Recent advances in 3-dimensional (3D) imaging, such as the Vectra XT 3D imaging system (Vectra; Canfield Imaging Systems), have enabled simulations for breast augmentation. These simulations are popular and valuable in preoperative consultations, but no studies have evaluated the system's predictive volume accuracy with a large sample size or explored the patient-specific and surgical factors influencing these simulations. A prospective cohort study was conducted on patients undergoing breast augmentation simulations with Vectra between January of 2021 and December of 2022. Predictive volumetric accuracy was analyzed with a ±10% precision threshold and Bland-Altman visualization. Linear breast measurements were compared using mean and median absolute error analysis, and a generalized linear model with generalized estimating equations was used to identify factors affecting volume prediction accuracy. The study included 78 patients (154 breasts). Accurate predictions (±10% error) were achieved in 73% of cases, with an average margin of error of 7.52%. The most accurate measurement was sternal-nipple ( P = 0.64) and the least accurate measurement was nipple-inframammary fold ( P = 0.001). Factors influencing prediction accuracy included pregnancy history, preoperative breast volume, and implant volume. Vectra accurately predicts postoperative breast volumes within a 10% margin of error, although precision decreases with larger breasts (greater than 650 cc). Patient-specific factors, such as pregnancy history and preoperative breast characteristics, should guide preoperative consultations. The system enhances patient education, shared decision-making, and participation in care, reinforcing its value in preoperative planning for breast augmentation.

Termites play important ecological roles in soil formation and nutrient cycling, but they also cause significant damage to crops and rural infrastructure in sub-Saharan Africa. Despite their economic importance, information on farmers' perceptions, indigenous knowledge, and local termite management practices remains limited in many termite-prone areas of Ethiopia. Therefore, this study assessed farmers' perceptions of termite infestation, its impacts, and indigenous management practices in Dodota district, Southeast Ethiopia. A household questionnaire survey was conducted using 120 randomly selected households determined through Cochran's sampling formula at a 95% confidence level and 5% margin of error. Field data were collected from April to May 2025 through household interviews and focus group discussions. Descriptive statistical analysis was performed using SPSS version 26 software. The study assessed farmers' knowledge of termite biology, perceptions of termite impacts, crop susceptibility, indigenous control practices, and the ecological importance of termites in the agricultural landscape. Most respondents were male (88.3%), married (95.8%), and within the middle-aged category of 34-60 years (65.8%). All farmers were aware of termites, and 91.7% correctly associated mound construction with termite activity. Farmers used three major criteria to identify different termite castes: body size (66.7%), body structure (16.7%), and feeding tunnels (10.0%). The major negative impacts reported by farmers included land consumption due to mound formation (43.3%), crop destruction (36.7%), and damage to homes (13.3%). Farmers also recognized several beneficial effects of termites, including improvement of soil porosity (22.5%), soil moisture retention (15.8%), and soil fertility (5.8%). Indigenous termite control practices included application of wood ash (6.7%), smoking termite mounds (13.3%), addition of organic matter (16.7%), destruction of mounds and removal of queens (6.7%), and promotion of natural predators (9.2%). Farmers possessed substantial indigenous knowledge of termite occurrence, impacts, and management practices. Integrating this knowledge with scientifically validated and environmentally sustainable approaches could improve termite management in smallholder farming systems. Future research should focus on evaluating indigenous practices and developing locally adaptable integrated termite management strategies.

Declining academic engagement among junior high school students remains a persistent problem that warrants investigation into its psychosocial determinants. This study aimed to determine the combined influence of parenting style and peer influence as predictors of students’ academic engagement in Davao City. Employing a quantitative, non-experimental descriptive–correlational research design, the study examined the levels of parenting style, peer influence, and academic engagement, as well as the relationships among these variables. Using Slovin’s formula with a 5% margin of error, a sample of 180 students was selected from a total population of 330. Data were collected through validated survey questionnaires and analyzed using appropriate statistical tools, including mean, standard deviation, Pearson’s Product–Moment Correlation Coefficient (Pearson’s r), and multiple linear regression analysis to determine significant relationships and predictive influence. The findings revealed that students generally exhibited moderate to high levels of Parenting Style, Peer Influence, and Academic Engagement, indicating that they experienced fair to strong parental guidance and peer interactions while actively participating in academic activities. These overall levels provide context for understanding the significant relationships observed between Parenting Style, Peer Influence, and Academic Engagement. Furthermore, the combined effect of Parenting Style and Peer Influence demonstrates that students’ academic engagement is best supported when both family and peer environments provide guidance and encouragement. This means that involving both parents and peers enhances students’ motivation, participation, and overall academic success. These results highlight the important roles of family and peers in fostering students’ engagement in academic activities and provide valuable implications for educators and school administrators in developing programs and interventions that support students’ academic involvement.

Implementing interventions to enhance employee well-being has become a trend in public sector organizations. This is due to the increasing level of stress among employees globally. Given the rise in employee stress, leaders must prioritize employee well-being or risk being blindsided by employee stress. The purpose of this study is to determine the relationship between the level of employee well-being (using the PERMA Workplace Profile) and the extent of implementation of the interventions promoting employee well-being. This study employed a non-experimental, quantitative, descriptive design. Research participants were purposively taken from the five (5) provinces of Northern Mindanao (Region 10), in Southern Philippines. The sample size was determined using Cochran’s formula with a 95% confidence level and a 5% margin of error. The data gathered were analyzed using descriptive statistics, and the relationship between participants’ PERMA Workplace profiles and the extent of implementation of well-being interventions was determined using correlation and regression analyses. The results reveal a high level of PERMA Workplace Profile among participating LGU employees and a great extent of implementation of wellbeing interventions in the LGUs where they belong. The null hypothesis is rejected, confirming a significant relationship between the well-being profile and the well-being interventions. The study concludes that LGUs can achieve the highest return on well-being by prioritizing interventions promoting good relationships and recognizing the accomplishments of employees.

When reinforced concrete (RC) slabs are subject to low velocity impacts (LVI), they will experience highly non-linear behaviors based upon the combined effects of flexural deformations; tensile membrane actions; punching shears; and local crushing as well as the evolving nature of damage in these structures. Most analytical models have been developed under restrictive simplifications; lack complete partitioning of energies; and depend heavily upon computationally intensive finite element simulations. An analytical-energy-based approach is presented to predict the non-linear dynamic behavior of RC slabs subjected to both LVIs and equivalent blast loads. The proposed model integrates an improved two degree-of-freedom (2-DoF) dynamic interaction system with an explicit multi-mechanism energy partitioning procedure which can be used to quantify all of the major forms of energy consumption including flexural; membrane; shear; crushing; damping and damage. The analytical framework has incorporated strain rate enhancement through use of dynamic increase factors (DIFs); punching shear capacity through application of the critical shear crack theory (CSCT); and geometrically non-linear membrane behavior via Xie's formulation for large deflection membrane behavior. A wide-ranging parametric analysis was performed to examine the effect of impact energy; slab thickness; reinforcement ratio; boundary conditions and advanced materials systems such as normal strength concrete (NSC); ultra high-performance concrete (UHPC); and ultra high-performance fibre reinforced concrete (UHPFRC). Results indicated that an increase in the magnitude of impact energy from 1 to 6kJ resulted in an approximate 301% increase in slab displacement. Furthermore, an increase in the slab thickness from 60 to 125mm resulted in a reduction of approximately 68% in slab displacement. It was also found that when compared with RC slabs made with NSC, those made with UHPFRC had displacements decreased by approximately 61% and reduced damage dissipation energy by approximately 69% due to higher levels of crack bridging and membrane resistance. Lastly, it was determined that twisted and hooked end fibres had the greatest energy absorbing capability and provided the longest delay to punching shear failure. Validation studies were conducted against existing experimental data and comparative non-linear ABAQUS CDP simulations indicating prediction error margins generally less than ± 5-10% and good correlation in terms of slab displacement response; crack development; and failure progression. The validation studies also showed that the method could be easily adapted for equivalent blast load analyses using impulse equivalency principles thus enabling a rapid predictive tool for designing RC slab systems resistant to either low velocity impact or blast loads.

β-Thalassemia is an inherited hemoglobin disorder caused by reduced or absent synthesis of the β-globin chain, leading to variable clinical severity and substantial lifelong health burden. Because prevention depends heavily on awareness, screening, and informed reproductive decision-making, understanding public knowledge, attitudes, and practices is essential for designing effective control strategies. An analytical cross-sectional study was conducted between November 2025 and January 2026 using a structured, self-administered Arabic questionnaire distributed online through Google Forms. The survey was adapted from a previously validated thalassemia KAP instrument and disseminated using snowball sampling through social media platforms. The minimum required sample size was 358, calculated using a single-proportion formula with an expected awareness proportion of 36.8% from a previous young-adult β-thalassemia KAP study, 95% confidence level, and 5% margin of error. A total of 424 Palestinian participants were included in the final analysis. Participants who had never heard of thalassemia were classified as unaware and excluded from domain-based KAP scoring analyses. Knowledge, attitude, and practice domains were scored using Bloom's 60% cut-off criterion. Descriptive statistics, chi-square tests, t-tests, ANOVA, correlation analyses, and multivariable logistic regression were used for data analysis. Overall, 87.0% of participants had heard of thalassemia. Among aware participants (n = 369), 46.1% demonstrated adequate knowledge, 73.7% had a positive attitude, and 82.7% had positive practice. Knowledge was stronger for hereditary concepts, carrier status, and the role of consanguinity, but weaker for disease treatability, clinical subtypes, and prevention policies. Females had significantly higher knowledge and practice scores than males, while positive attitude did not differ significantly by sex. Previous screening uptake was limited, but endorsement of premarital testing, prenatal diagnosis, and knowledge sharing was high. Knowledge score was positively correlated with practice score and independently predicted positive practice (aOR = 1.35, 95% CI: 1.15-1.57, p < 0.001). Female sex, increasing age, and higher education were independently associated with adequate knowledge. Although awareness of beta thalassemia among Palestinian young adults was high, important gaps remained in functionally relevant knowledge and in the translation of preventive intentions into actual screening behavior. The independent association between knowledge and positive practice suggests that strengthening practical genetic literacy may be a key pathway for improving prevention-oriented behavior. These findings support the need for culturally appropriate educational strategies, premarital counseling, and community-based initiatives to enhance informed decision-making and strengthen thalassemia prevention efforts in Palestine. Nurse-led health education, premarital counseling, and accessible community-based screening may help translate awareness into informed preventive action.

Drying shrinkage remains a critical durability challenge in cementitious materials, where conventional prediction methods are limited by low temporal resolution and lack of interpretability. This study presents an interpretable machine learning (ML) framework for real-time prediction of drying shrinkage in fly ash-containing concrete, integrating high-frequency experimental measurements with physically meaningful input features. A data set comprising 79,008 hly observations across 16 mix designs and different curing conditions was used to train and evaluate various ML models. Ensemble and nonlinear models, including Random Forest, Extra Trees, K-Nearest Neighbors, and Multi-Layer Perceptron, achieved high predictive accuracy (R 2 ≈ 0.99) on the test set with low error margins under both hourly and daily temporal representations. Besides, SHapley Additive exPlanations (SHAP) analysis reveals that multiscale temporal variables govern shrinkage evolution, with short-term capillary moisture loss and long-term pore-scale desorption captured through distinct temporal features. Material parameters, particularly fly ash incorporation, exhibit secondary but consistent influence through microstructural refinement mechanisms based on C-S-H phase nucleation and formation. The proposed framework enables a transition from periodic testing toward continuous, data-driven monitoring, offering a pathway for real-time quality control and reduced physical testing of sustainable concrete infrastructures.

Background. Adolescent consumption patterns are influenced by the rise of social media platforms, such as TikTok Shop, which combine entertainment and shopping on a single platform. Aims. This study investigates the impact of impulse buying on TikTok Shop on the fashion consumption behavior of female students at SMK Negeri 2 in Cirebon City. Methods. The study employed a quantitative method and involved 77 respondents, selected using the Slovin formula with a 10% margin of error. Data were collected via a Likert-scale questionnaire and analyzed using SPSS 2023 through validity, reliability, normality, linearity, and simple linear regression tests. Result. The analysis results indicate that impulse buying has a significant influence on consumption behavior. This means that the higher the students’ tendency to make impulsive purchases, the higher their consumption behavior. There is a significant and positive relationship between the tendency toward impulse buying and the students’ consumer behavior, with a correlation coefficient (R) of 0.548 and a coefficient of determination (R²) of 0.301. This implies that a one-unit increase in impulse buying will increase consumer behavior by 0.451, in accordance with the regression equation Y = 7.621 + 0.451X. Conclusion. The results of this study are expected to help parents, educators, and digital marketers understand how social media, particularly TikTok, influences adolescent consumption behavior. Implementation. These findings can also serve as a reference for teaching adolescents about financial literacy and how to exercise self-control while shopping in the internet age.

As the global demand for hip and knee arthroplasty procedures grow, advancements in orthopedic research are vital for progress. Pilot and feasibility trials play a critical role in optimising research efficiency, highlighting methodological gaps, and maximising study insights. However, the completeness of reporting in hip and knee arthroplasty pilot and feasibility trials remains unclear. The primary objective was to evaluate reporting completeness, assessed using the CONSORT checklist extension to pilot and feasibility trials. Secondary objectives included evaluating the reporting of key feasibility items and exploring factors associated with reporting completeness. This methodological survey analysed pilot and feasibility trials in hip and knee arthroplasty. A PubMed search identified relevant manuscripts published between January 1, 2017, and December 31, 2023. A minimum acceptable sample size of 147 (of 278 eligible) was identified, based on a calculation involving an estimated reporting completeness of 25% of the CONSORT checklist, a 95% confidence interval, and a 5% margin of error. Descriptive statistics were reported, and a multivariable linear regression with robust (HC1) standard errors was completed. Of 278 eligible publications identified from PubMed, a random sample of 147 studies was included. Reporting completeness was low, with studies accounting for a mean of 54.98% (16.95) of applicable CONSORT extension items. Notably, pilot-specific objectives and future study implications were often underreported. Similarly, manuscripts missed approximately 71.35% (10/14) of key feasibility elements. Studies that referenced reporting guidelines in the manuscript text were associated with better reporting, while those that failed to disclose their funding sources and lacked more key feasibility items demonstrated weaker reporting completeness. Hip and knee arthroplasty pilot and feasibility trials exhibit suboptimal reporting completeness with several missing feasibility elements. Improved adherence to CONSORT guidelines and enhanced transparency are needed.

Coupled-cluster theoryprovides an accurate description of electronicground and excited states. While it has been rigorously establishedthat the coupled-cluster ground state energy is size extensive andlocal excitations are size intensive in the thermodynamic limit, theseparability of other properties in coupled-cluster theory is subjectto known limitations. In particular, it was shown [Stanton, J. Chem. Phys.1994,101,8928–8937] that multicenter two-particle density matrices arenot asymptotically separable in general. In the present work, an analogousanalysis is applied to the excitonic coupling of local excitationsof two separate molecules, using both the equation-of-motion (EOM)and linear-response (LR) formalism. It is shown that for both formalismsthe two-electron transition density associated with the excitoniccoupling does not exactly separate into a product of local one-electrontransition densities. Numerical examples are provided for Ne2, (CO)2, and (C2H4)2,using coupled-cluster expansions up to single, double, triple, andquadruple excitations (CCSDTQ). Small but noticeable deviations onthe order of 5–10% are reported for approximations like CCSDand its second-order approximate variant CC2. As soon as triple excitationsare included, the separability error becomes significantly smaller.Exploratory computations for the coupling of larger chromophores suchas perylene or cumarine dyes using the CC2 method indicate that theseparability error can grow up to orders of 20%. While these deviationsare typically below the error margin of this method, the separabilityerror could be of relevance in the design and benchmarking of localcoupled-cluster approaches for excited states.

HighlightsWhat are the main findings?Quantified the signal drift characteristics (1.5–2% error) of FBG sensors under cyclic loading; revealed its core source is bonding/anchoring loosening.Revealed the evolution difference in cement sheath plastic strain between uncon-strained triaxial tests and constrained full-scale CCFS.What are the implications of the main findings?Proposed an optimized parallel winding + end fixing layout method to improve long-term monitoring reliability.Clarified the inhibition mechanism of stress redistribution on plastic accumulation, and defined the conservatism of traditional triaxial tests for cement sheath sealing capacity evaluation.Accurately characterizing the accumulated plastic deformation of the cement sheath is essential for evaluating wellbore integrity. Fiber Bragg Grating (FBG) technology, noted for its strong immunity to external interference, is employed for in situ monitoring under harsh downhole conditions. This study investigates the accumulated plastic deformation behavior of set cement through uniaxial cyclic loading–unloading tests and proposes a real-time, high-precision, and non-destructive monitoring scheme by integrating FBG sensors into the Casing–Cement–Formation system (CCFS). The results reveal that under uniaxial conditions, cumulative plastic strain increases with stress amplitude, with the plastic strain in a single cycle capable of reaching up to 0.2%. Under identical conditions, FBG measurements exhibit a drift phenomenon, resulting in an error margin of approximately 1.5–2%. Furthermore, within the CCFS, plastic strain exhibits linear accumulation during the initial 2–5 cycles, followed by a deceleration in the accumulation rate. This deceleration is attributed to the redistribution of internal stress induced by plastic strain accumulation. Notably, the addition of silica fume and latex significantly mitigates this deformation. Collectively, these findings validate the effectiveness of FBG technology for downhole integrity assessment and offer a pathway for early failure detection and targeted maintenance.

The increasing reliance on digital devices in modern workplaces has raised significant concerns about computer vision syndrome (CVS), particularly among professionals in screen-intensive environments, like the banking sector. Despite its widespread prevalence, limited research exists on CVS within the Bangladeshi banking sector. This study aims to estimate the prevalence of CVS among bank employees in Bangladesh and to evaluate the key demographic, occupational, and environmental risk factors associated with its occurrence. A cross-sectional study was conducted using a structured, self-administered questionnaire. The required sample size was calculated a prior based on an expected prevalence of 85.2%, a 5% margin of error, and a 95% confidence level, resulting in a target sample of 201 Bangladeshi bank employees. The presence of CVS was determined using the validated CVS-Q tool. Descriptive statistics were conducted to summarize demographic and socioeconomic characteristics of the study participants. Logistic regression analysis was performed to identify significant predictors of CVS. The prevalence of CVS among participants was 55.2%. The most reported symptoms were headache (79%), burning eyes (63%), and eye pain (52%). Logistic regression analysis revealed that increased daily computer use (odds ratios [OR]: 2.05; 95% CI: 1.07-4.83) and very bright monitor (OR: 113; 95% CI: 1.88-18,710) were significantly associated with higher odds of CVS. Conversely, larger family size (OR: 0.63; 95% CI: 0.39-0.92), higher weekly overtime (OR: 0.77; 95% CI: 0.60-0.91), and number of leave days taken for eye problems (OR: 0.03; 95% CI: 0.00-0.41) were associated with reduced odds of CVS. CVS is a prevalent occupational health concern among Bangladeshi bank employees, driven by modifiable factors such as screen brightness and prolonged computer use. Findings underscore the need for ergonomic interventions, regular eye screening, and educational measures to mitigate CVS and promote workplace well-being.

This study aims to analyze waste disposal and recovery data for 2022 and 2024 in Türkiye to reveal changes in national waste management and to make future predictions using multivariate machine learning and artificial neural network (ANN) models. Trends in disposal and recovery capacity were identified, and the main variables affecting waste processing volumes were modeled. Multiple linear regression and feedforward ANN models were created using the capacity and processing quantities obtained from Türkiye İstatistik Kurumu's data. The ANN model was trained with three hidden layers and a 64-32-16 neuron structure, with a learning rate of 0.001. The dataset was divided into 80% training and 20% testing. The machine learning model's test set accuracy coefficient was calculated as R2 = 0.87, and the mean absolute error (MAE) was 1.96 million tons. The ANN model demonstrated even higher performance, reaching R2 = 0.93 and an MAE of 1.41 million tons. ANNs accurately predicted total recycled waste to be 51.5 million tons in 2024, while the amount of landfilled waste was predicted with only a 3.2% margin of error. Model analyses revealed that increasing landfill capacity significantly increases disposal volume, while increasing the number of recycling facilities affects recovery volume through a strong, nonlinear relationship. The results show that ANN models can predict Türkiye's waste management trends with high accuracy and prove that they provide a powerful decision support tool that can be used in future policy planning.

Acoustic guide pad: a simple innovation that dramatically reduces the risk of misdiagnosis in respiratory muscle ultrasound examinations.

Evaluation of dual-energy CT virtual monoenergetic imaging for target delineation in rectal cancer radiotherapy.

Work motivation is an essential factor in supporting employee performance and the effectiveness of higher education institutions. However, employee motivation may decline when organizational justice, organizational support, and psychological attachment to work are not optimally managed. This study aims to analyze the effect of organizational justice and organizational support on work motivation, with work engagement as a mediating variable. This research employed a quantitative approach with descriptive and verification methods. The population consisted of 1,158 educational staff at the Indonesian University of Education, while the sample comprised 298 respondents determined using the Slovin formula with a 5% margin of error and selected through simple random sampling. Data were collected through questionnaires and analyzed using path analysis and the Sobel test. The results show that organizational justice and organizational support have a positive and significant effect on work motivation. Organizational justice and organizational support also have a positive and significant effect on work engagement. Furthermore, work engagement has a positive and significant effect on work motivation and significantly mediates the relationship between organizational justice and organizational support on work motivation. These findings indicate that fair organizational practices and institutional support can strengthen employee motivation, particularly when employees are psychologically engaged in their work. The study contributes to human resource management literature by emphasizing the mediating role of work engagement in explaining how organizational factors influence employee motivation in higher education institutions.

The escalating utilization of urban underground space frequently necessitates the construction of surface structures in close proximity to subterranean tunnels. This study investigates the bearing capacity of rigid strip footings above dual square tunnels in Hoek-Brown rock masses under inclined loading. Utilizing rigorous Finite Element Limit Analysis (FELA) with adaptive mesh refinement, the true ultimate bearing capacity is bracketed with a strict upper and lower bound error margin of less than 3%. The analysis systematically quantifies the influence of key parameters across extensive ranges: tunnel depth ratio (H/D ∈ [1.5, 3.0]), spacing ratio (S/D ∈ [1, 6], Geological Strength Index (GSI ∈ [50, 100]), and intact rock constant (mi = 7, 10, 15, 17, 25). A pivotal contribution is the precise identification of a load "critical angle" threshold, ranging from arctan(0.4) to arctan(0.5) depending on the tunnel depth. Below this angle, the failure mechanism is a deep-seated, tunnel-coupled mode; beyond it, the system transitions abruptly to a shallow, surface-controlled sliding failure where the tunnels have negligible impact. Furthermore, the study establishes quantitative engineering guidelines: narrow pillar spacings (S/D = 1) pose an extreme risk of catastrophic failure, whereas dual-tunnel interaction becomes negligible at S/D ≥ 4. Crucially, to bridge the gap between theoretical numerical analysis and practical application, a deep learning-based algorithm was employed to distill the extensive simulation dataset into an explicit, highly accurate, and interpretable predictive formula. Incorporating all investigated variables, this intelligent formula-alongside the comprehensive dimensionless failure envelopes-equips geotechnical engineers with a robust tool for rapid stability evaluations and design optimization.

Development and application of a CNS PBPK model that integrates CSF dynamics to optimize dosing regimens of antibacterial agents in pediatric patients with meningitis.

ABSTRACTCabotegravir (CAB), combined with rilpivirine, is the first long‐acting injectable therapy approved for HIV‐1 maintenance treatment. While adherence and patient satisfaction have been improved, pharmacokinetic (PK) variability remains a concern. Two population PK models have been developed: one based on data from phase I–III trials and the other on routine clinical data. However, neither model has undergone thorough external evaluation. The aim of this study was to evaluate the predictive performance of these models using an independent prospective dataset to evaluate their suitability to support model‐informed precision dosing (MIPD). External validation was performed using data from the French observational and multicenter (n = 14) ANRS0255 CARLAPOP study (736 HIV‐infected patients, representing 2192 concentrations). Models were implemented using MONOLIX software and evaluated using goodness‐of‐fit, prediction‐based, and simulation‐based diagnostics. For Han's model, regarding plasma concentrations following intramuscular administration, Median Prediction Error (MDPE) was −1.2% (PRED) and −4.4% (IPRED); Median Absolute Prediction Error (MDAPE) was 36.6% (PRED) and 17.9% (IPRED). For Thoueille's model, MDPE was −24.2% (PRED) and −9.2% (IPRED); MDAPE was 39.0% (PRED) and 15.3% (IPRED). However, < 70% of predictions were within a 20% error margin with both models. Graphical analyses of Thoueille's model showed systemic bias, particularly in women, nonsmokers, and patients with higher body mass index. Therefore, neither model was considered reliable enough for MIPD application in our population. Although Han et al.'s model demonstrated higher predictive performances, further improvements are required before it can be reliably applied for MIPD in daily routine.

Micro Arc Oxidation (MAO) is an advanced electrochemical surface-processing technique capable of producing hard, dense and adherent ceramic coatings on aluminium alloys, exhibiting superior wear and corrosion properties than traditional anodizing. This research project produced MAO coating on CMT-welded AA6082-T6 aluminium alloy joints and the effect of current density, the oxidation time and the distance in between the electrodes on the coating characteristics were systematically studied using Response Surface Methodology (RSM) based parametric mathematical modelling (PMM). The PMMs generated very accurate predictions of the porosity and hardness of the aluminium alloy joints with an error margin less than 2% and 99% confidence level. Detailed characterization was done to test model predictions. The analysis of the SEM showed how the crater-type discharge channels, micropores, and the patterns of molten-oxide resolidification changed, and optimal parameters resulted in homogeneous and compact structures. The thickness variation between about 58μm to 110μm with current density was established by cross-sectional SEM. The XRD patterns showed that, γ-Al2O3, θ-Al2O3 and α- Al2O3 phases existed and were transformed with α- Al2O3 enrichment at moderate current density (0.19A/cm2) that increased peak hardness. High density of current (0.25A/cm2) inhibited the formation of α- Al2O3 and high porosity. Optimized MAO conditions of 0.19A/cm2 current density, 20min oxidation time, and 6-cm spacing between electrodes gave minimum porosity (2.07 vol) and maximum hardness (1459.36 HV). The coatings also exhibited positive wear behaviour, and decreased friction coefficient was observed due to high density of ceramic phase. The overall results of modelling and characterization offer a solid system of application of MAO coating on welded aluminium structures in the high-performance engineering applications.