Development Of Model Research Articles

Development and Validation of the Diagnostic Model of 7 Gene in Endometriosis.

To explore the diagnostic biomarkers for diagnosing endometriosis. Endometriosis is a benign, progressive, estrogen-dependent gynecological disorder that has highly variant prevalence. Therefore, it is essential to develop reliable diagnostic biomarkers for endometriosis diagnosis. To explore the diagnostic biomarkers for endometriosis diagnosis. Based on transcriptome data from GSE145701, we identified potential therapeutic targets through the intersection of endometriosis-related genes from weighted gene correlation network analysis (WGCNA) and differential expression analysis. Aprotein-protein interaction (PPI) was constructed. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were employed for functional enrichment analysis. The intersection of hub genes from topological analysis and module genes from module-based network analysis were selected as core targets, which were used for diagnostic model construction. Its robustness was validated using GSE7305 and GSE134056. Associations of core targets with immune characteristics and pathways were further evaluated. Molecular docking was employed to evaluate the docking affinity between core targets and drugs. Additionally, western blot and quantitative real-time polymerase chain reaction were also carried out to validate molecular docking results. A diagnostic model was constructed using 7 core targets, which had a high diagnostic ability for endometriosis. CTSK was positively correlated with immune scores, while CDH2 was negatively correlated with immune scores. CTSK, HGF, and EPCAM were positively correlated with energy metabolism and inflammation-related pathways, while RUNX2, FN1, NCAM1, and CDH2 were positively correlated with epithelial-to-mesenchymal transition (EMT) and unfolded protein response (UPR). Moreover, FN1 had good docking affinity with Elagolix, Esmya, and Proellex. NCAM1 might be a promising target modulated by Elagolix. In vitro experiment revealed that the expression of FN1 in human normal endometrial cell lines (hEEC) gradually decreased with the increase of Esmya concentration, indicating that FN1 was a target for Esmya. These results may facilitate the in-depth understanding of the development of endometriosis, and guide early diagnostic as well as clinical treatments for patients with endometriosis.

Memory and anticipation: two main theorems for Markov regime-switching stochastic processes

We present two main theorems for stochastic processes with a Markov regime-switching model. First, we work on an existence-uniqueness theorem for a Stochastic Differential Delay Equation with Jumps and Regimes (SDDEJRs). Then we provide the duality between an SDDEJR and an Anticipated Backward Stochastic Differential Equation with Jumps and Regimes (ABSDEJRs). Our goal is to provide two technical and fundamental theorems for the future theoretical and applied developments of time-delayed and time-advanced models.

Desain Media Pembelajaran Hyperlink Berbasis Canva pada Materi Asmaul Husna Kelas X

This research aims to develop hyperlink-based learning media using Canva application on Asmaul Husna material for grade X students. This learning media is designed as an innovative solution that utilizes interactive features to increase student interest, understanding, and involvement in the learning process. The method used is Research and Development (R&D) with the 4-D development model (Define, Design, Develop, and Disseminate), although in this study only until the development stage. The validation results show that this media is very feasible to use, with a material expert eligibility score of 89.5%, and a media expert of 78.5%. So that the validation results obtained from the hyperlink feature on Canva on class X asmaul husna material can be used as one of the learning media that makes it easier for students to access various additional content such as videos and articles, thus creating a more dynamic and interactive learning experience.

Modeling the Dynamics of Blood Circulation in the Autonomous Cardiopulmonary Complex

The paper describes the development of computer modelling of hemodynamic systems, which helps to better understand physiology and to move from real experiments to simulation models. Using the object-oriented environment Modelica, an accurate model of the system "heart + lungs" was created. The performed numerical simulation confirmed the adequacy of the model and its applicability for more complex haemodynamic studies. Mathematical modelling of the "heart + lungs" system was carried out to create a digital twin. The main requirements to the model: reproduction of experimental data on animals and prediction of results in a wide range of parameters. The Modelica environment was used to create a circulatory model based on the quasi one-dimensional approximation. The modelling confirmed the adequacy of the digital twin for the key parameters of pressure and heart rate. The simulation results of two experiments showed good agreement with real data, which confirms the applicability of the model for complex haemodynamic problems.

Wind-driven and buoyancy effects for modeling natural ventilation in buildings at urban scale

This work proposes a new model to evaluate the air changes per hour (ach) due to natural infiltrations in buildings. This modeling already exists at building scale, but the new model will implement the hourly ventilation load in a physical-based modeling for space heating and cooling in buildings at urban scale. The proposed improvement considers the wind and buoyancy effects in the calculation of hourly achs in a high-density urban context. A three-zone air flow lumped modeling is applied to describe the air flow in buildings; the air flow rate due to infiltrations is calculated depending only on leakages’ characteristics and pressure variations in various climate conditions. The non-linear equations system of mass and energy conservation is solved by an iterative procedure using the Newton-Raphson numerical method. Besides, two different methodologies are compared to evaluate the external dynamic and static pressure conditions on building façades: experimental values (pressure coefficients Cp) and CFD simulations. For the latter, the air flow field in the urban canyons is described by the windy conditions and by imposing a temperature gradient due to solar irradiation between the windward and leeward facades. This methodology is applied to three urban canyons in Turin, with typical aspect ratios and orientations for some local climate conditions considering both heating and cooling seasons. Comparing the results of hourly ach obtained from the Cp method, the CFD technique allows to modulate the ach considering the impact of the canyon dimension, wind and buoyancy effect of non-isothermal condition, in varying the wind speed on the façades of buildings for different scenarios. It also overcomes the limit of field of applications of Cp, especially in high-density built urban environments. The encouraging results of this work will lead to future developments of the three-zone lumped model and its numerical solution techniques.

Innovative machine learning for drilling fluid density prediction: a novel central force search-adaptive XGBoost in HPHT environments

Oil and gas industries are facing a special dilemma when it comes to high-pressure, high-temperature (HPHT) drilling as the accurate forecasting of the drilling fluid density (DFD) is a vital factor for safe and efficient operations. Complicated relationships and inconsistencies in HPHT situations are rarely mapped by current forecasting models, while their buggy performance and safety risks during drilling can be underestimated. In this research, we propose a novel machine learning (ML) approach to enhance the accuracy of DFD anticipation under HPHT conditions: central force search-adaptive extreme gradient boosting (CFS-XGB). This paper uses a dataset that has drilling variables together with the DFD for HPHT situations to examine the accuracy of the CFS-XGB model. Excluding the abnormalities of data or mistakes, the reliability of the original data is maintained by applying min–max normalization. After that, finding the important features with the help of the boosted principal component analysis (BPCA) approach to the normalized data will ensure a major improvement in the CFS-XGB methodology’s prediction efficacy. This research is experimented in the Python platform, and the performance of the proposed CFS-XGB method is analyzed in terms of MSE, R2, and AAPRE metrics. The suggested approach performs better than the current methods in forecasting the drilling fluid concentration in HPHT settings, according to the experimental data. This development in predictive modeling helps increase the productivity and safety of drilling operations, which will eventually help the oil and gas sector manage the challenges posed by HPHT drilling settings.

Developing E-worksheet-based TPACK model for Junior High School Students in English Language Learning Context

Background:The goal of this study is at developing an E-Worksheet based on TPACK Model by using Adobe Animate CC and AI Voice. This research was conducted to overcome the students’ problem, the teacher’s problem, and instructional problem that occurred in Language classroom. Methodology:This study is a research and development project that used of the 4D development methodology. The four steps of this model's development are defining, designing, developing, and distributing. Observations, tests, interviews, and questionnaires are examples of research instruments. The e-worksheet was validated by 5 validators, including 2 material and language expert lecturers and 3 media expert lecturers. Findings:The percent formula is used to determine its practicality. The electronic worksheet is classified as very valid with the level of 84.2 % in material or content of product and very valid with the level of 85.9% in media of product. The result of the practicality of the electronic words indicated that the total mean score for all the items in the questioner was 4.5 with a percentage of 90 falling into the category of very practical. The results of the effectiveness test were obtained with a value of 0.81 in the high category. Conclusion:Based on the results of the study it can be concluded that developing an E-Worksheet in Describing words material based on the TPACK Model by using Adobe Animate CC and AI Voice for seventh grade students of Junior Islamic High School is valid, practical, and effective. Originality:The use of TPACK as a resource for creating teaching materials is quite effective

Validitas Petunjuk Praktikum Ekologi: Meningkatkan Kualitas Pembelajaran Berbasis Lingkungan

This research aims to determine the validity of ecology practicum instructions. This type of research is development research using the 4D model (Define, Design, Develop, and Disseminate). However, this research modified it to 3D, namely Define, Design, and Develop. The research instrument used was a validation sheet. The data analysis technique used is the percentage technique. The research showed that the validation results from content/material experts were 77%, in terms of appearance it was 75%, and in terms of language it was 87%. Thus, it can be concluded that the ecology practicum instructions are declared valid and do not need to be revised.

Machine Learning Aided Modeling of Granular Materials: A Review

AbstractArtificial intelligence (AI) has become a buzzy word since Google’s AlphaGo beat a world champion in 2017. In the past five years, machine learning as a subset of the broader category of AI has obtained considerable attention in the research community of granular materials. This work offers a detailed review of the recent advances in machine learning-aided studies of granular materials from the particle-particle interaction at the grain level to the macroscopic simulations of granular flow. This work will start with the application of machine learning in the microscopic particle-particle interaction and associated contact models. Then, different neural networks for learning the constitutive behaviour of granular materials will be reviewed and compared. Finally, the macroscopic simulations of practical engineering or boundary value problems based on the combination of neural networks and numerical methods are discussed. We hope readers will have a clear idea of the development of machine learning-aided modelling of granular materials via this comprehensive review work.

Выбор аналитической модели и количественных показателей для геоинформационного анализа устойчивого развития территориальных образований

Currently, there are no generally accepted algorithms for assessing, analyzing and forecasting the sustainable development of administrative-territorial units. The authors introduce the definition of this term, which implies the successful functioning of a human community over time carrying out economic activities without threats to its environment. Also, an analytical model for representation of its regional integrated indicator in the form of a vector with differential components, which characterize economic growth, environmental and social metrics, in a three-dimensional affine space, is proposed. Furthermore, we present a multiplicative criterion for assessing partial criteria of sustainable development efficiency and the basic expression for its parameters computation based on the set of economic, environmental and social ones, including such as the availability and quality of natural resources, regional labour resources, eco-factors, etc. The proposed vector model is based on the geoinformation approach and can be deployed within the framework of GIS of sustainable development for geo-analysis, modeling and mapping of natural resource potential and ecological state of intensively advancing territories, considering the dynamics of this process. Important properties of the geoinformation approach to assessing territorial entities’ forward growth, spatial and semantic scalability, are noted. In addition, the functional requirements and preliminary architecture of the core, modules, clients and layers of the related thematic geographic information system for the management, planning and forecasting the said indicator within the framework of digital economy are described

Deep models for multi-view 3D object recognition: a review

This review paper focuses on the progress of deep learning-based methods for multi-view 3D object recognition. It covers the state-of-the-art techniques in this field, specifically those that utilize 3D multi-view data as input representation. The paper provides a comprehensive analysis of the pipeline for deep learning-based multi-view 3D object recognition, including the various techniques employed at each stage. It also presents the latest developments in CNN-based and transformer-based models for multi-view 3D object recognition. The review discusses existing models in detail, including the datasets, camera configurations, view selection strategies, pre-trained CNN architectures, fusion strategies, and recognition performance. Additionally, it examines various computer vision applications that use multi-view classification. Finally, it highlights future directions, factors impacting recognition performance, and trends for the development of multi-view 3D object recognition method.

A Development of English for Tourism Book Based on Tourism Places in Dompu to Improve Students’ Speaking Skills

The aim of this research is to develop a valid, practical, and effective book of English for Tourism based on tourism places in Dompu. This study is a R&D (Research and Development) approach using the 4D model (Define, Design, Develop, and Disseminate). The book that had been developed was implemented in the English learning process at the class XI of Tourism Vocational High School Dompu, Department of Travel and Tourism Business with a total of 27 students, in academic year 2024. The validity test results of the developed book obtained from media expert is 88% (very valid) and 92% (very valid) from material expert. Moreover, the practicality test result from students and teacher was at the level “practical” with average score 3,8 or 76%. Lastly, based on students’ speaking test result, the average total score obtained by 27 students is 77. On other word, 100% students or all 27 students obtained average scores more than or equal to 70. It means all students have successfully done the task with good scores. Based on that, the use of English for tourism book based on tourism places in Dompu is effective to improve students’ speaking skill. Another remarkable finding in this reseach is that both teacher and students strongly agree that the material in book is able to stimulate students to be active in the learning process.

Retraction Note: A systematic review of developments in the 2-tuple linguistic model and its applications in decision analysis

Retraction Note: A systematic review of developments in the 2-tuple linguistic model and its applications in decision analysis

Dual syntax aware graph attention networks with prompt for aspect-based sentiment analysis

Aspect-based sentiment analysis (ABSA) is a challenging task due to the presence of multiple aspect words with different sentiment polarities in a sentence. Recently, pre-trained language models like BERT have been widely used as context encoders in ABSA. Graph neural networks have also been employed to extract syntactic and semantic information from sentence parsing trees, resulting in superior results. However, dependency trees may establish irrelevant dependencies for sentences with irregular syntax and complex structures. Additionally, previous methods have not fully utilized recent developments in pre-trained language models. Therefore, we propose a Dual Syntax aware Graph attention networks with Prompt (DSGP) model to address these issues. Our model utilizes prompt templates to maximize the potential of pre-trained models and masked vector outputs of templates as supplementary aspect feature representations. We also leverage both dependency trees and constituent trees with graph attention networks to extract different types of syntactic information. The dependency tree captures syntactic correlation between words, while the constituent tree provides a high-level formation of the sentence. Finally, the output from the prompt and parsing trees is fused and fed into a standard classifier. Experimental results on four public datasets demonstrate the competitive performance of our model.

Development of scale and model for evaluating the individual performance appraisals—Process in public management

The need for strategic alignment within HR management increased managers’ concern about individual behavior and how this behavior was related to the achievement of goals. In public management, effectively managing employees’ performance has been necessary since Weber’s bureaucratic administration. The individual performance appraisal is the right tool to assess employees’ competencies. Thus, we proposed the following research question: Which factors, as pointed out by theory, have the most significant influence on the individual performance appraisal process? The quantitative method was applied to answer this question, developing and testing a scale via EFA and a hypothetical model via SEM-CB. The results indicated a scale with 25 items able to access the main points of the IPA process and a hypothetical model with 7 constructs that indicate the influence on employee engagement. The main finding is the significant influence of feedback on the whole process. The main theoretical contribution was the construction of the MIPAS scale, and the practical contribution was to identify the points where managers should focus on improving the IPA process with their subordinates.

The Effectiveness of the Dilemma-STEAM Learning Model Based on Motion Graphic Videos in the Subject of Parabolic Motion

Abstract This study aims to develop and evaluate the feasibility and effectiveness of the Dilemma-STEAM learning model using motion graphic videos in teaching Parabolic Motion. The developed motion graphic videos incorporate five Dilemma-STEAM learning syntaxes: Reflection, Exploration, Elaboration, Integration, and Transformation, which address the topic of Parabolic Motion. The development model utilized in this Research and Development (R&D) method follows the 4D model (Define, Design, Develop, and Disseminate) with the following stages: 1. Needs analysis, 2. Product design, 3. Product development, 4. Feasibility testing and product trials. Based on needs analysis conducted through field observations, it was found that students require learning media in the form of motion graphics for the subject of Parabolic Motion using the Dilemma-STEAM learning model. Feasibility tests by subject matter experts, media experts, and learning experts were conducted using feasibility questionnaires, yielding an average feasibility percentage of 92% for subject matter experts, 89% for media experts, and 78% for learning experts. Additionally, readability tests and trials of the Dilemma-STEAM learning model were conducted with eleventh-grade students, with average effectiveness percentages of 78% and 75%, respectively. Based on the obtained average percentages, it is concluded that the Dilemma-STEAM learning model based on motion graphic videos is feasible and effective as a learning medium for eleventh-grade Physics.

Flipbook Platform E-Module Development: Geographic Information Systems Subject

This research aims to determine the feasibility and effectiveness of e-modules with the Flipbook platform in the Geographic Information Systems (GIS) Class XI Geospatial Engineering subject at SMK Negeri 1 Percut Sei Tuan. This research applies the Research and Development (R&D) method with the ADDIE model (Analysis, Design, Develop, Implement, and Evaluate). The research subjects were 30 students of Class XI Geospatial Engineering at SMK Negeri 1 Percut Sei Tuan. This research involved validation tests by material experts, media experts, and learning design experts, as well as trial stages, namely individual trials on 3 students, small group trials on 6 students, and limited field trials on 30 students. The research results show that the E-Module with the Flipbook platform is suitable for use based on media expert validation of 89.17% (very feasible), material expert validation of 92.13% (very feasible), and learning design expert validation of 88.71% (very feasible). In the individual trial, the score was 91.47% (very decent), the small group trial got a score of 92.33% (very decent), and the field trial got a score of 96.14% (very decent). The use of E-Modules with the Flipbook platform in GIS subjects is more effective than conventional whiteboard media, as can be seen from the results of tcount = 1.91 and tTable = 1.672, where 1.91 > 1.672 at the significance level α = 0.05. This shows that H0 is is rejected, and the alternative hypothesis (Ha) is accepted. This means that there is a noticeable difference in student learning results between the experimental and control classes with a significance level of 5%. The effectiveness value of the E-Module with the Flipbook platform is higher, namely 84.7%, compared to student learning outcomes using whiteboard media of 80.8%.

Prejudice model 1.0: A predictive model of prejudice.

The present research develops a predictive model of prejudice. For nearly a century, psychology and other fields have sought to scientifically understand and describe the causes of prejudice. Numerous theories of prejudice now exist. Yet these theories are overwhelmingly defined verbally and thus lack the ability to precisely predict when and to what extent prejudice will emerge. The abundance of theory also raises the possibility of undetected overlap between constructs theorized to cause prejudice. Predictive models enable falsification and provide a way for the field to move forward. To this end, here we present 18 studies with ∼5,000 participants in seven phases of model development. After initially identifying major theorized causes of prejudice in the literature, we used a model selection approach to winnow constructs into a parsimonious predictive model of prejudice (Phases I and II). We confirm this model in a preregistered out-of-sample test (Phase III), test variations in operationalizations and boundary conditions (Phases IV and V), and test generalizability on a U.S. representative sample, an Indian sample, and a U.K. sample (Phase VI). Finally, we consulted the predictions of experts in the field to examine how well they align with our results (Phase VII). We believe this initial predictive model is limited and bad, but by developing a model that makes highly specific predictions, drawing on the state of the art, we hope to provide a foundation from which research can build to improve science of prejudice. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Model development for bespoke large language models for digital triage assistance in mental health care

Contemporary large language models (LLMs) may have utility for processing unstructured, narrative free-text clinical data contained in electronic health records (EHRs) — a particularly important use-case for mental health where a majority of routinely-collected patient data lacks structured, machine-readable content.A significant problem for the United Kingdom’s National Health Service (NHS) are the long waiting lists for specialist mental healthcare. According to NHS data (NHS Digital, 2024), in each month of 2023, there were between 370,000 and 470,000 individual new referrals into secondary mental healthcare services. Referrals must be triaged by clinicians, using clinical information contained in the patient’s EHR to arrive at a decision about the most appropriate mental healthcare team to assess and potentially treat these patients.The ability to efficiently recommend a relevant team by ingesting potentially voluminous clinical notes could help services both reduce referral waiting times and with the right technology, improve the evidence available to justify triage decisions.We present and evaluate three different approaches for LLM-based, end-to-end ingestion of variable-length clinical EHR data to assist clinicians when triaging referrals. Our model is able to deliver triage recommendations consistent with existing clinical practices and its architecture was implemented on a single GPU, making it practical for implementation in resource-limited NHS environments where private implementations of LLM technology will be necessary to ensure confidential clinical data are appropriately controlled and governed. Code available at: https://github.com/NtaylorOX/BespokeLLM_Triage.

What can we learn from one-to-one trials in Instructional design? A case from module development

The one-to-one trial phase within the instructional design development model has frequently been neglected in prior research. This stage should provide much in-depth insight considering its nature, which is in-depth face-to-face data collection between designers and participants. In this study, we aim to analyze a particular instance of module development during the one-to-one trial phase. This study is part of the media development stage with the ADDIE model (Analyze, Design, Develop, Implement, and Evaluate) at the implementation stage. We used quantitative data collection through scoring and qualitative data through interviews with six participants involved in the one-to-one trial. The findings revealed that students provided critical feedback on two aspects of the module, particularly its design appeal and suitability for independent learning, aligning with an average participant score of 3 on a 1-5 scale. These results provide an overview that one-to-one as a foundation and initiation for a broader trial plays an essential role in providing a starting point for an implementation where quantitative and qualitative data provide holistic insight.