Bayesian Analysis of Multi-Factorial Experimental Designs Using SEM.

Latent repeated measures ANOVA (L-RM-ANOVA) has recently been proposed as an alternative to traditional repeated measures ANOVA. L-RM-ANOVA builds upon structural equation modeling and enables researchers to investigate interindividual differences in main/interaction effects, examine custom contrasts, incorporate a measurement model, and account for missing data. However, L-RM-ANOVA uses maximum likelihood and thus cannot incorporate prior information and can have poor statistical properties in small samples. We show how L-RM-ANOVA can be used with Bayesian estimation to resolve the aforementioned issues. We demonstrate how to place informative priors on model parameters that constitute main and interaction effects. We further show how to place weakly informative priors on standardized parameters which can be used when no prior information is available. We conclude that Bayesian estimation can lower Type 1 error and bias, and increase power and efficiency when priors are chosen adequately. We demonstrate the approach using a real empirical example and guide the readers through specification of the model. We argue that ANOVA tables and incomplete descriptive statistics are not sufficient information to specify informative priors, and we identify which parameter estimates should be reported in future research; thereby promoting cumulative research.

A comprehensive study on engineering and sustainability characteristics with emphasizing on 3R's approach in building construction.

The study assesses the mechanical efficiency, long-lasting characteristics, microstructure, and sustainability of sustainable concrete (SC) samples through several optimization methods, emphasizing the significance of the 3Rs (recycle, reuse, reduce) approach in the construction sector. The study uses advanced techniques like the Taguchi method, grey relational analysis (GRA), analysis of variance (ANOVA), and signal-noise ratio (SNR) to optimize parameters affecting the performance of SC. In this study, the properties of SC are assessed by considering various parameters. These parameters include the use of 10 %, 20 %, and 30 % of ground granulated blast furnace slag (GGBFS) as a replacement for fly ash (FA). Additionally, six different binder contents ranging from 300 kg/m3 to 600 kg/m3 are examined. The study also investigates three different molarities of sodium hydroxide (NaOH) (8 M, 12 M, and 16 M), three different ratios of alkaline activators (AA) (1.5, 2.0, and 2.5), three different AA to-binder ratios (0.30, 0.35, and 0.40), and curing temperature (CT) of 30 °C, 60 °C, and 90 °C. The study includes fresh properties such as fresh density (FD) and slump, mechanical properties such as tensile strength (TS), flexural strength (FS), modulus of elasticity (MOE), and compressive strength (CS), and durability studies such as dry density (DD), impact strength, water absorption (WA), and sorptivity. The blended proportions were obtained using the Taguchi method. The study shows that GGBFS accelerates geopolymerization in FA-based concrete, reducing setting time and early-age CS. FA is crucial for setting time, workability, and CS enhancement. GGBFS increases the densities of fresh and hardened concrete, with a highly correlated increase, allowing accurate hardened density prediction with a coefficient of 0.9057. The CS of the cube SC surpassed 40 MPa, irrespective of variables such as the AA ratio, CT, and NaOH molarity. The trail mix with a binder concentration of 600 kg/m3, 30 % GGBFS content, 12 M NaOH molarity, 1.5 AA ratio, 0.35 AA to binder ratio, and 90 °C CT exhibited the greatest strength. Mixtures containing 10 % GGBFS can attain a CS above 30 MPa after 28 days, making them suitable for structural purposes. The T18 mix exhibited a compact Calcium (alumino) silicate hydrate (C-A-S-H) and N-A-S-H gel, whereas the T3 mix displayed a varied and permeable structure. The study used GRA, ANOVA, and SNR methods to analyze properties varying by six variables, finding GGBFS content as the most influencing parameter. The study found that the SC had a lower sustainability score than the OPC mix, but had better energy efficiency.

Optimization of traction parameters for lumbar scoliosis.

BACKGROUND: Scoliosis is a high incidence disease that endangers the physical and mental health of adolescents. Traction therapy, as a conservative treatment plan, is helpful to improve the recovery speed of patients by studying the influence of different traction factors on the therapeutic effect. METHODS: Based on the thin layer CT data of the lumbar spine of a 16-year-old patient with scoliosis, Mimics21.0 was used to extract the 3D digital model, and Geomagic Wrap2021 was used to perform the smooth surface. After that, SolidWorks was used to manually construct the structures, such as the intervertebral disc, and Ansys17.0 was used to add constraints, ligaments, and other features. Three-factor ANOVA was carried out after an orthogonal experiment that considered traction mode, traction angle, and traction force was finished. RESULTS: ① A three-dimensional biomechanical model of lumbar scoliosis was created. ② The model's correctness was confirmed by comparing it to the corpse and other finite element models, as well as by verifying it under a range of working settings. ③ Traction force (P = 0.000), traction angle (P = 0.000), the interaction between traction force and traction angle (P = 0.000), and the interaction between traction mode and traction angle (P = 0.045) were all significant. ④ The interaction between traction force and traction angle has the most significant effect on Cobb, and traction with a certain angle is better than traditional axial traction. ⑤ Traction mode is not significant, but the interaction between traction mode and traction angle is significant. CONCLUSIONS: A certain angle of traction can aid in improving outcomes and the traction force can be suitably decreased in the clinical formulation of the traction plan. The uniformity of correcting effect is more favorable when higher fixation techniques like positive suspension or traction bed traction are used, as opposed to overhanging traction.

Seasonal influence of physicochemical and climatic parameters on phytoplankton diversity and abundance pattern in community managed semi-impacted floodplain wetland.

Wetlands provide numerous ecological services and are key habitats for aquatic flora and fauna. In the Beledanga wetland, the current study was conducted for 3 years, from July 2019 to June 2021, to evaluate the seasonal influence of physicochemical parameters on phytoplankton diversity and abundance patterns. Overall 48 genera of phytoplankton were observed. Bacillariophyceae (27%) contributed the maximum to the total phytoplankton density. The total abundance of phytoplankton was found utmost during monsoon (4.081 × 103 unit l-1) and least during post-monsoon (3.316 × 103 unit l-1). One-way analysis of variance indicated significant seasonal differences (p < 0.05) for some genera. The study gave the idea about the most influencing physic-chemical parameters (dissolved oxygen, turbidity, total hardness, Ca2+, and total nitrogen) on the growth of phytoplankton with the help of different multivariate and univariate analysis (canonical correspondence analysis and Karl Pearson's correlation). The study again highlighted that climate parameters (temperature and rainfall) had some effect on the phytoplanktonic groups. Our study conceded that N:P in the studied wetland was less than the Redfield ratio (16:1) in all three seasons, while the Si:P ratio was noticed in the high range (15:1) during pre-monsoon. The value of the Shannon diversity index and Margalef's species richness index were noticed to be > 3, which signified quite rich in phytoplankton diversity. But the value of Algal Pollution Index, which describes the ecological pollution level based on the present algal genera was observed high throughout all seasons, indicating organic load. So in future the studied wetland may get adversely affected with influence of anthropogenic activities. Therefore, for sustainable biodiversity of the waterbody, the anthropogenic activities (retting and intensification of agricultural farming) and macrophytes need to be controlled and regulated.

Influence of Machining Parameters on the Dimensional Accuracy of Drilled Holes in Engineering Plastics.

This paper explores the interaction between cutting parameters and the geometric accuracy of machined holes in a variety of engineering plastics, with the aim of improving manufacturing processes in the plastic processing industry. In the context of fast and precise manufacturing technology, the accuracy of drilled holes in polymers is of paramount importance, given their essential role in the assembly and functionality of finished parts. The objective of this research was to determine the influence of cutting speed and feed rate on the diameter and cylindricity of machined holes in six diverse types of plastics using a multilevel factorial design for analysis. The key message conveyed to the reader highlights that careful selection of cutting parameters is crucial to achieving high standards of accuracy and repeatability in plastic processing. The methodology involved structured experiments, looking at the effect of changing cutting parameters on a set of six polymer materials. A CNC machining center for drills and high-precision measuring machines were used to evaluate the diameter and cylindricity of the holes. The results of ANOVA statistical analysis showed a significant correlation between cutting parameters and hole sizes for some materials, while for others the relationship was less evident. The conclusions drawn highlight the importance of optimizing cutting speed and feed rate according to polymer type to maximize accuracy and minimize deviations from cylindricity. It was also observed that, under selected processing conditions, high- and medium-density polyurethane showed the best results in terms of accuracy and cylindricity, suggesting potential optimized directions for specific industrial applications.

Analyzing the long-term effects of e-glass mortar on sustainability and radiation shielding using ANOVA.

Significant investigations were performed on the use and impact on physical properties along with mechanical strength of the recycled and reused e-glass waste powder. However, it has been modeled how recycled display e-waste glass may affect the characteristics and qualities of dune sand mortar. This study investigates the long-term feasibility of using recycled display e-glass waste as a partial substitute for dune sand at varying percentages (5%, 10%, 15%, and 20%). The main focus is on evaluating its effectiveness in radiation shielding, strength properties, and durability for long-term development under the heating environmental process. Statistical analyses, including analysis of variance, are used to assess the significance of factors and their interactions on these characteristics. Additionally, a regression equation derived from the model offers insights into the quantitative relationship between the factors and properties. The results of the experiments led to the conclusion that the most effective proportion of e-glass waste to include in mortar is 20%, with the weight of dune sand. Including e-glass waste, they significantly increased the five characteristics of the mortar, making it suitable for high-strength mortar applications continue up to 68 MPa. The ANOVA model used in this study was trained using the same experimental research design and was critical in predicting the properties of the mortar. The model produced an accurate result with an R2 value greater than 0.99. E-glass replacements exhibit remarkable radiation shielding, characterized by pozzolanic activity and superior internal bonding due to its compact texture, contributing to enhanced long-term strength.

Study on the Molding Factors of Preparing High-Strength Laminated Bamboo Composites.

To promote the development of the 'Bamboo as a Substitute for Steel' proposal, rotary cut bamboo veneers were applied to prepare a kind of high-strength laminated bamboo composite, which was achieved through the hot press molding method in this study. Orthogonal experiments of L9 (33) were performed, with hot-pressing temperature, pressure, and time considered as three influencing factors. Physical properties like density and moisture content, and mechanical properties like modulus of rupture (MOR), modulus of elasticity (MOE), shear strength, and compressive strength were tested for the samples. It can be obtained from the results of range analysis and ANOVA that higher density and lower moisture content were correlated with higher mechanical strength. Within the selected range of tested factors, a hot-pressing temperature and time of 150 °C and 10 min can contribute to higher density and lower moisture content, and the combination of 150 °C and 50 MPa can produce greater mechanical strength. In the thickness direction, the laminated bamboo composites displayed a notable compressed structure.

mi-Mic: a novel multi-layer statistical test for microbiota-disease associations.

mi-Mic, a novel approach for microbiome differential abundance analysis, tackles the key challenges of such statistical tests: a large number of tests, sparsity, varying abundance scales, and taxonomic relationships. mi-Mic first converts microbial counts to a cladogram of means. It then applies a priori tests on the upper levels of the cladogram to detect overall relationships. Finally, it performs a Mann-Whitney test on paths that are consistently significant along the cladogram or on the leaves. mi-Mic has much higher true to false positives ratios than existing tests, as measured by a new real-to-shuffle positive score.

A comparative study of surfactant distribution and fate (western and eastern) Egyptian Mediterranean coasts focusing on its environmental toxicity.

One of the most difficult-to-manage new contaminants constantly released into the environment is linear alkylbenzene sulphonate (LAS), an anionic surfactant. Significant volumes of LAS are received by the Mediterranean coast of Egypt. The current study is a comprehensive assessment of the environmental fate of the LAS 1505 km off the Mediterranean coast of Egypt in the fall of 2023 in order to track its geographic spread and eventual demise in the water column. Critical analysis of LAS revealed that it is vertically distributed in various ways according to sources, uses, production amounts, and salinity levels. The vertical variation of LAS can be explained by its amphiphilic structure. A significant increase in surfactant concentration (>300 µg/L) was recorded in 66% and 43% of the total samples, ranging from 301.128 to 455.36 and from 304.556 to 486.135 for the western and eastern sides along the Egyptian Mediterranean coast, respectively. Evaluation of the average acute and chronic risk quotient (RQ) along the investigated locations revealed that fish were the most susceptible to LAS in both long and short exposure periods. The presented results also indicated significant LAS toxicity to three trophic levels (RQ values > 1). LAS toxicity to marine organisms was greater in the western than in eastern coastal regions according to acute and chronic mixture risk characterization ratios (RCRmix). The three trophic levels in the study area had the following order of acute relative contribution (RC) to LAS toxicity: fish > invertebrates > algae. The ANOVA test results showed that in both the western and eastern regions, LAS varied significantly (p < 0.05) with salinity (1.04E-60 and 5.44E-42) and depth (6.02E-65 and 1.59E-47), respectively. In addition, a significant difference was observed using the ANOVA test between the eastern and western regions of the Egyptian Mediterranean coast.

The Influence of Fused Deposition Modeling Parameters on the Properties of PA6/PA66 Composite Specimens by the Taguchi Method and Analysis of Variance.

Fused deposition modeling (FDM) is widely used in the rapid prototyping of polymers. Polyamide (PA) has excellent mechanical properties, but its application in FDM is limited due to its high water absorption, warpage, and forming shrinkage. The material of the filament and the printing parameters of the printer are two critical aspects that affect the performance of a component. The prepared PA6/PA66 (composite polyamide [COPA], PA6:PA66 = 85:15) composite (COPA: acrylonitrile butadiene styrene [ABS]: maleic anhydride grafted acrylonitrile butadiene styrene [ABS-g-MAH]: polyethylene = 800:133:67:100) has low water absorption (0.39%) and high dimensional stability, which has a good application prospect in FDM. The influence of eight FDM parameters, including three rarely reported, on the properties of PA6/PA66 composite specimens was investigated by the Taguchi method. The significance of influencing factors was evaluated by analysis of variance (ANOVA) and the stability by signal-noise ratio. When the layer thickness was 0.15 mm, the infill pattern was zigzags, the build plate adhesion type was brim, and the distance from the nozzle to the printing platform and the layer thickness (ΔL) was 0.05 mm; the specimens' dimensional accuracy, surface quality, and mechanical properties were better than other levels. The layer thickness and infill pattern were the two most important factors. The switch of the cooling fan and the temperature printing platform played a significant role in the specimens' dimensional accuracy and surface quality. ΔL tremendously influenced the thickness and warping degree of the specimens. The preparation of high-performance PA composites and the investigation of multiparameters by the Taguchi method provide a possible solution for applying polyamide in FDM.

Enhancing sustainability in self-compacting concrete by optimizing blended supplementary cementitious materials.

Within concrete engineering, the uptake of self-compacting concrete (SCC) represents a notable trend, delivering improved workability and placement efficiency. However, challenges persist, notably in achieving optimal performance while mitigating environmental impacts, particularly in cement consumption. However, simply reducing the cement content in the mix design can directly compromise the structural-concrete requirements. Towards these challenges, global trends emphasize the utilization of appropriate waste materials in blended concrete. This study explored a promising strategy by integrating supplementary cementitious materials (SCMs) to contribute to the United Nations' Sustainable Development Goals (SDGs) in addition to the engineering contributions. It suggests an optimal combination of Metakaolin (MK) and Limestone Powder (LP) to partially substitute cement. The research methodology employs the response surface method (RSM) to systematically explore the ideal ingredient ratios. Through a comprehensive analysis of orthogonal array of 16 mixes, encompassing both mixture and process variables, this study aims to explain the effects of MK and LP addition on the rheological and mechanical properties of SCC with varying cement replacement levels. In terms of mixture constituents, the total composition of cement, MK, and LP was fixed at 100%, while coarse aggregate (CA), fine aggregate (FA), and the water-to-binder ratio were held as process variables. In order to assess the rheological properties of the mix-design, various tests including slump flow, L-box, and sieve segregation were conducted. Additionally, to evaluate mechanical strength, samples were tested for compressive strength at both 7 and 28 days. Findings from the experiments reveal higher concentrations of MK result in reduced workability and hardened properties. Through RSM-based designed experimentation covering both rheological and mechanical aspects, it is observed that the optimal cement replacement level lies between 40 and 55%. The findings of this study contribute to the advancement of sustainable and structurally robust concrete practices, offering insights into the optimal utilization of SCMs to meet both engineering requirements and environmental sustainability goals.

Experiment and Analysis of Variance for Stabilizing Fine-Grained Soils with Cement and Sawdust Ash as Liner Materials.

Due to volume change and low strength, fine-grained soils are problematic in construction. Stabilization with cement and sawdust ash (SDA) by-products can improve engineering properties. This study aimed to investigate the effectiveness of cement and sawdust ash (SDA) in stabilizing fine-grained soils for liner applications. Varying proportions of cement (0-9%) and SDA (0-10%) were added to soil samples (n = 24). Specimens were tested for unconfined compressive strength (UCS), hydraulic conductivity (HC), and volumetric shrinkage strain (VSS). Two-way ANOVA analyzed stabilization effects. Optimal stabilization occurred with 6% cement and 6% SDA, resulting in significant increases in UCS (51 to 375 kN/m2) and decreases in HC (1.7 × 10-8 to 4.7 × 10-10 m/s) and VSS (12.8 to 3.51%) compared to untreated soil. ANOVA indicated that both cement and SDA had statistically significant (p < 0.05) effects on improving all three engineering properties. The addition of 6% cement and 6% SDA significantly improved the expansive soil's strength, hydraulic conductivity, and volume change properties. ANOVA confirmed the quantitative improvements and the significance of both stabilizers. Stabilization using the by-product SDA has the potential to be a sustainable soil improvement method.

Evaluating the Diagnostic Performance of Large Language Models on Complex Multimodal Medical Cases.

Large language models showed interpretative reasoning in solving diagnostically challenging medical cases.

Performance Prediction and Optimization of Nanofluid-Based PV/T Using Numerical Simulation and Response Surface Methodology.

A numerical investigation was carried out in ANSYS Fluent® on a photovoltaic/thermal (PV/T) system with MXene/water nanofluid as heat transfer fluid (HTF). The interaction of different operating parameters (nanofluid mass fraction, mass flow rate, inlet temperature and incident radiation) on the output response of the system (thermal efficiency, electrical efficiency, thermal exergy efficiency, and electrical exergy efficiency) was studied using a predictive model generated using response surface methodology (RSM). The analysis of variance (ANOVA) method was used to evaluate the significance of input parameters affecting the energy and exergy efficiencies of the nanofluid-based PV/T system. The nanofluid mass flow rate was discovered to be having an impact on the thermal efficiency of the system. Electrical efficiency, thermal exergy efficiency, and electrical exergy efficiency were found to be greatly influenced by incident solar radiation. The percentage contribution of each factor on the output response was calculated. Input variables were optimized using the desirability function to maximize energy and exergy efficiency. The developed statistical model generated an optimum value for the mass flow rate (71.84 kgh-1), the mass fraction (0.2 wt%), incident radiation (581 Wm-2), and inlet temperature (20 °C). The highest overall energy and exergy efficiency predicted by the model were 81.67% and 18.6%, respectively.

Nonparametric estimation of measurement uncertainty arising from sampling.

BACKGROUND: Increasingly, measurement uncertainty has been used by pure and applied analytical chemistry to ensure decision-making in commercial transactions and technical-scientific applications. Until recently, it was considered that measurement uncertainty boiled down to analytical uncertainty; however, over the last two decades, uncertainty arising from sampling has also been considered. However, the second version of the EURACHEM guide, published in 2019, assumes that the frequency distribution is approximately normal or can be normalized through logarithmic transformations, without treating data that deviate from the normality. RESULTS: Here, six examples (four from Eurachem guide) were treated by classical ANOVA and submitted to an innovative nonparametric approach for estimating the uncertainty contribution arising from sampling. Based on bootstrapping method, confidence intervals were used to guarantee metrological compatibility between the uncertainty ratios arising from the results of the traditional parametric tests and the unprecedented proposed nonparametric methodology. SIGNIFICANCE AND NOVELTY: The present study proposed an innovative methodology for covering this gap in the literature based on nonparametric statistics (NONPANOVA) using the median absolute deviation concepts. Supplementary material based on Excel spreadsheets was developed, assisting users in the statistical treatment of their real examples.

Smoothing regression and impact measures for accidents of traffic flows.

Traffic pattern identification and accident evaluation are essential for improving traffic planning, road safety, and traffic management. In this paper, we establish classification and regression models to characterize the relationship between traffic flows and different time points and identify different patterns of traffic flows by a negative binomial model with smoothing splines. It provides mean response curves and Bayesian credible bands for traffic flows, a single index, and the log-likelihood difference, for traffic flow pattern recognition. We further propose an impact measure for evaluating the influence of accidents on traffic flows based on the fitted negative binomial model. The proposed method has been successfully applied to real-world traffic flows, and it can be used for improving traffic management.

Experimental investigation on punch shear strength of poly lactic acid specimens for biomedical applications.

The designed biomedical implants require excellent shear strength primarily for mechanical stability against forces in human body. However, metallic implants undergo stress shielding with release of toxic ions in the body. Thus, Fused Deposition Modeling (FDM) has made significant progress in the biomedical field through the production of customized implants. The mechanical behavior is highly dependent on printing parameters, however, the effect of these parameters on punch shear strength of ASTM D732-02 standard specimens has not been explored. Thus, in the current study, the effect of infill density (IFD), printing speed (PTS), wall thickness (WLT), and layer thickness (LYT) has been investigated on the punch shear strength using Response Surface Methodology. The Analysis of Variance (ANOVA) has been performed for predicting statistical model with 95% confidence interval. During the statistical analysis, the terms with p-value lower than 0.05 were considered significant and the influence of process parameters has been examined using microscopic images. The surface plots have been used for discussing the effect of interactions between printing parameters. The statistical results revealed IFD as the most significant contributing factor, followed by PTS, LYT, and WLT. The study concluded by optimization of printing parameters for obtaining the highest punch shear strength.

Urine Metabolite Profiles after the Consumption of a Low- and a High-Digestible Protein Meal, and Comparison of Urine Normalization Techniques.

In the context of dietary transition toward plant proteins, it is necessary to ensure protein security in populations. It would thus be of interest to identify biomarkers of altered protein digestibility in populations. We examined the association between urinary metabolites and the acute intake of low- or highly digestible protein in healthy volunteers. The urine samples were collected before and 9 h after the ingestion of a meal containing either no protein, zein (low-digestible) or whey protein isolate (highly digestible). The liquid chromatography-high resolution mass spectrometry metabolomics approach was used for the profiling of the urinary metabolites. For the standardization of metabolomics data sets, osmolality-based, standard normal variates (SNV) and probabilistic quotient normalization (PQN) techniques were used. The ANOVA-based factorial method, AComDim_ICA, was used for chemometrics analysis. The osmolality adjustment has a beneficial effect and the subsequent mathematical normalization improves the chemometric analysis further. Some changes in the urinary metabolomes were observed 9 h after the meal in the three groups. However, there was no difference in the urine metabolome between groups. No biomarker of protein digestibility can be identified after the ingestion of a single meal, even when marked differences in the digestion efficiency of protein have been observed.

Bootstrap tests for simultaneous monotone ordering of effects in a two-way ANOVA.

In a two-way additive analysis of variance (ANOVA) model, we consider the problem of testing for homogeneity of both row and column effects against their simultaneous ordering. The error variances are assumed to be heterogeneous with unbalanced samples in each cell. Two simultaneous test procedures are developed-the first one using the likelihood ratio test (LRT) statistics of two independent hypotheses and another based on the consecutive pairwise differences of estimators of effects. The parametric bootstrap (PB) approach is used to find critical points of both the tests and the asymptotic accuracy of the bootstrap is established. An extensive simulation study shows that the proposed tests achieve the nominal size and have very good power performance. The robustness of the tests is also analyzed under deviation from normality. An "R" package is developed and shared on "GitHub" for ease of implementation of users. The proposed tests are illustrated using a real data set on the mortality due to alcoholic liver disease and it is shown that age and gender have a significant impact on the increasing incidence of mortality.

Optimization of 3D printer settings for recycled PET filament using analysis of variance (ANOVA).

Fused Deposition Modeling (FDM) 3D printing creates components by layering extruded material. Printer parameters such as layer height and infill density can greatly impact the mechanical properties and quality of the printed parts. One critical factor to be considered in analysis is the anisotropy nature of printed components, considering all cross-sectional area (CSA) profiles for less than 100% infill density. This paper investigates the effect of the anisotropy nature of 3D printed CSA has on stress calculations and hence mechanical properties of the specimen through Design of Experiment (DOE). Analysis of variance (ANOVA) is utilised to evaluate the results. Printed specimens were tensile tested as per ASTM D638-14. Raw data was analysed using various CSA profiles, taking changes in infill density and layer height into account. Fixed parameter such as shell count, top and bottom layers, nozzle diameter, Hexagonal pattern were defined. Specimens Ultimate Tensile Strength (UTS) values increased on average by 30% using average profile CSA data compared to using external specimen dimensions. Further analysis assessing printer parameters affect on recycled Polyethylene Terephthalate (rPET) specimen's Young's Modulus (YM) and UTS was studied. One significant finding from this study suggests that the thickness of each layer has the most significant impact on the material properties of 3D printed rPET, as observed through the analysis of tensile test data obtained from 3D printed samples. A 3D printed rPET specimen with 30% infill density and 0.25 mm layer height has a higher YM (1175 MPa) and UTS (39 MPa) compared to a specimen with 75% infill density and 0.1 mm layer height (1159 MPa, 31 MPa). However careful interpretation of the results is required because for the same 30% infill parameter at 0.2 mm layer height the YM (936 MPa) and UTM (28 MPa) are significantly lower than at 0.25 mm layer height. If a higher value of YM and UTS is required an infill setting of 50% and layer height of 0.25 mm gave the highest values, YM (1330 MPa) and UTS (43 MPa).