Statistical Performance Analysis Research Articles

Bimanual Fundamentals: Validation of a New Curriculum for Virtual Reality Training of Laparoscopic Skills.

Background. To determine face and construct validity for the new Bimanual Fundamentals curriculum for the Simendo® Virtual Reality Laparoscopy Simulator and prove its efficiency as a training and objective assessment tool for surgical resident’s advanced psychomotor skills. Methods. 49 participants were recruited: 17 medical students (novices), 15 residents (intermediates), and 17 medical specialists (experts) in the field of gynecology, general surgery, and urology in 3 tertiary medical centers in the Netherlands. All participants performed the 5 exercises of the curriculum for 3 consecutive times on the simulator. Intermediates and experts filled in a questionnaire afterward, regarding the reality of the simulator and training goals of each exercise. Statistical analysis of performance was performed between novices, intermediates, and experts. Parameters such as task time, collisions/displacements, and path length right and left were compared between groups. Additionally, a total performance score was calculated for each participant. Results. Face validity scores regarding realism and training goals were overall positive (median scores of 4 on a 5-point Likert scale). Participants felt that the curriculum was a useful addition to the previous curricula and the used simulator would fit in their residency programs. Construct validity results showed significant differences on the great majority of measured parameters between groups. The simulator is able to differentiate between performers with different levels of laparoscopic experience. Conclusions. Face and construct validity for the new Bimanual Fundamental curriculum for the Simendo virtual reality simulator could be established. The curriculum is suitable to use in resident’s training programs to improve and maintain advanced psychomotor skills.

Morphological control of Cu2ZnSn(S,Se)4 absorber films via inverted annealing for high-performance solar cells

In recent years, the method of forming Cu2ZnSn(S,Se)4 (CZTSSe) by heating precursors has been widely used, but the chalcogen vapor conditions and the placement of the sample have not been disclosed completely. Here, we demonstrate a novel inverted annealing process that enables the reaction between the chalcogen vapor and precursors to remain constant. As a result, we obtained CZTSSe absorber films with an improved morphology and a uniformly distributed elemental composition. The inverted annealed samples showed less variation in the S and Se content as well as in the statistical analysis of the device performance compared with those prepared via the original annealing process. These variations in the S and Se content were confirmed several times through X-ray diffraction, energy-dispersive X-ray, and external quantum efficiency analysis. A pn-junction analysis using a Mott-Schottky plot was also conducted. We anticipate that the inverted annealing process will become widely used as it is easily applicable and effective in improving the morphology of CZTSSe absorber films.

A Robust Design Methodology for Synchronous Reluctance Motors

The improvement of the robustness of the performance of electrical machines in presence of neglected features, such as manufacturing tolerances and deviations in material properties and parameters, is increasingly demanded. The available optimization procedures do not account for manufacturing tolerances, leaving the decision process incomplete. The article proposes a methodology for the selection of the more robust design towards manufacturing tolerances among the best candidates, detected by an optimization procedure. Statistical tools for tolerance analysis and worst-case analysis are discussed and adopted for the scope. The proposed methodology is used to select a robust design with respect to torque ripple deviations due to geometric tolerances in the laminations of a Synchronous Reluctance Motor with fluid shaped barriers. The statistical performance analysis and the worst-case analysis provide an estimation of the performance deterioration in the presence of the tolerances in the manufacturing process.

Statistical analysis of academic performance (by an example from the faculty of economics of Voronezh State University)

Importance. We live in a world in which educational processes are one of the constituent components of the modern society life. It is a well-known fact that higher education processes play an important role in achieving both individual and collective effectiveness. Therefore, they are subject to careful state and public monitoring. Objectives. Identification of statistically significant determinants of students' academic performance and in order to improve the quality of the educational process. Methods. During the research, we used parametric and nonparametric methods of data analysis and machine learning. We formed the research information base using data on educational activities, academic performance, points on the Unified State Exam, as well as generalized address data of students in the bachelor's degree in the areas of "Economics", "Management" 2013-2015 recruitment. Results. In the empirical part of the study, we tested several working hypotheses about the relationship of student academic performance with home region, gender, source of funding for educational activities, and the results of entrance tests. We also tested the hypothesis about the relationship between the completion of the educational activities of students due to academic failure and their low entrance score. We found that school-related stress has little to no impact on academic performance. Female students show better results in both areas of training. Girls who study at the expense of the federal budget demonstrate greater motivation for high academic performance compared to students at their own expense. There were no statistically significant linear relationships between the entrance scores and student performance. It would be unfair not to mention that fact that students completing their educational activities due to academic failure entered mainly with low total USE scores. Therefore it makes sense to significantly strengthen fundamental training, namely the classroom load in the disciplines of the basic part of the curriculum, develop convergent educational areas, and also strengthen interdisciplinary ties and the practice of studying the logic of cognition in order to smooth out the heterogeneity in the formation of competencies among students.

A density-based statistical analysis of graph clustering algorithm performance

Abstract We introduce graph clustering quality measures based on comparisons of global, intra- and inter-cluster densities, an accompanying statistical significance test and a step-by-step routine for clustering quality assessment. Our work is centred on the idea that well-clustered graphs will display a mean intra-cluster density that is higher than global density and mean inter-cluster density. We do not rely on any generative model for the null model graph. Our measures are shown to meet the axioms of a good clustering quality function. They have an intuitive graph-theoretic interpretation, a formal statistical interpretation and can be tested for significance. Empirical tests also show they are more responsive to graph structure, less likely to breakdown during numerical implementation and less sensitive to uncertainty in connectivity than the commonly used measures.

A modified grey wolf optimizer for improving wind plant energy production

<span>The main problem of existing wind plant nowadays is that the optimum controller of single turbine degrades the total energy production of wind farm when it is located in a large wind plant. This is owing to its greedy control policy that can not cope with turbulence effect between turbines. This paper proposes a Modified Grey Wolf Optimizer (M-GWO) to improvise the controller parameter of an array of turbines such that the total energy production of wind plant is increased. The modification employed to the original GWO is in terms of the updated mechanism. This modification is expected to improve the variation of exploration and exploitation rates while avoiding the premature convergence condition. The effectiveness of the M-GWO is applied to maximize energy production of a row of ten turbines. The model of the wind plant is derived based on the real Horns Rev wind plant in Denmark. The statistical performance analysis shows that the M-GWO provides the highest total energy production as compared to the standard GWO, Particle Swarm Optimization (PSO) and Safe Experimentation Dynamics (SED) methods.</span>

Nonlinear H∞ Measurement Feedback Control Algorithm for Quadrotor Position Tracking

This paper presents a new framework to solve the position tracking problem of a quadrotor helicopter. This algorithm consists of a cascade architecture which employs a measurement feedback nonlinear H∞ algorithm in the outer-loop to track the reference trajectory via noisy measurements, and a state-feedback nonlinear H∞ in the inner-loop to stabilize the attitude dynamics. The obtained structure for the outer-loop includes a robust controller-estimator combination which satisfies the stability and convergence of both parts. Analytical results show the asymptotic stability and output boundedness for both inner and outer loops and consequently the cascade system. The Monte-Carlo simulations are performed to numerically examine the robust performance and the region of attraction of the overall system. Different environmental disturbances and modeling uncertainties are considered in 1000 simulations to verify the robust performance and boundedness of all states. Moreover, to quantify the performance of the algorithm, two numerical indexes; Integrated Square error (ISE), and Integral of the Absolute value of the control Derivative (IADU) are suggested and utilized for statistical analysis of the state tracking performance and control effort, respectively. Finally, a Software-in-the-Loop (SIL) test is performed using PX4-FMU open source autopilot and Raspberry-Pi 3 as an auxiliary processor to show the capability of the algorithm for hardware implementation.

Surgical Workflow Analysis in Cerebral Aneurysm Coiling

Minimally‐invasive endovascular procedures have been established as fundamental and incredibly effective interventions in the treatment of aneurysms. Daily operations incorporate thousands of hours of experience sourced from the operating room and extensive literature. In particular, neurointerventional angiography requires extensive hands‐on training under the guidance of an expert interventionalist in order to learn nuanced navigational and procedural strategies. The presence of a high learning curves and inter‐center training variability creates difficulties in developing standardized training and assessment models in this field. Surgical workflow analysis has been gaining traction in recent years as a favorable technical approach to understanding surgical procedures. Through the breakdown of clinical performance into its core steps and events, workflow models allow for the visualization, statistical analysis and standardization of performance from clinically representative arrays of data points. Simulation‐based training in cerebral aneurysm diagnosis and coiling has seen great technical advancement over the last decade, with the production of affordable, tactile computer‐based simulators. Although these tools hold much potential, there is little data supporting the extent of their abilities to teach and standardize clinically‐transferable skills. Using surgical workflow analysis software, we aim to dissect the core steps and events that occur during a cerebral aneurysm coiling procedure in order to identify the average occurrence and length of significant procedural steps and events. Using this data, we aim to develop a standardized model of performance that can be applied as a guide to simulation‐based training and as a performance metric to simulation‐based clinical skills transfer. A total of three video cameras are placed around the Angio Suite focusing on significant areas of clinical importance: (1) the femoral access site and hands of the surgical team, (2) the imaging monitors, and (3) angiography pedals. Video data from 20 patients undergoing a cerebral angiography coiling procedure are collected and analyzed using Annotate. Data analysis will focus on quantitative data, such as event occurrence and length, and summative qualitative data on procedural overview. This data will be used to develop an averaged expert performance model for this procedure. The results of this study will provide important guidelines for translational simulation‐based training work that is representative of the steps performed in the Angio Suite. Workflow analysis in cerebral aneurysm coiling can provide answers to many uncertainties existing in the field, such as metrics for performance among experts, accuracy of simulation‐based training in comparison to clinical practice and potential areas of development in clinical training. Furthermore, the development of a performance model in neurointerventional angiography would create potential for automated workflow analyses and application of statistical data towards the treatment recommendations for novel and difficult cases.Support or Funding InformationCIHR‐CGSD

A statistical analysis of the financial performance of organic and conventional farms in the Czech Republic with respect to their size

Organic farming represents an environment-friendly alternative to conventional farming techniques. The aim of this paper is to assess whether there are differences in financial performance of the Czech agricultural enterprises as measured by financial analysis indicators in relation to the selected farming system (conventional vs. organic), the farm size (with respect to organic and conventional farms) and the interaction of these two factors in the period 2012–2016. A three-step approach including two-way multivariate analysis of variance (MANOVA), non-parametric two-way analysis of variance (ANOVA), specifically the Scheirer-Ray-Hare test, and post hoc Dunn’s test has been employed. The data sample comprises of 172 conventional and 136 organic farms, divided into three size categories. The effect of the selected farming system is manifested in return on sales, cost ratios, and asset turnover ratio. The farm size affects return on assets, cost ratios, liquidity and debt. The interaction of the factors has only been detected as regards return on assets.

Cotton Dyeing with Vat Dyes using Alkaline Pectinase and Iron (II) Salt

Use of sodium hydrosulfite in the dyeing of cotton with vat dyes is criticized for generation of sulfur compounds leading to air and water pollution. In this study, attempts were made to dye cotton with alkaline pectinase along with iron (II) salt as an alternative formulation. A 43 Box-Behnken design was used for statistical analysis of performance for this new reducing system and to obtain optimum parameters for cotton dyeing. The results showed that alkaline pectinase along with iron (II) salt was quite effective in developing comparable dyebath potential, dye receptivity on cotton with comparable dye strength, and colorfastness, as compared to the hydrosulfite-based reducing system. Dyebath stability in presence and absence of dye also showed superior results compared to that of the hydrosulfite system.

Computational Implementation and Asymptotic Statistical Performance Analysis of Range Frequency Autocorrelation Function for Radar High-Speed Target Detection

The range frequency autocorrelation function (RFAF) based algorithm is proposed for radar target detection and motion parameter estimation in our previous work. In the RFAF-based method, the symmetric autocorrelation function (SAF) is constructed with respect to the range frequency, and three dimensional (slow time, range frequency, and shift frequency) energy accumulation can be completed coherently. In this article, as a further study of the RFAF-based method, the fast implementation and detailed asymptotic statistical performance analyses of RFAF are studied. First, on the basis of the frequency circular convolution theorem, the fast implementation is proposed. Then, to evaluate the anti-noise performance and estimation accuracy, the output signal to noise ratio (SNR) and asymptotic mean square errors (AMSEs) of estimated parameters are derived in closed forms. Theoretical analyses and numerical simulation results reveal that the RFAF-based method outperforms several state-of-the-art algorithms in terms of anti-noise performance and parameter estimation accuracy, and its computational efficiency is high.

Real-Time Bottom Tracking Using Side Scan Sonar Data Through One-Dimensional Convolutional Neural Networks

As one of the most commonly used acoustic systems in seabed surveys, the altitude of the side scan sonar from the seafloor is always difficult to determine, especially when raw signal levels and gain information are unavailable. The inaccurate sonar altitudes would limit the applications of sonar image geocoding, target detection, and sediment classification. The sonar altitude can be obtained by using bottom tracking methods, but traditional methods often require manual thresholds or complex post-processing procedures, which cannot ensure accurate and real-time bottom tracking. In this paper, a real-time bottom tracking method of side scan data is proposed based on a one-dimensional convolution neural network. First, according to the characteristics of side scan backscatter strength sequences, positive (bottom sequences) and negative (water column and seabed sequences) samples are extracted to establish the sample sets. Second, a one-dimensional convolution neural network is carefully designed and trained by using the sample set to recognize the bottom sequences. Third, a complete processing procedure of the real-time bottom tracking method is established by traversing each side scan ping data and recognizing the bottom sequences. The auxiliary methods for improving real-time performance and sample data augmentation are also explained in detail. The proposed method is implemented on the measured side scan data from the marine area in Meizhou Bay. The trained network model achieves a 100% recognition of the initial sample set as well as 100% bottom tracking accuracy of the training survey line. The average bottom tracking accuracy of the testing survey lines excluding missed pings reaches 99.2%. By comparison with multi-beam bathymetric data and the statistical analysis of real-time performance, the experimental results prove the validity and accuracy of the proposed real-time bottom tracking method.

Deep Learning for Predicting Enhancing Lesions in Multiple Sclerosis from Noncontrast MRI.

Background Enhancing lesions on MRI scans obtained after contrast material administration are commonly thought to represent disease activity in multiple sclerosis (MS); it is desirable to develop methods that can predict enhancing lesions without the use of contrast material. Purpose To evaluate whether deep learning can predict enhancing lesions on MRI scans obtained without the use of contrast material. Materials and Methods This study involved prospective analysis of existing MRI data. A convolutional neural network was used for classification of enhancing lesions on unenhanced MRI scans. This classification was performed for each slice, and the slice scores were combined by using a fully connected network to produce participant-wise predictions. The network input consisted of 1970 multiparametric MRI scans from 1008 patients recruited from 2005 to 2009. Enhanced lesions on postcontrast T1-weighted images served as the ground truth. The network performance was assessed by using fivefold cross-validation. Statistical analysis of the network performance included calculation of lesion detection rates and areas under the receiver operating characteristic curve (AUCs). Results MRI scans from 1008 participants (mean age, 37.7 years ± 9.7; 730 women) were analyzed. At least one enhancing lesion was observed in 519 participants. The sensitivity and specificity averaged across the five test sets were 78% ± 4.3 and 73% ± 2.7, respectively, for slice-wise prediction. The corresponding participant-wise values were 72% ± 9.0 and 70% ± 6.3. The diagnostic performances (AUCs) were 0.82 ± 0.02 and 0.75 ± 0.03 for slice-wise and participant-wise enhancement prediction, respectively. Conclusion Deep learning used with conventional MRI identified enhanced lesions in multiple sclerosis from images from unenhanced multiparametric MRI with moderate to high accuracy. © RSNA, 2019.

Implementation of controlling technologies as a method to increase sustainability of the enterprise activities

We are currently facing that business environment is not stable due to globalisation processes in economics, cyclical changes and other disturbing factors. Hence, it is necessary to search such reserves that would improve efficiency of the business entity. The main objective of these reserves is to buffer negative impacts of external disturbances on financial and economic sustainability of enterprises. One of methods designed to improve the enterprise efficiency involves application of controlling technologies. In the present article the author examines results of statistical analysis of economic performance of more than 70 Russian and foreign enterprises. The author also analyses economic performance according to the author's integral indicator that shows effectiveness of primary implementation of the controlling system. On the ground of this analysis, the author determines the mean level of increase in efficiency of the enterprise's activity due to introduction of the controlling system. The analysis proves that, on average, efficiency of the economic entity's operation increases by about 17%.

Evaluation of the experimental project performance assessment mode with the application of descriptive statistical analysis method

In order to solve the problem that currently, the performance assessment mode of most experimental projects only adopts the individual assessment while neglecting teamwork, puts forward the experimental project performance assessment mode based on team communication, which takes all the students participating in the project as a team, and divides the team into several groups according to project contents and tasks. Team members complete the project tasks jointly through team communication and group and individual quantitative scoring will be carried out according to the completion of tasks. After conducting the descriptive statistical analysis of an experimental project assessment performance of 223 students from 8 classes of 4 undergraduate majors in a university through Stata15.1, the results show that the results of the project assessment performance data all show a normal distribution by major, class, group and individual, the assessment results are ideal, and the assessment mode is feasible. The innovation is to use the Stata, and use the descriptive statistical analysis method to make an in-depth and careful statistical analysis of the experimental project performance assessment mode based on the team communication proposed. And the normality test is carried out to make an objective evaluation to the assessment results.

Impact of Sunshine Duration and Clearness Index on Diffuse Solar Radiation Estimation in Mountainous Climate

In this paper, measured data of solar radiation was applied to develop forty-three (43) empirical models for estimation of monthly average diffuse solar radiation using clearness index, sunshine duration and a combination of them as predictors. The data covered a period of two years from May 2015 to April 2017 and was measured at Mehran University of Engineering and Technology, Hyderabad, Pakistan. Through a comprehensive statistical performance analysis, 43 dimensional models developed were tested for constructing the most accurate regression model to predict the monthly mean daily diffuse solar radiation in Hyderabad, Pakistan. On the whole, the model 42 – a hybrid of sunshine duration and clearness index predictors of diffuse fraction outperformed the remaining models proposed in this study. The best model (model 42) was then compared with 5 models and 5 measured data of diffuse solar radiation available in the literature and the NASA database by applying statistical indicators such as MBE, MPE, RMSE, RRMSE, R 2 and GPI. Through the analysis, the hybrid of sunshine duration and clearness index predictors of diffuse fraction model (model 42) was selected as the most appropriate model. The study concluded that the proposed hybrid model can serve as a baseline for the design of photovoltaic systems and estimate the monthly mean daily diffuse solar radiation on the horizontal surface for Hyderabad, Pakistan and other locations with similar local climate conditions. Citation: Nwokolo, S.C. and Otse, C.Q. (2019). Impact of Sunshine Duration and Clearness Index on Diffuse Solar Radiation Estimation in Mountainous Climate. Trends in Renewable Energy, 5, 307-332. DOI: 10.17737/tre.2019.5.3.00107

Statistical analysis of chain company employee performance based on SOM neural network and fuzzy model

Statistical analysis of chain company employee performance based on SOM neural network and fuzzy model

Prior design for tomographic volume fraction estimation in pneumatic conveying systems from capacitive data

Pneumatic conveying systems have become a standard technique for the transport of bulk materials such as powdery or granulates. The spatial dependence of the material density and the stream velocity in such transport systems require a volumetric measurement principle for flow measurement. In this paper we analyse the capability to estimate the volume fraction from capacitive sensing data using electrical capacitance tomography (ECT). In particular, we investigate the capability of back-projection type imaging algorithms. The ill-posed nature of the imaging problem of ECT require the incorporation of prior knowledge in the design of the estimator. We analyse the different flow profiles in pneumatic conveying in order to generate specific sample-based prior information to improve the estimation performance and robustness. We discuss the construction of different linear image reconstruction algorithms and present a framework, which allows a detailed statistical analysis of the estimator performance. Simulation studies show the estimation behaviour of different algorithms with respect to the incorporated prior information. We demonstrate, that the incorporation of specific prior knowledge leads to an improved estimator behaviour; for example, reduced variance and unbiased estimates. We implemented laboratory experiments in order to analyse the presented approach for the application in real pneumatic conveying processes. We demonstrate the improved robust estimation behaviour by means of comparative reconstruction results obtained with different algorithms and priors. Furthermore, the uncertainty of the estimated volume fraction is analysed in steady state conveying processes. Hereby, it is demonstrated, that appropriate prior information improves the estimation performance also for measurements coming from real pneumatic conveying processes, making ECT a suitable tool for the volume fraction estimation in such transport systems.

Impact and economic assessment on solar PV mirroring system – A feasibility report

This paper presents a review of existing PV augmented mirroring schemes along with an enhanced energy extraction scheme for an existing solar PV system. Here, an array of plane mirrors is placed in the inter row spacing of the PV arrays at a suitable angle (θms) to enhance the sunlight harvesting on the panel. The theory of solar PV mirroring (SPVm) system is presented considering the variation of sun elevation angle (θe) at the solar noon for all day of a year. The performance of the proposed system is monitored and compared with respect to a conventional SPV system of the same capacity for a duration of one year from January 2018 to December 2018 along with the weather conditions at the test site (Tiruchirappalli, India, 10.7542° N, 78.7862° E). The condition for optimal mirror angle is obtained based on the global irradiation impinged on the surface of the solar PV module at the test site. Detailed statistical performance analysis on annual energy yield is calculated for the test period. Also, the limitation of the proposed system due to increase in PV cell temperature is discussed. It is observed from the experimental result that an energy extraction of around 30% higher than the conventional SPV system is obtained. Finally, a feasibility analysis on the impact and economic aspects on an industrial PV plant of 5 MW capacity in the same location is carried out. The system degradation inclusive of PV cell temperature and mirror degradation is suitably considered in the feasibility study. It is found that an expected IRR for SPV and SPVm system is around 11.99% and 13.45%, respectively.

Scavenging carbon deposition on alumina supported cobalt catalyst during renewable hydrogen-rich syngas production by methane dry reforming using artificial intelligence modeling technique

Methane dry reforming is a thermo-catalytical process that utilizes two principal components of greenhouse gases for the production of hydrogen-rich syngas . One major shortcoming of the methane dry reforming as a potential route for renewable hydrogen-rich syngas production is catalyst deactivation through carbon deposition . In this study, an artificial neural network approach was employed for predictive modeling of the deactivation of alumina supported cobalt catalyst used to catalyze methane dry reforming reaction. The effect of methane/carbon dioxide (CH 4 /CO 2 ) ratio, reaction temperature and nitrogen (N 2 ) flowrate on the carbon deposition were investigated using full factorial experimental design . Two artificial neural network modeling techniques namely multilayer perceptron neural network (MLPNN) and radial basis function (RBFNN) were employed for the prediction of carbon deposition per gram catalyst using data obtained from 170 experimental runs. The hidden neurons were optimized to obtain 16 and 20 units respectively for the MLPNN and the RBFNN resulting in the network architecture of 3, 16, 1 and 3, 20, 1, respectively. The statistical analysis of the network performance resulted in mean standard error (MSE) values of 0.048 and 0.00285 for training the MLPNN algorithm above and below stoichiometric conditions with corresponding R 2 values of 0.945 and 0.965. While MSE values of 0.0073 and 0.00015 were obtained for the training of the RBFNN algorithm above and below stoichiometric conditions with R 2 of 0.987 for both cases. Base on the statistical analysis the RBFNN model was adjudicated as a better predictor of the carbon deposition during the hydrogen-rich syngas production than the MLPNN model. The three input parameters were found to have varying levels of importance in the prediction of the carbon deposition. The reaction temperature was observed to be the most important parameters that influence the prediction of carbon deposition above stochiometric while CH 4 /CO 2 was the most important parameters that influence the prediction of carbon deposition below stoichiometric conditions. • MLPNN and RBFNN models were developed and employed to predict carbon deposition on Co/αAl 2 O 3 . • Optimized hidden neurons with 16 and 20 units respectively were obtained for MLPNN and RBFNN. • Levenberg-Marquardt algorithm was employed to train the MLPNN and RBFNN. • Both MLPNN and RBFNN accurately predict carbon deposition with minimal residuals. • RBFNN with MSE of 0.00015 and R 2 of 0.985 predicts better than MLPNN with MSE of 0.00285 and R 2 of 0.965