Calculation Of Variance Research Articles

Sensitivity-based dynamic performance assessment for model predictive control with Gaussian noise

Economic model predictive control and tracking model predictive control are two popular advanced process control strategies used in various of fields. Nevertheless, for a given process, which controller should be chosen to achieve better performance is uncertain when noise exists. To this end, a sensitivity-based performance assessment approach is proposed to pre-evaluate the dynamic economic and tracking performance of them and guide the controller selection in this work. First, their controller gains around the optimal steady state are evaluated using the sensitivities of corresponding constrained dynamic programming problems. Second, the controller gains are substituted into the control loop to derive the propagation of process and measurement noise. Subsequently, the Taylor expansion is introduced to simplify the calculation of variance and mean of each variable. Finally, the tracking and economic performance surfaces are plotted and the performance indices are precisely calculated through integrating the objective functions and the probability density functions. Moreover, boundary moving (i.e., back off) and target moving can be pre-configured to guarantee the stability of controlled processes based on the proposed approach. Extensive simulations under different cases prove that the proposed approach can provide useful guidance on performance assessment and controller design.

Increasing transparency in machine learning through bootstrap simulation and shapely additive explanations.

Machine learning methods are widely used within the medical field. However, the reliability and efficacy of these models is difficult to assess, making it difficult for researchers to identify which machine-learning model to apply to their dataset. We assessed whether variance calculations of model metrics (e.g., AUROC, Sensitivity, Specificity) through bootstrap simulation and SHapely Additive exPlanations (SHAP) could increase model transparency and improve model selection. Data from the England National Health Services Heart Disease Prediction Cohort was used. After comparison of model metrics for XGBoost, Random Forest, Artificial Neural Network, and Adaptive Boosting, XGBoost was used as the machine-learning model of choice in this study. Boost-strap simulation (N = 10,000) was used to empirically derive the distribution of model metrics and covariate Gain statistics. SHapely Additive exPlanations (SHAP) to provide explanations to machine-learning output and simulation to evaluate the variance of model accuracy metrics. For the XGBoost modeling method, we observed (through 10,000 completed simulations) that the AUROC ranged from 0.771 to 0.947, a difference of 0.176, the balanced accuracy ranged from 0.688 to 0.894, a 0.205 difference, the sensitivity ranged from 0.632 to 0.939, a 0.307 difference, and the specificity ranged from 0.595 to 0.944, a 0.394 difference. Among 10,000 simulations completed, we observed that the gain for Angina ranged from 0.225 to 0.456, a difference of 0.231, for Cholesterol ranged from 0.148 to 0.326, a difference of 0.178, for maximum heart rate (MaxHR) ranged from 0.081 to 0.200, a range of 0.119, and for Age ranged from 0.059 to 0.157, difference of 0.098. Use of simulations to empirically evaluate the variability of model metrics and explanatory algorithms to observe if covariates match the literature are necessary for increased transparency, reliability, and utility of machine learning methods. These variance statistics, combined with model accuracy statistics can help researchers identify the best model for a given dataset.

Influence of Kirschner-Wire Insertion Angle on Construct Biomechanics following Tibial Tuberosity Osteotomy Fixation in Dogs.

The aim of this study was to evaluate the effect of Kirschner wire insertion angle on the biomechanical characteristics following tibial tuberosity osteotomy fixation in dogs. Twelve pairs of cadaveric tibia were harvested and randomly assigned to two treatment groups. Kirschner wires were placed either transversely (0 degrees) or placed caudodistally (30 degrees) with respect to the tibial tuberosity osteotomy. Each limb acted as its own respective control. Radiographic analysis allowed for the calculation of Kirschner wire insertion angle variance. Constructs were tested to monotonic failure while evaluating yield, peak, and failure forces, construct stiffness, and failure mode. Kirschner wire insertion angles were 1.1 ± 2.2 degrees and 30.5 ± 2.3 degrees, respectively, for 0-degree and 30-degree groups (p <0.0001). Yield (p = 0.0095), peak (p <0.024) and failure loads (p <0.030) were all significantly greater for Kirschner wires inserted at an angle of 0 degrees compared with 30 degrees. Construct stiffness did not differ regardless of insertion angle (p = 0.068). Failure mode did not differ (p = 0.87) with tibial tuberosity avulsion and Kirschner wire pull-out seen in the majority of constructs (67%). Kirschner wires placed transversely (0 degrees) for tibial tuberosity osteotomy fixation were biomechanically superior, increasing yield, peak, and failure forces by 1.6 times, 1.3 times, and 1.4 times, respectively, to those placed in a caudodistal (30 degrees) orientation. Kirschner wire insertion angle is an important consideration following tibial tuberosity osteotomy in dogs, with Kirschner wires placed at 0 degrees conferring increased resistance of the repair to construct deformation.

Peningkatan Kadar Protein Terlarut pada Larutan Tepung Jagung Menggunakan Saccharomyces cerevisiae

Poultry livestock are generally given feed in the form of corn kernels where the corn kernels contain high carbohydrates but have deficiencies of several amino acids. Amino acid deficiency can be overcome by increasing the protein content in the corn kernels. One way to increase the protein content in corn kernels is to carry out a fermentation process in corn flour solution. Therefore, this study aims to determine the effect of corn starch solution to increase dissolved protein levels in Saccharomyces cerevisiae biomass through a fermentation process. This study used a completely randomized design consisting of 8 treatments and 3 replications. The treatment volumes of corn flour solution were 0 mL, 5 mL, 10 mL, 15 mL, 20 mL, 25 mL, 30 mL and 35 mL. Data analysis used the calculation of variance and the BJND test. The results showed that the highest average dissolved protein content and total cell biomass of S. cerevisiae in the 15 ml treatment were 13.52%/g and 15.63 x 107 CFU/mL, the lowest average was in the 35 ml corn flour solution treatment, namely 0. 90%/gr and 3.01 x 107 CFU/mL. Based on the research, it can be concluded that corn flour solution can increase the dissolved protein content in S. cerevisiae biomass up to volume 15 mL/2 mL fermenter through the fermentation process. Based on the results of this study, it can be used as a basis for considering the use of S. cerevisiae fermenters to increase the nutritional value of corn seed-based animal feed.

Performance Comparison of Crossflow Turbine Configuration Upper Blade Convex and Curvature by Computational Method

A pico-hydro-type crossflow turbine (CFT) with an off-grid system configuration is a suitable option to increase the electrification ratio in remote or rural areas because it has a simple shape and can be applied in run-of-river conditions. Yet, a comprehensive study is necessary for the CFT to be applied to run-of-river conditions (low head and extreme fluctuation discharge), since this is categorized as an impulse turbine. One solution to optimize the CFT’s performance in this context is to increase the lift force. Hence, this study investigated the effect of the upper blade of the CFT with convex and curved configurations using the computational fluid dynamics (CFD) method. The CFD transient approach uses a moving mesh feature, and the solver is pressure-based in low-head conditions (5 m pressure). The CFD results and analysis of variance (ANOVA) calculation results from this study reveal that the upper CFT affects the performance of the turbine. The relationship of the CFT performance with the rotation and specific speed is parabolic. The express empirical law relation for performance to rotation is a four-order polynomial, and for performance to a specific speed, a three-order polynomial. Based on empirical laws, a CFT with a convex blade is recommended for conditions with low head and extreme fluctuation discharge since it has a wider range of specific speeds than a curved blade, propeller, or Kaplan, Pelton, or Francis turbine. Doi: 10.28991/CEJ-2023-09-01-012 Full Text: PDF

Problems experienced by nursing students in clinical training during the COVID-19 pandemic: a descriptive study

Objective: In this study, the attitudes of nursing students towards Covid-19 disease and the problems they experienced in the clinical learning environment during the pandemic were examined. Material and Methods: The research was a descriptive study. The population of the research is 2nd-3rd-4th-year students (n=264) studying at the nursing school who clinical placement during the pandemic. Data was collected online at the end of the clinical placement period (August 1-30,2021) using the "Student Information Form", "Questionnaire Form for Identifying Problems in Clinical Practice" and "Multidimensional Covid-19 Scale". Number and percentage calculation, t-test and one-way analysis of variance were used in data analysis. Results: It was determined that the students got high scores from the Multidimensional Covid-19 Scale (Mean=79.44±16.87). Mean scores were high for students who had lost their families due to COVID-19; who were worried about their patients having COVID-19; and who were directed to the care of COVID-infected patients without adequate protective equipment. The difference was statistically significant (p&lt;0.05). The most common problems students experienced were related to fear of making mistakes and harming patients (61.7%), fear of being infected with Covid-19 (61.7%), fear that patient has Covid-19 (60.6%), and feelings of inadequacy about entering clinical practice (58.7%). Conclusions: To prepare nursing students for emergencies related to the pandemic and manage similar future situations, nursing schools and hospitals should collaborate to develop strategies and coping mechanisms.

Efficient machine learning model to predict fineness, in a vertical raw meal of Morocco cement plant

Soft sensor enables computing parameters that can be physically impossible to measure. This work aims to develop a soft sensor for raw meal fineness in a vertical roller mill of a cement plant. In previous research, some key indicators of the process were ignored. It is well known that vertical raw meal is feeded with heterogenous material that impacts raw meal fineness, and therefore, can deteriorates prediction accuracy of the soft sensor. To deal with this constraint, one-year history of industrial data was exploited and processed by a robust multivariate outlier identification method, followed by process expert outlier verification. Then a particular attention was given to process and material quality parameters using a statistical method to test parameters significance on fineness based on data variance calculation. Eight process and four quality parameters were retained to develop robust nonlinear model using artificial neural network: Bayesian regularization and Levenberg Marquart, then compared with Support vector regression with different acquisition function and kernels to tune and select optimum model hyperparameters. The best model performance was obtained with the Bayesian regularization algorithm.

Validation of the Musical Identity Measure: Exploring musical identity as a variable across multiple types of musicians

This article introduces the validation of a Musical Identity Measure (MIM), developed to support individuals’ self-conceptions in relation to their musical activities (e.g., performance, composition, music technology). Initial model validation was carried out using a principal axis factor analysis with a diverse and international sample of 336 musicians. The factor analysis revealed a four-factor measure: Musical Calling, Musical Self-Efficacy, Emotional Attachment, and Growth Mindset. Confirmatory factor analysis with the 25-item measure suggested that the model fit would be improved with the removal of three items, resulting in the same four-factor model with 22 items. Further validation with a different dataset confirmed MIM as a strong fit as a bifactor model. Measurement invariance tests confirmed that the bifactor structure was the same for male and female participants; individual measurement invariance in relation to age could not be fully examined due to variance in group sizes. Subsequent analysis of variance (ANOVA) calculations suggested gender differences in musical self-efficacy and highlighted possible changes in MIM factors across the lifespan. MIM has the potential to provide individuals with insights into their motivations to engage with musical activities, to help identify areas requiring additional support or guidance, and to support future-oriented decision making. The measure may also support educators and researchers wishing to understand and support the processes of musical development and skill acquisition.

Taxation as a tool of implementation of the EU Green Deal in Ukraine

AbstractThe European Green Deal, a new economic growth strategy, involves changing the model of development of the European continent in the direction of better harmony with the environment. Tax policy can be a valuable tool for state regulation of the green transition. The article aims to answer the question, is the current state of environmental taxation in Ukraine in line with EU best practices? Could we suggest ways to improve domestic tax tools through harmonization with the EU Green Deal? To assess the level of harmonization of environmental taxation in Ukraine and the EU, the sigma convergence method was used for calculation of variance and the assessment of convergence (divergence) of indicators of tax revenues from environmental taxation. The primary areas regulated by environmental taxation are outlined. This step revealed that in Ukraine, the transport tax and energy taxation do not contain an environmental component, and land fees do not use their environmental potential in both Ukraine and the EU. An analysis of the dynamics of environmental payments in the EU and Ukraine was made in terms of convergence (divergence) of indicators. On the basis of the European experience of ecological taxation, new elements to the strategy of reforming ecological taxation in Ukraine were proposed. The reforming of environmental taxation could increase the effectiveness of the incentive function of taxes, simplify and improve the quality of tax administration, promote the formation of a transparent system of state control, and strengthen accountability for corruption and negligence in the public sector.

Geometrically interpretable Variance Hyper Rectangle learning for pattern classification

Many current intrinsically interpretable machine learning models can only handle the data that are linear, low-dimensional, and relatively independent attributes and often with discrete attribute values, while the models that are capable of handling high-dimensional nonlinear data, like deep learning, have very poor interpretability. Based on the geometric characteristics, a new idea of accurately wrapping the data region with minimum-volume geometry is proposed for pattern classification. The Variance Hyper Rectangle (VHR) model presented in this paper is a realization of the idea. The VHR model uses the minimum-volume hyper rectangles, obtained through projection variance calculation, to wrap the regions occupied by a category of data, hence it has strong and clear geometric interpretability. In addition, the VHR model is well suited for large data volume, as it approaches the linear complexity in both time and space. Extensive qualitative and quantitative experiments are performed on seven real-world data sets, demonstrating that VHR outperforms the state-of-the-art interpretable methods while running quickly.

Circular statistics vector for improving coherent plane wave compounding image in Fourier domain

In this work, a circular statistics vector (CSV) weighting Fourier domain (FD) beamforming for ultrasound plane-wave images was proposed to achieve better image quality with a high frame rate. Firstly, the cosine and sine components of the instantaneous phase are extracted from undelayed RF signals. Secondly, the FD beamformed cosine and sine components are used to establish the CSV. Finally, the FD beamformed amplitude image is weighted by the CSV. The resolution, contrast, and computation complexity were used to assess the performance of the proposed method. The results revealed that FD_CSV could significantly reduce the computational load compared to the conventional DAS_CSV on the equal improvement of image quality. Besides, compared to coherence factor (CF), phase coherence factor (PCF), etc., based on variance calculation, the CSV based on mean resultant vector calculation can effectively preserve the speckle due to the more tolerant to phase errors. The proposed FD_CSV weighting method has successfully conducted high image quality and low computational load.

Effects of deuteron optical model potentials on the production cross-section calculations of 66-68Ga from natZn and natGe

Since it was reported that several radioisotopes might be advantageous for different medical uses in diagnostics and therapeutics, they have gained significance. Theoretical and experimental researches on such radioisotopes have contributed to the literature on a wide variety of topics, including production route research, getting activation and yield values, optimum production method analysis, clinical effects and production cross-section calculations. This study was motivated by the studies described in this domain as well as those published in the literature. The primary purpose of this study is to investigate the effects of deuteron optical model potentials in various reactions where [Formula: see text] radioisotopes, which are known to be medically important, have been generated with deuteron induced particles on [Formula: see text] and [Formula: see text] targets. The experimental data from the literature and the calculation results generated using the five deuteron optical model potentials accessible in the TALYS code (version 1.95) were compared in line with the objective of this study. The experimental results in the literature and the data obtained as an outcome of the computations are graphically illustrated in order to comprehend these comparisons. Furthermore, to perform a numerical assessment, mean weighted deviation and relative variance calculations were also performed.

Two-Stage Transit Signal Priority Control Method to Improve Reliability of Bus Operation Considering Stochastic Process

When bus priority control is implemented, if there is a bus station between upstream detector and downstream signalized intersection, it will lead to the unpredictability of bus dwell time caused by the stochastic number of passengers waiting at the station. This uncertainty means that the bus’s arrival time at the intersection cannot be predicted quite accurately. Therefore, the signal priority control strategy made in advance may fail, which brings about a negative impact on the operation for both buses and cars at the intersection. To solve this problem, this paper proposes a two-stage transit signal priority (TTSP) control method. The first stage is robust optimal signal control (ROC) and aims to minimize bus delay expectation and variance. Bus delay expectation and variance calculation models considering the uncertainty of bus dwell time and the uncertainty of buses in line ahead in an exclusive bus lane are proposed based on mathematical statistics theory to formulate the stochastic process of bus operation. However, extreme situations cannot be avoided by the first-stage control completely. To make up for the deficiency of ROC, this paper adds the second-level real-time priority control, which is enabled in the case of the first-stage control failure to ensure the priority of public transit. The simulation test results show that the proposed TTSP method preforms better in reducing bus delay expectation, from 9.2 s to 3.2 s, reaching 65.2%. In addition, bus delay standard deviation decreases from 8.3 s to 3.9 s by 53.1%, improving the reliability of bus operation.

Risiko Pendapatan Usahatani Tumpang Sari Cabai Rawit dan Tembakau di Subak Gede Sukawati, Kabupaten Gianyar

Income Risks of Capsicum Frutescens L. and Nicotiana Tabacum Farming in Subak Gede Sukawati, Gianyar Regency. Land size, planting distance, capital, maintenance, and weather affect production yields and farmers' incomes. Therefore, farmers must be able to take the right decisions to minimize farming risks. This study aims to determine the risk of production, price, and income of capsicum frutescens L and nicotiana tabacum intercropping farming in Subak Gede Sukawati, Gianyar Regency. Data were analyzed using the calculation of variance, standard deviation, coefficient of variation, and descriptively. Production risk in capsicum frutescens L and nicotiana tabacum intercropping shows the lowest risk with a coefficient of variation of 0.14 when compared to capsicum frutescens L and nicotiana tabacum monoculture. The risk of capsicum frutescens L production is higher (0.34) than nicotiana tabacum (0.30). The price risk in nicotiana tabacum farming, which is 0.12, indicates a higher price risk than the risk in capsicum frutescens L farming (0.02). As for income risk, the lowest risk occurs in capsicum frutescens L and nicotiana tabacum intercropping farming, which is 0.13. This risk is lower when compared to the monoculture of capsicum frutescens L (0.38) and nicotiana tabacum (0.30). The selection of cropping patterns in Subak Gede Sukawati, among others, is based on habits carried out for generations, product selling prices, maximizing income, and production costs.

A 3D Lagrangian stochastic particle model for the concentration variance dispersion

A new scheme for the concentration variance calculation is assessed using field experiment data. The scheme is introduced in a Lagrangian stochastic particle model. The model provides run-time mean concentrations and concentrations’ variance 3D fields; thus, it does not need any off-line post-processing. The model is tested against the FFT-07 field experiment which involves a series of tracer releases. It is a short-range (500 m) highly instrumented experiment. In this work, measurement of tracer concentrations, emitted from a ground level point source is used to assess the ability of the new model in predicting the mean concentration, concentration variance, and the concentration fluctuation intensity at the ground level with a high spatial resolution. The results of the intercomparison are shown and discussed in terms of statistical plots and indices.

MULTI-SPECTRAL EDGE DETECTION FOR ENHANCED EXTRACTION AND CLASSIFICATION OF HOMOGENEOUS REGIONS IN REMOTELY SENSED IMAGES

Abstract. Mediterranean environments are characterized by high spatial and temporal heterogeneity due to their climatological, lithological, soil and vegetation geo-diversity and their high population density which cause growing land-use transformations at the rural-urban fringe. Remote sensing mapping and monitoring land cover in these environments under such conditions is a challenging task. Instead of the common per pixel approach we suggest combining application of an object-oriented classification based on image objects separation through edge detection with unsupervised classification. The main elements of our methodology are: (1) separating image areas into vegetation/ non-vegetation regions utilizing NDVI threshold; (2) calculation of the spatial variance at different bands; (3) image objects extraction through enhancement of the differences between edge pixels and regions of homogeneity; (4) per-object classification for the homogenous areas; (5) overlaying large unclassified image areas by the results of ISODATA (Iterative Self-Organizing Data Analysis) unsupervised classification. Our methodology was applied on multi-spectral images acquired by the VENμS remote sensing system. The study area consists of a typical rural area in semi-arid climate regions undergoing increasing urbanization. Six test areas were selected representing different spatial combinations of natural/ planted forests, agriculture and built-up land-use/ land cover types. While bare fields were poorly classified, areas of low vegetation cover were classified with producer/user accuracies below 60%, built-up areas and roads, cultivated areas, shrublands and bata (dwarf-shrubs) and rocky areas gained good producer/ user classification accuracies.

Investigation of level density and Gama strength function for photoneutron reaction in medical linacs in Beamline

Photoneutron reaction cross-sections of 197Au, 187Re, 186W, 181Ta, 94,95,96,97,98,100Mo isotopes were calculated through the TALYS 1.95 nuclear reaction code. The energy range of the incident photon chosen as 7 MeV–30 MeV corresponded to the range of the giant dipole resonance region, which is also an applicable energy range in radiotherapy for many commercial medical linear accelerators. Calculations were performed using three phenomenological level density models available in code, namely the Constant Temperature Fermi Gas Model, the Back-shifted Fermi Gas Model, and the Generalized Superfluid Model. The most convenient level density model for each reaction was chosen using relative variance calculations. The cross-section calculations were repeated using gamma strength function models, Kopecky-Uhl generalized Lorentzian model, Brink-Axel Lorentzian model, and Goriely's hybrid model and the best level density model was kept constant. The calculated data and the experimental data from the international Experimental Nuclear Reaction Data Library were analysed and compared graphically.

Characterizing warm atmospheric boundary layer over land by combining Raman and Doppler lidar measurements.

PBL plays a critical role in the atmosphere by transferring heat, moisture, and momentum. The warm PBL has a distinct diurnal cycle including daytime convective mixing layer (ML) and nighttime residual layer developments. Thus, for PBL characterization and process study, simultaneous determinations of PBL height (PBLH) and ML height (MLH) are necessary. Here, new approaches are developed to provide reliable PBLH and MLH to characterize warm PBL evolution. The approaches use Raman lidar (RL) water vapor mixing ratio (WVMR) and Doppler lidar (DL) vertical velocity measurements at the Southern Great Plains (SGP) atmospheric observatory, which was established by the Atmospheric Radiation Measurement (ARM) user facility. Compared with widely used lidar aerosol measurements for PBLH, WVMR is a better trace for PBL vertical mixing. For PBLH, the approach classifies PBL water vapor structures into a few general patterns, then uses a slope method and dynamic threshold method to determine PBLH. For MLH, wavelet analysis is used to re-construct 2-D variance from DL vertical wind velocity measurements according to the turbulence eddy size to minimize the impacts of gravity wave and eddy size on variance calculations; then, a dynamic threshold method is used to determine MLH. Remotely-sensed PBLHs and MLHs are compared with radiosonde measurements based on the Richardson number method. Good agreements between them confirm that the proposed new algorithms are reliable for PBLH and MLH characterization. The algorithms are applied to warm seasons' RL and ML measurements at the SGP site for five years to study warm season PBL structure and processes. The weekly composited diurnal evolutions of PBLHs and MLHs in warm climate were provided to illustrate diurnal and seasonal PBL evolutions. This reliable PBLH and MLH dataset will be valuable for PBL process study, model evolution, and PBL parameterization improvement.

Large-scale behavior of energy spectra of the quantum random antiferromagnetic Ising chain with mixed transverse and longitudinal fields

In recent years it became clear that the metallic regime of systems that exhibit a many body localization (MBL) behavior show properties which are quite different than the vanilla metallic region of the single particle Anderson regime. Here we show that the large scale energy spectrum of a canonical microscopical model featuring MBL, displays a non-universal behavior at intermediate scales, which is distinct from the deviation from universality seen in the single particle Anderson regime. The crucial step in revealing this behavior is a global unfolding of the spectrum performed using the singular value decomposition (SVD) which takes into account the sample to sample fluctuations of the spectra. The spectrum properties may be observed directly in the singular value amplitudes via the scree plot, or by using the SVD to unfold the spectra and then perform a number of states variance calculation. Both methods reveal an intermediate scale of energies which follow super Posissonian statistics.

Analysis of Driving Control Characteristics in Typical Road Types

Vehicle control requirements for longitudinal and lateral driver control are varied in different road geometries; this makes it irrational and superfluous to represent driving control characteristics with repetitive indices. To address this problem, the present study used multiple cross-analysis methods of vehicle running state parameters from experienced drivers in order to deeply study driving control characteristics in different road geometries. Six common road geometries with different driving control emphases were selected as typical road types and twenty-five experienced drivers were asked to perform an actual driving test. Taking the indices in the long straight road as the control variable, the indices in other roads were compared with it and judged according to the three methods: the overall distribution by box plots, significant difference test by analysis of variance (ANOVA) and relative distance calculation by technique for order preference by similarity to an ideal solution (TOPSIS). Moreover, the weight of the driving control characteristic index was calculated through the entropy weight method to reflect its importance. In this paper, the relationships between road geometry and driving control characteristics explicate the influence mechanism and interaction of road geometry on driving behavior, and the indicators that can reflect the control characteristics in different road types are obtained.