Models For Analysis Of Data Research Articles

The Review of Modeling and Rendering 3D Environment: A Survey

This paper explores several topics in the field of 3D Environments, including ray tracing, rendering techniques, algorithms and optimization, data processing, real-time rendering, modeling, physical simulation, and material modeling. The paper covers a large variety of applications and techniques that are widely used in computer graphics and visualization. This paper also points out that significant progress has been made in ray tracing, texture filtering, image processing, motion synthesis, geometric modeling, and physical simulation. These advances have improved the visual quality, performance, and fidelity of computer-generated images. In addition, the article mentions the trend of integrating machine learning, deep learning, and data analysis techniques into computer graphics and visualization, especially in data processing and analysis models, to improve the results of rendering and simulation. Overall, this article explores details of the current state of the computer graphics and visualization field, highlighting the various technologies and applications which shapes the field.

Unique lipid composition maintained by extracellular blockade leads to prooncogenicity

Lipid-mediated inflammation is involved in the development and malignancy of cancer. We previously demonstrated the existence of a novel oncogenic mechanism utilizing membrane lipids of extracellular vesicles in Epstein–Barr virus (EBV)-positive lymphomas and found that the lipid composition of lymphoma cells is skewed toward ω-3 fatty acids, which are anti-inflammatory lipids, suggesting an alteration in systemic lipid composition. The results showed that arachidonic acid (AA), an inflammatory lipid, was significantly reduced in the infected cells but detected at high levels in the sera of EBV-positive patients lead to the finding of the blockade of extracellular AA influx by downregulating FATP2, a long-chain fatty acid transporter that mainly transports AA in EBV-infected lymphoma cells. Low AA levels in tumor cells induced by downregulation of FATP2 expression confer resistance to ferroptosis and support tumor growth. TCGA data analysis and xenograft models have demonstrated that the axis plays a critical role in several types of cancers, especially poor prognostic cancers, such as glioblastoma and melanoma. Overall, our in vitro, in vivo, in silico, and clinical data suggest that several cancers exert oncogenic activity by maintaining their special lipid composition via extracellular blockade.

Flow past a random array of statistically homogeneously distributed stationary Platonic polyhedrons: Data analysis, Probability maps and Deep Learning models

We perform particle-resolved direct numerical simulations (PR-DNS) of the flow past a random array of stationary Platonic polyhedrons seeded in a tri-periodic box at Reynolds number Re=1, 10, and 100 and solid volume fraction 0.05, 0.1 and 0.2. Using Platonic polyhedrons enables us to examine the effects of particle angularity on the hydrodynamic force and torque exerted on the stationary particles in a controlled manner. We report the average drag, lift, and torque coefficients as well as their respective distribution. We explore the relationship between the particle angularity, particle angular position, flow disturbances created by the neighboring particles, and the hydrodynamic force and torque exerted on each individual particle. We attempt to extend our probability map based model (MPP), our Physics Informed neural network (PINN) model previously developed for spheres to Platonic polyhedrons, and propose an additional NN architecture. We show that ignoring the angular position and actual shape of the reference and neighboring particles constitutes a reasonable first order approximation that significantly simplifies the construction of deterministic models of hydrodynamic forces and torques at low Re=1 to moderate Re=10. At high Re=100, both the MPP model and the PINN model without any additional knowledge of the particle shape and angular position fail to properly predict the force and torque fluctuations. We then design a Convolutional Neural Network (CNN) architecture that is able to maintain a satisfactory performance at Re=100 but requires additional input data in the form of a coarse-grained velocity field around each particle. Overall, predicting the torque fluctuation remains a significant challenge.

Exploring stereotypes, bias, and expectations of women in the open data context

This study aims to provide a qualitative analysis of the knowledge, beliefs, and perspectives of prominent women working in different sectors in the context of open data. The analysis is based on semi-structured interviews with an outline developed for the interview script. The content obtained was analyzed by attending to categories that deal directly with a gender perspective, covering existing biases, and possible modes of transformation through actions at individual, collective, and institutional levels that incorporate gender sensitivity when working with open data. The findings show that gender bias persists in the process of collecting, analyzing, and interpreting data, as well as in the formation of work groups. The solution, highlighted by all interviewees, involves the development of interoperable and consistent data collection and analysis models that integrate a gender perspective right from the outset, as well as the training of professionals in fields associated with the use of data. Central to this process is the role of government agencies, which should promote efficient public policies giving greater visibility to the roles of women in areas of knowledge associated with open data. Finally, this research also points to the need for innovations such as artificial intelligence to overcome these challenges.

Effects of Public Service Motivation on R&D Project-Based Team Learning Where Psychological Safety Is a Mediator and Project Management Style Is a Moderator

While public service motivation (PSM) and teamwork are widely recognized as crucial drivers for effective public service delivery, researchers primarily analyze these factors independently and at a personal level. The existing literature rarely explores the interplay between PSM, the project team learning process (PTLP), and psychological safety (PS) within research and development (R&D) project teams, particularly in national R&D organizations. This study addresses this gap by proposing a theoretical model that examines the combined effect of individual motivation and team collaboration, mediated by PS, on R&D PTLP. Additionally, it investigates the moderating influence of project management (PM) styles—fully agile and partially agile—on these relationships. The proposed method utilizes partial least squares structural equation modeling (PLS-SEM) for quantitative data analysis. Our findings revealed a positive relationship between PSM, PS, and R&D PTLP, with PS acting as a significant mediator. Notably, the relationship between PSM and R&D PTLP was stronger under fully agile project management compared to partially agile settings. These findings suggest that both project teams and organizations should prioritize promoting PS and consider the moderating effects of project management styles to foster a sustainable R&D team learning process, particularly within national R&D institutions.

The influence of community park characteristics on satisfaction in Guangzhou: Moderating and mediating effects analysis

Community parks in old urban areas have problems such as outdated facilities and low quality, which inhibit the development of healthy aging. However, limited research has examined the correlation between such park characteristics and park satisfaction among elderly individuals. Additionally, the mechanisms underlying this association remain unclear. In this investigation, a moderated mediation framework grounded in self-determination theory was formulated to probe the interplay between these variables, with a specific focus on the mediating influence of social self-efficacy among the elderly and the moderating effect of use intention. A questionnaire survey (N = 319) was conducted in Shamian Park, Guangzhou, employing structural equation modeling for data analysis. Findings indicate that, even after controlling for demographic factors, park characteristics not only exert a direct influence on satisfaction but also exhibit an indirect impact through the mediation of social self-efficacy, with both pathways being moderated by use intention. This study has implications for how to improve the design of community parks in old urban areas in developing countries to better meet the basic needs of the elderly and promote healthy aging.

Exploring the Influence of Cumulative Ecological Risk on Belief in Fake News: A Longitudinal Study

Objectives: The dangers to society and individual persons of believing fake news are becoming more apparent, and it is increasingly important to explore the factors that influence believing fake news. Due to persons developing and having a unique consciousness, personality, and set of behaviors across interconnected ecological risks, we aimed to evaluate the relationship between the cumulative effects of multiple ecological risks and believing fake news. Methods: We conducted a survey over 2 time periods at 4 public universities in Beijing, China. We used cluster sampling and obtained 406 valid samples for each of the 2 time periods. We constructed a reflective formative second-order model using least partial squares structural equation modeling (PLS-SEM) for data analysis and analysed the model type through quadratic term effects. Results: First, there was a bidirectional impact between cumulative ecological risk and believing fake news, ie, not only did it show the importance of the environment in which individuals find themselves in shaping their beliefs about fake news, but it also demonstrated the dangers of fake news; second, cognitive bias mediates the relationship between cumulative ecological risk and believing in fake news, demonstrating the mechanism between cumulative ecological risk and believing in fake news; and third, the cumulative effect of ecological risk affects believing fake news following an exacerbation model. Conclusions: The tendency towards believing in fake news grows exponentially as a person experiences multiple risks from family, school, and peers. This study provides both theoretical and practical knowledge to combat fake news effectively.

Enhanced Sarcasm and Emotion Detection Through Unified Model of Transformer and FCNets

In this study, we present a unified model for efficient emotion and sarcasm identification in many languages. We accomplish resilient performance by integrating hybrid transformers and fully connected networks (FCNets) in our methodology. Extracting datasets in several languages, doing exploratory data analysis, preprocessing, extracting features, training and testing models, and finally deploying the system are all essential processes in the suggested strategy. Using a variety of models throughout training, we apply deep learning approaches to the problem of sarcasm and emotion recognition. The Flask framework chooses the model with the best performance to deploy. When applied to situations involving many languages, our method shows to be far more efficient and effective at identifying instances of sarcasm and emotion. Our model gives accuracy of 93.130 % for identifying sarcasm and emotion.

Conceptually Funded Usability Evaluation of an Application for Leveraging Descriptive Data Analysis Models for Cardiovascular Research.

The focus of this study, and the subject of this article, resides in the conceptually funded usability evaluation of an application of descriptive models to a specific dataset obtained from the East Slovak Institute of Heart and Vascular Diseases targeting cardiovascular patients. Delving into the current state-of-the-art practices, we examine the extent of cardiovascular diseases, descriptive data analysis models, and their practical applications. Most importantly, our inquiry focuses on exploration of usability, encompassing its application and evaluation methodologies, including Van Welie's layered model of usability and its inherent advantages and limitations. The primary objective of our research was to conceptualize, develop, and validate the usability of an application tailored to supporting cardiologists' research through descriptive modeling. Using the R programming language, we engineered a Shiny dashboard application named DESSFOCA (Decision Support System For Cardiologists) that is structured around three core functionalities: discovering association rules, applying clustering methods, and identifying association rules within predefined clusters. To assess the usability of DESSFOCA, we employed the System Usability Scale (SUS) and conducted a comprehensive evaluation. Additionally, we proposed an extension to Van Welie's layered model of usability, incorporating several crucial aspects deemed essential. Subsequently, we rigorously evaluated the proposed extension within the DESSFOCA application with respect to the extended usability model, drawing insightful conclusions from our findings.

QoS prediction using EMD-BiLSTM for II-IoT-secure communication systems

Abstract To address the challenges of secure and reliable communication and system quality of service (QoS) prediction in intelligent production lines (IPL) in the Industrial Intelligent Internet of Things (II-IOT) environment, a redundant collaborative security model-based communication architecture is designed. First, the redundant collaborative security communication model is introduced to construct the network communication architecture of IPL, including the industrial-site mechanical floor, data awareness layer, and gateway and application layer. Then, to leverage the advantages of the empirical-mode decomposition (EMD) method and the bidirectional long short-term memory (BiLSTM) model in time-series data analysis and processing, an EMD-BiLSTM-based QoS prediction model is proposed that can synchronously achieve one-step and multi-step prediction of QoS attributes. The proposed model exhibits a prediction accuracy of up to 94.01% on the SourceForge dataset, with prediction, recall, and F1 values as high as 91.37, 90.60, and 90.99%, respectively. The proposed EMD-BiLSTM model can achieve better performance than the state-of-the-art QoS prediction models, indicating that the proposed model can be more effectively used to improve the reliable communication level of II-IoT.

Enhanced Cardiovascular Disease Prediction Modelling using Machine Learning Techniques: A Focus on CardioVitalnet

ABSTRACT Aiming at early detection and accurate prediction of cardiovascular disease (CVD) to reduce mortality rates, this study focuses on the development of an intelligent predictive system to identify individuals at risk of CVD. The primary objective of the proposed system is to combine deep learning models with advanced data mining techniques to facilitate informed decision-making and precise CVD prediction. This approach involves several essential steps, including the preprocessing of acquired data, optimized feature selection, and disease classification, all aimed at enhancing the effectiveness of the system. The chosen optimal features are fed as input to the disease classification models and into some Machine Learning (ML) algorithms for improved performance in CVD classification. The experiment was simulated in the Python platform and the evaluation metrics such as accuracy, sensitivity, and F1_score were employed to assess the models’ performances. The ML models (Extra Trees (ET), Random Forest (RF), AdaBoost, and XG-Boost) classifiers achieved high accuracies of 94.35%, 97.87%, 96.44%, and 99.00%, respectively, on the test set, while the proposed CardioVitalNet (CVN) achieved 87.45% accuracy. These results offer valuable insights into the process of selecting models for medical data analysis, ultimately enhancing the ability to make more accurate diagnoses and predictions.

Optimization of data analysis models for low‐resource Eurasian languages using machine translation

Optimization of data analysis models for low‐resource Eurasian languages using machine translation

Explaining the predictions of kernel SVM models for neuroimaging data analysis

Machine learning methods have shown great performance in many areas, including neuroimaging data analysis. However, model performance is only one objective in neuroimaging analysis. Gaining insight from the data is also critical in this field, such as identifying regions where detected signals are relevant to cognitive and diagnostic tasks. To fulfill this need, enabling the explainability of a model’s decision-making process is critical. Predictions of complex machine learning models are notoriously difficult to explain. This limits the use of complex models like kernel support vector machines (SVM) in neuroimaging analysis. Recently, several permutation-based methods have been developed to explain these complex models. However, the explanation results are affected by class-irrelevant features like suppressor variables and high background noise variables. This problem may also happen when explaining linear models. One possible reason is that the permutation process will produce unrealistic data instances when features are not independent, e.g. correlated. These unrealistic data instances will influence the explanation results. In neuroimaging analysis, the activation pattern, the estimated weight of the assumed generative model corresponding to the current classifier, is used to deal with this problem for linear models. This method does not rely on a permutation process but rather on the available data information. In this paper, we propose a novel method of Explanation through Activation Pattern (EAP) to explain the SVM models with different types of kernels for neuroimaging data analysis. Our method can generate a global feature importance score by estimating the activation pattern of kernel SVM models. We evaluate our method against three popular methods on both simulation datasets and a publicly available EEG/MEG dataset on visual tasks. The experimental results demonstrate that the proposed EAP method can provide explanations with low computational cost and is less affected by class-irrelevant features than the other three methods. In the experiment using the MEG/EEG dataset of visual tasks, the proposed EAP method provides agreement results with the brain’s electrical activity patterns reported in the literature on the visual tasks EEG/MEG data and is significantly faster than the other explanation methods.

Associations between wastewater gut microbiome and community obesity rates: Potential microbial biomarkers for surveillance

Gut microbes are crucial for human health, which are usually accumulated in urban wastewater systems. Seven wastewater treatment plants in Australia with distinct population obesity rates between 18% and 33% were selected for wastewater sampling and analysis. Human gut microbiome were detected using metagenomic sequencing to investigate their associations with the community obesity rate. To unravel this complex relationship, a range of algorithm models, including linear discriminant analysis effect size (LEfSe), similarity percentage analysis (SIMPER), statistical analysis of metagenomic profiles (STAMP), linear models for microarray and RNA-Seq data analysis (LIMMA), Relief, ratio approach for identifying differential abundance (RAIDA), least absolute shrinkage and selection operator (LASSO), support vector machine (SVM), Boruta, DESeq2 and analysis of compositions of microbiomes with bias correction (ANCOM-BC), were used to identify potential bacterial biomarkers for obesity in the wastewater microbiome. Among these algorithm models, LEfSe, LIMMA, SIMPER and SVM are effective in identifying multiple microbial biomarkers. Specific human gut microbes, including Ruminococcus_E, Agathobacter, Fusicatenibacter, Anaerobutyricum, Blautia_A and Neisseria, were identified as potential consensus microbial biomarkers for obesity in the population. A high obesity rate is mainly characterized by a high abundance of pathogenic bacteria and microorganisms associated with xenobiotic biodegradation and metabolism, endocrine and metabolic diseases, and transcription pathways. This study underscores the innovative potential of leveraging human gut microbes in wastewater as biomarkers for monitoring obesity levels across communities, offering a novel, cost-effective, and indirect approach to public health surveillance.

The Impact of Digital Transformation on International Trade Based on Big Data Analysis Models and Threshold Effects

Currently, the world is experiencing a wave of digitalization, and the digital economy has become a significant engine for the growth of import and export trade. This paper deeply analyzes the impact of the digital economy on import and export trade from dimensions such as technology, society, and institutions. Based on panel data from 42 cities in China’s three major urban clusters (the Yangtze River Delta, the Pearl River Delta, and the Beijing-Tianjin-Hebei region) from 2011 to 2021, using the mediation effect model, threshold effect model, and K-means clustering algorithm, it examines the impact of digital economic development on import and export trade and its transmission mechanism. The research findings indicate that the development of the digital economy significantly promotes import and export trade; the level of technological innovation plays a mediating role in the relationship between digital economic development and import and export trade; there is a single threshold positive promotion effect of technological innovation level in the process of digital economic development affecting import and export trade; the degree of urbanization exhibits a single threshold effect in the process of digital economic development affecting import and export trade, specifically transitioning from inhibiting to promoting effects once the threshold value of urbanization is surpassed. Furthermore, there is a certain regional heterogeneity in the above conclusions among the three major urban clusters. The research results have certain guiding significance for optimizing the structure of foreign trade, promoting high-quality development of import and export trade, and achieving high-level economic and social development.

Bayesian Quantile Regression Models for Complex Survey Data Under Informative Sampling

Abstract The interest in considering the relation among random variables in quantiles instead of the mean has emerged in various fields, and data collected from complex survey designs are of fundamental importance to different areas. Despite the extensive literature on survey data analysis and quantile regression models, research papers exploring quantile regression estimation accounting for an informative design have primarily been restricted to a frequentist framework. In this paper, we introduce different Bayesian methods relying on the survey-weighted estimator and the estimating equations. A model-based simulation study evaluates the proposed methods compared to alternative approaches and a naïve model fitting ignoring the informative sampling design under different scenarios. In addition, we illustrate and conduct a prior sensitivity analysis in a design-based simulation study that uses data from Prova Brasil 2011.

Research on the Impact of the Public Safety Emergencies on Women Riders’ Preference of Shanghai Real-Time Crowdsourcing Logistics Platform

This study explores the impact of public safety emergencies on the preferences of women riders within Shanghai’s real-time crowdsourcing logistics platform. It employs quantitative research methods, utilizing LDA topic modeling and ERNIE categorization model for data analysis. The research identifies six key topics influencing riders’ preferences: Tip Order Information, Sharing and Volunteering, Epidemic Delivery Rules, Quality of Work and Life, Epidemic Control Measures, and Liability Exemption and Reward. The study reveals a cognitive bias among riders towards positive utilities, indicating a generally optimistic emotional state which influences their utility preferences. The findings suggest that the riders prioritize social interests and responsibilities during the pandemic, demonstrating adaptability to new work environments and appreciation for supportive measures by platforms. The study provides insights into the nuances of women riders’ preferences, emphasizing the need for targeted strategies by platforms and authorities to enhance job satisfaction and address challenges faced by women riders.

A Systematic Review of Big Data Driven Education Evaluation

The rapid development of artificial intelligence has driven the transformation of educational evaluation into big data-driven. This study used a systematic literature review method to analyzed 44 empirical research articles on the evaluation of big data education. Firstly, it has shown an increasing trend year by year, and is mainly published in thematic journals such as educational technology, science education, and language teaching. Chinese and American researchers have made the greatest contributions in this field. Secondly, the algorithmic models for big data education evaluation research are diverse, the text modality is the most popular, the evaluation subjects are mainly college students, with fewer primary and secondary school students, and science is the discipline that most commonly applies big data education evaluation. The evaluation objectives of big data education evaluation mainly focus on five aspects: high-order thinking analysis, learning performance prediction, learning emotion recognition, teaching management decision-making, and evaluation mode optimization, and the text modality is widely used for data collection in high-order thinking analysis; regardless of the evaluation objectives, higher education students are the most widely evaluated objects; the science discipline is the main field of using big data technology to empower teaching evaluation. Thirdly, the current research topics of big data education evaluation mainly focus on online learning behavior and environmental participation evaluation, process assessment of learning motivation and emotional analysis, development and optimization of subject domain big data models, cognitive diagnosis and high-order thinking skills evaluation, and design of learning analysis frameworks based on data mining.

Characterizing Precipitation Behaviors of H− in the Martian Atmosphere

AbstractSolar wind protons can charge exchange with the extensive hydrogen corona of Mars, resulting in a significant flux of energetic neutral atoms (ENAs). As these solar wind hydrogen ENAs precipitate into the upper atmosphere, they can experience electron attachment or detachment, resulting in populations of H− and H+, respectively, with upstream velocity. We seek to characterize the behavior of H− in the ionosphere of Mars through a combination of in situ data analysis and mathematical models. Observations indicate that measurable H− precipitation in the ionosphere of Mars is rare, occurring during only 1.8% of available observations. These events occur primarily during high energy solar wind conditions near perihelion. We also compare H− fluxes to those of H+ and find that H− fluxes are ∼4.5 times less than H+, indicating preferential conversion of hydrogen ENAs to H+. We develop a simple model describing the evolution of the charged and neutral fraction of ENAs and H− ions versus altitude. We find that 0.29%–0.78% of ENAs are converted to H− for solar wind energies 1–3 keV. We also predict that the effects of photodetachment on the H‐H− system are non‐negligible.

The Effect of Brand Awareness and Brand Image on Customer Purchase Decisions and Customer Advocacy Behavior: A Study on Consumers of Kapal Api Coffee Brand

The evolution of marketing, from traditional approaches to Marketing 4.0, has shifted the focus towards consumer behavior and advocacy. In this digital era, consumer empowerment and the significance of peer recommendations have reshaped marketing strategies. This study aims to investigate the relationships between brand awareness, brand image, consumer purchasing decisions, and customer advocacy behavior, with a specific focus on Kapal Api coffee consumers. Customer Advocacy Behavior, defined as consumer recommendations strengthening a brand, gains prominence in Marketing 4.0. Distinct from influencers, brand heroes advocate based on positive brand experiences. The study aligns with the 5A model, emphasizing the role of customer advocacy after the purchase decision. The research adopts a descriptive survey approach, utilizing a quantitative design. The sample consists of 258 Kapal Api coffee consumers, and data is collected through online surveys. The study applies Structural Equation Modeling (SEM) for data analysis. The analysis reveals significant positive relationships between brand awareness, brand image, purchasing decisions, and customer advocacy behavior. The study highlights the pivotal role of brand awareness and image in shaping consumer decisions and influencing post-purchase advocacy behavior. In conclusion, the research contributes valuable insights into the dynamics of brand awareness, brand image, purchasing decisions, and customer advocacy behavior within the context of Kapal Api coffee consumers. The findings emphasize the importance of cultivating strong brand awareness and a positive brand image to enhance consumer engagement and advocacy.