass variability that would otherwise alias into monthly mean global gravity fields. The most recent release is based on the global ERA5 reanalysis and ECMWF operational data together with simulations from the general ocean circulation model MPIOM consistently forced with fields from the corresponding atmospheric dataset. As background models are inevitably imperfect, residual errors will consequently propagate into the resulting geodetic products. Accounting for uncertainties of the background model data in a statistical sense, however, has been shown before to be a useful approach to mitigate the impact of residual errors leading to temporal aliasing artefacts. In light of the changes made in the new release RL07 of AOD1B, previous uncertainty assessments are deemed too pessimistic and thus need to be revisited. We here present an analysis of the residual errors in AOD1B RL07 based on ensemble statistics derived from different atmospheric reanalyses, including ERA5, MERRA2 and JRA55. For the oceans, we investigate the impact of both the forced and intrinsic variability through differences in MPIOM simulation experiments. The atmospheric and oceanic information is then combined to produce a new time-series of true errors, called AOe07, which is applicable in combination with AOD1B RL07. AOe07 is further complemented by a new spatial error variance–covariance matrix. Results from gravity field recovery simulation experiments for the planned Mass-Change and Geosciences International Constellation (MAGIC) based on GFZ’s EPOS software demonstrate improvements that can be expected from rigorously implementing the newly available stochastic information from AOD1B RL07 into the gravity field estimation process.

Impact of Assimilation of the Tropical Cyclone Strong Winds Observed by Synthetic Aperture Radar on Analyses and Forecasts

Abstract A few high-wind observations have been obtained from satellites over the ocean around tropical cyclones (TCs), but the impact of data assimilation of such observations over the sea on forecasting has not been clear. The spaceborne synthetic aperture radar (SAR) provides high-resolution and wide-area ocean surface wind speed data around the center of a TC. In this study, the impact of data assimilation of the ocean surface wind speed of SAR (OWSAR) on regional model forecasts was investigated. The assimilated data were estimated from SAR on board Sentinel-1 and RADARSAT-2. The bias of OWSAR depends on wind speed, the observation error variance depends on wind speed and incidence angle, and the spatial observation error correlation depends on the incidence angle. The observed OWSAR is screened using the variational quality control method with the Huber norm. In the case of Typhoon Hagibis (2019), OWSAR assimilation modified the TC low-level inflow, which also modified the TC upper-level outflow. The propagation of this OWSAR assimilation effect from the surface to the upper troposphere was given by a four-dimensional variational method that searches for the optimal solution within strong constraints on the time evolution of the forecast model. Statistical validation confirmed that errors in the TC intensity forecast decreased over lead times of 15 h, but this was not statistically significant. The validation using wind profiler observations showed that OWSAR assimilation significantly improved the accuracy of wind speed predictions from the middle to the upper level of the troposphere. Significance Statement The purpose of this study was to demonstrate the impact of the assimilation of ocean surface wind speed by synthetic aperture radar (SAR) on regional model predictions. In the case of tropical cyclones, ocean surface wind speed assimilation modified inflows in the lower layer and outflows in the upper layer. The results indicate that the SAR assimilation improves the accuracy of wind speed forecasts in the middle to upper troposphere.

Distributed field mapping for mobile sensor teams using a derivative‐free optimisation algorithm

AbstractThe authors propose a distributed field mapping algorithm that drives a team of robots to explore and learn an unknown scalar field using a Gaussian Process (GP). The authors’ strategy arises by balancing exploration objectives between areas of high error and high variance. As computing high error regions is impossible since the scalar field is unknown, a bio‐inspired approach known as Speeding‐Up and Slowing‐Down is leveraged to track the gradient of the GP error. This approach achieves global field‐learning convergence and is shown to be resistant to poor hyperparameter tuning of the GP. This approach is validated in simulations and experiments using 2D wheeled robots and 2D flying miniature autonomous blimps.

Physics-Informed Neural Networks for Solving High-Index Differential-Algebraic Equation Systems Based on Radau Methods

As is well known, differential algebraic equations (DAEs), which are able to describe dynamic changes and underlying constraints, have been widely applied in engineering fields such as fluid dynamics, multi-body dynamics, mechanical systems, and control theory. In practical physical modeling within these domains, the systems often generate high-index DAEs. Classical implicit numerical methods typically result in varying order reduction of numerical accuracy when solving high-index systems. Recently, the physics-informed neural networks (PINNs) have gained attention for solving DAE systems. However, it faces challenges like the inability to directly solve high-index systems, lower predictive accuracy, and weaker generalization capabilities. In this paper, we propose a PINN computational framework, combined Radau IIA numerical method with an improved fully connected neural network structure, to directly solve high-index DAEs. Furthermore, we employ a domain decomposition strategy to enhance solution accuracy. We conduct numerical experiments with two classical high-index systems as illustrative examples, investigating how different orders and time-step sizes of the Radau IIA method affect the accuracy of neural network solutions. For different time-step sizes, the experimental results indicate that utilizing a 5th-order Radau IIA method in the PINN achieves a high level of system accuracy and stability. Specifically, the absolute errors for all differential variables remain as low as 10−6, and the absolute errors for algebraic variables are maintained at 10−5. Therefore, our method exhibits excellent computational accuracy and strong generalization capabilities, providing a feasible approach for the high-precision solution of larger-scale DAEs with higher indices or challenging high-dimensional partial differential algebraic equation systems.

An accelerated stochastic extragradient-like algorithm with new stepsize rules for stochastic variational inequalities

In this paper, we devise a stochastic extragradient-like algorithm incorporated with inertial terms, which requires a single projection onto our feasible set and employs a stochastic approximation version of an Armijo-type line search scheme along a feasible direction, for solving pseudomonotone stochastic variational inequalities. In the algorithm, two different stepsize strategies are employed to update steplength sequences without using the prior knowledge of the Lipschitz constant of involved operators. In the process of the stochastic approximation, we iteratively reduce the variance of stochastic errors. The almost sure convergence, the complexity analysis, and rates are provided in a dimensional space under reasonable conditions. Finally, some numerical experiments with graphical illustrations are reported to demonstrate the applicability and the efficiency of our algorithm in comparison with some projection type methods in the literature.

Development Structural Equation Modelling with Second Order Measurement Model and Mixed Scale Data

This research will develop structural equation modeling involving latent variables with a second order measurement model, as well as mixed measurement scales using simulation data. Applied to simple structural modeling which has one exogenous variable, one mediating endogenous variable, and one endogenous dependent variable. Based on the results of the analysis, if the coefficient of the measurement model and the structural model is greater, the error variance is 0.1, causing the correlation between indicators in each variable to be stronger. Another effect caused by the greater closeness of the coefficient of the measurement model and the structural model is that the greater the percentage of diversity generated in the measurement model. The results of the inner model analysis show that there is an insignificant relationship, namely the relationship between the Y1 variable and the Y2 variable but for other path relationships it shows significant. The total coefficient of determination for the second order SEM shows that the greater the closeness of the coefficient of the measurement model and the structural model in the error variance condition of 0.1, the greater the R2 value.

Beyond a facility: A cross-sectional survey on WASH service levels and informal social accountability in childcare centres in Nairobi's informal settlements

Abstract Access to clean water, sanitation, and hygiene (WASH) services is crucial for a healthy start in life. Social accountability has a potential for enhancing WASH services in childcare centres. However, there are inadequate studies to understand how informal social accountability mechanisms contributes to WASH service provision. To address this gap, we conducted a cross-sectional survey in Korogocho and Viwandani informal settlements in Nairobi, Kenya, to explore the relationship between different levels of WASH services (ranging from basic to limited or nonexistent) and indicators of informal social accountability, including rewards, sanctions, voice, and responsiveness. We employed multinomial regression analysis, utilizing a robust error variance estimator to account for potential biases. Our findings revealed disparities in WASH service provision between the two studied areas, with childcare centres in Korogocho exhibiting higher access to basic WASH services compared to those in Viwandani. Our analysis also highlighted a significant association between informal social accountability mechanisms and the provision of WASH services. Notably, the sanction mechanism exhibited a correlation with all WASH services, suggesting its pivotal role in shaping service delivery outcomes. In light of these findings, it is imperative to prioritize efforts aimed at reinforcing social accountability mechanisms in WASH service delivery frameworks.