Parametric Methods Research Articles

Evaluation of undergraduate academic programs through data envelopment analysis and time-to-degree estimates at Spanish public universities

This article proposes nonparametric and parametric methods for conducting meaningful evaluations of academic programs. In particular, it proposes the use of data envelopment analysis (DEA) along with regression analysis for the evaluation of Spanish public universities' undergraduate curricula. Unlike the radial DEA models, which assume an equiproportional expansion of all outputs to achieve efficiency, the current study also relies on the use of non-radial DEA models for assessing the efficiency of Spanish public universities. A non-radial DEA measure (the so-called Russell measure) allows for unlike proportional augmentations in each positive output. In this study, non-radial DEA measures allow us to identify different levels of inefficiency for each output considered (social sciences and non-social sciences degrees). Specifically, the results show that the inefficiency in the production of scientific and technical degrees is greater than that in the production of humanities and social sciences degrees, although there are differences among institutions. Bachelor's degree production time is also estimated, and a duration analysis explains the time to degree. A worrying result is the "excessive" time it takes for a university to produce four-year undergraduate degree programs. The mean graduation time is 5.7 years. The Cox proportional hazards regression shows that a shorter time to graduation is associated with higher teaching quality as well as a higher faculty-to-student ratio. Parametric survival analysis using a lognormal distribution with gamma frailty also verifies these latter results.

Robotic-Assisted Ankle Rehabilitation Utilizing Electrical Stimulation and Virtual Reality Training Paradigms

This study investigates the ankle rehabilitation systems in great detail, including technical features, clinical considerations, patient-related factors, and economic factors. Using a parameterized method, different parameters were tested to find out how well, efficiently, and easily these systems could be used. It was looked at how technological features like robotic configuration, sensory feedback, and control methods can be used to make rehabilitation more personalized. To make sure the best results for patients, clinical considerations focused on practices based on evidence, safety features, and integration with clinical workflows. To look at the human-centered parts of ankle rehabilitation, things like user experience, adherence, and result measures were looked at. To find out if putting ankle rehabilitation systems into healthcare situations would be financially viable, economic factors like cost-effectiveness, reimbursement, and return on investment were looked at. Numbers were added to give quantitative information about each parameter, which made it easier to do a thorough review of ankle rehabilitation systems. Overall, this study gives important information to doctors, hospital managers, and others involved in improving the outcomes of ankle-related patients by choosing the best rehabilitation programs and making sure they are carried out properly.

The XXL survey

We model the evolution of active galactic nuclei (AGN) by constructing their radio LFs. We used a set of surveys of varying area and depth, namely, the deep COSMOS survey of 1916 AGN sources; the wide, shallow 3CRR, 7C, and 6CE surveys, together containing 356 AGN; and the intermediate XXL-North and South fields consisting of 899 and 1484 sources, respectively. We also used the CENSORS, BRL, Wall & Peacock, and Config surveys, respectively consisting of 150, 178, 233, and 230 sources. Together, these surveys account for 5446 AGN sources and constrained the LFs at high redshift and over a wide range of luminosities (up to z ≈ 3 and log(L/W Hz−1)∈[22, 29]). We concentrated on parametric methods within the Bayesian framework, which allowed us to perform model selection between a set of different models. By comparing the marginalised likelihoods and both the Akaike information criterion and the Bayesian information criterion, we show that the luminosity-dependent density evolution (LDDE) model fits the data best, with evidence ratios varying from “strong” (> 10) to “decisive” (> 100), according to the Jeffreys’ interpretation. The best-fitting model gives insight into the physical picture of AGN evolution, where AGN evolve differently as a function of their radio luminosity. We determined the number density, luminosity density, and kinetic luminosity density as a function of redshift, and we observed a flattening of these functions at higher redshifts, which is not present in simpler models. We explain these trends by our use of the LDDE model. Finally, we divided our sample into subsets according to the stellar mass of the host galaxies in order to investigate a possible bimodality in evolution. We found a difference in LF shape and evolution between these subsets. All together, these findings point to a physical picture where the evolution and density of AGN cannot be explained well by simple models but require more complex models either via AGN sub-populations, where the total AGN sample is divided into sub-samples according to various properties, such as optical properties and stellar mass, or via luminosity-dependent functions.

The Effect of Spousal Support on Postpartum Depression and Quality of Life

Objective: The study was conducted to determine the effect of spousal support on postpartum depression and quality of life.
 Methods: The descriptive and correlational study was conducted with 201 mothers between the 4th and 12th week postpartum in a Lokman Hekim Hospital of Ankara. The data were collected with the Personal Information Form, the Spouse Support Scale, the Edinburg Postpartum Depression Scale, and the World Health Organization Quality of Life Scale-Short Form. The data were evaluated using the SPSS 22.0 program, parametric methods, correlation and regression analyses and descriptive statistics. 
 Results: The mean scores of the participants were 66.23±18.19 on the Spouse Support Scale, 6.14±7.90 on the Edinburg Postpartum Depression Scale and 15.09±3.72 on the World Health Organization Quality of Life Scale. The postpartum depression rate was 25.4%. There was a negative and high correlation between the Spouse Support Scale and the Edinburgh Postpartum Depression Scale (r=-.84, p

Design and implementation of origami robot ROS-based SLAM and autonomous navigation.

In this study an innovative parameterized water-bomb wheel modeling method based on recursive solving are introduced, significantly reducing the modeling workload compared to traditional methods. A multi-link supporting structure is designed upon the foundation of the water-bomb wheel model. The effectiveness of the supporting structure is verified through simulations and experiments. For robots equipped with this water-bomb wheel featuring the multi-link support, base on the kinematic model of multi-link structure, a mapping algorithm that incorporates parameterized kinematic solutions and IMU-fused parameterized odometry is proposed. Based on this algorithm, SLAM and autonomous navigation experiments are carried out in simulation environment and real environment respectively. Compared with the traditional algorithm, this algorithm the precision of SLAM is enhanced, achieving high-precision SLAM and autonomous navigation with a robot error rate below 5%.

Hierarchical Principal Components for Data-Driven Multiresolution fMRI Analyses.

Understanding the organization of neural processing is a fundamental goal of neuroscience. Recent work suggests that these systems are organized as a multiscale hierarchy, with increasingly specialized subsystems nested inside general processing systems. Current neuroimaging methods, such as independent component analysis (ICA), cannot fully capture this hierarchy since they are limited to a single spatial scale. In this manuscript, we introduce multiresolution hierarchical principal components analysis (hPCA) and compare it to ICA using simulated fMRI datasets. Furthermore, we describe a parametric statistical filtering method developed to focus analyses on biologically relevant features. Lastly, we apply hPCA to the Human Connectome Project (HCP) to demonstrate its ability to estimate a hierarchy from real fMRI data. hPCA accurately estimated spatial maps and time series from networks with diverse hierarchical structures. Simulated hierarchies varied in the degree of branching, such as two-way or three-way subdivisions, and the total number of levels, with varying equal or unequal subdivision sizes at each branch. In each case, as well as in the HCP, hPCA was able to reconstruct a known hierarchy of networks. Our results suggest that hPCA can facilitate more detailed and comprehensive analyses of the brain's network of networks and the multiscale regional specializations underlying neural processing and cognition.

Assessing the Efficiency of Foreign Investment in a Certification Procedure Using an Ensemble Machine Learning Model

Many methods exist for solving the problem of evaluating efficiency in different processes. They are divided into two basic groups, parametric and non-parametric methods, which can have significant differences in the results. In this study, the authors consider the process of assessing the business climate depending on realized foreign investments. Due to the expected difference in efficiency assessment using different approaches, the goal of this paper is to create an optimization model of an ensemble for efficiency assessment that uses both types of methods with the aim of creating a symmetrical approach that achieves better results than each type of method individually. The proposed solution simultaneously analyzes the impact of different factors on foreign investments in order to determine the most important factors and thus enable each local government to ensure the best possible efficiency in this process. The innovative idea of this study is in the inclusion of classification and feature selection methods of machine learning to fulfill the set goal. Our research, focused on a specific case study in various cities across the Republic of Serbia, evaluated the effectiveness of that process. This study extends previous research and confirms the published results, highlighting the advantages of the newly proposed model.

Multichannel detection of evoked responses using critical values corrected by a parametric bootstrap: Frequency-domain cholesky correction

Multivariate Objective Response Detection (MORD) techniques aim to detect evoked responses in multichannel electroencephalographic (EEG) recordings. They provide enhanced statistical power, allowing the detection of small signals in shorter or noisy recordings. However, the correlation between the signals in multichannel recordings can lead to false positive rates greater than the nominal significance level of the tests. To address this, we propose a parametric bootstrap approach that adjusts the critical values based on the correlation between EEG channels in the time domain, a method called time-domain Cholesky correction (TDCC). In that first approach, we assumed that correlation (or, more precisely, coherence) is constant across all frequency bands. However, this is unlikely to hold true, as signal-to-noise ratios (where the signal is the evoked response and noise all other signal components) may vary across frequencies. Thus, in the current work, we propose an alternative parametric bootstrap method for estimating the critical values of MORD techniques based on the correlation in the frequency domain (FDCC, frequency-domain Cholesky-corrected critical values). The proposed methods are evaluated using simulated data and an auditory steady-state response (ASSR) database in the 40 Hz range. The proposed method controlled the false positive rate well, with increased sensitivity compared to single-channel methods. When compared to TDCC, FDCC achieved similar performance but with the advantage of being, on average, 27 times faster in terms of computational time required to estimate the critical values.

MORPHOMETRIC PARAMETERS OF MATURITY OF PLACENTAL CHORINAL VILLI IN PREMATURE LABOR

Aim – to establish some morphometric parameters of placental villi in premature birth in comparison with physiological pregnancy.Material and methods. The material (30 placentas in premature births and 30 placentas in physiological pregnancies) was fixed for 22-24 hours in a neutral buffered 10% formalin solution. Next, the material was dehydrated in an ascending battery of alcohols and embedded into paraffin at 580С. Serial histological sections 5 μm thick were made on a sled microtome. After deparaffinization, serial histological sections were stained with hematoxylin and eosin, and with a chromotrope - water blue according to N.Z.Slinchenko. In addition to the descriptive method of histopathological examination, computer morphometry was performed on previously obtained digital copies of optical microscopic images of the tissue (Delta Optical Evolution 100 microscope {planachromatic lenses according to the required optical magnification} and Olympus SP550UZ digital camera). Digital copies of the image were analyzed using a freeware copy of the ImageJ v1.52 computer program specialized for digital histometric studies. In particular, the percentage of different types of chorionic villi was calculated on digital copies of optical images using the unrepeatable score test method. The obtained digital data were processed statistically. In particular, with the help of a copy of the free (freeware) computer program for statistical calculations PAST v 4.14, a preliminary test for the normality of the distribution was applied according to the Wilkie-Hahn-Shapiro test. For all studied statistical samples, according to this criterion, the hypothesis about the normality of the distribution was not rejected (at p=0.05), therefore, parametric methods of statistical analysis were used: calculation of the arithmetic mean and its error, the odd two-sided Student's test. Along with the Student's test, the non-parametric Mann-Whitney U test was also used. Results. It was considered most important to start assessing the state of the placenta according to the percentage ratios between different types of villous formations. In particular, according to modern ideas about different types of chorionic villi in the II and III trimesters of pregnancy, the following types of villous formations of the placenta were distinguished: stem villi ("early" and late"), intermediate villi (immature and mature), terminal villi, terminal "specialized" villi, mesenchymal villi, embryonic villi, trophoblastic and villous processes. The total amount of the specified villi should be equal to 100%. The division of villi into primary, secondary, and tertiary villi is relevant only for the 1st trimester of pregnancy, therefore, it is not suitable for studying the morphological features of premature birth.Conclusions. According to the obtained morphometric data, the immaturity of the chorionic tree is noted in premature birth compared to physiological pregnancy. This can be seen from a noticeable increase in the percentage of stem "early" villi, intermediate immature and mature villi, mesenchymal and embryonic villi, trophoblastic and villous processes, but with a significant decrease in the percentage of terminal villi.

Electrically conducting mixed convective nanofluid flow past a nonlinearly slender Riga plate subjected to viscous dissipation and activation energy

This paper reports the mass and energy transmission characteristics of an electrically conducting mixed convective nanofluid flow past a stretching Riga plate. An additional effect of viscous dissipation, Arrhenius activation energy and heat source is also studied. The energy and mass transmissions are evaluated by a zero-mass flux of nanoparticle and convective boundary conditions. Buongiorno’s relations are proposed for the Brownian motion and thermophoretic diffusion. The similarity substitutions are employed to derive the non-dimensional set of modeled equations. The obtained set of equations is numerically processed via parametric continuation method (PCM). Several flow factors affecting the velocity, energy, and mass distributions are graphically discussed. It has been perceived that the fluid velocity field declines with the influence of velocity power index (m), while improves with the upshot of modified Hartmann number (Q). The effect of Schmidt number and chemical reaction diminishes the concentration profile [Formula: see text]. Furthermore, the energy curve enhances with the effect of thermophoresis factor, Biot and Eckert number.

AN COMPARATIVE ANALYSIS OF WORKING CAPITAL AND PROFITABILITY IN ONLINE SERVICES SECTOR WITH REFERENCE TO IRCTC AND NYKAA

The Online Services Sector is one of the most important industry in India. In India there are many large and developing industries (Crude oil, Coal, Refinery, Cement, Textile, Steel, FMCG, Agriculture) and has significant contribution to GDP of India. The main Objective of the study is to analyse the Working Capital and Profitability of company during the study period is 2019-20 to 2022-23. Data Collection is based on secondary data collection method. Sample Selection is judgemental non parametric sampling method is used. For Data Analysis T-test is applied for Analysis of Working Capital and Profitability, Findings is that there is a significant difference between Current Ratio and Quick Ratio of company, and there is no significant difference between Inventory Turnover Ratio of both company. Due to their Working Capital Ratio Profitability of company also effected so NYKAA should maintain their Working Capital Ratio for increase their Profitability.

An intelligent recommendation system for personalised parametric garment patterns by integrating designer’s knowledge and 3D body measurements

Garment pattern-making is one of the most important parts of the apparel industry. However, traditional pattern-making is an experience-based work, very time-consuming and ignores the body shape difference. This paper proposes a parametric design method for garment pattern based on body dimensions acquired from a body scanner and body features (body feature points and three segmented body part shape classification) identified by designers according to their professional knowledge. By using this method, we construct a men’s shirt pattern recommendation system oriented to personalised fit. The system consists of two databases and three models. The two databases include a relational database (Database I) and a personalised basic pattern (PBP) database (Database II). The Database I is based on manual and three-dimensional (3D) measurements of human bodies by using designer’s knowledge. And Database I is a relational database, which is organised in terms of the relational model of the body part shape and its key body feature dimensions. After a deep analysis of measured data, the irrelevant measured dimensions to human body shape have been excluded by designers and extract representative human body feature dimensions. In addition, the relations between body shapes and previously identified body feature dimensions have been modelled. From the above relational model, we label key feature point positions on the corresponding 3D body model obtained from 3D body scanning and correct the whole 3D human upper body model into the semantically interpretable one. The 3D personalised basic pattern is drawn on the corrected model based on these key feature points. By using three-dimensional to two-dimensional (3D-to-2D) flattening technology, a 2D flatten graph of the 3D personalised basic pattern of the interpretable model is obtained and slightly adjusted to the form suitable for industrial production, i.e., PBP and the PBP database (Database II) is built. In addition, the three models include a basic pattern parametric model (Model I) (characterizing the relations between the basic pattern and its key influencing human dimensions (chest girth and back length)), a regression model (Model II) which enables to infer from basic pattern to PBP for three body parts based on the one-to-one correspondence of key points between the PBPs and the basic patterns and a personalised shirt pattern parametric model (Model III) (characterizing the structural relations between the personalised shirt pattern (PBPshirt) and PBP). The initial input items of the recommendation system are the body dimension constraint parameters, including chest girth, back length and the body feature dimensions used to determine each body part shape as well as three shirt style constraint parameters (slim, regular and loose). By using Model I, the corresponding basic pattern can be generated through the user’s chest girth and back length. Body feature dimensions determine the three body parts’ shapes. Then, Model II is used to generate the PBP for the corresponding body parts shape. Based on the shirt style chosen by the user, Mode III is used to generate the PBPshirt from the PBP. The output of the recommendation system is a fit-oriented PBPshirt. Moreover, if the PBPshirt is unsatisfactory after a virtual try-on, four adjustable parameters (front side-seam dart, back side-seam dart, waist dart and garment bodice length) are designed to adjust the PBPshirt generated by the proposed recommendation system.

Coherent Chaotic Communication Using Generalized Runge–Kutta Method

Computer simulation of continuous chaotic systems is usually performed using numerical methods. The discretization may introduce new properties into finite-difference models compared to their continuous prototypes and can therefore lead to new types of dynamical behavior exhibited by discrete chaotic systems. It is known that one can control the dynamics of a discrete system using a special class of integration methods. One of the applications of such a phenomenon is chaos-based communication systems, which have recently attracted attention due to their high covertness and broadband transmission capability. Proper modulation of chaotic carrier signals is one of the key problems in chaos-based communication system design. It is challenging to modulate and demodulate a chaotic signal in the same way as a conventional signal due to its noise-like shape and broadband characteristics. Therefore, the development of new modulation–demodulation techniques is of great interest in the field. One possible approach here is to use adaptive numerical integration, which allows control of the properties of the finite-difference chaotic model. In this study, we describe a novel modulation technique for chaos-based communication systems based on generalized explicit second-order Runge–Kutta methods. We use a specially designed test bench to evaluate the efficiency of the proposed modulation method and compare it with state-of-the-art solutions. Experimental results show that the proposed modulation technique outperforms the conventional parametric modulation method in both coverage and noise immunity. The obtained results can be efficiently applied to the design of advanced chaos-based communication systems as well as being used to improve existing architectures.

An additive model system for heartwood, sapwood and bark diameter – A working example in Pinus koraiensis Siebold & Zucc. plantations

Accurate estimations of a tree stem’s heartwood diameter (HD), sapwood width (SW), bark thickness (BT) and diameter outside bark (DOB) are essential for volume and biomass calculations, forest management, and optimal timber production. Understanding these components also provides valuable insights into the ecological health and functioning of forest ecosystems. The additive model system, enhanced by machine learning algorithms, offers new tools for estimation. We constructed parametric taper models and nonparametric multilayer perceptron models to compare the accuracy of HD, SW, BT, and DOB estimates based on 3199 upper stem diameter measurements obtained from 103 sample Pinus koraiensis Siebold & Zucc. trees in Northeast China. In the context of the additive model system for tree stem internal components, we utilized nonlinear seemingly unrelated regression (NSUR) as a parametric method and multitask learning (MTL) as a nonparametric method. In the comparison of additive model systems, MTL (nonparametric) performed better than did NSUR (parametric) by improving the accuracy and generalizability of the stem and internal component estimates. The coefficient of determination (R2) for DOB, HD, SW, and BT increased by 0.31%, 1.39%, 4.21%, and 4.31%, respectively. Based on these results, we conclude that the MTL provides a valid option for improving HD, SW, BT, and DOB and enhancing the volume prediction of stem components.

A Symmetric Kernel Smoothing Estimation of the Time-Varying Coefficient for Medical Costs

In longitudinal studies, subjects are repeatedly observed at a set of distinct time points until the terminal event time. The time-varying coefficient model extends the parametric method and captures the dynamic trajectories of time-dependent covariate effects, thus enabling it to describe the potential relationship between the longitudinal variable and the observed time points. In this study, we propose a novel approach to the estimation of medical costs using a symmetric kernel smoothing method in the time-varying coefficient joint model. A smooth function of medical costs is derived by weighting the values of longitudinal data at all distinct observed time points via the combination of the kernel method and the inverse probability weighting method. For the simulation study, we first set up the true functions of time-varying coefficients; we then generated random samples for covariates and censored survival times. Subsequently, the longitudinal data of response variables could be produced. Further, numerical simulation experiments were conducted by using the proposed method and applying R code to the generated data. The estimated results for the parameters and non-parametric functions were compared with different settings. The numerical results illustrate that as the sample size increases, the bias and model-based standard errors decrease, and the performance improves with larger sample sizes. The estimates of functions in the model almost coincide with the true functions, as shown in the figures of the simulation study. Furthermore, the consistency of the obtained estimator is demonstrated via theoretical analysis, and a numerical simulation is performed to illustrate the performance of the proposed estimators. The proposed model is applied to a real-world data set acquired from a multicenter automatic defibrillator implantation trial (MADIT).

Parametric Design Method and Lift/Drag Characteristics Analysis for a Wide-Range, Wing-Morphing Glide Vehicle

The parametric design method is widely utilized in the preliminary design stage for hypersonic vehicles; it ensures the fast iteration of configuration, generation, and optimization. This study proposes a novel parametric method for a wide-range, wing-morphing glide vehicle. The whole configuration, including a waverider fuselage, a rotating wing, a blunt leading edge, rudders, etc., can be easily described using 27 key parameters. In contrast to the typical parametric method, the new method takes internal payloads into consideration during the shape optimization process. That is, the vehicle configuration can be flexibly adjusted depending on the internal payloads; these payloads may be of random amounts and have different shapes. The code for the new parametric design method is developed using the secondary development tools of UG (UG 10.0) commercial software. The lift and drag characteristics over a wide operational range (H = 6–25 km, M = 2.5–8.5, AOA = 0–10°) were numerically investigated, as was the influence of the retracting angle of the morphing wings. It was found that, for the mode of the fully deployed wings, the lift-to-drag ratio (L/D) remained at a high level (≥4.7) over a Mach range of 4.0–8.5 and an AOA range of 4–7°. For the mode of the fully retracted wings, the drag coefficient remained smaller than 0.02 over a Mach range of 4.0–8.5 and an AOA range of 0–5°. A wide L/D of 0.3–4.7 could be achieved by controlling the retracting angle of the wings, thus demonstrating a good potential for flight maneuverability. The flexible change in L/D proved to be a combined result of varying pressure distribution and edge-flow spillage. This will aid in the further optimization of lift/drag characteristics.

LRANet: Towards Accurate and Efficient Scene Text Detection with Low-Rank Approximation Network

Recently, regression-based methods, which predict parameterized text shapes for text localization, have gained popularity in scene text detection. However, the existing parameterized text shape methods still have limitations in modeling arbitrary-shaped texts due to ignoring the utilization of text-specific shape information. Moreover, the time consumption of the entire pipeline has been largely overlooked, leading to a suboptimal overall inference speed. To address these issues, we first propose a novel parameterized text shape method based on low-rank approximation. Unlike other shape representation methods that employ data-irrelevant parameterization, our approach utilizes singular value decomposition and reconstructs the text shape using a few eigenvectors learned from labeled text contours. By exploring the shape correlation among different text contours, our method achieves consistency, compactness, simplicity, and robustness in shape representation. Next, we propose a dual assignment scheme for speed acceleration. It adopts a sparse assignment branch to accelerate the inference speed, and meanwhile, provides ample supervised signals for training through a dense assignment branch. Building upon these designs, we implement an accurate and efficient arbitrary-shaped text detector named LRANet. Extensive experiments are conducted on several challenging benchmarks, demonstrating the superior accuracy and efficiency of LRANet compared to state-of-the-art methods. Code is available at: https://github.com/ychensu/LRANet.git

Numerical investigation of MHD hybrid nanofluid flow with heat transfer subject to thermal radiation across two coaxial cylinders

Numerically the hybrid nanofluid (HNF) flow comprised of aluminum alloys (AA7072 and AA7075) across to coaxial cylinders is reported in the current analysis. The fluid flow has been examined under the significances of viscous dissipation, thermal radiation, and exponential heat source/sink. The HNF is produced by the addition of AA7072 and AA7075 nanoparticles (NPs) in water. Aluminum alloys are mostly used for the electric module wrapping, electronic machinery, automotive body, solar and wind energy controlling, and energy generation. The flow has been modeled in the form of momentum, continuity, and energy equations. Numerical approach parametric continuation method (PCM) is used to solve the modeled equations. The consequences of flow and energy parameters on the temperature, velocity, skin friction, and Nusselt number have been revealed through figures. It has been noticed that the fluid velocity drops, whereas the energy curve develops with the escalating influence of magnetic field. Furthermore, the rising numbers of AA7072 and AA7075 NPs in the water diminish the fluid velocity and temperature profile. Energy curve accelerates with the growing values of Brinkman number. It can be observed that the energy transmission rate upsurges up to 13.17% by the addition of AA7072 and AA7075 NPs in the water.

Error Analysis of Normal Surface Measurements Based on Multiple Laser Displacement Sensors.

The robotic drilling of assembly holes is a crucial process in aerospace manufacturing, in which measuring the normal of the workpiece surface is a key step to guide the robot to the correct pose and guarantee the perpendicularity of the hole axis. Multiple laser displacement sensors can be used to satisfy the portable and in-site measurement requirements, but there is still a lack of accurate analysis and layout design. In this paper, a simplified parametric method is proposed for multi-sensor normal measurement devices with a symmetrical layout, using three parameters: the sensor number, the laser beam slant angle, and the laser spot distribution radius. A normal measurement error distribution simulation method considering the random sensor errors is proposed. The measurement error distribution laws at different sensor numbers, the laser beam slant angle, and the laser spot distribution radius are revealed as a pyramid-like region. The influential factors on normal measurement accuracy, such as sensor accuracy, quantity and installation position, are analyzed by a simulation and verified experimentally on a five-axis precision machine tool. The results show that increasing the laser beam slant angle and laser spot distribution radius significantly reduces the normal measurement errors. With the laser beam slant angle ≥15° and the laser spot distribution radius ≥19 mm, the normal measurement error falls below 0.05°, ensuring normal accuracy in robotic drilling.

Realistic estimation framework of radioactive release distributions into the environment during nuclear power plant accidents

Since the level 2 PSA of OPR-1000 was the requirement for regulatory purposes, Cs-137 release estimation was contained as the Nuclear Safety Act of ROK in which the Cs-137 release frequency exceeding 100 TBq was determined to happen less than 1.0E-6 per year after the Fukushima Daiichi Accident. However, Cs-137 release estimation from the conventional level 2 PSA of OPR-1000 provided uncertainty due to dominant accident sequence consideration. Thus, this study aimed to develop systematic methods through the overall framework to quantify realistic uncertainty concerns of radioactive material release using sensitivity and uncertainty analysis methods and apply them to OPR-1000. This framework helped to quantify confidential value for the Cs-137 release under the BEPU approach using both parametric and non-parametric methods to cover both realistic and conservative points. Uncertainty propagation analysis showed the unexpected uncertainty increase of Cs-137 release exceeding 100 TBq. The non-parametric uncertainty analysis provided higher conservative concerns for safety than the realistic concerns in terms of economics when compared with the parametric uncertainty analysis. Wilks’ uncertainty analysis showed the importance to consider conservative Cs-137 release in order to reach the higher safety need. Sensitivity analysis showed reasonable relationships between engineering safety parameters with the Cs-137 release.