Distribution Of Parameter Values Research Articles

Estimation of Marshall–Olkin Extended Generalized Extreme Value Distribution Parameters under Progressive Type-II Censoring by Using a Genetic Algorithm

In this article, we consider the statistical analysis of the parameter estimation of the Marshall–Olkin extended generalized extreme value under liner normalization distribution (MO-GEVL) within the context of progressively type-II censored data. The progressively type-II censored data are considered for three specific distribution patterns: fixed, discrete uniform, and binomial random removal. The challenge lies in the computation of maximum likelihood estimations (MLEs), as there is no straightforward analytical solution. The classical numerical methods are considered inadequate for solving the complex MLE equation system, leading to the necessity of employing artificial intelligence algorithms. This article utilizes the genetic algorithm (GA) to overcome this difficulty. This article considers parameter estimation through both maximum likelihood and Bayesian methods. For the MLE, the confidence intervals of the parameters are calculated using the Fisher information matrix. In the Bayesian estimation, the Lindley approximation is applied, considering LINEX loss functions and square error loss, suitable for both non-informative and informative contexts. The effectiveness and applicability of these proposed methods are demonstrated through numerical simulations and practical real-data examples.

Geomorphic risk maps for river migration using probabilistic modeling – a framework

Abstract. Lateral migration of meandering rivers poses erosional risks to human settlements, roads, and infrastructure in alluvial floodplains. While there is a large body of scientific literature on the dominant mechanisms driving river migration, it is still not possible to accurately predict river meander evolution over multiple years. This is in part because we do not fully understand the relative contribution of each mechanism and because deterministic mathematical models are not equipped to account for stochasticity in the system. Besides, uncertainty due to model structure deficits and unknown parameter values remains. For a more reliable assessment of risks, we therefore need probabilistic forecasts. Here, we present a workflow to generate geomorphic risk maps for river migration using probabilistic modeling. We start with a simple geometric model for river migration, where nominal migration rates increase with local and upstream curvature. We then account for model structure deficits using smooth random functions. Probabilistic forecasts for river channel position over time are generated by Monte Carlo runs using a distribution of model parameter values inferred from satellite data. We provide a recipe for parameter inference within the Bayesian framework. We demonstrate that such risk maps are relatively more informative in avoiding false negatives, which can be both detrimental and costly, in the context of assessing erosional hazards due to river migration. Our results show that with longer prediction time horizons, the spatial uncertainty of erosional hazard within the entire channel belt increases – with more geographical area falling within 25 % < probability < 75 %. However, forecasts also become more confident about erosion for regions immediately in the vicinity of the river, especially on its cut-bank side. Probabilistic modeling thus allows us to quantify our degree of confidence – which is spatially and temporally variable – in river migration forecasts. We also note that to increase the reliability of these risk maps, we need to describe the first-order dynamics in our model to a reasonable degree of accuracy, and simple geometric models do not always possess such accuracy.

Compare Some Shrinkage Bayesian Estimation Method forGumbel-Max Distribution with Simulation

The Gumbel max distribution is one of the most important statistical distributions because it has many applications, especially in the field of water research and the prediction of earthquakes and volcanoes. This work includes a comparison of several Shrinkage Bayesian methods, namely the Shrinkage Bayesian Square Loss Function, Shrinkage Bayesian Lenix Function, and Shrinkage Bayesian Unix Function to estimate the distribution parameter. The research also includes several simulated experiments according to the sample size change and the real value of the distribution parameter. The number of experiments is 15 simulated experiments based on the difference sample size and true distribution parameter values. The results of the simulation experiments are compared depending on each of the absolute least difference criteria ( and the mean squared error (). The comparison results show that the estimation method is affected by the sample size, and the real value of the distribution parameter. The best estimation method is the Shrinkage Bayesian Lenix Function. The Bayesian methods can be applied to other statistical distributions such that the Lindley Weibull distribution and logistics distribution.

Comparison between the standard Bayesian method and the hierarchical Bayesian method for estimating the reliability function of the inverse Lomax distribution with type II observational dat

In this paper, the shape and size parameters (without imposing either parameter) and the survival function of the inverse Lomax distribution in the presence of observational data of the second type are estimated using different Bayesian methods, which included the standard Bayesian method and the hierarchical Bayesian method under a symmetric loss function, which is the quadratic loss function, and in order to compare The two methods used Monte Carlo simulation, where different sample sizes and three models of default values for distribution parameters (Ѳ,λ) , were used and the statistical measure, mean integral square error (IMSE), was used. Simulation experiments showed the superiority of the hierarchical Bayesian method because it has the least mean square integral error (IMSE)

A precise modeling method of three‐dimensional discrete fracture network based on rectangular joint model

AbstractAs the weak structure and main seepage channel of rock mass, the discrete fracture network (DFN) has a significant influence on the physical and mechanical properties of rock mass. In this paper, based on the rectangular joint model, two algorithms are proposed to realize the precise modeling of the DFN in any polyhedron. First, the equivalence of DFN modeling with the rectangular joint model and the elliptical joint model is theoretically deduced. On this basis, the chain splicing algorithm suitable for the rectangular joint model is proposed, which can generate the DFN model according to the actual distribution of joint spacing, trace, and bridge length. Aiming at the concave boundary in the model domain, the segmentation‐stitching operation is put forward, and the continuity of the joint when passing through the interface is ensured. Finally, the effectiveness of the above algorithm is verified by several simple cases, and the relationship between the size of the geometrical representative elementary volume (REV) and discontinuity parameters is discussed. The results show that when the mean values of the joint distribution parameters are the same, even if the distribution rules are different, the number of joints generated in the same spatial region is approximately equal. The size of the geometrical REV is independent of the spacing and bridge length and is approximately three to five times the mean of the trace. The relevant algorithms and conclusions in this paper can be used as the basis for subsequent rock mass stability analysis and seepage calculation.

Flow characteristics of dispersed two-phase flows in an 8 × 8 rod bundle

Two-phase flows in rod bundles or tube bundles appear in heat transfer systems. The bundle-average (or one-dimensional) void fraction is a critical parameter in overall system design, performance evaluation, and safety assessment. Accurate bundle-average void fraction prediction requires a precise interfacial momentum transfer term formulated using the distribution parameter and drift velocity, two essential drift-flux parameters in the drift-flux model. The currently utilized modeling approach is based on the distribution parameter and drift velocity determined together through drift-flux plots. This results in possible compensation errors between the distribution parameter and drift velocity in the model validation process. The independent validation of the constitutive equations for the distribution parameter and drift velocity has been challenging due to experimental difficulty in measuring detailed two-phase flow structures in rod bundles. The present study proposes an approximation methodology to obtain subchannel average two-phase flow parameters using local two-phase flow data measured at two local points: the subchannel center and minimum gap center in a rod bundle. The distributions of subchannel average two-phase flow parameters are invaluable in benchmarking subchannel analysis codes. Comprehensive subchannel average void fraction and gas velocity mappings are given to discuss the effects of gas and liquid velocities, pressure, spacer grid, and developing length on the two-phase flow characteristics in the rod bundle. The bundle-average distribution parameter values calculated by subchannel average two-phase flow parameters are used for independent validation of the constitutive equations of the distribution parameter. The bundle-average drift velocity values back-calculated using the distribution parameter values are also used for independent validation of the constitutive equation of the drift velocity.

Determination of Life Microinsurance Premium Using the Commercial Rate Method

Microinsurance is insurance that is intended for people who have low incomes which is made with the aim that all levels of society can have insurance with affordable prices. Life insurance is a protection program for families in the event of unwanted things, such as death or permanent disability, to policy holders. This study aims to determine the life microinsurance premium. The data sample used is data on claim and benefit paid by life insurance company obtained from the official website of Otoritas Jasa Keuangan (OJK) Indonesia, which is assumed to have a log-normal distribution. The research method is to test the distribution of claims from the sample data using the Kolmogorov-Smirnov test. Then determine the value of the claim distribution parameter, and then calculating life microinsurance premium using the Commercial Rate method. The results obtained in the form of premium for life microinsurance that are payable by low-income people.

Service life prediction and time-variant reliability of reinforced concrete structures in harsh marine environment considering multiple factors: A case study for Qingdao Bay Bridge

Chloride attack is the primary cause of performance deterioration and life attenuation of reinforced concrete (RC) structures in marine environments. In an attempt to accurately predict the reliability and service life of RC structures under the co-effects of multiple factors, a comprehensively modified chloride-transport-based model is proposed in this paper. Based on Fick's second law, a multifactor integrated deterministic chloride transport model is developed considering the time dependence, concrete mix proportion, chloride binding capacity, freeze–thaw cycles, sustained load, temperature, humidity and concrete deterioration. Based on extensive literature review combined with the results of multitudinous long-term field exposure tests used in the durability design of Hong Kong-Zhuhai-Macao Bridge and Shenzhen-Zhongshan Bridge, the probability distribution types and parameter values of the governing variables are determined. Taking Qingdao Bay Bridge as an example, the time-variant reliability index, failure probability and reliability-index-based corrosion initiation are determined by using the equivalent normalization method (JC method) and Monte Carlo simulation technique for different environmental zones including atmospheric/splash/tidal/submerged zones. The sensitivity of the reliability index to the control variables and the effects of age index, chloride binding capacity, numbers of freeze–thaw cycles, stress level and concrete deterioration on the reliability index are quantitatively investigated. The results show that the time-variant reliability of different environmental zones is sequenced as: atmospheric zone > submerged zone > tidal zone > splash zone; the sensitivity of reliability to the governing variables sequenced as: concrete cover thickness > surface chloride concentration > chloride diffusion coefficient > critical chloride concentration; the time dependence of the diffusion coefficient has a significant effect on the time to corrosion initiation and cannot be ignored in the predictive model for both the high-performance or ordinary concrete; the number of freeze–thaw cycles and the tensile stress level are relatively insensitive but substantially detrimental to the reliability of corrosion initiation.

The effect of the volume of tests and the number of participants on the assessment of qualification in interlaboratory comparison tests

The results of a comparative analysis of statistical indicators for assessing the proficiency determined during interlaboratory comparison tests (ICT) are presented. The main statistical indicators were selected: the assigned value, standard deviation for assessment the proficiency and standard uncertainty of the assigned value. Statistical indicators were determined in accordance with Algorithm A, GOST R 50779.60. Comparison of the indicators was carried out on the basis of numerical experiment data (random number generation) for the ultimate tensile strength of a D16AT aluminum alloy depending on the number of samples and the number of participants in the ICT qualification program. The calculation and subsequent statistical analysis of the proficiency test scores was based on the values generated by the software. Random number generation is performed for a characteristic that has a normal distribution law with the parameters of the mean equal to 450 MPa and standard deviation equal to 5 MPa. The values of the normal distribution parameters chosen as an example corresponded to the average level of tensile strength values of standard specimens made of D16AT aluminum alloy sheets. The numerical experiment (generation of random values) was carried out for given cases of the number of participants and the number of proficiency testing samples (PTS) independently of each other using separate samples of the appropriate size. A total of 36,000 ultimate tensile strength values were generated for the proficiency testing samples made of D16AT aluminum alloy sheets. As a result of calculations, 48 average values were determined (according to the number of considered implementations of ICT programs, depending on the number of participants and the proficiency testing samples) for each selected statistical indicator of the ICT qualification program. The average value of each statistical indicator was determined on samples with a bulk of 250 to 2000 generated (experimental) values, depending on the number of participants and the number of proficiency testing samples. The analysis thus performed made it possible to evaluate the influence of the volume of tests and the number of participants on the assessment of the proficiency in interlaboratory comparative tests. It is shown that for the selected range of the number of ICT participants (from 5 to 20) and the number of PTS (from 5 to 10), the value of the assigned xpt value does not depend either on the number of tested PTS or the number of ICT participants. The maximum discrepancy between the assigned value and the given level of tensile strength (450 MPa) is 0.13%, which falls within the error (rounding) for this level of values and cannot lead to significant errors in proficiency testing during ICT. The standard deviation for the qualification assessment, regardless of the number of participants, decreases with an increase in the number of proficiency testing samples, but such a decrease is insignificant and does not exceed the standard deviation (SD) value of the ultimate tensile strength (for the selected model for generating experimental values, the SD value is accepted to be 5 MPa). In contrast to the assigned value and the standard deviation for the qualification assessment, it is shown that the standard uncertainty of the assigned value for the ultimate tensile strength of standard samples made of D16AT aluminum alloy sheets depends on the number of participants and the number of proficiency testing samples tested by each participant and decreases with an increase in the number of participants in ICT programs and the number of proficiency testing samples.

Coevolution of Extreme Sea Levels and Sea‐Level Rise Under Global Warming

AbstractDesign of coastal defense structures like seawalls and breakwaters can no longer be based on stationarity assumption. In many parts of the world, an anticipated sea‐level rise (SLR) due to climate change will constitute present‐day extreme sea levels inappropriate for future coastal flood risk assessments since it will significantly increase their probability of occurrence. Here, we first show that global annual maxima sea levels (AMSLs) have been increasing in magnitude over the last decades, primarily due to a positive shift in mean sea level (MSL). Then, we apply non‐stationary extreme value theory to model the extremal behavior of sea levels with MSL as a covariate and quantify the evolution of AMSLs in the following decades using revised probabilistic sea‐level rise projections. Our analysis reveals that non‐stationary distributions exhibit distinct differences compared to simply considering stationary conditions with a change in location parameter equal to the amount of MSL rise. With the use of non‐stationary distributions, we show that by the year 2050 many locations will experience their present‐day 100‐yr return level as an event with return period less than 15 and 9 years under the moderate (RCP4.5) and high (RCP8.5) representative concentration pathways. Also, we find that by the end of this century almost all locations examined will encounter their current 100‐yr return level on an annual basis, even if CO2 concentration is kept at moderate levels (RCP4.5). Our assessment accounts for large uncertainty by incorporating ambiguities in both SLR projections and non‐stationary extreme value distribution parameters via a Monte Carlo simulation.

Statistical model for assessing the reliability of non-destructive testing systems by solving inverse problems

Objectives. The wear monitoring of metal structural elements of power plants—in particular, pipelines of nuclear power plants—is an essential means of ensuring safety during their operation. Monitoring the state of the pipeline by direct inspection requires a considerable amount of labor, as well as, in some cases, the suspension of power plant operation. In order to reduce costs during monitoring measures, it is proposed to use mathematical modeling. This work aimes to develop a mathematical model of a diagnostic system for assessing the probability of detection of defects by solving inverse problems.Methods. A binomial model for assessing the reliability of monitoring, comprising the Berens-Hovey parametric model of the probability of detection of defects and a parametric model based on studying test samples, was analyzed. As an alternative to this binomial model, a computational method for assessing the reliability of non-destructive testing systems by solving an inverse problem was proposed. To determine the parameters of the defect detection probability curve, the model uses data obtained by various monitoring teams over a long period of power plant operation. To serve as initial data, the defect distribution density over one or more of the following characteristics can be used: depth, length, and/or cross-sectional area of the defect. Using the proposed mathematical model, a series of test calculations was performed based on nine combinations of initial data. The combinations differed in the confidence coefficient of the initial monitoring system, the parameters of the distribution of defects, and the sensitivity of the monitoring system.Results. The calculation data were used to construct curves of the probability density of detected defects as a function of the defect size, recover the values of the defect distribution parameters under various test conditions, and estimate the error of recovering the parameters. The degree of imperfection of the system was estimated using the curve of the detection probability of a defect by a certain monitoring system.Conclusions. Under constraints on the data sample size, the proposed methodology allows the metal monitoring results to be applied with greater confidence than currently used methods at the same time as evaluating the efficiency of monitoring carried out by individual test teams or laboratories. In future, this can be used to form the basis of a recommendation of the involvement of a particular team to perform diagnostic work.

Alternative to mean and least squares methods used in processing the results of scientific and technical experiments

The nomenclature of controlled parameters and norms of measurement accuracy determines the reliability of control and its laboriousness. On average, the labor-intensiveness of control is about 10% of the total labor-intensiveness of manufacturing objects, and in a number of industries it is much higher (aerospace engineering). The simplest task in determining a rational nomenclature of controlled parameters of ob-jects is the exclusion of excessive parameters, while it is necessary to determine the probability that the deviations of some Х2 parameter within the specified tolerances are the same as the deviations of the Х1 parameter within its specified tolerances. At the same time, inequalities are defined that determine the smallest value of this probability. The general principle of solving this problem is the determination of conditional proba-bilities (if two parameters are considered) P Р11, Р12; , or Р21, Р22. At the same time, if the obtained values: Р11, Р12, are more likely than (or equal to) the permissible value of Rdop, then it is advisable to control only parameter Х1, and exclude Х2 from the no-menclature of parameters. If: Р21, Р22 , are greater than (or equal to) the permissible value of Rdop, then Х1 is turned off. An example of the exclusion of a redundant control parameter is given. The method can be generalized for any number of parameters, for which it is necessary to use correlation matrices or a simple enumeration of parameter combinations. A computer program has been developed that can be used if the following information is available: 1) the number of controlled parameters (n); 2) values of toler-ances (Xni, Xvi), which parameters are subject to control; 3) numerical characteristics of distributions of parameter values within the specified tolerances - the average value of and the average squared deviation σХі,; 4) types of distribution laws of parameter values; 5) data on correlations between parameters and values of pairwise correlation coefficients ( rij ); 6) the value of the probabilities of control errors Р12, Р21 (with two parameters). Calculations should be adjusted as statistical data accumulate.

Position-dependent correlation function of weak-lensing convergence

We provide a systematic study of the position-dependent correlation function in weak-lensing convergence maps and its relation to the squeezed limit of the three-point correlation function (3PCF) using state-of-the-art numerical simulations. We relate the position-dependent correlation function to its harmonic counterpart, i.e., the position-dependent power spectrum or equivalently the integrated bispectrum (IB). We use a recently proposed improved fitting function, BiHalofit, for the bispectrum to compute the theoretical predictions as a function of source redshifts. In addition to low redshift results (${z}_{s}=1.0--2.0$) we also provide results for maps inferred from lensing of the cosmic microwave background (CMB), i.e., ${z}_{s}=1100$. We include a Euclid-type realistic survey mask and noise. In agreement with the recent studies on the position-dependent power spectrum, we find that the results from simulations are consistent with the theoretical expectations when appropriate corrections are included. Performing a rough estimate, we find that the signal-to-noise (S/N) for the detection of position-dependent correlation function from Euclid-type mask with ${f}_{\mathrm{sky}}=0.35$, can range between 6--12 depending on the value of the intrinsic ellipticity distribution parameter ${\ensuremath{\sigma}}_{\ensuremath{\epsilon}}=0.3--1.0$. For reconstructed $\ensuremath{\kappa}$ maps using an ideal CMB survey the $\mathrm{S}/\mathrm{N}\ensuremath{\approx}1.8$. We also found that a 10% deviation in ${\ensuremath{\sigma}}_{8}$ can be detected using IB for the optimistic case of ${\ensuremath{\sigma}}_{\ensuremath{\epsilon}}=0.3$ with a $\mathrm{S}/\mathrm{N}\ensuremath{\approx}5$. The S/N for such detection in case of ${\mathrm{\ensuremath{\Omega}}}_{M}$ is lower.

АНАЛИЗ ПОГРЕШНОСТИ БАЗИРОВАНИЯ ЗАГОТОВКИ ПРИ УСТАНОВКЕ НА ДВЕ ОПОРНЫЕ ПРИЗМЫ С УЧЕТОМ ПОГРЕШНОСТИ ФОРМЫ И МЕСТОРАСПОЛОЖЕНИЯ

The study objective is the development of devices for high-accuracy machining of machine parts. The problem to which the article is devoted is the analysis of the joint influence of location vees on the accuracy of workpiece locating. Research methods: modeling the contact of the workpiece with the location vees using the theory of dimensional connections, regression analysis of modeling results of installing workpieces in the location vees. The novelty of the work: the formation of the workpiece error of locating when it is installed on a device with two location vees and a random distribution of the accuracy parameters of the base surfaces is considered. Study results: a model of self-installation of the workpiece on two location vees is developed and dependences are obtained for estimating the workpiece error of locating, taking into account the normal distribution of accuracy parameter values within their tolerances. Conclusions: taking into account the probability of distribution of the values of base surface accuracy parameters within the tolerances reduces the values of the workpiece error of locating, which makes it possible to reduce the requirements for the accuracy of the devices reasonably.

Photoprotective Effects of D1 Protein Turnover and the Lutein Cycle on Three Ephemeral Plants under Heat Stress

To clarify the characteristics of photoinhibition and the primary defense mechanisms of ephemeral plant leaves against photodestruction under high temperature stress, inhibitors and the technology to determine chlorophyll fluorescence were used to explore the protective effects of D1 protein turnover and the lutein cycle in the high temperature stress of the leaves of three ephemeral plants. The results showed that the maximum light conversion efficiency (Fv/Fm) of the ephemeral plant leaves decreased, and the initial fluorescence (Fo) increased under 35°C ± 1°C heat stress for 1–4 h or on sunny days in the summer. Both Fv/Fm and Fo could be recovered after 8 h of darkness or afternoon weakening of the external temperature. Streptomycin sulfate (SM) or dithiothreitol (DTT) accelerated the decrease of Fv/Fm and the photochemical quenching coefficient (qP) in the leaves of three ephemeral plants at high temperature, and the decrease was greater in the SM than in the DTT treatment. When the high temperature stress was prolonged, the Y(II) values of light energy distribution parameters of PSII decreased, and the Y(NPQ) and Y(NO) values increased gradually in all the treatment groups of the three ephemeral plants. The results showed that the leaves of the three ephemeral plants had their own highly advanced mechanisms to protect against photodamage, which inhibited the turnover of D1 protein and xanthophyll cycle. This can damage the PSII reaction center in the leaves of the three ephemeral plants under high temperature. The protective effect of D1 protein turnover on heat stress in <i>Erodium oxyrrhynchum</i> and <i>Senecio subdentatus</i> was greater than that of the lutein cycle, while the protective effect of lutein cycle was greater than that of D1 protein turnover in <i>Heliotropium acutiflorum</i> subjected to heat damage.

Enhanced optical and dielectric properties of polymer dispersed liquid crystal for display applications

The compound material prepared by dispersing polymer into liquid crystal is of importance due to the improvement in its electro-optical properties. Polymer dispersed liquid crystal (PDLC) is a functional material, of less than micron size liquid crystal, chemically dispersed in a polymer matrix. We report the influences of different concentrations of polymer, on the optical and dielectric behaviour of cholesteric liquid crystals. The fabry perot scattering studies in addition to polarizing optical microscopy were used to study the optical properties. Several novel phases in addition with the known phases were found for the compound materials. The dispersion also results in the large value of dielectric anisotropy which indicates possibility of this material to be used in forming a photo cured network. Our investigation shows a non-zero value of distribution parameter and also increase in dielectric constant, which is useful for display applications.

Statistical analysis of progressively first-failure-censored data via beta-binomial removals

&lt;abstract&gt;&lt;p&gt;Progressive first-failure censoring has been widely-used in practice when the experimenter desires to remove some groups of test units before the first-failure is observed in all groups. Practically, some test groups may haphazardly quit the experiment at each progressive stage, which cannot be determined in advance. As a result, in this article, we propose a progressively first-failure censored sampling with random removals, which allows the removal of the surviving group(s) during the execution of the life test with uncertain probability, called the beta-binomial probability law. Generalized extreme value lifetime model has been widely-used to analyze a variety of extreme value data, including flood flows, wind speeds, radioactive emissions, and others. So, when the sample observations are gathered using the suggested censoring plan, the Bayes and maximum likelihood approaches are used to estimate the generalized extreme value distribution parameters. Furthermore, Bayes estimates are produced under balanced symmetric and asymmetric loss functions. A hybrid Gibbs within the Metropolis-Hastings method is suggested to gather samples from the joint posterior distribution. The highest posterior density intervals are also provided. To further understand how the suggested inferential approaches actually work in the long run, extensive Monte Carlo simulation experiments are carried out. Two applications of real-world datasets from clinical trials are examined to show the applicability and feasibility of the suggested methodology. The numerical results showed that the proposed sampling mechanism is more flexible to operate a classical (or Bayesian) inferential approach to estimate any lifetime parameter.&lt;/p&gt;&lt;/abstract&gt;

Effects of martensitic phase transformation on fatigue indicator parameters determined by a crystal plasticity model

Fatigue indicator parameters are a powerful tool for materials design. These parameters represent the potency of various microstructures to incubate fatigue cracks in metal alloys and are useful in ranking deleterious microstructural features on which to focus process improvements. These fatigue indicator parameters are generally a function of irreversible plastic strain; however, for alloys with solid-state phase transformations (such as Nitinol), reversible transformation strains occur along with plastic strain. This article addresses the question of whether modeling a reversible transformation will affect the predicted value of a fatigue indicator parameter. For Nitinol, the effects of a reversible phase transformation on the fatigue indicator parameter and fatigue life are studied for a polygranular microstructure with and without an inclusion. The crystal mechanics of both plasticity and phase transformations are modeled using the finite element method. It is shown that the usage of a crystal-mechanics-based phase transformation model affects the values of fatigue indicator parameters, the shape of predicted strain–life curves, the distribution of fatigue indicator parameter values in the microstructure, and also the rank ordering of deleterious microstructural features.

Prediction of intensity of dispersion in surfactant solutions in vertical sparged bubble column

AbstractDispersion of liquid in bubble column is an important process in chemical and allied industries. In the present work, the dispersion characteristics of liquid phase in a bubble column is studied in three different surfactant solutions. A comparative analysis on the dispersion characteristics of liquid phase in different surfactants has been carried out. The gas holdup and mixing time are also analysed. The effect of different physiochemical properties and operating variables on the dispersion coefficient and gas holdup is examined. A novel model for the estimation of dispersion due to bubble motion has been developed which incorporates the effect of gas holdup, liquid viscosity and circulation velocity. The contribution of dispersion coefficient due to the bubble motion in overall dispersion is also examined. The value of velocity distribution parameter in three different surfactant solutions is also compared. Generalised correlations for gas holdup, dispersion coefficient and mixing time are also developed based on the physicochemical properties of liquid. This research may be useful for the selection and application of these three surfactants in industries. This article will also help researchers for designing multiphase flow system where role of dispersion due to the motion of bubbles is significant.

Raspberries-LITRP Database: RGB Images Database for the Industrial Applications of Red Raspberries’ Automatic Quality Estimation

This work presents a free new database designed from a real industrial process to recognize, identify, and classify the quality of the red raspberry accurately, automatically, and in real time. Raspberry trays with recently harvested fresh fruit enter the industry’s selection and quality control process to be categorized and subsequently their purchase price is determined. This selection is carried out from a sample of a complete batch to evaluate the quality of the raspberry. This database aims to solve one of the major problems in the industry: evaluating the largest amount of fruit possible and not a single sample. This major dataset enables researchers in various disciplines to develop practical machine-learning (ML) algorithms to improve red raspberry quality in the industry, by identifying different diseases and defects in the fruit, and by overcoming limitations by increasing the performance detection rate accuracy and reducing computation time. This database is made up of two packages and can be downloaded free from the Laboratory of Technological Research in Pattern Recognition repository at the Catholic University of the Maule. The RGB image package contains 286 raw original images with a resolution of 3948 × 2748 pixels from raspberry trays acquired during a typical process in the industry. Furthermore, the labeled images are available with the annotations for two diseases (86 albinism labels and 164 fungus rust labels) and two defects (115 over-ripeness labels, and 244 peduncle labels). The MATLAB code package contains three well-known ML methodological approaches, which can be used to classify and detect the quality of red raspberries. Two are statistical-based learning methods for feature extraction coupled with a conventional artificial neural network (ANN) as a classifier and detector. The first method uses four predictive learning from descriptive statistical measures, such as variance, standard deviation, mean, and median. The second method uses three predictive learning from a statistical model based on the generalized extreme value distribution parameters, such as location, scale, and shape. The third ML approach uses a convolution neural network based on a pre-trained fastest region approach (Faster R-CNN) that extracts its features directly from images to classify and detect fruit quality. The classification performance metric was assessed in terms of true and false positive rates, and accuracy. On average, for all types of raspberries studied, the following accuracies were achieved: Faster R-CNN 91.2%, descriptive statistics 81%, and generalized extreme value 84.5%. These performance metrics were compared to manual data annotations by industry quality control staff, accomplishing the parameters and standards of agribusiness. This work shows promising results, which can shed a new light on fruit quality standards methodologies in the industry.