Credibility Of Model Research Articles

Latent Pitfalls in Microstructure-Based Modeling for Thermally Aged 9Cr-1Mo-V Steel (Grade 91)

Abstract A case study was conducted on a mechanistic model development that predicted tensile strength deterioration with thermal aging of 9Cr-1Mo-V steel in supporting the 60-year design life expected for advanced nuclear reactors. For property prediction beyond practical testing times, mechanistic modeling is highly desired, as it taps into the physics of structure–property relationships and therefore can generate reliable results for extrapolation. Meanwhile, as mechanistic models are often complicated, reflecting the intricacy of microstructure and strengthening mechanisms, pitfalls that are difficult to detect often exist. This paper discusses latent pitfalls that are common in mechanistic modeling or specific in this 9Cr-1Mo-V case development through using the American Society of Mechanical Engineers verification and validation in computational solid mechanics (ASME V&V 10) standard for evaluating credibility of modeling in materials engineering. Suggestions are also made for enhancing reliability of microstructure-based modeling.

A critical review on approaches to generate and validate virtual population for physiologically based pharmacokinetic models: Methodologies, case studies and way forward.

In silico modeling and simulation techniques such as physiologically based pharmacokinetic (PBPK) and physiologically based biopharmaceutics modeling (PBBM) have demonstrated various applications in drug discovery and development. Virtual bioequivalence leverages these computation tools to predict bioequivalence between reference and test formulations thereby demonstrating possibilities to reduce human studies. A pre-requisite for virtual bioequivalence is development of validated virtual population that depicts the same variability as that of observed in clinic. This development, validation and optimization of virtual population is a key attribute of virtual bioequivalence based on which conclusion of bioequivalence is made. Various strategies for optimization of virtual population based on appropriate considerations of physicochemical, physiological and disposition aspects are demonstrated with the help of six diverse case studies of immediate and modified release formulations. Once the virtual population is optimized to match in vivo variability, it can be used for various applications such as biowaivers, dissolution specification justification, f2 mismatch, establishing dissolution safe space, etc. In this review article, we attempted to describe various methodologies and approaches for optimization of virtual population using Gastroplus. Strategies based on optimization of virtual population with emphasis on specific and sensitive parameters were portrayed. We have further elucidated considerations related to study design, in vivo variability, sample size for optimization of virtual population from Gastroplus perspective. We believe that this review article provides a step-by-step process for virtual population optimization for interest of biopharmaceutics modeling scientists in order to ensure reliable and credible physiological models.

Models vetted against prediction error and parameter sensitivity standards can credibly evaluate ecosystem management options

A new standard for assessing model credibility is developed. This standard consists of parameter estimation, prediction error assessment, and a parameter sensitivity analysis that is driven by outside individuals who are skeptical of the model’s credibility (hereafter, skeptics). Ecological/environmental models that have a one-step-ahead prediction error rate that is better than naive forecasting — and are not excessively sensitive to small changes in their parameter values are said here to be vetted. A procedure is described that can perform this assessment on any model being evaluated for possible participation in an ecosystem management decision. Uncertainty surrounding the model’s ability to predict future values of its output variables and in the estimates of all of its parameters should be part of any effort to vett a model. The vetting procedure described herein, Prediction Error Rate-Deterministic Sensitivity Analysis (PER-DSA), incorporates these two aspects of model uncertainty. DSA in particular, requires participation by skeptics and is the reason why a successful DSA gives a model sufficient credibility to have a voice in ecosystem management decision making. But these models need to be stochastic and represent the mechanistic processes of the system being modeled. For such models, performing a PER-DSA can be computationally expensive. A cluster computing algorithm to speed-up these computations is described as one way to answer this challenge. This new standard is illustrated through a PER-DSA of a population dynamics model of South African rhinoceros (Ceratotherium simum simum).

Enhancing the lateral performance of Chuan-dou timber frame infilled with vertical wood panels

Chuan-dou timber frame system represents a prevalent form of traditional wooden architecture, extensively employed in Southwest China. The wood infilling wall stands as a primary manifestation of infill wall construction in Chuan-dou timber frame system, and the existence of infilled walls significantly impacts the lateral behavior of Chuan-dou timber frames. In this paper, the internal force analysis of the timber frame specimen infilled with vertical panels (TFVP) had been undertaken by the credible finite element model, elucidating the detailed enhancement mechanism of wood vertical infilled panels on the lateral performance of the timber frame. 10 models with diverse geometric parameters were formulated to investigate the influence of geometric factors on the vertical panels, including width and thickness. The research findings showed that the presence of Dijiaofang and wood infilled walls could result in a substantial enhancement in the load capacities and initial lateral stiffness of the timber frames. Among them, the enhancement effect of Dijiaofang comes from half-tenon joints (HTJs), while the reinforcement of wood vertical infilled panels arises from the interplay between adjacent panels, as well as the squeezing and friction behavior between the infilled wall and Chuan-dou timber frame. Furthermore, the parametric analysis indicated that the geometry of vertical panels has discernible effects on the lateral performance of TFVP specimens.

A new adaptive approach for bridging the gap in insurance premium estimation

This paper proposes a new adaptive approach called adaptive mean (ADM) that combines the strengths of trimming and winsorization to minimize the mean square error (MSE). ADM will contribute significantly to insurers by enabling them to mitigate risk exposure caused by inaccurate premium estimation and establish more precise premiums for their clients. Furthermore, the proposed mean offers several pros compared to the conventional mean. It facilitates the natural and intuitive calculation of risk loading, identifies and measures the impact of large claims, and analyzes premium susceptibility to skewed risk by capturing the tails of the relevant loss models. In addition, this paper reviews two established, robust credibility methods (trimming and winsorization). It compares them with our proposed method, resulting in a more reliable and robustly accurate method for credibility premium estimation. We presented and analyzed two real data sets from engineering insurance companies and the Wisconsin Office of the Insurance Commissioner to highlight the advances of ADM in minimizing the MSE and building more robust credibility models that are less vulnerable to outlier events and model uncertainty.

Assessing Credibility in Bayesian Networks Structure Learning.

Learning Bayesian networks from data aims to create a Directed Acyclic Graph that encodes significant statistical relationships between variables and their joint probability distributions. However, when using real-world data with limited knowledge of the original dynamical system, it is challenging to determine if the learned DAG accurately reflects the underlying relationships, especially when the data come from multiple independent sources. This paper describes a methodology capable of assessing the credible interval for the existence and direction of each edge within Bayesian networks learned from data, without previous knowledge of the underlying dynamical system. It offers several advantages over classical methods, such as data fusion from multiple sources, identification of latent variables, and extraction of the most prominent edges with their respective credible interval. The method is evaluated using simulated datasets of various sizes and a real use case. Our approach was verified to achieve results comparable to the most recent studies in the field, while providing more information on the model's credibility.

PBBM Considerations for Base Models, Model Validation, and Application Steps: Workshop Summary Report.

The proceedings from the 30th August 2023 (Day 2) of the workshop "Physiologically Based Biopharmaceutics Models (PBBM) Best Practices for Drug Product Quality: Regulatory and Industry Perspectives" are provided herein. Day 2 covered PBBM case studies from six regulatory authorities which provided considerations for model verification, validation, and application based on the context of use (COU) of the model. PBBM case studies to define critical material attribute (CMA) specification settings, such as active pharmaceutical ingredient (API) particle size distributions (PSDs) were shared. PBBM case studies to define critical quality attributes (CQAs) such as the dissolution specification setting or to define the bioequivalence safe space were also discussed. Examples of PBBM using the credibility assessment framework, COU and model risk assessment, as well as scientific learnings from PBBM case studies are provided. Breakout session discussions highlighted current trends and barriers to application of PBBMs including: (a) PBBM credibility assessment framework and level of validation, (b) use of disposition parameters in PBBM and points to consider when iv data are not available, (c) conducting virtual bioequivalence trials and dealing with variability, (d) model acceptance criteria, and (e) application of PBBMs for establishing safe space and failure edges.

Mechanisms that potentially contribute to the development of post-streptococcal glomerulonephritis.

Post-streptococcal glomerulonephritis (PSGN) is primarily associated with preceding Group A streptococcal skin or throat infections, now mainly observed in economically disadvantaged communities. This condition significantly predisposes individuals to later-life chronic kidney disease and concurrent renal complications, with the elderly experiencing increased severity and less favourable outcomes. Streptococcal pyrogenic exotoxin B and nephritis associated plasmin receptor are identified nephritogenic antigens (nephritogens). Pathogenesis of PSGN is multifactorial. It can involve the formation of antigen-antibody immune complexes, causing inflammatory damage to renal glomeruli. Deposition of circulating immune complexes or in situ formation of immune complexes in glomeruli, or both, results in glomerulonephritis. Additionally, molecular mimicry is hypothesised as a mechanism, wherein cross-reactivity between anti-streptococcal antibodies and glomerular intrinsic matrix proteins leads to glomerulonephritis. Besides, as observed in clinical studies, streptococcal inhibitor of complement, a streptococcal-secreted protein, can also be associated with PSGN. However, the interplay between these streptococcal antigens in the pathogenesis of PSGN necessitates further investigation. Despite the clinical significance of PSGN, the lack of credible animal models poses challenges in understanding the association between streptococcal antigens and the disease process. This review outlines the postulated mechanisms implicated in the development of PSGN with possible therapeutic approaches.

Dominance of Artificial Intelligence and Machine Learning Algorithms in Real-Time Traffic Flow prediction and Route Optimization in Autonomous Vehicles

The evolution of autonomous vehicles has elicited significant interest in understanding how real-time data may be used to provide enhanced driving experiences. This research project explored AI and Machine Learning methodologies applied for traffic forecasting and route optimization, and their implications for autonomous vehicles and urban mobility. For this project, the road traffic flow Dataset was utilized from Kaggle, containing 48,000 records of the flow of traffic, each including the following key features. In our work, we deployed some credible and well-established machine learning models: linear regression and random forest. These algorithms were separately trained by using a part of preprocessed data. The MSE for the Random Forest model was significantly lower, which means that the Random Forest Regressor had much smaller errors in estimating the volume of traffic compared to the Linear Regression model. The Random Forest Model had a high R² score, proving that this model explains a great deal of variance in the volume of traffic. This means that the 'model of random forest regressors' excellently fitted the data, snatching most of the important patterns and relationships between input features and target variables.

360 Degree Risk Assessment for Reliable ContingentWorkforce Operations - A Business Credibility Model

The gig economy, while providing big tech companies with unparalleled access to specialized talent and operational agility, simultaneously introduces a complex spectrum of risks, ranging from financial liabilities to regulatory non-compliance and performance inefficiencies. Traditional risk management frameworks fall short of addressing the dynamic nature of these challenges. This paper presents the 360° Risk Assessment Model, an advanced, data-driven framework leveraging machine learning to deliver a real-time, multi-faceted evaluation of risk. By dynamically quantifying and recalibrating risk scores, this model empowers enterprises to strategically mitigate potential losses, optimize decision-making, and enhance the governance of their contingent workforce ecosystem.

An Operational Quality Model of Embedded Software Aligned with ISO 25000

Embedded systems omnipresent in everyday life and industry are mainly composed of hardware and software that must comply with a number of standards and regulations. However, there is no consensus on the quality characteristics and sub-characteristics of embedded software. This paper presents the steps for modeling an operational quality model for embedded software aligned with the ISO 25000 series of quality models for traditional computer systems. From a literature review composed of 40 studies on quality modeling for embedded systems and software, 85 of the most frequent quality characteristics and sub-characteristics were first identified, including a subset of 16 referenced or cited in at least 25% of the literature. Next, the design of a quality model for embedded software aligned with the ISO 25000 series was proposed with 13 characteristics and 27 sub-characteristics. The operational aspect of this quality model for embedded software is addressed next through a set of measures and measurement functions from ISO 25000 to aggregate the results of the quantification of the characteristics and sub-characteristics. A survey involving 25 embedded software specialists is presented next to gauge, using Fleiss’ Kappa criteria, their agreement with the proposed quality model. Furthermore, the computed importance weights derived from the survey participants’ individual opinions were compared with those derived from an analysis of 40 embedded software studies, bolstering the credibility of the model. The results of this study suggest that the proposed quality model can serve as a framework for evaluating and understanding the quality characteristics across diverse expertise levels. Furthermore, the convergence between the survey and the literature strengthens the model's credibility by anchoring it in both established literature and practitioners’ agreements.

Development of a prognostic model for NSCLC based on differential genes in tumour stem cells

Non-small cell lung cancer (NSCLC) constitutes a significant portion of lung cancers and cytotoxic drugs (e.g. cisplatin) are currently the first-line treatment. However, NSCLC has developed resistance to this drug, which limits the therapeutic effect and thus affects prognosis. NSCLC sc-RNA-seq data were downloaded from the GEO database and Ku Leuven Laboratory for Functional Epigenetics, and bulk RNA-seq data were obtained from the TCGA database. The “Seurat” package was employed for scRNA-seq data processing, and the uniform manifold approximation and projection (UMAP) were applied for downscaling and cluster identification. Use the FindAllMarkers function to find differential genes (DEGs) for tumor stem cells. Then, we performed univariate regression analyses on the DEGs to identify potential prognostic genes. We created a machine learning framework based on potential prognostic genes, which combines 10 machine learning methods and their 101 combinations to get the optimal prognostic risk model. The model was evaluated in the training set and validation set. A nomogram was developed to provide physicians with a quantitative tool for prognosis prediction. Finally, we evaluated the expression and functionality of SLC2A1. We discovered 22 cell clusters containing 218379 cells by examining single-cell RNA sequencing datasets (GSE148071, KU_lom, GSE131907, GSE136246, GSE127465). Tumour cells were isolated for subpopulation analysis and 162 differential genes from SOX2_cancer were obtained. After univariate Cox analysis, we found 23 genes with prognostic potential prognostic value and utilized them to develop 101‑combination machine learning computational framework. We eventually picked the best performing ‘StepCox[both] + RSF’, which includes 8 genes. The model has a relatively high prediction accuracy in both TCGA and GEO datasets. In in vitro investigations, targeted suppression of the SLC2A1 gene resulted in significant reductions in proliferation, invasion and migration in A549 cells. In addition, a significant reduction in cisplatin resistance was seen in A549/DDP cells. The outcomes demonstrated the precision and credibility of the prognostic model for NSCLC, highlighting its potential significance in the treatment and prognosis of individuals affected by this disease. SLC2A1 may become a promising prognostic marker and a potential therapeutic target, offering valuable insights to inform clinical treatment decisions.

Examining Relations Between Sexualizing Media Exposure and Sexting Attitudes and Behaviors among U.S. Adolescents.

Based on social cognitive processes (Bandura, 2009), sexualizing media likely provide adolescents with credible role models for their own sexualized self-presentation in other mediated contexts, such as sexting. A survey of 6,093 US adolescents was conducted (Mage = 15.27 years, SD = 1.37; 69.1% cisgender girls; 26.8% cisgender boys, 4.2% gender-minority participants) to examine relations between three types of sexualizing media exposure (reality television, music videos, pornography) and active sexting behaviors (i.e., requesting and sending). Among cisgender boys, exposure to sexually oriented reality television was positively related to active sexting behaviors, but this link was not found for cisgender girls or gender-minority participants. Music video exposure was related to active sexting behaviors only among cisgender boys and pornography consumption was positively related to active sexting behaviors among all gender groups. A positive outcome expectancy (i.e., sexting is fun) consistently mediated these relations. A negative outcome expectancy (i.e., sexting is risky) only mediated the relations between pornography consumption and active sexting behaviors among cisgender girls. Results are discussed in light of social cognitive theory (Bandura, 2009) and the 3AM Model (Wright, 2011).

Global Models, Victim Disconnect and Demands for International Intervention: The Dilemma of Decoloniality and Transitional Justice in Sri Lanka

Abstract This article uses Sri Lanka as a case study to impel engagement of decoloniality with transitional justice. It identifies gaps in the literature critical of transitional justice, specifically structural interpretations of power hierarchies, state-centrism and disregard of ethnicity and religion. It thereby uses a decolonial analytical lens on empirical findings from Sri Lanka’s failed transitional justice process to identify and understand continuing colonial power structures, including epistemic coloniality. The empirical findings offer three new insights. First, an ideational, structural and procedural disconnection between victims and the global transitional justice model is noted. The article traces how victim positioning and this disconnection were disregarded in favour of an internationally authoritative, credible and universal model of transitional justice. Second, the ethno-religious challenges to transitional justice, which include its reliance on the state as a neutral provider of justice, are highlighted. The third finding, however, on victim demands for greater international involvement, presents a dilemma to future decolonial consideration of transitional justice. Despite only using decoloniality as an analytical tool, the article nevertheless demonstrates the need for deeper reform, including at the epistemic level, for transformation to occur within the field.

Small and Medium Enterprises’ Brand Personality and Customer Satisfaction: The Mediating Role of Product Quality

The purpose of this study was to examine the influence of Tsitsou’s brand personality scale on customer satisfaction and the mediating effect of product quality on the relationship between brand personality dimensions and customer satisfaction. Data were gathered from 240 customers of Small and Medium Enterprises (SMEs) products and services through the use of a cross-sectional survey. Data analyses were done by descriptive statistics and inferential statistics by the use of SPSS Version 23 and Smart PLS-3 which was used for structural equation modelling. The results of the study revealed that all the dimensions of Tsitsou’s brand personality model (competitiveness, prestige, morality, authenticity, and credibility) had significant positive relationships with customer satisfaction. Product quality indirectly mediated the relationships among competitiveness, prestige, morality, authenticity, credibility and customer satisfaction. The results have substantiated that Tsitsou’s brand personality scale can be applied to SMEs with particular reference to developing economies. Therefore, it can be recommended that business practitioners employ brand personality traits to market their products and services to boost customer satisfaction.

Development of a coupled agent-based generation expansion planning tool with a power dispatch model

Power companies need to adapt their generation expansion planning in response to changing market, climate and regulatory conditions as global warming, electrification, and technology breakthroughs continue. To fortify energy system resilience, it is critical to understand the collective effects of their autonomous decisions on power systems operations and reliability. To this end, we developed an integrated framework, an agent-based model (ABM) coupled with a power dispatch model (PDM) (referred to as ABM-PDM), tested on the Texas 123-bus transmission system in the Electric Reliability Council of Texas (ERCOT) region. Agents (power generation companies) can invest in natural gas, solar, and wind technologies to maximize profits from 2021 to 2050, using market information from the PDM based on their capital budget and perceived costs, financial incentives for renewable energy, and climate risks. We applied ABM-PDM to assess how power companies respond to future technological advancements and climate change. After demonstrating model credibility, we explored 25 combinations of cost and capacity factors reflecting a variety of technological evolution trajectories. Results indicated that to replace wind over solar for replacing existing fossil-fuel power plants due to lower costs and higher capacity factors. Additionally, as more agents invest, the energy market becomes more competitive, and system-wide electricity prices drop. We also analyzed the impacts of temperature increases on investments using seven projections, from 0 to 6 °C, during the modeling period. The results showed that as temperatures rise, agents invest more to accommodate the increasing loads. ABM-PDM incorporates risk attitude and learning into companies’ decision-making, providing additional information on generation expansion for the non-optimal future of power systems.

Bayesian credibility model with heavy tail random variables: calibration of the prior and application to natural disasters and cyber insurance

Bayesian credibility model with heavy tail random variables: calibration of the prior and application to natural disasters and cyber insurance

Quantile balanced credibility model

This paper introduces a model into the methodology of greatest accuracy approach in credibility theory, we extend Pitselis (2013) quantile credibility model by including a different type of losses which is called the weighted balanced loss function (WBLF henceforth) in order to establish novel credibility premiums under the balanced squared error loss function. More specifically, we further explain how quantiles under the WBLF can be incorporated into the classical Bühlmann’s (1967) credibility model. A new model of quantile credibility under the WBLF is presented, where the premiums are estimated using the non-parametric approach where premiums must be estimated based on data only, without postulating any type of distribution. Furthermore, we establish the credibility estimators and provide an application to real data of the results using R software to evaluate the effectiveness of our proposed model, and conclude with a comparison with the Pitselis (2013) model and the Bühlmann (1967) model to show the effectiveness of our premium.

Integrating plasma exosomal miRNAs, ultrasound radiomics and tPSA for the diagnosis and prediction of early prostate cancer: a multi-center study.

This multi-center study aims to explore the roles of plasma exosomal microRNAs (miRNAs), ultrasound (US) radiomics, and total prostate-specific antigen (tPSA) levels in early prostate cancer detection. We analyzed the publicly available dataset GSE112264 to identify the differentially expressed miRNAs associated with prostate cancer. Then, PyRadiomics was used to extract image features, and least absolute shrinkage and selection operator (LASSO) was used to screen the data. Subsequently, according to strict inclusion and exclusion criteria, the internal dataset (n = 199) was used to construct a diagnostic model, and the receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA), and DeLong test were used to evaluate its diagnostic performance. Finally, we used an external dataset (n = 158) for further validation. The number of features extracted by PyRadiomics was 851, and the number of features screened by LASSO was 23. We combined the hsa-miR-320c, hsa-miR-944, radiomics, and tPSA features to construct a joint model. The area under the ROC curve of the combined model was 0.935. In the internal validation, the area under the curve (AUC) of the training set was 0.943, and the AUC of the test set was 0.946. The AUC of the external data set was 0.910. The calibration curve and decision curve were consistent with the performance of the combined model. There was a significant difference in the prediction ability between the combined prediction model and the single index prediction model, indicating the high credibility and accuracy of the combined model in predicting PCa. The combined prediction model, consisting of plasma exosomal miRNAs (hsa-miR-320c and hsa-miR-944), US radiomics, and clinical tPSA, can be utilized for the early diagnosis of prostate cancer.

The Relationships between Service Quality, Reputation, and Performance in Hospitality

The importance of service quality and a positive reputation, individually leading to enhanced performance of hospitality organizations, is undisputed. However, little is known about their interplay and how they collectively explain the variability in performance. The purpose of this article is to contribute to the understanding of the individual and collective impact of service quality and reputation on variability in performance, categorized as customer satisfaction and loyalty. This study was conducted using quantitative research methods with data derived from hotel guests in Iceland in the summer of 2023. We gathered 1487 valid answers from guests staying at any of the 9 selected hotels, all belonging to the same Icelandic chain. The factor analysis revealed a credible four-factor model that consists of tangible service, performed service, reputation, and performance. All three independent factors had a statistically positive connection with variabilities in performance, and the collective explanation ratio for the model was strong. The independent factors were correlated to a satisfactory level, and tangible service had the strongest unique connection to performance.