Modeling Approach Research Articles

Estimating software reliability using size-biased modelling

Software testing is an important step in software development where inputs are administered repeatedly to detect bugs present in the software. In this paper, we have considered the estimation of total number of bugs and software reliability as a size-biased sampling problem by introducing the concept of eventual bug size as a latent variable. We have developed a Bayesian generalised linear mixed model (GLMM) using software testing detection data to estimate software reliability and stopping phase. The model uses size-biased approach where the probability of detecting a bug is an increasing function of eventual size of the bug which is as an index for the potential number of inputs that may eventually pass through the bug. We have tested the sensitivity of the reliability estimates by varying the number of inputs and detection probability via a simulation study and have found that the key parameters could be accurately estimated. Further, we have applied our model to two empirical data sets – one from a commercial software and the other from ISRO launch mission software testing data set. The hierarchical modelling approach provides a unified modelling framework that may find applications in other fields (e.g. hydrocarbon explorations) apart from software management.

Effect of blockchain technology adoption on construction supply chain: a structural equation modelling (SEM) approach

Purpose The construction industry is frequently scrutinised by the public for a variety of issues, including waste, inefficiency, narrow profit margins, scheduling setbacks, budget overruns, quality concerns, trust deficits, transparency issues, coordination challenges, communication issues and fraud. The purpose of this paper is to assess the effect of blockchain technology adoption on the construction supply chain. Design/methodology/approach This study used a quantitative research approach through a questionnaire survey that was conducted among professionals in the Nigerian construction industry using the snowball sampling method, which resulted in a selection of 155 respondents. The collected data were analysed using partial least squares structural equation modelling, enabling a thorough assessment of the proposed relationships and offering valuable insights specific to the construction industry. Findings The study’s findings validated the conceptual framework established. The results indicated that implementing blockchain across all stages of the construction supply chain has the potential to improve the construction process. The study also revealed that blockchain technology will significantly affect the construction supply chain in a positive manner. Research limitations/implications This research was carried out in the South-western region which is one of the six geo-political zones in Nigeria using a cross-sectional survey method. The study holds implications not only for local construction practices but will also contribute to the broader discourse on national construction sector challenges and possible solutions. Practical implications The findings of this study will be immensely beneficial to both professionals, practitioners and stakeholders in the Nigerian construction industry in learning about the potential of blockchain technology application in improving the construction supply chain. Originality/value The study in this paper constructed and evaluated a conceptual framework by exploring the connections between the variables. The results have significant implications for the construction sector, as they provide avenues for enhancing the construction process and the overall supply chain. These findings are valuable for researchers examining the potential effects of blockchain technology on the construction supply chain.

Knowledge attitude and convenience on self-medication practices among university students in Bangladesh exploration using structural equation modeling approach

Self-medication is a prevalent practice among university students globally and is a significant public health concern. However, previous research has been limited in scope, focusing primarily on adolescents or the general public, leaving a gap in understanding the causal relationships associated with self-medication; thus, this study aimed to investigate the factors influencing self-medication practices among university students in Bangladesh by developing a comprehensive causal model. Data from 417 students across five public universities were collected using the simple random walk technique by a team of 10 members. The study utilized constructs of knowledge, attitude, and convenience related to self-medication as independent variables, while self-medication practice as the dependent variable. One-way ANOVA and structural equation modeling (SEM) were employed to develop a causal model of self-medication practice among university students in Bangladesh. The findings revealed that students with better medication knowledge and adverse drug reactions (ADRs) were more likely to practice self-medication. A positive attitude towards self-medication and ADRs was also significantly associated with higher self-medication practice scores. Additionally, those who perceived self-medication as convenient and prescribed medication as inconvenient had higher self-medication practice scores. The attitude towards self-medication had the most substantial negative effect on self-medication practice, followed by the inconvenience of prescribed medication and the convenience of self-medication. The model explained 87% of the variance in self-medication practice, indicating a good fit for the data. University students in Bangladesh possess intermediate knowledge of medication and primary knowledge of ADRs. They exhibit a positive attitude towards self-medication and ADRs. Physical convenience favors self-medication, while the inconvenience of prescribed medication contributes to its lower preference. Policymakers should focus on evidence-based guidelines to reduce the extent of unnecessary self-medication practice and to enhance the quantity and accessibility of prescribed medications to address the issue effectively.

Development and validation of a biomechanically fidelic surgical training knee model.

Knee arthroplasty technique is constantly evolving and the opportunity for surgeons to practice new techniques is currently highly dependent on the availability of cadaveric specimens requiring certified facilities. The high cost, limited supply, and heterogeneity of cadaveric specimens has increased the demand for synthetic training models, which are currently limited by a lack of biomechanical fidelity. Here, we aimed to design, manufacture, and experimentally validate a synthetic knee surgical training model which reproduces the flexion dependent varus-valgus (VV) and anterior-posterior (AP) mechanics of cadaveric knees, while maintaining anatomic accuracy. A probabilistic finite element modeling approach was employed to design physical models to exhibit passive cadaveric VV and AP mechanics. Seven synthetic models were manufactured and tested in a six-degree-of-freedom hexapod robot. Overall, the synthetic models exhibited cadaver-like VV and AP mechanics across a wide range of flexion angles with little variation between models. In the extended position, two models showed increased valgus rotation (<0.5°), and three models showed increased posterior tibial translation (<1.7 mm) when compared to the 95% confidence interval (CI) of cadaveric measurements. At full flexion, all models showed VV and AP mechanics within the 95% CI of cadaveric measurements. Given the repeatable mechanics exhibited, the knee models developed in this study can be used to reduce the current reliance on cadaveric specimens in surgical training.

Predicting the rutting behaviour of asphalt concrete in the modified wheel tracking test using DEM and a cohesive viscoelastic–elastoplastic-damage contact model

AbstractThis study used an advanced modelling approach capable of capturing the complex behaviour of asphalt concrete to model the modified wheel tracking test using a recent advanced experimental test set-up in accordance with ASTM D8292-20. The modelling approach uses the discrete element method (DEM) to naturally produce the heterogeneous internal structure and governs the behaviour of asphalt concrete at the grain level by an interparticle contact model. The contact model used is capable of characterising the rate and time dependency, viscoelastic-damage, and plastic-damage behaviour of asphalt concrete utilising the coupling of an elastoplastic-damage law with a viscoelastic-damage law. Unlike the conventional wheel tracking tests run in a fixed boundary condition (fully confined), the modified wheel tracking test considers the effect of boundary conditions on the rutting behaviour of asphalt mixes. Through comparisons and verifications with laboratory data of the rutting test at different boundary conditions (fully confined and unconfined), the modelling approach shows its capability of capturing the rutting behaviour of asphalt concrete in the modified wheel tracking test. Micromechanics analysis shows that the third (tertiary) stage of rutting behaviour is due to the weakening of the internal structure of the asphalt samples with contact bond breaks over time, which is found in the unconfined test. Meanwhile, the tertiary stage hardly occurs in the fully confined test once densification leads to contact of the aggregate–aggregate skeleton, forming a rigid structure to resist the load with lateral support from the fixed boundary condition. Finally, a parametric study was also conducted to provide further insight into the current testing set-up, including the effect of the sample size and boundary condition on the rutting behaviour of asphalt concrete.

Evaluating dynamic tree-species-shifting and height development caused by ungulate browsing in forest regeneration using a process-based modeling approach

Ungulate browsing can prevent the successful regeneration and development of forest-owner-intended and climate-resilient admixed tree species, compromising the future provision of multiple ecosystem services. A reduction of the height increment of palatable tree species caused by ungulate browsing results in a competition shift in favor of less palatable species. While this key process of losing admixed tree species is well understood, forest growth models have not yet captured its full complexity. Therefore, our objectives were to simulate (i) the loss of susceptible tree species from different browsing regimes and its impact on stand development, (ii) changes in the height increment of saplings and trees, and (iii) the shifts in social status during stand development without pre-defined limits a sapling can resist browsing. To address these objectives, we used the process-based model iLand to simulate the exposure of Norway spruce, Silver fir, and European beech stands to varying browsing densities. We successfully reproduced expected dynamic shifts in species composition and height development 24 years after establishment, depending on ungulate pressure and species-specific competitive strength. Stand composition and growth differences already occur with slight changes in ungulate density. When saplings remained in stand structure, independent of the number being browsed, our results led to more individuals with substantially smaller dimensions. We found that dynamic tree species shifting can be simulated realistically by a process-based modeling approach, which can be used to show the strong impact of ungulates on stand development.

Multi-scale lumped modeling of micro-channels cooling structure for W7-X divertor unit target module

A novel concept for the cooling system of the W7-X divertor unit target module has been proposed, which involves tiles equipped with parallel arrays connected by hundreds of sub-millimeter rectangular micro-channels (MCs), obtained using Additive Manufacturing techniques. The structural material proposed for the heat sink substrate is galvanized copper, while the plasma facing material is tungsten. To reduce the high computational cost of thermal-hydraulic simulations of the tiles, a multi-scale lumped modeling approach has been built in previous studies. This involves replacing a group of hydraulic parallel MCs with a porous strip (PS), suitably calibrated to reproduce similar thermal-hydraulic behavior of the MCs. The aim of the current work is to verify that the PS-model is also suitable for the thermo-mechanical stress evaluation, comparing the results from arrays with MCs and PS. Since the heat sink and plasma-facing tiles are bonded, the interfacial delamination and shear stresses are analyzed, being crucial for structural integrity. The analyses, carried out in the elastic regime, show that the PS model returns conservative results when compared to the MCs model, with an overestimation of the delamination and shear stresses at the free edge. Results from both models reveal nearly identical interfacial stress predictions at the free surface edge. Moreover, it is found that both MCs and PS blocks, located near the bond interface, contribute to high-stress fluctuations that could lead to the delamination of the bond interface, suggesting that the distance of the microchannel from the interface should be increased. The PS model can reliably be used to design and verify the most convenient series/parallel connection of the tiles in the divertor unit target module, taking operational constraints into consideration.

Nutrient contents and microbial communities as mediators of the effects of land-use in ecosystem functioning in alpine ecosystems from Central Mexico

Abstract Purpose The interplay between microbial taxonomy, functional diversity, and land-use changes across diverse ecosystems must be better understood. This study explores, through a statistical modeling approach, the intricate relationships between microbial communities, soil properties (nutrient contents and physicochemical parameters), and land-use changes, using stable isotopes ratio (δ13C/δ15N) as ecosystem function indicator. Methods Using Structural Equation Modeling (SEM) this study aims to investigate how soil microbial communities, soil properties and ecosystem function are interconnected in three ecosystems undergoing land-use change in Nevado de Toluca, Mexico. Results Multivariate analyses of microbial communities and soil parameters for the different land-uses reveal significant variation in both microbial composition and soil properties (i.e. nutrient contents) mainly explained by the land-use history of the studied ecosystems. Through the SEM approach, it was possible to disentangle the relative contribution of land-use change, microbial community composition and nutrient contents to the ecosystem function defined as the δ13C/δ15N ratio. Conclusions Microbial communities are the main drivers of soil ecosystem functioning. However, through SEM approach it is possible to formally test direct and indirect relationships impacting microbial communities and the resulting functional consequences, contributing to a mechanistic understanding of the ecological implications of land-use change, aiding in sustainable land-use decision-making.

Accurate and robust linear representation for containership stability conditions with ballast water

Stowage planning involves optimizing the placement of cargo on a ship while ensuring the ship's stability conditions are met. This optimization problem is typically formulated as a mixed-integer linear programming problem. However, a challenge arises in representing stability conditions as linear constraints since these conditions have non-linear relationships to the decision variables. Although there are methods for linearizing these stability conditions, they lack a systematic modeling approach and numerical verification of accuracy and reliability. In particular, the complex and diverse nature of ballast water contributes to significant non-linearity, posing a considerable challenge in developing an accurate linear approximation. This study proposes linear prediction methods that incorporate ballast water to address major stability conditions such as trim, drafts, shear force, bending moment, torsion, and transverse metacentric height. We introduce a comprehensive training and testing methodology for prediction, and validate the proposed method by comparing it with true values on two ships of different capacities, demonstrating its accuracy and scalability.

Aeroelastic analysis of a lightweight topology-optimized sandwich panel

Sandwich structures with lattice cores are novel, lightweight composite structures and are widely used in the aerospace industry. Besides, the aeroelastic behavior of sandwich panels in a supersonic flow regime still needs to be thoroughly studied. This work investigates the supersonic flutter of a sandwich panel whose core is topology-optimized. A finite element model of a sandwich panel based on the layerwise theory, coupled with the first-order piston theory, is presented. The sandwich panel core is assessed using a topology optimization approach with flutter loading constraints. The subsequent analytical homogenization scheme is developed to provide the equivalent mechanical properties of the topology-optimized panel. The modeling approach is fully validated, and the results demonstrate that the sandwich panel is capable of enlarging the flutter-free operational flight range when compared with other conventional panel designs. A parametric analysis of the topology-optimized sandwich panel regarding the critical flutter conditions is performed.

Unsupervised multimodal modeling of cognitive and brain health trajectories for early dementia prediction

Predicting the course of neurodegenerative disorders early has potential to greatly improve clinical management and patient outcomes. A key challenge for early prediction in real-world clinical settings is the lack of labeled data (i.e., clinical diagnosis). In contrast to supervised classification approaches that require labeled data, we propose an unsupervised multimodal trajectory modeling (MTM) approach based on a mixture of state space models that captures changes in longitudinal data (i.e., trajectories) and stratifies individuals without using clinical diagnosis for model training. MTM learns the relationship between states comprising expensive, invasive biomarkers (β-amyloid, grey matter density) and readily obtainable cognitive observations. MTM training on trajectories stratifies individuals into clinically meaningful clusters more reliably than MTM training on baseline data alone and is robust to missing data (i.e., cognitive data alone or single assessments). Extracting an individualized cognitive health index (i.e., MTM-derived cluster membership index) allows us to predict progression to AD more precisely than standard clinical assessments (i.e., cognitive tests or MRI scans alone). Importantly, MTM generalizes successfully from research cohort to real-world clinical data from memory clinic patients with missing data, enhancing the clinical utility of our approach. Thus, our multimodal trajectory modeling approach provides a cost-effective and non-invasive tool for early dementia prediction without labeled data (i.e., clinical diagnosis) with strong potential for translation to clinical practice.

An emergent ontology for digitally modeling existing buildings: examining Kyoto’s Nishiki market

PurposeThis project examines digital modeling strategies for existing buildings. In this context, it aims to question assumptions about the need for geometric accuracy and the efficacy of predefined ontologies. As a counterpoint to prevailing digital modeling strategies, this project proposes a digital modeling approach using a project-specific, emergent ontology.Design/methodology/approachNishiki Market, in Kyoto, Japan, is studied as a test case. The emergent-ontology modeling process is introduced with an initial minimal set of operations including basic fold and trim operations applicable to surfaces. As the model develops iteratively, new situations are encountered for which existing rules are insufficient. In response, the model maker’s subjective judgment is invoked to introduce new operations, and ontological rules are allowed to expand.FindingsThe emergent-ontology approach, when executed on the Nishiki Market test case, enables representation of specific architectural qualities, highlighting semantic distinctions between digitally modeled elements of real-world features. The modeling approach generated project-specific knowledge, informing disciplinary understanding. Ontological emergence enabled semantic relationships to be disclosed and newly constructed.Originality/valueThe project proposes a novel methodology using an emergent ontology for digitally modeling existing buildings. Instead of remaining within the limitations a predefined ontology, the model maker’s subjective decisions shape the model’s ongoing development. This interpretive approach allows project-specific knowledge generation while challenging prevailing assumptions about accuracy and consistency in digital models of existing buildings.

Unveiling the dynamics of seam performance: a regression analysis approach for predictive modeling

Abstract The performance of seams is crucial for the garment manufacturing industry's sustainability. This study delves into the factors impacting seam slippage, strength, and efficiency. By analyzing fabric specifications (weight and weave), machine settings (presser foot pressure and speed), and seam direction, the study uncovers their influence on seam properties. Samples of 100% cotton plain and twill fabrics, both of low (120g/m^2to140g/m^2) and high (275 g/m^2to320 g/m^2) weights, were chosen. Lock stitch type (301) and superimposed seam class were employed, with samples stitched in both weft and warp directions using the same stitch type, seam class, and substantial core spun sewing thread. An experimental design was created using variable parameters (fabric weight, weave, machine speed, and presser foot pressure), resulting in a 2^3 X 3^2 regression analysis. This design facilitated the development of 72 stitched samples. The regression analysis yielded an optimized solution, with square regressions indicating significant seam strength (85%) and efficiency (93%). In summary, this research provides insights into optimizing seam performance by understanding the interplay of fabric, machine, and stitching parameters, crucial for enhancing the quality and durability of garment seams.

Transcranial alternating current stimulation over frontal eye fields mimics attentional modulation of visual processing.

Attentional control over sensory processing has been linked to neural alpha oscillations and related inhibition of cerebral cortex. Despite the wide consensus on the functional relevance of alpha oscillations for attention, precise neural mechanisms of how alpha oscillations shape perception and how this top-down modulation is implemented in cortical networks remain unclear. Here, we tested the hypothesis that alpha oscillations in frontal eye fields (FEF) are causally involved in the top-down regulation of visual processing in humans (male and female). We applied sham-controlled, intermittent transcranial alternating current stimulation (tACS) over bilateral FEF at either 10 Hz (alpha) or 40 Hz (gamma) to manipulate attentional preparation in a visual discrimination task. Under each stimulation condition, we measured psychometric functions for contrast perception and introduced a novel linear mixed modeling approach for statistical control of neurosensory side effects of the electric stimulation. TACS at alpha frequency reduced the slope of the psychometric function, resulting in improved sub-threshold and impaired super-threshold contrast perception. Side effects on the psychometric functions were complex and showed large interindividual variability. Controlling for the impact of side effects on the psychometric parameters by using covariates in the linear mixed model analysis reduced this variability and strengthened the perceptual effect. We propose that alpha tACS over FEF mimicked a state of endogenous attention by strengthening a fronto-occipitoparietal network in the alpha band. We speculate that this network modulation enhanced phasic gating in occipitoparietal cortex leading to increased variability of single-trial psychometric thresholds, measurable as a reduction of psychometric slope.Significance statement Attention is fundamental to the voluntary control of perception and behavior. Yet, precise underlying neural mechanisms remain unclear. Here, we provide evidence for a vital role of frontal alpha oscillations in the regulation of gating of visual information by using intermittent transcranial alternating current stimulation (tACS). We show that modulation of frontal alpha oscillations affected the slope of psychometric functions of visual contrast perception, leading to contrast-dependent improvement and impairment of perception. Our data adds to work on alpha oscillations in spatial attention and studies on the psychometrics of attention. Furthermore, we introduce a novel approach for the statistical control of tACS side effects and thereby contribute to the ongoing debate on outcome variability in studies using transcranial neurostimulation methods.

Today’s positive affect predicts tomorrow’s experience of meaningful coincidences: a cross-lagged multilevel analysis

ABSTRACT The perception of meaningful patterns in random arrangements and unrelated events takes place in our everyday lives, coined apophenia, synchronicity, or the experience of meaningful coincidences. However, we do not know yet what predicts this phenomenon. To investigate this, we re-analyzed a combined data set of two daily diary studies with a total of N = 169 participants (mean age 29.95 years; 54 men). We investigated if positive or negative affect (PA, NA) predicts the number of meaningful coincidences on the following day (or vice versa). By means of a cross-lagged multilevel modelling approach (Bayesian estimation) we evaluated with which of two theoretical assumptions the data are more in line. First, if meaningful coincidences are facilitated by a broader and more flexible thinking style, PA should positively predict meaningful coincidences at the following day. However, if the experience of meaningful coincidences signifies a strategy to cope with negative feeling states, NA should predict the experience of meaningful coincidences during the following day. In favour of a more flexible thinking style, we found that PA predicted the number of perceived coincidences the following day. We did not find any effect for NA, and therefore, no evidence arguing for the coping mechanism hypothesis of meaningful coincidences.

Modeling approach for Ti3C2 MXene-based fluorescent aptasensor for amoxicillin biosensing in water matrices

Amoxicillin, a member of the penicillin family, is primarily utilized for the treatment of various bacterial infections affecting ears, nose, throat, urinary tract, and skin. Given its widespread application in medicine, agriculture, environment, and food industry, the precise and sensitive detection of amoxicillin is important. This study introduces a novel approach to developing a sensitive and selective fluorescent aptasensor relying on fluorescence resonance energy transfer (FRET) for the specific detection of amoxicillin. The carboxyfluorescein-labeled aptamer serves as a energy donor, while MXene functions as an energy acceptor, and acting as a quencher. To achieve optimal detection efficiency, a dual optimization strategy utilizing RSM-CCD and ANN-GA was used to fine-tune experimental conditions. The fluorescence measurements revealed an expansive linear range extending from 100 to 2400 ng mL−1, accompanied by an exceptionally low detection limit of 1.53 ng mL−1. Additionally, it shows an excellent selectivity towards amoxicillin over other antibiotics commonly found in water matrices. The aptasensor demonstrates good stability and reproducibility; effectiveness of the aptasensor was validated by testing in real water samples. This remarkable sensitivity and broad dynamic range affirm the efficacy aptasensor in accurately detecting varying concentrations of amoxicillin in wastewater bodies.

The relationship between mathematics self-efficacy and mathematics achievement: multilevel analysis with NAEP 2019

BackgroundThis empirical study aims to investigate the association between mathematics self-efficacy and mathematics achievement gaps among students in Grades 4, 8, and 12, utilizing data from the 2019 National Assessment of Educational Progress (NAEP). The study also considers student-level (e.g., mathematics self-efficacy, gender, race/ethnicity) and school-level (e.g., school location, proportion of underrepresented students) demographics to provide a comprehensive analysis of the factors contributing to mathematics achievement gaps.MethodsA two-level cross-sectional multilevel modeling approach was employed to analyze the variance in mathematics achievement, partitioning it into within- and between-school components. This approach allowed for an examination of association between mathematics self-efficacy and achievement gaps while considering various student and school characteristics. The rationale behind this methodology lies in its ability to capture the hierarchical nature of educational data and provide a nuanced understanding of the factors associated with mathematics achievement.ResultsThe analysis of the NAEP data revealed substantial variability in mathematics achievement across schools in the United States at all grade levels. Furthermore, mathematics self-efficacy emerged as a robust predictor of students' mathematics achievement, exhibiting significant effect sizes for Grades 4, 8, and 12. Remarkably, when students' mathematics self-efficacy was held constant, the mathematics achievement gaps among different student subgroups by gender, race/ethnicity, ELL, IEP, NSLP status narrowed, highlighting the importance of self-efficacy in addressing these disparities. The study also identified the presence of significant school contextual effects, further emphasizing the role of the educational environment in shaping mathematics achievement.ConclusionsThis study underscores the critical role of mathematics self-efficacy in influencing mathematics achievement gaps among students. By acknowledging the association between self-efficacy beliefs and mathematics achievement, policymakers and educators can develop targeted interventions to enhance students' confidence and motivation in mathematics, ultimately promoting equitable educational outcomes. The findings also emphasize the significance of school-level factors, calling for comprehensive approaches that consider both individual and contextual factors in narrowing achievement gaps. The implications of adopting a self-efficacy perspective to address mathematics achievement gaps extend to educational policy, curriculum development, and instructional practices, ultimately fostering more inclusive and effective mathematics education for all students.

Direct and Inverse Kinematics of a 3RRR Symmetric Planar Robot: An Alternative of Active Joints

Existing direct and inverse kinematic models of planar parallel robots assume that the robot’s active joints are all at the bases. However, this approach becomes excessively complex when modeling a planar parallel robot in which the active joints are within one single kinematic chain. To address this problem, our article unveils an alternative for a 3RRR symmetric planar robot modeling technique for the derivation of the robot workspace and the analysis of its direct and inverse kinematics. The workspace was defined using a system of inequalities, and the direct and inverse kinematics models were generated using vectorial analysis and an optimized geometrical approach, respectively. The resulting models are systematically presented and validated. Two final model renditions are delivered supplying a thorough equation analysis and an applicability discussion based on the importance of the robot’s mobile platform orientation. The advantages of this model are discussed in comparison to the traditional modeling approach: whereas conventional techniques require the solution of complex eighth-degree polynomials for the analysis of the active joint configuration of these robots, these models provide an efficient back-of-the-envelope analysis approach that requires the solution of a simple second-degree polynomial.

Evaluation of the Feasibility of the Prediction of the Surface Morphologiesof AWJ-Milled Pockets by Statistical Methods Based on Multiple Roughness Indicators

Improvement of the surface quality of machined parts is essential in order to avoid excessive and costly post-processing. Although non-conventional processes can efficiently carry out the machining of difficult-to-cut materials with high productivity, they may also, for various reasons, be related to increased surface roughness. In order to optimize the surface quality of generated surfaces in a reliable way, surface profiles obtained during these processes must be adequately modeled. However, given that most studies have focused on Ra or Rz indicators or are based on the assumption of a normal distribution for the profile heights, relevant models cannot accurately represent the surface characteristics that exist in a real machined surface with a high degree of accuracy. Thus, in the present study, a new modeling approach based on the use of a statistical probability distribution for the surface profile height is proposed. After six different distributions were evaluated on the basis of a three-stage procedure involving different roughness indicators pertaining to the abrasive waterjet (AWJ) milling of pockets, it was found that, although it is not possible to model the nominal values of every roughness parameter simultaneously, in several cases, it is possible to approximate the values of critical indicators such as Ra, Rz, Rsk, Rku and Rp/Rv ratio by Weibull distribution with a sufficient degree of accuracy.

The dynamics of employability capitals for the transition to work: career identity, cultural capital, job interview self-efficacy and self-perceived employability

Purpose This study aims to understand how employability capitals’ dynamics foster self-perceived employability (SPE) among students and graduates, which is still being empirically explored. Building upon the Employability Capital Growth Model and the Social Cognitive Career Theory’s career self-management model, we aimed to understand how different capitals associate by testing a serial mediation model connecting career identity (reflecting career identity capital) and SPE through the serial mediation of cultural capital and job interview self-efficacy (ISE) (an element of psychological capital).Design/methodology/approach We adopted a two-wave design involving 227 Italian University students and graduates. We recruited participants through multi-channel communication. The hypothesised relationships were analysed employing the structural equation modelling approach with the SPSS AMOS statistical package.Findings The results indicated that career identity, cultural capital, ISE and SPE are meaningfully related. In particular, in line with our expectations, we observed that career identity predicts cultural capital, which is positively associated with ISE which, ultimately, impacts SPE.Originality/value Our work adds to existing research by advancing the understanding of employability capitals, explaining how they interact and influence SPE, which is crucial for a sustainable transition into the workforce. At a practical level, our findings call upon, and guide, efforts from various stakeholders in the graduate career ecosystem (i.e. universities and their partners) to offer students and graduates meaningful experiences to form and use their employability capitals.