Model Assessment Research Articles

Sustainable management of polyethylene terephthalate waste flow using a fuzzy frank weighted assessment model

The consequences for the ecosystem of polyethylene terephthalate (PET) waste are becoming increasingly significant and widespread. Companies managing PET waste strive to enhance sustainability in all areas. The development of systematic decision-making approaches and frameworks for PET waste management is strongly needed. This research aims to present a new methodological framework for the categorization of the most efficient PET waste management solutions. The introduced fuzzy Frank weighted sum product assessment (FWESPA) model enables rational and flexible reasoning by nonlinearly processing uncertain information. A nonlinear aggregation function is proposed for the fusion of fuzzy strategic options. It is advantageous in simulating the impact of strategic options on a final decision. An integral part of the introduced fuzzy FWESPA model is a reverse sorting algorithm. This innovative algorithm can improve the performance of traditional normalization techniques. Also, an improved fuzzy Frank ordinal priority approach linear model is formulated to define the significance of evaluation criteria. The comprehensive real-life study demonstrates the proposed decision-analytics-based approach. The results showed the following rankings of considered alternatives: “recycling” (ℤA2 = 0.8565) > “energy recovery” (ℤA1 = 0.7364) > “remanufacturing” (ℤA4 = 0.690) > “incineration” (ℤA3 = 0.6592). Based on the results presented, alternatives “recycling” (A2) and “energy recovery” (A1) represent dominant alternatives with a slight advantage of recycling. Research findings can be used when deciding the appropriate way to enhance PET waste handling. The findings also describe the benefits and limitations of each treatment option for PET waste, as well as highlight the crucial challenges.

Power-law modeling for swift viscoelastic assessment in biological soft tissues

Power-law modeling for swift viscoelastic assessment in biological soft tissues

4 Hands-on 2: Environmental evaluation of feeding strategies with agent-based modeling and life cycle assessment: From theory to practice. Sponsored by EAAP

Abstract Livestock farming systems (LFS) have detrimental effects on the environment, associated mainly to feed production, enteric methane and emissions from manure. Life Cycle Assessment (LCA) is a standardized internationally recognized methodology to assess the various environmental impacts associated to a product, considering all the processes occurring during its life cycle. Cradle-to-farm-gate LCA is commonly used to assess the environmental impacts of LFS. However, LFS are complex because they are structured by multiple interactions between biological and human-controlled processes, at various organization levels. Their behavior and the extent to which feeding strategies can mitigate this environmental burden is not always easily predictable. Dynamic mechanistic models of LFS can simulate the effects of feeding strategies on both animal performance and the associated excretion of nutrients. These models are useful tools to produce the life cycle inventories needed in life cycle assessment. In the pig-fattening unit, the animal performance with multiphase group feeding depends largely on the variability of growth potential among pigs and the farmer makes decisions that can target either the group of pigs or each individual (e.g., when choosing pigs to deliver to slaughterhouse). Therefore, simulating the growth trajectory of each individual pig and events like deliveries to slaughterhouse makes it possible to estimate the response of the pig population rather than the average pig to feeding strategies. Individual-based models (IBM) are particularly adequate because they make it possible to simulate each individual and to build the response of the population from the aggregation of individuals. Agent-based models are IBM that rely on self-governing agents made of properties, behavioral rules and resources that allow each agent to make decisions upon the occurrence of an event, which can be triggered randomly or not. This hands-on will focus on a specific example that illustrates how and why agent-based models are useful tools to assess the effects of feeding strategies through LCA. To keep it easily tractable, it will focus only on the impacts on climate change and acidification of a pen of fattening pigs according to the feeding strategy applied. In a first step, we will develop a simplified individual-based model of a pig-fattening pen (including pigs and the farmer as agents), which simulates the effects of feeding strategies on animal performance (growth rate, feed conversion ratio, slaughter weight, N excretion). In a second step, we will upgrade this model by introducing a module which makes the calculation of the environmental impacts through LCA. In a third step, we will use the model to simulate the effects of various feeding strategies according to the variability of growth potential among pigs in the pen. For all steps, we will work with Python language using pre-developed Jupyter notebooks.

Time varying M with starvation mortality in a state-space stock assessment model: Part 2: Atlantic cod (Gadus morhua) on the southern Grand Bank of Newfoundland

State-space models are now a common tool for modeling time-varying ecological phenomena. This extends to state-space stock assessment models (SSAMs), recognized as pivotal components within the evolving landscape of next-generation stock assessment methodologies. Though methods are rapidly evolving, the estimation of time-varying rates of natural mortality (M) remains a challenge, and the sensitivity of stock assessments and management advice to assumed M values underscores the pressing need for improved estimation methods. Using southern Grand Bank (SGB) Atlantic cod as a case study, we introduce a novel approach to estimate time-varying M. We first convert a length-based starvation M index into an age-based index, which we then include in an age-based SSAM to estimate two components of M: starvation M and a remainder component. This produces a new SGB cod SSAM with time-varying total stock M. This model produces a large decrease (68 %) in the size of the model process errors (i.e., their standard deviation) and better fit compared to a model that did not account for time-varying M, indicating that the starvation M index improves our model of stock productivity. By leveraging readily available information on fish body condition and the proportion of fish in really poor condition, the proposed methods offer a valuable solution to the challenges associated with estimating time-varying M. The proposed methods offer a tractable solution to the common struggles associated with quantifying changes in fish productivity, which is crucial for the management of dynamic systems.

Using anthropometric parameters to predict insulin resistance among patients without diabetes mellitus

Anthropometric parameters are widely used in the clinical assessment of hypertension, type 2 diabetes, and cardiovascular disease. However, few studies have compared the association between different anthropometric parameters and insulin resistance (IR). This study was aimed at investigating the relationship between 6 indicators, including body mass index (BMI), calf circumference (CC), arm circumference (AC), thigh circumference (TC), waist circumference (WC), waist-height ratio (WHtR), and IR. Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) was used to measure IR. Weighted linear regression was used to assess the relationship between different parameters and IR. The receiver operating characteristic curve (ROC) was employed to compare the strength of the relationship between different anthropometric parameters and IR. A total of 8069 participants were enrolled in our study, including 4873 without IR and 3196 with IR. The weighted linear regression results showed that BMI, CC, AC, TC and WC were significantly correlated with IR, except WHtR. After adjusting for multiple confounding factors, we found that BMI, AC and WC were significantly positively correlated with IR, while TC was significantly negatively correlated with IR. Logistic regression results showed that a larger TC was associated with a decreased risk of IR. In addition, BMI and WC had similar areas under the curve (AUC: 0.780, 95% CI 0.770–0.790; AUC: 0.774, 95% CI 0.763–0.784, respectively), which were higher than TC and AC (AUC: 0.698, 95% CI 0.687–0.710, AUC: 0.746, 95% CI 0.735–0.757, respectively). To our knowledge, this is the first study to report a negative correlation between TC and IR among patients without diabetes mellitus. Therefore, TC may be a new tool to guide public health and a clinical predictor of IR in non-diabetic patients.

Microbiota-targeted interventions and clinical implications for maternal-offspring health: An umbrella review of systematic reviews and meta-analyses of randomised controlled trials.

Microbes in the human body are the determinants of life-long health and disease. Microbiome acquisition starts in utero and matures during early childhood through breastfeeding. However, maternal gut dysbiosis affects the maternal-offspring microbiome interplay. Lines of evidence on dysbiosis-targeted interventions and their effect on maternal-offspring health and gut microbiome are inconsistent and inconclusive. Therefore, this study summarised studies to identify the most common microbiota-targeted intervention during pregnancy and lactation and to comprehensively evaluate its effects on maternal and offspring health. This umbrella review was conducted by systematically searching databases such as PubMed and the Web of Science from inception to 2 September 2023. The quality was assessed using the Assessment of Multiple Systematic Reviews-2 checklist. The Grading of Recommendations Assessment, Development, and Evaluation was used for grading the strength and certainty of the studies. The overlap of primary studies was quantified by the corrected covered area score. A total of 17 systematic reviews and meta-analyses with 219 randomised controlled trials, 39 113 mothers, and 20 915 infants were included in this study. About 88% of studies had moderate and above certainty of evidence. Probiotics were the most common and effective interventions at reducing gestational diabetes risk (fasting blood glucose with the mean difference (MD) = -2.92, -0.05; I2 = 45, 98.97), fasting serum insulin (MD = -2.3, -2.06; I2 = 45, 77), glycated haemoglobin (Hb A1c) = -0.16; I2 = 0.00)), Homeostatic Model Assessment of insulin resistance (HOMA-IR) (MD = -20.55, -0.16; I2 = 0.00, 72.00), and lipid metabolism (MD = -5.47, 0.98; I2 = 0.00, 90.65). It was also effective in preventing and treating mastitis (risk ratio (RR) = 0.49; I2 = 2.00), relieving anxiety symptoms (MD = -0.99, 0.01; I2 = 0.00, 70.00), depression in lactation (MD = -0.46, -0.22; I2 = 0.00, 74.00) and reducing recto-vaginal bacterial colonisation (odds ratio (OR) = 0.62; I2 = 4.80), and with no adverse events. It also effectively remodelled the infant gut microbiome (MD = 0.89; I2 = 95.01) and prevented infant allergies. However, studies on pregnancy outcomes and preeclampsia incidences are limited. Our findings from high-quality studies identify that probiotics are the most common microbiome interventions during pregnancy and lactation. Probiotics have a strong impact on maternal and offspring health through maintaining gut microbiome homeostasis. However, further studies are needed on the effect of microbiota-targeted interventions on maternal cardiometabolic health, pregnancy, and neonatal outcomes. This umbrella review was registered with PROSPERO, CRD42023437098.

Subclinical left ventricular structural and functional alterations in children with obesity: is body mass or insulin resistance the main issue?

Obesity is an independent risk factor for cardiovascular diseases. The study aims to assess the left ventricular structure and functions in children with obesity. This study included 29 patients with metabolic syndrome, 31 patients with obesity without metabolic syndrome, and 30 healthy children of similar age and gender. Demographic, anthropometric, and biochemical findings and left ventricular structure and functions evaluated by conventional pulsed wave Doppler and tissue Doppler echocardiography were compared. The left ventricular mass index and relative wall thickness were significantly higher in children with obesity compared to controls. The mean left ventricular mass index of children with metabolic syndrome was also higher than for those without it. Most children with obesity had normal left ventricular geometry; concentric hypertrophy (27.6%) was more common in children with metabolic syndrome, and eccentric hypertrophy (25.7%) was more common in those without. The early to late diastolic mitral annular velocity ratios obtained with conventional pulsed wave Doppler echocardiography and tissue Doppler echocardiography (E/A and Em/Am, respectively) were lower in children with obesity than controls. In addition, the ratio obtained by tissue Doppler echocardiography was lower in children with metabolic syndrome than without. The homeostatic model assessment of insulin resistance, systolic blood pressure, and body mass index has been identified as independent factors for left ventricular structures and functions. Obesity causes subclinical left ventricular hypertrophy and diastolic dysfunction. Additional metabolic syndrome-related risks lead to further deterioration of cardiac morphology and functions.

Investigating the efficacy of acupuncture in treating patients with metabolic-associated fatty liver disease: a protocol for a randomised controlled clinical trial

IntroductionAcupuncture is widely used for metabolic-associated fatty liver disease (MAFLD) treatment; however, the clinical efficacy has not been confirmed due to the lack of high-level evidence-based clinical practice. The purpose...

An Empirical Study of Data Mining Technology in English Learning Outcome Prediction

This study employs data mining to construct a predictive model for English learning outcomes, aiming to identify crucial factors affecting student performance and inform teaching enhancements. Through extensive data collection and meticulous preprocessing, a robust predictive model is established. Detailing the theoretical framework and methodologies such as machine learning, data preprocessing, feature selection, and model assessment, the study ensures model accuracy and reliability. Data sourcing and processing steps transform raw data into a model-ready feature set. Model validation reveals high prediction accuracy and generalizability, with key features significantly impacting achievement predictions, deepening insight into learning determinants and guiding targeted teaching strategies. The research concludes with a summary of findings, acknowledging limitations, and suggesting future avenues. This data-driven English learning achievement prediction model offers promising educational applications, enhancing decision-making and personalized learning.

Maximum information coefficient combined with IMPA‐ELM model for load regulation assessment

Maximum information coefficient combined with <scp>IMPA</scp>‐<scp>ELM</scp> model for load regulation assessment

Adaptive multi-granularity trust management scheme for UAV visual sensor security under adversarial attacks

The big data provided by unmanned aerial vehicle (UAV) visual sensors offers essential information resources for activities across various industries. However, various adversarial threats are inevitable throughout the lifecycle of data generation, transmission, and utilization, leading to serious security risks. Trust assessment of visual sensors is a prerequisite for securing UAVs, but the multidimensionality of the trust elements and the uncertainty of the evidence limit its practical application. To advance this research, we innovatively propose a trust management scheme based on multi-granularity evidence fusion within the framework of belief functions (BFs) theory to adaptively respond to both known and unknown threats. We first propose a direct trust assessment model for known threats, which constructs multidimensional coarse-grained trust elements (MCTEs) and integrates multiple lightweight sub-models for basic belief assignment (BBA) to meet the need for fast response. Then, to address the unknown threats, we introduce pre-trained models to build multidimensional fine-grained trust elements (MFTEs) to construct trust recommendation models for indirect trust assessment for visual sensors. In addition, to accurately characterize the trustworthiness of visual sensors, we also introduce a BBA-weighted fusion method to achieve more reasonable trust aggregation by weakening highly conflicting evidence sources. Finally, to validate the effectiveness of the proposed method, we conducted a comprehensive trust assessment and security experiment on UAV aerial images. The results indicate that the proposed method demonstrates excellent performance and is beneficial for enhancing UAV security in adversarial attack scenarios.

Comparative assessment of simple and detailed energy performance models for urban energy modelling based on digital twin and statistical typology database for the renovation of existing building stock

The renovation wave calls for an integrated, participatory, and neighbourhood to neighbourhood approach tailored to the local environments. This can be hindered by the lack of geometric and performance input data about the existing building stock. A parametric digital urban building energy modelling (UBEM) tool was developed for local municipalities to automate calculations for comprehensive renovation strategies utilizing public databases specifically at the neighbourhood level. This article compares the accuracy of two energy performance calculation models (seasonal heat balance method and hourly resistance–capacitance method) used within the tool to detailed energy simulation software and measured energy consumption data of existing group of residential buildings in a same neighbourhood. The accuracy for estimating the total primary energy demand was roughly below 10 % for both simplified models. The seasonal calculation method showed consistent overestimation of space heating, depending on building characteristics. Solar and internal heat gains significantly affect the accuracy of simplified heat balance calculations, particularly in well-insulated buildings, while the more detailed 5R1C lumped capacitance hourly method provides improved accuracy for space heating demand. The comparison of uniform (proportional) and project based window area distribution used in the calculation models showed only marginal difference. The study validated simple seasonal and 5R1C hourly calculation methods using data from an apartment building neighbourhood with 22 apartment buildings. Despite larger deviations in the case of individual buildings, the average deviation from measured heating demand was only around 3 %, the seasonal method slightly overestimating and the 5R1C hourly method slightly underestimating the measured values. The primary energy demand including typical values of domestic hot water and household electricity was overestimated by both simplified methods due to lower electricity and water consumption in reality although the difference was below 7 % for the entire district. This concludes that pooling the building envelope heat loss calculation on a district level improves accuracy on average allowing better assessment of comparative renovation strategies.

Bias in fishery stock assessment models.

Bias in fishery stock assessment models.

Korean Red Ginseng Extract Powder Mitigates Fasting And Postprandial Hyperglycemia in Type 2 Diabetic Mice.

Type 2 diabetes mellitus (T2DM) involves insulin resistance and elevated blood sugar levels, causing complications. Red ginseng extract powder (RGEP) from Panax ginseng Meyer shows promise for diabetes treatment. However, its efficacy in managing T2DM remains unclear. Therefore, this study aims to evaluate the effectiveness of RGEP in a mouse model of T2DM. The efficacy of RGEP in treating T2DM was assessed in db/db mice. Mice were divided into seven groups: control, db/db, metformin, and RGEP at 50, 100, 200, and 400 mg/kg. Administered orally for 9 weeks, RGEP effects on glucose regulation and insulin sensitivity were assessed through various metabolic parameters. In addition, mRNA expression levels of genes associated with hepatic gluconeogenesis and insulin sensitivity were examined. Fasting blood sugar showed a significant decrease in all RGEP concentration groups, but OGTT and insulin tolerance test showed a significant decrease at the RGEP concentration of 400 mg/kg, indicating enhanced glycemic control. Moreover, RGEP dose-dependently decreased serum glucose, HbA1c levels, and homeostatic model assessment of insulin resistance values, suggesting its effectiveness in reducing insulin resistance in db/db mice. Furthermore, RGEP downregulated mRNA expression of key components in the gluconeogenesis pathway (G6Pase, FOXO1, GLUT4, and PEPCK), insulin sensitivity (leptin, insulin1, PTP1B, GLP-1, and DPP-4), and mitochondria energy metabolism (PGC1) in either the liver or pancreas, while simultaneously upregulating GLP-1 expression. In conclusion, these findings highlight the potential of RGEP as a complementary therapy for T2DM, indicating therapeutic efficacy in managing diabetic complications through improved metabolic parameters.

A Single Dose of Phosphoinositide-3-kinase Inhibitor Alpelisib Induces Insulin Resistance in Healthy Adults: A Randomized Feasibility Study.

Our objective is to test a single dose of the phosphoinositide-3-kinase (PI3K) inhibitor alpelisib as a tool for acute modeling of insulin resistance in healthy volunteers. This single-center, double-blind, phase 1 clinical trial randomized healthy adults to take a single oral dose of alpelisib 300 mg (n = 5) or placebo (n = 6) at bedtime, followed by measurement of glucose, insulin, and C-peptide levels after an overnight fast and during a 3-hour, 75-g oral glucose tolerance test. Fasting plasma glucose trended higher with alpelisib (mean ± S.D.: 93 ± 11 mg/dL) versus placebo (84 ± 5 mg/dL), while mean fasting serum insulin increased nearly fivefold (23 ± 12 µU/mL vs. 5 ± 3 µU/mL, respectively) and Homeostasis Model Assessment of Insulin Resistance scored 5.4 ± 3.1 for alpelisib and 1.1 ± 0.6 for placebo. During OGTT, incremental area under the curve (AUC) for insulin was over fourfold greater with alpelisib (22 ± 15 mU/mL x min) than placebo (5 ± 2 mU/mL x min); glucose AUC trended higher with alpelisib. Single-dose alpelisib was well tolerated and produced metabolic alterations consistent with acute induction of IR, validating its use for mechanistic study of insulin action in humans. (ClinicalTrials.gov registration: NCT05733455).

Meta-analysis of randomized controlled trials of the effects of synbiotics, probiotics, or prebiotics in controlling glucose homeostasis in non-alcoholic fatty liver disease patients.

Background: Probiotics, prebiotics, and synbiotics have been suggested as a possible therapy for non-alcoholic fatty liver disease (NAFLD). However, their efficacy in improving blood glucose levels in NAFLD patients remains uncertain. Objective: The aim of this study was to assess the effects of supplementation with probiotics, prebiotics, or synbiotics on fasting blood glucose (FBG) levels in NAFLD patients. Methods: We searched PubMed, Web of Science, and Google Scholar for relevant trials published up to March 2024. Out of 3369 identified studies, 24 randomized controlled trials (RCTs) were included. Results: Probiotic, prebiotic, or synbiotic supplementation substantially reduced FBG (n = 23; standard mean difference (SMD) = -0.17; 95% confidence interval (CI): -0.30, -0.03; P = 0.02), fasting insulin levels (n = 12; SMD = -0.28; 95% CI: -0.49, -0.07; P = 0.01), and homeostatic model assessment for insulin resistance (HOMA-IR; n = 14; SMD = -0.28; 95% CI: -0.47, -0.09; P = 0.004). However, glycosylated hemoglobin (HbA1c; n = 3; SMD = -0.17; 95% CI: -0.48, 0.13; P = 0.27) was not significantly affected. The FBG-decreasing effect diminished as the body mass index (BMI) of volunteers increased in the baseline. Additionally, the number of probiotic strains and geographic region were shown to significantly affect FBG levels. Conclusion: This meta-analysis indicates that supplementation with probiotics, prebiotics, or synbiotics may aid in controlling glucose homeostasis in patients with NAFLD.

An evaluation of the consistency of data and models for performance assessment: Divalent metal sorption on montmorillonite

A detailed analysis of literature data on the retention of divalent metal by purified montmorillonite or montmorillonite-rich material (e.g. bentonite) was conducted. Data for which sufficient experimental conditions were available were modeled with the 2SPNE SC/CE model available in the literature, and with a “minimalist” model, which uses a minimum number of parameters to reproduce the data.A successful data modeling could only be achieved if considering (i) cation exchange competition with aqueous Ca2+ and Mg2+, originating from the dissolution of the clay mineral itself or accessory minerals and (ii) precipitation, at high pH, of phases incorporating the divalent metal of interest. However, in many studies, the concentration of aqueous Ca2+ and Mg2+ and, more generally, a full characterization of solution composition (e.g., Si, Al, dissolved organic carbon, alkalinity) is not provided, thus impeding accurate data modeling. Further difficulties stem from uncertainties related to the nature of the solids precipitating at high pH and missing or inaccurate thermodynamic data related to mineral solubility.Log KD values cannot be predicted from available literature data with an accuracy better than ±1 log unit, without sorption data obtained on a material representative of in situ conditions. In addition, the most reactive sites, commonly referred to as strong sites, which have been characterized only on some, but not all, reference clay materials, should not be considered in blind predictions based on the mineralogical composition of clay materials.

In vitro assessment of vascular injury following stent retriever retraction in clinically-relevant endothelialized silicone models.

Mechanical thrombectomy devices have potential to injure the vessel during treatment of acute ischemic stroke. The goal of the current work was to tailor in vitro endothelialized silicone models for stent retriever assessment and to evaluate endothelial injury following treatment by various stent retriever designs and sizes. Clinically-relevant neurovascular geometries were first modeled out of silicone, then sterilized, coated with fibronectin, placed in bioreactors, seeded with human endothelial cells, and cultivated under flow. Several sizes of two different commercially available stent retrievers were then deployed in, and retracted through, vessels. Vessels were immediately harvested and stained. Endothelial injury, identified as denudation, was quantified using ImageJ. Results illustrated that endothelial injury ranged from 16-18% in wire/microcatheter-only treated vessels, 37-61% in 1-pass treatments, and 52-70% in 2-pass treatments. Overall this work showcases an in vitro approach for early stage assessment of the extent and location of vascular injury following stent retriever retraction.

Choosing Wisely Between Radiotherapy Dose-Fractionation Schedules: The Molecular Graded Prognostic Assessment for Elderly Glioblastoma Patients.

This study aimed to develop a graded prognostic assessment (GPA) model integrating genomic characteristics for elderly patients with glioblastoma (eGBM), and to compare the efficacy of different radiotherapy schedules. This multi-institutional retrospective study included patients aged ≥65 years who underwent surgical resection followed by radiotherapy with or without temozolomide (TMZ) for newly diagnosed eGBM. Based on the significant factors identified in the multivariate analysis for overall survival (OS), the molecular GPA for eGBM (eGBM-molGPA) was established. A total of 334 and 239 patients who underwent conventionally fractionated radiotherapy (CFRT) and hypofractionated radiotherapy (HFRT) were included, respectively, with 86% of patients receiving TMZ-based chemoradiation. With a median follow-up of 17.4 months (range, 3.3-149.9), the median OS was 18.7 months for CFRT+TMZ group, 15.1 months for HFRT+TMZ group, and 10.4 months for radiotherapy alone group (CFRT+TMZ vs. HFRT+TMZ: HR 1.52, p<0.001; CFRT+TMZ vs. radiotherapy alone: HR 2.52, p<0.001). In a combined analysis with the NOA-08 and NORDIC trials, CFRT+TMZ group exhibited the highest survival rates among all treatment groups. The eGBM-molGPA, which integrated four clinical and three molecular parameters, stratified patients into low-, intermediate-, and high-risk groups. CFRT+TMZ significantly improved OS compared to HFRT+TMZ or radiotherapy alone in the low-risk (p=0.023) and intermediate-risk groups (p<0.001). However, in the high-risk group, there was no significant difference in OS between treatment options (p=0.770). CFRT+TMZ may be more effective than HFRT+TMZ or radiotherapy alone for selected eGBM patients. The novel eGBM-molGPA model can guide treatment selection for this patient population.

Enhancing IT maturity in the Devops focus area: a comprehensive assessment with COBIT 2019

The research will investigate the following research inquiries: by utilizing the COBIT 2019 design toolkit, what is the level of DevOps maturity within the company according to COBIT 2019, and what recommendations are required to enhance the maturity level in DevOps. The researchers conducted a collaborative workshop with the critical management team to obtain reliable and accurate information. The workshop engaged participants from the development and operations teams to gather and analyze data about current DevOps practices. Based on the data, it is known that out of the 26 processes within the DevOps focus area, they have been measured and resulted in a maturity level of 3.15. The value of 3.15 indicates that the DevOps maturity level falls under the ''Defined'' category, meaning that the processes within the DevOps focus area have been implemented consistently. The case study research has reached its outcomes, and the researcher can conclude that the study has successfully achieved its objectives and benefits. This research has conducted an assessment of the maturity level for the DevOps focus area using the Process Assessment Model (PAM) as a measurement tool.