Fitting Model Research Articles

Experimental Study on Optimization of Consolidation Parameters of Silty Clay Based on Response Surface Methodology: A Case Study on the Protection and Restoration of the Ming and Qing Dynasty Hangzhou Seawall Site

The preservation of the ancient seawall site is a focal point and challenge in the protection of historical relics along Hangzhou’s Grand Canal in China. This endeavor holds significant historical and contemporary value in uncovering and perpetuating Hangzhou’s cultural heritage. Researchers investigating the Linping section of the seawall site aimed to address soil site deterioration by selecting environmentally friendly alkali-activated slag cementitious materials and applying the response surface method (RSM) to conduct solidification experiments on the seawall soil. Researchers used the results of unconfined compressive strength tests and microscopic electron microscopy analysis, considering the comprehensive performance of soil solidification mechanisms and mechanical properties, to establish a least-squares regression fitting model to optimize the solidification material process parameters. The experimental results indicate that the optimal mass ratio of lime, gypsum, and slag for achieving the best solidification process parameters for the seawall soil, with a 28-day curing period, is 1:1.9:6.2. This ratio was subsequently applied to the restoration and reconstruction of the seawall site, with parts of the restored seawall exhibited in a museum to promote the sustainable conservation of urban cultural heritage. This study provides theoretical support and practical guidance for the protection and restoration of soil sites.

CANUCS: An Updated Mass and Magnification Model of A370 with JWST

We present an updated mass and magnification model of galaxy cluster A370 using new NIRCam, NIRISS, and NIRSpec data from the Canadian NIRISS Unbiased Cluster Survey (CANUCS). Using Lenstool and a combination of archival Hubble Space Telescope (HST) and MUSE data with new JWST data as constraints, we derive an improved gravitational lensing model and extract magnifications of background galaxies with uncertainties. Using our best fit model, we perform a search for new multiply imaged systems via predicted positions. We report no new multiply imaged systems with identifiable redshifts, likely due to already very deep HST and Spitzer data but confirm a z ∼ 8 multiply imaged system by measuring its redshift with NIRISS and NIRSpec spectra. We find that the overall shape of the critical curve for a source at z = 9.0 is similar to previous models of A370, with small changes. We investigate the z ∼ 8 galaxy with two images observable with an apparent magnitude in the F125W band of 26.0 ± 0.2 and 25.6 ± 0.1. After correcting for the magnifications of the images, 7.4−0.3+0.5 and 9.4−0.4+0.5 , we use spectral energy distribution fitting to find an intrinsic stellar mass of log(M */M ⊙) = 7.49−0.05+0.04 , intrinsic star formation rate of 2.8−0.3+0.4 M ⊙ yr−1, and M UV of −21.3 −0.2+0.2 , which is close to the knee of the luminosity function at that redshift. Our model, and corresponding magnification, shear, and convergence maps are available on request and will be made publicly available on MAST in a CANUCS data release (doi:10.17909/ph4n-6n76).

Fit models’ roles in identifying fit issues in the apparel technical design process and implications for improving 3D virtual fitting

Fit sessions are essential in making well-fitted clothing. During these sessions, apparel fit is determined iteratively by a team of designers, technical designers, fit models, and merchandisers. With the advent of the digitalization of new product development processes in the fashion industry, fit sessions have been seen as a bottleneck for they are still held in person. However, the process is currently still irreplaceable, and what makes the feedback provided by fit models important is an area that has never been tapped. Therefore, the present study aimed to understand fit models’ role in fit sessions as well as how they assess the fit of garments and deliver their feedback. On-site observation during fit sessions and individual interviews with fit models were conducted. It was found that fit models gave comfort, fit, and tactile comments by testing garments while standing and moving between several postures. They had knowledge that overlapped with those of technical designers and designers and this empowerment enabled them to take part in the decision-making process in fit sessions. It also was found that fit models’ feedback on garments was essential as they were the first people to try on the garments and present the customers’ points of view.

Di-2-ethylhexyl phthalate (DEHP) exposure increase female infertility

This study explores the relationship between Di-2-ethylhexyl phthalate (DEHP) exposure and female infertility. The analysis included 998 female participants aged between 18 and 44 years. We analyzed data from the National Health and Nutrition Examination Survey (2013–2018) using multiple logistic regression and generalized linear models to assess the impact of DEHP on infertility. Additionally, we employed curve fitting and two-piecewise linear regression models to investigate potential nonlinear correlations, conducting subgroup analyses based on age, BMI, alcohol consumption, smoking status, hypertension, and diabetes. Our results, after adjusting for confounders, revealed a positive association between DEHP exposure and infertility. This association was significant whether DEHP was treated as a continuous variable (odds ratio OR = 1.28, 95 % confidence interval CI: 1.08–1.52, P = 0.0072) or as a categorical variable (P for trend = 0.0038). A non-linear relationship was identified, with an inflection point at − 3.35 (∑DEHP = 0.0981 × 10−9 mol/mg creatinine). Effect sizes were 1.55 (1.01–2.36) on the left side of the inflection point and 0.73 (0.43–1.23) on the right side. Subgroup analysis indicated that the correlation was consistent across stratified variables. In conclusion, our findings suggest a non-linear association between DEHP exposure and female infertility, with a positive correlation within a specific dose range, but no further increase in risk beyond a certain threshold.

Development of a glue bonded shear connections aimed for mass timber composites

Mass timber composites (MTCs) generally require stiff glued connections to meet serviceability design criteria; however, achieving stiff glued connections without conventional pressing infrastructure can necessitate alternative gap filling adhesives. A readily available construction adhesive with potential for use with alternative pressing methods is investigated to develop the needed information for its commercial application in MTCs. The glue line thickness, surface coverage, three contact pressures, and how these parameters influence the load-slip curve of the connection when loaded in pure shear are investigated. The average glue line thickness ranged from 0.76 to 0.33 mm, with lower pressures having thicker glue lines and higher pressures having thinner glue lines. The planning precision was found to be more critical than clamping pressure for glue line thickness. Based on glue bond thickness testing and fabrication experience it is recommended to use 1.2–1.4 mm2 of adhesive per mm width to create full coverage glue bonds without excessive adhesive squeeze out. When tested under pure shear, specimens were observed to fail with a mixture of wood and glue, with more glue failure present as the pressure was increased. The initial stiffness of the connection tended to increase with clamping pressure, while the strength tended to decrease. Two bi-linear data fit models are applied to determine the yield point and simplify the load-slip behaviour for use in design analysis. Overall, the 75% max load method better fit the behaviour and was simpler to apply. The shear strength and stiffness achieved experimentally across all pressures were comparable to other glue connection literature results, and when applied in the design of a 10 m long MTC ribbed panel all pressures achieved near fully composite action. Overall, the adhesive has shown to be an easy to apply and structurally viable connection alternative for MTCs.

Application of Response Surface Methodology for concentration of fluorosilicic acid by extraction technique with benzyl alcohol as extractant

As a kind of alternative of natural fluorinated minerals and raw material for producing anhydrous hydrogen fluoride, fluorosilicic acid has attracted more and more attention. In order to raise its utilization rate, dilute fluorosilicic acid solution coming from chemical enterprises needs to be concentrated. The extraction technology with benzyl alcohol as extraction agent was used to concentrating fluorosilicic acid aqueous solution. In order to determine the optimizing operation conditions of the extracting process, single - factor experiments were carried out to confirm the significant affecting factors and their operating range. Then the Response Surface Methodology (RSM) was conducted to accomplish regression of a nonlinear quadratic polynomial model to the experimental data and statistical analysis. Based on these studies, the optimal technical parameters of extracting process are acquired by optimization of the model using the Design-Expert 13 software. The statistical analysis for the fitting response surface quadratic equation model indicated that the model was reliable and accurate with very low p-values (<0.0001), the predicted values with the model equation had remarkable consistency in experimental data. By a great number of optimizations carried out with the Design Expert 13 software based on the fitting model equation, the optimal extractive processing conditions for concentrating fluorosilicic acid aqueous solution were obtained: the phase ratio 7, the extraction time 120.11 min and the extraction temperature 39.70 °C. The optimal concentration of fluorosilicic acid is 26.68 %. In order to increase the concentration of fluorosilicic acid, three-stage cross-current extraction for dilute fluorosilicic acid solution was carried out at the optimizing extracting conditions, 17.43 wt% fluorosilicic acid solution can be concentrated up to 37.46 wt%. The extractant-benzyl alcohol can be recovered with vacuum distillation and recovery rate reached up to 98 %.

Guiding trust: The role of labeling information in the market for gluten-free pasta (2019–2023)

Labeling transparency is crucial in today's consumer landscape, ensuring informed choices and trust. Food labels are the first piece of information consumers have and become critical when foods must meet dietary restrictions. With its widespread consumption, gluten-free pasta is a fitting model to evaluate labels’ contribution to food literacy. The scope of this study is to examine the clean status and transparency of gluten-free pasta labels in the global marketplace analyzing by ingredient composition, nutritional facts, nutritional claims, and the implementation of quantitative ingredient declarations (QUID). To achieve this, Mintel Global New Product Database (GNPD) was used to retrieve a total of 1188 products sold from 2019 to 2023. Findings revealed variations in gluten-free pasta ingredients, with fresh and dry types varying in nutrients. Branded and private-label pasta differed in salt content, while pulses-based pasta was fiber and protein-rich but pricier. Organic pasta did not consistently show superior qualities, and whole grains did not change nutrition or price. Vegan options were similar in nutrition and price to vegetarian choices. While nutritional claims generally complied with regulations, inconsistent use of QUID highlighted the need for stricter labeling standards to ensure product transparency and support consumer decision-making. Our findings underline the critical role of clear and accurate labeling in empowering consumers and advocating for enhanced regulatory oversight.

An interactive R-based custom quantification program for semi-quantitative analysis of triacylglycerols in bovine milk.

The R programming language, RStudio, and open-source software solutions for analysis of liquid chromatography-mass spectrometry (LC-MS) data have been used with user-written R-based custom quantification programs (CQP) for semi-quantification of triacylglycerols (TAGs) in bovine milk lipid extracts. Using the peak-finding capabilities of the package "xcms" in RStudio, peaks were integrated, and retention times aligned, normalized, and then used for semi-quantitative analysis of a custom set of four extraction internal standards (EISs) and 29 TAG regioisomers using the choice of four analytical internal standards (AISs). Alternating stereospecific numbering (sn) 1,3 TAG regioisomers (standards 1, 3, and 5 of six calibration standards) and sn-1,2 TAG regioisomers (standards 2, 4, and 6 of six standards) were used to make a set of six calibration standards, which were used for quantification using a linear fit model, polynomial fit model, power fit model, level-bracketed linear fit, replicate-bracketed polynomial fit, replicate-bracketed power fit, and replicate- and level-bracketed linear fit and response factors. For example, the linear fit for EIS1 gave an unacceptable coefficient of determination (CoD), r2 = 0.9616, whereas the polynomial fit gave r2 = 0.9908 and the power fit gave r2 = 0.9928, while the double-bracketed linear fit gave CoDs of r2 = 0.9960, 0.9848, and 0.9781 for the three brackets, yet gave the least % difference to known calibration concentrations. For unparalleled transparency, the CQP produced webpages that allowed every step in the data processing and quantification sequence to be verified and reproduced, and contained interactive figures. The data are publicly available using a digital object identifier (DOI). The R code can be downloaded and used with the downloadable data to reproduce the results, to modify the code and further customize the results, or to copy and paste and adapt the code to other quantification applications.

Zero-shot prompt-based video encoder for surgical gesture recognition.

In order to produce a surgical gesture recognition system that can support a wide variety of procedures, either a very large annotated dataset must be acquired, or fitted models must generalize to new labels (so-called zero-shot capability). In this paper we investigate the feasibility of latter option. Leveraging the bridge-prompt framework, we prompt-tune a pre-trained vision-text model (CLIP) for gesture recognition in surgical videos. This can utilize extensive outside video data such as text, but also make use of label meta-data and weakly supervised contrastive losses. Our experiments show that prompt-based video encoder outperforms standard encoders in surgical gesture recognition tasks. Notably, it displays strong performance in zero-shot scenarios, where gestures/tasks that were not provided during the encoder training phase are included in the prediction phase. Additionally, we measure the benefit of inclusion text descriptions in the feature extractor training schema. Bridge-prompt and similar pre-trained+prompt-tuned video encoder models present significant visual representation for surgical robotics, especially in gesture recognition tasks. Given the diverse range of surgical tasks (gestures), the ability of these models to zero-shot transfer without the need for any task (gesture) specific retraining makes them invaluable.

Modelling wound area in studies of wound healing interventions

BackgroundExperimental studies of wound healing often use survival analysis and time to event outcomes or differences in wound area at a specific time point. However, these methods do not use a potentially large number of observations made over the course of a trial and may be inefficient. A model-based approach can leverage all trial data, but there is little guidance on appropriate models and functional forms to describe wound healing.MethodsWe derive a general statistical model and review a wide range of plausible mathematical models to describe wound healing. We identify a range of possible derived estimands and their derivation from the models. Using data from a trial of an intervention to promote ulcer healing in patients affected by leprosy that included three measurement methods repeated across the course of the study, we compare the goodness-of-fit of the models using a range of methods and estimate treatment effects and healing rate functions with the best-fitting models.ResultsOverall, we included 5,581 ulcer measurements of 1,578 unique images from 130 patients. We examined the performance of a range of models. The square root, log square root, and log quadratic models were the best fitting models across all outcome measurement methods. The estimated treatment effects magnitude and sign varied by time post-randomisation, model type, and outcome type, but across all models there was little evidence of effectiveness. The estimated effects were significantly more precise than non-parametric alternatives. For example, estimated differences from the three outcome measurements at 42-days post-randomisation were − 0.01 cm2 (-0.77, 0.74), -0.44 cm2 (-1.64, 0.76), and 0.11 cm2 (-0.87, 1.08) using a non-parametric method versus − 0.03 cm2 (-0.14, 0.06), 0.06 cm2 (-0.05, 0.17), and 0.03 cm2 (-0.07, 0.17) using a square-root model.ConclusionsModel-based analyses can dramatically improve the precision of estimates but care must be taken to carefully compare and select the best fitting models. The (log) square-root model is strongly recommended reflecting advice from a century ago.

A crack identification scheme based on neural network surrogate model and XFEM

Crack detection and identification is of great significance to the safety issues of engineering structures. In this paper, an intelligent crack identification scheme based on extended finite element and neural network surrogate model is proposed to realize the accurate identification of crack parameters. The method firstly employs extended finite element forward analysis to obtain the displacement data of measurement points on geometric models with different crack lengths, and inputs them as sample data to train the agent model, establishes a neural network-based inverse analysis model for crack identification, and automatically updates the threshold and weight of the neural network by using the Gray Wolf optimization algorithm to finally compute the globally optimal results. In the screening of the surrogate model, this paper verifies the advantages of the neural network surrogate model in data fitting and crack information extraction by comparing and analyzing the characteristics of neural network, support vector machine and other surrogate models, and optimizing the neural network surrogate model by adopting the Gray Wolf optimization algorithm. Finally, several numerical examples of different types of cracks are given to verify the validity of the proposed method, and the results show that the proposed method can accurately invert the geometric information of cracks.

Heterogeneity in the development of differential reinforcement: examining predictive power of definitions and differential association from a social learning perspective

Social learning theory is a prominent framework for understanding the relevance of socialization processes for predicting behavior. While recent research has focused on placing these processes within developmental contexts, there remain gaps in understanding how differential reinforcement for involvement in antisocial behavior may develop across adolescence and emerging adulthood and how definitions and differential association predict development. All 11 waves of the Pathways to Desistance dataset were analyzed, comprising the responses of 1354 justice-involved youth across seven years post-adjudication. Group-based trajectory modeling was used to identify developmental patterns of differential reinforcement and regression modeling was used to examine predictors of development. Results indicated that a two-group model of development best fit the reinforcement data (High, Low). Greater endorsement of antisocial definitions and greater differential association with deviant peers and a significant and positive interaction between these constructs predicted increased odds of assignment to the High group.

Heating patterns and temperature distribution of projectile surface in lunar regolith penetration

During penetration, a large quantity of friction-induced heat is generated, significantly increasing the projectile surface temperature. Considering that the temperature variation depends on the physical properties of the target being penetrated, understanding this relationship can aid in extraterrestrial material behavior for detection and analysis efforts. The study investigated the patterns of heat generation and the distribution of temperature on the surface of a projectile as it penetrates lunar regolith. For discrete medium penetration, large deviations appear in temperature prediction due to particle extrusion flow. Thus, a heat flux density model on the projectile surface by introducing a relative velocity factor (RVF) for correction was established. The particle flow characteristics simulation and fitting model of the introduced factor were also obtained. We constructed a theoretical relationship between the resistance and stress model parameters using dynamic modeling. Experimental projectiles recording penetration acceleration and temperature at the points of interest on the projectile surface were designed and tested to obtain recorded data. The temperature field in this process was simulated in COMSOL software to calculate the projectile surface's heat flux density and temperature distribution. The results indicate that the developed model is effective. This research infers the physical characteristics of the penetrating target under specified penetration conditions and provides more dimensional information for lunar regolith exploration.

Housing instability patterns among low-income, urban Black young adults in California and associations with mental health outcomes: baseline data from a randomized waitlist-controlled trial

BackgroundDeep-rooted racial residential segregation and housing discrimination have given rise to housing disparities among low-income Black young adults in the US. Most studies have focused on single dimensions of housing instability, and thus provide a partial view of how Black young adults experience multiple, and perhaps overlapping, experiences of housing instability including homelessness, frequent moves, unaffordability, or evictions. We aimed to illuminate the multiple forms of housing instability that Black young adults contend with and examine relationships between housing instability and mental health outcomes.MethodsUsing baseline data from the Black Economic Equity Movement (BEEM) guaranteed income trial with 300 urban low-income Black young adults (aged 18–24), we conducted a three-stage latent class analysis using nine housing instability indicators. We identified distinct patterns by using fit indices and theory to determine the optimal number of latent classes. We then used multinomial logistic regression to identify subpopulations disproportionately represented within unstable housing patterns. Finally, we estimated associations between housing experience patterns and mental health outcomes: depression, anxiety, and hope.ResultsWe found high prevalence of housing instability with 27.3% of participants reporting experiences of homelessness in the prior year and 39.0% of participants reporting multiple measures of housing instability. We found the 4-class solution to be the best fitting model for the data based on fit indices and theory. Latent classes were characterized as four housing experience patterns: 1) more stably housed, 2) unaffordable and overcrowded housing, 3) mainly unhoused, and 4) multiple dimensions of housing instability. Those experiencing unaffordable and overcrowded housing and being mainly unhoused were more than four times as likely to have symptoms of depression (Unaffordable: aOR = 4.57, 95% CI: 1.64, 12.72; Unhoused: aOR = 4.67, 95% CI:1.18, 18.48) and more than twice as likely to report anxiety (Unaffordable: aOR = 2.28, 95% CI: 1.03, 5.04; Unhoused: aOR = 3.36, 95% CI: 1.12, 10.05) compared to the more stably housed pattern. We found that hope scores were similarly high across patterns.ConclusionsHigh prevalence of housing instability and mental health challenges among low-income Black young adults demands tailored interventions to reduce instability, given widening racial disparities and implications for future well-being into adulthood.

Monte Carlo simulation of shielding materials in storage drums for 241Am-Be disused sealed radioactive sources

Disused Sealed Radioactive Sources (DSRS) containing neutron sources such as 241Am-Be require careful management due to neutron radiation. However, finding readily available and effective combination layer shielding materials for practical use to safely contain 241Am-Be can be challenging. The main objective of this study is to investigate the configuration of shielding materials and determine the maximum activity of 241Am-Be sources that can be safely stored in a 200-L drum. A three-layer shielding approach using a 200-L drum as a storage container, with sequential layers of lead (Pb), polyethylene (PE), and ordinary Portland concrete (OPC), achieves the lowest dose rates compared to other combination sequences, as shown by Monte Carlo simulations. With a fixed lead thickness and varying polyethylene and ordinary Portland concrete thicknesses, Monte Carlo simulations using the Particle and Heavy Ion Transport code System (PHITS) demonstrate that this drum design can safely accommodate activities ranging from 22.01 Ci to 72.92 Ci of 241Am-Be. The fitted model equation determines the required polyethylene thickness for any activity within this range. Additionally, case-based simulation results indicate that Indonesia's total inventory of 241Am-Be DSRS can be stored in three 200-L drums with a polyethylene thickness of 15 cm. This configuration meets international standards, ensuring the dose rate does not exceed 2 mSv/h at the surface and 0.1 mSv/h at 1 m from the drum's surface.

Accelerated CEST imaging through deep learning quantification from reduced frequency offsets.

To shorten CEST acquisition time by leveraging Z-spectrum undersampling combined with deep learning for CEST map construction from undersampled Z-spectra. Fisher information gain analysis identified optimal frequency offsets (termed "Fisher offsets") for the multi-pool fitting model, maximizing information gain for the amplitude and the FWHM parameters. These offsets guided initial subsampling levels. A U-NET, trained on undersampled brain CEST images from 18 volunteers, produced CEST maps at 3 T with varied undersampling levels. Feasibility was first tested using retrospective undersampling at three levels, followed by prospective in vivo undersampling (15 of 53 offsets), reducing scan time significantly. Additionally, glioblastoma grade IV pathology was simulated to evaluate network performance in patient-like cases. Traditional multi-pool models failed to quantify CEST maps from undersampled images (structural similarity index [SSIM] <0.2, peak SNR <20, Pearson r <0.1). Conversely, U-NET fitting successfully addressed undersampled data challenges. The study suggests CEST scan time reduction is feasible by undersampling 15, 25, or 35 of 53 Z-spectrum offsets. Prospective undersampling cut scan time by 3.5 times, with a maximum mean squared error of 4.4e-4, r = 0.82, and SSIM = 0.84, compared to the ground truth. The network also reliably predicted CEST values for simulated glioblastoma pathology. The U-NET architecture effectively quantifies CEST maps from undersampled Z-spectra at various undersampling levels.

Prediction of fresh herbage yield using data mining techniques with limited plant quality parameters

The purpose of this study was to ascertain the fresh herbage yield, fertilizer dosage, and plant characteristics of the Sorghum-Sudangrass hybrid grown in arid and semi-arid regions, as well as their interrelationships. For this reason, data from the Sorghum-Sudangrass hybrid were used to assess the predictive performance of several data mining techniques, including CHAID, CART, MARS, and Bagging MARS. Plant traits were measured in Konya and Sanliurfa during 2021 and 2022. The descriptive statistical values were calculated as follows: plant height 306.27 cm, stem diameter 9.47 mm, fresh herbage yield 10852.51 kg/da, crude protein ratio 9.66%, acid detergent fiber 33.39%, neutral detergent fiber 51.85%, acid detergent lignin 9.76%, dry matter digestibility 62.88%, dry matter intake 2.34%, and relative feed value 114.68 (average values). The predictive capacities of the fitted models were assessed using model fit statistics such as the coefficient of determination (R²), adjusted R², root mean square error (RMSE), mean absolute percentage error (MAPE), standard deviation ratio (SD ratio), and Akaike Information Criterion (AIC). With the lowest values for RMSE, MAPE, SD ratio, and AIC (246, 1.926, 0.085, and 845, respectively), and the highest R² value (0.993) and adjusted R² value (0.989), the MARS algorithm was determined to be the best model for characterizing fresh herbage yield. As a solid alternative to other data mining techniques, the MARS algorithm was shown to be the most appropriate model for forecasting fresh herbage production.

Removal of hexavalent chromium using activated and magnetic Ziziphus Jujube seed biochar — a column study

ABSTRACT In this study, activated and magnetic biochar prepared from Ziziphus Jujube Seeds (ZJS) is used to remove Hexavalent Chromium (Cr(VI)) using Fixed Bed Column Experiment. Physicochemical characteristics of the activated and magnetic biochar such as functional groups, surface area, morphological study, and magnetic properties were analyzed. The column experiment was carried out for varying bed heights (2, 4, 6, 8, 10 cm), flow rates (20, 30 ml/min), and initial chromium concentrations (40, 60, 80 ppm) to determine adsorption performance of the studied biochar. The characteristic study found that magnetic biochar (MB) has good magnetic properties and crystallinity nature with essential functional groups such as C-O, FeO. The study found that Magnetic Biochar (MB) effectively removed 99% of hexavalent chromium through column adsorption under optimal conditions, including bed height of 6 cm, 60ppm Chromium concentration, and flow rate of 20 ml/min. Kinetic model study showed that Magnetic Biochar (MB) showed very good agreement with Adam Bohart Model, with R2 of 0.97, which is best fit model than other model studies whereas the other model showed a lesser R2 value. The study reveals that magnetic biochar from Ziziphus Jujube Seed is optimal adsorbent for removal of Cr(VI) through column adsorption experiment.

Bayesian inference for unit Gamma distribution

In this article, we develop Bayesian inference for the unit-Gamma (GU) distribution, initially introduced by Grassia (1977). This distribution is highly flexible, allowing it to take various forms within the interval (0,1), encompassing both symmetric and asymmetric shapes. Such flexibility makes it an attractive alternative to traditional distributions in this range, like the Kumaraswamy and beta models. We propose a parameterization based on quantiles, a particularly advantageous approach when dealing with datasets that include outliers, since the median, for instance, is a more robust estimator compared to the mean in such cases. Our work covers parameter estimation, model fit assessment, model comparison, and influence analysis. All procedures were implemented using Markov Chain Monte Carlo (MCMC) methods via Just Another Gibbs Sampling (JAGS) through the R2jags package in R, an open-source software. Furthermore, we demonstrate the effectiveness of this methodology by applying it to a real-world dataset, highlighting its practical utility.

3D printing food flow in different extruders based on crazy and adaptive salp swarm algorithm-deep extreme learning machine improved-lattice Boltzmann method

Extrusion is significant in achieving 3D printing emulsion. The piston and screw extruders are the common structures to achieve the extrusion. The chocolate emulsion is taken for example, two extruders are numerically investigated and compared based on the fluid dynamic analysis. To conduct the simulations, the crazy and adaptive salp swarm algorithm-deep extreme learning machine (CASSA-DELM) is proposed to predict the viscosity of the chocolate emulsion, which is used to replace the traditional fitted model. The built model can avoid non-consistency in the whole shearing rate range. Then, an improved lattice Boltzmann method (I-LBM) is introduced to process the non-Newtonian behavior of the emulsion. In the simulation, the CASSA-DELM model provides the viscosities for each iteration in I-LBM based on the obtained shearing rates. Because the attachment(s) may be generated on the walls, the no-attachment and with-attachment(s) cases are explored, and the necessary results are obtained, which indicate that the piston extruder is more suitable for extruding the single component of food fluid. The screw extruder is recommended for the multiple components of food fluid because the vortex in the X-Y cross-section contributes to further mixing action for the emulsion containing different materials, such as the investigated chocolate emulsion. The indirect experiments are conducted to validate the above conclusions. The current work can contribute to improving the extruding theory of material extrusion technologies.