Objective: Diabetes mellitus is a metabolic disorder that develops over time and affects the cardiovascular system, eyes, kidneys, nerves, and blood sugar levels. The aim of this investigation was to ...determine the prevalence of diabetic mellitus patients, identify the associating risk factors using a multilevel longitudinal model, and understand the multilevel model changes for the level-1 and level-2 models. Material and Methods: We examined such types of scenarios using multilevel longitudinal models such as the simple random intercept multilevel model, the random coefficient model, and the null model. Results: There were 248 individuals with diabetes mellitus enrolled in the study for follow-up measurements over 4 time points, among these 248 individuals, 211 had complete data for all four time points. Based on the intraclass correlation coefficient, much of the variability (88.35%) in diabetes mellitus patients was accounted for by the follow-up time in this study, whereas 11.65% of the variability could not be accounted for by the follow-up time. Moreover, the data analysis suggested that sex had a significant effect on diabetes mellitus patients with the progression of time. Conclusion: Based on the results of our study, sex, baseline fasting and educational status had a significant effect on diabetes mellitus patients over time. The educational status of diabetes mellitus patients was found to have a significant effect throughout the follow-up time; this shows that when treating diabetes mellitus patients, the physician should beware of the nature of the disease and how to management diabetes requires a high level of awareness and motivation on part of the patients regarding self-care.
The development of the water management system has so far reached the stage of utilizing IoT technology in the monitoring and operation process. An essential factor in water that affects the quality ...of a substance is pH. The research aims to analyze and ensure that the devices have a small pH measurement error rate with TLC4502 & E201C. The calibration process was carried out using linear regression, and value of 0.99 was obtained. Analysis was carried out using one-way ANOVA and Tukey HSD methods, and it was found that all data pairs rejected the null hypothesis (H0) and accepted the alternate hypothesis (H1). This hypothesis indicated a significant difference in the relative measurement error of pH in each condition. The standard error value of each measurement after filtration was 0.00, with an uncertainty value ranging from 0.07 to 0.02. If the sensor can provide measurement results with low error and high accuracy, then the sensor can be widely circulated and used. Through this research, the feasibility of a measuring instrument was developed based on the perspective of errors and high accuracy. A quality measuring instrument certainly helpful in various fields from the fisheries, hydroponics, and environmental sectors.
Fused deposition modelling (FDM) is a fast growing rapid prototyping (RP) technology due to its ability to build functional parts having complex geometrical shape in reasonable time period. The ...quality of built parts depends on many process variables. In this study, five important process parameters such as layer thickness, orientation, raster angle, raster width and air gap are considered. Their influence on three responses such as tensile, flexural and impact strength of test specimen is studied. Experiments are conducted based on central composite design (CCD) in order to reduce experimental runs. Empirical models relating response and process parameters are developed. The validity of the models is tested using analysis of variance (ANOVA). Response surface plots for each response is analysed and optimal parameter setting for each response is determined. The major reason for weak strength may be attributed to distortion within or between the layers. Finally, concept of desirability function is used for maximizing all responses simultaneously.
The present investigation optimizes process parameters such tool size, peak current, pulse on time, and dielectric pressure in the EDM process. The material removal rate affects the machining ...operation's quality. The goal of the present work is to maximize material removal rate using Taguchi technique-based optimization of machining parameters. On an EDM, the experimental study for machining operations was carried out. For EN-31, machining operations are carried out using an orthogonal array with L9. The ANOVA technique was used to evaluate the experimental results. Using a technique called as grey relational analysis, the optimum conditions for material removal rate are determined. The optimum approach for a set of linked parameters is evaluated using the grey relation grade.
The reliable and cost-effective operation of battery packs relies on state of power (SOP) algorithms to estimate the available power of the system. The challenges in developing these algorithms ...include the nonlinear behavior of batteries under high-power demands and the impact of temperature, state of charge (SOC), stack pressure and previous load history at high C-rates. This study employs analysis of variance (ANOVA) and design of experiments (DOE) to assess the impact of key factors on the power output of lithium-ion LCO pouch cells. The findings demonstrate that the effect of cell-to-cell variation on power output is more pronounced than degradation and random errors of the experiments. Further analysis shows that temperature and state of charge have a significant influence on power availability (p-value <0.05), while stack pressure does not show a significant impact within the tested ranges (20–60 kPa). Notably, the load history factor approached the significance threshold with a p-value of 0.06, highlighting its potential importance in highly dynamic load profiles at increased C-rates. This research underscores the critical factors influencing battery performance and emphasizes the necessity of meticulous statistical methods in the development of accurate power estimation methods.
•Temperature and SOC are the most influential factors on power availability.•Effects of stack pressure (20–60 kPa) were negligible.•Load history can potentially influence power availability.•Cell-to-cell variations were more significant than experimental errors.
We present a method that can be used to optimize CFD analyses of ventilation performance in buildings; the method was successfully applied in other disciplines. In the method, three statistical ...methods are used - the Taguchi method, ANOVA, and Grey Relational Analysis (GRA) to achieve optimization. We then model the risk of draft and the mean age of air in a two-dimensional benchmark test case to exemplify the application of the method. We use five independent variables for modeling: temperature, air velocity, and three room dimensions, each with five levels. In short, the method is implemented as follows: At first, using an L25 orthogonal array in the Taguchi method, we decreased the total number of cases to be modeled from 3125 (55) to 25. Then, we modeled the 25 cases with CFD and used the Taguchi method and ANOVA analyses to find the best solutions for draught risk or the age of air, while with the GRA method we found the best solution for both of them considered jointly. Finally, new CFD models for the variables describing the best solutions were run to confirm their superiority within the 25 cases selected for modeling within the orthogonal L25 array. They were not compared against all possible 3125 cases because the computational time would have been prohibitive. Nevertheless, we believe, that the results are credible in identifying the best solutions. This is based on experience in other disciplines. We, therefore, propose that the presented method is considered when complex problems are analyzed related to ventilation performance requiring large computational power.
•It is confirmed that Taguchi, ANOVA and GRA methods are useful for numerical indoor ventilation studies.•It is possible to find optimum solutions for the same set of parameters even if the resulting functions are found to have different sensitivity to the parameters examined.•The data that can be obtained from the results of a large number of cases (3125) that are not feasible to solve was obtained with a low error level despite the significantly reduced number of cases (25).•Although the mathematical relationship between the investigated parameters and the resulting functions is not known, it has been shown that the optimization can be done successfully.
•Models to include market integration in electricity price forecasting are proposed.•The forecasters lead to accuracy improvements that are statistically significant.•Deep neural networks are used as ...based models of the larger modeling framework.•A forecasters that predicts prices in various markets leads to the best results.•A novel feature selection algorithm based on functional ANOVA is proposed.
Motivated by the increasing integration among electricity markets, in this paper we propose two different methods to incorporate market integration in electricity price forecasting and to improve the predictive performance. First, we propose a deep neural network that considers features from connected markets to improve the predictive accuracy in a local market. To measure the importance of these features, we propose a novel feature selection algorithm that, by using Bayesian optimization and functional analysis of variance, evaluates the effect of the features on the algorithm performance. In addition, using market integration, we propose a second model that, by simultaneously predicting prices from two markets, improves the forecasting accuracy even further. As a case study, we consider the electricity market in Belgium and the improvements in forecasting accuracy when using various French electricity features. We show that the two proposed models lead to improvements that are statistically significant. Particularly, due to market integration, the predictive accuracy is improved from 15.7% to 12.5% sMAPE (symmetric mean absolute percentage error). In addition, we show that the proposed feature selection algorithm is able to perform a correct assessment, i.e. to discard the irrelevant features.
This article focuses on the optimization of process parameters such as core area, core material and voltage for the design of power transformer. It employs Taguchi orthogonal array technique for ...designing the experiments and its analysis. Utility transformers are usually specified with the losses associated at design stage. The area of the core cross-section applied voltage, as well as the core material all has impact core loss deterioration. The impact of such variables influencing core loss is investigated by executing the model. A small proportion of core as well as the coil assembly experiments is simulated using the Taguchi approach with the orthogonal array. In this study, the core as well as the coil assembly of an 8MVA, 33/11KV, 3 Phase Transformer is modelled in ANSYS MAXWELL software. MINITAB software is used to assess the program's anticipated core loss in order to discover the optimal arrangement for three control variables.
PDF
