Credibility Of Model Research Articles

Finite element modeling of fracture compression by compression plates.

Dynamic compression plating is a common type of fracture fixation used to compress between bone fragments. The quality of compression across the fracture is important for postoperative stability and primary bone healing. Compression quality may be affected by surgical variations in plate prebend, screw location, screw torque, fracture gap, and implant material. Computational modeling provides a tool for systematically examining these factors, and for visualizing the mechanisms involved. The purpose of this study was to develop a finite element model of dynamic compression plating that includes screw insertion under torque control, establish model credibility through sensitivity analyses and experimental validation, and use the model to examine the effects of surgical variables on fracture compression and postoperative stability. Model-predicted compressive pressures had good agreement with corresponding synthetic bones experiments under a variety of conditions. Models demonstrated that introducing a 1.5or 3 mm plate prebend (using a 4.5 mm narrow LCP plate) eliminated gapping at the far cortex, which is consistent with clinical recommendations. However, models also revealed that plate prebend led to sharp decreases in fracture compressive force, exceeding 80% in some cases. A 1.5 mm plate prebend resulted in the most uniform pressures across the fracture. Testing of a simplified model form used in previous computational modeling studies showed large inaccuracies for constructs with plate prebend. This study provides the first experimentally validated computational models of dynamic compression plate fracture fixation, and reveals important effects of plate prebend and fracture gap on fracture compression quality.

Magnetic isotope effects: a potential testing ground for quantum biology.

One possible explanation for magnetosensing in biology, such as avian magnetoreception, is based on the spin dynamics of certain chemical reactions that involve radical pairs. Radical pairs have been suggested to also play a role in anesthesia, hyperactivity, neurogenesis, circadian clock rhythm, microtubule assembly, etc. It thus seems critical to probe the credibility of such models. One way to do so is through isotope effects with different nuclear spins. Here we briefly review the papers involving spin-related isotope effects in biology. We suggest studying isotope effects can be an interesting avenue for quantum biology.

Can ChatGPT help patients answer their otolaryngology questions?

Over the past year, the world has been captivated by the potential of artificial intelligence (AI). The appetite for AI in science, specifically healthcare is huge. It is imperative to understand the credibility of large language models in assisting the public in medical queries. To evaluate the ability of ChatGPT to provide reasonably accurate answers to public queries within the domain of Otolaryngology. Two board-certified otolaryngologists (HZ, RS) inputted 30 text-based patient queries into the ChatGPT-3.5 model. ChatGPT responses were rated by physicians on a scale (accurate, partially accurate, incorrect), while a similar 3-point scale involving confidence was given to layperson reviewers. Demographic data involving gender and education level was recorded for the public reviewers. Inter-rater agreement percentage was based on binomial distribution for calculating the 95% confidence intervals and performing significance tests. Statistical significance was defined as p < .05 for two-sided tests. In testing patient queries, both Otolaryngology physicians found that ChatGPT answered 98.3% of questions correctly, but only 79.8% (range 51.7%-100%) of patients were confident that the AI model was accurate in its responses (corrected agreement = 0.682; p < .001). Among the layperson responses, the corrected coefficient was of moderate agreement (0.571; p < .001). No correlation was noted among age, gender, or education level for the layperson responses. ChatGPT is highly accurate in responding to questions posed by the public with regards to Otolaryngology from a physician standpoint. Public reviewers were not fully confident in believing the AI model, with subjective concerns related to less trust in AI answers compared to physician explanation. Larger evaluations with a representative public sample and broader medical questions should immediately be conducted by appropriate organizations, governing bodies, and/or governmental agencies to instill public confidence in AI and ChatGPT as a medical resource. 4.

Updating and recalibrating causal probabilistic models on a new target population

ObjectiveVery often the performance of a Bayesian Network (BN) is affected when applied to a new target population. This is mainly because of differences in population characteristics. External validation of the model performance on different populations is a standard approach to test model’s generalisability. However, a good predictive performance is not enough to show that the model represents the unique population characteristics and can be adopted in the new environment. MethodsIn this paper, we present a methodology for updating and recalibrating developed BN models – both their structure and parameters - to better account for the characteristics of the target population. Attention has been given on incorporating expert knowledge and recalibrating latent variables, which are usually omitted from data-driven models. ResultsThe method is successfully applied to a clinical case study about the prediction of trauma-induced coagulopathy, where a BN has already been developed for civilian trauma patients and now it is recalibrated on combat casualties. ConclusionThe methodology proposed in this study is important for developing credible models that can demonstrate a good predictive performance when applied to a target population. Another advantage of the proposed methodology is that it is not limited to data-driven techniques and shows how expert knowledge can also be used when updating and recalibrating the model.

Multiple programmed cell death patterns and immune landscapes in bladder cancer: Evidence based on machine learning and multi-cohorts.

Bladder cancer (BLCA) is the most prevalent malignant neoplasm of the urinary tract, and ranks seventh as the most frequent systemic neoplasm in males. Dysregulation of programmed cell death (PCD) has been implicated in various stages of cancer progression, including tumorigenesis, invasion, and metastasis. However, the correlation between multiple PCD modes and BLCA is lacking. Thus, a risk prediction model was built based on 12 models of PCD to predict prognosis and immunotherapy response in patients with BLCA. The RNA sequencing transcriptome data of BLCA were collected from the Cancer Genome Atlas Program (TCGA) and GEO datasets. Univariate Cox and LASSO regression analyzes were performed to identify PCD-related genes (PCDRGs) significant for prognosis. Multivariate Cox regression analysis was used to develop a prognostic model for PCD. Survival analysis and chi-squared test were employed to analyze the survival variations between different risk groups. Univariate and multivariate Cox analyses were performed to evaluate the model as an independent prognostic predictor. A nomogram was formulated using both clinical data and the model to predict the survival rates of BLCA patients. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were performed to analyze and elucidate the molecular mechanisms and pathways operating within different risk score groups. Furthermore, the immune landscape was investigated and the efficacy of various anti-tumor drugs was evaluated for BLCA. Finally, consensus clustering analysis was adopted to explore the association between different PCD clusters and clinical characteristics. Assessment of the public datasets and multivariate Cox analysis yielded 1254 PCDRGs, of which 10 PCDRGs for BLCA were identified. Based on the PCDRGs, a prognostic model was built for BLCA patient prognosis. Compared with the low-risk group, the high-risk group had a poorer prognosis. The model predicted area under the curve (AUC) values of 0.751, 0.753, and 0.763, respectively, for 1-, 3-, and 5-year survival of BLCA patients. The nomogram further demonstrated the credibility of the prognosis model. The low-risk group patients exhibited lower TIDE scores and higher TMB scores, implying better response of the low-risk group to immunotherapy. The consensus clustering analysis indicated that compared with PCD cluster A, PCD cluster B was significantly more expressed in PCDRGs, suggesting a closer relation of PCD cluster B to PCDRGs. Patients in PCD cluster B had lower risk scores. To summarize, the effects of 12 PCD patterns on BLCA were synthesized and the correlation between PCD and BLCA was explored. These findings provide new and convincing evidence for individualized treatment of BLCA, and help guide the treatment strategy and improve the prognosis of BLCA patients.

Generating bulk RNA-Seq gene expression data based on generative deep learning models and utilizing it for data augmentation

Large-scale high-throughput transcriptome sequencing data holds significant value in biomedical research. However, practical challenges such as difficulty in sample acquisition often limit the availability of large sample sizes, leading to decreased reliability of the analysis results. In practice, generative deep learning models, such as Generative Adversarial Networks (GANs) and Diffusion Models (DMs), have been proven to generate realistic data and may be used to solve this promblem. In this study, we utilized bulk RNA-Seq gene expression data to construct different generative models with two data preprocessing methods: Min-Max-GAN, Z-Score-GAN, Min-Max-DM, and Z-Score-DM. We demonstrated that the generated data from the Min-Max-GAN model exhibited high similarity to real data, surpassing the performance of the other models significantly. Furthermore, we trained the models on the largest dataset available to date, achieving MMD (Maximum Mean Discrepancy) of 0.030 and 0.033 on the training and independent datasets, respectively. Through SHAP (SHapley Additive exPlanations) explanations of our generative model, we also enhanced our model's credibility. Finally, we applied the generated data to data augmentation and observed a significant improvement in the performance of classification models. In summary, this study establishes a GAN-based approach for generating bulk RNA-Seq gene expression data, which contributes to enhancing the performance and reliability of downstream tasks in high-throughput transcriptome analysis.

Integration of Rural Revitalization Strategy and Modernized Agricultural Governance Based on Intelligent Big Data Analysis

Abstract This paper explores the integration mechanism of rural revitalization strategy and agricultural modernization governance. The relationship between the two is complementary and closely linked, and rural revitalization can bring new impetus to agricultural modernization. The logistic development model is proposed to carry out a stage-by-stage study on the integration development of agricultural modernization governance and rural revitalization, and the subsystem orderliness and composite system synergy models are established by using the geometric mean method and the linear weighting method, respectively, and the degree of integration development is judged by measuring the integration status. The model constructed in this paper, as well as the governance level of rural revitalization strategies and agricultural modernization in place B, are measured through empirical research. The experiment shows that the two variables in this paper and their logarithmic means are 100, and the means are 0.3436, 0.3463, -1.3217, and -0.9874, respectively. In the stability test, the number of scatters in the region is 92, and the truthfulness and predictive intervals are in [-1,1], which makes the model stability and credibility good. After the implementation of the rural revitalization strategy for 11 years, the level of the comprehensive development index of agricultural modernization in the rural areas of place B in 2020 is 0.3189, 0.2473, 0.2434, 0.2645, respectively, which is in the stage of steady growth.

A Review Paper on the Credibility Model for Electronic Service Quality using Big Data

A Review Paper on the Credibility Model for Electronic Service Quality using Big Data

Development and validation of a prognostic nomogram model incorporating routine laboratory biomarkers for preoperative patients with endometrial cancer

BackgroundSome biomarkers collected from routine laboratory tests have shown important value in cancer prognosis. The study aimed to evaluate the prognostic significance of routine laboratory biomarkers in patients with endometrial cancer (EC) and to develop credible prognostic nomogram models for clinical application.MethodsA total of 727 patients were randomly divided into a training set and a validation set. Cox proportional hazards models were used to evaluate each biomarker’s prognostic value, and independent prognostic factors were used to generate overall survival (OS) and progression-free survival (PFS) nomgrams. The efficacy of the nomograms were evaluated by Harrell’s concordance index (C-index), receiver operating characteristic (ROC) curves, decision curve analysis (DCA), calibration curves, X-tile analysis and Kaplan‒Meier curves.ResultsTen significant biomarkers in multivariate Cox analysis were integrated to develop OS and PFS nomograms. The C-indices of the OS- nomogram in the training and validation sets were 0.885 (95% confidence interval (CI), 0.810–0.960) and 0.850 (95% CI, 0.761–0.939), respectively; those of the PFS- nomogram in the training and validation sets were 0.903 (95% CI, 0.866–0.940) and 0.825 (95% CI, 0.711–0.939), respectively. ROC, DCA and calibration curves showed better clinical application value for the nomograms incorporating routine laboratory biomarkers. X-tile analysis and Kaplan‒Meier curves showed that the nomograms were stable and credible in evaluating patients at different risks.ConclusionsNomogram models incorporating routine laboratory biomarkers, including NLR, MLR, fibrinogen, albumin and AB blood type, were demonstrated to be simple, reliable and favourable in predicting the outcomes of patients with EC.

A Review of the Mechanism of the Influencer

Opportunities for marketing innovation have emerged in the new media era as a result of the fast diversification of social media platforms and the power of social media influencers. Influencer marketing, which includes user-generated content, multi-agent information distribution guided by social media influencers, and the expansion of influencers' advocacy and endorsement of goods and services, has its roots on social media platforms. The majority of recent studies on influencer marketing concentrate on specific influencers and followers. On the basis of the source credibility model, cognitive consistency theory, matching hypothesis, trust transfer theory, meaning transfer theory, etc., it investigates the influence processes of influencers on specific followers. The main topics of the current studies on the influencer marketing mechanism are the interactions between influencers and businesses or products, and the relationships between influencers and customers. By reviewing the latest developments in current research, this study sorts out and analyzes the mechanism of influencer marketing and interprets how influencers influence followers' behavior from the perspectives of sociology and psychology.

Prediction of concrete strength using MCMC and GPR methods

Concrete strength prediction is a complex nonlinear regression task that involves multiple ingredients and age as key factors. In order to achieve accurate predictions, the Markov Chain Monte Carlo (MCMC) and Gaussian Process Regression (GPR) techniques are employed. The dataset, sourced from Kaggle repositories, comprises a comprehensive collection of 1030 data points. Alongside the existing features (content of ingredients, age and strength), we introduce new ones, including water-cement ratio, sand ratio, and water-binder ratio, to enhance the model's credibility. To determine the optimal kernel function, the dataset is partitioned into training and testing subsets. Notably, the MCMC method yields an R2 of 0.41, while GPR demonstrates a significantly improved R2 of 0.89. Further investigation is warranted to refine the model's fit and optimize its predictive capacity.

Kredibilitas Endorser Perempuan dan Laki-laki pada Iklan Pantene Miracle Hair Supplement

&lt;p&gt;&lt;em&gt;This study aims to show that there are differences in the credibility of female and male celebrity endorsers in Pantene Miracle Hair Supplement video ads on Youtube. As well as to show that there are different responses between female and male audiences. The importance of using endorsers as communicators in advertising is because it is considered to affect the persuasion and process of communicating the information to their audiences, especially endorsers with strong credibility. In addition, the credibility of celebrity endorsers and gender endorsers is must be considered when targeting audiences with certain genders. A number of previous comparative studies compared the credibility of female and male celebrity endorsers by female and male audiences show contradict results. This study uses the source credibility model that suggest by Ohanian. This type of research is quantitative comparative with survey methods. The sampling technique uses nonprobability with quota sampling and use 125 participants. The hypothesis tested by paired and independent t-tests. This study finds results that; female celebrity endorsers are more credible than male celebrity endorsers; there is no difference in credibility for female celebrity endorsers by female and male audiences; Also, male celebrity endorsers are considered more credible by female audiences than by male audiences.&lt;/em&gt;&lt;/p&gt;

Integrating a heatscape index and a Patch CA model to predict land surface temperature under multiple scenarios of landscape composition and configuration

Effectively predicting and mitigating urban heat island (UHI) through regulation of urban landscape patterns remains a global challenge. This paper introduces a new framework for predicting land surface temperatures (LST). We introduce a heatscape index (SCSS) to establish a relationship between landscape patterns and LST within spatial grids. Furthermore, the framework employs an urban CA model, equipped with a sophisticated patch generation engine, to forecast urban landscapes under multiple scenarios. LST under these scenarios is then calculated based on the established regression relationship. Application of the framework in Wuhan yields valuable insights: (1) The regression coefficient (R2 = 0.85) between SCSS and LST indicates that changes in SCSS can well explain the spatial heterogeneity in LST, especially in areas with evident UHI effects. (2) The simulated and retrieved LST for 2015 shows high spatial consistency, achieving an R2 value of 0.81, indicating the model's credibility. (3) Scenario simulations demonstrate that rapid and compact urban land expansion would exacerbate the UHI effect, leading to extreme high-temperature areas expanding towards suburban regions and significant temperature increases in urban fringe areas. The proposed framework provides powerful tools for policymakers and urban planners to mitigate the UHI effect and enhance urban sustainability.

Aero-structural design of joined-wing aircraft based on high-fidelity model

The joined-wing configuration reduces induced drag and structural weight by connecting the rear wing to the front wing. In addition, the rear wing can replace the role of the horizontal tail of a conventional aircraft, thus eliminating the aerodynamic drag and weight associated with the horizontal tail. This particular shape creates a highly coupled relationship between aerodynamics and structure, which must be fully considered during the overall design process to enhance aircraft performance. In this research, an aero-structural design model of the joined-wing aircraft is constructed based on high-fidelity computational fluid dynamics and structural finite element methods. The model is able to obtain accurate aerodynamic loads for the non-planar wing and to simulate the statically indeterminate structure of the closed wing configuration. The influence of the joined-wing shape parameters on the aerodynamic and structural disciplines, as well as the influence of geometric nonlinear characteristics, deformation constraints and buckling constraints on the structural weight are all taken into consideration. The model is applied to complete the aero-structural design optimization of a high-altitude long-endurance joined-wing aircraft, and wind tunnel tests are conducted. The test results verify the credibility of the design model proposed and the validity of the design environment.

Optimization and validation of universal response functions for interior spruce (Picea glauca, Picea engelmannii, and their hybrids)

Ecological models, including various forms of population transfer and response functions, have been widely employed to predict the performance of forest trees under future climate conditions. However, the accuracy of model predictions remains a challenge due to the lack of systematic model optimization and validation processes. In this study, we explored a series of candidate universal response functions (URFs) based on comprehensive provenance trials for interior spruce, a tree hybrid complex of great ecological and economic importance in British Columbia. We first developed URFs with various numbers of climate variables and their combinations, then evaluated and validated these models with conventional statistical methods. Although these models showed high predictive power and performed well in statistical validation, the map visualizations of these models varied substantially. Therefore, we further conducted a pixel-wise spatial validation based on site productivity data to identify the model with the best performance. Finally, we analyzed the impact of climate change on interior spruce based on the final model. Our results revealed that: 1) a minimum number of independent climate variables is required to generate a reliable and informative ecological model; 2) spatial validation is critical to ensure the credibility of ecological models; 3) among-population variation resulting from local adaptation is relatively weak, indicating a potential for modest productivity gains from selecting well-adapted populations for stand establishment; and 4) most of the current interior spruce habitat is projected to be vulnerable to anthropogenic climate change, largely due to temperature rise, with a major upslope shift predicted.

Mendel's closet: genetics, eugenics and the exceptions of sex in Edwardian Britain

This article situates formative Mendelian and chromosomal precepts and rhetoric as an integral part of ‘reproductive’ physiology and the broader sexological terrain in Edwardian Britain. Alongside the discovery of ‘internal secretions’ (hormones), the discovery of the sex chromosomes, made around the same time as the rediscovery of Mendel's laws of heredity at the turn of the twentieth century, transformed the ways in which questions about sex determination and sex development were considered. Approaches were diverse as leading biologists including William Bateson, Leonard Doncaster, Reginald Crundall Punnett, Geoffrey Watkins Smith and their interlocutors negotiated the multiple, often conflicting, sociopolitical interpretations, uses and abuses that Mendelian approaches to sex were amenable to. Most contentiously, it was recognized that any credible model of sex biology had to account for all manner of sex phenomena, including parthenogenesis, intersexualities and transformations of sex, and that it was the variations of sex that best provided insights into the otherwise hidden mechanisms that shaped sex characteristics. Such a move, however, embroiled the new sexological genetics and the developing discipline of ‘reproductive’ physiology with vexed debates about feminism, homosexuality and eugenics. The article charts how the ensuing tensions played out across scholarly and popular platforms, including Britain's newspapers.

A closed-loop analysis approach for ensuring stormwater source control design solution to achieve the intended goals

Stormwater source controls have been adopted worldwide to address hydrological and environmental impairments caused by the spread of impervious surfaces in cities. Current design method in China uses 30-year daily rainfall records to generate relationship of rainfall volume capture ratio (αg) and daily design storm, and then uses design storm to propose design solution. However, source control performance differs from rain to rain, and hence the design solution's actual effect may deviate from αg. Borrowing closed-loop feedback concept from business domain, this study proposes closed-loop analysis (CLA) which uses design solution's 30-year simulated result as data feedback to check design solution's effectiveness and then make improvements if necessary. It consists of four methods: 1) hourly design storm statistical method, for addressing the weakness of current daily design storm; 2) design solution model credibility examination method, for guaranteeing credibility of 30-year simulated results for CLA; 3) appropriate design storms determination method for source control without underdrain; 4) additional design parameters optimization method for source control with underdrain. Taking Xiamen city for example, case study results shows that design solution's 30-year simulated results were consistent/comparable with sizing calculation formula that was used to propose design solution, and therefore they were credible for CLA. Appropriate design storms ensured design solutions without underdrain to achieve the intended αg±3 %. Optimal design parameters combinations ensured design solutions with underdrain to achieve αg but also restore natural runoff events with pre- and post-development runoff frequency spectra similarity being 0.670–0.691. Based on stormwater mathematical model, CLA can drive source control design computation to a new methodological stage.

The Influence of Source Credibility and Inspiration on Tourists’ Travel Planning Through Travel Vlogs

This study investigated the effects of viewing travel vlog experiences on online tourists’ travel planning behavior. Based on integrating cognitive emotion theory (CET) and the source credibility model (SCM), this study illuminates the mechanics of inspiration formation and its mediation role between credible vlogger characteristics and travel planning behavior. The study surveyed 423 online tourists in Vietnam who use travel vlogs to plan their travels. The study found that sincerity, professionalism, and attraction all positively affect inspiration, but attraction had the biggest effect. Inspiration partially mediated the relationship between sincerity and travel planning behavior, while the influence of professionalism and attraction on travel planning behavior was fully mediated by inspiration. The study suggests that travel vloggers need to create products that clearly portray their ethos and can connect emotionally with online tourists. Hospitality and tourism businesses should collaborate with travel vloggers to enhance their brand image.

A modelling framework for modern heritage buildings energy simulation

The latest European polices highlight the urgent need to rehabilitate the existing building stock, responsible for 40 % of the EU's total energy consumption. In this process, a key role is played by thermal simulations, assessing the effective energy performances. However, significant discrepancies between real and simulated consumptions are frequently outlined. Inaccurate results are particularly dangerous for modern buildings, which, rarely protected, are often altered by invasive retrofitting solutions, with little regard for their heritage and cultural value. This paper introduces a comprehensive framework for the building energy simulation, ensuring the necessary model credibility. It consists of dynamic modelling, calibration and validation, enhancing the usefulness of the final results. A validated model is in fact the premise to propose a well-balanced retrofitting scenario, improving the current energy performances, reducing the operational costs, and preserving the historical values of existing buildings. As operative case-study, Chauderon administrative building in Lausanne (1969–1974), designed by the Atelier AAA in collaboration with Jean Prouvé, has been selected. Today, the complex is well-preserved in its original materiality and represents an iconic example of the modern aesthetics, with an expressed need for retrofitting. Following the proposed framework, a reliable model in WUFIplus has been created and validated according to ASHRAE 14, allowing to reliably test the efficacy of future retrofitting scenarios. The final aim of this process is to minimize the risk of inappropriate interventions. For high-quality or recognized post-World War II building stock, the use of a calibrated and validated model is justified and recommended over the static or simplified modelling approaches still commonly employed today.

How do gravity alterations affect animal and human systems at a cellular/tissue level?

The present white paper concerns the indications and recommendations of the SciSpacE Science Community to make progress in filling the gaps of knowledge that prevent us from answering the question: “How Do Gravity Alterations Affect Animal and Human Systems at a Cellular/Tissue Level?” This is one of the five major scientific issues of the ESA roadmap “Biology in Space and Analogue Environments”. Despite the many studies conducted so far on spaceflight adaptation mechanisms and related pathophysiological alterations observed in astronauts, we are not yet able to elaborate a synthetic integrated model of the many changes occurring at different system and functional levels. Consequently, it is difficult to develop credible models for predicting long-term consequences of human adaptation to the space environment, as well as to implement medical support plans for long-term missions and a strategy for preventing the possible health risks due to prolonged exposure to spaceflight beyond the low Earth orbit (LEO). The research activities suggested by the scientific community have the aim to overcome these problems by striving to connect biological and physiological aspects in a more holistic view of space adaptation effects.