Statistical Analysis Model Research Articles

A mathematical framework for the statistical interpretation of biological growth models

Biological entities are inherently dynamic. As such, various ecological disciplines use mathematical models to describe temporal evolution. Typically, growth curves are modelled as sigmoids, with the evolution modelled by ordinary differential equations. Among the various sigmoid models, the logistic, Gompertz and Richards equations are well-established and widely used for the purpose of fitting growth data in the fields of biology and ecology. The present paper puts forth a mathematical framework for the statistical analysis of population growth models. The analysis is based on a mathematical model of the population–environment relationship, the theoretical foundations of which are discussed in detail. By applying this theory, stochastic evolutionary equations are obtained, for which the logistic, Gompertz, Richards and Birch equations represent a limiting case. To substantiate the models of population growth dynamics, the results of numerical simulations are presented. It is demonstrated that a variety of population growth models can be addressed in a comparable manner. It is suggested that the discussed mathematical framework for statistical interpretation of the joint population-environment evolution represents a promising avenue for further research.

Assessing aortic morphology post aortic valve replacement using unsupervised hierarchical clustering and statistical shape modelling

Abstract Introduction There are different surgical procedures used to treat aortic valve disease such as aortic valve replacement (AVR), the Ozaki procedure, the Ross procedure, and the valve-sparing procedure. There may be postoperative side effects associated with aortic morphology. Computational analyses that assess morphology are thus necessary in such scenario. Statistical shape modelling is used to assess three-dimensional morphology, discover unique shape features, create mean shapes, and create shape modes that display morphological variability. Hierarchical cluster analysis is a machine learning method used to classify a population into subgroups. Both of these statistical methods were applied to aortic valve replacement patients to evaluate morphological variability after aortic valve replacement (AVR) surgery. Purpose The purpose of this study is to assess the aortic morphology of AVR patients, identify morphological differences between different surgical procedures, identify subgroups postoperatively using statistical shape modelling and hierarchical cluster analysis, and assessing possible correlations between the resultant clusters and function i.e. ejection fraction. Methods Computed tomography and cardiac magnetic resonance images were used to reconstruct aortas into 3D models using segmentation and 3D reconstruction software. For each patient, two models were created (i.e. ascending aorta only and aorta including the arch and the descending aorta). N=35 patients were included in the study. Statistical shape analysis was run to create templates and shape modes. Statistical environment and language software was used to run hierarchical cluster analysis. Results Overall, the hierarchical cluster analysis did not classify the aortas based on type of surgery, but most patients in the Ross group demonstrated morphological differences from the other surgical groups and many of the aortas belonging to the Ross group were clustered together in both clustering analyses. No significant differences between clusters were seen (p=0.47 for ascending, p=0.19 for whole aorta). No correlation was found between clusters and ejection fraction (p=0.75, r2<0.01), possibly indicating that post-AVR aortic morphology in this sample does not reflect overt functional changes. Conclusion Both statistical shape modelling and hierarchical cluster analyses were successful in assessing morphological variability and identifying subgroups in this population. This framework could therefore be extended to larger samples to inform on surgical decision-making allowing surgeons to choose the appropriate surgery for the patient or design prosthetic valves that are more customised to the patient’s anatomy.Cluster-Ejection Fraction Correlation

Dynamic Optimization of Exclusive Bus Lane Location Considering Reliability: A Case Study of Beijing

For metropolises like Beijing, heavy congestions cause transit passengers’ unreliable travel time, including in vehicle time and waiting time. Comparing with other managerial measures, designing a lane for bus use only is an effective method to improve travel reliability, for it can eliminate the influence on bus-driving conditions. This paper proposes a reliable and practical method to determine exclusive bus lanes (EBL). A reliability-based optimization model is established, in which the tradeoff among bus and private car passengers’ travel time, reliability, and EBL construction cost are considered. Based on the actual network, a user equilibrium demand assignment model is applied to estimate the dynamic bus flow distribution. Since the model is nonlinear, a two-step method is proposed where tangent lines are introduced to constitute an envelope curve to linearize the model. This work conducts the statistical modeling and fitting analysis with actual bus trajectory data, collected on EBL in Beijing during peak hours. Passenger travel time distributions are fitted to estimate the statistical passenger travel time; Lognormal distribution and Gaussian distribution are the best fit. The optimization results indicate that the passenger travel time reliability can be improved by 5.5% by the optimized EBL location scheme. This study will provide a theoretical basis and methodological support for improving the service level of the public transportation system in large cities through the scientific planning of exclusive bus lanes.

A 45-year updating wind and wave hindcast over the Oman Sea and the Arabian Sea

This study focuses on developing a comprehensive and reliable wind and wave hindcast for the Oman Sea and Arabian Sea, spanning a significant 45-year period (1979–2024). The objective is to capture the intricate wind and wave climate of the region, characterized by distinct monsoon cycles and occasional tropical cyclones. The availability of extensive wave data over four decades would be an irreplaceable tool for researchers and engineers, enabling improved accuracy in extreme value analysis, sediment transport studies, and wave-induced current simulations. Despite limited field observations, all available measured data within the region were collected, analyzed for configuration of the models and utilized for model calibration and validation. The hindcast data was generated using the Weather Research and Forecasting (WRF) model for winds and WAVEWATCH III (WW3) model for waves, and evaluated against observational and measured wind and wave parameters. A comprehensive statistical analysis of the hindcast model's performance reveals adequate agreement with measured data, as evidenced by root mean square errors (RMSEs) of 1.23 m/s for wind speeds and 0.37 m for significant wave heights in average. These results underscore the model's reliability for research and engineering applications, particularly in the Arabian Sea, with a focus on the northern coastlines of the Oman Sea. The specific model configuration employed in this study holds significant potential for future investigations in the northern Indian Ocean, offering a valuable tool for understanding and predicting climatical conditions in this region.

Pulmonary Emphysema Disease Classification Model Using Deep Structured Techniques

Emphysema is normally employed in the sense of morphology, and thus, imaging the modalities has a significant part in detecting the disorder. Most importantly, the High-Resolution Computed Tomography (HRCT) is a trustworthy component for illustrating the emphysema pathology. Hence, the pulmonary emphysema diagnosis at the starting stage is very important and can secure the life of a human. When there is a failure in the technique, there is a possibility of the death that displays the importance of effective diagnosis. The endotracheal intubation’s delay is prevented by implementing distinct mechanisms that result in various requirements. The presented work develops a deep learning-aided pulmonary emphysema disease classification model that is implemented to defeat the above difficulties. At first, the images are accumulated from a standard dataset. Then, the collected images are provided to the phase of segmentation. Here, the newly developed Adaptive Trans-Residual Dense UNet (ATRDUNet) is employed to perform the segmentation process. Also, the recommended ATRDUNet model helps to locate the disease in the appropriate location. Moreover, the performance of the segmentation process is enriched to fine-tune the parameter using the Fitness Revised Position of Wild Horse Optimizer (FRP-WHO). While optimizing the parameters effectively, it tends to handle the complex issues to strengthen the model accurately. The segmented results from the ATRDUNet are passed to Vision Transformer-based Residual DenseNet with LSTM layer (ViTrans-RDLSTM) for getting the final classified outcome. To this end, the analysis of the suggested model is correlated to find the effectiveness of the recommended model. However, these results are examined with the help of standard performance measures. Also, the experimentation is carried out using the standard “Automatic emphysema detection using weakly labeled HRCT lung images” dataset to attain better outcomes in the developed model. The findings of the recommended approach show 95% in terms of accuracy. Also, the statistical analysis of the developed model shows 23.9%, 12.3%, 15.2%, and 13.0% better performance than BWO, CO, HBA, and WHO in terms of mean analysis to show enriched performance.

Evaluating Airport Service Quality Based on the Statistical and Predictive Analysis of Skytrax Passenger Reviews

This study leverages approximately 7500 reviews from Skytrax to explore the determinants of airport service quality and their influence on passenger recommendations. The dataset includes various features such as terminal cleanliness, terminal seating, terminal signs, food and beverages, airport shopping, WiFi connectivity, and airport staff. The research employs a comprehensive methodology encompassing statistical data analysis, predictive modelling, and interaction effects analysis. The descriptive analysis of time-series data highlighted trends and fluctuations in service quality and recommendations, providing insights into temporal dynamics. Multiple machine learning models, including logistic regression, Random Forest, SVM, KNN, Gradient Boosting, and Neural Networks, were developed in this study and cross-validated for airport recommendation based on Skytrax’s online reviews. Among others, Gradient Boosting emerged as the most accurate model with an 88.15% mean accuracy. Interaction effects revealed significant combined influences, such as terminal cleanliness and terminal seating, on passenger recommendations. This multifaceted approach offers robust insights into factors influencing airport recommendations and guides improvements in airport management to enhance passenger satisfaction. Future work will focus on a general-purpose machine learning framework and its toolbox development for airport service quality analysis based on online reviews from various sources.

Statistical Analysis and Several Estimation Methods of New Alpha Power-Transformed Pareto Model with Applications in Insurance

This article defines a new distribution using a novel alpha power-transformed method extension. The model obtained has three parameters and is quite effective in modeling skewed, complex, symmetric, and asymmetric datasets. The new approach has one additional parameter for the model. Certain distributional and mathematical properties are investigated, notably reliability, quartile, moments, skewness, kurtosis, and order statistics, and several approaches of estimation, notably the maximum likelihood, least square, weighted least square, maximum product spacing, Cramer-Von Mises, and Anderson Darling estimators of the model parameters were obtained. A Monte Carlo simulation study was conducted to evaluate the performance of the proposed techniques of estimation of the model parameters. The actuarial measures are computed for our recommended model. At the end of the paper, two insurance applications are illustrated to check the potential and utility of the suggested distribution. Evaluation using four selection criteria indicates that our recommended model is the most appropriate probability model for modeling insurance datasets.

A generalizable and interpretable model for early warning of pest-induced crop diseases using environmental data

Pest infestations and resulting crop diseases threaten global food security. Traditional pest and disease monitoring methods are time-consuming and prone to delays, thus necessitating the development of effective prediction strategies that facilitate early and timely detection. In response to this challenge, this research proposes a generalizable and interpretable machine learning model to predict two major rice pests—Green Leafhopper and Yellow Stem Borer—and a No Pest class using environmental data collected from various regions in India. The dataset, comprising factors such as temperature, humidity, and rainfall, underwent rigorous preprocessing. Key features like temperature difference, humidity difference, and vapor pressure deficit were engineered to enhance the model’s performance. Twelve baseline models were trained and their performance was evaluated using F1 scores and AUC values due to the imbalanced nature of the dataset. Through statistical analysis of baseline models, Random Forest, Balanced Random Forest, XGBoost, and CatBoost models, were selected for hyperparameter tuning via Optuna. The tuned CatBoost model demonstrated superior performance, achieving AUC values of 0.99 for Green Leafhoppers, 0.98 for Yellow Stem Borers, 1.00 for No Pest class and an overall F1 score of 0.9414 with a mean AUC value of 0.9912 across all classes. Additionally, Explainable Artificial Intelligence techniques, particularly SHAP, were employed to interpret the model’s predictions, identifying the relative importance of environmental factors in pest occurrence. This interpretability aligns the model’s predictions with established agronomic knowledge, enhancing its practical utility for early pest and disease detection. The generalizability of the proposed model suggests it can be adapted to other crops and regions, offering a valuable tool for early warning systems in agriculture, promoting sustainable practices, and reducing crop losses due to pests and diseases.

Degree of Food Processing Is Associated With Glycemic Control in African American Adults With Type 2 Diabetes: Findings From Texas Strength Through Resilience in Diabetes Education Clinical Trial

BackgroundType 2 diabetes mellitus (T2DM) disproportionately affects African American (AA) populations. Despite the importance of diet in diabetes management, the association of diet quality and/or the degree of food processing with glycemic control in AA adults with T2DM remains unclear. ObjectiveThis study aimed to examine associations between diet quality scores and the degree of processing in the diet with glycosylated hemoglobin (HbA1c) level in AA adults with T2DM. DesignThis cross-sectional study used baseline data from participants in Texas Strength Through Resilience in Diabetes Education, an ongoing clinical trial. Participants/settingParticipants involved in this analysis (N = 273) were AA adults with T2DM recruited through local churches in Austin, TX, and the surrounding areas from August 2020 through April 2023. Main outcome measuresParticipants provided 2 24-hour dietary recalls (1 weekend and 1 weekday) and a blood sample to measure HbA1c level. Healthy Eating Index 2015, Alternative Healthy Eating Index 2010, and Alternate Mediterranean Diet scores were calculated. The NOVA method was used to calculate the percentage of grams and calories that came from ultraprocessed foods and unprocessed or minimally processed foods. Statistical analyses performedLinear regression and analysis of variance models tested associations between the diet quality scores and degree of food processing with HbA1c level, adjusting for demographic covariates. Models were stratified by insulin use after finding a significant interaction with ultraprocessed foods and unprocessed or minimally processed foods. ResultsRegression analyses revealed that the percentage of grams in the total diet from ultraprocessed foods was positively associated with HbA1c level (βadj = .015; Padj = .032), whereas unprocessed or minimally processed foods were inversely associated with HbA1c level (βadj = –.014; Padj = .043). There was no significant association between any diet quality score and HbA1c level. ConclusionsIn AA adults with T2DM, only the degree of food processing was associated with HbA1c level. Future research should explore whether a causal relationship exists between food processing and HbA1c level and investigate mechanisms by which ultraprocessed foods may affect glycemic control.

Independent isomeric yield ratios of fission products in the epi-cadmium neutron induced fission of 235U

In the epi-cadmium neutron induced fission of 235U, independent isomeric yield ratios (IR) of fission products 130,132Sb, 131,133Te, 134,136I, 135Xe and 138Cs have been measured by using an off-line gamma-ray spectrometric technique. The average neutron energy of the epi-cadmium reactor neutron spectrum is 1.9 MeV. From the IR values, the root mean square fragment angular momenta (JRMS) were deduced by using spin dependent statistical model analysis. The IR and JRMS values of considered fission products in the epi-cadmium neutron induced fission of 235U were compared with the literature data in the thermal neutron induced fission of 235U to examine the influence of excitation energy on nuclear structure effect.

Social Networks and Digital Influencers in the Online Purchasing Decision Process

Social networks have become a significant part of people’s daily lives, particularly in the purchasing process. In this context, digital influencers have played an essential role in shaping consumers’ opinions. In this sense, studying the role of social networks and influencers in the online product purchase decision process was considered pertinent. We selected two technology adoption models to fulfill this purpose: the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). We conducted an exploratory study using a quantitative approach, with data being collected through a questionnaire distributed online. The statistical analysis of the empirical model employed a partial least squares structural equation modeling (PLS-SEM) technique. Analyzing the 135 responses allowed us to conclude that social networks are relevant in consumers’ purchase decision process. This research highlights the importance of the credibility of influencers, emotional connections with audiences, and the dynamics of the media in shaping consumer behavior. It highlights the strong influence of digital influencers on purchasing decisions and makes a methodological contribution with a rigorous empirical model. This study also suggests avenues for future research in order to deepen the understanding of influencer and social media marketing strategies.

About the Residual Time of Human Life

Many researchers believe that human lifetime in the interval from 20 to 80 years is approximately described by the Gompertz distribution. It is natural to consider the problem of the distribution of the residual human lifetime at the final stage, that is, after 80 years. To do this, in this study, a statistical analysis of some statistical models on real data is carried out. Several models are presented, which, according to the authors, can adequately describe data on mortality after 80 years.

Analysis of Erythrocyte Parameters in Multiple and Long-Term Blood Donors from Northern Pomerania (Poland)

Background/Aims: Assessment of the levels of vital blood parameters in donors is essential to evaluate their health status, ensure their suitability for donation, preserve the integrity of the circulatory system, and facilitate comprehensive health monitoring. The aim of our study was to analyse the levels of haemoglobin, haematocrit, erythrocyte count, MCV, MCH, and MCHC in 12 groups of first-time donors and experienced donors of both sexes at the John Paul II Regional Blood Donation and Treatment Centre in Słupsk, northern Poland. The donors were divided into three age groups (18-30 years, 31-45 years, and 46-65 years). Methods: Using MANOVA multivariate significance tests, we examined the main effects of donor-related factors (age, sex, donor stage) on morphological blood parameters to evaluate different haematological parameters, such as Hb, Ht, RBC, MCV, MCH, and MCHC, and identified statistically significant relationships between all variables. Results: The multivariate analysis of these three main factors showed that the variation in haemoglobin (Hb) levels accounted for 46% of the explained dependence in this statistical model. In particular, approximately half of the variability in the multivariate statistical analysis was attributed to the role of Hb and haematocrit (Ht). In addition, the β-coefficient values for Hb and Ht were statistically higher in relation to donor sex and donor type (single versus repeat). These β-coefficient values from our data represent the strength and direction of the relationship between the haematological parameters (Hb and Ht) and the specific donor characteristics. A higher β-coefficient indicates a stronger influence of donor sex and donor type on these parameters, suggesting that these factors contribute significantly to the variation in the Hb and Ht levels. Based on our results, the comprehensive analysis of the entire statistical model of metabolic biomarkers revealed the following hierarchy: Hb > Ht > MCHC > MCV > RBC > MCH. The results obtained showed strong statistical relationships, as indicated by the high values of the key statistical indicators in our analysis. The coefficient of determination (R²) showed that the model explained a significant proportion of the variance in the data, while the F-test statistic confirmed the significance of the predictors. Conclusion: These strong statistical dependencies provided a clear justification for selecting this model over others, as it effectively represented the underlying relationships within the data. These statistics help to assess how well the model matches the actual data, thereby helping to reduce the risks associated with blood donation, optimise donor safety, and maintain the quality and efficiency of blood transfusion services.

Enhancing Manufacturing Processing Stability and Efficiency with Linear-Regression Analysis: Modeling on a Flow-Drill Screw (FDS) Joining Process

The instability (in processing time) in the flow-drill screwing process is undesired but inescapable due to variations in material property, gauge, and process parameters. A substantial number of materials and lab labor need to be used to test and control the variability of the real manufacturing joining process. To enhance the stability and efficiency of the screwing process, this study seeks multi-disciplinary collaboration by applying linear-regression modeling. Six hundred and forty-eight data points were collected and split into an 80% training set for model building and a 20% test set for model validation. A multiple linear-regression model was built. The results indicated that, compared to variable base level (6000 rpm rotational speed and 1100 N downforce), higher rotational speed (8000 rpm, 7000 rpm), greater downforce (1200 N, 1300 N), and their interaction were significantly associated with passage (processing) time, while the switch point did not significantly affect passage time. The interaction plot and effect size were adopted to provide measurements of the effect magnitude on processing time. The coefficient of determination indicated that 86% of the variability in the passage time can be explained by this model. Statistical analysis, such as data visualization, statistical modeling, and other data-driven analysis methods, can be used to detect underlying relationships between variables, investigate variations, and make predictions in the manufacturing process. The outcomes from the data-driven analysis can benefit from improving the economical manufacturing system, refining the processing setting, and reducing test material costs, labor, and lead time.

Feline respiratory disease complex: insights into the role of viral and bacterial co-infections.

Feline respiratory disease complex (FRDC) is a highly prevalent syndrome in cats that often result in fatal outcomes. FRDC etiology is complex, and often, multiple viral and bacterial pathogens are simultaneously associated with disease causation. There is limited information about the role of co-infections in pathogenesis and the current prevalence of pathogens in North America. We aimed to conduct a study using technical advances in molecular diagnosis and statistical modeling analysis to elucidate the occurrence of pathogens and how co-infections affect disease severity. We attained information from three diagnostic laboratories in North America regarding the occurrence of Bordetella bronchiseptica, Chlamydia felis, Mycoplasma, Felid alphaherpesvirus 1 (FeHV-1), feline calicivirus (FCV), and influenza A, along with age, seasonality, sex, and clinical signs. We also evaluated the role of co-infections in disease severity. These pathogens were also investigated in clinically normal cats (control). The most detected pathogens were Mycoplasma, FCV, and FeHV-1. Most pathogens were detected in the control group, highlighting the challenge of interpreting positive testing results. Co-infections of Mycoplasma and FCV, as well as Mycoplasma and FeHV-1, were important predictors of disease severity. Age, sex, and season had a minor impact on pathogen occurrence. This study provides new insights into FRDC and underlines the relevance of diagnostic panels to screen for a range of pathogens, providing knowledge for timely diagnosis and therapeutic interventions.

Additively manufactured carbon fiber reinforced-polyamide component: a study based on statistical modeling and regression analysis

This paper presents the experimental assessment of the hardness characteristic of additively manufactured polyamide (PA 6) composite reinforced with carbon micro-fibers. The carbon fiber-reinforced polyamide (CFPA) components were manufactured using the additive manufacturing technique—fused deposition modeling (FDM). The experiments were conducted for testing the hardness of the samples using a Shore-D hardness tester. The novel contributions of the work towards the manufacturing fraternity include selecting a scantly researched material like CFPA, and the elaborative investigation of hardness variation with the alteration of the prime parameters pertaining to FDM. The effect of the print-related parameters, namely, layer height (LH), infill density (ID), and raster orientation (RO) on the hardness of the CFPA component was studied, and the results were analyzed using statistical analysis tool ‘analysis of variance (ANOVA)’. Moreover, a regression model was developed to predict the output response, i.e. hardness for different combinations of the input parameters. Considering an ID of 100% and an RO of 0°, the hardness value of 93.89 at 0.1 mm LH reduced to 88.44 at 0.3 mm LH, depicting a reduction of 5.81%. An increasing trend was observed for hardness with the increase in ID for all the levels of LH and RO. The highest value of hardness (93.89) was achieved at an ID of 100%, with the LH and RO values kept at 0.1 mm and 0°, respectively. The ANOVA suggested that the effect of all three parameters is significant in the study, ID being the most affecting parameter with an effect contribution of 37.88%. The fitness of the adopted model was well justified by the high R-sq value of 0.9618 and significantly low error values in the range of 0.002–0.08.

Adsorption of cadmium ion from water by using non-toxic iron oxide adsorbent; experimental and statistical modelling analysis

Adsorption of cadmium ion from water by using non-toxic iron oxide adsorbent; experimental and statistical modelling analysis

The Influence of E-Service Quality and E-WOM on Customer Satisfaction of Shopee Users (Study on Business Administration Students at Bali State Polytechnic)

This study aims to determine the extent of the influence of e-service quality and e-WOM on customer satisfaction with the Shopee application. The population in this study is 92 students from the Business Administration Department at Bali State Polytechnic who use the Shopee application for shopping, especially for fashion items, with an unknown total number of users. The method used in this research is quantitative, with a Total Sampling technique. Hypothesis testing in this study employs statistical model analysis consisting of validity tests, reliability tests, classical assumption tests, multiple linear regression tests, T-tests, F-tests, and R² determination coefficients using SPSS22 software. The results show that there is an influence between the independent variables, namely e-service quality and e-WOM, on the dependent variable, customer satisfaction.

Integrating human factors into the distribution model of goods and fast-moving consumer goods for effective inventory control

Multiple factors contribute to the occurrences of disruptions in workflow management within the e-commerce system that focuses on the distribution of goods and other fast-moving consumer goods (FMCG). These disruptions have particularly affected and increased lead times and caused frequent shipment delays. The causes of these disruptions, especially in the context of distribution of goods and FMCG towards inventory control, have been widely speculated upon in recent years. Given the recent interruptions, the study presented in this paper used a methodology called statistical model analysis (SMA) to study the impact of human factors (HFs) on the system performance in a warehouse distribution system. A case study was taken on a particular company called ABC and XYZ to model the impact of some specific HF such as job fatigue, and job rotation to name a few and to uncover the underlying reasons behind the ongoing disruptions within the distribution system. Specifically, the result of this study aims to provide a data-driven understanding of these issues and one of the contributions of this study is to enhance our understanding of the significance of these HFs, rather than focusing on the equipment and materials as seen in prior research. Through this data-based approach study, stakeholders in operations management, e-commerce, and the supply chain system would be well-informed in many ways to resolve the challenges faced by humans in the system towards enhancing their overall system performance.

Statistical optimization, kinetic modeling, and techno-economic analysis for the production of high molecular mass dextran using sugarcane industrial waste-molasses

This study focuses on the transformation of industrial wastes to wealth by utilizing treated sugarcane molasses (TCM) to produce dextran. The fermentation conditions for maximum dextran production were initially optimized using central-composite design in shake-flasks. The highest titer of dextran (60.0 ± 2.0 g/L) was obtained with optimized variables of 150 g/L substrate (TCM), 12.8 g/L yeast extract, 39.8 g/L K2HPO4, and 48 h fermentation time. Then, the profiles of dextran production, TCM consumption, and microbial growth were fitted by kinetic models to obtain the following kinetic parameters: 0.35 h−1 maximum specific growth rate (μmax), 0.48 g dextran/g substrate yield coefficient (Yps), 0.07 maintenance coefficient (ms), and 10.73 g product/g cell growth-associated constant (α). For determining the scale-up factors, the fermentation conditions were replicated in a 3 L fermenter at various stirring speeds (50–250 rpm), and a scale-up strategy based on constant P/V was used to predict the power consumption (1.88–285.51 W) for a pilot-scale of 2000 L working volume fermenter at various stirring speeds (10.8–54 rpm). The dextran produced was characterized using gel permeation chromatography to determine the molecular mass variations (3–4000 kDa) with fermentation conditions. The rheological variations of fermentation broth at different stirring speeds were also studied and related to the molecular mass of the dextran produced. Techno-economic analysis for dextran production explored a gross margin of 22.65 %, a return on investment of 16.80 %, and a pay-back time of 5.95 years.